From af4f3fd52351827ca75065df8df2b98450b2972a Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 8 Feb 2023 17:51:41 -0500 Subject: [PATCH 01/61] WIP Signed-off-by: Adam Li --- doc/references.bib | 17 ++++ doc/whats_new/v0.1.rst | 1 + dodiscover/constraint/__init__.py | 3 +- dodiscover/constraint/intervention.py | 130 ++++++++++++++++++++++++++ 4 files changed, 150 insertions(+), 1 deletion(-) create mode 100644 dodiscover/constraint/intervention.py diff --git a/doc/references.bib b/doc/references.bib index dbb5fbfe7..154de67a7 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -16,6 +16,23 @@ @article{Colombo2012 url = {https://doi.org/10.1214/11-AOS940} } +@article{Jaber2020causal, + title={Causal discovery from soft interventions with unknown targets: Characterization and learning}, + author={Jaber, Amin and Kocaoglu, Murat and Shanmugam, Karthikeyan and Bareinboim, Elias}, + journal={Advances in neural information processing systems}, + volume={33}, + pages={9551--9561}, + year={2020} +} + +@article{Kocaoglu2019characterization, + title={Characterization and learning of causal graphs with latent variables from soft interventions}, + author={Kocaoglu, Murat and Jaber, Amin and Shanmugam, Karthikeyan and Bareinboim, Elias}, + journal={Advances in Neural Information Processing Systems}, + volume={32}, + year={2019} +} + @article{Lopez2016revisiting, title = {Revisiting classifier two-sample tests}, author = {Lopez-Paz, David and Oquab, Maxime}, diff --git a/doc/whats_new/v0.1.rst b/doc/whats_new/v0.1.rst index 74be27fbd..86e69556e 100644 --- a/doc/whats_new/v0.1.rst +++ b/doc/whats_new/v0.1.rst @@ -43,6 +43,7 @@ Changelog - |Feature| Add conditional k-sample (discrepancy) test, :class:`dodiscover.cd.BregmanCDTest`, by `Adam Li`_ (:pr:`82`) - |Feature| Add conditional mutual information test, :class:`dodiscover.ci.CMITest`, by `Adam Li`_ (:pr:`83`) - |Feature| Add classifier conditional mutual information test, :class:`dodiscover.ci.ClassifierCMITest`, by `Adam Li`_ (:pr:`85`) +- |Feature| Add Psi-FCI and I-FCI algorithm for handling soft-interventional data, :class:`dodiscover.constraint.PsiFCI` by `Adam Li`_ (:pr:``) Code and Documentation Contributors ----------------------------------- diff --git a/dodiscover/constraint/__init__.py b/dodiscover/constraint/__init__.py index 6bf409eff..a4b3469ea 100644 --- a/dodiscover/constraint/__init__.py +++ b/dodiscover/constraint/__init__.py @@ -1,3 +1,4 @@ +from .config import SkeletonMethods from .fcialg import FCI from .pcalg import PC -from .skeleton import LearnSemiMarkovianSkeleton, LearnSkeleton, SkeletonMethods +from .skeleton import LearnSemiMarkovianSkeleton, LearnSkeleton diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py new file mode 100644 index 000000000..0b55e8cee --- /dev/null +++ b/dodiscover/constraint/intervention.py @@ -0,0 +1,130 @@ +from typing import Optional, Tuple + +import networkx as nx +import pandas as pd + +from dodiscover._protocol import EquivalenceClass +from dodiscover.ci import BaseConditionalIndependenceTest +from dodiscover.constraint import SkeletonMethods +from dodiscover.context import Context +from dodiscover.typing import SeparatingSet + +from .fcialg import FCI + + +class PsiFCI(FCI): + def __init__( + self, + ci_estimator: BaseConditionalIndependenceTest, + alpha: float = 0.05, + min_cond_set_size: Optional[int] = None, + max_cond_set_size: Optional[int] = None, + max_combinations: Optional[int] = None, + skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + apply_orientations: bool = True, + max_iter: int = 1000, + max_path_length: Optional[int] = None, + pds_skeleton_method: SkeletonMethods = SkeletonMethods.PDS, + known_intervention_targets: bool = False, + **ci_estimator_kwargs, + ): + """Interventional (Psi) FCI algorithm. + + The I-FCI (or Psi-FCI) algorithm is an algorithm that accepts + multiple sets of data that may pertain to observational and/or + multiple interventional datasets under a known (I-FCI), or unknown (Psi-FCI) + intervention target setting. Our API consolidates them here under + one class, but you can control the setting using our hyperparameter. + See :footcite:`Kocaoglu2019characterization` for more information on + I-FCI and :footcite:`Jaber2020causal` for more information on Psi-FCI. + + The Psi-FCI algorithm is complete for the Psi-PAG equivalence class. + However, the I-FCI has not been shown to be complete for the I-PAG + equivalence class. Note that the I-FCI algorithm may change without + notice. + + Parameters + ---------- + ci_estimator : Callable + The conditional independence test function. The arguments of the estimator should + be data, node, node to compare, conditioning set of nodes, and any additional + keyword arguments. + alpha : float, optional + The significance level for the conditional independence test, by default 0.05. + min_cond_set_size : int, optional + Minimum size of the conditioning set, by default None, which will be set to '0'. + Used to constrain the computation spent on the algorithm. + max_cond_set_size : int, optional + Maximum size of the conditioning set, by default None. Used to limit + the computation spent on the algorithm. + max_combinations : int, optional + The maximum number of conditional independence tests to run from the set + of possible conditioning sets. By default None, which means the algorithm will + check all possible conditioning sets. If ``max_combinations=n`` is set, then + for every conditioning set size, 'p', there will be at most 'n' CI tests run + before the conditioning set size 'p' is incremented. For controlling the size + of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used + in conjunction with ``keep_sorted`` parameter to only test the "strongest" + dependences. + skeleton_method : SkeletonMethods + The method to use for testing conditional independence. Must be one of + ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. + apply_orientations : bool + Whether or not to apply orientation rules given the learned skeleton graph + and separating set per pair of variables. If ``True`` (default), will + apply Zhang's orientation rules R0-10, orienting colliders and certain + arrowheads and tails :footcite:`Zhang2008`. + max_iter : int + The maximum number of iterations through the graph to apply + orientation rules. + max_path_length : int, optional + The maximum length of any discriminating path, or None if unlimited. + selection_bias : bool + Whether or not to account for selection bias within the causal PAG. + See :footcite:`Zhang2008`. Currently not implemented. + pds_skeleton_method : SkeletonMethods + The method to use for learning the skeleton using PDS. Must be one of + ('pds', 'pds_path'). See Notes for more details. + known_intervention_targets : bool, optional + If `True`, then will run the I-FCI algorithm. If `False`, will run the + Psi-FCI algorithm. By default False. + ci_estimator_kwargs : dict + Keyword arguments for the ``ci_estimator`` function. + """ + super().__init__( + ci_estimator, + alpha, + min_cond_set_size, + max_cond_set_size, + max_combinations, + skeleton_method, + apply_orientations, + max_iter=max_iter, + max_path_length=max_path_length, + selection_bias=False, + pds_skeleton_method=pds_skeleton_method, + **ci_estimator_kwargs, + ) + self.known_intervention_targets = known_intervention_targets + + def learn_skeleton( + self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None + ) -> Tuple[nx.Graph, SeparatingSet]: + return super().learn_skeleton(data, context, sep_set) + + def fit(self, data: pd.DataFrame, context: Context) -> None: + return super().fit(data, context) + + def orient_edges(self, graph: EquivalenceClass): + return super().orient_edges(graph) + + def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: + import pywhy_graphs as pgraph + + # convert the undirected skeleton graph to its PAG-class, where + # all left-over edges have a "circle" endpoint + if self.known_intervention_targets: + pag = pgraph.IPAG(incoming_circle_edges=graph, name="IPAG derived with I-FCI") + else: + pag = pgraph.PsiPAG(incoming_circle_edges=graph, name="PsiPAG derived with Psi-FCI") + return pag From 70f666a849a103251e79325ff96c791793a4eb8d Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 13 Feb 2023 17:07:53 -0500 Subject: [PATCH 02/61] Working prototype of interventional skeleton Signed-off-by: Adam Li --- doc/api.rst | 2 + doc/conditional_independence.rst | 26 + doc/constraint_causal_discovery.rst | 31 + doc/index.rst | 7 +- doc/use.rst | 12 +- doc/user_guide.rst | 22 + dodiscover/__init__.py | 2 +- dodiscover/_protocol.py | 8 + dodiscover/base.py | 194 +++++ dodiscover/cd/bregman.py | 10 +- dodiscover/cd/kernel_test.py | 1 + dodiscover/ci/cmi_test.py | 2 +- dodiscover/ci/kernel_test.py | 8 +- dodiscover/ci/kernel_utils.py | 15 +- dodiscover/ci/monte_carlo.py | 4 +- dodiscover/constraint/_classes.py | 5 +- dodiscover/constraint/fcialg.py | 32 +- dodiscover/constraint/intervention.py | 38 +- dodiscover/constraint/skeleton.py | 682 ++++++++++++++++-- dodiscover/context.py | 144 ++-- dodiscover/context_builder.py | 352 ++++++++- dodiscover/metrics.py | 2 +- dodiscover/typing.py | 4 +- examples/README.txt | 2 + pyproject.toml | 2 +- .../constraint/test_intervene_skeleton.py | 62 ++ tests/unit_tests/constraint/test_skeleton.py | 91 ++- tests/unit_tests/test_base.py | 42 ++ tests/unit_tests/test_context_builder.py | 199 ++++- 29 files changed, 1773 insertions(+), 228 deletions(-) create mode 100644 doc/conditional_independence.rst create mode 100644 doc/constraint_causal_discovery.rst create mode 100644 doc/user_guide.rst create mode 100644 dodiscover/base.py create mode 100644 tests/unit_tests/constraint/test_intervene_skeleton.py create mode 100644 tests/unit_tests/test_base.py diff --git a/doc/api.rst b/doc/api.rst index d4d7a5c3d..7c7b8b301 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -1,3 +1,5 @@ +.. _api_ref: + ### API ### diff --git a/doc/conditional_independence.rst b/doc/conditional_independence.rst new file mode 100644 index 000000000..133a16a36 --- /dev/null +++ b/doc/conditional_independence.rst @@ -0,0 +1,26 @@ +.. _conditional_independence: + +======================== +Conditional Independence +======================== + +.. currentmodule:: dodiscover.ci + +TBD. + +Partial (Pearson) Correlation +----------------------------- +TBD. + +Discrete, Categorical and Binary Data +------------------------------------- +TBD. + + +Kernel-Approaches +----------------- +TBD. + +Conditional Mutual Information +------------------------------ +TBD. \ No newline at end of file diff --git a/doc/constraint_causal_discovery.rst b/doc/constraint_causal_discovery.rst new file mode 100644 index 000000000..07a337a20 --- /dev/null +++ b/doc/constraint_causal_discovery.rst @@ -0,0 +1,31 @@ +.. _constraint_causal_discovery: + +================================== +Constraint-based causal discovery +================================== + +.. currentmodule:: dodiscover.constraint + +The following are a set of methods intended for regression in which +the target value is expected to be a linear combination of the features. +In mathematical notation, if :math:`\hat{y}` is the predicted +value. + +(Non-parametric) Markovian SCMs with Observational Data +------------------------------------------------------- + + +(Non-parametric) Semi-Markovian SCMs with Observational Data +------------------------------------------------------------ + + +(Non-parametric) SCMs with Interventional Data +---------------------------------------------- + + +Robust learning +--------------- +Conservative orientations, etc. + + + diff --git a/doc/index.rst b/doc/index.rst index f068a093f..a6956c290 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -9,7 +9,12 @@ algorithms and data structures of ``networkx``. We encourage you to use the package for your causal inference research and also build on top with relevant Pull Requests. -See our examples for walk-throughs of how to use the package. +See our examples for walk-throughs of how to use the package, or + +Please refer to our :ref:`user_guide` for details on all the tools that we +provide. You can also find an exhaustive list of the public API in the +:ref:`api_ref`. You can also look at our numerous :ref:`examples ` +that illustrate the use of ``dodiscover`` in many different contexts. Contents -------- diff --git a/doc/use.rst b/doc/use.rst index 78a74b93c..c7bb0dbad 100644 --- a/doc/use.rst +++ b/doc/use.rst @@ -1,11 +1,11 @@ :orphan: -Using dodiscover -===================== - -To be able to effectively use dodiscover, look at some of the basic examples here -to learn everything you need! +Examples and Tutorials using DoDiscover +======================================= +To be able to effectively use dodiscover, you can look at some of the basic examples here +to learn everything you need from concepts to explicit code examples. .. include:: auto_examples/index.rst - :start-after: :orphan: \ No newline at end of file + :start-after: :orphan: + diff --git a/doc/user_guide.rst b/doc/user_guide.rst new file mode 100644 index 000000000..66fbe10e7 --- /dev/null +++ b/doc/user_guide.rst @@ -0,0 +1,22 @@ +.. Places parent toc into the sidebar + +:parenttoc: True + +.. title:: User guide: contents + +.. _user_guide: + +========== +User Guide +========== + +.. toctree:: + :numbered: + :maxdepth: 3 + + constraint_causal_discovery.rst + .. scores_causal_discovery.rst + .. visualizations.rst + .. datasets.rst + .. computing.rst + .. common_pitfalls.rst diff --git a/dodiscover/__init__.py b/dodiscover/__init__.py index 446ee292a..50014dbb8 100644 --- a/dodiscover/__init__.py +++ b/dodiscover/__init__.py @@ -8,4 +8,4 @@ from ._protocol import EquivalenceClass, Graph from ._version import __version__ # noqa: F401 from .constraint import FCI, PC -from .context_builder import ContextBuilder, make_context +from .context_builder import ContextBuilder, InterventionalContextBuilder, make_context diff --git a/dodiscover/_protocol.py b/dodiscover/_protocol.py index d0cc9b9e7..91709e069 100644 --- a/dodiscover/_protocol.py +++ b/dodiscover/_protocol.py @@ -51,6 +51,14 @@ def to_undirected(self) -> nx.Graph: """ pass + def subgraph(self, nodes): + """Get subgraph based on nodes.""" + pass + + def copy(self): + """Create a copy of the graph.""" + pass + class EquivalenceClass(Graph, Protocol): """Protocol for equivalence class of graphs.""" diff --git a/dodiscover/base.py b/dodiscover/base.py new file mode 100644 index 000000000..62599e39b --- /dev/null +++ b/dodiscover/base.py @@ -0,0 +1,194 @@ +import inspect +import warnings +from collections import defaultdict +from copy import deepcopy + +from ._version import __version__ + + +class InconsistentVersionWarning(UserWarning): + """Warning raised when an estimator is unpickled with a inconsistent version. + + Parameters + ---------- + estimator_name : str + Estimator name. + + current_dodiscover_version : str + Current dodiscover version. + + original_dodiscover_version : str + Original dodiscover version. + """ + + def __init__(self, *, estimator_name, current_dodiscover_version, original_dodiscover_version): + self.estimator_name = estimator_name + self.current_dodiscover_version = current_dodiscover_version + self.original_dodiscover_version = original_dodiscover_version + + def __str__(self): + return ( + f"Trying to unpickle estimator {self.estimator_name} from version" + f" {self.original_dodiscover_version} when " + f"using version {self.current_dodiscover_version}. This might lead to breaking" + " code or " + "invalid results. Use at your own risk. " + ) + + +class BasePyWhy: + """Base class for all PyWhy class objects. + + TODO: add parameter validation and data validation from sklearn. + TODO: add HTML representation. + + Notes + ----- + All learners and context should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + """ + + @classmethod + def _get_param_names(cls): + """Get parameter names for the estimator.""" + # fetch the constructor or the original constructor before + # deprecation wrapping if any + init = getattr(cls.__init__, "deprecated_original", cls.__init__) + if init is object.__init__: + # No explicit constructor to introspect + return [] + + # introspect the constructor arguments to find the model parameters + # to represent + init_signature = inspect.signature(init) + # Consider the constructor parameters excluding 'self' + parameters = [ + p + for p in init_signature.parameters.values() + if p.name != "self" and p.kind != p.VAR_KEYWORD + ] + for p in parameters: + if p.kind == p.VAR_POSITIONAL: + raise RuntimeError( + "dodiscover estimators should always " + "specify their parameters in the signature" + " of their __init__ (no varargs)." + " %s with constructor %s doesn't " + " follow this convention." % (cls, init_signature) + ) + # Extract and sort argument names excluding 'self' + return sorted([p.name for p in parameters]) + + def get_params(self, deep=True): + """ + Get parameters for this Context. + + TODO: can update this when we build a causal-Pipeline similar to sklearn's Pipeline. + + Parameters + ---------- + deep : bool, default=True + If True, will return the parameters for this estimator and + contained subobjects that are estimators. + + Returns + ------- + params : dict + Parameter names mapped to their values. + """ + out = dict() + for key in self._get_param_names(): + value = getattr(self, key) + + if deep and hasattr(value, "get_params") and not isinstance(value, type): + # future proof for pipeline objects + deep_items = value.get_params().items() + out.update((key + "__" + k, val) for k, val in deep_items) + elif deep and not isinstance(value, type): + # this ensures a deepcopy is applied, which is useful for graphs + value = deepcopy(value) + + out[key] = value + return out + + def set_params(self, **params): + """Set the parameters of this estimator. + + The method works on simple estimators as well as on nested objects. + The latter have parameters of the form ``__`` + so that it's possible to update each component of a nested object. + + Parameters + ---------- + **params : dict + Estimator parameters. + + Returns + ------- + self : estimator instance + Estimator instance. + """ + if not params: + # Simple optimization to gain speed (inspect is slow) + return self + valid_params = self.get_params(deep=True) + + nested_params = defaultdict(dict) # grouped by prefix + for key, value in params.items(): + key, delim, sub_key = key.partition("__") + if key not in valid_params: + local_valid_params = self._get_param_names() + raise ValueError( + f"Invalid parameter {key!r} for estimator {self}. " + f"Valid parameters are: {local_valid_params!r}." + ) + + if delim: + nested_params[key][sub_key] = value + else: + setattr(self, key, value) + valid_params[key] = value + + for key, sub_params in nested_params.items(): + valid_params[key].set_params(**sub_params) + + return self + + def __getstate__(self): + if getattr(self, "__slots__", None): + raise TypeError( + "You cannot use `__slots__` in objects inheriting from " + "`dodiscover.base.BasePyWhy`." + ) + + try: + state = super().__getstate__() + if state is None: + # For Python 3.11+, empty instance (no `__slots__`, + # and `__dict__`) will return a state equal to `None`. + state = self.__dict__.copy() + except AttributeError: + # Python < 3.11 + state = self.__dict__.copy() + + if type(self).__module__.startswith("dodiscover."): + return dict(state.items(), _dodiscover_version=__version__) + else: + return state + + def __setstate__(self, state): + if type(self).__module__.startswith("dodiscover."): + pickle_version = state.pop("_dodiscover_version", "pre-0.18") + if pickle_version != __version__: + warnings.warn( + InconsistentVersionWarning( + estimator_name=self.__class__.__name__, + current_dodiscover_version=__version__, + original_dodiscover_version=pickle_version, + ), + ) + try: + super().__setstate__(state) + except AttributeError: + self.__dict__.update(state) diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index d292a2725..0224c1f52 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -1,4 +1,4 @@ -from typing import Set, Tuple +from typing import Optional, Set, Tuple import numpy as np import pandas as pd @@ -42,10 +42,10 @@ def __init__( self, metric: str = "rbf", distance_metric: str = "euclidean", - kwidth: float = None, + kwidth: Optional[float] = None, null_reps: int = 1000, - n_jobs: int = None, - random_state: int = None, + n_jobs: Optional[int] = None, + random_state: Optional[int] = None, ) -> None: self.metric = metric self.distance_metric = distance_metric @@ -125,7 +125,7 @@ def _statistic(self, X: ArrayLike, Y: ArrayLike, group_ind: ArrayLike) -> float: return conditional_div def compute_null( - self, X: ArrayLike, Y: ArrayLike, null_reps: int = 1000, random_state: int = None + self, X: ArrayLike, Y: ArrayLike, null_reps: int = 1000, random_state: Optional[int] = None ): rng = np.random.default_rng(random_state) diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index 51b2b99c2..a9f9a2893 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -99,6 +99,7 @@ def test( Set of Y variables. group_col : Column The column denoting, which group (i.e. environment) each sample belongs to. + This is typically the F-node. Returns ------- diff --git a/dodiscover/ci/cmi_test.py b/dodiscover/ci/cmi_test.py index e48395229..bab463fc0 100644 --- a/dodiscover/ci/cmi_test.py +++ b/dodiscover/ci/cmi_test.py @@ -85,7 +85,7 @@ def __init__( n_jobs: int = -1, n_shuffle_nbrs: int = 5, n_shuffle: int = 100, - random_seed: int = None, + random_seed: Optional[int] = None, ) -> None: self.k = k self.n_shuffle_nbrs = n_shuffle_nbrs diff --git a/dodiscover/ci/kernel_test.py b/dodiscover/ci/kernel_test.py index 6c8cb4e2b..2aae14ba4 100644 --- a/dodiscover/ci/kernel_test.py +++ b/dodiscover/ci/kernel_test.py @@ -21,11 +21,11 @@ def __init__( kernel_z: str = "rbf", null_size: int = 1000, approx_with_gamma: bool = True, - kwidth_x: float = None, - kwidth_y: float = None, - kwidth_z: float = None, + kwidth_x: Optional[float] = None, + kwidth_y: Optional[float] = None, + kwidth_z: Optional[float] = None, threshold: float = 1e-5, - n_jobs: int = None, + n_jobs: Optional[int] = None, ): """Kernel (Conditional) Independence Test. diff --git a/dodiscover/ci/kernel_utils.py b/dodiscover/ci/kernel_utils.py index 4d7234576..9bb0f8f11 100644 --- a/dodiscover/ci/kernel_utils.py +++ b/dodiscover/ci/kernel_utils.py @@ -101,9 +101,9 @@ def kl_divergence_score(y_stat_q: ArrayLike, y_stat_p: ArrayLike, eps: float) -> def corrent_matrix( data: ArrayLike, metric: str = "rbf", - kwidth: float = None, + kwidth: Optional[float] = None, distance_metric="euclidean", - n_jobs=None, + n_jobs: Optional[int] = None, ) -> ArrayLike: """Compute the centered correntropy of a matrix. @@ -227,7 +227,10 @@ def compute_kernel( def _estimate_kwidth( - X: ArrayLike, method="scott", distance_metric: str = None, n_jobs: int = None + X: ArrayLike, + method="scott", + distance_metric: Optional[str] = None, + n_jobs: Optional[int] = None, ) -> float: """Estimate kernel width. @@ -272,9 +275,9 @@ def _estimate_kwidth( def _kernel_estimate_propensity_scores( K: ArrayLike, group_ind: ArrayLike, - penalty: float = None, - n_jobs: int = None, - random_state: int = None, + penalty: Optional[float] = None, + n_jobs: Optional[int] = None, + random_state: Optional[int] = None, ) -> ArrayLike: """Estimate propensity scores given kernel and propensities. diff --git a/dodiscover/ci/monte_carlo.py b/dodiscover/ci/monte_carlo.py index 3f20d509d..71349df6d 100644 --- a/dodiscover/ci/monte_carlo.py +++ b/dodiscover/ci/monte_carlo.py @@ -1,3 +1,5 @@ +from typing import Optional + import numpy as np import scipy.spatial from numpy.typing import ArrayLike @@ -5,7 +7,7 @@ def generate_knn_in_subspace( - z_arr: ArrayLike, method: str = "knn", k: int = 1, n_jobs: int = None + z_arr: ArrayLike, method: str = "knn", k: int = 1, n_jobs: Optional[int] = None ) -> ArrayLike: """Generate kNN in subspace. diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index dce215fd3..dc3087e7c 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -153,15 +153,12 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: constructor. """ self.context_ = make_context(context).build() - graph = self.context_.init_graph - self.init_graph_ = graph - self.fixed_edges_ = self.context_.included_edges # create a reference to the underlying data to be used self.X_ = data # initialize graph object to apply learning - self.separating_sets_ = self._initialize_sep_sets(self.init_graph_) + self.separating_sets_ = self._initialize_sep_sets(self.context_.init_graph) # learn skeleton graph and the separating sets per variable graph, self.separating_sets_ = self.learn_skeleton( diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index 0f92c07a3..2dcc6def7 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -801,33 +801,6 @@ def _apply_rules_1to10(self, graph: EquivalenceClass, sep_set: SeparatingSet): def learn_skeleton( self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None ) -> Tuple[nx.Graph, SeparatingSet]: - import pywhy_graphs - - from dodiscover import make_context - - # initially learn the skeleton - skel_graph, sep_set = super().learn_skeleton(data, context, sep_set) - - # convert the undirected skeleton graph to a PAG, where - # all left-over edges have a "circle" endpoint - pag = pywhy_graphs.PAG(incoming_circle_edges=skel_graph, name="PAG derived with FCI") - - # orient colliders - self.orient_unshielded_triples(pag, sep_set) - - # convert the adjacency graph - new_init_graph = pag.to_undirected() - - # Update the Context: - # add the corresponding intermediate PAG now to the context - # new initialization graph - for (_, _, d) in new_init_graph.edges(data=True): - if "test_stat" in d: - d.pop("test_stat") - if "pvalue" in d: - d.pop("pvalue") - context = make_context(context).graph(new_init_graph).state_variable("PAG", pag).build() - # # now compute all possibly d-separating sets and learn a better skeleton skel_alg = LearnSemiMarkovianSkeleton( self.ci_estimator, @@ -836,7 +809,8 @@ def learn_skeleton( min_cond_set_size=self.min_cond_set_size, max_cond_set_size=self.max_cond_set_size, max_combinations=self.max_combinations, - skeleton_method=self.pds_skeleton_method, + skeleton_method=self.skeleton_method, + second_stage_skeleton_method=self.pds_skeleton_method, keep_sorted=False, max_path_length=self.max_path_length, **self.ci_estimator_kwargs, @@ -849,7 +823,7 @@ def learn_skeleton( return skel_graph, sep_set def fit(self, data: pd.DataFrame, context: Context) -> None: - return super().fit(data, context) + super().fit(data, context) def orient_edges(self, graph: EquivalenceClass): # orient colliders again diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index 0b55e8cee..ae6164137 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -1,4 +1,4 @@ -from typing import Optional, Tuple +from typing import List, Optional, Tuple import networkx as nx import pandas as pd @@ -112,8 +112,40 @@ def learn_skeleton( ) -> Tuple[nx.Graph, SeparatingSet]: return super().learn_skeleton(data, context, sep_set) - def fit(self, data: pd.DataFrame, context: Context) -> None: - return super().fit(data, context) + def fit(self, data: List[pd.DataFrame], context: Context): + """Learn the relevant causal graph equivalence class. + + From the pairs of datasets, we take all combinations and + construct F-nodes corresponding to those. + + Parameters + ---------- + data : List[pd.DataFrame] + The list of different datasets assigned to different + environments. We assume the first dataset is always + observational. + context : Context + _description_ + + Returns + ------- + _type_ + _description_ + """ + if not isinstance(data, list): + raise RuntimeError("The input datasets must be in a Python list.") + + n_datasets = len(data) + intervention_targets = context.intervention_targets + + if n_datasets - 1 != len(intervention_targets): + raise RuntimeError( + f"There are {n_datasets} passed in, but {len(intervention_targets)} " + f"intervention targets. There must be a matching (number of datasets - 1) and " + f"intervention targets." + ) + + super().fit(data, context) def orient_edges(self, graph: EquivalenceClass): return super().orient_edges(graph) diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index b05123451..f70320630 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1,18 +1,23 @@ import logging from collections import defaultdict +from copy import deepcopy +from functools import reduce from itertools import chain, combinations -from typing import Iterable, Optional, Set, SupportsFloat, Tuple, Union +from typing import Iterable, List, Optional, Set, SupportsFloat, Tuple, Union import networkx as nx import numpy as np import pandas as pd -from dodiscover.ci import BaseConditionalIndependenceTest +from dodiscover.cd import BaseConditionalDiscrepancyTest +from dodiscover.ci import BaseConditionalIndependenceTest, Oracle from dodiscover.constraint.config import SkeletonMethods +from dodiscover.constraint.utils import is_in_sep_set from dodiscover.typing import Column, SeparatingSet +from .._protocol import EquivalenceClass from ..context import Context -from ..context_builder import make_context +from ..context_builder import ContextBuilder, InterventionalContextBuilder, make_context logger = logging.getLogger() @@ -94,7 +99,10 @@ def _assign_weight(u, v, edge_attr): class LearnSkeleton: - """Learn a skeleton graph from a Markovian causal model. + """Learn a skeleton graph from observational data without latent confounding. + + A skeleton graph from a Markovian causal model can be learned completely + with this procedure. Parameters ---------- @@ -142,6 +150,17 @@ class LearnSkeleton: testing 'x' || 'y' given some conditioning set (key name 'pvalue'). sep_set_ : dictionary of dictionary of list of set Mapping node to other nodes to separating sets of variables. + context_ : Context + The result context. Encodes causal assumptions. + min_cond_set_size_ : int + The inferred minimum conditioning set size. + max_cond_set_size_ : int + The inferred maximum conditioning set size. + max_combinations_ : int + The inferred maximum number of combinations of 'Z' to test per + :math:`X \\perp Y | Z`. + n_iters_ : int + The number of iterations the skeleton has been learned. Notes ----- @@ -195,12 +214,14 @@ class LearnSkeleton: """ adj_graph_: nx.Graph - sep_set_: SeparatingSet - remove_edges: Set - context: Context + context_: Context min_cond_set_size_: int max_cond_set_size_: int max_combinations_: int + sep_set_: SeparatingSet + n_iters_: int = 0 + + remove_edges: Set def __init__( self, @@ -212,7 +233,7 @@ def __init__( max_combinations: Optional[int] = None, skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, keep_sorted: bool = False, - **ci_estimator_kwargs, + ci_estimator_kwargs=None, ) -> None: self.ci_estimator = ci_estimator self.sep_set = sep_set @@ -234,10 +255,7 @@ def __init__( def _initialize_params(self) -> None: """Initialize parameters for learning skeleton. - Parameters - ---------- - nodes : list of nodes - The list of nodes that will be present in the learned skeleton graph. + Basic parameters that are used by any constraint-based causal discovery algorithms. """ # error checks of passed in arguments if self.max_combinations is not None and self.max_combinations <= 0: @@ -248,11 +266,11 @@ def _initialize_params(self) -> None: f"Skeleton method must be one of {SkeletonMethods}, not {self.skeleton_method}." ) - if self.sep_set is None: + if self.sep_set is None and not hasattr(self, "sep_set_"): # keep track of separating sets self.sep_set_ = defaultdict(lambda: defaultdict(list)) - else: - self.sep_set_ = self.sep_set + elif not hasattr(self, "sep_set_"): + self.sep_set_ = self.sep_set # type: ignore # control of the conditioning set if self.max_cond_set_size is None: @@ -268,6 +286,9 @@ def _initialize_params(self) -> None: else: self.max_combinations_ = self.max_combinations + if self.ci_estimator_kwargs is None: + self.ci_estimator_kwargs = dict() + def evaluate_edge( self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None ) -> Tuple[float, float]: @@ -297,7 +318,7 @@ def evaluate_edge( self.n_ci_tests += 1 return test_stat, pvalue - def fit(self, data: pd.DataFrame, context: Context) -> None: + def fit(self, data: pd.DataFrame, context: Context): """Run structure learning to learn the skeleton of the causal graph. Parameters @@ -307,11 +328,10 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: context : Context A context object. """ - self.context = make_context(context).build() + self.context_ = context.copy() # get the initialized graph - adj_graph = self.context.init_graph - X = data + adj_graph = deepcopy(self.context_.init_graph.copy()) # track progress of the algorithm for which edges to remove to ensure stability self.remove_edges = set() @@ -322,8 +342,10 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: # the size of the conditioning set will start off at the minimum size_cond_set = self.min_cond_set_size_ + # allow us to query the iteration stage of the causal discovery algorithm + # allowing us to run multiple iterations of the skeleton discovery edge_attrs = set(chain.from_iterable(d.keys() for *_, d in adj_graph.edges(data=True))) - if "test_stat" in edge_attrs or "pvalue" in edge_attrs: + if self.n_iters_ == 0 and "test_stat" in edge_attrs or "pvalue" in edge_attrs: raise RuntimeError( "Running skeleton discovery with adjacency graph " "with 'test_stat' or 'pvalue' is not supported yet." @@ -361,7 +383,7 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: continue # ignore fixed edges - if (x_var, y_var) in self.context.included_edges.edges: + if (x_var, y_var) in self.context_.included_edges.edges: continue # compute the possible variables used in the conditioning set @@ -369,7 +391,6 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: adj_graph, x_var, y_var, - skeleton_method=self.skeleton_method, ) logger.debug( @@ -408,7 +429,7 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: break # compute conditional independence test - test_stat, pvalue = self.evaluate_edge(X, x_var, y_var, set(cond_set)) + test_stat, pvalue = self.evaluate_edge(data, x_var, y_var, set(cond_set)) # if any "independence" is found through inability to reject # the null hypothesis, then we will break the loop comparing X and Y @@ -441,6 +462,7 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: break self.adj_graph_ = adj_graph + self.n_iters_ += 1 def _summarize_xy_comparison( self, x_var: Column, y_var: Column, removed_edge: bool, pvalue: float @@ -458,9 +480,13 @@ def _summarize_xy_comparison( ) def _compute_candidate_conditioning_sets( - self, adj_graph: nx.Graph, x_var: Column, y_var: Column, skeleton_method: SkeletonMethods + self, adj_graph: nx.Graph, x_var: Column, y_var: Column ) -> Set[Column]: - """Compute candidate conditioning sets. + r"""Compute candidate conditioning sets. + + For a given 'X' and 'Y', this method implements a graphical algorithm that + enumerates possible variables that are part of 'Z', the conditioning set. + One can then test the following null hypothesis :math:`H_0: X \perp Y | Z`. Parameters ---------- @@ -470,16 +496,20 @@ def _compute_candidate_conditioning_sets( The 'X' node. y_var : node The 'Y' node. - skeleton_method : SkeletonMethods - The skeleton method, which dictates how we choose the corresponding - conditioning sets. Returns ------- - possible_variables : Set + possible_variables : Set of Column The set of nodes in 'adj_graph' that are candidates for the conditioning set. + + Notes + ----- + The :attr:`skeleton_method` dictates how we choose the corresponding conditioning sets. + For more information, see :class:`SkeletonMethods`. """ + skeleton_method = self.skeleton_method + if skeleton_method == SkeletonMethods.COMPLETE: possible_variables = set(adj_graph.nodes) elif skeleton_method == SkeletonMethods.NBRS: @@ -566,8 +596,12 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. skeleton_method : SkeletonMethods - The method to use for testing conditional independence. Must be one of - ('pds', 'pds_path'). See Notes for more details. + The method to use for determining conditioning sets when testing conditional + independence of the first stage. See :class:`LearnSkeleton` for details. + second_stage_skeleton_method : SkeletonMethods | None + The method to use for determining conditioning sets when testing conditional + independence of the first stage. Must be one of ('pds', 'pds_path'). See Notes + for more details. If `None`, then no second stage skeleton discovery phase will be run. keep_sorted : bool Whether or not to keep the considered conditioning set variables in sorted dependency order. If True (default) will sort the existing dependencies of each variable @@ -579,6 +613,29 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): ci_estimator_kwargs : dict Keyword arguments for the ``ci_estimator`` function. + Attributes + ---------- + adj_graph_ : nx.Graph + The discovered graph from data. Stored using an undirected + graph. The graph contains edge attributes for the smallest value of the + test statistic encountered (key name 'test_stat'), the largest pvalue seen in + testing 'x' || 'y' given some conditioning set (key name 'pvalue'). + sep_set_ : dictionary of dictionary of list of set + Mapping node to other nodes to separating sets of variables. + context_ : Context + The result context. Encodes causal assumptions. + min_cond_set_size_ : int + The inferred minimum conditioning set size. + max_cond_set_size_ : int + The inferred maximum conditioning set size. + max_combinations_ : int + The inferred maximum number of combinations of 'Z' to test per + :math:`X \\perp Y | Z`. + n_iters_ : int + The number of iterations the skeleton has been learned. + max_path_length_ : int + Th inferred maximum path length any single discriminating path is allowed to take. + Notes ----- To learn the skeleton of a Semi-Markovian causal model, one approach is to consider @@ -608,6 +665,8 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): .. footbibliography:: """ + max_path_length_: int + def __init__( self, ci_estimator: BaseConditionalIndependenceTest, @@ -616,10 +675,11 @@ def __init__( min_cond_set_size: int = 0, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.PDS, + skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + second_stage_skeleton_method: Optional[SkeletonMethods] = SkeletonMethods.PDS, keep_sorted: bool = False, max_path_length: Optional[int] = None, - **ci_estimator_kwargs, + ci_estimator_kwargs=None, ) -> None: super().__init__( ci_estimator, @@ -630,41 +690,75 @@ def __init__( max_combinations, skeleton_method, keep_sorted, - **ci_estimator_kwargs, + ci_estimator_kwargs=ci_estimator_kwargs, ) - if max_path_length is None: - max_path_length = np.inf + + self.second_stage_skeleton_method = second_stage_skeleton_method self.max_path_length = max_path_length + def _orient_unshielded_triples(self, graph: EquivalenceClass, sep_set: SeparatingSet) -> None: + """Orient colliders given a graph and separation set. + + Parameters + ---------- + graph : EquivalenceClass + The partial ancestral graph (PAG). + sep_set : SeparatingSet + The separating set between any two nodes. + """ + # for every node in the PAG, evaluate neighbors that have any edge + for u in graph.nodes: + for v_i, v_j in combinations(graph.neighbors(u), 2): + # Check that there is no edge of any type between + # v_i and v_j, else this is a "shielded" collider. + # Then check to see if 'u' is in the separating + # set. If it is not, then there is a collider. + if v_j not in graph.neighbors(v_i) and not is_in_sep_set( + u, sep_set, v_i, v_j, mode="any" + ): + if graph.has_edge(v_i, u, graph.circle_edge_name): + graph.orient_uncertain_edge(v_i, u) + if graph.has_edge(v_j, u, graph.circle_edge_name): + graph.orient_uncertain_edge(v_j, u) + def _compute_candidate_conditioning_sets( - self, adj_graph: nx.Graph, x_var: Column, y_var: Column, skeleton_method: SkeletonMethods + self, adj_graph: nx.Graph, x_var: Column, y_var: Column ) -> Set[Column]: import pywhy_graphs as pgraph # get PAG from the context object - pag = self.context.state_variable("PAG") + pag = self.context_.state_variable("PAG", on_missing="ignore") - if skeleton_method == SkeletonMethods.PDS: - # determine how we want to construct the candidates for separating nodes - # perform conditioning independence testing on all combinations - possible_variables = pgraph.pds( - pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore - ) - elif skeleton_method == SkeletonMethods.PDS_PATH: - # determine how we want to construct the candidates for separating nodes - # perform conditioning independence testing on all combinations - possible_variables = pgraph.pds_path( - pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore - ) - - if self.keep_sorted: - # Note it is assumed in public API that 'test_stat' is set - # inside the adj_graph - possible_variables = sorted( - possible_variables, - key=lambda n: adj_graph.edges[x_var, n]["test_stat"], - reverse=True, - ) # type: ignore + if pag is None: + # if PAG has not been set as a state variable, then we are learning a skeleton + # without PDS information. I.e. the normal LearnSkeleton algorithm + return super()._compute_candidate_conditioning_sets(adj_graph, x_var, y_var) + else: + if not all(x in pag.nodes for x in [x_var, y_var]): + raise RuntimeError("wtf..") + skeleton_method = self.second_stage_skeleton_method + + if skeleton_method == SkeletonMethods.PDS: + # determine how we want to construct the candidates for separating nodes + # perform conditioning independence testing on all combinations + possible_variables = pgraph.pds( + pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore + ) + elif skeleton_method == SkeletonMethods.PDS_PATH: + # determine how we want to construct the candidates for separating nodes + # perform conditioning independence testing on all combinations + possible_variables = pgraph.pds_path( + pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore + ) + + if self.keep_sorted: + # Note it is assumed in public API that 'test_stat' is set + # inside the adj_graph + possible_variables = sorted( + possible_variables, + key=lambda n: adj_graph.edges[x_var, n]["test_stat"], + reverse=True, + ) # type: ignore if x_var in possible_variables: possible_variables.remove(x_var) @@ -673,5 +767,471 @@ def _compute_candidate_conditioning_sets( return possible_variables - def fit(self, data: pd.DataFrame, context: Context) -> None: - return super().fit(data, context) + def fit(self, data: pd.DataFrame, context: Context): + import pywhy_graphs as pgraphs + + if self.max_path_length is None: + self.max_path_length_ = np.inf + else: + self.max_path_length_ = self.max_path_length + + # initially learn the skeleton without using PDS information + super().fit(data, context) + + # if there is no second stage skeleton method to be run, then we + # will stop with the skeleton here + if self.second_stage_skeleton_method is None: + return self + + # convert the undirected skeleton graph to a PAG, where + # all left-over edges have a "circle" endpoint + sep_set = self.sep_set_ + skel_graph = self.adj_graph_ + pag = pgraphs.PAG(incoming_circle_edges=skel_graph, name="PAG derived with FCI") + + # orient colliders + self._orient_unshielded_triples(pag, sep_set) + + # convert the adjacency graph + new_init_graph = pag.to_undirected() + + # Update the Context: + # add the corresponding intermediate PAG now to the context + # new initialization graph + for (_, _, d) in new_init_graph.edges(data=True): + if "test_stat" in d: + d.pop("test_stat") + if "pvalue" in d: + d.pop("pvalue") + context = ( + make_context(context).init_graph(new_init_graph).state_variable("PAG", pag).build() + ) + + if not all(x in context.state_variable("PAG").nodes for x in data.columns): + raise RuntimeError("wtf..") + + # now compute all possibly d-separating sets and learn a better skeleton + super().fit(data, context) + return self + + +class LearnInterventionSkeleton(LearnSemiMarkovianSkeleton): + """Learn skeleton using observational and interventional data. + + Parameters + ---------- + ci_estimator : BaseConditionalIndependenceTest + The conditional independence test function. + cd_estimator : BaseConditionalDiscrepancyTest + The conditional discrepancy test function. + sep_set : dictionary of dictionary of list of set + Mapping node to other nodes to separating sets of variables. + If ``None``, then an empty dictionary of dictionary of list of sets + will be initialized. + alpha : float, optional + The significance level for the conditional independence test, by default 0.05. + min_cond_set_size : int + The minimum size of the conditioning set, by default 0. The number of variables + used in the conditioning set. + max_cond_set_size : int, optional + Maximum size of the conditioning set, by default None. Used to limit + the computation spent on the algorithm. + max_combinations : int, optional + The maximum number of conditional independence tests to run from the set + of possible conditioning sets. By default None, which means the algorithm will + check all possible conditioning sets. If ``max_combinations=n`` is set, then + for every conditioning set size, 'p', there will be at most 'n' CI tests run + before the conditioning set size 'p' is incremented. For controlling the size + of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used + in conjunction with ``keep_sorted`` parameter to only test the "strongest" + dependences. + skeleton_method : SkeletonMethods + The method to use for testing conditional independence. Must be one of + ('pds', 'pds_path'). See Notes for more details. + keep_sorted : bool + Whether or not to keep the considered conditioning set variables in sorted + dependency order. If True (default) will sort the existing dependencies of each variable + by its dependencies from strongest to weakest (i.e. largest CI test statistic value + to lowest). This can be used in conjunction with ``max_combinations`` parameter + to only test the "strongest" dependences. + max_path_length : int, optional + The maximum length of any discriminating path, or None if unlimited. + ci_estimator_kwargs : dict + Keyword arguments for the ``ci_estimator`` function. + cd_estimator_kwargs : dict + Keyword arguments for the ``cd_estimator`` function. + + Notes + ----- + With interventional data, one may either know the interventional targets from each + experimental distribution dataset, or one may not know the explicit targets. If the + interventional targets are known, then the skeleton discovery algorithm of + :footcite:`Kocaoglu2019characterization` is used. That is we learn the skeleton of a + IPAG. Otherwise, we will not know the intervention targets, and use the skeleton discovery + algorithm described in :footcite:`Jaber2020causal`. To define intervention targets, one + must use the :class:`dodiscover.InterventionalContextBuilder`. + + References + ---------- + .. footbibliography:: + """ + + def __init__( + self, + ci_estimator: BaseConditionalIndependenceTest, + cd_estimator: BaseConditionalDiscrepancyTest, + sep_set: Optional[SeparatingSet] = None, + alpha: float = 0.05, + min_cond_set_size: int = 0, + max_cond_set_size: Optional[int] = None, + max_combinations: Optional[int] = None, + skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + second_stage_skeleton_method: SkeletonMethods = SkeletonMethods.PDS, + keep_sorted: bool = False, + max_path_length: Optional[int] = None, + known_intervention_targets: bool = False, + ci_estimator_kwargs=None, + cd_estimator_kwargs=None, + ) -> None: + super().__init__( + ci_estimator, + sep_set, + alpha, + min_cond_set_size, + max_cond_set_size, + max_combinations, + skeleton_method, + second_stage_skeleton_method, + keep_sorted, + max_path_length, + ci_estimator_kwargs, + ) + + self.cd_estimator = cd_estimator + self.known_intervention_targets = known_intervention_targets + self.cd_estimator_kwargs = cd_estimator_kwargs + + def evaluate_fnode_edge( + self, + data: List[pd.DataFrame], + X: Set[Column], + Y: Set[Column], + Z: Optional[Set[Column]] = None, + ) -> Tuple[float, float]: + """Test an edge from an F-node to a regular node for X || Y | Z. + + Parameters + ---------- + data : pd.DataFrame + The dataset + X : column + A column in ``data``. This is assumed to be the F-node. + Y : column + A column in ``data``. + Z : set, optional + A list of columns in ``data``, by default None. + + Returns + ------- + test_stat : float + Test statistic. + pvalue : float + The pvalue. + """ + if Z is None: + Z = set() + + # extract the F-node name + group_col: Column = reduce(lambda x: x, X) # type: ignore + + # get the sigma-map for this F-node + distribution_idx = self.context_.sigma_map[group_col] + + # get the distributions across the two distributions + data_i = data[distribution_idx[0]] + data_j = data[distribution_idx[1]] + + # name the group column the F-node, so Oracle works as expected + data_i[group_col] = 0 + data_j[group_col] = 1 + data = pd.concat((data_i, data_j), axis=0) + + # compare conditional distributions P(Y | X) vs P'(Y | X), where 'group_col' + # indicates which distribution data came from + if isinstance(self.cd_estimator, Oracle): + # test graphically if Y is d-separated from F-node given Z + test_stat, pvalue = self.cd_estimator.test( + data, {group_col}, Y, Z, **self.cd_estimator_kwargs + ) + else: + # test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) + test_stat, pvalue = self.cd_estimator.test( + data, Z, Y, group_col, **self.cd_estimator_kwargs + ) + + self.n_ci_tests += 1 + return test_stat, pvalue + + def _compute_candidate_conditioning_sets( + self, adj_graph: nx.Graph, x_var: Column, y_var: Column + ) -> Set[Column]: + """Override the computation for conditioning sets. + + Parameters + ---------- + adj_graph : nx.Graph + _description_ + x_var : Column + _description_ + y_var : Column + _description_ + + Returns + ------- + Z : Set[Column] + _description_ + """ + f_nodes = self.context_.f_nodes + + # if F-nodes is not defined, then we are simply doing learning in the observational setting + if len(f_nodes) == 0: + # compute the possible variables used in the conditioning set + return super()._compute_candidate_conditioning_sets(adj_graph, x_var, y_var) + else: + if y_var in f_nodes: + raise RuntimeError("This should not be the case") + + # get only neighboring sets of Y-vars, or PDS that depend on Y + possible_variables = set(adj_graph.neighbors(y_var)) + return possible_variables + + def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], context: Context): + self.context_ = make_context(context, create_using=InterventionalContextBuilder).build() + + # get the initialized graph + adj_graph = self.context_.init_graph + f_nodes = self.context_.f_nodes + + print("Starting learning int skeleton: ", f_nodes) + + # the size of the conditioning set will start off at the minimum + size_cond_set = self.min_cond_set_size_ + + # track progress of the algorithm for which edges to remove to ensure stability + self.remove_edges = set() + + # Outer loop: iterate over 'size_cond_set' until stopping criterion is met + # - 'size_cond_set' > 'max_cond_set_size' or + # - All (X, Y) pairs have candidate conditioning sets of size < 'size_cond_set' + while 1: + cont = False + # initialize set of edges to remove at the end of every loop + self.remove_edges = set() + + # loop through every node + for x_var in f_nodes: + possible_adjacencies = set(adj_graph.neighbors(x_var)) + + logger.info(f"Considering node {x_var}...\n\n") + + for y_var in possible_adjacencies: + # a node cannot be a parent to itself in DAGs + if y_var == x_var: + continue + + # if Y is also a F-node, then they are automatically assumed d-separated + if y_var in f_nodes: + pvalue = 1.0 + test_stat = 0.0 + + # post-process the CI test results + removed_edge = self._postprocess_ci_test( + adj_graph, x_var, y_var, set(), test_stat, pvalue + ) + + # summarize the comparison of XY + self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) + continue + + # TODO: allow ignoring fixed edges + # if self.context_.included_edges.has_edge(x_var, y_var): + # continue + # if self.context_.excluded_edges.has_edge(x_var, y_var): + # pvalue = 1.0 + # test_stat = 0.0 + + # # post-process the CI test results + # removed_edge = self._postprocess_ci_test( + # adj_graph, x_var, y_var, {}, test_stat, pvalue + # ) + + # # summarize the comparison of XY + # self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) + # continue + + # compute the possible variables used in the conditioning set + possible_variables = self._compute_candidate_conditioning_sets( + adj_graph, + x_var, + y_var, + ) + + logger.debug( + f"Adj({x_var}) without {y_var} with size={len(possible_adjacencies) - 1} " + f"with p={size_cond_set}. The possible variables to condition on are: " + f"{possible_variables}." + ) + + # check that number of adjacencies is greater then the + # cardinality of the conditioning set + if len(possible_variables) < size_cond_set: + logger.debug( + f"\n\nBreaking for {x_var}, {y_var}, {len(possible_adjacencies)}, " + f"{size_cond_set}, {possible_variables}" + ) + continue + else: + cont = True + + # generate iterator through the conditioning sets + conditioning_sets = _iter_conditioning_set( + possible_variables=possible_variables, + x_var=x_var, + y_var=y_var, + size_cond_set=size_cond_set, + ) + + # now iterate through the possible parents + for comb_idx, cond_set in enumerate(conditioning_sets): + # check the number of combinations of possible parents we have tried + # to use as a separating set + if ( + self.max_combinations_ is not None + and comb_idx >= self.max_combinations_ + ): + break + + # compute conditional independence test + test_stat, pvalue = self.evaluate_fnode_edge( + interv_data, {x_var}, {y_var}, set(cond_set) + ) + + # if any "independence" is found through inability to reject + # the null hypothesis, then we will break the loop comparing X and Y + # and say X and Y are conditionally independent given 'cond_set' + if pvalue > self.alpha: + break + + # post-process the CI test results + removed_edge = self._postprocess_ci_test( + adj_graph, x_var, y_var, cond_set, test_stat, pvalue + ) + + # summarize the comparison of XY + self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) + + # finally remove edges after performing + # conditional independence tests + logger.info(f"For p = {size_cond_set}, removing all edges: {self.remove_edges}") + + # Remove non-significant links + # Note: Removing edges at the end ensures "stability" of the algorithm + # with respect to the randomness choice of pairs of edges considered in the inner loop + adj_graph.remove_edges_from(self.remove_edges) + + # increment the conditioning set size + size_cond_set += 1 + + # only allow conditioning set sizes up to maximum set number + if size_cond_set > self.max_cond_set_size_ or cont is False: + break + + self.adj_graph_ = adj_graph + + def _learn_skeleton_with_observations(self, obs_data: pd.DataFrame, context: Context): + f_nodes = context.f_nodes + + # get the init graph that does not contain any F-nodes + obs_context_bld = make_context(context, create_using=ContextBuilder) + init_graph = deepcopy(context.init_graph) + + # get the subgraph of non-f nodes + non_f_nodes = set(init_graph.nodes) - set(f_nodes) + obs_init_graph = init_graph.subgraph(non_f_nodes) + + # now learn the observational subgraph + obs_context_bld.init_graph(obs_init_graph) + obs_context = obs_context_bld.build() + super().fit(obs_data, obs_context) + + def fit(self, data: List[pd.DataFrame], context: Context) -> None: + """Fit data and context. + + Parameters + ---------- + data : List[pd.DataFrame] + List of dataframes corresponding to different distributions of data. + context : Context + Context object. + """ + if self.cd_estimator_kwargs is None: + self.cd_estimator_kwargs = dict() + + # error-check the datasets passed in match the intervention contexts + if len(data) != context.num_distributions: + raise RuntimeError( + f"The number of datasets does not match the number of interventions. " + f"You passed in {len(data)} different datasets, whereas " + f"there are {len(context.intervention_targets)} different interventions " + f"specified. It is assumed that the first dataset is observational, " + f"while the rest are interventional." + ) + + orig_context = context.copy() + f_nodes = context.f_nodes + + # it is fine to run the first stage of the FCI algorithm, as this will + # not result in removing any edges among the F-nodes + obs_data = data[0] + self._learn_skeleton_with_observations(obs_data, context) + + # keep track of the observational skeleton graph + obs_skel_graph = self.adj_graph_.copy() + + # all separating sets are either: + # i) augmented with all F-nodes, or + # ii) augmented with all F-nodes except intervention index 'i' + # R9 allows us to leverage F-nodes being not in separating sets to + + # augment all separating sets that have non-empty sets with all + # F-nodes to keep consistency with the algorithm + for x_var, y_vars in self.sep_set_.items(): + for y_var in y_vars: + sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + if len(sep_sets) > 0: + for idx in range(len(sep_sets)): + self.sep_set_[x_var][y_var][idx].update(f_nodes) + + # index all datasets, where the first one may be observational + non_f_nodes = self.context_.get_non_f_nodes() + + # reset the init graph and this time learn the skeleton using + # interventional distributions + # create a complete subgraph of F-nodes with all other nodes + for node in f_nodes: + for obs_node in set(f_nodes).union(set(non_f_nodes)): + if node == obs_node: + continue + self.adj_graph_.add_edge(node, obs_node, test_stat=np.inf, pvalue=-1e-5) + + # reset context and add observational skeleton + self.context_ = ( + make_context(orig_context, create_using=InterventionalContextBuilder) + .init_graph(self.adj_graph_.copy()) + .build() + ) + self.context_.add_state_variable("obs_skel_graph", obs_skel_graph) + + # now, we'll fit the data using interventional data by looping over all + # combinations of F-nodes and their neighbors + self._learn_skeleton_with_interventions(data, self.context_) diff --git a/dodiscover/context.py b/dodiscover/context.py index 8830af853..71dc5f3c3 100644 --- a/dodiscover/context.py +++ b/dodiscover/context.py @@ -1,12 +1,16 @@ -from typing import Any, Dict, Set +from dataclasses import dataclass, field +from typing import Any, Dict, FrozenSet, List, Set, Tuple +from warnings import warn import networkx as nx from ._protocol import Graph -from .typing import Column, NetworkxGraph +from .base import BasePyWhy +from .typing import Column -class Context: +@dataclass(eq=True, frozen=True) +class Context(BasePyWhy): """Context of assumptions, domain knowledge and data. This should NOT be instantiated directly. One should instead @@ -28,6 +32,12 @@ class Context: Included edges without direction. excluded_edges : nx.Graph Excluded edges without direction. + state_variables : Dict + Name of intermediate state variables during the learning process. + intervention_targets : list of tuples + List of intervention targets (known, or unknown), which correspond to + the nodes in the graph (known), or indices of datasets that contain + interventions (unknown). Raises ------ @@ -42,57 +52,43 @@ class Context: is used in conjunction with a discovery algorithm. Setting the a priori explicit direction of an edge is not supported yet. + + **Testing for equality** + + Currently, testing for equality is done on all attributes that are not + graphs. Defining equality among graphs is ill-defined, and as such, we + leave testing of the internal graphs to users. Some checks of equality + for example can be :func:`nx.is_isomorphic` for checking isomorphism + among two graphs. """ - _variables: Set[Column] - _latents: Set[Column] - _init_graph: Graph - _included_edges: nx.Graph - _excluded_edges: nx.Graph - _state_variables: Dict[str, Any] - - def __init__( - self, - variables: Set[Column], - latents: Set[Column], - init_graph: Graph, - included_edges: NetworkxGraph, - excluded_edges: NetworkxGraph, - state_variables: Dict[str, Any], - ) -> None: - # set to class - self._state_variables = state_variables - self._variables = variables - self._latents = latents - self._init_graph = init_graph - self._included_edges = included_edges - self._excluded_edges = excluded_edges - - @property - def included_edges(self) -> nx.Graph: - return self._included_edges - - @property - def excluded_edges(self) -> nx.Graph: - return self._excluded_edges - - @property - def init_graph(self) -> Graph: - return self._init_graph - - @property - def observed_variables(self) -> Set[Column]: - return self._variables - - @property - def latent_variables(self) -> Set[Column]: - return self._latents - - @property - def state_variables(self) -> Dict[str, Any]: - return self._state_variables - - def add_state_variable(self, name: str, var: Any) -> None: + observed_variables: Set[Column] + latent_variables: Set[Column] + state_variables: Dict[str, Any] + init_graph: Graph = field(compare=False) + included_edges: nx.Graph = field(compare=False) + excluded_edges: nx.Graph = field(compare=False) + + ######################################################## + # for interventional data + ######################################################## + # the number of distributions we expect to have access to + num_distributions: int = field(default=1) + + # whether or not observational distribution is present + obs_distribution: bool = field(default=True) + + # (optional) known intervention targets, corresponding to nodes in the graph + intervention_targets: List[Tuple[Column]] = field(default_factory=list) + + # (optional) mapping F-nodes to their symmetric difference intervention targets + symmetric_diff_map: Dict[Any, FrozenSet] = field(default_factory=dict) + + # sigma-map mapping F-nodes to their distribution indices + sigma_map: Dict[Any, Tuple] = field(default_factory=dict) + f_nodes: List = field(default_factory=list) + + def add_state_variable(self, name: str, var: Any) -> "Context": """Add a state variable. Called by an algorithm to persist data objects that @@ -105,22 +101,54 @@ def add_state_variable(self, name: str, var: Any) -> None: var : any Any state variable. """ - self._state_variables[name] = var + self.state_variables[name] = var + return self - def state_variable(self, name: str) -> Any: + def state_variable(self, name: str, on_missing: str = "raise") -> Any: """Get a state variable. Parameters ---------- name : str The name of the state variable. + on_missing : one of {'raise', 'warn', 'ignore'} + Behavior if ``name`` is not in the dictionary of state variables. + If 'raise' (default) will raise a RuntimeError. If 'warn', will + raise a UserWarning. If 'ignore', will return `None`. Returns ------- state_var : Any The state variable. """ - if name not in self._state_variables: - raise RuntimeError(f"{name} is not a state variable: {self._state_variables}") - - return self._state_variables[name] + if name not in self.state_variables and on_missing != "ignore": + err_msg = f"{name} is not a state variable: {self.state_variables}" + if on_missing == "raise": + raise RuntimeError(err_msg) + elif on_missing == "warn": + warn(err_msg) + + return self.state_variables.get(name) + + def copy(self) -> "Context": + """Create a deepcopy of the context.""" + return Context(**self.get_params(deep=True)) + + ############################################################### + # Methods for interventional data. + ############################################################### + def get_non_f_nodes(self) -> Set: + """Get the set of non f-nodes.""" + non_f_nodes = set() + f_nodes = set(self.f_nodes) + for node in self.init_graph.nodes: + if node not in f_nodes: + non_f_nodes.add(node) + return non_f_nodes + + def reverse_sigma_map(self) -> Dict: + """Get the reverse sigma-map.""" + reverse_map = dict() + for node, mapping in self.sigma_map.items(): + reverse_map[mapping] = node + return reverse_map diff --git a/dodiscover/context_builder.py b/dodiscover/context_builder.py index dd331e0be..ea8f9b406 100644 --- a/dodiscover/context_builder.py +++ b/dodiscover/context_builder.py @@ -1,30 +1,54 @@ -from copy import copy, deepcopy -from typing import Any, Dict, Optional, Set, Tuple, cast +import types +from copy import copy +from itertools import combinations +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, cast import networkx as nx +import numpy as np import pandas as pd from ._protocol import Graph from .context import Context from .typing import Column, NetworkxGraph +CALLABLES = types.FunctionType, types.MethodType + class ContextBuilder: - """A builder class for creating Context objects ergonomically. + """A builder class for creating observational data Context objects ergonomically. + + The ContextBuilder is meant solely to build Context objects that work + with observational datasets. The context builder provides a way to capture assumptions, domain knowledge, and data. This should NOT be instantiated directly. One should instead use `dodiscover.make_context` to build a Context data structure. """ - _graph: Optional[Graph] = None + _init_graph: Optional[Graph] = None _included_edges: Optional[NetworkxGraph] = None _excluded_edges: Optional[NetworkxGraph] = None _observed_variables: Optional[Set[Column]] = None _latent_variables: Optional[Set[Column]] = None _state_variables: Dict[str, Any] = dict() - def graph(self, graph: Graph) -> "ContextBuilder": + def __init__(self) -> None: + # perform an error-check on subclass definitions of ContextBuilder + for attribute, value in self.__class__.__dict__.items(): + if isinstance(value, CALLABLES) or isinstance(value, property): + continue + if attribute.startswith("__"): + continue + + if not hasattr(self, attribute[1:]): + raise RuntimeError( + f"Context objects has class attributes that do not have " + f"a matching class method to set the attribute, {attribute}. " + f"The form of the attribute must be '_' and a " + f"corresponding function name ''." + ) + + def init_graph(self, graph: Graph) -> "ContextBuilder": """Set the partial graph to start with. Parameters @@ -37,7 +61,47 @@ def graph(self, graph: Graph) -> "ContextBuilder": self : ContextBuilder The builder instance """ - self._graph = graph + self._init_graph = graph + return self + + def excluded_edges(self, exclude: Optional[NetworkxGraph]) -> "ContextBuilder": + """Set exclusion edge constraints to apply in discovery. + + Parameters + ---------- + excluded : Optional[NetworkxGraph] + Edges that should be excluded in the resultant graph + + Returns + ------- + self : ContextBuilder + The builder instance + """ + if self._included_edges is not None: + for u, v in exclude.edges: # type: ignore + if self._included_edges.has_edge(u, v): + raise RuntimeError(f"{(u, v)} is already specified as an included edge.") + self._excluded_edges = exclude + return self + + def included_edges(self, include: Optional[NetworkxGraph]) -> "ContextBuilder": + """Set inclusion edge constraints to apply in discovery. + + Parameters + ---------- + included : Optional[NetworkxGraph] + Edges that should be included in the resultant graph + + Returns + ------- + self : ContextBuilder + The builder instance + """ + if self._excluded_edges is not None: + for u, v in include.edges: # type: ignore + if self._excluded_edges.has_edge(u, v): + raise RuntimeError(f"{(u, v)} is already specified as an excluded edge.") + self._included_edges = include return self def edges( @@ -63,6 +127,46 @@ def edges( self._excluded_edges = exclude return self + def observed_variables(self, observed: Optional[Set[Column]] = None) -> "ContextBuilder": + """Set observed variables. + + Parameters + ---------- + observed : Optional[Set[Column]] + Set of observed variables, by default None. If neither ``latents``, + nor ``variables`` is set, then it is presumed that ``variables`` consists + of the columns of ``data`` and ``latents`` is the empty set. + """ + if self._latent_variables is not None and any( + obs_var in self._latent_variables for obs_var in observed # type: ignore + ): + raise RuntimeError( + f"Latent variables are set already {self._latent_variables}, " + f'which contain variables you are trying to set as "observed".' + ) + self._observed_variables = observed + return self + + def latent_variables(self, latents: Optional[Set[Column]] = None) -> "ContextBuilder": + """Set latent variables. + + Parameters + ---------- + latents : Optional[Set[Column]] + Set of latent "unobserved" variables, by default None. If neither ``latents``, + nor ``variables`` is set, then it is presumed that ``variables`` consists + of the columns of ``data`` and ``latents`` is the empty set. + """ + if self._observed_variables is not None and any( + latent_var in self._observed_variables for latent_var in latents # type: ignore + ): + raise RuntimeError( + f"Observed variables are set already {self._observed_variables}, " + f'which contain variables you are trying to set as "latent".' + ) + self._latent_variables = latents + return self + def variables( self, observed: Optional[Set[Column]] = None, @@ -145,13 +249,13 @@ def build(self) -> Context: if self._observed_variables is None: raise ValueError("Could not infer variables from data or given arguments.") - empty_graph = lambda: nx.empty_graph(self._observed_variables, create_using=nx.Graph) + empty_graph = self._empty_graph_func(self._observed_variables) return Context( - init_graph=self._interpolate_graph(), + init_graph=self._interpolate_graph(self._observed_variables), included_edges=self._included_edges or empty_graph(), excluded_edges=self._excluded_edges or empty_graph(), - variables=self._observed_variables, - latents=self._latent_variables or set(), + observed_variables=self._observed_variables, + latent_variables=self._latent_variables or set(), state_variables=self._state_variables, ) @@ -166,7 +270,7 @@ def _interpolate_variables( if observed is not None and latents is not None: if columns - set(observed) != set(latents): raise ValueError( - "If observed and latents are set, then they must be " + "If observed and latents are both set, then they must " "include all columns in data." ) elif observed is None and latents is not None: @@ -183,26 +287,215 @@ def _interpolate_variables( latents = set(cast(Set[Column], latents)) return (observed, latents) - def _interpolate_graph(self) -> nx.Graph: + def _interpolate_graph(self, graph_variables) -> nx.Graph: if self._observed_variables is None: raise ValueError("Must set variables() before building Context.") - complete_graph = lambda: nx.complete_graph(self._observed_variables, create_using=nx.Graph) - has_all_variables = lambda g: set(g.nodes) == set(self._observed_variables) + complete_graph = lambda: nx.complete_graph(graph_variables, create_using=nx.Graph) + has_all_variables = lambda g: set(g.nodes).issuperset(set(self._observed_variables)) # initialize the starting graph - if self._graph is None: + if self._init_graph is None: return complete_graph() else: - if not has_all_variables(self._graph): + if not has_all_variables(self._init_graph): raise ValueError( - f"The nodes within the initial graph, {self._graph.nodes}, " + f"The nodes within the initial graph, {self._init_graph.nodes}, " f"do not match the nodes in the passed in data, {self._observed_variables}." ) - return self._graph + return self._init_graph + + def _empty_graph_func(self, graph_variables) -> Callable: + empty_graph = lambda: nx.empty_graph(graph_variables, create_using=nx.Graph) + return empty_graph -def make_context(context: Optional[Context] = None) -> ContextBuilder: +class InterventionalContextBuilder(ContextBuilder): + """A builder class for creating observational+interventional data Context objects. + + The InterventionalContextBuilder is meant solely to build Context objects that work + with observational + interventional datasets. + + The context builder provides a way to capture assumptions, domain knowledge, + and data. This should NOT be instantiated directly. One should instead use + `dodiscover.make_context` to build a Context data structure. + + Notes + ----- + The number of distributions and/or interventional targets must be set in order + to build the `Context` object here. + """ + + _intervention_targets: List[Tuple[Column]] = [] + _num_distributions: Optional[int] = None + _obs_distribution: bool = True + + def obs_distribution(self, has_obs_distrib: bool): + """Whether or not we have access to the observational distribution.""" + self._obs_distribution = has_obs_distrib + return self + + def num_distributions(self, num_distribs: int): + """Set the number of data distributions we are expected to have access to. + + Note this must include observational too if observational is assumed present. + To assume that we do not have access to observational data, use the + :meth:`InterventionalContextBuilder.obs_distribution` to turn off that assumption. + + Parameters + ---------- + num_distribs : int + Number of distributions we will have access to. Will set the number of + distributions to be ``num_distribs + 1`` if ``_obs_distribution is True`` (default). + """ + if len(self._intervention_targets) > 0 and ( + len(self._intervention_targets) + int(self._obs_distribution) != num_distribs + ): + raise RuntimeError( + f"Setting the number of distributions {num_distribs} does not match the number of " + f"intervention targets {len(self._intervention_targets)}." + ) + self._num_distributions = num_distribs + return self + + def intervention_targets(self, targets: List[Tuple[Column]]): + """Set known intervention targets of the data. + + Will also automatically infer the F-nodes that will be present + in the graph. For more information on F-nodes see ``pywhy-graphs``. + + Parameters + ---------- + interventions : List of tuples + A list of tuples of nodes that are known intervention targets. + Assumes that the order of the interventions marked are those of the + passed in the data. + + If intervention targets are unknown, then this is not necessary. + """ + if ( + self._num_distributions is not None + and len(targets) + int(self._obs_distribution) != self._num_distributions + ): + raise RuntimeError( + f"Setting the number of intervention targets {targets} does not match the " + f"number of distributions set {self._num_distributions} (it is assumed " + f"there are {int(self._obs_distribution)} observational distributions)." + ) + self._intervention_targets = targets + self._num_distributions = len(targets) + int(self._obs_distribution) + return self + + def build(self) -> Context: + """Build the Context object. + + Returns + ------- + context : Context + The populated Context object. + """ + if self._observed_variables is None: + raise ValueError("Could not infer variables from data or given arguments.") + if self._num_distributions is None: + raise ValueError( + "There is no intervention context set. Are you sure you are using " + "the right contextbuilder? If you only have observational data " + "use `ContextBuilder` instead of `InterventionContextBuilder`." + ) + + # get F-nodes and sigma-map + f_nodes, sigma_map, symmetric_diff_map = self._create_augmented_nodes() + graph_variables = set(self._observed_variables).union(set(f_nodes)) + + empty_graph = self._empty_graph_func(graph_variables) + return Context( + init_graph=self._interpolate_graph(graph_variables), + included_edges=self._included_edges or empty_graph(), + excluded_edges=self._excluded_edges or empty_graph(), + observed_variables=self._observed_variables, + latent_variables=self._latent_variables or set(), + state_variables=self._state_variables, + intervention_targets=self._intervention_targets, + f_nodes=f_nodes, + sigma_map=sigma_map, + symmetric_diff_map=symmetric_diff_map, + obs_distribution=self._obs_distribution, + num_distributions=self._num_distributions, + ) + + def _create_augmented_nodes(self) -> Tuple[List, Dict, Dict]: + """Create augmented nodes, sigma map and optionally a symmetric difference map. + + Given a number of distributions attributed to interventions, one constructs + F-nodes to add to the causal graph via one of two procedures: + + - (known targets): For all pairs of intervention targets, form the + symmetric difference and then assign this to a new F-node. + This is ``n_targets choose 2`` + - (unknown targets): For all pairs of incoming distributions, form + a new F-node. This is ``n_distributions choose 2`` + + The difference is the additional information is encoded in the known + targets case. That is we know the symmetric difference mapping for each + F-node. + + Returns + ------- + Tuple[List, Dict[Any, Tuple], Dict[Any, FrozenSet]] + _description_ + """ + augmented_nodes = [] + sigma_map = dict() + symmetric_diff_map = dict() + + # add the empty intervention if there is assumed observational data + if self._obs_distribution: + distribution_targets_idx = [0] + else: + distribution_targets_idx = [] + + # now map all distribution targets to their indexed distribution + int_dist_idx = np.arange(int(self._obs_distribution), self._num_distributions).tolist() + distribution_targets_idx.extend(int_dist_idx) + + # store known-targets, which are sets of nodes + targets = [] + if len(self._intervention_targets) > 0: + if self._obs_distribution: + targets.append(()) + targets.extend(copy(list(self._intervention_targets))) # type: ignore + + for idx, (jdx, kdx) in enumerate(combinations(distribution_targets_idx, 2)): + if jdx == kdx: + continue + f_node = ("F", idx) + augmented_nodes.append(f_node) + sigma_map[f_node] = (jdx, kdx) + + # if we additionally know the intervention targets + if len(self._intervention_targets) > 0: + print(jdx, kdx, len(targets), distribution_targets_idx) + i_target: Set = set(targets[jdx]) + j_target: Set = set(targets[kdx]) + + # form symmetric difference and store its frozenset + # (so that way order is not important) + f_node_targets = frozenset(i_target.symmetric_difference(j_target)) + symmetric_diff_map[f_node] = f_node_targets + return augmented_nodes, sigma_map, symmetric_diff_map + + def _interpolate_graph(self, graph_variables) -> nx.Graph: + init_graph = super()._interpolate_graph(graph_variables) + + # do error-check + if not all(node in init_graph for node in graph_variables): + raise RuntimeError( + "Not all nodes (observational and f-nodes) are part of the init graph." + ) + return init_graph + + +def make_context(context: Optional[Context] = None, create_using=ContextBuilder) -> ContextBuilder: """Create a new ContextBuilder instance. Returns @@ -216,11 +509,22 @@ def make_context(context: Optional[Context] = None) -> ContextBuilder: variables, ``(1, 2, 3)``. >>> context_builder = make_context() >>> context = context_builder.variables([1, 2, 3]).build() + + Notes + ----- + `Context` objects are dataclasses that creates a dictionary-like access + to causal context metadata. Copying relevant information from a `Context` + object into a `ContextBuilder` is all supported with the exception of + state variables. State variables are not copied over. To set state variables + again, one must build the Context and then call :meth:`Context.state_variable`. """ - result = ContextBuilder() + result = create_using() if context is not None: - result.graph(deepcopy(context.init_graph)) - result.edges(deepcopy(context.included_edges), deepcopy(context.excluded_edges)) - result.variables(copy(context.observed_variables), copy(context.latent_variables)) - result.state_variables(deepcopy(context.state_variables)) + # we create a copy of the ContextBuilder with the current values + # in the context + ctx_params = context.get_params() + for param, value in ctx_params.items(): + if getattr(result, param, None) is not None: + getattr(result, param)(value) + return result diff --git a/dodiscover/metrics.py b/dodiscover/metrics.py index 28d683efe..f47b636e3 100644 --- a/dodiscover/metrics.py +++ b/dodiscover/metrics.py @@ -15,7 +15,7 @@ def confusion_matrix_networks( true_graph: Graph, pred_graph: Graph, labels: Optional[NDArray] = None, - normalize: str = None, + normalize: Optional[str] = None, ): """Compute the confusion matrix comparing a predicted graph from the true graph. diff --git a/dodiscover/typing.py b/dodiscover/typing.py index ddbbd3d69..4a90b9f0a 100644 --- a/dodiscover/typing.py +++ b/dodiscover/typing.py @@ -1,9 +1,9 @@ -from typing import Dict, List, Set, Union +from typing import Dict, List, Set, Tuple, Union import networkx as nx # Pandas DataFrame columns that are also compatible with Graph nodes -Column = Union[int, float, str] +Column = Union[None, int, float, str, Tuple] # The separating set used in constraint-based causal discovery SeparatingSet = Dict[Column, Dict[Column, List[Set[Column]]]] diff --git a/examples/README.txt b/examples/README.txt index 141afd482..77b91b485 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -1,3 +1,5 @@ +.. _general_examples: + Examples -------- diff --git a/pyproject.toml b/pyproject.toml index c16e394d4..d7134cc91 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -201,7 +201,7 @@ convention = 'numpy' ignore-decorators = '(copy_doc|property|.*setter|.*getter|pyqtSlot|Slot)' match = '^(?!setup|__init__|test_).*\.py' match-dir = '^dodiscover.*' -add_ignore = 'D100,D104,D107' +add_ignore = 'D100,D104,D105,D107' [tool.mypy] ignore_missing_imports = true diff --git a/tests/unit_tests/constraint/test_intervene_skeleton.py b/tests/unit_tests/constraint/test_intervene_skeleton.py new file mode 100644 index 000000000..52457e986 --- /dev/null +++ b/tests/unit_tests/constraint/test_intervene_skeleton.py @@ -0,0 +1,62 @@ +import networkx as nx +import pywhy_graphs as pgraphs + +from dodiscover import InterventionalContextBuilder, make_context +from dodiscover.ci import Oracle +from dodiscover.constraint.skeleton import LearnInterventionSkeleton +from dodiscover.constraint.utils import dummy_sample + + +def test_fnode_skeleton(): + """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`.""" + # first create the oracle + directed_edges = [ + ("x", "w"), + ("w", "y"), + ("z", "y"), + ] + bidirected_edges = [("x", "z"), ("z", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x"}) + oracle = Oracle(graph) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + ("x", "w"), + ("x", "z"), + ("x", "y"), + ("z", "y"), + ("w", "y"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_node(("F", 0)) + + # define the learner and the context + learner = LearnInterventionSkeleton( + ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=True + ) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .intervention_targets([("x",)]) + .build() + ) + learner.fit(data, context) + + # first check the observational skeleton + skel_graph = learner.adj_graph_ + obs_skel_graph = learner.context_.state_variable("obs_skel_graph") + + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + assert nx.is_isomorphic(expected_skeleton, skel_graph) + + +def test_fnode_skeleton_errors(): + pass diff --git a/tests/unit_tests/constraint/test_skeleton.py b/tests/unit_tests/constraint/test_skeleton.py index 0b622e85e..dff8fe9e2 100644 --- a/tests/unit_tests/constraint/test_skeleton.py +++ b/tests/unit_tests/constraint/test_skeleton.py @@ -3,7 +3,6 @@ import pandas as pd import pytest import pywhy_graphs -from flaky import flaky from dodiscover import make_context from dodiscover.ci import GSquareCITest, Oracle @@ -105,12 +104,13 @@ def test_learn_skeleton_with_data(indep_test_func, data_matrix, g_answer): assert skel_graph.has_edge(*edge), error_msg +@pytest.mark.parametrize("skel_method", [LearnSkeleton, LearnSemiMarkovianSkeleton]) @pytest.mark.parametrize("G", [collider(), common_cause_and_collider(), complex_graph()]) -def test_learn_skeleton_oracle(G): +def test_learn_skeleton_oracle(G, skel_method): df = dummy_sample(G) oracle = Oracle(G) alpha = 0.05 - alg = LearnSkeleton(ci_estimator=oracle, alpha=alpha) + alg = skel_method(ci_estimator=oracle, alpha=alpha) context = make_context().variables(data=df).build() alg.fit(df, context) @@ -121,7 +121,63 @@ def test_learn_skeleton_oracle(G): assert nx.is_isomorphic(skel_graph, G.to_undirected()) -@flaky +def test_learn_skeleton_pds_disabled_first_stage(): + """Test that we can disable the first stage of the algorithm.""" + # reconstruct the PAG the way FCI would + edge_list = [("D", "A"), ("B", "E"), ("F", "B"), ("C", "F"), ("C", "H"), ("H", "D")] + latent_edge_list = [("A", "B"), ("D", "E")] + graph = pywhy_graphs.ADMG( + incoming_directed_edges=edge_list, incoming_bidirected_edges=latent_edge_list + ) + ci_estimator = Oracle(graph) + sample = dummy_sample(graph) + context = make_context().variables(data=sample).build() + + # generate the expected PAG + edge_list = [ + ("A", "B"), + ("D", "A"), + ("B", "E"), + ("B", "F"), + ("F", "C"), + ("C", "H"), + ("H", "D"), + ("D", "E"), + ("A", "E"), # Note: this is the extra edge + ] + expected_skel = nx.Graph(edge_list) + + # learn the skeleton of the graph now with the first stage skeleton + alg = LearnSemiMarkovianSkeleton(ci_estimator=ci_estimator, second_stage_skeleton_method=None) + alg.fit(sample, context) + assert alg.context_.state_variable("PAG", on_missing="ignore") is None + assert nx.is_isomorphic(expected_skel, alg.adj_graph_) + + +@pytest.mark.parametrize("skel_method", [LearnSkeleton, LearnSemiMarkovianSkeleton]) +def test_method_does_not_change_context(skel_method): + # reconstruct the PAG the way FCI would + edge_list = [("D", "A")] + latent_edge_list = [("A", "B"), ("D", "E")] + graph = pywhy_graphs.ADMG( + incoming_directed_edges=edge_list, incoming_bidirected_edges=latent_edge_list + ) + ci_estimator = Oracle(graph) + sample = dummy_sample(graph) + context = make_context().variables(data=sample).build() + context_copy = context.copy() + + # after the first stage, we learn a skeleton as in Figure 16 + firstalg = skel_method(ci_estimator=ci_estimator) + firstalg.fit(sample, context) + + # context should not change as a copy is made internally + assert context == context_copy + assert nx.is_isomorphic(context.init_graph, context_copy.init_graph) + assert nx.is_isomorphic(context.included_edges, context_copy.included_edges) + assert nx.is_isomorphic(context.excluded_edges, context_copy.excluded_edges) + + def test_learn_pds_skeleton(): """Test example in Causation, Prediction and Search book. @@ -171,6 +227,8 @@ def test_learn_pds_skeleton(): latent_edge_list = [("A", "B"), ("D", "E")] uncertain_edge_list = [ ("A", "E"), + ("A", "D"), + ("E", "B"), ("E", "A"), ("B", "F"), ("F", "C"), @@ -179,24 +237,24 @@ def test_learn_pds_skeleton(): ("H", "C"), ("D", "H"), ] - first_stage_pag = pywhy_graphs.PAG( + exp_first_stage_pag = pywhy_graphs.PAG( edge_list, incoming_bidirected_edges=latent_edge_list, incoming_circle_edges=uncertain_edge_list, ) # learn the skeleton of the graph now with the first stage skeleton - context = ( - make_context(context) - .graph(first_stage_pag.to_undirected()) - .state_variable("PAG", first_stage_pag) - .build() - ) + print(context.init_graph.edges(data=True)) alg = LearnSemiMarkovianSkeleton(ci_estimator=ci_estimator) alg.fit(sample, context) - skel_graph = alg.adj_graph_ - # generate the expected PAG + # the first stage PAG should be equal to what we learned + first_stage_pag = alg.context_.state_variable("PAG") + for edge_type, subgraph in exp_first_stage_pag.get_graphs().items(): + assert nx.is_isomorphic(first_stage_pag.get_graphs(edge_type), subgraph) + + # now test that the expected skeleton is the same as the learned skeleton + skel_graph = alg.adj_graph_ edge_list = [ ("D", "A"), ("B", "E"), @@ -217,8 +275,9 @@ def test_learn_pds_skeleton(): incoming_bidirected_edges=latent_edge_list, incoming_circle_edges=uncertain_edge_list, ) - for edge in expected_pag.to_undirected().edges: + expected_skel = expected_pag.to_undirected() + for edge in expected_skel.edges: assert skel_graph.has_edge(*edge) for edge in skel_graph.edges: - assert expected_pag.to_undirected().has_edge(*edge) - assert nx.is_isomorphic(skel_graph, expected_pag.to_undirected()) + assert expected_skel.has_edge(*edge) + assert nx.is_isomorphic(skel_graph, expected_skel) diff --git a/tests/unit_tests/test_base.py b/tests/unit_tests/test_base.py new file mode 100644 index 000000000..5e89182bd --- /dev/null +++ b/tests/unit_tests/test_base.py @@ -0,0 +1,42 @@ +import pytest + +from dodiscover.base import BasePyWhy + +# TODO: add pickling tests + + +class MyLearner(BasePyWhy): + def __init__(self, l1=0, empty=None): + self.l1 = l1 + self.empty = empty + + +class K(BasePyWhy): + def __init__(self, c=None, d=None): + self.c = c + self.d = d + + +class T(BasePyWhy): + def __init__(self, a=None, b=None): + self.a = a + self.b = b + + +def test_str(): + # Smoke test the str of the base estimator + my_estimator = MyLearner() + str(my_estimator) + + +def test_get_params(): + test = T(K(), K) + + assert "a__d" in test.get_params(deep=True) + assert "a__d" not in test.get_params(deep=False) + + test.set_params(a__d=2) + assert test.a.d == 2 + + with pytest.raises(ValueError): + test.set_params(a__a=2) diff --git a/tests/unit_tests/test_context_builder.py b/tests/unit_tests/test_context_builder.py index 42e3feebb..a970ef92b 100644 --- a/tests/unit_tests/test_context_builder.py +++ b/tests/unit_tests/test_context_builder.py @@ -1,9 +1,13 @@ +import dataclasses +import math +from copy import copy, deepcopy + import networkx as nx import numpy as np import pandas as pd import pytest -from dodiscover import make_context +from dodiscover import ContextBuilder, InterventionalContextBuilder, make_context seed = 12345 @@ -24,12 +28,15 @@ def test_build_with_initial_graph(): graph = nx.DiGraph() graph.add_edges_from([("x", "y")]) data = make_df() - ctx = make_context().graph(graph).variables(data=data).build() + ctx = make_context().init_graph(graph).variables(data=data).build() assert ctx.init_graph is graph # if the initial graph does not match the variables passed in, then raise an error with pytest.raises(ValueError, match="The nodes within the initial graph*"): - make_context().graph(graph).variables(observed="x").build() + make_context().init_graph(graph).variables(observed="blah").build() + + # this should work since 'x' is in the subset of the initial graph + ctx = make_context().init_graph(graph).variables(observed="x").build() def test_build_with_observed_and_latents(): @@ -48,6 +55,16 @@ def test_build_with_observed_and_latents(): ctx = ctx_builder.variables(latents="x", data=df).build() assert ctx.observed_variables == {"y"} + cp_ctx_builder = make_context() + with pytest.raises(RuntimeError, match="Observed variables are set already"): + cp_ctx_builder.observed_variables({"x"}) + cp_ctx_builder.latent_variables({"x"}) + + cp_ctx_builder = deepcopy(ctx_builder) + with pytest.raises(RuntimeError, match="Latent variables are set already"): + cp_ctx_builder.latent_variables({"x"}) + cp_ctx_builder.observed_variables({"x"}) + def test_build_context_errors(): ctx_builder = make_context() @@ -61,5 +78,179 @@ def test_build_context_errors(): # if we specify latent and observed variables, they should match up with # the columns of the dataset - with pytest.raises(ValueError, match="If observed and latents are set"): + with pytest.raises(ValueError, match="If observed and latents are both set"): ctx_builder.variables(observed="x", latents="z", data=df) + + +def test_context_set_errors(): + ctx_builder = make_context() + df = make_df() + ctx = ctx_builder.variables(data=df).build() + + with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): + ctx.init_graph = nx.empty_graph(0) + + +def test_context_set_edges(): + ctx_builder = make_context() + df = make_df() + + # an error should be raised in inclusion/exclusion edges do not match + inc_graph = nx.Graph() + inc_graph.add_edge("x", "y") + ctx_builder.variables(data=df).included_edges(inc_graph) + with pytest.raises(RuntimeError, match="^(.*)is already specified as an included edge"): + ctx_builder.excluded_edges(inc_graph) + + inc_graph = nx.Graph() + ctx_builder = ctx_builder.included_edges(None) + inc_graph.add_edge("x", "y") + ctx_builder.excluded_edges(inc_graph) + + with pytest.raises(RuntimeError, match="^(.*) is already specified as an excluded edge"): + ctx_builder.included_edges(inc_graph) + + +class BadContextBuilder(ContextBuilder): + random_attribute: str = "hi" + + +def test_context_builder_extension_error(): + """All context builders should follow a specific pattern for definine private attributes.""" + + with pytest.raises(RuntimeError, match="Context objects has class attributes that do not have"): + BadContextBuilder().observed_variables(["x"]).build() + + +def test_context_set_get(): + ctx_builder = make_context() + df = make_df() + ctx = ( + ctx_builder.variables(data=df) + .init_graph(nx.empty_graph(df.columns)) + .included_edges(nx.DiGraph([("x", "y")])) + .build() + ) + + # making the contexBuilder with that context should result in the exact same copy + ctx2 = make_context(ctx).build() + assert ctx == ctx2 + + # directly setting fields should not be allowed + with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): + ctx.intervention_targets = ["new"] + + # however, altering via functions is fine + ctx.add_state_variable("new", 0) + assert ctx.state_variables == {"new": 0} + + # basic smoke-check of functionality of dataclasses + assert all( + x not in dataclasses.asdict(ctx) for x in ("get_f_nodes", "get_params", "get_sigma_map") + ) + + ctx_copy = ctx.copy() + assert ctx == ctx_copy + + +def test_context_state_variables(): + ctx_builder = make_context() + df = make_df() + ctx = ( + ctx_builder.variables(data=df) + .init_graph(nx.empty_graph(df.columns)) + .included_edges(nx.DiGraph([("x", "y")])) + .build() + ) + + with pytest.raises(RuntimeError, match="^(.*) is not a state variable:"): + ctx.state_variable("pag") + with pytest.warns(UserWarning, match="^(.*) is not a state variable:"): + ctx.state_variable("pag", on_missing="warn") + assert ctx.state_variable("pag", on_missing="ignore") is None + + +def test_context_interventions(): + ctx_builder = make_context(create_using=InterventionalContextBuilder) + df = make_df() + + # check InterventionalContextBuilder errors that should be raised + with pytest.raises(ValueError, match="There is no intervention context set"): + copy(ctx_builder).variables(data=df).init_graph( + nx.empty_graph(df.columns + ["blah"]) + ).build() + + with pytest.raises(RuntimeError, match="Not all nodes"): + copy(ctx_builder).variables(data=df).init_graph( + nx.empty_graph(list(df.columns) + ["blah"]) + ).num_distributions(5).build() + + with pytest.raises(RuntimeError, match="Setting the number of distribution"): + copy(ctx_builder).variables(data=df).intervention_targets([("x",)]).num_distributions( + 5 + ).build() + + with pytest.raises(RuntimeError, match="Setting the number of intervention targets"): + copy(ctx_builder).variables(data=df).num_distributions(5).intervention_targets( + [("x",)] + ).build() + + # check InterventionalContextBuilder building with + # known-interventional targets + # now build context + ctx = ( + copy(ctx_builder) + .variables(data=df) + .intervention_targets([("x",), ("x", "y"), ("y",)]) + .build() + ) + assert ctx.obs_distribution is True + assert len(ctx.intervention_targets) == 3 + expected_num_f_nodes = math.comb(4, 2) + assert len(ctx.sigma_map) == expected_num_f_nodes + assert len(ctx.f_nodes) == expected_num_f_nodes + assert ctx.symmetric_diff_map.keys() == ctx.sigma_map.keys() + for val in [("x",), ("x", "y"), ("y",)]: + assert frozenset(val) in ctx.symmetric_diff_map.values() + + # check that there are (4 choose 2) = 6 F-nodes + assert len(ctx.f_nodes) == 6 + + # now build context by also specifying number of distributions should be the same + ctx2 = ( + copy(ctx_builder) + .variables(data=df) + .num_distributions(4) + .intervention_targets([("x",), ("x", "y"), ("y",)]) + .build() + ) + assert ctx == ctx2 + + # making a copy should not change anything + ctx3 = make_context(ctx, create_using=InterventionalContextBuilder).build() + assert ctx == ctx3 + + # now build context without intervention targets + ctx = copy(ctx_builder).variables(data=df).num_distributions(4).build() + + # the symmetric diff map is now empty because we do not know the targets + assert ctx.symmetric_diff_map == dict() + assert set(ctx.sigma_map.keys()) == set(ctx.f_nodes) + + +def test_context_interventions_without_observational(): + ctx_builder = make_context(create_using=InterventionalContextBuilder) + df = make_df() + + # now build context without observational data + ctx = ( + copy(ctx_builder) + .variables(data=df) + .obs_distribution(False) + .num_distributions(3) + .intervention_targets([("x",), ("x", "y"), ("y",)]) + .build() + ) + + # check that there are (3 choose 2) = 3 F-nodes + assert len(ctx.f_nodes) == 3 From 32416a4f1748398f719dee2d9a245759caf479fa Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 14 Feb 2023 11:37:15 -0500 Subject: [PATCH 03/61] WIP Signed-off-by: Adam Li --- doc/constraint_causal_discovery.rst | 54 ++++++- doc/references.bib | 9 ++ doc/user_guide.rst | 1 + dodiscover/constraint/__init__.py | 2 +- dodiscover/constraint/fcialg.py | 39 ++++- dodiscover/constraint/intervention.py | 166 ++++++++++++--------- dodiscover/constraint/skeleton.py | 28 ++-- tests/unit_tests/constraint/test_fcialg.py | 76 +++++++++- tests/unit_tests/constraint/test_pcalg.py | 18 ++- 9 files changed, 296 insertions(+), 97 deletions(-) diff --git a/doc/constraint_causal_discovery.rst b/doc/constraint_causal_discovery.rst index 07a337a20..66d69fbc0 100644 --- a/doc/constraint_causal_discovery.rst +++ b/doc/constraint_causal_discovery.rst @@ -6,13 +6,51 @@ Constraint-based causal discovery .. currentmodule:: dodiscover.constraint -The following are a set of methods intended for regression in which -the target value is expected to be a linear combination of the features. -In mathematical notation, if :math:`\hat{y}` is the predicted -value. +The following are a set of methods intended for (non-parametric) structure learning +of causal graphs (i.e. causal discovery) given observational and/or interventional data +by checking constraints in the form of conditional independences (CI). At a high level +all constraint-based causal discovery, tests CI statements, where we use different CI +statistical tests to test the following null hypothesis: + +:math:`H_0: X \perp Y | Z` and :math:`H_A: X \not\perp Y | Z` + +For a given node ``X`` and ``Y`` in the underlying causal graph of interest, and +a conditioning set, ``Z``. CI tests can have a variety of different assumptions +that make one better than another in different settings and data assumptions. +For more information on the CI tests themselves, see :ref:`conditional_independence`. + +Fundamental Assumptions of Constraint-Based Causal Discovery +------------------------------------------------------------ +The fundamental assumptions of all algorithms in this section are the Markov property assumption +and the causal faithfulness assumption :footcite:`Spirtes1993` and :footcite:`Pearl_causality_2009`. +The Markov assumption states that all d-separation statements in the causal graph imply a +corresponding CI statement in the data. This is a core-assumption that users from graphical modeling +may be familiar with. On the other hand, the causal faithfulness assumption states that all +CI statements in the data map to a d-separation statement. That is, there are no accidental +CI that occur in the data, which are not represented by a d-separation statement in the underlying +causal graph. The causal faithfulness assumption is a very problematic assumption because in practice +one might have data that is very weakly dependent, such that a CI test under a specified :math:`\alpha` +level would fail to reject the null hypothesis and conclude the variables in question are CI. In higher +dimensions this can occur a large percentage of the time as demonstrated in :footcite:`uhler2013geometry`. + +Tackling constraint-based causal discovery is a large and active area of research. (Non-parametric) Markovian SCMs with Observational Data ------------------------------------------------------- +If one assumes that the underlying structural causal model (SCM) is Markovian, +then the Peter and Clarke (PC) algorithm has been shown to be sound and complete +for learning a completed partially directed acyclic graph (CPDAG) :footcite:`Meek1995`. + +The PC algorithm and its variants assume Markovianity, which is also known as +causal-sufficiency in the literature. In other words, it assumes a lack of latent +confounders, where there is no latent variable that is a confounder of the observed data. + +The PC algorithm learns a CPDAG in three stages: + +1. skeleton discovery: This first phase is the process of leveraging CI tests to test + edges for conditional independence. +2. unshielded triplet orientation: +3. deterministic path orientations: (Non-parametric) Semi-Markovian SCMs with Observational Data @@ -23,6 +61,14 @@ value. ---------------------------------------------- +Choosing the conditioning sets +------------------------------ +To describe. + +Hyperparameters and controlling overfitting +------------------------------------------- +To describe. + Robust learning --------------- Conservative orientations, etc. diff --git a/doc/references.bib b/doc/references.bib index 154de67a7..2f0ea855c 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -104,6 +104,15 @@ @article{Sen2017model year = {2017} } +@article{uhler2013geometry, + title={Geometry of the faithfulness assumption in causal inference}, + author={Uhler, Caroline and Raskutti, Garvesh and B{\"u}hlmann, Peter and Yu, Bin}, + journal={The Annals of Statistics}, + pages={436--463}, + year={2013}, + publisher={JSTOR} +} + @inproceedings{Yu2020Bregman, title = {Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications}, author = {Yu, Shujian and Shaker, Ammar and Alesiani, Francesco and Principe, Jose}, diff --git a/doc/user_guide.rst b/doc/user_guide.rst index 66fbe10e7..7b7ecaaf8 100644 --- a/doc/user_guide.rst +++ b/doc/user_guide.rst @@ -15,6 +15,7 @@ User Guide :maxdepth: 3 constraint_causal_discovery.rst + conditional_independence.rst .. scores_causal_discovery.rst .. visualizations.rst .. datasets.rst diff --git a/dodiscover/constraint/__init__.py b/dodiscover/constraint/__init__.py index a4b3469ea..761edfc13 100644 --- a/dodiscover/constraint/__init__.py +++ b/dodiscover/constraint/__init__.py @@ -1,4 +1,4 @@ from .config import SkeletonMethods from .fcialg import FCI from .pcalg import PC -from .skeleton import LearnSemiMarkovianSkeleton, LearnSkeleton +from .skeleton import LearnInterventionSkeleton, LearnSemiMarkovianSkeleton, LearnSkeleton diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index 2dcc6def7..22e0f7fe7 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -525,9 +525,10 @@ def _apply_rule7(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) def _apply_rule8(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) -> bool: """Apply rule 8 of FCI algorithm. - If A -> u -> C, or A -o u -> C - and A o-> C, then orient A o-> C as A -> C. - + If: + - A -> u -> C, or A -o u -> C, (the second condition is only present with selection bias) + - and A o-> C, + - then orient A o-> C as A -> C. Parameters ---------- @@ -553,10 +554,18 @@ def _apply_rule8(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) if graph.has_edge(c, a, graph.circle_edge_name) and graph.has_edge( a, c, graph.directed_edge_name ): - # check that A -> u or A -o u - condition_one = graph.has_edge(a, u, graph.directed_edge_name) or graph.has_edge( + # check that A -> u + condition_one_Adirectu = graph.has_edge( + a, u, graph.directed_edge_name + ) and not graph.has_edge(u, a, graph.circle_edge_name) + # check that A -o u + # Note: this is not possible without first running R5-7 because a tail with a circle edge + # would not occur through any of the other rules. + condition_one_Acircleu = graph.has_edge( a, u, graph.circle_edge_name - ) + ) and not graph.has_edge(u, a) + condition_one = condition_one_Adirectu or condition_one_Acircleu + # check that u -> C condition_two = graph.has_edge(u, c, graph.directed_edge_name) and not graph.has_edge( c, u, graph.circle_edge_name @@ -784,6 +793,22 @@ def _apply_rules_1to10(self, graph: EquivalenceClass, sep_set: SeparatingSet): r9_add, r10_add, ] + if all(x in [u, a, c] for x in ["x6", "x4"]) and any(all_flags): + print(u, a, c) + print( + [ + r1_add, + r2_add, + r3_add, + r4_add, + r5_add, + r6_add, + r7_add, + r8_add, + r9_add, + r10_add, + ] + ) if any(all_flags) and not change_flag: logger.info(f"{change_flag} with " f"{all_flags}") change_flag = True @@ -801,7 +826,7 @@ def _apply_rules_1to10(self, graph: EquivalenceClass, sep_set: SeparatingSet): def learn_skeleton( self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None ) -> Tuple[nx.Graph, SeparatingSet]: - # # now compute all possibly d-separating sets and learn a better skeleton + # now compute all possibly d-separating sets and learn a better skeleton skel_alg = LearnSemiMarkovianSkeleton( self.ci_estimator, sep_set=sep_set, diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index ae6164137..4ed6f1c18 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -4,8 +4,9 @@ import pandas as pd from dodiscover._protocol import EquivalenceClass +from dodiscover.cd import BaseConditionalDiscrepancyTest from dodiscover.ci import BaseConditionalIndependenceTest -from dodiscover.constraint import SkeletonMethods +from dodiscover.constraint import LearnInterventionSkeleton, SkeletonMethods from dodiscover.context import Context from dodiscover.typing import SeparatingSet @@ -13,9 +14,79 @@ class PsiFCI(FCI): + """Interventional (Psi) FCI algorithm. + + The I-FCI (or Psi-FCI) algorithm is an algorithm that accepts + multiple sets of data that may pertain to observational and/or + multiple interventional datasets under a known (I-FCI), or unknown (Psi-FCI) + intervention target setting. Our API consolidates them here under + one class, but you can control the setting using our hyperparameter. + See :footcite:`Kocaoglu2019characterization` for more information on + I-FCI and :footcite:`Jaber2020causal` for more information on Psi-FCI. + + The Psi-FCI algorithm is complete for the Psi-PAG equivalence class. + However, the I-FCI has not been shown to be complete for the I-PAG + equivalence class. Note that the I-FCI algorithm may change without + notice. + + Parameters + ---------- + ci_estimator : Callable + The conditional independence test function. The arguments of the estimator should + be data, node, node to compare, conditioning set of nodes, and any additional + keyword arguments. + cd_estimator : BaseConditionalDiscrepancyTest + The conditional discrepancy test function. + alpha : float, optional + The significance level for the conditional independence test, by default 0.05. + min_cond_set_size : int, optional + Minimum size of the conditioning set, by default None, which will be set to '0'. + Used to constrain the computation spent on the algorithm. + max_cond_set_size : int, optional + Maximum size of the conditioning set, by default None. Used to limit + the computation spent on the algorithm. + max_combinations : int, optional + The maximum number of conditional independence tests to run from the set + of possible conditioning sets. By default None, which means the algorithm will + check all possible conditioning sets. If ``max_combinations=n`` is set, then + for every conditioning set size, 'p', there will be at most 'n' CI tests run + before the conditioning set size 'p' is incremented. For controlling the size + of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used + in conjunction with ``keep_sorted`` parameter to only test the "strongest" + dependences. + skeleton_method : SkeletonMethods + The method to use for testing conditional independence. Must be one of + ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. + apply_orientations : bool + Whether or not to apply orientation rules given the learned skeleton graph + and separating set per pair of variables. If ``True`` (default), will + apply Zhang's orientation rules R0-10, orienting colliders and certain + arrowheads and tails :footcite:`Zhang2008`. + max_iter : int + The maximum number of iterations through the graph to apply + orientation rules. + max_path_length : int, optional + The maximum length of any discriminating path, or None if unlimited. + pds_skeleton_method : SkeletonMethods + The method to use for learning the skeleton using PDS. Must be one of + ('pds', 'pds_path'). See Notes for more details. + known_intervention_targets : bool, optional + If `True`, then will run the I-FCI algorithm. If `False`, will run the + Psi-FCI algorithm. By default False. + ci_estimator_kwargs : dict + Keyword arguments for the ``ci_estimator`` function. + cd_estimator_kwargs : dict + Keyword arguments for the ``cd_estimator`` function. + + Notes + ----- + Selection bias is unsupported because it is still an active research area. + """ + def __init__( self, ci_estimator: BaseConditionalIndependenceTest, + cd_estimator: BaseConditionalDiscrepancyTest, alpha: float = 0.05, min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, @@ -26,71 +97,9 @@ def __init__( max_path_length: Optional[int] = None, pds_skeleton_method: SkeletonMethods = SkeletonMethods.PDS, known_intervention_targets: bool = False, - **ci_estimator_kwargs, + ci_estimator_kwargs=None, + cd_estimator_kwargs=None, ): - """Interventional (Psi) FCI algorithm. - - The I-FCI (or Psi-FCI) algorithm is an algorithm that accepts - multiple sets of data that may pertain to observational and/or - multiple interventional datasets under a known (I-FCI), or unknown (Psi-FCI) - intervention target setting. Our API consolidates them here under - one class, but you can control the setting using our hyperparameter. - See :footcite:`Kocaoglu2019characterization` for more information on - I-FCI and :footcite:`Jaber2020causal` for more information on Psi-FCI. - - The Psi-FCI algorithm is complete for the Psi-PAG equivalence class. - However, the I-FCI has not been shown to be complete for the I-PAG - equivalence class. Note that the I-FCI algorithm may change without - notice. - - Parameters - ---------- - ci_estimator : Callable - The conditional independence test function. The arguments of the estimator should - be data, node, node to compare, conditioning set of nodes, and any additional - keyword arguments. - alpha : float, optional - The significance level for the conditional independence test, by default 0.05. - min_cond_set_size : int, optional - Minimum size of the conditioning set, by default None, which will be set to '0'. - Used to constrain the computation spent on the algorithm. - max_cond_set_size : int, optional - Maximum size of the conditioning set, by default None. Used to limit - the computation spent on the algorithm. - max_combinations : int, optional - The maximum number of conditional independence tests to run from the set - of possible conditioning sets. By default None, which means the algorithm will - check all possible conditioning sets. If ``max_combinations=n`` is set, then - for every conditioning set size, 'p', there will be at most 'n' CI tests run - before the conditioning set size 'p' is incremented. For controlling the size - of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used - in conjunction with ``keep_sorted`` parameter to only test the "strongest" - dependences. - skeleton_method : SkeletonMethods - The method to use for testing conditional independence. Must be one of - ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. - apply_orientations : bool - Whether or not to apply orientation rules given the learned skeleton graph - and separating set per pair of variables. If ``True`` (default), will - apply Zhang's orientation rules R0-10, orienting colliders and certain - arrowheads and tails :footcite:`Zhang2008`. - max_iter : int - The maximum number of iterations through the graph to apply - orientation rules. - max_path_length : int, optional - The maximum length of any discriminating path, or None if unlimited. - selection_bias : bool - Whether or not to account for selection bias within the causal PAG. - See :footcite:`Zhang2008`. Currently not implemented. - pds_skeleton_method : SkeletonMethods - The method to use for learning the skeleton using PDS. Must be one of - ('pds', 'pds_path'). See Notes for more details. - known_intervention_targets : bool, optional - If `True`, then will run the I-FCI algorithm. If `False`, will run the - Psi-FCI algorithm. By default False. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. - """ super().__init__( ci_estimator, alpha, @@ -103,14 +112,37 @@ def __init__( max_path_length=max_path_length, selection_bias=False, pds_skeleton_method=pds_skeleton_method, - **ci_estimator_kwargs, + ci_estimator_kwargs=ci_estimator_kwargs, ) + self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets + self.cd_estimator_kwargs = cd_estimator_kwargs def learn_skeleton( self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None ) -> Tuple[nx.Graph, SeparatingSet]: - return super().learn_skeleton(data, context, sep_set) + # now compute all possibly d-separating sets and learn a better skeleton + skel_alg = LearnInterventionSkeleton( + self.ci_estimator, + self.cd_estimator, + sep_set=sep_set, + alpha=self.alpha, + min_cond_set_size=self.min_cond_set_size, + max_cond_set_size=self.max_cond_set_size, + max_combinations=self.max_combinations, + skeleton_method=self.skeleton_method, + second_stage_skeleton_method=self.pds_skeleton_method, + keep_sorted=False, + max_path_length=self.max_path_length, + ci_estimator_kwargs=self.ci_estimator_kwargs, + cd_estimator_kwargs=self.cd_estimator_kwargs, + ) + skel_alg.fit(data, context) + + skel_graph = skel_alg.adj_graph_ + sep_set = skel_alg.sep_set_ + self.n_ci_tests += skel_alg.n_ci_tests + return skel_graph, sep_set def fit(self, data: List[pd.DataFrame], context: Context): """Learn the relevant causal graph equivalence class. diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index f70320630..e1304e154 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -77,24 +77,28 @@ def _find_neighbors_along_path(G: nx.Graph, start, end) -> Set: The set of neighbors that are also along a path towards the 'end' node. """ - - def _assign_weight(u, v, edge_attr): - if u == node or v == node: - return np.inf - else: - return 1 - nbrs = set() + + # query all neighbors of X and then only add nodes that are in a valid path + # to end for node in G.neighbors(start): if not G.has_edge(start, node): raise RuntimeError(f"{start} and {node} are not connected, but they are assumed to be.") + # if we queried the edge we are testing, then pick that one + if node == end: + continue + # find a path from start node to end - path = nx.shortest_path(G, source=node, target=end, weight=_assign_weight) - if len(path) > 0: - if start in path: - raise RuntimeError("There is an error with the input. This is not possible.") - nbrs.add(node) + paths = nx.all_simple_paths(G, source=node, target=end) + for path in paths: + # the trivial path which indicates that 'node' is only connected to 'end' through 'start' + if path == (node, start, end): + continue + else: + # found a single path + nbrs.add(node) + break return nbrs diff --git a/tests/unit_tests/constraint/test_fcialg.py b/tests/unit_tests/constraint/test_fcialg.py index 01c133efb..785f4c8be 100644 --- a/tests/unit_tests/constraint/test_fcialg.py +++ b/tests/unit_tests/constraint/test_fcialg.py @@ -2,12 +2,14 @@ import networkx as nx import numpy as np +import pytest import pywhy_graphs import pywhy_graphs.networkx as pywhy_nx from pywhy_graphs import ADMG, PAG from dodiscover import FCI, make_context from dodiscover.ci import Oracle +from dodiscover.constraint.config import SkeletonMethods from dodiscover.constraint.utils import dummy_sample np.random.seed(12345) @@ -567,10 +569,6 @@ def test_fci_unobserved_confounder(self): assert set(pag.edges()) == set(expected_pag.edges()) - # expected_pag_digraph = expected_pag.compute_full_graph(to_networkx=True) - # pag_digraph = pag.compute_full_graph(to_networkx=True) - # assert nx.is_isomorphic(pag_digraph, expected_pag_digraph) - def test_fci_spirtes_example(self): """Test example in book. @@ -751,3 +749,73 @@ def test_fci_selection_bias(self): expected_pag.add_edge("D", "R", expected_pag.directed_edge_name) assert pag.nodes() == expected_pag.nodes() assert pag.edges() == expected_pag.edges() + + +@pytest.mark.parametrize( + "skeleton_method", [SkeletonMethods.NBRS, SkeletonMethods.NBRS_PATH, SkeletonMethods.COMPLETE] +) +@pytest.mark.parametrize("pds_skeleton_method", [SkeletonMethods.PDS_PATH, SkeletonMethods.PDS]) +def test_fci_complex(skeleton_method, pds_skeleton_method): + """ + Test FCI algorithm with more complex graph. + + Use Figure 2 from :footcite:`Colombo2012`. + + References + ---------- + .. footbibliography:: + """ + edge_list = [ + ("x4", "x1"), + ("x2", "x5"), + ("x3", "x2"), + ("x3", "x4"), + ("x2", "x6"), + ("x3", "x6"), + ("x4", "x6"), + ("x5", "x6"), + ] + latent_edge_list = [("x1", "x2"), ("x4", "x5")] + G = ADMG(edge_list, latent_edge_list) + sample = dummy_sample(G) + context = make_context().variables(data=sample).build() + oracle = Oracle(G) + ci_estimator = oracle + fci = FCI( + ci_estimator=ci_estimator, + max_iter=np.inf, + skeleton_method=skeleton_method, + pds_skeleton_method=pds_skeleton_method, + selection_bias=False, + ) + fci.fit(sample, context) + pag = fci.graph_ + + # double check the m-separation statement and PDS + assert pywhy_nx.m_separated(G, {"x1"}, {"x3"}, {"x4"}) + pdsep = pywhy_graphs.pds(G, "x1", "x3") + assert "x2" in pdsep + + expected_pag = PAG() + expected_pag.add_edges_from( + [("x6", "x5"), ("x2", "x3"), ("x4", "x3"), ("x6", "x4")], expected_pag.circle_edge_name + ) + expected_pag.add_edges_from( + [ + ("x4", "x1"), + ("x2", "x5"), + ("x3", "x2"), + ("x3", "x4"), + ("x2", "x6"), + ("x3", "x6"), + ("x4", "x6"), + ("x5", "x6"), + ], + expected_pag.directed_edge_name, + ) + expected_pag.add_edge("x1", "x2", expected_pag.bidirected_edge_name) + expected_pag.add_edge("x4", "x5", expected_pag.bidirected_edge_name) + + assert set(pag.edges()) == set(expected_pag.edges()) + for edge_type, subgraph in expected_pag.get_graphs().items(): + assert nx.is_isomorphic(subgraph, pag.get_graphs(edge_type)) diff --git a/tests/unit_tests/constraint/test_pcalg.py b/tests/unit_tests/constraint/test_pcalg.py index 60337a484..66d028c51 100644 --- a/tests/unit_tests/constraint/test_pcalg.py +++ b/tests/unit_tests/constraint/test_pcalg.py @@ -8,11 +8,25 @@ from dodiscover import make_context from dodiscover.ci import GSquareCITest, Oracle from dodiscover.constraint import PC +from dodiscover.constraint.config import SkeletonMethods from dodiscover.constraint.utils import dummy_sample from dodiscover.metrics import confusion_matrix_networks from dodiscover.testdata.testdata import bin_data, dis_data +@pytest.mark.parametrize( + "pc_kwargs", + [ + { + "min_cond_set_size": 0, + "max_cond_set_size": 3, + "max_combinations": 10, + "max_iter": 10, + "skeleton_method": SkeletonMethods.NBRS_PATH, + }, + {}, + ], +) @pytest.mark.parametrize( ("indep_test_func", "data_matrix", "g_answer", "alpha"), [ @@ -46,7 +60,7 @@ ), ], ) -def test_estimate_cpdag_testdata(indep_test_func, data_matrix, g_answer, alpha): +def test_estimate_cpdag_testdata(indep_test_func, data_matrix, g_answer, alpha, pc_kwargs): """Test PC algorithm for estimating the causal DAG. Test data is imported from pcalg, which is sensitive to the CI test @@ -56,7 +70,7 @@ def test_estimate_cpdag_testdata(indep_test_func, data_matrix, g_answer, alpha): """ data_df = pd.DataFrame(data_matrix) context = make_context().variables(data=data_df).build() - alg = PC(ci_estimator=indep_test_func, alpha=alpha) + alg = PC(ci_estimator=indep_test_func, alpha=alpha, **pc_kwargs) alg.fit(data_df, context) graph = alg.graph_ From 339bdad9e56e80c3c7bb72af756ea91f1b094520 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 14 Feb 2023 14:57:26 -0500 Subject: [PATCH 04/61] Merging Signed-off-by: Adam Li --- dodiscover/base.py | 4 ++-- dodiscover/constraint/__init__.py | 1 + dodiscover/constraint/fcialg.py | 4 ++-- dodiscover/constraint/skeleton.py | 3 ++- dodiscover/context.py | 6 +++--- dodiscover/context_builder.py | 9 +++++---- 6 files changed, 15 insertions(+), 12 deletions(-) diff --git a/dodiscover/base.py b/dodiscover/base.py index 62599e39b..05c9879ee 100644 --- a/dodiscover/base.py +++ b/dodiscover/base.py @@ -126,8 +126,8 @@ def set_params(self, **params): Returns ------- - self : estimator instance - Estimator instance. + self : instance + Learner instance. """ if not params: # Simple optimization to gain speed (inspect is slow) diff --git a/dodiscover/constraint/__init__.py b/dodiscover/constraint/__init__.py index 761edfc13..2d4fdd511 100644 --- a/dodiscover/constraint/__init__.py +++ b/dodiscover/constraint/__init__.py @@ -1,4 +1,5 @@ from .config import SkeletonMethods from .fcialg import FCI +from .intervention import PsiFCI from .pcalg import PC from .skeleton import LearnInterventionSkeleton, LearnSemiMarkovianSkeleton, LearnSkeleton diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index 22e0f7fe7..4c4d2bfa7 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -559,8 +559,8 @@ def _apply_rule8(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) a, u, graph.directed_edge_name ) and not graph.has_edge(u, a, graph.circle_edge_name) # check that A -o u - # Note: this is not possible without first running R5-7 because a tail with a circle edge - # would not occur through any of the other rules. + # Note: this is not possible without first running R5-7 because a tail with a + # circle edge would not occur through any of the other rules. condition_one_Acircleu = graph.has_edge( a, u, graph.circle_edge_name ) and not graph.has_edge(u, a) diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index e1304e154..a2d331a1c 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -92,7 +92,8 @@ def _find_neighbors_along_path(G: nx.Graph, start, end) -> Set: # find a path from start node to end paths = nx.all_simple_paths(G, source=node, target=end) for path in paths: - # the trivial path which indicates that 'node' is only connected to 'end' through 'start' + # the trivial path which indicates that 'node' is only connected to + # 'end' through 'start' if path == (node, start, end): continue else: diff --git a/dodiscover/context.py b/dodiscover/context.py index 71dc5f3c3..7df174669 100644 --- a/dodiscover/context.py +++ b/dodiscover/context.py @@ -34,7 +34,7 @@ class Context(BasePyWhy): Excluded edges without direction. state_variables : Dict Name of intermediate state variables during the learning process. - intervention_targets : list of tuples + intervention_targets : list of tuple List of intervention targets (known, or unknown), which correspond to the nodes in the graph (known), or indices of datasets that contain interventions (unknown). @@ -58,7 +58,7 @@ class Context(BasePyWhy): Currently, testing for equality is done on all attributes that are not graphs. Defining equality among graphs is ill-defined, and as such, we leave testing of the internal graphs to users. Some checks of equality - for example can be :func:`nx.is_isomorphic` for checking isomorphism + for example can be :func:`networkx.is_isomorphic` for checking isomorphism among two graphs. """ @@ -111,7 +111,7 @@ def state_variable(self, name: str, on_missing: str = "raise") -> Any: ---------- name : str The name of the state variable. - on_missing : one of {'raise', 'warn', 'ignore'} + on_missing : {'raise', 'warn', 'ignore'} Behavior if ``name`` is not in the dictionary of state variables. If 'raise' (default) will raise a RuntimeError. If 'warn', will raise a UserWarning. If 'ignore', will return `None`. diff --git a/dodiscover/context_builder.py b/dodiscover/context_builder.py index ea8f9b406..057a47744 100644 --- a/dodiscover/context_builder.py +++ b/dodiscover/context_builder.py @@ -323,7 +323,7 @@ class InterventionalContextBuilder(ContextBuilder): Notes ----- The number of distributions and/or interventional targets must be set in order - to build the `Context` object here. + to build the `dodiscover.Context` object here. """ _intervention_targets: List[Tuple[Column]] = [] @@ -512,11 +512,12 @@ def make_context(context: Optional[Context] = None, create_using=ContextBuilder) Notes ----- - `Context` objects are dataclasses that creates a dictionary-like access - to causal context metadata. Copying relevant information from a `Context` + `dodiscover.Context` objects are dataclasses that creates a dictionary-like access + to causal context metadata. Copying relevant information from a `dodiscover.Context` object into a `ContextBuilder` is all supported with the exception of state variables. State variables are not copied over. To set state variables - again, one must build the Context and then call :meth:`Context.state_variable`. + again, one must build the Context and then call + :meth:`dodiscover.Context.state_variable`. """ result = create_using() if context is not None: From 3d025916b7088bb3744ab40397b9fee3333e8e73 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 14 Feb 2023 16:24:01 -0500 Subject: [PATCH 05/61] Merge Signed-off-by: Adam Li --- tests/unit_tests/constraint/test_fcialg.py | 74 +--------------------- 1 file changed, 2 insertions(+), 72 deletions(-) diff --git a/tests/unit_tests/constraint/test_fcialg.py b/tests/unit_tests/constraint/test_fcialg.py index a63dffb5b..64c84ad4f 100644 --- a/tests/unit_tests/constraint/test_fcialg.py +++ b/tests/unit_tests/constraint/test_fcialg.py @@ -636,8 +636,8 @@ def test_fci_spirtes_example(self): assert nx.is_isomorphic(skel_graph.to_undirected(), expected_pag.to_undirected()) assert set(expected_pag.edges()) == set(pag.edges()) - @pytest.mark.parametrize("skeleton_method", [SkeletonMethods.NBRS, SkeletonMethods.COMPLETE]) - @pytest.mark.parametrize("pds_skeleton_method", [SkeletonMethods.PDS]) + @pytest.mark.parametrize("skeleton_method", [SkeletonMethods.NBRS, SkeletonMethods.NBRS_PATH, SkeletonMethods.COMPLETE]) + @pytest.mark.parametrize("pds_skeleton_method", [SkeletonMethods.PDS, SkeletonMethods.PDS_PATH]) @pytest.mark.parametrize("selection_bias", [True, False]) def test_fci_complex(self, skeleton_method, pds_skeleton_method, selection_bias): """ @@ -780,73 +780,3 @@ def test_fci_selection_bias(self): expected_pag.add_edge("D", "R", expected_pag.directed_edge_name) assert pag.nodes() == expected_pag.nodes() assert pag.edges() == expected_pag.edges() - - -@pytest.mark.parametrize( - "skeleton_method", [SkeletonMethods.NBRS, SkeletonMethods.NBRS_PATH, SkeletonMethods.COMPLETE] -) -@pytest.mark.parametrize("pds_skeleton_method", [SkeletonMethods.PDS_PATH, SkeletonMethods.PDS]) -def test_fci_complex(skeleton_method, pds_skeleton_method): - """ - Test FCI algorithm with more complex graph. - - Use Figure 2 from :footcite:`Colombo2012`. - - References - ---------- - .. footbibliography:: - """ - edge_list = [ - ("x4", "x1"), - ("x2", "x5"), - ("x3", "x2"), - ("x3", "x4"), - ("x2", "x6"), - ("x3", "x6"), - ("x4", "x6"), - ("x5", "x6"), - ] - latent_edge_list = [("x1", "x2"), ("x4", "x5")] - G = ADMG(edge_list, latent_edge_list) - sample = dummy_sample(G) - context = make_context().variables(data=sample).build() - oracle = Oracle(G) - ci_estimator = oracle - fci = FCI( - ci_estimator=ci_estimator, - max_iter=np.inf, - skeleton_method=skeleton_method, - pds_skeleton_method=pds_skeleton_method, - selection_bias=False, - ) - fci.fit(sample, context) - pag = fci.graph_ - - # double check the m-separation statement and PDS - assert pywhy_nx.m_separated(G, {"x1"}, {"x3"}, {"x4"}) - pdsep = pywhy_graphs.pds(G, "x1", "x3") - assert "x2" in pdsep - - expected_pag = PAG() - expected_pag.add_edges_from( - [("x6", "x5"), ("x2", "x3"), ("x4", "x3"), ("x6", "x4")], expected_pag.circle_edge_name - ) - expected_pag.add_edges_from( - [ - ("x4", "x1"), - ("x2", "x5"), - ("x3", "x2"), - ("x3", "x4"), - ("x2", "x6"), - ("x3", "x6"), - ("x4", "x6"), - ("x5", "x6"), - ], - expected_pag.directed_edge_name, - ) - expected_pag.add_edge("x1", "x2", expected_pag.bidirected_edge_name) - expected_pag.add_edge("x4", "x5", expected_pag.bidirected_edge_name) - - assert set(pag.edges()) == set(expected_pag.edges()) - for edge_type, subgraph in expected_pag.get_graphs().items(): - assert nx.is_isomorphic(subgraph, pag.get_graphs(edge_type)) From 1b4a18410f55087558db45e02e088f5dbb1f58ca Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 17 Feb 2023 16:48:43 -0500 Subject: [PATCH 06/61] Adding updated psifci working tests Signed-off-by: Adam Li --- .vscode/settings.json | 3 + doc/api.rst | 2 +- doc/conditional_independence.rst | 33 ++- doc/conf.py | 1 + doc/constraint_causal_discovery.rst | 78 +++++- doc/index.rst | 5 +- doc/references.bib | 75 ++++-- dodiscover/__init__.py | 2 +- dodiscover/_protocol.py | 6 +- dodiscover/ci/g_test.py | 8 +- dodiscover/constraint/__init__.py | 2 +- dodiscover/constraint/_classes.py | 19 +- dodiscover/constraint/config.py | 29 ++- dodiscover/constraint/fcialg.py | 35 +-- dodiscover/constraint/intervention.py | 206 +++++++++++++-- dodiscover/constraint/pcalg.py | 8 +- dodiscover/constraint/skeleton.py | 84 ++---- dodiscover/context_builder.py | 17 +- .../unit_tests/conditional/ci/test_g_test.py | 11 +- tests/unit_tests/constraint/__init__.py | 0 tests/unit_tests/constraint/test_fcialg.py | 33 ++- .../constraint/test_intervene_skeleton.py | 85 +++++- tests/unit_tests/constraint/test_pcalg.py | 4 +- tests/unit_tests/constraint/test_psifcialg.py | 243 ++++++++++++++++++ tests/unit_tests/test_context_builder.py | 6 +- 25 files changed, 777 insertions(+), 218 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 tests/unit_tests/constraint/__init__.py create mode 100644 tests/unit_tests/constraint/test_psifcialg.py diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 000000000..a7d0fc7b7 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "esbonio.sphinx.confDir": "" +} \ No newline at end of file diff --git a/doc/api.rst b/doc/api.rst index 7c7b8b301..5c7331419 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -46,7 +46,7 @@ Constraint-based structure learning LearnSkeleton LearnSemiMarkovianSkeleton - SkeletonMethods + ConditioningSetSelection PC FCI diff --git a/doc/conditional_independence.rst b/doc/conditional_independence.rst index 133a16a36..026703a00 100644 --- a/doc/conditional_independence.rst +++ b/doc/conditional_independence.rst @@ -6,6 +6,34 @@ Conditional Independence .. currentmodule:: dodiscover.ci +Testing for invariances in the data, such as conditional independence (CI) can be represented graphically +in the form of d-separation statements under the causal faithfulness assumption. Given this, one +is interested in high-powered and well-controlled CI tests that can be used to test for CI in data. + +CI tests are framed as a statistical hypothesis test, with the following null hypothesis for a given +pair of variables ``(X, Y)`` and a conditioning set ``Z`` (which may be empty): + +:math:`H_0: X \perp Y | Z`, or written in terms of their distribution :math:`H_0: P(Y | X, Z) = P(Y | Z)` + +Similarly, the alternative hypothesis is written as: + +:math:`H_0: X \not\perp Y | Z`, or written in terms of their distribution :math:`H_0: P(Y | X, Z) \neq P(Y | Z)` + +Then typically, one posits an acceptable Type I error rate (false positive), typically :math:`\alpha = 0.05` +and then either attempts to sample from the null distribution, or characterizes the asymptotic distribution +of the test statistic. In both approaches a pvalue is computed, which is compared to :math:`\alpha`. The pvalue +states the "probability that we observe our data (e.g. test statistic) under the null hypothesis". By rejecting +the null hypothesis, one claims that :math:`X \not\perp Y | Z`, so that X and Y are in fact (conditionally) +dependent given Z. Note that if one fails to reject the null hypothesis, it is simply by convention that we +claim X and Y are conditionally independent. It is not necessarily the case, and it is plausible that there +is a weak dependency that is unable to be captured by our proposed CI test, and/or data samples. + +It is because of this reason, one would typically like the most powerful test given assumptions about +the data. With that in mind, there are various approaches to CI testing that are typically more powerful +with certain assumptions on the underlying data distribution. + +Conditional Mutual Information +------------------------------ TBD. Partial (Pearson) Correlation @@ -16,11 +44,10 @@ Discrete, Categorical and Binary Data ------------------------------------- TBD. - Kernel-Approaches ----------------- TBD. -Conditional Mutual Information ------------------------------- +Classifier-based Approaches +--------------------------- TBD. \ No newline at end of file diff --git a/doc/conf.py b/doc/conf.py index 69762332e..96511bff7 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -205,6 +205,7 @@ "joblib": ("https://joblib.readthedocs.io/en/latest", None), "matplotlib": ("https://matplotlib.org/stable", None), "torch": ("https://pytorch.org/docs/master/", None), + "pywhy_graphs": ("https://www.pywhy.org/pywhy-graphs/dev/", None), } intersphinx_timeout = 5 diff --git a/doc/constraint_causal_discovery.rst b/doc/constraint_causal_discovery.rst index 66d69fbc0..23f751c88 100644 --- a/doc/constraint_causal_discovery.rst +++ b/doc/constraint_causal_discovery.rst @@ -23,9 +23,12 @@ Fundamental Assumptions of Constraint-Based Causal Discovery ------------------------------------------------------------ The fundamental assumptions of all algorithms in this section are the Markov property assumption and the causal faithfulness assumption :footcite:`Spirtes1993` and :footcite:`Pearl_causality_2009`. + The Markov assumption states that all d-separation statements in the causal graph imply a corresponding CI statement in the data. This is a core-assumption that users from graphical modeling -may be familiar with. On the other hand, the causal faithfulness assumption states that all +may be familiar with. + +On the other hand, the causal faithfulness assumption states that all CI statements in the data map to a d-separation statement. That is, there are no accidental CI that occur in the data, which are not represented by a d-separation statement in the underlying causal graph. The causal faithfulness assumption is a very problematic assumption because in practice @@ -33,7 +36,8 @@ one might have data that is very weakly dependent, such that a CI test under a s level would fail to reject the null hypothesis and conclude the variables in question are CI. In higher dimensions this can occur a large percentage of the time as demonstrated in :footcite:`uhler2013geometry`. -Tackling constraint-based causal discovery is a large and active area of research. +Tackling violations of faithfulness in constraint-based causal discovery is a large and active +area of research. (Non-parametric) Markovian SCMs with Observational Data ------------------------------------------------------- @@ -41,29 +45,83 @@ If one assumes that the underlying structural causal model (SCM) is Markovian, then the Peter and Clarke (PC) algorithm has been shown to be sound and complete for learning a completed partially directed acyclic graph (CPDAG) :footcite:`Meek1995`. -The PC algorithm and its variants assume Markovianity, which is also known as -causal-sufficiency in the literature. In other words, it assumes a lack of latent +The :class:`dodiscover.PC` algorithm and its variants assume Markovianity, which is +also known as causal-sufficiency in the literature. In other words, it assumes a lack of latent confounders, where there is no latent variable that is a confounder of the observed data. The PC algorithm learns a CPDAG in three stages: 1. skeleton discovery: This first phase is the process of leveraging CI tests to test - edges for conditional independence. -2. unshielded triplet orientation: -3. deterministic path orientations: - + edges for conditional independence. Along the way, connections of the graph are trimmed + (when CI is detected) and the separating sets among pairs of variables are tracked. + + A separating set is a set of nodes in the graph that d-separate a pair of variables. + Note that a pair of variables may contain many d-separators, and thus there may be + many separating sets. +2. unshielded triplet orientation: This takes triplets on a path of the form ``X *-* Y *-* Z``, + where the triplet path is "unshielded" meaning ``X`` and ``Z`` are not connected. Then + it checks that ``Y`` is not in the separating set of X and Z. Given these two conditions, + Y must be a collider and is oriented as ``X *-> Y <-* Z``. The stars in the path indicate + that it can be any kind of edge endpoint (e.g. in a PAG it could be a circle endpoint edge). +3. deterministic path orientations: Once all colliders are oriented, there are a set of + deterministic logical rules that allow us to orient more edges. In the PC algorithm, + these are the so-called "Meek's orientation rules", which are 4 rules that are applied + repeatedly until no more changes to the graph are made :footcite:`Meek1995`. + +The resulting graph is an equivalence class of DAGs without latent confounders, the CPDAG. +For more information on CPDAGs, one can also see :class:`pywhy_graphs.CPDAG`. (Non-parametric) Semi-Markovian SCMs with Observational Data ------------------------------------------------------------ - +If one assumes that the underlying SCM is Semi-Markovian, then the "Fast Causal Inference" +(FCI) algorithm has been shown to be sound and complete for learning a partial ancestral +graph (PAG) :footcite:`zhang2008ancestralgraphs,Zhang2008`. + +The FCI algorithm and its variants assume Semi-Markovianity, which assumes the +possible presence of latent confounders and even selection bias in the observational data. + +The :class:`dodiscover.FCI` algorithm follows the three stages of learning that the PC +algorithm does, but with a few minor modifications that we will outline here: + +1. skeleton discovery: The skeleton discovery phase is now composed of two stages. The first + stage is the same as the PC algorithm. The second phase, takes the output graph of the first + phase and tries to orient colliders. This results in a PAG that can be queried for the + potentially d-separating (PDS) sets for any pair of variables ``(X, Y)``. The skeleton + discovery phase is restarted from scratch, but now the conditioning sets are chosen from + the PDS sets. The PDS set approach is described in :footcite:`Colombo2012` and + :footcite:`Spirtes1993`. +2. deterministic path orientations: The four orientation rules of the PC algorithm are still + the same, but in the FCI case, we add an additional six orientation rules. The additional + rules account for latent confounding and selection bias. Three of those rules + only apply if we assume selection bias is present. (Non-parametric) SCMs with Interventional Data ---------------------------------------------- +When we have access to experimental data, there are multiple datasets corresponding to multiple +distributions (e.g. observational and different interventions), we can improve causal discovery. +If one assumes we have access to multiple distributions, one may know the targets of +each intervention, where one can apply the I-FCI algorithm to learn an Interventional-PAG +(I-PAG) :footcite:`Kocaoglu2019characterization`. + +Alternatively, one may assume they do not know where the intervention was applied in each +distribution. In this case, one may apply the :math:`Psi`-FCI algorithm to learn a +:math:`Psi`-PAG :footcite:`Jaber2020causal`. +.. autosummary:: + :toctree: generated/ + + PsiFCI Choosing the conditioning sets ------------------------------ -To describe. +We briefly describe how ``dodiscover`` chooses conditioning sets, ``Z`` that are tested given +a pair of nodes ``(X, Y)``. The test we are doing is :math:`X \perp Y | Z`, where ``Z`` can +be the empty set. There are multiple strategies for choosing ``Z``. + +.. autosummary:: + :toctree: generated/ + + ConditioningSetSelection Hyperparameters and controlling overfitting ------------------------------------------- diff --git a/doc/index.rst b/doc/index.rst index a6956c290..de95af9fd 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -24,8 +24,9 @@ Contents :caption: Getting started: installation - api - use + Reference API + Usage + User Guide tutorials/index whats_new diff --git a/doc/references.bib b/doc/references.bib index 2f0ea855c..c52d35d08 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -16,21 +16,31 @@ @article{Colombo2012 url = {https://doi.org/10.1214/11-AOS940} } +@inproceedings{correa2020calculus, + title = {A calculus for stochastic interventions: Causal effect identification and surrogate experiments}, + author = {Correa, Juan and Bareinboim, Elias}, + booktitle = {Proceedings of the AAAI conference on artificial intelligence}, + volume = {34}, + number = {06}, + pages = {10093--10100}, + year = {2020} +} + @article{Jaber2020causal, - title={Causal discovery from soft interventions with unknown targets: Characterization and learning}, - author={Jaber, Amin and Kocaoglu, Murat and Shanmugam, Karthikeyan and Bareinboim, Elias}, - journal={Advances in neural information processing systems}, - volume={33}, - pages={9551--9561}, - year={2020} + title = {Causal discovery from soft interventions with unknown targets: Characterization and learning}, + author = {Jaber, Amin and Kocaoglu, Murat and Shanmugam, Karthikeyan and Bareinboim, Elias}, + journal = {Advances in neural information processing systems}, + volume = {33}, + pages = {9551--9561}, + year = {2020} } @article{Kocaoglu2019characterization, - title={Characterization and learning of causal graphs with latent variables from soft interventions}, - author={Kocaoglu, Murat and Jaber, Amin and Shanmugam, Karthikeyan and Bareinboim, Elias}, - journal={Advances in Neural Information Processing Systems}, - volume={32}, - year={2019} + title = {Characterization and learning of causal graphs with latent variables from soft interventions}, + author = {Kocaoglu, Murat and Jaber, Amin and Shanmugam, Karthikeyan and Bareinboim, Elias}, + journal = {Advances in Neural Information Processing Systems}, + volume = {32}, + year = {2019} } @article{Lopez2016revisiting, @@ -105,12 +115,12 @@ @article{Sen2017model } @article{uhler2013geometry, - title={Geometry of the faithfulness assumption in causal inference}, - author={Uhler, Caroline and Raskutti, Garvesh and B{\"u}hlmann, Peter and Yu, Bin}, - journal={The Annals of Statistics}, - pages={436--463}, - year={2013}, - publisher={JSTOR} + title = {Geometry of the faithfulness assumption in causal inference}, + author = {Uhler, Caroline and Raskutti, Garvesh and B{\"u}hlmann, Peter and Yu, Bin}, + journal = {The Annals of Statistics}, + pages = {436--463}, + year = {2013}, + publisher = {JSTOR} } @inproceedings{Yu2020Bregman, @@ -142,6 +152,17 @@ @article{Zhang2008 keywords = {Ancestral graphs, Automated causal discovery, Bayesian networks, Causal models, Markov equivalence, Latent variables} } +@article{zhang2008ancestralgraphs, + author = {Jiji Zhang}, + title = {Causal Reasoning with Ancestral Graphs}, + journal = {Journal of Machine Learning Research}, + year = {2008}, + volume = {9}, + number = {47}, + pages = {1437--1474}, + url = {http://jmlr.org/papers/v9/zhang08a.html} +} + @inproceedings{Zhang2011, author = {Zhang, Kun and Peters, Jonas and Janzing, Dominik and Sch\"{o}lkopf, Bernhard}, title = {Kernel-Based Conditional Independence Test and Application in Causal Discovery}, @@ -181,17 +202,17 @@ @book{Spirtes1993 } @article{xie2020generalized, - title = {Generalized independent noise condition for estimating latent variable causal graphs}, - author = {Xie, Feng and Cai, Ruichu and Huang, Biwei and Glymour, Clark and Hao, Zhifeng and Zhang, Kun}, - journal = {Advances in Neural Information Processing Systems}, - volume = {33}, - pages = {14891--14902}, - year = {2020} + title = {Generalized independent noise condition for estimating latent variable causal graphs}, + author = {Xie, Feng and Cai, Ruichu and Huang, Biwei and Glymour, Clark and Hao, Zhifeng and Zhang, Kun}, + journal = {Advances in Neural Information Processing Systems}, + volume = {33}, + pages = {14891--14902}, + year = {2020} } @article{dai2022independence, - title = {Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models}, - author = {Dai, Haoyue and Spirtes, Peter and Zhang, Kun}, - journal = {arXiv preprint arXiv:2210.11021}, - year = {2022} + title = {Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models}, + author = {Dai, Haoyue and Spirtes, Peter and Zhang, Kun}, + journal = {arXiv preprint arXiv:2210.11021}, + year = {2022} } diff --git a/dodiscover/__init__.py b/dodiscover/__init__.py index 50014dbb8..1ca3fc985 100644 --- a/dodiscover/__init__.py +++ b/dodiscover/__init__.py @@ -7,5 +7,5 @@ from . import metrics # noqa: F401 from ._protocol import EquivalenceClass, Graph from ._version import __version__ # noqa: F401 -from .constraint import FCI, PC +from .constraint import FCI, PC, PsiFCI from .context_builder import ContextBuilder, InterventionalContextBuilder, make_context diff --git a/dodiscover/_protocol.py b/dodiscover/_protocol.py index 91709e069..84e32defc 100644 --- a/dodiscover/_protocol.py +++ b/dodiscover/_protocol.py @@ -15,7 +15,7 @@ def edges(self, data=None) -> Iterable: """Return an iterable over edge tuples in graph.""" pass - def has_edge(self, u, v, edge_type) -> bool: + def has_edge(self, u, v, edge_type="any") -> bool: """Check if graph has an edge for a specific edge type.""" pass @@ -31,11 +31,11 @@ def remove_edges_from(self, edges) -> None: """Remove a set of edges from the graph.""" pass - def add_edge(self, u, v, edge_type) -> None: + def add_edge(self, u, v, edge_type="all") -> None: """Add an edge to the graph.""" pass - def remove_edge(self, u, v, edge_type) -> None: + def remove_edge(self, u, v, edge_type="all") -> None: """Remove an edge from the graph.""" pass diff --git a/dodiscover/ci/g_test.py b/dodiscover/ci/g_test.py index 5b30644fe..6bb42a7c7 100644 --- a/dodiscover/ci/g_test.py +++ b/dodiscover/ci/g_test.py @@ -382,7 +382,7 @@ def g_square_discrete( class GSquareCITest(BaseConditionalIndependenceTest): - def __init__(self, data_type: str = "binary"): + def __init__(self, data_type: str = "binary", levels: Optional[List] = None): r"""G squared CI test for discrete or binary data. For details of the test see :footcite:`Neapolitan2003`. @@ -392,6 +392,8 @@ def __init__(self, data_type: str = "binary"): data_type : str, optional The type of data, which can be "binary", or "discrete". By default "binary". + levels : List, optional + Levels of each column in the data matrix (as a list()). Notes ----- @@ -411,6 +413,7 @@ def __init__(self, data_type: str = "binary"): .. footbibliography:: """ self.data_type = data_type + self.levels = levels def test( self, @@ -418,7 +421,6 @@ def test( x_vars: Set[Column], y_vars: Set[Column], z_covariates: Optional[Set[Column]] = None, - levels: Optional[List] = None, ) -> Tuple[float, float]: """Abstract method for all conditional independence tests. @@ -450,7 +452,7 @@ def test( if self.data_type == "binary": stat, pvalue = g_square_binary(df, x_var, y_var, z_covariates) elif self.data_type == "discrete": - stat, pvalue = g_square_discrete(df, x_var, y_var, z_covariates, levels=levels) + stat, pvalue = g_square_discrete(df, x_var, y_var, z_covariates, levels=self.levels) else: raise ValueError( f"The acceptable data_type for G Square CI test is " diff --git a/dodiscover/constraint/__init__.py b/dodiscover/constraint/__init__.py index 2d4fdd511..f21aed4ce 100644 --- a/dodiscover/constraint/__init__.py +++ b/dodiscover/constraint/__init__.py @@ -1,4 +1,4 @@ -from .config import SkeletonMethods +from .config import ConditioningSetSelection from .fcialg import FCI from .intervention import PsiFCI from .pcalg import PC diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index dc3087e7c..764330104 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -7,9 +7,8 @@ import pandas as pd from dodiscover.ci.base import BaseConditionalIndependenceTest -from dodiscover.constraint.skeleton import LearnSkeleton, SkeletonMethods +from dodiscover.constraint.skeleton import ConditioningSetSelection, LearnSkeleton from dodiscover.context import Context -from dodiscover.context_builder import make_context from dodiscover.typing import Column, SeparatingSet from .._protocol import EquivalenceClass @@ -46,8 +45,6 @@ class BaseConstraintDiscovery: Whether or not to apply orientation rules given the learned skeleton graph and separating set per pair of variables. If ``True`` (default), will apply orientation rules for specific algorithm. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. Attributes ---------- @@ -69,13 +66,11 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, - **ci_estimator_kwargs, ): self.alpha = alpha self.ci_estimator = ci_estimator - self.ci_estimator_kwargs = ci_estimator_kwargs self.apply_orientations = apply_orientations self.skeleton_method = skeleton_method @@ -152,17 +147,14 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: Control over the constraints imposed by the algorithm can be passed into the class constructor. """ - self.context_ = make_context(context).build() - - # create a reference to the underlying data to be used - self.X_ = data + self.context_ = context.copy() # initialize graph object to apply learning self.separating_sets_ = self._initialize_sep_sets(self.context_.init_graph) # learn skeleton graph and the separating sets per variable graph, self.separating_sets_ = self.learn_skeleton( - self.X_, self.context_, self.separating_sets_ + data, self.context_, self.separating_sets_ ) # convert networkx.Graph to relevant causal graph object @@ -203,7 +195,7 @@ def evaluate_edge( """ if Z is None: Z = set() - test_stat, pvalue = self.ci_estimator.test(data, {X}, {Y}, Z, **self.ci_estimator_kwargs) + test_stat, pvalue = self.ci_estimator.test(data, {X}, {Y}, Z) return test_stat, pvalue def learn_skeleton( @@ -257,7 +249,6 @@ def learn_skeleton( max_combinations=self.max_combinations, skeleton_method=self.skeleton_method, keep_sorted=False, - **self.ci_estimator_kwargs, ) skel_alg.fit(data, context) diff --git a/dodiscover/constraint/config.py b/dodiscover/constraint/config.py index b78c335d2..ca3f07ed2 100644 --- a/dodiscover/constraint/config.py +++ b/dodiscover/constraint/config.py @@ -16,18 +16,39 @@ def __contains__(cls, item): return True -class SkeletonMethods(Enum, metaclass=MetaEnum): - """Available methods for learning a skeleton from data. +class ConditioningSetSelection(Enum, metaclass=MetaEnum): + """Available methods for selecting the conditioning sets when learning a skeleton. + + Given a pair of nodes in a graph, (X, Y), this enumeration selects a strategy + for choosing conditioning sets to be checked for conditional independence. Notes ----- Allows 'contains' checks because of the metaclass. For example, - one can do ``is 'complete' in SkeletonMethods``, which would - return True. + one can run ``"complete" in SkeletonMethods``, which would + return `True`. """ COMPLETE = "complete" + """Considers all possible combinations of nodes in the graph that are + not (X,Y). + """ + NBRS = "neighbors" + """Considers all current neighbors of (X,Y) in the graph. + """ + NBRS_PATH = "neighbors_path" + """Considers all neighbors of (X,Y) in the graph that are on a simple + path between the two nodes. + """ + PDS = "pds" + """Considers all potentially d-separating sets for (X,Y) in the graph. + This requires some initial collider information in the graph. + """ + PDS_PATH = "pds_path" + """Considers all PDS sets for (X,Y) in the graph that lie on a path + between the two nodes. + """ diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index 3c0971317..de752d948 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -6,7 +6,7 @@ import pandas as pd from dodiscover.ci.base import BaseConditionalIndependenceTest -from dodiscover.constraint.config import SkeletonMethods +from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.skeleton import LearnSemiMarkovianSkeleton from dodiscover.constraint.utils import is_in_sep_set from dodiscover.typing import Column, SeparatingSet @@ -67,8 +67,6 @@ class FCI(BaseConstraintDiscovery): pds_skeleton_method : SkeletonMethods The method to use for learning the skeleton using PDS. Must be one of ('pds', 'pds_path'). See Notes for more details. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. References ---------- @@ -91,13 +89,12 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, max_iter: int = 1000, max_path_length: Optional[int] = None, selection_bias: bool = True, - pds_skeleton_method: SkeletonMethods = SkeletonMethods.PDS, - **ci_estimator_kwargs, + pds_skeleton_method: ConditioningSetSelection = ConditioningSetSelection.PDS, ): super().__init__( ci_estimator, @@ -106,7 +103,6 @@ def __init__( max_cond_set_size=max_cond_set_size, max_combinations=max_combinations, skeleton_method=skeleton_method, - **ci_estimator_kwargs, ) self.max_iter = max_iter self.apply_orientations = apply_orientations @@ -747,7 +743,7 @@ def _apply_rule10( return added_arrows, a_to_u_path, a_to_v_path - def _apply_rules_1to10(self, graph: EquivalenceClass, sep_set: SeparatingSet): + def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingSet): idx = 0 finished = False while idx < self.max_iter and not finished: @@ -757,6 +753,7 @@ def _apply_rules_1to10(self, graph: EquivalenceClass, sep_set: SeparatingSet): for u in graph.nodes: for (a, c) in permutations(graph.neighbors(u), 2): logger.debug(f"Check {u} {a} {c}") + # apply R1-3 to orient triples and arrowheads r1_add = self._apply_rule1(graph, u, a, c) r2_add = self._apply_rule2(graph, u, a, c) @@ -797,22 +794,6 @@ def _apply_rules_1to10(self, graph: EquivalenceClass, sep_set: SeparatingSet): r9_add, r10_add, ] - if all(x in [u, a, c] for x in ["x6", "x4"]) and any(all_flags): - print(u, a, c) - print( - [ - r1_add, - r2_add, - r3_add, - r4_add, - r5_add, - r6_add, - r7_add, - r8_add, - r9_add, - r10_add, - ] - ) if any(all_flags) and not change_flag: logger.info(f"{change_flag} with " f"{all_flags}") change_flag = True @@ -842,7 +823,6 @@ def learn_skeleton( second_stage_skeleton_method=self.pds_skeleton_method, keep_sorted=False, max_path_length=self.max_path_length, - **self.ci_estimator_kwargs, ) skel_alg.fit(data, context) @@ -851,16 +831,13 @@ def learn_skeleton( self.n_ci_tests += skel_alg.n_ci_tests return skel_graph, sep_set - def fit(self, data: pd.DataFrame, context: Context) -> None: - super().fit(data, context) - def orient_edges(self, graph: EquivalenceClass): # orient colliders again self.orient_unshielded_triples(graph, self.separating_sets_) # run the rest of the rules to orient as many edges # as possible - self._apply_rules_1to10(graph, self.separating_sets_) + self._apply_orientation_rules(graph, self.separating_sets_) def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: import pywhy_graphs as pgraph diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index 4ed6f1c18..d44d4f8fd 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -1,4 +1,6 @@ -from typing import List, Optional, Tuple +import logging +from itertools import permutations +from typing import Any, Dict, FrozenSet, List, Optional, Tuple import networkx as nx import pandas as pd @@ -6,11 +8,14 @@ from dodiscover._protocol import EquivalenceClass from dodiscover.cd import BaseConditionalDiscrepancyTest from dodiscover.ci import BaseConditionalIndependenceTest -from dodiscover.constraint import LearnInterventionSkeleton, SkeletonMethods from dodiscover.context import Context -from dodiscover.typing import SeparatingSet +from dodiscover.typing import Column, SeparatingSet +from .config import ConditioningSetSelection from .fcialg import FCI +from .skeleton import LearnInterventionSkeleton + +logger = logging.getLogger() class PsiFCI(FCI): @@ -73,10 +78,6 @@ class PsiFCI(FCI): known_intervention_targets : bool, optional If `True`, then will run the I-FCI algorithm. If `False`, will run the Psi-FCI algorithm. By default False. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. - cd_estimator_kwargs : dict - Keyword arguments for the ``cd_estimator`` function. Notes ----- @@ -91,14 +92,12 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, max_iter: int = 1000, max_path_length: Optional[int] = None, - pds_skeleton_method: SkeletonMethods = SkeletonMethods.PDS, + pds_skeleton_method: ConditioningSetSelection = ConditioningSetSelection.PDS, known_intervention_targets: bool = False, - ci_estimator_kwargs=None, - cd_estimator_kwargs=None, ): super().__init__( ci_estimator, @@ -112,17 +111,15 @@ def __init__( max_path_length=max_path_length, selection_bias=False, pds_skeleton_method=pds_skeleton_method, - ci_estimator_kwargs=ci_estimator_kwargs, ) self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets - self.cd_estimator_kwargs = cd_estimator_kwargs def learn_skeleton( self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None ) -> Tuple[nx.Graph, SeparatingSet]: # now compute all possibly d-separating sets and learn a better skeleton - skel_alg = LearnInterventionSkeleton( + self.skeleton_learner_ = LearnInterventionSkeleton( self.ci_estimator, self.cd_estimator, sep_set=sep_set, @@ -134,14 +131,13 @@ def learn_skeleton( second_stage_skeleton_method=self.pds_skeleton_method, keep_sorted=False, max_path_length=self.max_path_length, - ci_estimator_kwargs=self.ci_estimator_kwargs, - cd_estimator_kwargs=self.cd_estimator_kwargs, ) - skel_alg.fit(data, context) + print(context) + self.skeleton_learner_.fit(data, context) - skel_graph = skel_alg.adj_graph_ - sep_set = skel_alg.sep_set_ - self.n_ci_tests += skel_alg.n_ci_tests + skel_graph = self.skeleton_learner_.adj_graph_ + sep_set = self.skeleton_learner_.sep_set_ + self.n_ci_tests += self.skeleton_learner_.n_ci_tests return skel_graph, sep_set def fit(self, data: List[pd.DataFrame], context: Context): @@ -168,19 +164,175 @@ def fit(self, data: List[pd.DataFrame], context: Context): raise RuntimeError("The input datasets must be in a Python list.") n_datasets = len(data) - intervention_targets = context.intervention_targets + n_distributions = context.num_distributions - if n_datasets - 1 != len(intervention_targets): + if n_datasets != n_distributions: raise RuntimeError( - f"There are {n_datasets} passed in, but {len(intervention_targets)} " - f"intervention targets. There must be a matching (number of datasets - 1) and " - f"intervention targets." + f"There are {n_datasets} passed in, but {n_distributions} " + f"total assumed distributions. There must be a matching number of datasets and " + f"'context.num_distributions'." ) super().fit(data, context) - def orient_edges(self, graph: EquivalenceClass): - return super().orient_edges(graph) + def _apply_rule11(self, graph: EquivalenceClass, f_nodes: List) -> Tuple[bool, List]: + """Apply "Rule 8" in I-FCI algorithm, which we call Rule 11. + + This orients all edges out of F-nodes. So patterns of the form + + ``('F', 0) *-* 'x'`` will become ``('F', 0) -> 'x'``. + + For original details of the rule, see :footcite:`Kocaoglu2019characterization`. + + Parameters + ---------- + graph : EquivalenceClass + The causal graph to apply rules to. + f_nodes : list + The list of f-nodes within the graph. + + Returns + ------- + added_arrows : bool + Whether or not arrows were added. + oriented_edges : List + A list of oriented edges. + + References + ---------- + .. footbibliography:: + """ + oriented_edges = [] + added_arrows = True + for node in f_nodes: + for nbr in graph.neighbors(node): + if nbr in f_nodes: + continue + + # remove all edges between node and nbr and orient this out + graph.remove_edge(node, nbr) + graph.remove_edge(nbr, node) + graph.add_edge(node, nbr, graph.directed_edge_name) + oriented_edges.append((node, nbr)) + return added_arrows, oriented_edges + + def _apply_rule12( + self, + graph: EquivalenceClass, + u: Column, + a: Column, + c: Column, + f_nodes: List, + symmetric_diff_map: Dict[Any, FrozenSet], + ) -> bool: + """Apply "Rule 9" of the I-FCI algorithm. + + Checks for inducing paths where 'u' is the F-node, and 'a' and 'c' are connected: + + 'u' -> 'a' *-* 'c' with 'u' -> 'c', then orient 'a' -> 'c'. + + For original details of the rule, see :footcite:`Kocaoglu2019characterization`. + + Parameters + ---------- + graph : EquivalenceClass + The causal graph. + u : Column + The candidate F-node + a : Column + Neighbors of the F-node. + c : Column + Neighbors of the F-node. + symmetric_diff_map : dict + A mapping from the F-nodes to the symmetric difference of the pair of + intervention targets each F-node represents. I.e. if F-node, F1 represents + the pair of intervention distributions with targets {'x'}, and {'x', 'y'}, + then F1 maps to {'y'} in the symmetric diff map. + + Returns + ------- + added_arrows : bool + Whether or not an orientation was made. + + References + ---------- + .. footbibliography:: + """ + added_arrows = False + if u in f_nodes and self.known_intervention_targets: + # get sigma map to map F-node to its symmetric difference target + S_set: FrozenSet = symmetric_diff_map.get(u, frozenset()) + + # check a *-* c + if ( + len(S_set) == 1 + and a in S_set + and (graph.has_edge(a, c) or graph.has_edge(c, a)) + and graph.has_edge(u, a) + and graph.has_edge(u, c) + ): + # remove all edges between a and c + graph.remove_edge(a, c) + graph.remove_edge(c, a) + + # then orient X -> Y + graph.add_edge(a, c, graph.directed_edge_name) + + added_arrows = True + return added_arrows + + def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingSet): + idx = 0 + finished = False + + # apply R11, which is called R8 in I-FCI / Psi-FCI orienting all F-nodes + f_nodes = self.context_.f_nodes + symmetric_diff_map = self.context_.symmetric_diff_map + _ = self._apply_rule11(graph, f_nodes) + + while idx < self.max_iter and not finished: + change_flag = False + logger.info(f"Running R1-10 for iteration {idx}") + + for u in graph.nodes: + for (a, c) in permutations(graph.neighbors(u), 2): + logger.debug(f"Check {u} {a} {c}") + + # apply R1-3 to orient triples and arrowheads + r1_add = self._apply_rule1(graph, u, a, c) + r2_add = self._apply_rule2(graph, u, a, c) + r3_add = self._apply_rule3(graph, u, a, c) + + # apply R4, orienting discriminating paths + r4_add, _ = self._apply_rule4(graph, u, a, c, sep_set) + + # apply R8 to orient more tails + r8_add = self._apply_rule8(graph, u, a, c) + + # apply R9-10 to orient uncovered potentially directed paths + r9_add, _ = self._apply_rule9(graph, a, u, c) + + # a and c are neighbors of u, so u is the endpoint desired + r10_add, _, _ = self._apply_rule10(graph, a, c, u) + + # apply R12, called R9 in I-FCI when we know the intervention targets + r12_add = self._apply_rule12(graph, u, a, c, f_nodes, symmetric_diff_map) + + # see if there was a change flag + all_flags = [r1_add, r2_add, r3_add, r4_add, r8_add, r9_add, r10_add, r12_add] + if any(all_flags) and not change_flag: + logger.info(f"{change_flag} with {all_flags}") + change_flag = True + + # check if we should continue or not + if not change_flag: + finished = True + if not self.selection_bias: + logger.info(f"Finished applying R1-4, and R8-10 with {idx} iterations") + if self.selection_bias: + logger.info(f"Finished applying R1-10 with {idx} iterations") + break + idx += 1 def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: import pywhy_graphs as pgraph diff --git a/dodiscover/constraint/pcalg.py b/dodiscover/constraint/pcalg.py index ec9e60201..6a7688b61 100644 --- a/dodiscover/constraint/pcalg.py +++ b/dodiscover/constraint/pcalg.py @@ -5,7 +5,7 @@ import networkx as nx from dodiscover.ci.base import BaseConditionalIndependenceTest -from dodiscover.constraint.config import SkeletonMethods +from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.utils import is_in_sep_set from dodiscover.typing import Column, SeparatingSet @@ -56,8 +56,6 @@ class PC(BaseConstraintDiscovery): max_iter : int The maximum number of iterations through the graph to apply orientation rules. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. Attributes ---------- @@ -83,10 +81,9 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, max_iter: int = 1000, - **ci_estimator_kwargs, ): super().__init__( ci_estimator, @@ -95,7 +92,6 @@ def __init__( max_cond_set_size=max_cond_set_size, max_combinations=max_combinations, skeleton_method=skeleton_method, - **ci_estimator_kwargs, ) self.max_iter = max_iter self.apply_orientations = apply_orientations diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index a2d331a1c..17027e76e 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -11,7 +11,7 @@ from dodiscover.cd import BaseConditionalDiscrepancyTest from dodiscover.ci import BaseConditionalIndependenceTest, Oracle -from dodiscover.constraint.config import SkeletonMethods +from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.utils import is_in_sep_set from dodiscover.typing import Column, SeparatingSet @@ -143,8 +143,6 @@ class LearnSkeleton: by its dependencies from strongest to weakest (i.e. largest CI test statistic value to lowest). This can be used in conjunction with ``max_combinations`` parameter to only test the "strongest" dependences. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. Attributes ---------- @@ -236,14 +234,12 @@ def __init__( min_cond_set_size: int = 0, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, + skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, keep_sorted: bool = False, - ci_estimator_kwargs=None, ) -> None: self.ci_estimator = ci_estimator self.sep_set = sep_set self.alpha = alpha - self.ci_estimator_kwargs = ci_estimator_kwargs self.skeleton_method = skeleton_method # control of the conditioning set @@ -266,9 +262,10 @@ def _initialize_params(self) -> None: if self.max_combinations is not None and self.max_combinations <= 0: raise RuntimeError(f"Max combinations must be at least 1, not {self.max_combinations}") - if self.skeleton_method not in SkeletonMethods: + if self.skeleton_method not in ConditioningSetSelection: raise ValueError( - f"Skeleton method must be one of {SkeletonMethods}, not {self.skeleton_method}." + f"Skeleton method must be one of {ConditioningSetSelection}, not " + f"{self.skeleton_method}." ) if self.sep_set is None and not hasattr(self, "sep_set_"): @@ -291,9 +288,6 @@ def _initialize_params(self) -> None: else: self.max_combinations_ = self.max_combinations - if self.ci_estimator_kwargs is None: - self.ci_estimator_kwargs = dict() - def evaluate_edge( self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None ) -> Tuple[float, float]: @@ -319,7 +313,7 @@ def evaluate_edge( """ if Z is None: Z = set() - test_stat, pvalue = self.ci_estimator.test(data, {X}, {Y}, Z, **self.ci_estimator_kwargs) + test_stat, pvalue = self.ci_estimator.test(data, {X}, {Y}, Z) self.n_ci_tests += 1 return test_stat, pvalue @@ -515,12 +509,12 @@ def _compute_candidate_conditioning_sets( """ skeleton_method = self.skeleton_method - if skeleton_method == SkeletonMethods.COMPLETE: + if skeleton_method == ConditioningSetSelection.COMPLETE: possible_variables = set(adj_graph.nodes) - elif skeleton_method == SkeletonMethods.NBRS: + elif skeleton_method == ConditioningSetSelection.NBRS: possible_variables = set(adj_graph.neighbors(x_var)) # possible_adjacencies.copy() - elif skeleton_method == SkeletonMethods.NBRS_PATH: + elif skeleton_method == ConditioningSetSelection.NBRS_PATH: # constrain adjacency set to ones with a path from x_var to y_var possible_variables = _find_neighbors_along_path(adj_graph, start=x_var, end=y_var) @@ -615,8 +609,6 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): to only test the "strongest" dependences. max_path_length : int, optional The maximum length of any discriminating path, or None if unlimited. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. Attributes ---------- @@ -680,11 +672,12 @@ def __init__( min_cond_set_size: int = 0, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, - second_stage_skeleton_method: Optional[SkeletonMethods] = SkeletonMethods.PDS, + skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + second_stage_skeleton_method: Optional[ + ConditioningSetSelection + ] = ConditioningSetSelection.PDS, keep_sorted: bool = False, max_path_length: Optional[int] = None, - ci_estimator_kwargs=None, ) -> None: super().__init__( ci_estimator, @@ -695,7 +688,6 @@ def __init__( max_combinations, skeleton_method, keep_sorted, - ci_estimator_kwargs=ci_estimator_kwargs, ) self.second_stage_skeleton_method = second_stage_skeleton_method @@ -743,13 +735,13 @@ def _compute_candidate_conditioning_sets( raise RuntimeError("wtf..") skeleton_method = self.second_stage_skeleton_method - if skeleton_method == SkeletonMethods.PDS: + if skeleton_method == ConditioningSetSelection.PDS: # determine how we want to construct the candidates for separating nodes # perform conditioning independence testing on all combinations possible_variables = pgraph.pds( pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore ) - elif skeleton_method == SkeletonMethods.PDS_PATH: + elif skeleton_method == ConditioningSetSelection.PDS_PATH: # determine how we want to construct the candidates for separating nodes # perform conditioning independence testing on all combinations possible_variables = pgraph.pds_path( @@ -861,10 +853,6 @@ class LearnInterventionSkeleton(LearnSemiMarkovianSkeleton): to only test the "strongest" dependences. max_path_length : int, optional The maximum length of any discriminating path, or None if unlimited. - ci_estimator_kwargs : dict - Keyword arguments for the ``ci_estimator`` function. - cd_estimator_kwargs : dict - Keyword arguments for the ``cd_estimator`` function. Notes ----- @@ -890,13 +878,11 @@ def __init__( min_cond_set_size: int = 0, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: SkeletonMethods = SkeletonMethods.NBRS, - second_stage_skeleton_method: SkeletonMethods = SkeletonMethods.PDS, + skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + second_stage_skeleton_method: ConditioningSetSelection = ConditioningSetSelection.PDS, keep_sorted: bool = False, max_path_length: Optional[int] = None, known_intervention_targets: bool = False, - ci_estimator_kwargs=None, - cd_estimator_kwargs=None, ) -> None: super().__init__( ci_estimator, @@ -909,12 +895,10 @@ def __init__( second_stage_skeleton_method, keep_sorted, max_path_length, - ci_estimator_kwargs, ) self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets - self.cd_estimator_kwargs = cd_estimator_kwargs def evaluate_fnode_edge( self, @@ -965,14 +949,10 @@ def evaluate_fnode_edge( # indicates which distribution data came from if isinstance(self.cd_estimator, Oracle): # test graphically if Y is d-separated from F-node given Z - test_stat, pvalue = self.cd_estimator.test( - data, {group_col}, Y, Z, **self.cd_estimator_kwargs - ) + test_stat, pvalue = self.cd_estimator.test(data, {group_col}, Y, Z) else: # test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test( - data, Z, Y, group_col, **self.cd_estimator_kwargs - ) + test_stat, pvalue = self.cd_estimator.test(data, Z, Y, group_col) self.n_ci_tests += 1 return test_stat, pvalue @@ -1017,8 +997,6 @@ def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], co adj_graph = self.context_.init_graph f_nodes = self.context_.f_nodes - print("Starting learning int skeleton: ", f_nodes) - # the size of the conditioning set will start off at the minimum size_cond_set = self.min_cond_set_size_ @@ -1058,22 +1036,6 @@ def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], co self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) continue - # TODO: allow ignoring fixed edges - # if self.context_.included_edges.has_edge(x_var, y_var): - # continue - # if self.context_.excluded_edges.has_edge(x_var, y_var): - # pvalue = 1.0 - # test_stat = 0.0 - - # # post-process the CI test results - # removed_edge = self._postprocess_ci_test( - # adj_graph, x_var, y_var, {}, test_stat, pvalue - # ) - - # # summarize the comparison of XY - # self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) - # continue - # compute the possible variables used in the conditioning set possible_variables = self._compute_candidate_conditioning_sets( adj_graph, @@ -1179,8 +1141,9 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: context : Context Context object. """ - if self.cd_estimator_kwargs is None: - self.cd_estimator_kwargs = dict() + # ensure data is a list + if isinstance(data, pd.DataFrame): + data = [data] # error-check the datasets passed in match the intervention contexts if len(data) != context.num_distributions: @@ -1188,7 +1151,8 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: f"The number of datasets does not match the number of interventions. " f"You passed in {len(data)} different datasets, whereas " f"there are {len(context.intervention_targets)} different interventions " - f"specified. It is assumed that the first dataset is observational, " + f"specified and {context.num_distributions} distributions assumed. " + f"It is assumed that the first dataset is observational, " f"while the rest are interventional." ) diff --git a/dodiscover/context_builder.py b/dodiscover/context_builder.py index 057a47744..0cfb2d186 100644 --- a/dodiscover/context_builder.py +++ b/dodiscover/context_builder.py @@ -1,7 +1,8 @@ import types from copy import copy from itertools import combinations -from typing import Any, Callable, Dict, List, Optional, Set, Tuple, cast +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union, cast +from warnings import warn import networkx as nx import numpy as np @@ -397,7 +398,7 @@ def build(self) -> Context: if self._observed_variables is None: raise ValueError("Could not infer variables from data or given arguments.") if self._num_distributions is None: - raise ValueError( + warn( "There is no intervention context set. Are you sure you are using " "the right contextbuilder? If you only have observational data " "use `ContextBuilder` instead of `InterventionContextBuilder`." @@ -407,6 +408,12 @@ def build(self) -> Context: f_nodes, sigma_map, symmetric_diff_map = self._create_augmented_nodes() graph_variables = set(self._observed_variables).union(set(f_nodes)) + # infer number of distributions + if self._num_distributions is None: + num_distributions = int(self._obs_distribution) + else: + num_distributions = self._num_distributions + empty_graph = self._empty_graph_func(graph_variables) return Context( init_graph=self._interpolate_graph(graph_variables), @@ -420,7 +427,7 @@ def build(self) -> Context: sigma_map=sigma_map, symmetric_diff_map=symmetric_diff_map, obs_distribution=self._obs_distribution, - num_distributions=self._num_distributions, + num_distributions=num_distributions, ) def _create_augmented_nodes(self) -> Tuple[List, Dict, Dict]: @@ -495,7 +502,9 @@ def _interpolate_graph(self, graph_variables) -> nx.Graph: return init_graph -def make_context(context: Optional[Context] = None, create_using=ContextBuilder) -> ContextBuilder: +def make_context( + context: Optional[Context] = None, create_using=ContextBuilder +) -> Union[ContextBuilder, InterventionalContextBuilder]: """Create a new ContextBuilder instance. Returns diff --git a/tests/unit_tests/conditional/ci/test_g_test.py b/tests/unit_tests/conditional/ci/test_g_test.py index 841544c91..ca92ed297 100644 --- a/tests/unit_tests/conditional/ci/test_g_test.py +++ b/tests/unit_tests/conditional/ci/test_g_test.py @@ -17,8 +17,8 @@ def test_g_error(): sets = [[], [2], [2, 3], [3, 4], [2, 3, 4]] df = pd.DataFrame.from_records(dm) with pytest.raises(ValueError, match="data_type"): - ci_estimator = GSquareCITest(data_type="auto") - ci_estimator.test(df, {x}, {y}, set(sets[0]), [3, 2, 3, 4, 2]) + ci_estimator = GSquareCITest(data_type="auto", levels=[3, 2, 3, 4, 2]) + ci_estimator.test(df, {x}, {y}, set(sets[0])) def test_g_discrete(): @@ -26,12 +26,12 @@ def test_g_discrete(): dm = np.array([testdata.dis_data]).reshape((10000, 5)) x = 0 y = 1 - ci_estimator = GSquareCITest(data_type="discrete") + ci_estimator = GSquareCITest(data_type="discrete", levels=[3, 2, 3, 4, 2]) df = pd.DataFrame.from_records(dm) sets = [[], [2], [2, 3], [3, 4], [2, 3, 4]] for idx in range(len(sets)): - _, p = ci_estimator.test(df, {x}, {y}, set(sets[idx]), [3, 2, 3, 4, 2]) + _, p = ci_estimator.test(df, {x}, {y}, set(sets[idx])) fr_p = frexp(p) fr_a = frexp(testdata.dis_answer[idx]) @@ -45,9 +45,10 @@ def test_g_discrete(): dm = np.array([testdata.dis_data]).reshape((2000, 25)) df = pd.DataFrame.from_records(dm) levels = np.ones((25,)) * 3 + ci_estimator = GSquareCITest(data_type="discrete", levels=levels) sets = [[2, 3, 4, 5, 6, 7]] with pytest.raises(RuntimeError, match="Not enough samples"): - ci_estimator.test(df, {x}, {y}, set(sets[0]), levels) + ci_estimator.test(df, {x}, {y}, set(sets[0])) def test_g_binary(): diff --git a/tests/unit_tests/constraint/__init__.py b/tests/unit_tests/constraint/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/unit_tests/constraint/test_fcialg.py b/tests/unit_tests/constraint/test_fcialg.py index 64c84ad4f..dfb55f9e7 100644 --- a/tests/unit_tests/constraint/test_fcialg.py +++ b/tests/unit_tests/constraint/test_fcialg.py @@ -9,7 +9,7 @@ from dodiscover import FCI, make_context from dodiscover.ci import Oracle -from dodiscover.constraint.config import SkeletonMethods +from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.utils import dummy_sample np.random.seed(12345) @@ -23,19 +23,20 @@ def setup_method(self): oracle = Oracle(G) fci = FCI(ci_estimator=oracle) + self.context_func = make_context self.G = G self.ci_estimator = oracle self.alg = fci def test_fci_skel_graph(self): sample = dummy_sample(self.G) - context = make_context().variables(data=sample).build() + context = self.context_func().variables(data=sample).build() skel_graph, _ = self.alg.learn_skeleton(sample, context) assert nx.is_isomorphic(skel_graph, self.G.to_undirected()) def test_fci_basic_collider(self): sample = dummy_sample(self.G) - context = make_context().variables(data=sample).build() + context = self.context_func().variables(data=sample).build() skel_graph, sep_set = self.alg.learn_skeleton(sample, context) graph = PAG(incoming_circle_edges=skel_graph) self.alg.orient_unshielded_triples(graph, sep_set) @@ -425,6 +426,9 @@ def test_fci_rule8_without_selection_bias(self): assert not self.alg._apply_rule8(G, "u", "A", "C") def test_fci_rule8_with_selection_bias(self): + if not self.alg.selection_bias: + pytest.skip(reason="No selection bias for this algorithm") + # If A -o u -> C and A o-> C then orient A o-> C as A -> C G = PAG() @@ -565,7 +569,7 @@ def test_fci_unobserved_confounder(self): latent_edge_list = [("x1", "x2")] G = ADMG(edge_list, incoming_bidirected_edges=latent_edge_list) sample = dummy_sample(G) - context = make_context().variables(data=sample).build() + context = self.context_func().variables(data=sample).build() oracle = Oracle(G) ci_estimator = oracle @@ -602,7 +606,7 @@ def test_fci_spirtes_example(self): graph = ADMG(edge_list, latent_edge_list) alg = FCI(ci_estimator=Oracle(graph)) sample = dummy_sample(graph) - context = make_context().variables(data=sample).build() + context = self.context_func().variables(data=sample).build() alg.fit(sample, context) pag = alg.graph_ skel_graph = alg.graph_ @@ -636,8 +640,17 @@ def test_fci_spirtes_example(self): assert nx.is_isomorphic(skel_graph.to_undirected(), expected_pag.to_undirected()) assert set(expected_pag.edges()) == set(pag.edges()) - @pytest.mark.parametrize("skeleton_method", [SkeletonMethods.NBRS, SkeletonMethods.NBRS_PATH, SkeletonMethods.COMPLETE]) - @pytest.mark.parametrize("pds_skeleton_method", [SkeletonMethods.PDS, SkeletonMethods.PDS_PATH]) + @pytest.mark.parametrize( + "skeleton_method", + [ + ConditioningSetSelection.NBRS, + ConditioningSetSelection.NBRS_PATH, + ConditioningSetSelection.COMPLETE, + ], + ) + @pytest.mark.parametrize( + "pds_skeleton_method", [ConditioningSetSelection.PDS, ConditioningSetSelection.PDS_PATH] + ) @pytest.mark.parametrize("selection_bias", [True, False]) def test_fci_complex(self, skeleton_method, pds_skeleton_method, selection_bias): """ @@ -662,7 +675,7 @@ def test_fci_complex(self, skeleton_method, pds_skeleton_method, selection_bias) latent_edge_list = [("x1", "x2"), ("x4", "x5")] G = ADMG(edge_list, latent_edge_list) sample = dummy_sample(G) - context = make_context().variables(data=sample).build() + context = self.context_func().variables(data=sample).build() oracle = Oracle(G) ci_estimator = oracle fci = FCI( @@ -721,7 +734,7 @@ def test_fci_fig6(self): assert pywhy_graphs.networkx.m_separated(G, {"A"}, {"D"}, {"B", "C"}) sample = dummy_sample(G) - context = make_context().variables(data=sample).build() + context = self.context_func().variables(data=sample).build() oracle = Oracle(G) ci_estimator = oracle fci = FCI(ci_estimator=ci_estimator, max_iter=np.inf, selection_bias=False) @@ -764,7 +777,7 @@ def test_fci_selection_bias(self): G._edge_graphs.pop("circle") sample = dummy_sample(G) - context = make_context().variables(data=sample).build() + context = self.context_func().variables(data=sample).build() oracle = Oracle(G) ci_estimator = oracle fci = FCI(ci_estimator=ci_estimator, max_iter=np.inf, selection_bias=True) diff --git a/tests/unit_tests/constraint/test_intervene_skeleton.py b/tests/unit_tests/constraint/test_intervene_skeleton.py index 52457e986..d0797741d 100644 --- a/tests/unit_tests/constraint/test_intervene_skeleton.py +++ b/tests/unit_tests/constraint/test_intervene_skeleton.py @@ -1,4 +1,5 @@ import networkx as nx +import pytest import pywhy_graphs as pgraphs from dodiscover import InterventionalContextBuilder, make_context @@ -7,7 +8,7 @@ from dodiscover.constraint.utils import dummy_sample -def test_fnode_skeleton(): +def test_fnode_skeleton_known_targets(): """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`.""" # first create the oracle directed_edges = [ @@ -58,5 +59,85 @@ def test_fnode_skeleton(): assert nx.is_isomorphic(expected_skeleton, skel_graph) +def test_fnode_skeleton_unknown_targets(): + """Test learning the skeleton for Figure 2 in :footcite:`Jaber2020causal`.""" + # first create the oracle + directed_edges = [ + ("x", "w"), + ("x", "y"), + ("y", "w"), + ("z", "y"), + ("z", "x"), + ] + bidirected_edges = [("x", "w")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x", "w"}) + oracle = Oracle(graph) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "w"), + ("x", "w"), + ("x", "z"), + ("x", "y"), + ("z", "y"), + ("z", "w"), + ("w", "y"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_node(("F", 0)) + + # define the learner and the context + learner = LearnInterventionSkeleton( + ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=False + ) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .num_distributions(2) + .build() + ) + learner.fit(data, context) + + # first check the observational skeleton + skel_graph = learner.adj_graph_ + obs_skel_graph = learner.context_.state_variable("obs_skel_graph") + + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + assert nx.is_isomorphic(expected_skeleton, skel_graph) + + def test_fnode_skeleton_errors(): - pass + # define the learner and the context + directed_edges = [ + ("x", "w"), + ("w", "y"), + ("z", "y"), + ] + bidirected_edges = [("x", "z"), ("z", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x"}) + oracle = Oracle(graph) + + data = [dummy_sample(non_f_graph)] + learner = LearnInterventionSkeleton( + ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=True + ) + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .intervention_targets([("x",)]) + .build() + ) + + with pytest.raises(RuntimeError, match="The number of datasets does not match"): + learner.fit(data, context) diff --git a/tests/unit_tests/constraint/test_pcalg.py b/tests/unit_tests/constraint/test_pcalg.py index 66d028c51..5c963b190 100644 --- a/tests/unit_tests/constraint/test_pcalg.py +++ b/tests/unit_tests/constraint/test_pcalg.py @@ -8,7 +8,7 @@ from dodiscover import make_context from dodiscover.ci import GSquareCITest, Oracle from dodiscover.constraint import PC -from dodiscover.constraint.config import SkeletonMethods +from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.utils import dummy_sample from dodiscover.metrics import confusion_matrix_networks from dodiscover.testdata.testdata import bin_data, dis_data @@ -22,7 +22,7 @@ "max_cond_set_size": 3, "max_combinations": 10, "max_iter": 10, - "skeleton_method": SkeletonMethods.NBRS_PATH, + "skeleton_method": ConditioningSetSelection.NBRS_PATH, }, {}, ], diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py new file mode 100644 index 000000000..1736a5641 --- /dev/null +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -0,0 +1,243 @@ +from itertools import permutations + +import networkx as nx +import numpy as np +import pytest +import pywhy_graphs as pgraphs +from pywhy_graphs import IPAG, PsiPAG + +from dodiscover import InterventionalContextBuilder, PsiFCI, make_context +from dodiscover.ci import Oracle +from dodiscover.constraint.utils import dummy_sample + +from .test_fcialg import Test_FCI + +np.random.seed(12345) + + +@pytest.mark.filterwarnings("ignore:There is no intervention context set.") +class Test_IFCI(Test_FCI): + def setup_method(self): + # construct a causal graph that will result in + # x -> y <- z + G = nx.DiGraph([("x", "y"), ("z", "y")]) + oracle = Oracle(G) + alg = PsiFCI(known_intervention_targets=True, ci_estimator=oracle, cd_estimator=oracle) + + self.context_func = lambda: make_context(create_using=InterventionalContextBuilder) + self.G = G + self.ci_estimator = oracle + self.alg = alg + + def test_rule11(self): + """Test that all F-nodes are oriented outwards properly.""" + # create a complete graph + sub_dir_graph = nx.complete_graph( + [("F", 0), ("F", 1), "a", "b", "c", "d"], create_using=nx.DiGraph + ) + G = IPAG(incoming_circle_edges=sub_dir_graph) + + # there must only be one kind of edge from F-nodes to its nbrs + f_nodes = [("F", 0), ("F", 1)] + self.alg._apply_rule11(G, f_nodes) + for f_node in f_nodes: + for nbr in G.neighbors(f_node): + if nbr in f_nodes: + continue + assert G.has_edge(f_node, nbr, G.directed_edge_name) + assert not G.has_edge(nbr, f_node) + + def test_rule12(self): + """Test rule "9" in the I-FCI paper from Figure 3.""" + # create a complete graph + directed_edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + ("z", "x"), + ("z", "y"), + ("x", "y"), + ("x", "w"), + ("w", "y"), + ] + circle_edges = [("y", "x"), ("x", "z"), ("y", "z"), ("y", "w")] + G = IPAG(incoming_directed_edges=directed_edges, incoming_circle_edges=circle_edges) + G.graph["F-nodes"][("F", 0)] = ["x"] + f_nodes = G.f_nodes + + # there must only be one kind of edge from F-nodes to its nbrs + symmetric_diff_map = { + ("F", 0): ["x"], + } + + # no arrows should be added if we are not operating over a F-node + for x, y, z in permutations(G.non_f_nodes, 3): + added_arrows = self.alg._apply_rule12(G, x, y, z, f_nodes, symmetric_diff_map) + assert not added_arrows + + # no arrows should be added if the conditions of the rule are not met + added_arrows = self.alg._apply_rule12(G, ("F", 0), "x", "z", f_nodes, symmetric_diff_map) + assert not added_arrows + + added_arrows = self.alg._apply_rule12(G, ("F", 0), "x", "y", f_nodes, symmetric_diff_map) + if self.alg.known_intervention_targets: + assert added_arrows + assert G.has_edge("x", "y", G.directed_edge_name) + assert not G.has_edge("y", "x") + else: + assert not added_arrows + assert G.has_edge("y", "x") + + def test_ifci_figure3(self): + """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`.""" + # first create the oracle + directed_edges = [ + ("x", "w"), + ("w", "y"), + ("z", "y"), + ] + bidirected_edges = [("x", "z"), ("z", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x"}) + oracle = Oracle(graph) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + ("x", "w"), + ("x", "z"), + ("x", "y"), + ("z", "y"), + ("w", "y"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_node(("F", 0)) + + # define the learner and the context + learner = PsiFCI(ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=True) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .intervention_targets([("x",)]) + .build() + ) + learner.fit(data, context) + + # first check the observational skeleton + skel_graph = learner.skeleton_learner_.adj_graph_ + obs_skel_graph = learner.skeleton_learner_.context_.state_variable("obs_skel_graph") + + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + assert nx.is_isomorphic(expected_skeleton, skel_graph) + + # now check the end graph + directed_edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + ("z", "x"), + ("z", "y"), + ("x", "y"), + ("x", "w"), + ("w", "y"), + ] + circle_edges = [("x", "z"), ("y", "z"), ("y", "w")] + expected_G = IPAG( + incoming_directed_edges=directed_edges, incoming_circle_edges=circle_edges + ) + expected_G.graph["F-nodes"][("F", 0)] = ["x"] + + learned_graph = learner.graph_ + for edge_type, subgraph in expected_G.get_graphs().items(): + assert nx.is_isomorphic(subgraph, learned_graph.get_graphs(edge_type)) + + +@pytest.mark.filterwarnings("ignore:There is no intervention context set.") +class Test_PsiFCI(Test_IFCI): + def setup_method(self): + # construct a causal graph that will result in + # x -> y <- z + G = nx.DiGraph([("x", "y"), ("z", "y")]) + oracle = Oracle(G) + alg = PsiFCI(known_intervention_targets=False, ci_estimator=oracle, cd_estimator=oracle) + + self.context_func = lambda: make_context(create_using=InterventionalContextBuilder) + self.G = G + self.ci_estimator = oracle + self.alg = alg + + def test_figure2_skeleton(self): + """Test learning the graph for Figure 2 in :footcite:`Jaber2020causal`.""" + # first create the oracle + directed_edges = [ + ("x", "w"), + ("x", "y"), + ("y", "w"), + ("z", "y"), + ("z", "x"), + ] + bidirected_edges = [("x", "w")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x", "w"}) + oracle = Oracle(graph) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "w"), + ("x", "w"), + ("x", "z"), + ("x", "y"), + ("z", "y"), + ("z", "w"), + ("w", "y"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_node(("F", 0)) + + # define the learner and the context + learner = PsiFCI(ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=False) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .num_distributions(2) + .build() + ) + learner.fit(data, context) + + # first check the observational skeleton + skel_graph = learner.skeleton_learner_.adj_graph_ + obs_skel_graph = learner.skeleton_learner_.context_.state_variable("obs_skel_graph") + + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + assert nx.is_isomorphic(expected_skeleton, skel_graph) + + # now check the end graph + directed_edges = [ + (("F", 0), "x"), + (("F", 0), "w"), + ("z", "x"), + ("z", "y"), + ("z", "w"), + ("x", "y"), + ("x", "w"), + ("y", "w"), + ] + circle_edges = [("x", "z"), ("w", "x"), ("w", "z"), ("w", "y")] + expected_G = PsiPAG( + incoming_directed_edges=directed_edges, incoming_circle_edges=circle_edges + ) + expected_G.graph["F-nodes"][("F", 0)] = ["x"] + + learned_graph = learner.graph_ + for edge_type, subgraph in expected_G.get_graphs().items(): + assert nx.is_isomorphic(subgraph, learned_graph.get_graphs(edge_type)) diff --git a/tests/unit_tests/test_context_builder.py b/tests/unit_tests/test_context_builder.py index a970ef92b..503ed447a 100644 --- a/tests/unit_tests/test_context_builder.py +++ b/tests/unit_tests/test_context_builder.py @@ -175,10 +175,8 @@ def test_context_interventions(): df = make_df() # check InterventionalContextBuilder errors that should be raised - with pytest.raises(ValueError, match="There is no intervention context set"): - copy(ctx_builder).variables(data=df).init_graph( - nx.empty_graph(df.columns + ["blah"]) - ).build() + with pytest.warns(UserWarning, match="There is no intervention context set"): + copy(ctx_builder).variables(data=df).init_graph(nx.empty_graph(df.columns)).build() with pytest.raises(RuntimeError, match="Not all nodes"): copy(ctx_builder).variables(data=df).init_graph( From 1738b8c9d1f9817c7e7ded55cbbcdd01ebfc3d05 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 17 Feb 2023 17:21:28 -0500 Subject: [PATCH 07/61] Fix docs Signed-off-by: Adam Li --- doc/api.rst | 3 +++ doc/conf.py | 6 +++++- doc/constraint_causal_discovery.rst | 14 ++++++++------ doc/user_guide.rst | 11 ++++++----- dodiscover/__init__.py | 7 ++----- dodiscover/constraint/fcialg.py | 4 ++-- dodiscover/constraint/intervention.py | 15 +++++++-------- dodiscover/constraint/pcalg.py | 2 +- dodiscover/constraint/skeleton.py | 10 +++++----- dodiscover/context.py | 4 ++-- dodiscover/context_builder.py | 14 +++++++------- 11 files changed, 48 insertions(+), 42 deletions(-) diff --git a/doc/api.rst b/doc/api.rst index 5c7331419..aecbd94c0 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -34,6 +34,7 @@ See docs for ``Context`` and ``make_context`` for more information. make_context ContextBuilder + InterventionalContextBuilder context.Context @@ -46,9 +47,11 @@ Constraint-based structure learning LearnSkeleton LearnSemiMarkovianSkeleton + LearnInterventionSkeleton ConditioningSetSelection PC FCI + PsiFCI Comparing causal discovery algorithms ===================================== diff --git a/doc/conf.py b/doc/conf.py index dd98fab89..aba2f88fb 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -111,6 +111,7 @@ "CPDAG", "PAG", "ADMG", + "PsiFCI", # networkx "node", "nodes", @@ -148,6 +149,7 @@ "nx.Graph": "networkx.Graph", "nx.DiGraph": "networkx.DiGraph", "nx.MultiDiGraph": "networkx.MultiDiGraph", + "nx": "networkx", "pgmpy.models.BayesianNetwork": "pgmpy.models.BayesianNetwork", # dodiscover "ADMG": "dodiscover.ADMG", @@ -155,6 +157,8 @@ "CPDAG": "dodiscover.CPDAG", "DAG": "dodiscover.DAG", "BaseConditionalIndependenceTest": "dodiscover.ci.BaseConditionalIndependenceTest", + "BaseConditionalDiscrepancyTest": "dodiscover.cd.BaseConditionalDiscrepancyTest", + "ConditioningSetSelection": "dodiscover.constraint.ConditioningSetSelection", "Context": "dodiscover.context.Context", "PC": "dodiscover.PC", "EquivalenceClass": "dodiscover.EquivalenceClass", @@ -173,7 +177,7 @@ "column": "pandas.DataFrame.columns", } -default_role = "py:obj" +default_role = "literal" # Tell myst-parser to assign header anchors for h1-h3. # myst_heading_anchors = 3 diff --git a/doc/constraint_causal_discovery.rst b/doc/constraint_causal_discovery.rst index 23f751c88..c10f3ac0d 100644 --- a/doc/constraint_causal_discovery.rst +++ b/doc/constraint_causal_discovery.rst @@ -3,8 +3,10 @@ ================================== Constraint-based causal discovery ================================== - -.. currentmodule:: dodiscover.constraint +.. automodule:: dodiscover.constraint + :no-members: + :no-inherited-members: +.. currentmodule:: dodiscover The following are a set of methods intended for (non-parametric) structure learning of causal graphs (i.e. causal discovery) given observational and/or interventional data @@ -45,7 +47,7 @@ If one assumes that the underlying structural causal model (SCM) is Markovian, then the Peter and Clarke (PC) algorithm has been shown to be sound and complete for learning a completed partially directed acyclic graph (CPDAG) :footcite:`Meek1995`. -The :class:`dodiscover.PC` algorithm and its variants assume Markovianity, which is +The :class:`dodiscover.constraint.PC` algorithm and its variants assume Markovianity, which is also known as causal-sufficiency in the literature. In other words, it assumes a lack of latent confounders, where there is no latent variable that is a confounder of the observed data. @@ -80,7 +82,7 @@ graph (PAG) :footcite:`zhang2008ancestralgraphs,Zhang2008`. The FCI algorithm and its variants assume Semi-Markovianity, which assumes the possible presence of latent confounders and even selection bias in the observational data. -The :class:`dodiscover.FCI` algorithm follows the three stages of learning that the PC +The :class:`dodiscover.constraint.FCI` algorithm follows the three stages of learning that the PC algorithm does, but with a few minor modifications that we will outline here: 1. skeleton discovery: The skeleton discovery phase is now composed of two stages. The first @@ -110,7 +112,7 @@ distribution. In this case, one may apply the :math:`Psi`-FCI algorithm to learn .. autosummary:: :toctree: generated/ - PsiFCI + constraint.PsiFCI Choosing the conditioning sets ------------------------------ @@ -121,7 +123,7 @@ be the empty set. There are multiple strategies for choosing ``Z``. .. autosummary:: :toctree: generated/ - ConditioningSetSelection + constraint.ConditioningSetSelection Hyperparameters and controlling overfitting ------------------------------------------- diff --git a/doc/user_guide.rst b/doc/user_guide.rst index 7b7ecaaf8..97242c587 100644 --- a/doc/user_guide.rst +++ b/doc/user_guide.rst @@ -16,8 +16,9 @@ User Guide constraint_causal_discovery.rst conditional_independence.rst - .. scores_causal_discovery.rst - .. visualizations.rst - .. datasets.rst - .. computing.rst - .. common_pitfalls.rst + +.. scores_causal_discovery.rst +.. visualizations.rst +.. datasets.rst +.. computing.rst +.. common_pitfalls.rst diff --git a/dodiscover/__init__.py b/dodiscover/__init__.py index 1ca3fc985..0b34f2bbf 100644 --- a/dodiscover/__init__.py +++ b/dodiscover/__init__.py @@ -1,11 +1,8 @@ -""" -DoDiscover - a library for Python-based Causal Discovery -""" - - +from . import cd # noqa: F401 from . import ci # noqa: F401 from . import metrics # noqa: F401 from ._protocol import EquivalenceClass, Graph from ._version import __version__ # noqa: F401 from .constraint import FCI, PC, PsiFCI +from .context import Context from .context_builder import ContextBuilder, InterventionalContextBuilder, make_context diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index de752d948..049679ca7 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -48,7 +48,7 @@ class FCI(BaseConstraintDiscovery): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : SkeletonMethods + skeleton_method : ConditioningSetSelection The method to use for testing conditional independence. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. apply_orientations : bool @@ -64,7 +64,7 @@ class FCI(BaseConstraintDiscovery): selection_bias : bool Whether or not to account for selection bias within the causal PAG. See :footcite:`Zhang2008`. - pds_skeleton_method : SkeletonMethods + pds_skeleton_method : ConditioningSetSelection The method to use for learning the skeleton using PDS. Must be one of ('pds', 'pds_path'). See Notes for more details. diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index d44d4f8fd..c5632aaeb 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -36,7 +36,7 @@ class PsiFCI(FCI): Parameters ---------- - ci_estimator : Callable + ci_estimator : BaseConditionalIndependenceTest The conditional independence test function. The arguments of the estimator should be data, node, node to compare, conditioning set of nodes, and any additional keyword arguments. @@ -59,7 +59,7 @@ class PsiFCI(FCI): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : SkeletonMethods + skeleton_method : ConditioningSetSelection The method to use for testing conditional independence. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. apply_orientations : bool @@ -72,7 +72,7 @@ class PsiFCI(FCI): orientation rules. max_path_length : int, optional The maximum length of any discriminating path, or None if unlimited. - pds_skeleton_method : SkeletonMethods + pds_skeleton_method : ConditioningSetSelection The method to use for learning the skeleton using PDS. Must be one of ('pds', 'pds_path'). See Notes for more details. known_intervention_targets : bool, optional @@ -132,7 +132,6 @@ def learn_skeleton( keep_sorted=False, max_path_length=self.max_path_length, ) - print(context) self.skeleton_learner_.fit(data, context) skel_graph = self.skeleton_learner_.adj_graph_ @@ -153,12 +152,12 @@ def fit(self, data: List[pd.DataFrame], context: Context): environments. We assume the first dataset is always observational. context : Context - _description_ + The context with interventional assumptions. Returns ------- - _type_ - _description_ + self : PsiFCI + The fitted learner. """ if not isinstance(data, list): raise RuntimeError("The input datasets must be in a Python list.") @@ -173,7 +172,7 @@ def fit(self, data: List[pd.DataFrame], context: Context): f"'context.num_distributions'." ) - super().fit(data, context) + return super().fit(data, context) def _apply_rule11(self, graph: EquivalenceClass, f_nodes: List) -> Tuple[bool, List]: """Apply "Rule 8" in I-FCI algorithm, which we call Rule 11. diff --git a/dodiscover/constraint/pcalg.py b/dodiscover/constraint/pcalg.py index 6a7688b61..8d9dee092 100644 --- a/dodiscover/constraint/pcalg.py +++ b/dodiscover/constraint/pcalg.py @@ -45,7 +45,7 @@ class PC(BaseConstraintDiscovery): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : SkeletonMethods + skeleton_method : ConditioningSetSelection The method to use for testing conditional independence. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. apply_orientations : bool diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 17027e76e..12e072396 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -134,7 +134,7 @@ class LearnSkeleton: of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : SkeletonMethods + skeleton_method : ConditioningSetSelection The method to use for testing conditional independence. Must be one of ('complete', 'neighbors', 'neighbors_path'). See Notes for more details. keep_sorted : bool @@ -505,7 +505,7 @@ def _compute_candidate_conditioning_sets( Notes ----- The :attr:`skeleton_method` dictates how we choose the corresponding conditioning sets. - For more information, see :class:`SkeletonMethods`. + For more information, see :class:`ConditioningSetSelection`. """ skeleton_method = self.skeleton_method @@ -594,10 +594,10 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : SkeletonMethods + skeleton_method : ConditioningSetSelection The method to use for determining conditioning sets when testing conditional independence of the first stage. See :class:`LearnSkeleton` for details. - second_stage_skeleton_method : SkeletonMethods | None + second_stage_skeleton_method : ConditioningSetSelection | None The method to use for determining conditioning sets when testing conditional independence of the first stage. Must be one of ('pds', 'pds_path'). See Notes for more details. If `None`, then no second stage skeleton discovery phase will be run. @@ -842,7 +842,7 @@ class LearnInterventionSkeleton(LearnSemiMarkovianSkeleton): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : SkeletonMethods + skeleton_method : ConditioningSetSelection The method to use for testing conditional independence. Must be one of ('pds', 'pds_path'). See Notes for more details. keep_sorted : bool diff --git a/dodiscover/context.py b/dodiscover/context.py index 7df174669..61d542d21 100644 --- a/dodiscover/context.py +++ b/dodiscover/context.py @@ -58,8 +58,8 @@ class Context(BasePyWhy): Currently, testing for equality is done on all attributes that are not graphs. Defining equality among graphs is ill-defined, and as such, we leave testing of the internal graphs to users. Some checks of equality - for example can be :func:`networkx.is_isomorphic` for checking isomorphism - among two graphs. + for example can be :func:`networkx.algorithms.isomorphism.is_isomorphic` + for checking isomorphism among two graphs. """ observed_variables: Set[Column] diff --git a/dodiscover/context_builder.py b/dodiscover/context_builder.py index 0cfb2d186..30bba1762 100644 --- a/dodiscover/context_builder.py +++ b/dodiscover/context_builder.py @@ -319,12 +319,12 @@ class InterventionalContextBuilder(ContextBuilder): The context builder provides a way to capture assumptions, domain knowledge, and data. This should NOT be instantiated directly. One should instead use - `dodiscover.make_context` to build a Context data structure. + :func:`dodiscover.make_context` to build a Context data structure. Notes ----- The number of distributions and/or interventional targets must be set in order - to build the `dodiscover.Context` object here. + to build the :class:`~.context.Context` object here. """ _intervention_targets: List[Tuple[Column]] = [] @@ -367,7 +367,7 @@ def intervention_targets(self, targets: List[Tuple[Column]]): Parameters ---------- - interventions : List of tuples + interventions : List of tuple A list of tuples of nodes that are known intervention targets. Assumes that the order of the interventions marked are those of the passed in the data. @@ -509,7 +509,7 @@ def make_context( Returns ------- - result : ContextBuilder + result : ContextBuilder, InterventionalContextBuilder The new ContextBuilder instance Examples @@ -521,12 +521,12 @@ def make_context( Notes ----- - `dodiscover.Context` objects are dataclasses that creates a dictionary-like access - to causal context metadata. Copying relevant information from a `dodiscover.Context` + :class:`~.context.Context` objects are dataclasses that creates a dictionary-like access + to causal context metadata. Copying relevant information from a Context object into a `ContextBuilder` is all supported with the exception of state variables. State variables are not copied over. To set state variables again, one must build the Context and then call - :meth:`dodiscover.Context.state_variable`. + :py:meth:`~.context.Context.state_variable`. """ result = create_using() if context is not None: From eebc6a5cd9b4aa6491cf4d1cfe259b02651cccc7 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 17 Feb 2023 17:23:15 -0500 Subject: [PATCH 08/61] Do not add vscode settings Signed-off-by: Adam Li --- .gitignore | 1 + .vscode/settings.json | 3 --- 2 files changed, 1 insertion(+), 3 deletions(-) delete mode 100644 .vscode/settings.json diff --git a/.gitignore b/.gitignore index 72a35bbeb..ef43177f6 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ __pycache__/ *$py.class .DS_Store +.vscode # C extensions *.so diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index a7d0fc7b7..000000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "esbonio.sphinx.confDir": "" -} \ No newline at end of file From f8d13d09b96e3c8134d9653abe809924bcb52ecf Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 24 Feb 2023 12:52:17 -0800 Subject: [PATCH 09/61] Update Signed-off-by: Adam Li --- doc/conditional_independence.rst | 137 +++++++++++++- doc/references.bib | 205 +++++++++++++-------- dodiscover/cd/base.py | 18 +- dodiscover/cd/bregman.py | 6 +- dodiscover/cd/kernel_test.py | 11 +- dodiscover/ci/base.py | 4 +- dodiscover/constraint/_classes.py | 10 +- dodiscover/constraint/fcialg.py | 20 +- dodiscover/constraint/intervention.py | 20 +- dodiscover/constraint/pcalg.py | 8 +- dodiscover/constraint/skeleton.py | 65 ++++--- dodiscover/context_builder.py | 66 +++---- examples/ex_psifci_alg.py | 144 +++++++++++++++ examples/plot_pc_alg.py | 12 +- tests/unit_tests/conditional/cd/test_cd.py | 31 ++-- tests/unit_tests/test_context_builder.py | 2 +- 16 files changed, 555 insertions(+), 204 deletions(-) create mode 100644 examples/ex_psifci_alg.py diff --git a/doc/conditional_independence.rst b/doc/conditional_independence.rst index 026703a00..f1cb0e041 100644 --- a/doc/conditional_independence.rst +++ b/doc/conditional_independence.rst @@ -34,20 +34,147 @@ with certain assumptions on the underlying data distribution. Conditional Mutual Information ------------------------------ -TBD. +Conditional mutual information (CMI) is a general formulation of CI, where CMI is defined as +:math:: + + \\int log \frac{p(x, y | z)}{p(x | z) p(y | z)} + +As we can see, CMI is equal to 0, if and only if :math:`p(x, y | z) = p(x | z) p(y | z)`, which +is exactly the definition of CI. CMI is completely non-parametric and thus requires no assumptions +on the underlying distributions. Unfortunately, CMI is notoriously difficult to estimate. There are +various proposals in the literature for estimating CMI, which we summarize here: + +- The Kraskov, Stogbauer and Grassberger (KSG) estimate approach estimates mutual information + via k-NN statistics :footcite:`kraskov_estimating_2004`. It was generalized to CMI by :footcite:`frenzel_partial_2007`. + This class of estimators is asymptotically correct, meaning if we had an infinite amount of data + we would obtain the true value of the CMI. However, it relies on statistics generated from the k-NN, + which if we implement the naive approach using a KDTree, then it generally suffers in high-dimensions. + In our examples, we see it suffer with dimensionality > 4 or 5. + + Estimates of CMI can be converted into a CI hypothesis test by permutation testing :footcite:`Runge2018cmi`. + One can generate estimated samples from the null distribution by permuting samples in an intelligent manner + and then the CMI value generated from the actual observed data can be compared to the CMI values computed + on the permutated datasets to estimate a pvalue. + + It is worth noting that if one has "robust" estimates of k-NN using for example a model that + is effective in high-dimensions, then the KSG estimator for CMI may be quite good. For example, + one can use variants of Random Forests to generate adaptive nearest-neighbor estimates in high-dimensions + or on manifolds, such that the KSG estimator is still quite good. + +.. autosummary:: + :toctree: generated/ + + ci.CMITest + +- The Classifier Divergence approach estimates CMI using a classification model. + +.. autosummary:: + :toctree: generated/ + + ci.ClassifierCMITest + +- Direct posterior estimates can be implemented with a classification model by directly + estimating :math:`P(y|x)` and :math:`P(y|x,z)`, which can be used as plug-in estimates + to the equation for CMI. Partial (Pearson) Correlation ----------------------------- -TBD. +Partial correlation based on the Pearson correlation is equivalent to CMI in the setting +of normally distributed data. Computing partial correlation is fast and efficient and +thus attractive to use. However, this **relies on the assumption of multivariate Gaussianity**, +which may be unrealistic in certain datasets. + +.. autosummary:: + :toctree: generated/ + + ci.FisherZCITest Discrete, Categorical and Binary Data ------------------------------------- -TBD. +If one has discrete data, then the test to use is based on Chi-square tests. The :math:`G^2` +class of tests will construct a contingency table based on the number of levels across +each discrete variable. An exponential amount of data is needed for increasing levels +for a discrete variable. + +.. autosummary:: + :toctree: generated/ + + ci.GSquareCITest Kernel-Approaches ----------------- -TBD. +Kernel-based tests are attractive since they are semi-parametric and use kernel-based ideas +that have been shown to be robust in the machine-learning field. The Kernel CI test is a test +that computes a test statistic from kernels of the data and uses permutation testing to +generate samples from the null distribution :footcite:`Zhang2011`, which are then used to +estimate a pvalue. + +.. autosummary:: + :toctree: generated/ + + ci.KernelCITest Classifier-based Approaches --------------------------- -TBD. \ No newline at end of file +Another suite of approaches that rely on permutation testing is the classifier-based approach. +By shuffling data in an intelligent way, one can setup a hypothesis test for CI based on the +predicted probabilities from a classification-model. Intuitively, if the shuffled data is similar +to the unshuffled data, such that the classification-model achieves non-trivial performance +(e.g. >50\% accuracy on a balanced dataset), then one fails to reject the null hypothesis and would +state that the original data was in fact CI :footcite:`Sen2017model`. + +.. autosummary:: + :toctree: generated/ + + ci.ClassifierCITest + +======================= +Conditional Discrepancy +======================= + +.. currentmodule:: dodiscover.cd + +Conditional discrepancy (CD) is another form of conditional invariances that may be exhibited by data. The +general question is whether or not the following two distributions are equal: + +:math:`P_{i=j}(y|x) =? P_{i=k}(y|x)` + +where :math:`P_i(.)` denote the distribution that explicitly comes from +a different group, or environment, denoted by the discrete indices :math:`i`. This is also +known in some cases as conditional k-sample testing, if there are a finite k number of groups +for :math:`P_i`. CD testing is important because it detects other kinds of invariances besides +CI. + +Discrete, Categorical and Binary Data +------------------------------------- +If one has entirely discrete data, then the problem of CD can be converted into a CI test that +leverages the Chi-square class of tests. Since ``y`` and ``x`` are discrete and so are the +indices of the distribution, one can convert the CD test: + +:math:`P_{i=j}(y|x) =? P_{i=k}(y|x)` into the CI test :math:`P(y|x,i) = P(y|x)`, which can +be tested with the Chi-square CI tests. + +Kernel-Approaches +----------------- +Kernel-based tests are attractive since they are semi-parametric and use kernel-based ideas +that have been shown to be robust in the machine-learning field. The Kernel CD test is a test +that computes a test statistic from kernels of the data and uses a weighted permutation testing +based on the estimated propensity scores to generate samples from the null distribution +:footcite:`Park2021conditional`, which are then used to estimate a pvalue. + +.. autosummary:: + :toctree: generated/ + + cd.KernelCDTest + +Bregman-Divergences +------------------- +The Bregman CD test is a divergence-based test +that computes a test statistic from estimated Von-Neumann divergences of the data and uses a +weighted permutation testing based on the estimated propensity scores to generate samples from the null distribution +:footcite:`Yu2020Bregman`, which are then used to estimate a pvalue. + +.. autosummary:: + :toctree: generated/ + + cd.BregmanCDTest diff --git a/doc/references.bib b/doc/references.bib index c52d35d08..8f3c1c376 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -43,13 +43,6 @@ @article{Kocaoglu2019characterization year = {2019} } -@article{Lopez2016revisiting, - title = {Revisiting classifier two-sample tests}, - author = {Lopez-Paz, David and Oquab, Maxime}, - journal = {arXiv preprint arXiv:1610.06545}, - year = {2016} -} - @article{Meek1995, author = {Meek, Christopher}, year = {2013}, @@ -60,15 +53,6 @@ @article{Meek1995 journal = {Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU} } -@inproceedings{Mukherjee2020ccmi, - title = {CCMI: Classifier based conditional mutual information estimation}, - author = {Mukherjee, Sudipto and Asnani, Himanshu and Kannan, Sreeram}, - booktitle = {Uncertainty in artificial intelligence}, - pages = {1083--1093}, - year = {2020}, - organization = {PMLR} -} - @book{Neapolitan2003, author = {Neapolitan, Richard}, year = {2003}, @@ -80,40 +64,6 @@ @book{Neapolitan2003 doi = {10.1145/1327942.1327961} } - -@inproceedings{Park2021conditional, - title = {Conditional distributional treatment effect with kernel conditional mean embeddings and U-statistic regression}, - author = {Park, Junhyung and Shalit, Uri and Sch{\"o}lkopf, Bernhard and Muandet, Krikamol}, - booktitle = {International Conference on Machine Learning}, - pages = {8401--8412}, - year = {2021}, - organization = {PMLR} -} - - -@inproceedings{Runge2018cmi, - title = {Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information}, - author = {Runge, Jakob}, - booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, - pages = {938--947}, - year = {2018}, - editor = {Storkey, Amos and Perez-Cruz, Fernando}, - volume = {84}, - series = {Proceedings of Machine Learning Research}, - month = {09--11 Apr}, - publisher = {PMLR}, - pdf = {http://proceedings.mlr.press/v84/runge18a/runge18a.pdf}, - url = {https://proceedings.mlr.press/v84/runge18a.html} -} - -@article{Sen2017model, - title = {Model-powered conditional independence test}, - author = {Sen, Rajat and Suresh, Ananda Theertha and Shanmugam, Karthikeyan and Dimakis, Alexandros G and Shakkottai, Sanjay}, - journal = {Advances in neural information processing systems}, - volume = {30}, - year = {2017} -} - @article{uhler2013geometry, title = {Geometry of the faithfulness assumption in causal inference}, author = {Uhler, Caroline and Raskutti, Garvesh and B{\"u}hlmann, Peter and Yu, Bin}, @@ -123,21 +73,6 @@ @article{uhler2013geometry publisher = {JSTOR} } -@inproceedings{Yu2020Bregman, - title = {Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications}, - author = {Yu, Shujian and Shaker, Ammar and Alesiani, Francesco and Principe, Jose}, - booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on - Artificial Intelligence, {IJCAI-20}}, - publisher = {International Joint Conferences on Artificial Intelligence Organization}, - editor = {Christian Bessiere}, - pages = {2777--2784}, - year = {2020}, - month = {7}, - note = {Main track}, - doi = {10.24963/ijcai.2020/385}, - url = {https://doi.org/10.24963/ijcai.2020/385} -} - @article{Zhang2008, title = {On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias}, journal = {Artificial Intelligence}, @@ -163,20 +98,6 @@ @article{zhang2008ancestralgraphs url = {http://jmlr.org/papers/v9/zhang08a.html} } -@inproceedings{Zhang2011, - author = {Zhang, Kun and Peters, Jonas and Janzing, Dominik and Sch\"{o}lkopf, Bernhard}, - title = {Kernel-Based Conditional Independence Test and Application in Causal Discovery}, - year = {2011}, - isbn = {9780974903972}, - publisher = {AUAI Press}, - address = {Arlington, Virginia, USA}, - booktitle = {Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence}, - pages = {804–813}, - numpages = {10}, - location = {Barcelona, Spain}, - series = {UAI'11} -} - # Books @book{Pearl_causality_2009, @@ -216,3 +137,129 @@ @article{dai2022independence journal = {arXiv preprint arXiv:2210.11021}, year = {2022} } + +% Conditional Testing + +@article{frenzel_partial_2007, + title = {Partial {Mutual} {Information} for {Coupling} {Analysis} of {Multivariate} {Time} {Series}}, + volume = {99}, + doi = {10.1103/PhysRevLett.99.204101}, + abstract = {We propose a method to discover couplings in multivariate time series, based on partial mutual information, an information-theoretic generalization of partial correlation. It represents the part of mutual information of two random quantities that is not contained in a third one. By suitable choice of the latter, we can differentiate between direct and indirect interactions and derive an appropriate graphical model. An efficient estimator for partial mutual information is presented as well.}, + journal = {Physical review letters}, + author = {Frenzel, Stefan and Pompe, Bernd}, + month = dec, + year = {2007}, + pages = {204101}, + file = {Full Text PDF:/Users/adam2392/Zotero/storage/8ICFXVZG/Frenzel and Pompe - 2007 - Partial Mutual Information for Coupling Analysis o.pdf:application/pdf} +} + +@article{kraskov_estimating_2004, + title = {Estimating mutual information}, + volume = {69}, + url = {https://link.aps.org/doi/10.1103/PhysRevE.69.066138}, + doi = {10.1103/PhysRevE.69.066138}, + abstract = {We present two classes of improved estimators for mutual information M(X,Y), from samples of random points distributed according to some joint probability density μ(x,y). In contrast to conventional estimators based on binnings, they are based on entropy estimates from k-nearest neighbor distances. This means that they are data efficient (with k=1 we resolve structures down to the smallest possible scales), adaptive (the resolution is higher where data are more numerous), and have minimal bias. Indeed, the bias of the underlying entropy estimates is mainly due to nonuniformity of the density at the smallest resolved scale, giving typically systematic errors which scale as functions of k∕N for N points. Numerically, we find that both families become exact for independent distributions, i.e. the estimator ˆM(X,Y) vanishes (up to statistical fluctuations) if μ(x,y)=μ(x)μ(y). This holds for all tested marginal distributions and for all dimensions of x and y. In addition, we give estimators for redundancies between more than two random variables. We compare our algorithms in detail with existing algorithms. Finally, we demonstrate the usefulness of our estimators for assessing the actual independence of components obtained from independent component analysis (ICA), for improving ICA, and for estimating the reliability of blind source separation., This article appears in the following collections:}, + number = {6}, + urldate = {2023-01-27}, + journal = {Physical Review E}, + author = {Kraskov, Alexander and Stögbauer, Harald and Grassberger, Peter}, + month = jun, + year = {2004}, + note = {Publisher: American Physical Society}, + pages = {066138}, + file = {APS Snapshot:/Users/adam2392/Zotero/storage/GRW23BYU/PhysRevE.69.html:text/html;Full Text PDF:/Users/adam2392/Zotero/storage/NJT9QCVA/Kraskov et al. - 2004 - Estimating mutual information.pdf:application/pdf} +} + +@article{Lopez2016revisiting, + title = {Revisiting classifier two-sample tests}, + author = {Lopez-Paz, David and Oquab, Maxime}, + journal = {arXiv preprint arXiv:1610.06545}, + year = {2016} +} + +@inproceedings{Mukherjee2020ccmi, + title = {CCMI: Classifier based conditional mutual information estimation}, + author = {Mukherjee, Sudipto and Asnani, Himanshu and Kannan, Sreeram}, + booktitle = {Uncertainty in artificial intelligence}, + pages = {1083--1093}, + year = {2020}, + organization = {PMLR} +} + +@inproceedings{Park2021conditional, + title = {Conditional distributional treatment effect with kernel conditional mean embeddings and U-statistic regression}, + author = {Park, Junhyung and Shalit, Uri and Sch{\"o}lkopf, Bernhard and Muandet, Krikamol}, + booktitle = {International Conference on Machine Learning}, + pages = {8401--8412}, + year = {2021}, + organization = {PMLR} +} + +@inproceedings{Runge2018cmi, + title = {Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information}, + author = {Runge, Jakob}, + booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, + pages = {938--947}, + year = {2018}, + editor = {Storkey, Amos and Perez-Cruz, Fernando}, + volume = {84}, + series = {Proceedings of Machine Learning Research}, + month = {09--11 Apr}, + publisher = {PMLR}, + pdf = {http://proceedings.mlr.press/v84/runge18a/runge18a.pdf}, + url = {https://proceedings.mlr.press/v84/runge18a.html} +} + +@article{Sen2017model, + title = {Model-powered conditional independence test}, + author = {Sen, Rajat and Suresh, Ananda Theertha and Shanmugam, Karthikeyan and Dimakis, Alexandros G and Shakkottai, Sanjay}, + journal = {Advances in neural information processing systems}, + volume = {30}, + year = {2017} +} + +@inproceedings{Yu2020Bregman, + title = {Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications}, + author = {Yu, Shujian and Shaker, Ammar and Alesiani, Francesco and Principe, Jose}, + booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on + Artificial Intelligence, {IJCAI-20}}, + publisher = {International Joint Conferences on Artificial Intelligence Organization}, + editor = {Christian Bessiere}, + pages = {2777--2784}, + year = {2020}, + month = {7}, + note = {Main track}, + doi = {10.24963/ijcai.2020/385}, + url = {https://doi.org/10.24963/ijcai.2020/385} +} + +@inproceedings{Zhang2011, + author = {Zhang, Kun and Peters, Jonas and Janzing, Dominik and Sch\"{o}lkopf, Bernhard}, + title = {Kernel-Based Conditional Independence Test and Application in Causal Discovery}, + year = {2011}, + isbn = {9780974903972}, + publisher = {AUAI Press}, + address = {Arlington, Virginia, USA}, + booktitle = {Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence}, + pages = {804–813}, + numpages = {10}, + location = {Barcelona, Spain}, + series = {UAI'11} +} + +% Example refs + +@article{sachsdataset2005, + author = {Karen Sachs and Omar Perez and Dana Pe'er and Douglas A. Lauffenburger and Garry P. Nolan }, + title = {Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data}, + journal = {Science}, + volume = {308}, + number = {5721}, + pages = {523-529}, + year = {2005}, + doi = {10.1126/science.1105809}, + url = {https://www.science.org/doi/abs/10.1126/science.1105809}, + eprint = {https://www.science.org/doi/pdf/10.1126/science.1105809}, + abstract = {Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.} +} + diff --git a/dodiscover/cd/base.py b/dodiscover/cd/base.py index 2537cb1df..a28679e4d 100644 --- a/dodiscover/cd/base.py +++ b/dodiscover/cd/base.py @@ -55,21 +55,33 @@ def _check_test_input( @abstractmethod def test( - self, df: pd.DataFrame, x_vars: Set[Column], y_vars: Set[Column], group_col: Column + self, + df: pd.DataFrame, + y_vars: Set[Column], + group_col: Column, + x_vars: Optional[Set[Column]], ) -> Tuple[float, float]: """Abstract method for all conditional discrepancy tests. + Tests the null hypothesis: :math:`P(Y | X, group) = P(Y | X)`, where + we are trying to determine if Y is (conditionally) independent from + the group denoting the distribution, given X. + + Another way of viewing this test is testing whether or not :math:`P_i(Y|X) = P_j(Y|X)`, + where :math:`P_i(.)` and :math:`P_j(.)` denote distributions from different groups + or environments denoted by the group_col. + Parameters ---------- df : pd.DataFrame The dataframe containing the dataset. - x_vars : Set of column - A column in ``df``. y_vars : Set of column A column in ``df``. group_col : column A column in ``df`` that indicates which group of distribution each sample belongs to with a '0', or '1'. + x_vars : Set of column, optional + A column in ``df``. Returns ------- diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index a2d6c61e3..ee8e29b8c 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -74,7 +74,11 @@ def __init__( self.propensity_est = propensity_est def test( - self, df: pd.DataFrame, x_vars: Set[Column], y_vars: Set[Column], group_col: Column + self, + df: pd.DataFrame, + y_vars: Set[Column], + group_col: Column, + x_vars: Optional[Set[Column]], ) -> Tuple[float, float]: # check test input self._check_test_input(df, x_vars, y_vars, group_col) diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index 5f0e53726..ff4b0c3dc 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -1,4 +1,4 @@ -from typing import Set, Tuple +from typing import Optional, Set, Tuple import numpy as np import pandas as pd @@ -92,9 +92,9 @@ def __init__( def test( self, df: pd.DataFrame, - x_vars: Set[Column], y_vars: Set[Column], group_col: Column, + x_vars: Optional[Set[Column]], ) -> Tuple[float, float]: """Compute k-sample test statistic and pvalue. @@ -103,7 +103,12 @@ def test( H_0: P(Y|X) = P'(Y|X) where the different distributions arise from the different datasets - collected denoted by the ``group_col`` parameter. + collected denoted by the ``group_col`` parameter. It can also be written + as:: + + H_0: P(Y|X,T) = P(Y|X) + + meaning that :math:`Y \\perp X | T`, where T is the group indicator. Parameters ---------- diff --git a/dodiscover/ci/base.py b/dodiscover/ci/base.py index d307ae8f9..7fb5eb986 100644 --- a/dodiscover/ci/base.py +++ b/dodiscover/ci/base.py @@ -34,7 +34,9 @@ def _check_test_input( if any(col not in df.columns for col in y_vars): raise ValueError(f"The y variables {y_vars} are not all in the DataFrame.") if z_covariates is not None and any(col not in df.columns for col in z_covariates): - raise ValueError("The z conditioning set variables are not all in the DataFrame.") + raise ValueError( + f"The z conditioning set variables {z_covariates} are not all in the DataFrame with {df.columns}." + ) if not self._allow_multivariate_input and (len(x_vars) > 1 or len(y_vars) > 1): raise RuntimeError(f"{self.__class__} does not support multivariate input for X and Y.") diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index 764330104..f8b81879a 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -38,8 +38,8 @@ class BaseConstraintDiscovery: parents still, by default None. If None, then will not be used. If set, then the conditioning set will be chosen lexographically based on the sorted test statistic values of 'ith Pa(X) -> X', for each possible parent node of 'X'. - skeleton_method : SkeletonMethods - The method to use for testing conditional independence. Must be one of + condsel_method : ConditioningSetSelection + The method to use for selecting the conditioning sets. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. apply_orientations : bool Whether or not to apply orientation rules given the learned skeleton graph @@ -66,13 +66,13 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, ): self.alpha = alpha self.ci_estimator = ci_estimator self.apply_orientations = apply_orientations - self.skeleton_method = skeleton_method + self.condsel_method = condsel_method # constraining the conditional independence tests if max_cond_set_size is None: @@ -247,7 +247,7 @@ def learn_skeleton( min_cond_set_size=self.min_cond_set_size, max_cond_set_size=self.max_cond_set_size, max_combinations=self.max_combinations, - skeleton_method=self.skeleton_method, + condsel_method=self.condsel_method, keep_sorted=False, ) skel_alg.fit(data, context) diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index 049679ca7..f926c3b34 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -48,8 +48,8 @@ class FCI(BaseConstraintDiscovery): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : ConditioningSetSelection - The method to use for testing conditional independence. Must be one of + condsel_method : ConditioningSetSelection + The method to use for selecting the conditioning sets. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. apply_orientations : bool Whether or not to apply orientation rules given the learned skeleton graph @@ -64,8 +64,8 @@ class FCI(BaseConstraintDiscovery): selection_bias : bool Whether or not to account for selection bias within the causal PAG. See :footcite:`Zhang2008`. - pds_skeleton_method : ConditioningSetSelection - The method to use for learning the skeleton using PDS. Must be one of + pds_condsel_method : ConditioningSetSelection + The method to use for selecting the conditioning sets using PDS. Must be one of ('pds', 'pds_path'). See Notes for more details. References @@ -89,12 +89,12 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, max_iter: int = 1000, max_path_length: Optional[int] = None, selection_bias: bool = True, - pds_skeleton_method: ConditioningSetSelection = ConditioningSetSelection.PDS, + pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, ): super().__init__( ci_estimator, @@ -102,13 +102,13 @@ def __init__( min_cond_set_size=min_cond_set_size, max_cond_set_size=max_cond_set_size, max_combinations=max_combinations, - skeleton_method=skeleton_method, + condsel_method=condsel_method, ) self.max_iter = max_iter self.apply_orientations = apply_orientations self.max_path_length = max_path_length self.selection_bias = selection_bias - self.pds_skeleton_method = pds_skeleton_method + self.pds_condsel_method = pds_condsel_method def orient_unshielded_triples(self, graph: EquivalenceClass, sep_set: SeparatingSet) -> None: """Orient colliders given a graph and separation set. @@ -819,8 +819,8 @@ def learn_skeleton( min_cond_set_size=self.min_cond_set_size, max_cond_set_size=self.max_cond_set_size, max_combinations=self.max_combinations, - skeleton_method=self.skeleton_method, - second_stage_skeleton_method=self.pds_skeleton_method, + condsel_method=self.condsel_method, + second_stage_condsel_method=self.pds_condsel_method, keep_sorted=False, max_path_length=self.max_path_length, ) diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index c5632aaeb..e06aa0802 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -59,8 +59,8 @@ class PsiFCI(FCI): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : ConditioningSetSelection - The method to use for testing conditional independence. Must be one of + condsel_method : ConditioningSetSelection + The method to use for selecting the conditioning sets. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. apply_orientations : bool Whether or not to apply orientation rules given the learned skeleton graph @@ -72,8 +72,8 @@ class PsiFCI(FCI): orientation rules. max_path_length : int, optional The maximum length of any discriminating path, or None if unlimited. - pds_skeleton_method : ConditioningSetSelection - The method to use for learning the skeleton using PDS. Must be one of + pds_condsel_method : ConditioningSetSelection + The method to use for selecting the conditioning sets using PDS. Must be one of ('pds', 'pds_path'). See Notes for more details. known_intervention_targets : bool, optional If `True`, then will run the I-FCI algorithm. If `False`, will run the @@ -92,11 +92,11 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, max_iter: int = 1000, max_path_length: Optional[int] = None, - pds_skeleton_method: ConditioningSetSelection = ConditioningSetSelection.PDS, + pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, known_intervention_targets: bool = False, ): super().__init__( @@ -105,12 +105,12 @@ def __init__( min_cond_set_size, max_cond_set_size, max_combinations, - skeleton_method, + condsel_method, apply_orientations, max_iter=max_iter, max_path_length=max_path_length, selection_bias=False, - pds_skeleton_method=pds_skeleton_method, + pds_condsel_method=pds_condsel_method, ) self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets @@ -127,8 +127,8 @@ def learn_skeleton( min_cond_set_size=self.min_cond_set_size, max_cond_set_size=self.max_cond_set_size, max_combinations=self.max_combinations, - skeleton_method=self.skeleton_method, - second_stage_skeleton_method=self.pds_skeleton_method, + condsel_method=self.condsel_method, + second_stage_condsel_method=self.pds_condsel_method, keep_sorted=False, max_path_length=self.max_path_length, ) diff --git a/dodiscover/constraint/pcalg.py b/dodiscover/constraint/pcalg.py index 8d9dee092..9468eaf2d 100644 --- a/dodiscover/constraint/pcalg.py +++ b/dodiscover/constraint/pcalg.py @@ -45,8 +45,8 @@ class PC(BaseConstraintDiscovery): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : ConditioningSetSelection - The method to use for testing conditional independence. Must be one of + condsel_method : ConditioningSetSelection + The method to use for selecting the conditioning set. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. apply_orientations : bool Whether or not to apply orientation rules given the learned skeleton graph @@ -81,7 +81,7 @@ def __init__( min_cond_set_size: Optional[int] = None, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, max_iter: int = 1000, ): @@ -91,7 +91,7 @@ def __init__( min_cond_set_size=min_cond_set_size, max_cond_set_size=max_cond_set_size, max_combinations=max_combinations, - skeleton_method=skeleton_method, + condsel_method=condsel_method, ) self.max_iter = max_iter self.apply_orientations = apply_orientations diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 12e072396..6a8d73ccc 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -134,8 +134,8 @@ class LearnSkeleton: of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : ConditioningSetSelection - The method to use for testing conditional independence. Must be one of + condsel_method : ConditioningSetSelection + The method to use for selecting the conditioning set. Must be one of ('complete', 'neighbors', 'neighbors_path'). See Notes for more details. keep_sorted : bool Whether or not to keep the considered conditioning set variables in sorted @@ -205,7 +205,7 @@ class LearnSkeleton: Different methods for learning the skeleton: There are different ways to learn the skeleton that are valid under various - assumptions. The value of ``skeleton_method`` completely defines how one + assumptions. The value of ``condsel_method`` completely defines how one selects the conditioning set. - 'complete': This exhaustively conditions on all combinations of variables in @@ -234,13 +234,13 @@ def __init__( min_cond_set_size: int = 0, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, keep_sorted: bool = False, ) -> None: self.ci_estimator = ci_estimator self.sep_set = sep_set self.alpha = alpha - self.skeleton_method = skeleton_method + self.condsel_method = condsel_method # control of the conditioning set self.min_cond_set_size = min_cond_set_size @@ -262,10 +262,10 @@ def _initialize_params(self) -> None: if self.max_combinations is not None and self.max_combinations <= 0: raise RuntimeError(f"Max combinations must be at least 1, not {self.max_combinations}") - if self.skeleton_method not in ConditioningSetSelection: + if self.condsel_method not in ConditioningSetSelection: raise ValueError( f"Skeleton method must be one of {ConditioningSetSelection}, not " - f"{self.skeleton_method}." + f"{self.condsel_method}." ) if self.sep_set is None and not hasattr(self, "sep_set_"): @@ -504,17 +504,17 @@ def _compute_candidate_conditioning_sets( Notes ----- - The :attr:`skeleton_method` dictates how we choose the corresponding conditioning sets. + The :attr:`condsel_method` dictates how we choose the corresponding conditioning sets. For more information, see :class:`ConditioningSetSelection`. """ - skeleton_method = self.skeleton_method + condsel_method = self.condsel_method - if skeleton_method == ConditioningSetSelection.COMPLETE: + if condsel_method == ConditioningSetSelection.COMPLETE: possible_variables = set(adj_graph.nodes) - elif skeleton_method == ConditioningSetSelection.NBRS: + elif condsel_method == ConditioningSetSelection.NBRS: possible_variables = set(adj_graph.neighbors(x_var)) # possible_adjacencies.copy() - elif skeleton_method == ConditioningSetSelection.NBRS_PATH: + elif condsel_method == ConditioningSetSelection.NBRS_PATH: # constrain adjacency set to ones with a path from x_var to y_var possible_variables = _find_neighbors_along_path(adj_graph, start=x_var, end=y_var) @@ -594,10 +594,10 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : ConditioningSetSelection + condsel_method : ConditioningSetSelection The method to use for determining conditioning sets when testing conditional independence of the first stage. See :class:`LearnSkeleton` for details. - second_stage_skeleton_method : ConditioningSetSelection | None + second_stage_condsel_method : ConditioningSetSelection | None The method to use for determining conditioning sets when testing conditional independence of the first stage. Must be one of ('pds', 'pds_path'). See Notes for more details. If `None`, then no second stage skeleton discovery phase will be run. @@ -649,7 +649,7 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): Different methods for learning the skeleton: There are different ways to learn the skeleton that are valid under various - assumptions. The value of ``skeleton_method`` completely defines how one + assumptions. The value of ``condsel_method`` completely defines how one selects the conditioning set. - 'pds': This conditions on the PDS set of 'x_var'. Note, this definition does @@ -672,8 +672,8 @@ def __init__( min_cond_set_size: int = 0, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, - second_stage_skeleton_method: Optional[ + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + second_stage_condsel_method: Optional[ ConditioningSetSelection ] = ConditioningSetSelection.PDS, keep_sorted: bool = False, @@ -686,11 +686,11 @@ def __init__( min_cond_set_size, max_cond_set_size, max_combinations, - skeleton_method, + condsel_method, keep_sorted, ) - self.second_stage_skeleton_method = second_stage_skeleton_method + self.second_stage_condsel_method = second_stage_condsel_method self.max_path_length = max_path_length def _orient_unshielded_triples(self, graph: EquivalenceClass, sep_set: SeparatingSet) -> None: @@ -733,15 +733,15 @@ def _compute_candidate_conditioning_sets( else: if not all(x in pag.nodes for x in [x_var, y_var]): raise RuntimeError("wtf..") - skeleton_method = self.second_stage_skeleton_method + condsel_method = self.second_stage_condsel_method - if skeleton_method == ConditioningSetSelection.PDS: + if condsel_method == ConditioningSetSelection.PDS: # determine how we want to construct the candidates for separating nodes # perform conditioning independence testing on all combinations possible_variables = pgraph.pds( pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore ) - elif skeleton_method == ConditioningSetSelection.PDS_PATH: + elif condsel_method == ConditioningSetSelection.PDS_PATH: # determine how we want to construct the candidates for separating nodes # perform conditioning independence testing on all combinations possible_variables = pgraph.pds_path( @@ -777,7 +777,7 @@ def fit(self, data: pd.DataFrame, context: Context): # if there is no second stage skeleton method to be run, then we # will stop with the skeleton here - if self.second_stage_skeleton_method is None: + if self.second_stage_condsel_method is None: return self # convert the undirected skeleton graph to a PAG, where @@ -842,7 +842,7 @@ class LearnInterventionSkeleton(LearnSemiMarkovianSkeleton): of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used in conjunction with ``keep_sorted`` parameter to only test the "strongest" dependences. - skeleton_method : ConditioningSetSelection + condsel_method : ConditioningSetSelection The method to use for testing conditional independence. Must be one of ('pds', 'pds_path'). See Notes for more details. keep_sorted : bool @@ -878,8 +878,8 @@ def __init__( min_cond_set_size: int = 0, max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, - skeleton_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, - second_stage_skeleton_method: ConditioningSetSelection = ConditioningSetSelection.PDS, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + second_stage_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, keep_sorted: bool = False, max_path_length: Optional[int] = None, known_intervention_targets: bool = False, @@ -891,8 +891,8 @@ def __init__( min_cond_set_size, max_cond_set_size, max_combinations, - skeleton_method, - second_stage_skeleton_method, + condsel_method, + second_stage_condsel_method, keep_sorted, max_path_length, ) @@ -947,12 +947,9 @@ def evaluate_fnode_edge( # compare conditional distributions P(Y | X) vs P'(Y | X), where 'group_col' # indicates which distribution data came from - if isinstance(self.cd_estimator, Oracle): - # test graphically if Y is d-separated from F-node given Z - test_stat, pvalue = self.cd_estimator.test(data, {group_col}, Y, Z) - else: - # test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test(data, Z, Y, group_col) + # test graphically if Y is d-separated from F-node given Z + # or test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) + test_stat, pvalue = self.cd_estimator.test(data, {group_col}, Y, Z) self.n_ci_tests += 1 return test_stat, pvalue diff --git a/dodiscover/context_builder.py b/dodiscover/context_builder.py index 30bba1762..82434c2ec 100644 --- a/dodiscover/context_builder.py +++ b/dodiscover/context_builder.py @@ -327,12 +327,15 @@ class InterventionalContextBuilder(ContextBuilder): to build the :class:`~.context.Context` object here. """ - _intervention_targets: List[Tuple[Column]] = [] + _intervention_targets: Optional[List[Tuple[Column]]] = None _num_distributions: Optional[int] = None _obs_distribution: bool = True def obs_distribution(self, has_obs_distrib: bool): - """Whether or not we have access to the observational distribution.""" + """Whether or not we have access to the observational distribution. + + By default, this is True and assumed to be the first distribution. + """ self._obs_distribution = has_obs_distrib return self @@ -349,13 +352,6 @@ def num_distributions(self, num_distribs: int): Number of distributions we will have access to. Will set the number of distributions to be ``num_distribs + 1`` if ``_obs_distribution is True`` (default). """ - if len(self._intervention_targets) > 0 and ( - len(self._intervention_targets) + int(self._obs_distribution) != num_distribs - ): - raise RuntimeError( - f"Setting the number of distributions {num_distribs} does not match the number of " - f"intervention targets {len(self._intervention_targets)}." - ) self._num_distributions = num_distribs return self @@ -374,17 +370,7 @@ def intervention_targets(self, targets: List[Tuple[Column]]): If intervention targets are unknown, then this is not necessary. """ - if ( - self._num_distributions is not None - and len(targets) + int(self._obs_distribution) != self._num_distributions - ): - raise RuntimeError( - f"Setting the number of intervention targets {targets} does not match the " - f"number of distributions set {self._num_distributions} (it is assumed " - f"there are {int(self._obs_distribution)} observational distributions)." - ) self._intervention_targets = targets - self._num_distributions = len(targets) + int(self._obs_distribution) return self def build(self) -> Context: @@ -404,16 +390,30 @@ def build(self) -> Context: "use `ContextBuilder` instead of `InterventionContextBuilder`." ) - # get F-nodes and sigma-map - f_nodes, sigma_map, symmetric_diff_map = self._create_augmented_nodes() - graph_variables = set(self._observed_variables).union(set(f_nodes)) - - # infer number of distributions + # infer intervention targets and number of distributions + if self._intervention_targets is None: + intervention_targets = [] + else: + intervention_targets = self._intervention_targets if self._num_distributions is None: - num_distributions = int(self._obs_distribution) + num_distributions = int(self._obs_distribution) + len(intervention_targets) else: num_distributions = self._num_distributions + # error-check if intervention targets was set that it matches the distributions + if len(intervention_targets) > 0: + if len(intervention_targets) + int(self._obs_distribution) != num_distributions: + raise RuntimeError( + f"Setting the number of distributions {num_distributions} does not match the number of " + f"intervention targets {len(intervention_targets)}." + ) + + # get F-nodes and sigma-map + f_nodes, sigma_map, symmetric_diff_map = self._create_augmented_nodes( + intervention_targets, num_distributions + ) + graph_variables = set(self._observed_variables).union(set(f_nodes)) + empty_graph = self._empty_graph_func(graph_variables) return Context( init_graph=self._interpolate_graph(graph_variables), @@ -422,7 +422,7 @@ def build(self) -> Context: observed_variables=self._observed_variables, latent_variables=self._latent_variables or set(), state_variables=self._state_variables, - intervention_targets=self._intervention_targets, + intervention_targets=intervention_targets, f_nodes=f_nodes, sigma_map=sigma_map, symmetric_diff_map=symmetric_diff_map, @@ -430,7 +430,9 @@ def build(self) -> Context: num_distributions=num_distributions, ) - def _create_augmented_nodes(self) -> Tuple[List, Dict, Dict]: + def _create_augmented_nodes( + self, intervention_targets, num_distributions + ) -> Tuple[List, Dict, Dict]: """Create augmented nodes, sigma map and optionally a symmetric difference map. Given a number of distributions attributed to interventions, one constructs @@ -462,16 +464,17 @@ def _create_augmented_nodes(self) -> Tuple[List, Dict, Dict]: distribution_targets_idx = [] # now map all distribution targets to their indexed distribution - int_dist_idx = np.arange(int(self._obs_distribution), self._num_distributions).tolist() + int_dist_idx = np.arange(int(self._obs_distribution), num_distributions).tolist() distribution_targets_idx.extend(int_dist_idx) # store known-targets, which are sets of nodes targets = [] - if len(self._intervention_targets) > 0: + if len(intervention_targets) > 0: if self._obs_distribution: targets.append(()) - targets.extend(copy(list(self._intervention_targets))) # type: ignore + targets.extend(copy(list(intervention_targets))) # type: ignore + # create F-nodes, their symmetric difference mapping and sigma-mapping to intervention targets for idx, (jdx, kdx) in enumerate(combinations(distribution_targets_idx, 2)): if jdx == kdx: continue @@ -480,8 +483,7 @@ def _create_augmented_nodes(self) -> Tuple[List, Dict, Dict]: sigma_map[f_node] = (jdx, kdx) # if we additionally know the intervention targets - if len(self._intervention_targets) > 0: - print(jdx, kdx, len(targets), distribution_targets_idx) + if len(intervention_targets) > 0: i_target: Set = set(targets[jdx]) j_target: Set = set(targets[kdx]) diff --git a/examples/ex_psifci_alg.py b/examples/ex_psifci_alg.py new file mode 100644 index 000000000..7539d4b0f --- /dev/null +++ b/examples/ex_psifci_alg.py @@ -0,0 +1,144 @@ +""" +.. _ex-psifci-algorithm: + +========================================= +Causal discovery with interventional data +========================================= + +We will simulate some observational data from a Structural Causal Model (SCM) and +demonstrate how we will use the PC algorithm. + +The PC algorithm works on observational data when there are no unobserved latent +confounders. That means for any observed set of variables, there is no common causes +that are unobserved. In other words, all exogenous variables then are assumed to be +independent. + +In this example, we will introduce the main abstractions and concepts used in +dodiscover for causal discovery: + +- learner: Any causal discovery algorithm that has a similar scikit-learn API. +- context: Causal assumptions. + +.. currentmodule:: dodiscover +""" + + +# %% +# Authors: Adam Li +# +# License: BSD (3-clause) + +from pywhy_graphs.viz import draw +from dodiscover.ci import GSquareCITest, Oracle +from dodiscover import PsiFCI, Context, make_context, InterventionalContextBuilder + +import pandas as pd +import bnlearn + +import pooch + +# %% +# Pull in the Sachs Dataset +# ------------------------- +# The Sachs dataset is a famous dataset in causal discovery because of its real-life +# applicability and access to experimental data that analyzed the causal network of +# 11 proteins using knockouts and spikings :footcite:`sachsdataset2005`. The pathways +# for those proteins are already known, so it is an ideal dataset for benchmarking +# causal discovery algorithms. +# +# We will download a preprocessed version of the dataset from the following +# url: https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz +# +# Ref: https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_example +df = bnlearn.import_example("sachs", n=2000) + +# use pooch to download robustly from a url +url = "https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz" +file_path = pooch.retrieve( + url=url, + known_hash="md5:39ee257f7eeb94cb60e6177cf80c9544", +) + +df = pd.read_csv(file_path, delimiter=" ") + +# the ground-truth dag is shown here +ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False) +fig = bnlearn.plot(ground_truth_dag) + +# .. note:: +# The Sachs dataset has previously been preprocessed, and the steps are described +# in bnlearn, at the web-page https://www.bnlearn.com/research/sachs05/. +print(df.head()) +print(df.shape) + +# %% +# Preprocess the dataset +# ---------------------- +# Since the data is one dataframe, we need to process it into a form +# that is acceptable by dodiscover's :class:`PsiFCI`` algorithm. We +# will form a list of separate dataframes. +unique_ints = df["INT"].unique() + +# get the list of intervention targets and list of dataframe associated with each intervention +intervention_targets = [df.columns[idx] for idx in unique_ints] +data_cols = [col for col in df.columns if col != "INT"] +data = [] +for interv_idx in unique_ints: + _data = df[df["INT"] == interv_idx][data_cols] + data.append(_data) + +print(len(data), len(intervention_targets)) +# %% +# Setup constraint-based learner +# ------------------------------ +# Since we have access to interventional data, the causal discovery algorithm +# we will use that leverages CI and CD tests to estimate causal constraints +# is the Psi-FCI algorithm :footcite:`Jaber2020causal`. + +# Our dataset is comprised of discrete valued data, so we will utilize the +# G^2 (Chi-square) CI test. +ci_estimator = GSquareCITest(data_type="discrete") + +# Since our data is entirely discrete, we can also use the G^2 test as our +# CD test. +cd_estimator = GSquareCITest(data_type="discrete") + +alpha = 0.05 + +learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha) + +# create context with information about the interventions +ctx_builder = make_context(create_using=InterventionalContextBuilder) +ctx: Context = ( + ctx_builder.variables(data=data[0]) + .intervention_targets(intervention_targets) + .obs_distribution(False) + .build() +) + +# %% +# Run the learning process +# ------------------------ +# We have setup our causal context and causal discovery learner, so we will now +# run the algorithm using the :meth:`PsiFCI.fit` API, which is similar to scikit-learn's +# `fit` design. All fitted attributes contain an underscore at the end. +learner = learner.fit(data, ctx) + +# %% +# Visualize the results +# --------------------- +# Now that we have learned the graph, we will show it here. Note differences and similarities +# to the ground-truth DAG that is "assumed". Moreover, note that this reproduces Supplementary +# Figure 8 in :footcite:`Jaber2020causal`. +est_pag = learner.graph_ + +# draw the full graph +draw(est_pag, direction="LR") + +# if we do not want to visualize the F-nodes, then we can view the subgraph +est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes()) +draw(est_pag_no_fnodes, direction="LR") + +# References +# ---------- +# .. footbibliography:: diff --git a/examples/plot_pc_alg.py b/examples/plot_pc_alg.py index e6b303fcb..f86fe4ddc 100644 --- a/examples/plot_pc_alg.py +++ b/examples/plot_pc_alg.py @@ -1,9 +1,9 @@ """ .. _ex-pc-algorithm: -==================================================================================== -PC algorithm for causal discovery from observational data without latent confounders -==================================================================================== +============================================================= +Basic causal discovery with DoDiscover using the PC algorithm +============================================================= We will simulate some observational data from a Structural Causal Model (SCM) and demonstrate how we will use the PC algorithm. @@ -13,6 +13,12 @@ that are unobserved. In other words, all exogenous variables then are assumed to be independent. +In this example, we will introduce the main abstractions and concepts used in +dodiscover for causal discovery: + +- learner: Any causal discovery algorithm that has a similar scikit-learn API. +- context: Causal assumptions. + .. currentmodule:: dodiscover """ # %% diff --git a/tests/unit_tests/conditional/cd/test_cd.py b/tests/unit_tests/conditional/cd/test_cd.py index 8dbd58f65..a22b017dc 100644 --- a/tests/unit_tests/conditional/cd/test_cd.py +++ b/tests/unit_tests/conditional/cd/test_cd.py @@ -54,33 +54,33 @@ def test_cd_tests_error(cd_func): sample_df = single_env_scm(n_samples=10) cd_estimator = cd_func() with pytest.raises(ValueError, match="The group col"): - cd_estimator.test(sample_df, {x}, {y}, group_col="blah") + cd_estimator.test(sample_df, {y}, group_col="blah", x_vars={x}) with pytest.raises(ValueError, match="The x variables are not all"): - cd_estimator.test(sample_df, {"blah"}, y_vars={y}, group_col="group") + cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={"blah"}) with pytest.raises(ValueError, match="The y variables are not all"): - cd_estimator.test(sample_df, {x}, y_vars={"blah"}, group_col="group") + cd_estimator.test(sample_df, y_vars={"blah"}, group_col="group", x_vars={x}) # all the group indicators have different values now from 0/1 sample_df["group"] = sample_df["group"] + 3 with pytest.raises(RuntimeError, match="Group indications in"): - cd_estimator.test(sample_df, {x}, {y}, group_col="group") + cd_estimator.test(sample_df, {y}, group_col="group", x_vars={x}) # test pre-fit propensity scores, or custom propensity model with pytest.raises( ValueError, match="Both propensity model and propensity estimates are specified" ): cd_estimator = cd_func(propensity_model=RandomForestClassifier(), propensity_est=[0.5, 0.5]) - cd_estimator.test(sample_df, {x}, y_vars={y}, group_col="group") + cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={x}) with pytest.raises(ValueError, match="There are 3 group pre-defined estimates"): cd_estimator = cd_func(propensity_est=np.ones((10, 3)) * 0.5) - cd_estimator.test(sample_df, {x}, y_vars={y}, group_col="group") + cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={x}) with pytest.raises(ValueError, match="There are 100 pre-defined estimates"): cd_estimator = cd_func(propensity_est=np.ones((100, 2)) * 0.5) - cd_estimator.test(sample_df, {x}, y_vars={y}, group_col="group") + cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={x}) @pytest.mark.parametrize( @@ -112,16 +112,21 @@ def test_cd_simulation(cd_func, df, env_type, cd_kwargs): alpha = 0.1 if env_type == "single": - _, pvalue = cd_estimator.test(df, {"x"}, {"x1"}, group_col=group_col) + _, pvalue = cd_estimator.test( + df, + {"x1"}, + group_col, + {"x"}, + ) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" - _, pvalue = cd_estimator.test(df, {"x"}, {"z"}, group_col=group_col) + _, pvalue = cd_estimator.test(df, {"z"}, group_col, {"x"}) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" - _, pvalue = cd_estimator.test(df, {"x"}, {"y"}, group_col=group_col) + _, pvalue = cd_estimator.test(df, {"y"}, group_col, {"x"}) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" elif env_type == "multi": - _, pvalue = cd_estimator.test(df, {"x"}, {"z"}, group_col=group_col) + _, pvalue = cd_estimator.test(df, {"z"}, group_col, {"x"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" - _, pvalue = cd_estimator.test(df, {"x"}, {"y"}, group_col=group_col) + _, pvalue = cd_estimator.test(df, {"y"}, group_col, {"x"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" - _, pvalue = cd_estimator.test(df, {"x1"}, {"z"}, group_col=group_col) + _, pvalue = cd_estimator.test(df, {"z"}, group_col, {"x1"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" diff --git a/tests/unit_tests/test_context_builder.py b/tests/unit_tests/test_context_builder.py index 503ed447a..b8495c5d4 100644 --- a/tests/unit_tests/test_context_builder.py +++ b/tests/unit_tests/test_context_builder.py @@ -188,7 +188,7 @@ def test_context_interventions(): 5 ).build() - with pytest.raises(RuntimeError, match="Setting the number of intervention targets"): + with pytest.raises(RuntimeError, match="Setting the number of distribution"): copy(ctx_builder).variables(data=df).num_distributions(5).intervention_targets( [("x",)] ).build() From 997619c733bcc0922f696d4cc987413fc3aa85b4 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 27 Feb 2023 11:25:18 -1000 Subject: [PATCH 10/61] Add example Signed-off-by: Adam Li --- dodiscover/ci/g_test.py | 12 +++++++--- dodiscover/constraint/_classes.py | 1 + dodiscover/constraint/skeleton.py | 39 +++++++++++++++++-------------- examples/ex_psifci_alg.py | 3 +++ 4 files changed, 35 insertions(+), 20 deletions(-) diff --git a/dodiscover/ci/g_test.py b/dodiscover/ci/g_test.py index 6bb42a7c7..0942447f9 100644 --- a/dodiscover/ci/g_test.py +++ b/dodiscover/ci/g_test.py @@ -68,10 +68,16 @@ def _calculate_contingency_tble( else: # discrete case if zidx == 0: - kdx += data[z][row_idx] # data[row_idx, z] + # print(zidx, z, row_idx, data.shape) + row = data.iloc[row_idx] + kdx += row[z] # data[row_idx, z] else: lprod = np.prod(list(map(lambda x: levels[x], sep_set[:zidx]))) # type: ignore - kdx += data[z][row_idx] * lprod + row = data.iloc[row_idx] + kdx += row[z] * lprod + + if np.isnan(kdx): + print(kdx, zidx, z, data[z][row_idx], lprod) # increment the co-occurrence found contingency_tble[idx, jdx, kdx] += 1 @@ -337,7 +343,7 @@ def g_square_discrete( ) if levels is None: - levels = np.amax(data, axis=0) + 1 + levels = (np.amax(data, axis=0) + 1).astype(int) n_samples = data.shape[0] s_size = len(sep_set) dof = (levels[x] - 1) * (levels[y] - 1) * np.prod(list(map(lambda x: levels[x], sep_set))) diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index f8b81879a..897957943 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -169,6 +169,7 @@ def fit(self, data: pd.DataFrame, context: Context) -> None: # store resulting data structures self.graph_ = graph + return self def evaluate_edge( self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 6a8d73ccc..00605023b 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -937,8 +937,8 @@ def evaluate_fnode_edge( distribution_idx = self.context_.sigma_map[group_col] # get the distributions across the two distributions - data_i = data[distribution_idx[0]] - data_j = data[distribution_idx[1]] + data_i = data[distribution_idx[0]].copy() + data_j = data[distribution_idx[1]].copy() # name the group column the F-node, so Oracle works as expected data_i[group_col] = 0 @@ -984,7 +984,7 @@ def _compute_candidate_conditioning_sets( raise RuntimeError("This should not be the case") # get only neighboring sets of Y-vars, or PDS that depend on Y - possible_variables = set(adj_graph.neighbors(y_var)) + possible_variables = set(adj_graph.neighbors(y_var)) - set(f_nodes) return possible_variables def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], context: Context): @@ -1000,6 +1000,25 @@ def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], co # track progress of the algorithm for which edges to remove to ensure stability self.remove_edges = set() + # first remove all connections among f-nodes + for x_var in f_nodes: + for y_var in f_nodes: + if x_var == y_var: + continue + + pvalue = 1.0 + test_stat = 0.0 + + # post-process the CI test results + removed_edge = self._postprocess_ci_test( + adj_graph, x_var, y_var, set(), test_stat, pvalue + ) + + # summarize the comparison of XY + self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) + # Remove edges + adj_graph.remove_edges_from(self.remove_edges) + # Outer loop: iterate over 'size_cond_set' until stopping criterion is met # - 'size_cond_set' > 'max_cond_set_size' or # - All (X, Y) pairs have candidate conditioning sets of size < 'size_cond_set' @@ -1019,20 +1038,6 @@ def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], co if y_var == x_var: continue - # if Y is also a F-node, then they are automatically assumed d-separated - if y_var in f_nodes: - pvalue = 1.0 - test_stat = 0.0 - - # post-process the CI test results - removed_edge = self._postprocess_ci_test( - adj_graph, x_var, y_var, set(), test_stat, pvalue - ) - - # summarize the comparison of XY - self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) - continue - # compute the possible variables used in the conditioning set possible_variables = self._compute_candidate_conditioning_sets( adj_graph, diff --git a/examples/ex_psifci_alg.py b/examples/ex_psifci_alg.py index 7539d4b0f..7a0c8acc2 100644 --- a/examples/ex_psifci_alg.py +++ b/examples/ex_psifci_alg.py @@ -116,6 +116,9 @@ .build() ) +print(ctx.init_graph) +print(ctx.f_nodes) + # %% # Run the learning process # ------------------------ From 8ee328ede4eec30b7292740ff39b3d549dd3ee44 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 27 Feb 2023 11:29:13 -1000 Subject: [PATCH 11/61] Add example Signed-off-by: Adam Li --- dodiscover/ci/base.py | 3 ++- dodiscover/constraint/skeleton.py | 2 +- dodiscover/context_builder.py | 7 ++++--- examples/ex_psifci_alg.py | 4 ++-- 4 files changed, 9 insertions(+), 7 deletions(-) diff --git a/dodiscover/ci/base.py b/dodiscover/ci/base.py index 7fb5eb986..a2209e24e 100644 --- a/dodiscover/ci/base.py +++ b/dodiscover/ci/base.py @@ -35,7 +35,8 @@ def _check_test_input( raise ValueError(f"The y variables {y_vars} are not all in the DataFrame.") if z_covariates is not None and any(col not in df.columns for col in z_covariates): raise ValueError( - f"The z conditioning set variables {z_covariates} are not all in the DataFrame with {df.columns}." + f"The z conditioning set variables {z_covariates} are not all in the " + f"DataFrame with {df.columns}." ) if not self._allow_multivariate_input and (len(x_vars) > 1 or len(y_vars) > 1): diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 00605023b..78129997d 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -10,7 +10,7 @@ import pandas as pd from dodiscover.cd import BaseConditionalDiscrepancyTest -from dodiscover.ci import BaseConditionalIndependenceTest, Oracle +from dodiscover.ci import BaseConditionalIndependenceTest from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.utils import is_in_sep_set from dodiscover.typing import Column, SeparatingSet diff --git a/dodiscover/context_builder.py b/dodiscover/context_builder.py index 82434c2ec..326631687 100644 --- a/dodiscover/context_builder.py +++ b/dodiscover/context_builder.py @@ -404,8 +404,8 @@ def build(self) -> Context: if len(intervention_targets) > 0: if len(intervention_targets) + int(self._obs_distribution) != num_distributions: raise RuntimeError( - f"Setting the number of distributions {num_distributions} does not match the number of " - f"intervention targets {len(intervention_targets)}." + f"Setting the number of distributions {num_distributions} does not match the " + f"number of intervention targets {len(intervention_targets)}." ) # get F-nodes and sigma-map @@ -474,7 +474,8 @@ def _create_augmented_nodes( targets.append(()) targets.extend(copy(list(intervention_targets))) # type: ignore - # create F-nodes, their symmetric difference mapping and sigma-mapping to intervention targets + # create F-nodes, their symmetric difference mapping and sigma-mapping to + # intervention targets for idx, (jdx, kdx) in enumerate(combinations(distribution_targets_idx, 2)): if jdx == kdx: continue diff --git a/examples/ex_psifci_alg.py b/examples/ex_psifci_alg.py index 7a0c8acc2..223b5c335 100644 --- a/examples/ex_psifci_alg.py +++ b/examples/ex_psifci_alg.py @@ -29,7 +29,7 @@ # License: BSD (3-clause) from pywhy_graphs.viz import draw -from dodiscover.ci import GSquareCITest, Oracle +from dodiscover.ci import GSquareCITest from dodiscover import PsiFCI, Context, make_context, InterventionalContextBuilder import pandas as pd @@ -49,7 +49,7 @@ # We will download a preprocessed version of the dataset from the following # url: https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz # -# Ref: https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_example +# Ref: https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_example # noqa df = bnlearn.import_example("sachs", n=2000) # use pooch to download robustly from a url From f281fa169b9d25347dc85b601d58e22490a98506 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 27 Feb 2023 16:37:56 -1000 Subject: [PATCH 12/61] Add example Signed-off-by: Adam Li --- doc/conditional_independence.rst | 16 ++++++++-------- examples/ex_psifci_alg.py | 11 +++++++---- pyproject.toml | 1 + 3 files changed, 16 insertions(+), 12 deletions(-) diff --git a/doc/conditional_independence.rst b/doc/conditional_independence.rst index f1cb0e041..6143a3ae1 100644 --- a/doc/conditional_independence.rst +++ b/doc/conditional_independence.rst @@ -64,14 +64,14 @@ various proposals in the literature for estimating CMI, which we summarize here: .. autosummary:: :toctree: generated/ - ci.CMITest + CMITest - The Classifier Divergence approach estimates CMI using a classification model. .. autosummary:: :toctree: generated/ - ci.ClassifierCMITest + ClassifierCMITest - Direct posterior estimates can be implemented with a classification model by directly estimating :math:`P(y|x)` and :math:`P(y|x,z)`, which can be used as plug-in estimates @@ -87,7 +87,7 @@ which may be unrealistic in certain datasets. .. autosummary:: :toctree: generated/ - ci.FisherZCITest + FisherZCITest Discrete, Categorical and Binary Data ------------------------------------- @@ -99,7 +99,7 @@ for a discrete variable. .. autosummary:: :toctree: generated/ - ci.GSquareCITest + GSquareCITest Kernel-Approaches ----------------- @@ -112,7 +112,7 @@ estimate a pvalue. .. autosummary:: :toctree: generated/ - ci.KernelCITest + KernelCITest Classifier-based Approaches --------------------------- @@ -126,7 +126,7 @@ state that the original data was in fact CI :footcite:`Sen2017model`. .. autosummary:: :toctree: generated/ - ci.ClassifierCITest + ClassifierCITest ======================= Conditional Discrepancy @@ -165,7 +165,7 @@ based on the estimated propensity scores to generate samples from the null distr .. autosummary:: :toctree: generated/ - cd.KernelCDTest + KernelCDTest Bregman-Divergences ------------------- @@ -177,4 +177,4 @@ weighted permutation testing based on the estimated propensity scores to generat .. autosummary:: :toctree: generated/ - cd.BregmanCDTest + BregmanCDTest diff --git a/examples/ex_psifci_alg.py b/examples/ex_psifci_alg.py index 223b5c335..a8c8cf26a 100644 --- a/examples/ex_psifci_alg.py +++ b/examples/ex_psifci_alg.py @@ -75,7 +75,7 @@ # Preprocess the dataset # ---------------------- # Since the data is one dataframe, we need to process it into a form -# that is acceptable by dodiscover's :class:`PsiFCI`` algorithm. We +# that is acceptable by dodiscover's :class:`constraint.PsiFCI` algorithm. We # will form a list of separate dataframes. unique_ints = df["INT"].unique() @@ -123,7 +123,7 @@ # Run the learning process # ------------------------ # We have setup our causal context and causal discovery learner, so we will now -# run the algorithm using the :meth:`PsiFCI.fit` API, which is similar to scikit-learn's +# run the algorithm using the :meth:`constraint.PsiFCI.fit` API, which is similar to scikit-learn's # `fit` design. All fitted attributes contain an underscore at the end. learner = learner.fit(data, ctx) @@ -136,11 +136,14 @@ est_pag = learner.graph_ # draw the full graph -draw(est_pag, direction="LR") +dot_graph = draw(est_pag, direction="LR") +dot_graph.render(outfile="psi_pag_full.png", view=True) # if we do not want to visualize the F-nodes, then we can view the subgraph est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes()) -draw(est_pag_no_fnodes, direction="LR") +dot_graph = draw(est_pag_no_fnodes, direction="LR") +dot_graph.render(outfile="psi_pag.png", view=True) + # References # ---------- diff --git a/pyproject.toml b/pyproject.toml index 77b093856..91b928566 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -146,6 +146,7 @@ type_check = 'mypy -p dodiscover -p tests --config-file pyproject.toml' unit_test = 'pytest tests/unit_tests --cov=dodiscover --cov-report=xml --cov-config=pyproject.toml' integration_test = 'pytest tests/integration_tests' build_docs = 'make -C doc clean html' +build_docs_noplot = 'make -C doc clean html-noplot' [[tool.poe.tasks.clean]] sequence = ['_clean_cache', '_clean_pyc', '_clean_so', '_clean_build', '_clean_ctags', '_clean_test'] From cebb60e994b101335d05e5c52f5ad8b2f975b158 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 28 Feb 2023 15:50:01 -1000 Subject: [PATCH 13/61] Run poetry update to add pooch Signed-off-by: Adam Li --- poetry.lock | 56 +++++++++++++++++++++++++------------------------- pyproject.toml | 1 + 2 files changed, 29 insertions(+), 28 deletions(-) diff --git a/poetry.lock b/poetry.lock index 69c546d90..5e5f75a56 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1500,7 +1500,6 @@ category = "dev" optional = false python-versions = "*" files = [ - 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x_vars: Set[Column], y_vars: Set[Column], group_col: Column, + x_vars: Optional[Set[Column]], ): - if any(col not in df.columns for col in x_vars): + if x_vars is not None and any(col not in df.columns for col in x_vars): raise ValueError("The x variables are not all in the DataFrame.") if any(col not in df.columns for col in y_vars): raise ValueError("The y variables are not all in the DataFrame.") diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index ee8e29b8c..b42f69f4f 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -78,12 +78,16 @@ def test( df: pd.DataFrame, y_vars: Set[Column], group_col: Column, - x_vars: Optional[Set[Column]], + x_vars: Optional[Set[Column]] = None, ) -> Tuple[float, float]: # check test input - self._check_test_input(df, x_vars, y_vars, group_col) + self._check_test_input(df, y_vars, group_col, x_vars) + + if x_vars is not None: + x_cols = list(x_vars) + else: + x_cols = None - x_cols = list(x_vars) y_cols = list(y_vars) group_ind = df[group_col].to_numpy() if set(np.unique(group_ind)) != {0, 1}: diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index ff4b0c3dc..36f30204a 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -94,7 +94,7 @@ def test( df: pd.DataFrame, y_vars: Set[Column], group_col: Column, - x_vars: Optional[Set[Column]], + x_vars: Optional[Set[Column]] = None, ) -> Tuple[float, float]: """Compute k-sample test statistic and pvalue. @@ -115,13 +115,13 @@ def test( df : pd.DataFrame The dataset containing the columns denoted by ``x_vars``, ``y_vars``, and the ``group_col``. - x_vars : Set[Column] - Set of X variables. y_vars : Set[Column] Set of Y variables. group_col : Column The column denoting, which group (i.e. environment) each sample belongs to. This is typically the F-node. Must be binary. + x_vars : Set[Column] + Set of X variables. Returns ------- @@ -131,9 +131,13 @@ def test( The computed p-value. """ # check test input - self._check_test_input(df, x_vars, y_vars, group_col) + self._check_test_input(df, y_vars, group_col, x_vars) + + if x_vars is not None: + x_cols = list(x_vars) + else: + x_cols = None - x_cols = list(x_vars) y_cols = list(y_vars) group_ind = df[group_col].to_numpy() diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index 897957943..7c11e94a5 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -123,7 +123,7 @@ def orient_edges(self, graph: EquivalenceClass) -> None: "skeleton graph given a separating set." ) - def fit(self, data: pd.DataFrame, context: Context) -> None: + def fit(self, data: pd.DataFrame, context: Context): """Fit constraint-based discovery algorithm on dataset 'X'. Parameters diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 78129997d..09e3886ad 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -949,7 +949,7 @@ def evaluate_fnode_edge( # indicates which distribution data came from # test graphically if Y is d-separated from F-node given Z # or test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test(data, {group_col}, Y, Z) + test_stat, pvalue = self.cd_estimator.test(data, Y, group_col, Z) self.n_ci_tests += 1 return test_stat, pvalue From de979cc2d35ae76685bf52ff8591a738e5427b0d Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 28 Feb 2023 16:06:00 -1000 Subject: [PATCH 15/61] Remove verbosity Signed-off-by: Adam Li --- examples/ex_psifci_alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/ex_psifci_alg.py b/examples/ex_psifci_alg.py index a8c8cf26a..258b15058 100644 --- a/examples/ex_psifci_alg.py +++ b/examples/ex_psifci_alg.py @@ -50,7 +50,7 @@ # url: https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz # # Ref: https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_example # noqa -df = bnlearn.import_example("sachs", n=2000) +df = bnlearn.import_example("sachs", n=2000, verbose=False) # use pooch to download robustly from a url url = "https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz" From 33f6a04bbe555b6065da1fd67f8a693a7a17458f Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 2 Mar 2023 15:02:56 -0800 Subject: [PATCH 16/61] Change exmaple name Signed-off-by: Adam Li --- .../{ex_psifci_alg.py => plot_psifci_alg.py} | 0 poetry.lock | 292 +++++++++--------- 2 files changed, 146 insertions(+), 146 deletions(-) rename examples/{ex_psifci_alg.py => plot_psifci_alg.py} (100%) diff --git a/examples/ex_psifci_alg.py b/examples/plot_psifci_alg.py similarity index 100% rename from examples/ex_psifci_alg.py rename to examples/plot_psifci_alg.py diff --git a/poetry.lock b/poetry.lock index 5e5f75a56..0f91c6915 100644 --- a/poetry.lock +++ b/poetry.lock @@ -45,18 +45,18 @@ tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy [[package]] name = "babel" -version = "2.11.0" +version = "2.12.1" description = "Internationalization utilities" category = "dev" optional = false 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"pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [extras] graph-func = ["pywhy-graphs"] From 5c777ef06e1157da272d5503d4bf3ad70a7af1f2 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 2 Mar 2023 15:49:19 -0800 Subject: [PATCH 17/61] Try again Signed-off-by: Adam Li --- .circleci/config.yml | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 7625d5dc3..b5aae08a8 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1,10 +1,12 @@ version: 2.1 orbs: - python: circleci/python@2.0.3 + python: circleci/python@2.1.1 jobs: build_doc: + parameters: + tag: "3.10" executor: python/default steps: - restore_cache: @@ -62,6 +64,13 @@ jobs: echo "export DISPLAY=:99" >> $BASH_ENV echo "BASH_ENV:" cat $BASH_ENV + - run: + name: Install fonts needed for diagrams + command: | + mkdir -p $HOME/.fonts + curl https://codeload.github.com/adobe-fonts/source-code-pro/tar.gz/2.038R-ro/1.058R-it/1.018R-VAR | tar xz -C $HOME/.fonts + curl https://codeload.github.com/adobe-fonts/source-sans-pro/tar.gz/3.028R | tar xz -C $HOME/.fonts + fc-cache -f - run: name: Install pysal dependencies command: | From 1cac3f90fe76f84c8199fdb5b1417adaf45ad166 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 2 Mar 2023 16:15:22 -0800 Subject: [PATCH 18/61] Try again Signed-off-by: Adam Li --- .circleci/config.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index b5aae08a8..ee239c938 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,7 +6,9 @@ orbs: jobs: build_doc: parameters: - tag: "3.10" + tag: + type: string + default: "3.10" executor: python/default steps: - restore_cache: From aed5dd7941e8059537b4ea21cfa7a1966a81c21f Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 2 Mar 2023 17:25:33 -0800 Subject: [PATCH 19/61] Try again Signed-off-by: Adam Li --- .circleci/config.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index ee239c938..04c0d73ff 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -5,11 +5,7 @@ orbs: jobs: build_doc: - parameters: - tag: - type: string - default: "3.10" - executor: python/default + executor: python/3.10 steps: - restore_cache: name: Restore .git From a04afb1b59812d036e7f52e78441395798d1dffe Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 3 Mar 2023 08:58:14 -0800 Subject: [PATCH 20/61] Fix Signed-off-by: Adam Li --- .circleci/config.yml | 2 +- examples/plot_psifci_alg.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 04c0d73ff..bf83f580b 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -5,7 +5,7 @@ orbs: jobs: build_doc: - executor: python/3.10 + executor: python/default steps: - restore_cache: name: Restore .git diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 258b15058..49f0fc4eb 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -50,7 +50,6 @@ # url: https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz # # Ref: https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_example # noqa -df = bnlearn.import_example("sachs", n=2000, verbose=False) # use pooch to download robustly from a url url = "https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz" From 220bdb6b67668532e046d128b556450ed269cc9b Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 3 Mar 2023 10:34:41 -0800 Subject: [PATCH 21/61] Fix unit tests and try ci Signed-off-by: Adam Li --- .circleci/config.yml | 12 ++++++------ tests/unit_tests/constraint/test_fcialg.py | 10 +++++----- tests/unit_tests/constraint/test_pcalg.py | 2 +- tests/unit_tests/constraint/test_skeleton.py | 2 +- 4 files changed, 13 insertions(+), 13 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index bf83f580b..a8d65f1d6 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -63,12 +63,12 @@ jobs: echo "BASH_ENV:" cat $BASH_ENV - run: - name: Install fonts needed for diagrams - command: | - mkdir -p $HOME/.fonts - curl https://codeload.github.com/adobe-fonts/source-code-pro/tar.gz/2.038R-ro/1.058R-it/1.018R-VAR | tar xz -C $HOME/.fonts - curl https://codeload.github.com/adobe-fonts/source-sans-pro/tar.gz/3.028R | tar xz -C $HOME/.fonts - fc-cache -f + name: Install fonts needed for diagrams + command: | + mkdir -p $HOME/.fonts + curl https://codeload.github.com/adobe-fonts/source-code-pro/tar.gz/2.038R-ro/1.058R-it/1.018R-VAR | tar xz -C $HOME/.fonts + curl https://codeload.github.com/adobe-fonts/source-sans-pro/tar.gz/3.028R | tar xz -C $HOME/.fonts + fc-cache -f - run: name: Install pysal dependencies command: | diff --git a/tests/unit_tests/constraint/test_fcialg.py b/tests/unit_tests/constraint/test_fcialg.py index dfb55f9e7..5acccd8c8 100644 --- a/tests/unit_tests/constraint/test_fcialg.py +++ b/tests/unit_tests/constraint/test_fcialg.py @@ -641,7 +641,7 @@ def test_fci_spirtes_example(self): assert set(expected_pag.edges()) == set(pag.edges()) @pytest.mark.parametrize( - "skeleton_method", + "condsel_method", [ ConditioningSetSelection.NBRS, ConditioningSetSelection.NBRS_PATH, @@ -649,10 +649,10 @@ def test_fci_spirtes_example(self): ], ) @pytest.mark.parametrize( - "pds_skeleton_method", [ConditioningSetSelection.PDS, ConditioningSetSelection.PDS_PATH] + "pds_condsel_method", [ConditioningSetSelection.PDS, ConditioningSetSelection.PDS_PATH] ) @pytest.mark.parametrize("selection_bias", [True, False]) - def test_fci_complex(self, skeleton_method, pds_skeleton_method, selection_bias): + def test_fci_complex(self, condsel_method, pds_condsel_method, selection_bias): """ Test FCI algorithm with more complex graph. @@ -681,8 +681,8 @@ def test_fci_complex(self, skeleton_method, pds_skeleton_method, selection_bias) fci = FCI( ci_estimator=ci_estimator, max_iter=np.inf, - skeleton_method=skeleton_method, - pds_skeleton_method=pds_skeleton_method, + condsel_method=condsel_method, + pds_condsel_method=pds_condsel_method, selection_bias=selection_bias, ) fci.fit(sample, context) diff --git a/tests/unit_tests/constraint/test_pcalg.py b/tests/unit_tests/constraint/test_pcalg.py index 5c963b190..803cb2687 100644 --- a/tests/unit_tests/constraint/test_pcalg.py +++ b/tests/unit_tests/constraint/test_pcalg.py @@ -22,7 +22,7 @@ "max_cond_set_size": 3, "max_combinations": 10, "max_iter": 10, - "skeleton_method": ConditioningSetSelection.NBRS_PATH, + "condsel_method": ConditioningSetSelection.NBRS_PATH, }, {}, ], diff --git a/tests/unit_tests/constraint/test_skeleton.py b/tests/unit_tests/constraint/test_skeleton.py index dff8fe9e2..551dbb4d9 100644 --- a/tests/unit_tests/constraint/test_skeleton.py +++ b/tests/unit_tests/constraint/test_skeleton.py @@ -148,7 +148,7 @@ def test_learn_skeleton_pds_disabled_first_stage(): expected_skel = nx.Graph(edge_list) # learn the skeleton of the graph now with the first stage skeleton - alg = LearnSemiMarkovianSkeleton(ci_estimator=ci_estimator, second_stage_skeleton_method=None) + alg = LearnSemiMarkovianSkeleton(ci_estimator=ci_estimator, second_stage_condsel_method=None) alg.fit(sample, context) assert alg.context_.state_variable("PAG", on_missing="ignore") is None assert nx.is_isomorphic(expected_skel, alg.adj_graph_) From be5d05967dba34f6a950850c3774542debc0f753 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 3 Mar 2023 10:39:36 -0800 Subject: [PATCH 22/61] Try again Signed-off-by: Adam Li --- .circleci/config.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index a8d65f1d6..6b0e056a4 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -55,12 +55,12 @@ jobs: sudo apt update sudo apt-get update sudo apt install -qq graphviz optipng libxft2 graphviz-dev - echo "set -e" >> $BASH_ENV - echo "export OPENBLAS_NUM_THREADS=4" >> $BASH_ENV - echo "export XDG_RUNTIME_DIR=/tmp/runtime-circleci" >> $BASH_ENV - echo "export PATH=~/.local/bin/:$PATH" >> $BASH_ENV - echo "export DISPLAY=:99" >> $BASH_ENV - echo "BASH_ENV:" + echo 'set -e' >> $BASH_ENV + echo 'export OPENBLAS_NUM_THREADS=4' >> $BASH_ENV + echo 'export XDG_RUNTIME_DIR=/tmp/runtime-circleci' >> $BASH_ENV + echo 'export PATH=~/.local/bin/:$PATH' >> $BASH_ENV + echo 'export DISPLAY=:99' >> $BASH_ENV + echo 'BASH_ENV:' cat $BASH_ENV - run: name: Install fonts needed for diagrams From 74283c454df3ba6a2fcb6eb1cc3fcdf4f80f9c8e Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 3 Mar 2023 11:07:22 -0800 Subject: [PATCH 23/61] Comment out ground truth dag Signed-off-by: Adam Li --- .circleci/config.yml | 2 +- dodiscover/ci/base.py | 4 +++- dodiscover/constraint/skeleton.py | 4 ++-- examples/plot_psifci_alg.py | 6 +++--- poetry.lock | 6 +++--- 5 files changed, 12 insertions(+), 10 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 6b0e056a4..49aca977b 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -76,7 +76,7 @@ jobs: - python/install-packages: pkg-manager: poetry args: "-E graph_func -E viz --with docs" - cache-version: "v1" # change to clear cache + cache-version: "v2" # change to clear cache - run: name: Check poetry package versions command: | diff --git a/dodiscover/ci/base.py b/dodiscover/ci/base.py index a2209e24e..b535fe1bf 100644 --- a/dodiscover/ci/base.py +++ b/dodiscover/ci/base.py @@ -32,7 +32,9 @@ def _check_test_input( if any(col not in df.columns for col in x_vars): raise ValueError(f"The x variables {x_vars} are not all in the DataFrame.") if any(col not in df.columns for col in y_vars): - raise ValueError(f"The y variables {y_vars} are not all in the DataFrame.") + raise ValueError( + f"The y variables {y_vars} are not all in the DataFrame: {df.columns}." + ) if z_covariates is not None and any(col not in df.columns for col in z_covariates): raise ValueError( f"The z conditioning set variables {z_covariates} are not all in the " diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 09e3886ad..2c5c6aae6 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -949,7 +949,7 @@ def evaluate_fnode_edge( # indicates which distribution data came from # test graphically if Y is d-separated from F-node given Z # or test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test(data, Y, group_col, Z) + test_stat, pvalue = self.cd_estimator.test(data, Y, X, Z) self.n_ci_tests += 1 return test_stat, pvalue @@ -1082,7 +1082,7 @@ def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], co # compute conditional independence test test_stat, pvalue = self.evaluate_fnode_edge( - interv_data, {x_var}, {y_var}, set(cond_set) + interv_data, set({x_var}), set({y_var}), set(cond_set) ) # if any "independence" is found through inability to reject diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 49f0fc4eb..cb2f973af 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -60,9 +60,9 @@ df = pd.read_csv(file_path, delimiter=" ") -# the ground-truth dag is shown here -ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False) -fig = bnlearn.plot(ground_truth_dag) +# the ground-truth dag is shown here: XXX: comment in when errors are fixed +# ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False) +# fig = bnlearn.plot(ground_truth_dag) # .. note:: # The Sachs dataset has previously been preprocessed, and the steps are described diff --git a/poetry.lock b/poetry.lock index 0f91c6915..4a37eee66 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2463,14 +2463,14 @@ files = [ [[package]] name = "platformdirs" -version = "3.0.0" +version = "3.1.0" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." category = "dev" optional = false python-versions = ">=3.7" files = [ - 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self.graph = clearn_arr_to_graph(adj_mat, names, "cpdag") + self.graph = clearn_to_graph(adj_mat, names, "cpdag") diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index cb2f973af..0e128962b 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -61,8 +61,8 @@ df = pd.read_csv(file_path, delimiter=" ") # the ground-truth dag is shown here: XXX: comment in when errors are fixed -# ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False) -# fig = bnlearn.plot(ground_truth_dag) +ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False) +fig = bnlearn.plot(ground_truth_dag) # .. note:: # The Sachs dataset has previously been preprocessed, and the steps are described diff --git a/poetry.lock b/poetry.lock index 4a37eee66..e019b0d1d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -171,14 +171,14 @@ css = ["tinycss2 (>=1.1.0,<1.2)"] [[package]] name = "bnlearn" -version = "0.7.12" +version = "0.7.14" description = "Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods." category = "dev" optional = false python-versions = ">=3" files = [ - 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-3951,14 +3938,14 @@ files = [ [[package]] name = "tqdm" -version = "4.64.1" +version = "4.65.0" description = "Fast, Extensible Progress Meter" category = "dev" optional = false -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" +python-versions = ">=3.7" files = [ - {file = "tqdm-4.64.1-py2.py3-none-any.whl", hash = "sha256:6fee160d6ffcd1b1c68c65f14c829c22832bc401726335ce92c52d395944a6a1"}, - {file = "tqdm-4.64.1.tar.gz", hash = "sha256:5f4f682a004951c1b450bc753c710e9280c5746ce6ffedee253ddbcbf54cf1e4"}, + {file = "tqdm-4.65.0-py3-none-any.whl", hash = "sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671"}, + {file = "tqdm-4.65.0.tar.gz", hash = "sha256:1871fb68a86b8fb3b59ca4cdd3dcccbc7e6d613eeed31f4c332531977b89beb5"}, ] [package.dependencies] @@ -4177,4 +4164,4 @@ viz = ["pygraphviz"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "d43c687f97b3232a1a5107b49689226195007840b2439ed363ebc7f333c9c03c" +content-hash = "7bdc45ff215348c0eb60dd63b13f6251fc592fb4286259704188543bac1887d0" diff --git a/pyproject.toml b/pyproject.toml index e1df9ac83..550e2dccd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -71,7 +71,7 @@ pytest-cov = "^3.0.0" memory_profiler = { version = "^0.60.0" } flaky = "^3.7.0" causal-learn = "^0.1.3.1" # these packages are only needed for integration testing -bnlearn = "^0.7.12" +bnlearn = "^0.7.14" dowhy = { version = "^0.8" } typing-extensions = { version = "*" } # needed in dowhy's package joblib = { version = "^1.1.0" } # needed in dowhy's package diff --git a/tests/integration_tests/constraint/test_fci_causal_learn.py b/tests/integration_tests/constraint/test_fci_causal_learn.py index 585e53cff..db8d167aa 100644 --- a/tests/integration_tests/constraint/test_fci_causal_learn.py +++ b/tests/integration_tests/constraint/test_fci_causal_learn.py @@ -4,7 +4,7 @@ from causallearn.search.ConstraintBased.FCI import fci from dowhy import gcm from dowhy.gcm.util.general import set_random_seed -from pywhy_graphs.array.export import clearn_arr_to_graph +from pywhy_graphs.export import clearn_to_graph from scipy import stats from dodiscover import FCI, make_context @@ -119,7 +119,7 @@ def test_fci_against_causallearn(): dodiscover_graph = fci_alg.graph_ # first compare the adjacency structure - clearn_graph = clearn_arr_to_graph(clearn_graph.graph, arr_idx=data.columns, graph_type="pag") + clearn_graph = clearn_to_graph(clearn_graph.graph, arr_idx=data.columns, graph_type="pag") cm = confusion_matrix_networks(dodiscover_graph, clearn_graph) dia = np.diag_indices(cm.shape[0]) # indices of diagonal elements diff --git a/tests/integration_tests/constraint/test_pc_causal_learn.py b/tests/integration_tests/constraint/test_pc_causal_learn.py index 6e277c753..ef7508613 100644 --- a/tests/integration_tests/constraint/test_pc_causal_learn.py +++ b/tests/integration_tests/constraint/test_pc_causal_learn.py @@ -2,7 +2,7 @@ import networkx as nx import pytest from causallearn.search.ConstraintBased.PC import pc_alg -from pywhy_graphs.array.export import clearn_arr_to_graph +from pywhy_graphs.export import clearn_to_graph from sklearn.preprocessing import LabelEncoder from dodiscover import PC, make_context @@ -132,9 +132,7 @@ def test_pc_against_causallearn(dataset, ci_estimator, clearn_test, col_names, c # convert to pywhy graph nodes = [node.get_name() for node in clearn_graph.G.nodes] - clearn_pywhy_graph = clearn_arr_to_graph( - clearn_graph.G.graph, arr_idx=nodes, graph_type="cpdag" - ) + clearn_pywhy_graph = clearn_to_graph(clearn_graph.G.graph, arr_idx=nodes, graph_type="cpdag") print(pywhy_graph) print(clearn_pywhy_graph) print(clearn_graph.G.graph) From 291ec90b3958afe0bd5307f48681593a04fd35d8 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 7 Mar 2023 11:51:55 -0500 Subject: [PATCH 25/61] Try again Signed-off-by: Adam Li --- .circleci/config.yml | 3 +-- doc/_templates/docs-navbar.html | 2 +- doc/_templates/layout.html | 2 +- doc/_templates/version-switcher.html | 2 +- doc/conf.py | 3 +++ dodiscover/constraint/skeleton.py | 15 ++++++--------- examples/plot_psifci_alg.py | 2 ++ tests/unit_tests/conditional/ci/test_ci.py | 2 +- 8 files changed, 16 insertions(+), 15 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 49aca977b..0de35ee79 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -94,8 +94,7 @@ jobs: - run: name: Build documentation command: | - cd doc - poetry run make html + poetry run poe build_docs # Save the example test results - store_test_results: path: doc/_build/test-results diff --git a/doc/_templates/docs-navbar.html b/doc/_templates/docs-navbar.html index 3ab07e687..a7d1f4a77 100644 --- a/doc/_templates/docs-navbar.html +++ b/doc/_templates/docs-navbar.html @@ -10,7 +10,7 @@ diff --git a/doc/_templates/layout.html b/doc/_templates/layout.html index dbb4e67bb..790fefe1b 100755 --- a/doc/_templates/layout.html +++ b/doc/_templates/layout.html @@ -12,7 +12,7 @@ {% endblock %} {% block extrahead %} - + {{ super() }} diff --git a/doc/_templates/version-switcher.html b/doc/_templates/version-switcher.html index 13571117c..1626c1883 100644 --- a/doc/_templates/version-switcher.html +++ b/doc/_templates/version-switcher.html @@ -5,7 +5,7 @@
{%- for ver, txt in versions_dropdown.items() %} - {{ txt }} + {{ txt }} {%- endfor %}
diff --git a/doc/conf.py b/doc/conf.py index aba2f88fb..ee35a1597 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -56,8 +56,11 @@ "numpydoc", "IPython.sphinxext.ipython_console_highlighting", "nbsphinx", + "sphinx.ext.graphviz", ] +graphviz_output_format = 'png' + # configure sphinx-copybutton copybutton_prompt_text = r">>> |\.\.\. |\$ " copybutton_prompt_is_regexp = True diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 2c5c6aae6..5e453a36f 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -903,7 +903,7 @@ def __init__( def evaluate_fnode_edge( self, data: List[pd.DataFrame], - X: Set[Column], + X: Column, Y: Set[Column], Z: Optional[Set[Column]] = None, ) -> Tuple[float, float]: @@ -930,26 +930,23 @@ def evaluate_fnode_edge( if Z is None: Z = set() - # extract the F-node name - group_col: Column = reduce(lambda x: x, X) # type: ignore - # get the sigma-map for this F-node - distribution_idx = self.context_.sigma_map[group_col] + distribution_idx = self.context_.sigma_map[X] # get the distributions across the two distributions data_i = data[distribution_idx[0]].copy() data_j = data[distribution_idx[1]].copy() # name the group column the F-node, so Oracle works as expected - data_i[group_col] = 0 - data_j[group_col] = 1 + data_i[X] = 0 + data_j[X] = 1 data = pd.concat((data_i, data_j), axis=0) # compare conditional distributions P(Y | X) vs P'(Y | X), where 'group_col' # indicates which distribution data came from # test graphically if Y is d-separated from F-node given Z # or test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test(data, Y, X, Z) + test_stat, pvalue = self.cd_estimator.test(data, Y, {X}, Z) self.n_ci_tests += 1 return test_stat, pvalue @@ -1082,7 +1079,7 @@ def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], co # compute conditional independence test test_stat, pvalue = self.evaluate_fnode_edge( - interv_data, set({x_var}), set({y_var}), set(cond_set) + interv_data, x_var, set({y_var}), set(cond_set) ) # if any "independence" is found through inability to reject diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 0e128962b..c31a2b65e 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -142,7 +142,9 @@ est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes()) dot_graph = draw(est_pag_no_fnodes, direction="LR") dot_graph.render(outfile="psi_pag.png", view=True) +dot_graph.render(outfile="psi_pag.dot", view=True) +# .. graphviz:: psi_pag.dot # References # ---------- diff --git a/tests/unit_tests/conditional/ci/test_ci.py b/tests/unit_tests/conditional/ci/test_ci.py index 2421734b8..195c2d840 100644 --- a/tests/unit_tests/conditional/ci/test_ci.py +++ b/tests/unit_tests/conditional/ci/test_ci.py @@ -25,7 +25,7 @@ def test_ci_tests(ci_estimator): x = "x" y = "y" - with pytest.raises(ValueError, match="The z conditioning set variables are not all"): + with pytest.raises(ValueError, match="The z conditioning set variables .* are not all"): ci_estimator.test(sample_df, {x}, {y}, z_covariates=["blah"]) with pytest.raises(ValueError, match="The x variables.*are not all"): From f7288c4317166bf1c68650faa49b5720f742b5f7 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 7 Mar 2023 11:52:02 -0500 Subject: [PATCH 26/61] Try again Signed-off-by: Adam Li --- doc/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index ee35a1597..e9822656a 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -59,7 +59,7 @@ "sphinx.ext.graphviz", ] -graphviz_output_format = 'png' +graphviz_output_format = "png" # configure sphinx-copybutton copybutton_prompt_text = r">>> |\.\.\. |\$ " From 3c5ad3eba09fb8cab52bd312330dd23d3cad4e1d Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 7 Mar 2023 12:28:10 -0500 Subject: [PATCH 27/61] Adding scraper Signed-off-by: Adam Li --- doc/conf.py | 7 +++++++ dodiscover/cd/base.py | 9 +++++++-- dodiscover/cd/bregman.py | 7 ++++--- dodiscover/cd/kernel_test.py | 8 ++++---- dodiscover/constraint/skeleton.py | 1 - examples/plot_psifci_alg.py | 1 - 6 files changed, 22 insertions(+), 11 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index e9822656a..5e95e5c37 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -263,6 +263,13 @@ } scrapers = ("matplotlib",) +# Add pygraphviz png scraper, if available +try: + from pygraphviz.scraper import PNGScraper + + scrapers += (PNGScraper(),) +except ImportError: + pass sphinx_gallery_conf = { "doc_module": "dodiscover", diff --git a/dodiscover/cd/base.py b/dodiscover/cd/base.py index 91beed5c4..6f47f349c 100644 --- a/dodiscover/cd/base.py +++ b/dodiscover/cd/base.py @@ -27,7 +27,7 @@ def _check_test_input( self, df: pd.DataFrame, y_vars: Set[Column], - group_col: Column, + group_col: Set[Column], x_vars: Optional[Set[Column]], ): if x_vars is not None and any(col not in df.columns for col in x_vars): @@ -53,12 +53,17 @@ def _check_test_input( f"there are {len(df[group_col].unique())} samples." ) + if len(group_col) > 1: + raise RuntimeError( + f"Group column should be only one column (one node) in the data {group_col}." + ) + @abstractmethod def test( self, df: pd.DataFrame, y_vars: Set[Column], - group_col: Column, + group_col: Set[Column], x_vars: Optional[Set[Column]], ) -> Tuple[float, float]: """Abstract method for all conditional discrepancy tests. diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index b42f69f4f..854437cbd 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -77,11 +77,12 @@ def test( self, df: pd.DataFrame, y_vars: Set[Column], - group_col: Column, + group_col: Set[Column], x_vars: Optional[Set[Column]] = None, ) -> Tuple[float, float]: # check test input self._check_test_input(df, y_vars, group_col, x_vars) + group_col_var: Column = list(group_col)[0] if x_vars is not None: x_cols = list(x_vars) @@ -89,9 +90,9 @@ def test( x_cols = None y_cols = list(y_vars) - group_ind = df[group_col].to_numpy() + group_ind = df[group_col_var].to_numpy() if set(np.unique(group_ind)) != {0, 1}: - raise RuntimeError(f"Group indications in {group_col} column should be all 1 or 0.") + raise RuntimeError(f"Group indications in {group_col_var} column should be all 1 or 0.") # get the X and Y dataset X = df[x_cols].to_numpy() diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index 36f30204a..5a31ce5e2 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -93,7 +93,7 @@ def test( self, df: pd.DataFrame, y_vars: Set[Column], - group_col: Column, + group_col: Set[Column], x_vars: Optional[Set[Column]] = None, ) -> Tuple[float, float]: """Compute k-sample test statistic and pvalue. @@ -132,7 +132,7 @@ def test( """ # check test input self._check_test_input(df, y_vars, group_col, x_vars) - + group_col_var: Column = list(group_col)[0] if x_vars is not None: x_cols = list(x_vars) else: @@ -140,9 +140,9 @@ def test( y_cols = list(y_vars) - group_ind = df[group_col].to_numpy() + group_ind = df[group_col_var].to_numpy() if set(np.unique(group_ind)) != {0, 1}: - raise RuntimeError(f"Group indications in {group_col} column should be all 1 or 0.") + raise RuntimeError(f"Group indications in {group_col_var} column should be all 1 or 0.") # compute kernel for the X and Y data X = df[x_cols].to_numpy() diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 5e453a36f..dca688515 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1,7 +1,6 @@ import logging from collections import defaultdict from copy import deepcopy -from functools import reduce from itertools import chain, combinations from typing import Iterable, List, Optional, Set, SupportsFloat, Tuple, Union diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index c31a2b65e..b7a4bb21c 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -142,7 +142,6 @@ est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes()) dot_graph = draw(est_pag_no_fnodes, direction="LR") dot_graph.render(outfile="psi_pag.png", view=True) -dot_graph.render(outfile="psi_pag.dot", view=True) # .. graphviz:: psi_pag.dot From c9e185d139f3cbe8852366b9ae4d5c4073cdad05 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 7 Mar 2023 13:43:51 -0500 Subject: [PATCH 28/61] Adding unit tests fix Signed-off-by: Adam Li --- dodiscover/cd/base.py | 23 +++++++++--------- tests/unit_tests/conditional/cd/test_cd.py | 27 ++++++++++++---------- 2 files changed, 27 insertions(+), 23 deletions(-) diff --git a/dodiscover/cd/base.py b/dodiscover/cd/base.py index 6f47f349c..515459090 100644 --- a/dodiscover/cd/base.py +++ b/dodiscover/cd/base.py @@ -30,34 +30,35 @@ def _check_test_input( group_col: Set[Column], x_vars: Optional[Set[Column]], ): + if len(group_col) > 1: + raise ValueError( + f"Group column should be only one column (one node) in the data {group_col}." + ) + group_col_var: Column = list(group_col)[0] + if x_vars is not None and any(col not in df.columns for col in x_vars): raise ValueError("The x variables are not all in the DataFrame.") if any(col not in df.columns for col in y_vars): raise ValueError("The y variables are not all in the DataFrame.") - if group_col not in df.columns: - raise ValueError(f"The group column {group_col} is not in the DataFrame.") + if group_col_var not in df.columns: + raise ValueError(f"The group column {group_col_var} is not in the DataFrame.") if self.propensity_model is not None and self.propensity_est is not None: raise ValueError( "Both propensity model and propensity estimates are specified. Only one is allowed." ) if self.propensity_est is not None: - if self.propensity_est.shape[0] != len(df[group_col]): + if self.propensity_est.shape[0] != len(df[group_col_var]): raise ValueError( f"There are {self.propensity_est.shape[0]} pre-defined estimates, while " - f"there are {len(df[group_col])} unique groups." + f"there are {len(df[group_col_var])} unique groups." ) - if self.propensity_est.shape[1] != len(df[group_col].unique()): + if self.propensity_est.shape[1] != len(df[group_col_var].unique()): raise ValueError( f"There are {self.propensity_est.shape[1]} group pre-defined estimates, while " - f"there are {len(df[group_col].unique())} samples." + f"there are {len(df[group_col_var].unique())} samples." ) - if len(group_col) > 1: - raise RuntimeError( - f"Group column should be only one column (one node) in the data {group_col}." - ) - @abstractmethod def test( self, diff --git a/tests/unit_tests/conditional/cd/test_cd.py b/tests/unit_tests/conditional/cd/test_cd.py index a22b017dc..8cef4449f 100644 --- a/tests/unit_tests/conditional/cd/test_cd.py +++ b/tests/unit_tests/conditional/cd/test_cd.py @@ -54,33 +54,36 @@ def test_cd_tests_error(cd_func): sample_df = single_env_scm(n_samples=10) cd_estimator = cd_func() with pytest.raises(ValueError, match="The group col"): - cd_estimator.test(sample_df, {y}, group_col="blah", x_vars={x}) + cd_estimator.test(sample_df, {y}, group_col={"blah"}, x_vars={x}) with pytest.raises(ValueError, match="The x variables are not all"): - cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={"blah"}) + cd_estimator.test(sample_df, y_vars={y}, group_col={"group"}, x_vars={"blah"}) with pytest.raises(ValueError, match="The y variables are not all"): + cd_estimator.test(sample_df, y_vars={"blah"}, group_col={"group"}, x_vars={x}) + + with pytest.raises(ValueError, match="Group column should be only one column"): cd_estimator.test(sample_df, y_vars={"blah"}, group_col="group", x_vars={x}) # all the group indicators have different values now from 0/1 sample_df["group"] = sample_df["group"] + 3 with pytest.raises(RuntimeError, match="Group indications in"): - cd_estimator.test(sample_df, {y}, group_col="group", x_vars={x}) + cd_estimator.test(sample_df, {y}, group_col={"group"}, x_vars={x}) # test pre-fit propensity scores, or custom propensity model with pytest.raises( ValueError, match="Both propensity model and propensity estimates are specified" ): cd_estimator = cd_func(propensity_model=RandomForestClassifier(), propensity_est=[0.5, 0.5]) - cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={x}) + cd_estimator.test(sample_df, y_vars={y}, group_col={"group"}, x_vars={x}) with pytest.raises(ValueError, match="There are 3 group pre-defined estimates"): cd_estimator = cd_func(propensity_est=np.ones((10, 3)) * 0.5) - cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={x}) + cd_estimator.test(sample_df, y_vars={y}, group_col={"group"}, x_vars={x}) with pytest.raises(ValueError, match="There are 100 pre-defined estimates"): cd_estimator = cd_func(propensity_est=np.ones((100, 2)) * 0.5) - cd_estimator.test(sample_df, y_vars={y}, group_col="group", x_vars={x}) + cd_estimator.test(sample_df, y_vars={y}, group_col={"group"}, x_vars={x}) @pytest.mark.parametrize( @@ -115,18 +118,18 @@ def test_cd_simulation(cd_func, df, env_type, cd_kwargs): _, pvalue = cd_estimator.test( df, {"x1"}, - group_col, + {group_col}, {"x"}, ) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" - _, pvalue = cd_estimator.test(df, {"z"}, group_col, {"x"}) + _, pvalue = cd_estimator.test(df, {"z"}, {group_col}, {"x"}) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" - _, pvalue = cd_estimator.test(df, {"y"}, group_col, {"x"}) + _, pvalue = cd_estimator.test(df, {"y"}, {group_col}, {"x"}) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" elif env_type == "multi": - _, pvalue = cd_estimator.test(df, {"z"}, group_col, {"x"}) + _, pvalue = cd_estimator.test(df, {"z"}, {group_col}, {"x"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" - _, pvalue = cd_estimator.test(df, {"y"}, group_col, {"x"}) + _, pvalue = cd_estimator.test(df, {"y"}, {group_col}, {"x"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" - _, pvalue = cd_estimator.test(df, {"z"}, group_col, {"x1"}) + _, pvalue = cd_estimator.test(df, {"z"}, {group_col}, {"x1"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" From ea06719764dcb7f4b3a940a86c95d651b8980814 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 7 Mar 2023 18:10:21 -0500 Subject: [PATCH 29/61] Fix psifci Signed-off-by: Adam Li --- dodiscover/cd/base.py | 2 +- dodiscover/cd/bregman.py | 8 +-- dodiscover/cd/kernel_test.py | 12 ++-- dodiscover/constraint/skeleton.py | 29 ++++++---- examples/plot_psifci_alg.py | 7 +-- pyproject.toml | 1 + tests/unit_tests/constraint/test_psifcialg.py | 57 ++++++++++++++++++- 7 files changed, 85 insertions(+), 31 deletions(-) diff --git a/dodiscover/cd/base.py b/dodiscover/cd/base.py index 515459090..20add5f82 100644 --- a/dodiscover/cd/base.py +++ b/dodiscover/cd/base.py @@ -65,7 +65,7 @@ def test( df: pd.DataFrame, y_vars: Set[Column], group_col: Set[Column], - x_vars: Optional[Set[Column]], + x_vars: Set[Column], ) -> Tuple[float, float]: """Abstract method for all conditional discrepancy tests. diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index 854437cbd..687969b38 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -78,17 +78,13 @@ def test( df: pd.DataFrame, y_vars: Set[Column], group_col: Set[Column], - x_vars: Optional[Set[Column]] = None, + x_vars: Set[Column], ) -> Tuple[float, float]: # check test input self._check_test_input(df, y_vars, group_col, x_vars) group_col_var: Column = list(group_col)[0] - if x_vars is not None: - x_cols = list(x_vars) - else: - x_cols = None - + x_cols = list(x_vars) y_cols = list(y_vars) group_ind = df[group_col_var].to_numpy() if set(np.unique(group_ind)) != {0, 1}: diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index 5a31ce5e2..a2d68e524 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -1,4 +1,4 @@ -from typing import Optional, Set, Tuple +from typing import Set, Tuple import numpy as np import pandas as pd @@ -94,7 +94,7 @@ def test( df: pd.DataFrame, y_vars: Set[Column], group_col: Set[Column], - x_vars: Optional[Set[Column]] = None, + x_vars: Set[Column], ) -> Tuple[float, float]: """Compute k-sample test statistic and pvalue. @@ -121,7 +121,7 @@ def test( The column denoting, which group (i.e. environment) each sample belongs to. This is typically the F-node. Must be binary. x_vars : Set[Column] - Set of X variables. + Set of X variables. Can be the empty set. Returns ------- @@ -133,11 +133,7 @@ def test( # check test input self._check_test_input(df, y_vars, group_col, x_vars) group_col_var: Column = list(group_col)[0] - if x_vars is not None: - x_cols = list(x_vars) - else: - x_cols = None - + x_cols = list(x_vars) y_cols = list(y_vars) group_ind = df[group_col_var].to_numpy() diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index dca688515..45991899b 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -904,7 +904,7 @@ def evaluate_fnode_edge( data: List[pd.DataFrame], X: Column, Y: Set[Column], - Z: Optional[Set[Column]] = None, + Z: Set[Column], ) -> Tuple[float, float]: """Test an edge from an F-node to a regular node for X || Y | Z. @@ -916,8 +916,8 @@ def evaluate_fnode_edge( A column in ``data``. This is assumed to be the F-node. Y : column A column in ``data``. - Z : set, optional - A list of columns in ``data``, by default None. + Z : set + A list of columns in ``data``. Can be the empty set. Returns ------- @@ -926,9 +926,6 @@ def evaluate_fnode_edge( pvalue : float The pvalue. """ - if Z is None: - Z = set() - # get the sigma-map for this F-node distribution_idx = self.context_.sigma_map[X] @@ -986,6 +983,9 @@ def _compute_candidate_conditioning_sets( def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], context: Context): self.context_ = make_context(context, create_using=InterventionalContextBuilder).build() + # initialize learning parameters + self._initialize_params() + # get the initialized graph adj_graph = self.context_.init_graph f_nodes = self.context_.f_nodes @@ -1012,6 +1012,7 @@ def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], co # summarize the comparison of XY self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) + # Remove edges adj_graph.remove_edges_from(self.remove_edges) @@ -1157,9 +1158,16 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: orig_context = context.copy() f_nodes = context.f_nodes - # it is fine to run the first stage of the FCI algorithm, as this will - # not result in removing any edges among the F-nodes - obs_data = data[0] + if context.obs_distribution: + # it is fine to run the first stage of the FCI algorithm, as this will + # not result in removing any edges among the F-nodes + obs_data = data[0] + else: + # if we explicitly do not have access to the observational distribution, + # then we should choose the experimental dataset with the most samples + largest_data_idx = np.argmax([len(df) for df in data]) + obs_data = data[largest_data_idx] + self._learn_skeleton_with_observations(obs_data, context) # keep track of the observational skeleton graph @@ -1169,7 +1177,6 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: # i) augmented with all F-nodes, or # ii) augmented with all F-nodes except intervention index 'i' # R9 allows us to leverage F-nodes being not in separating sets to - # augment all separating sets that have non-empty sets with all # F-nodes to keep consistency with the algorithm for x_var, y_vars in self.sep_set_.items(): @@ -1180,7 +1187,7 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: self.sep_set_[x_var][y_var][idx].update(f_nodes) # index all datasets, where the first one may be observational - non_f_nodes = self.context_.get_non_f_nodes() + non_f_nodes = context.get_non_f_nodes() # reset the init graph and this time learn the skeleton using # interventional distributions diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index b7a4bb21c..966486a34 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -102,8 +102,7 @@ # CD test. cd_estimator = GSquareCITest(data_type="discrete") -alpha = 0.05 - +alpha = 0.1 learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha) # create context with information about the interventions @@ -134,6 +133,8 @@ # Figure 8 in :footcite:`Jaber2020causal`. est_pag = learner.graph_ +print(f"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG") + # draw the full graph dot_graph = draw(est_pag, direction="LR") dot_graph.render(outfile="psi_pag_full.png", view=True) @@ -143,8 +144,6 @@ dot_graph = draw(est_pag_no_fnodes, direction="LR") dot_graph.render(outfile="psi_pag.png", view=True) -# .. graphviz:: psi_pag.dot - # References # ---------- # .. footbibliography:: diff --git a/pyproject.toml b/pyproject.toml index 550e2dccd..4d7bbff0f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,6 +77,7 @@ typing-extensions = { version = "*" } # needed in dowhy's package joblib = { version = "^1.1.0" } # needed in dowhy's package tqdm = { version = "^4.64.0" } # needed in dowhy's package pre-commit = "^3.0.4" +pooch = "^1.7.0" [tool.poetry.group.docs] optional = true diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py index 1736a5641..be7e549d3 100644 --- a/tests/unit_tests/constraint/test_psifcialg.py +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -1,13 +1,17 @@ from itertools import permutations +import bnlearn import networkx as nx import numpy as np +import pandas as pd +import pooch import pytest import pywhy_graphs as pgraphs from pywhy_graphs import IPAG, PsiPAG +from pywhy_graphs.export import numpy_to_graph from dodiscover import InterventionalContextBuilder, PsiFCI, make_context -from dodiscover.ci import Oracle +from dodiscover.ci import GSquareCITest, Oracle from dodiscover.constraint.utils import dummy_sample from .test_fcialg import Test_FCI @@ -241,3 +245,54 @@ def test_figure2_skeleton(self): learned_graph = learner.graph_ for edge_type, subgraph in expected_G.get_graphs().items(): assert nx.is_isomorphic(subgraph, learned_graph.get_graphs(edge_type)) + + +def test_psifci_withsachs(): + + bnlearn.import_DAG() + + # use pooch to download robustly from a url + url = "https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz" + file_path = pooch.retrieve( + url=url, + known_hash="md5:39ee257f7eeb94cb60e6177cf80c9544", + ) + + df = pd.read_csv(file_path, delimiter=" ") + + # only use the observational data + unique_ints = df["INT"].unique() + intervention_targets = [df.columns[idx] for idx in unique_ints] + + # get the list of intervention targets and list of dataframe associated with each intervention + data_cols = [col for col in df.columns if col != "INT"] + data = [] + for interv_idx in unique_ints: + _data = df[df["INT"] == interv_idx][data_cols] + data.append(_data) + + ci_estimator = GSquareCITest(data_type="discrete") + alpha = 0.05 + learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=ci_estimator, alpha=alpha) + ctx_builder = make_context(create_using=InterventionalContextBuilder) + ctx = ( + ctx_builder.variables(data=data) + .intervention_targets(intervention_targets) + .obs_distribution(False) + .build() + ) + # learner.fit(data, ctx) + + # first try it with the oracle + # the ground-truth dag + ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False)["adjmat"] + adjmat = ground_truth_dag.to_numpy() + arr_idx = ground_truth_dag.columns.tolist() + print(df) + print(df.columns) + print(adjmat) + print(adjmat.shape) + G = numpy_to_graph(adjmat, arr_idx=arr_idx, graph_type="dag") + print(len(G.edges())) + + assert False From 3504d681911236a4056f2cef6a8243e74353edef Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 7 Mar 2023 18:46:18 -0500 Subject: [PATCH 30/61] Add PsiFCI working example Signed-off-by: Adam Li --- examples/plot_psifci_alg.py | 38 +++++++++---------- tests/unit_tests/constraint/test_psifcialg.py | 4 +- 2 files changed, 19 insertions(+), 23 deletions(-) diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 966486a34..872c0b921 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -1,23 +1,14 @@ """ .. _ex-psifci-algorithm: -========================================= -Causal discovery with interventional data -========================================= +========================================================= +Causal discovery with interventional data - Sachs dataset +========================================================= -We will simulate some observational data from a Structural Causal Model (SCM) and -demonstrate how we will use the PC algorithm. - -The PC algorithm works on observational data when there are no unobserved latent -confounders. That means for any observed set of variables, there is no common causes -that are unobserved. In other words, all exogenous variables then are assumed to be -independent. - -In this example, we will introduce the main abstractions and concepts used in -dodiscover for causal discovery: - -- learner: Any causal discovery algorithm that has a similar scikit-learn API. -- context: Causal assumptions. +We will analyze the Sachs dataset :footcite:`sachsdataset2005` and reproduce analyses +from the Supplemental Figure 8 in :footcite:`Jaber2020causal` demonstrating the +usage of the :class:`dodiscover.PsiFCI` algorithm for learning causal graphs +from observational and interventional data. .. currentmodule:: dodiscover """ @@ -126,8 +117,8 @@ learner = learner.fit(data, ctx) # %% -# Visualize the results -# --------------------- +# Analyze the results +# =================== # Now that we have learned the graph, we will show it here. Note differences and similarities # to the ground-truth DAG that is "assumed". Moreover, note that this reproduces Supplementary # Figure 8 in :footcite:`Jaber2020causal`. @@ -135,15 +126,22 @@ print(f"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG") -# draw the full graph +# %% +# Visualize the full graph including the F-node dot_graph = draw(est_pag, direction="LR") dot_graph.render(outfile="psi_pag_full.png", view=True) -# if we do not want to visualize the F-nodes, then we can view the subgraph +# %% +# Visualize the graph without the F-nodes est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes()) dot_graph = draw(est_pag_no_fnodes, direction="LR") dot_graph.render(outfile="psi_pag.png", view=True) +# Interpretation +# -------------- +# Looking at the supplemental figure 8b in :footcite:`Jaber2020causal`, we see that the +# learned PAG matches quite well. + # References # ---------- # .. footbibliography:: diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py index be7e549d3..401c052f3 100644 --- a/tests/unit_tests/constraint/test_psifcialg.py +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -281,7 +281,7 @@ def test_psifci_withsachs(): .obs_distribution(False) .build() ) - # learner.fit(data, ctx) + learner.fit(data, ctx) # first try it with the oracle # the ground-truth dag @@ -294,5 +294,3 @@ def test_psifci_withsachs(): print(adjmat.shape) G = numpy_to_graph(adjmat, arr_idx=arr_idx, graph_type="dag") print(len(G.edges())) - - assert False From 868ae9570a18654f12fe3394ff717d5063bf1f76 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 7 Mar 2023 18:48:39 -0500 Subject: [PATCH 31/61] Add PsiFCI working example Signed-off-by: Adam Li --- examples/plot_psifci_alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 872c0b921..79224edbb 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -93,7 +93,7 @@ # CD test. cd_estimator = GSquareCITest(data_type="discrete") -alpha = 0.1 +alpha = 0.05 learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha) # create context with information about the interventions From 3126d07d0c8faba87818bd4e244074b6d7055d9a Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 8 Mar 2023 14:21:28 -0500 Subject: [PATCH 32/61] Add covergae for tests and fix docs build Signed-off-by: Adam Li --- dodiscover/context_builder.py | 2 -- examples/plot_psifci_alg.py | 2 +- tests/unit_tests/test_context_builder.py | 3 +++ 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/dodiscover/context_builder.py b/dodiscover/context_builder.py index 326631687..3ec7045f3 100644 --- a/dodiscover/context_builder.py +++ b/dodiscover/context_builder.py @@ -477,8 +477,6 @@ def _create_augmented_nodes( # create F-nodes, their symmetric difference mapping and sigma-mapping to # intervention targets for idx, (jdx, kdx) in enumerate(combinations(distribution_targets_idx, 2)): - if jdx == kdx: - continue f_node = ("F", idx) augmented_nodes.append(f_node) sigma_map[f_node] = (jdx, kdx) diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 79224edbb..fd56403e7 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -7,7 +7,7 @@ We will analyze the Sachs dataset :footcite:`sachsdataset2005` and reproduce analyses from the Supplemental Figure 8 in :footcite:`Jaber2020causal` demonstrating the -usage of the :class:`dodiscover.PsiFCI` algorithm for learning causal graphs +usage of the :class:`dodiscover.constraint.PsiFCI` algorithm for learning causal graphs from observational and interventional data. .. currentmodule:: dodiscover diff --git a/tests/unit_tests/test_context_builder.py b/tests/unit_tests/test_context_builder.py index b8495c5d4..29e50f249 100644 --- a/tests/unit_tests/test_context_builder.py +++ b/tests/unit_tests/test_context_builder.py @@ -235,6 +235,9 @@ def test_context_interventions(): assert ctx.symmetric_diff_map == dict() assert set(ctx.sigma_map.keys()) == set(ctx.f_nodes) + # test reverse sigma map + assert set(ctx.reverse_sigma_map().values()) == set(ctx.f_nodes) + def test_context_interventions_without_observational(): ctx_builder = make_context(create_using=InterventionalContextBuilder) From 6a58647694434414d76dc3c9a8d8a5c2397b4f02 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 16 Mar 2023 17:17:12 -0400 Subject: [PATCH 33/61] Adding updated fix Signed-off-by: Adam Li --- Untitled.ipynb | 1319 +++++++++++++++++ dodiscover/constraint/_classes.py | 99 +- dodiscover/constraint/fcialg.py | 14 +- dodiscover/constraint/intervention.py | 15 + dodiscover/constraint/pcalg.py | 69 +- dodiscover/constraint/skeleton.py | 947 +++++++----- examples/plot_psifci_alg.py | 3 +- tests/unit_tests/constraint/test_psifcialg.py | 3 +- 8 files changed, 2076 insertions(+), 393 deletions(-) create mode 100644 Untitled.ipynb diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 000000000..2a3a2f323 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,1319 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c742672f-1415-4035-af44-ac501d009785", + "metadata": {}, + "outputs": [], + "source": [ + "from pywhy_graphs.viz import draw\n", + "from dodiscover.ci import GSquareCITest\n", + "from dodiscover import PsiFCI, Context, make_context, InterventionalContextBuilder\n", + "import networkx as nx\n", + "import pandas as pd\n", + "import bnlearn\n", + "import numpy as np\n", + "from pprint import pprint\n", + "from itertools import combinations\n", + "import pooch\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4c709a7c-1620-4d72-8905-f25306dc10d3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[bnlearn] >Set node properties.\n", + "[bnlearn] >Set edge properties.\n", + "[bnlearn] >Plot based on Bayesian model\n", + " Raf Mek Plcg PIP2 PIP3 Erk Akt PKA PKC P38 Jnk INT\n", + "0 1 1 1 2 3 2 1 3 1 2 1 8\n", + "1 1 1 1 1 3 3 2 3 1 2 1 8\n", + "2 1 1 2 2 3 2 1 3 2 1 1 8\n", + "3 1 1 1 1 3 2 1 3 1 3 1 8\n", + "4 1 1 1 1 3 2 1 3 1 1 1 8\n", + "(5400, 12)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# use pooch to download robustly from a url\n", + "url = \"https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz\"\n", + "file_path = pooch.retrieve(\n", + " url=url,\n", + " known_hash=\"md5:39ee257f7eeb94cb60e6177cf80c9544\",\n", + ")\n", + "\n", + "df = pd.read_csv(file_path, delimiter=\" \")\n", + "\n", + "# the ground-truth dag is shown here: XXX: comment in when errors are fixed\n", + "ground_truth_dag = bnlearn.import_DAG(\"sachs\", verbose=False)\n", + "fig = bnlearn.plot(ground_truth_dag)\n", + "\n", + "# .. note::\n", + "# The Sachs dataset has previously been preprocessed, and the steps are described\n", + "# in bnlearn, at the web-page https://www.bnlearn.com/research/sachs05/.\n", + "print(df.head())\n", + "print(df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7a908aeb-d993-454e-80a8-144aadf90923", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OutEdgeView([('Erk', 'Akt'), ('PKA', 'Akt'), ('PKA', 'Erk'), ('PKA', 'Jnk'), ('PKA', 'Mek'), ('PKA', 'P38'), ('PKA', 'Raf'), ('Mek', 'Erk'), ('PKC', 'Jnk'), ('PKC', 'Mek'), ('PKC', 'P38'), ('PKC', 'PKA'), ('PKC', 'Raf'), ('Raf', 'Mek'), ('PIP3', 'PIP2'), ('Plcg', 'PIP2'), ('Plcg', 'PIP3')])\n" + ] + } + ], + "source": [ + "pprint(ground_truth_dag['model'].to_directed().edges)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1aa43d9a-a88d-459b-9b32-388f794885c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6 6\n" + ] + } + ], + "source": [ + "# %%\n", + "# Preprocess the dataset\n", + "# ----------------------\n", + "# Since the data is one dataframe, we need to process it into a form\n", + "# that is acceptable by dodiscover's :class:`constraint.PsiFCI` algorithm. We\n", + "# will form a list of separate dataframes.\n", + "unique_ints = df[\"INT\"].unique()\n", + "\n", + "# get the list of intervention targets and list of dataframe associated with each intervention\n", + "intervention_targets = [df.columns[idx] for idx in unique_ints]\n", + "data_cols = [col for col in df.columns if col != \"INT\"]\n", + "data = []\n", + "for interv_idx in unique_ints:\n", + " _data = df[df[\"INT\"] == interv_idx][data_cols]\n", + " data.append(_data)\n", + "\n", + "print(len(data), len(intervention_targets))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e75376cb-d467-47fd-92bd-84ef21a35f4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Graph with 26 nodes and 325 edges\n", + "[('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n" + ] + } + ], + "source": [ + "# Our dataset is comprised of discrete valued data, so we will utilize the\n", + "# G^2 (Chi-square) CI test.\n", + "ci_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "# Since our data is entirely discrete, we can also use the G^2 test as our\n", + "# CD test.\n", + "cd_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "alpha = 0.05\n", + "learner = PsiFCI(\n", + " ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha\n", + ")\n", + "\n", + "# create context with information about the interventions\n", + "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", + "ctx: Context = (\n", + " ctx_builder.variables(data=data[0])\n", + " # .intervention_targets(intervention_targets)\n", + " .num_distributions(6)\n", + " .obs_distribution(False)\n", + " .build()\n", + ")\n", + "\n", + "print(ctx.init_graph)\n", + "print(ctx.f_nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3d6ac82a-6a5c-4c46-b8c1-7cd09101c5f8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1800 is too small. Need 1920.\n", + "Not enough samples. 1800 is too small. Need 1920.\n", + "Not enough samples. 1800 is too small. Need 1920.\n", + "Not enough samples. 1800 is too small. Need 1920.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1280.\n" + ] + } + ], + "source": [ + "# `fit` design. All fitted attributes contain an underscore at the end.\n", + "learner = learner.fit(data, ctx)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7c25044c-99f7-4255-87f7-9a5d845b77e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 47 edges in the resulting PAG\n" + ] + } + ], + "source": [ + "# %%\n", + "# Analyze the results\n", + "# ===================\n", + "# Now that we have learned the graph, we will show it here. Note differences and similarities\n", + "# to the ground-truth DAG that is \"assumed\". Moreover, note that this reproduces Supplementary\n", + "# Figure 8 in :footcite:`Jaber2020causal`.\n", + "est_pag = learner.graph_\n", + "\n", + "print(f\"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9f3e89fb-9ef6-4ad8-8198-164ba3032dd5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC\n", + "\n", + "PKC\n", + "\n", + "\n", + "\n", + "P38\n", + "\n", + "P38\n", + "\n", + "\n", + "\n", + "PKC->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Jnk\n", + "\n", + "Jnk\n", + "\n", + "\n", + "\n", + "PKC->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg\n", + "\n", + "Plcg\n", + "\n", + "\n", + "\n", + "PIP2\n", + "\n", + "PIP2\n", + "\n", + "\n", + "\n", + "Plcg->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "Plcg->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf\n", + "\n", + "Raf\n", + "\n", + "\n", + "\n", + "Mek\n", + "\n", + "Mek\n", + "\n", + "\n", + "\n", + "Raf->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Jnk->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Akt\n", + "\n", + "Akt\n", + "\n", + "\n", + "\n", + "Erk\n", + "\n", + "Erk\n", + "\n", + "\n", + "\n", + "Akt->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA\n", + "\n", + "PKA\n", + "\n", + "\n", + "\n", + "PKA->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)\n", + "\n", + "('F', 4)\n", + "\n", + "\n", + "\n", + "('F', 4)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 10)\n", + "\n", + "('F', 10)\n", + "\n", + "\n", + "\n", + "('F', 10)->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 10)->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 10)->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 9)\n", + "\n", + "('F', 9)\n", + "\n", + "\n", + "\n", + "('F', 9)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 9)->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 9)->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)\n", + "\n", + "('F', 5)\n", + "\n", + "\n", + "\n", + "('F', 5)->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 11)\n", + "\n", + "('F', 11)\n", + "\n", + "\n", + "\n", + "('F', 11)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 11)->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)\n", + "\n", + "('F', 1)\n", + "\n", + "\n", + "\n", + "('F', 1)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 13)\n", + "\n", + "('F', 13)\n", + "\n", + "\n", + "\n", + "('F', 13)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 13)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 13)->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 13)->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)\n", + "\n", + "('F', 3)\n", + "\n", + "\n", + "\n", + "('F', 3)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 6)\n", + "\n", + "('F', 6)\n", + "\n", + "\n", + "\n", + "('F', 6)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 12)\n", + "\n", + "('F', 12)\n", + "\n", + "\n", + "\n", + "('F', 12)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 12)->Plcg\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 12)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 12)->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)\n", + "\n", + "('F', 2)\n", + "\n", + "\n", + "\n", + "('F', 2)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)\n", + "\n", + "('F', 14)\n", + "\n", + "\n", + "\n", + "('F', 14)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 8)\n", + "\n", + "('F', 8)\n", + "\n", + "\n", + "\n", + "('F', 8)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 8)->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 7)\n", + "\n", + "('F', 7)\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "dot_graph = draw(est_pag, direction=\"TD\")\n", + "dot_graph\n", + "# dot_graph.render(outfile=\"psi_pag_full.png\", view=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "afb1ce35-7715-417c-aa77-eccd5ebf8280", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Plcg', 'PIP3', ('F', 10), ('F', 5)]\n" + ] + } + ], + "source": [ + "print(list(est_pag.neighbors('PIP2')))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "64b61853-3c5e-4cd4-bb9b-5b4e79a9e292", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ctx = learner.context_\n", + "\n", + "# get the distribution indices that are associated with the F-node\n", + "ctx.sigma_map[('F', 5)]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c12c86b9-d6e5-417e-88ec-9b203100500a", + "metadata": {}, + "outputs": [], + "source": [ + "# get the distributions across the two distributions\n", + "data_i = data[1].copy()\n", + "data_j = data[2].copy()\n", + "\n", + "# name the group column the F-node, so Oracle works as expected\n", + "data_i[('F', 5)] = 1\n", + "data_j[('F', 5)] = 0\n", + "sub_df = pd.concat((data_i, data_j), axis=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "841a883d-816e-4927-83e6-20a109fc3cc4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "inf\n", + "inf\n" + ] + } + ], + "source": [ + "print(learner.min_cond_set_size)\n", + "print(learner.max_cond_set_size)\n", + "print(learner.max_combinations)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6a0a1fb5-9938-40e2-a017-15800d8edc17", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "() 6.435791889158532e-18\n", + "('Plcg',) 6.593184879001655e-20\n", + "('Akt',) 1.200266118509522e-11\n", + "('PKC',) 8.024753178107371e-14\n", + "('Jnk',) 2.585768066145771e-11\n", + "('Erk',) 3.900071970577842e-13\n", + "('P38',) 3.299503701434742e-14\n", + "('PIP3',) 7.083138871715661e-05\n", + "('PKA',) 2.1393776567798484e-13\n", + "('Mek',) 3.693790590305435e-13\n", + "('Raf',) 4.606407772216647e-14\n", + "('Plcg', 'Akt') 8.460800997783422e-08\n", + "('Plcg', 'PKC') 8.990500531927801e-12\n", + "('Plcg', 'Jnk') 3.3857765002483844e-08\n", + "('Plcg', 'Erk') 6.590268332067125e-10\n", + "('Plcg', 'P38') 1.5225223700046747e-12\n", + "('Plcg', 'PIP3') 0.007261132771613868\n", + "('Plcg', 'PKA') 3.212849565105484e-10\n", + "('Plcg', 'Mek') 1.2833709367530019e-10\n", + "('Plcg', 'Raf') 1.4163604686420524e-10\n", + "('Akt', 'PKC') 9.873846189581637e-05\n", + "('Akt', 'Jnk') 0.0031219677903327\n", + "('Akt', 'Erk') 0.01369622199818355\n", + "('Akt', 'P38') 6.063070345314021e-05\n", + "('Akt', 'PIP3') 0.9024296015533677\n", + "('Akt', 'PKA') 0.0017714845018109568\n", + "('Akt', 'Mek') 0.003528741899265669\n", + "('Akt', 'Raf') 0.0008991224821112011\n", + "('PKC', 'Jnk') 9.33977408762107e-07\n", + "('PKC', 'Erk') 8.515213761827435e-06\n", + "('PKC', 'P38') 2.246388723926839e-07\n", + "('PKC', 'PIP3') 0.34768142211165515\n", + "('PKC', 'PKA') 6.332152314280369e-06\n", + "('PKC', 'Mek') 9.5098537319553e-06\n", + "('PKC', 'Raf') 1.2688094863821566e-06\n", + "('Jnk', 'Erk') 0.0001566261707614548\n", + "('Jnk', 'P38') 1.5862931844717754e-05\n", + "('Jnk', 'PIP3') 0.38466781469475697\n", + "('Jnk', 'PKA') 0.00021305054793702892\n", + "('Jnk', 'Mek') 0.00036729818160982603\n", + "('Jnk', 'Raf') 0.00013375039556901973\n", + "('Erk', 'P38') 3.025389145329581e-06\n", + "('Erk', 'PIP3') 0.571452260105889\n", + "('Erk', 'PKA') 0.0004884821515207358\n", + "('Erk', 'Mek') 0.0001085690456512515\n", + "('Erk', 'Raf') 7.50123313213848e-05\n", + "('P38', 'PIP3') 0.3624403399481475\n", + "('P38', 'PKA') 3.613841666449476e-06\n", + "('P38', 'Mek') 3.380684744791879e-06\n", + "('P38', 'Raf') 1.4059792077888952e-06\n", + "('PIP3', 'PKA') 0.5191109355735917\n", + "('PIP3', 'Mek') 0.652887562766674\n", + "('PIP3', 'Raf') 0.08503255960096978\n", + "('PKA', 'Mek') 3.275626021425967e-05\n", + "('PKA', 'Raf') 7.463914552917212e-05\n", + "('Mek', 'Raf') 0.00011080182503373675\n", + "('Plcg', 'Akt', 'PKC') 0.7891422366155535\n", + "('Plcg', 'Akt', 'Jnk') 0.9859814485468757\n", + "('Plcg', 'Akt', 'Erk') 0.9998631601456061\n", + "('Plcg', 'Akt', 'P38') 0.6491208080745487\n", + "('Plcg', 'Akt', 'PIP3') 0.9999999999998938\n", + "('Plcg', 'Akt', 'PKA') 0.9954496653208057\n", + "('Plcg', 'Akt', 'Mek') 0.9908709305733735\n", + "('Plcg', 'Akt', 'Raf') 0.9903519248786904\n", + "('Plcg', 'PKC', 'Jnk') 0.272016946202727\n", + "('Plcg', 'PKC', 'Erk') 0.34285852384885324\n", + "('Plcg', 'PKC', 'P38') 0.0178491632841892\n", + "('Plcg', 'PKC', 'PIP3') 0.9999776236130891\n", + "('Plcg', 'PKC', 'PKA') 0.24366692852731484\n", + "('Plcg', 'PKC', 'Mek') 0.2962573810055808\n", + "('Plcg', 'PKC', 'Raf') 0.2547341290948196\n", + "('Plcg', 'Jnk', 'Erk') 0.8750866000635122\n", + "('Plcg', 'Jnk', 'P38') 0.19238205860774288\n", + "('Plcg', 'Jnk', 'PIP3') 0.9999996689409033\n", + "('Plcg', 'Jnk', 'PKA') 0.8665894965460248\n", + "('Plcg', 'Jnk', 'Mek') 0.7819450525990416\n", + "('Plcg', 'Jnk', 'Raf') 0.8022937030878551\n", + "('Plcg', 'Erk', 'P38') 0.2746627826687863\n", + "('Plcg', 'Erk', 'PIP3') 0.9999999992414166\n", + "('Plcg', 'Erk', 'PKA') 0.9720017267019727\n", + "('Plcg', 'Erk', 'Mek') 0.8611375671390413\n", + "('Plcg', 'Erk', 'Raf') 0.914660041195928\n", + "('Plcg', 'P38', 'PIP3') 0.9999579261799895\n", + "('Plcg', 'P38', 'PKA') 0.24945969813307475\n", + "('Plcg', 'P38', 'Mek') 0.22021175370330875\n", + "('Plcg', 'P38', 'Raf') 0.26535481578322995\n", + "('Plcg', 'PIP3', 'PKA') 0.9999999947814776\n", + "('Plcg', 'PIP3', 'Mek') 0.9999999964268261\n", + "('Plcg', 'PIP3', 'Raf') 0.9999989292834803\n", + "('Plcg', 'PKA', 'Mek') 0.7872515434872753\n", + "('Plcg', 'PKA', 'Raf') 0.905685514521774\n", + "('Plcg', 'Mek', 'Raf') 0.8282818367662137\n", + "('Akt', 'PKC', 'Jnk') 0.9971393969975411\n", + "('Akt', 'PKC', 'Erk') 0.9999999458872947\n", + "('Akt', 'PKC', 'P38') 0.9893596174396212\n", + "('Akt', 'PKC', 'PIP3') 1.0\n", + "('Akt', 'PKC', 'PKA') 0.9999954872688738\n", + "('Akt', 'PKC', 'Mek') 0.9999986563760157\n", + "('Akt', 'PKC', 'Raf') 0.999971160867838\n", + "('Akt', 'Jnk', 'Erk') 0.999999993828627\n", + "('Akt', 'Jnk', 'P38') 0.9997396896479096\n", + "('Akt', 'Jnk', 'PIP3') 1.0\n", + "('Akt', 'Jnk', 'PKA') 0.9999999822575754\n", + "('Akt', 'Jnk', 'Mek') 0.9999999979889044\n", + "('Akt', 'Jnk', 'Raf') 0.999999981982114\n", + "('Akt', 'Erk', 'P38') 0.999999700454769\n", + "('Akt', 'Erk', 'PIP3') 1.0\n", + "('Akt', 'Erk', 'PKA') 0.9999999999999994\n", + "('Akt', 'Erk', 'Mek') 0.9999999999998971\n", + "('Akt', 'Erk', 'Raf') 0.999999999999427\n", + "('Akt', 'P38', 'PIP3') 1.0\n", + "('Akt', 'P38', 'PKA') 0.9999908229412479\n", + "('Akt', 'P38', 'Mek') 0.9999947787202634\n", + "('Akt', 'P38', 'Raf') 0.9999754637345232\n", + "('Akt', 'PIP3', 'PKA') 1.0\n", + "('Akt', 'PIP3', 'Mek') 1.0\n", + "('Akt', 'PIP3', 'Raf') 1.0\n", + "('Akt', 'PKA', 'Mek') 0.9999999999274537\n", + "('Akt', 'PKA', 'Raf') 0.9999999999881026\n", + "('Akt', 'Mek', 'Raf') 0.9999999999983371\n", + "('PKC', 'Jnk', 'Erk') 0.9651475133088108\n", + "('PKC', 'Jnk', 'P38') 0.5427124506088234\n", + "('PKC', 'Jnk', 'PIP3') 0.9999990051613937\n", + "('PKC', 'Jnk', 'PKA') 0.9675608555729773\n", + "('PKC', 'Jnk', 'Mek') 0.9769118017786198\n", + "('PKC', 'Jnk', 'Raf') 0.9278947746043861\n", + "('PKC', 'Erk', 'P38') 0.9148685242905765\n", + "('PKC', 'Erk', 'PIP3') 0.9999999999999999\n", + "('PKC', 'Erk', 'PKA') 0.9998858065715381\n", + "('PKC', 'Erk', 'Mek') 0.9995801816450322\n", + "('PKC', 'Erk', 'Raf') 0.9989918666456864\n", + "('PKC', 'P38', 'PIP3') 0.9999999999828385\n", + "('PKC', 'P38', 'PKA') 0.935827568587764\n", + "('PKC', 'P38', 'Mek') 0.9376631156192213\n", + "('PKC', 'P38', 'Raf') 0.891353553265502\n", + "('PKC', 'PIP3', 'PKA') 0.9999999999999962\n", + "('PKC', 'PIP3', 'Mek') 1.0\n", + "('PKC', 'PIP3', 'Raf') 0.9999999994789294\n", + "('PKC', 'PKA', 'Mek') 0.9981343131288912\n", + "('PKC', 'PKA', 'Raf') 0.9990695979081108\n", + "('PKC', 'Mek', 'Raf') 0.9997591440996322\n", + "('Jnk', 'Erk', 'P38') 0.9863194883217228\n", + "('Jnk', 'Erk', 'PIP3') 0.9999999999999992\n", + "('Jnk', 'Erk', 'PKA') 0.9999982941423281\n", + "('Jnk', 'Erk', 'Mek') 0.9999863603496597\n", + "('Jnk', 'Erk', 'Raf') 0.999978853319258\n", + "('Jnk', 'P38', 'PIP3') 0.9999999999923149\n", + "('Jnk', 'P38', 'PKA') 0.9958478989453659\n", + "('Jnk', 'P38', 'Mek') 0.9919983175172655\n", + "('Jnk', 'P38', 'Raf') 0.9880507060776124\n", + "('Jnk', 'PIP3', 'PKA') 1.0\n", + "('Jnk', 'PIP3', 'Mek') 1.0\n", + "('Jnk', 'PIP3', 'Raf') 0.9999999999998467\n", + "('Jnk', 'PKA', 'Mek') 0.9999850493880029\n", + "('Jnk', 'PKA', 'Raf') 0.9999931221643621\n", + "('Jnk', 'Mek', 'Raf') 0.9999980460494133\n", + "('Erk', 'P38', 'PIP3') 0.9999999999999999\n", + "('Erk', 'P38', 'PKA') 0.9998825118150718\n", + "('Erk', 'P38', 'Mek') 0.9987854820233211\n", + "('Erk', 'P38', 'Raf') 0.998993464768293\n", + "('Erk', 'PIP3', 'PKA') 1.0\n", + "('Erk', 'PIP3', 'Mek') 1.0\n", + "('Erk', 'PIP3', 'Raf') 1.0\n", + "('Erk', 'PKA', 'Mek') 0.9999999999490085\n", + "('Erk', 'PKA', 'Raf') 0.9999999998053\n", + "('Erk', 'Mek', 'Raf') 0.9999997290186001\n", + "('P38', 'PIP3', 'PKA') 0.9999999999999999\n", + "('P38', 'PIP3', 'Mek') 1.0\n", + "('P38', 'PIP3', 'Raf') 0.9999999999526511\n", + "('P38', 'PKA', 'Mek') 0.9984454305569591\n", + "('P38', 'PKA', 'Raf') 0.9995944645718454\n", + "('P38', 'Mek', 'Raf') 0.9994200382853249\n", + "('PIP3', 'PKA', 'Mek') 1.0\n", + "('PIP3', 'PKA', 'Raf') 1.0\n", + "('PIP3', 'Mek', 'Raf') 1.0\n", + "('PKA', 'Mek', 'Raf') 0.9999999489322792\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Not enough samples. 2400 is too small. Need 3240.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_2117/627510932.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_non_f_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msep_set\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_f_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_edge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msub_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PIP2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msep_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msep_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/_classes.py\u001b[0m in \u001b[0;36mevaluate_edge\u001b[0;34m(self, data, X, Y, Z)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mZ\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0mtest_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mci_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtest_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/ci/g_test.py\u001b[0m in \u001b[0;36mtest\u001b[0;34m(self, df, x_vars, y_vars, z_covariates)\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_square_binary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_covariates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 460\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"discrete\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m \u001b[0mstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_square_discrete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_covariates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 462\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 463\u001b[0m raise ValueError(\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/ci/g_test.py\u001b[0m in \u001b[0;36mg_square_discrete\u001b[0;34m(data, x, y, sep_set, levels)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mn_samples_req\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mn_samples_req\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Not enough samples. {n_samples} is too small. Need {n_samples_req}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0mcontingency_tble\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Not enough samples. 2400 is too small. Need 3240." + ] + } + ], + "source": [ + "non_f_nodes = ctx.get_non_f_nodes()\n", + "non_f_nodes.remove('PIP2')\n", + "\n", + "for p in range(len(ctx.get_non_f_nodes())):\n", + " for sep_set in combinations(non_f_nodes, p):\n", + " stat, pvalue = learner.evaluate_edge(sub_df, ('F', 5), 'PIP2', set(sep_set))\n", + " \n", + " print(sep_set, pvalue)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "daae0d6c-65cd-43aa-b8b2-b0a3b7b83c53", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7 4]\n", + "['PKA' 'PIP3']\n" + ] + } + ], + "source": [ + "# the intervention target indices\n", + "print(unique_ints[[2, 4]])\n", + "\n", + "print(np.array(intervention_targets)[[2, 4]])\n", + "# print(intervention_targets[7], intervention_targets[4])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c4668302-9da7-411f-9605-bffd863d06a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('F', 6), ('F', 12), ('F', 9), 'Raf', ('F', 5), ('F', 2), ('F', 8), ('F', 14), ('F', 11), ('F', 1), ('F', 0), ('F', 13), ('F', 3)]\n" + ] + } + ], + "source": [ + "print(list(est_pag.neighbors('Mek')))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "321acf1c-0519-4f92-9a8f-f7f6b6333da8", + "metadata": {}, + "outputs": [], + "source": [ + "for f_node in est_pag.f_nodes:\n", + " print(f_node)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d27c0dab-a6bf-48cf-90b7-f1298ca89b12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{}\n" + ] + } + ], + "source": [ + "print(ctx.state_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b6e4aa12-a6f6-4afd-b810-02a852fdb623", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'obs_skel_graph'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_2117/2603007690.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mobs_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'obs_skel_graph'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m: 'obs_skel_graph'" + ] + } + ], + "source": [ + "obs_graph = ctx.state_variables['obs_skel_graph']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b9daa53d-0216-4443-aa31-fbbdf8c19b4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC\n", + "\n", + "PKC\n", + "\n", + "\n", + "\n", + "P38\n", + "\n", + "P38\n", + "\n", + "\n", + "\n", + "PKC->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Jnk\n", + "\n", + "Jnk\n", + "\n", + "\n", + "\n", + "PKC->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg\n", + "\n", + "Plcg\n", + "\n", + "\n", + "\n", + "PIP2\n", + "\n", + "PIP2\n", + "\n", + "\n", + "\n", + "Plcg->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "Plcg->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf\n", + "\n", + "Raf\n", + "\n", + "\n", + "\n", + "Mek\n", + "\n", + "Mek\n", + "\n", + "\n", + "\n", + "Raf->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Jnk->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Akt\n", + "\n", + "Akt\n", + "\n", + "\n", + "\n", + "Erk\n", + "\n", + "Erk\n", + "\n", + "\n", + "\n", + "Akt->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA\n", + "\n", + "PKA\n", + "\n", + "\n", + "\n", + "PKA->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# %%\n", + "# Visualize the graph without the F-nodes\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes())\n", + "dot_graph = draw(est_pag_no_fnodes, direction=\"LR\")\n", + "dot_graph\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63f8ce08-1a2f-4e70-8b47-2172e78126e6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pywhy-discover", + "language": "python", + "name": "pywhy-discover" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index 7c11e94a5..fdf617b76 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -7,7 +7,7 @@ import pandas as pd from dodiscover.ci.base import BaseConditionalIndependenceTest -from dodiscover.constraint.skeleton import ConditioningSetSelection, LearnSkeleton +from dodiscover.constraint.skeleton import ConditioningSetSelection from dodiscover.context import Context from dodiscover.typing import Column, SeparatingSet @@ -24,7 +24,10 @@ class BaseConstraintDiscovery: ci_estimator : BaseConditionalIndependenceTest The conditional independence test function. The arguments of the estimator should be data, node, node to compare, conditioning set of nodes, and any additional - keyword arguments. + keyword arguments. It must implement the ``test`` function which accepts the data, + a set of X nodes, a set of Y nodes and an optional set of Z nodes, which returns a + ordered tuple of test statistic and pvalue associated with the null hypothesis + :math:`X \\perp Y | Z`. alpha : float, optional The significance level for the conditional independence test, by default 0.05. min_cond_set_size : int, optional @@ -36,8 +39,9 @@ class BaseConstraintDiscovery: max_combinations : int, optional Maximum number of tries with a conditioning set chosen from the set of possible parents still, by default None. If None, then will not be used. If set, then - the conditioning set will be chosen lexographically based on the sorted - test statistic values of 'ith Pa(X) -> X', for each possible parent node of 'X'. + only ``max_combinations`` of conditioning sets will be chosen at each iteration + of the algorithm. One can also set ``keep_sorted`` to make sure to choose the most + "dependent" variables in the conditioning set. condsel_method : ConditioningSetSelection The method to use for selecting the conditioning sets. Must be one of ('neighbors', 'complete', 'neighbors_path'). See Notes for more details. @@ -45,6 +49,14 @@ class BaseConstraintDiscovery: Whether or not to apply orientation rules given the learned skeleton graph and separating set per pair of variables. If ``True`` (default), will apply orientation rules for specific algorithm. + keep_sorted : bool + Whether or not to keep the considered conditioning set variables in sorted + dependency order. If True (default) will sort the existing dependencies of each variable + by its dependencies from strongest to weakest (i.e. largest CI test statistic value + to lowest). The conditioning set is chosen lexographically + based on the sorted test statistic values of 'ith Pa(X) -> X', for each possible + parent node of 'X'. This can be used in conjunction with ``max_combinations`` parameter + to only test the "strongest" dependences. Attributes ---------- @@ -54,6 +66,24 @@ class BaseConstraintDiscovery: The dictionary of separating sets, where it is a nested dictionary from the variable name to the variable it is being compared to the set of variables in the graph that separate the two. + + Notes + ----- + The design of constraint-based causal discovery algorithms proceeds at a high level + in two stages: + + 1. skeleton discovery + 2. orientation of edges + + The skeleton discovery stage is passed off to a dedicated class used for learning + Bayesian networks with conditional testing. All skeleton discovery methods return an + undirected networkx :class:`networkx.Graph` and a `SeparatingSet` data structure. + + The orientation of edges proceeds typically by: + + - converting the skeleton graph to a relevant `EquivalenceClass` + - orienting unshielded triples into colliders + - orienting edges """ graph_: Optional[EquivalenceClass] @@ -68,6 +98,7 @@ def __init__( max_combinations: Optional[int] = None, condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, + keep_sorted: bool = False, ): self.alpha = alpha self.ci_estimator = ci_estimator @@ -84,6 +115,7 @@ def __init__( if max_combinations is None: max_combinations = np.inf self.max_combinations = max_combinations + self.keep_sorted = keep_sorted # initialize the result properties we want to fit self.separating_sets_ = defaultdict(lambda: defaultdict(list)) @@ -115,9 +147,35 @@ def orient_unshielded_triples( graph: EquivalenceClass, sep_set: SeparatingSet, ) -> None: + """Orient unshielded triples in a graph. + + Parameters + ---------- + graph : EquivalenceClass + Causal graph + sep_set : SeparatingSet + Separating sets among all possible variables (I.e. a hash map of hash maps). + + Raises + ------ + NotImplementedError + All constraint-based discovery algorithms must implement this. + """ raise NotImplementedError() def orient_edges(self, graph: EquivalenceClass) -> None: + """Apply orientations to edges using logical rules. + + Parameters + ---------- + graph : EquivalenceClass + Causal graph. + + Raises + ------ + NotImplementedError + All constraint-based discovery algorithms must implement this. + """ raise NotImplementedError( "All constraint discovery algorithms need to implement a function to orient the " "skeleton graph given a separating set." @@ -205,10 +263,9 @@ def learn_skeleton( context: Context, sep_set: Optional[SeparatingSet] = None, ) -> Tuple[nx.Graph, SeparatingSet]: - """Learns the skeleton of a causal DAG using pairwise independence testing. + """Learns the skeleton of a causal DAG using pairwise (conditional) independence testing. - Encodes the skeleton via an undirected graph, `networkx.Graph`. Only - tests with adjacent nodes in the conditioning set. + Encodes the skeleton via an undirected graph, `networkx.Graph`. Parameters ---------- @@ -226,35 +283,13 @@ def learn_skeleton( sep_set : dict of dict of list of set The separating set per pairs of variables. - Raises - ------ - ValueError - If the nodes in the initialization graph do not match the variable - names in passed in data, ``X``. - ValueError - If the nodes in the fixed-edge graph do not match the variable - names in passed in data, ``X``. - Notes ----- Learning the skeleton of a causal DAG uses (conditional) independence testing to determine which variables are (in)dependent. This specific algorithm compares exhaustively pairs of adjacent variables. """ - skel_alg = LearnSkeleton( - self.ci_estimator, - sep_set=sep_set, - alpha=self.alpha, - min_cond_set_size=self.min_cond_set_size, - max_cond_set_size=self.max_cond_set_size, - max_combinations=self.max_combinations, - condsel_method=self.condsel_method, - keep_sorted=False, + raise NotImplementedError( + "All constraint discovery algorithms need to implement a function to orient the " + "skeleton graph given a separating set." ) - skel_alg.fit(data, context) - - skel_graph = skel_alg.adj_graph_ - sep_set = skel_alg.sep_set_ - self.n_ci_tests += skel_alg.n_ci_tests - - return skel_graph, sep_set diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index f926c3b34..78221e207 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -56,6 +56,14 @@ class FCI(BaseConstraintDiscovery): and separating set per pair of variables. If ``True`` (default), will apply Zhang's orientation rules R0-10, orienting colliders and certain arrowheads and tails :footcite:`Zhang2008`. + keep_sorted : bool + Whether or not to keep the considered conditioning set variables in sorted + dependency order. If True (default) will sort the existing dependencies of each variable + by its dependencies from strongest to weakest (i.e. largest CI test statistic value + to lowest). The conditioning set is chosen lexographically + based on the sorted test statistic values of 'ith Pa(X) -> X', for each possible + parent node of 'X'. This can be used in conjunction with ``max_combinations`` parameter + to only test the "strongest" dependences. max_iter : int The maximum number of iterations through the graph to apply orientation rules. @@ -91,6 +99,7 @@ def __init__( max_combinations: Optional[int] = None, condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, + keep_sorted: bool = False, max_iter: int = 1000, max_path_length: Optional[int] = None, selection_bias: bool = True, @@ -103,9 +112,10 @@ def __init__( max_cond_set_size=max_cond_set_size, max_combinations=max_combinations, condsel_method=condsel_method, + keep_sorted=keep_sorted, + apply_orientations=apply_orientations, ) self.max_iter = max_iter - self.apply_orientations = apply_orientations self.max_path_length = max_path_length self.selection_bias = selection_bias self.pds_condsel_method = pds_condsel_method @@ -821,7 +831,7 @@ def learn_skeleton( max_combinations=self.max_combinations, condsel_method=self.condsel_method, second_stage_condsel_method=self.pds_condsel_method, - keep_sorted=False, + keep_sorted=self.keep_sorted, max_path_length=self.max_path_length, ) skel_alg.fit(data, context) diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index e06aa0802..cf66f244c 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -67,6 +67,14 @@ class PsiFCI(FCI): and separating set per pair of variables. If ``True`` (default), will apply Zhang's orientation rules R0-10, orienting colliders and certain arrowheads and tails :footcite:`Zhang2008`. + keep_sorted : bool + Whether or not to keep the considered conditioning set variables in sorted + dependency order. If True (default) will sort the existing dependencies of each variable + by its dependencies from strongest to weakest (i.e. largest CI test statistic value + to lowest). The conditioning set is chosen lexographically + based on the sorted test statistic values of 'ith Pa(X) -> X', for each possible + parent node of 'X'. This can be used in conjunction with ``max_combinations`` parameter + to only test the "strongest" dependences. max_iter : int The maximum number of iterations through the graph to apply orientation rules. @@ -94,6 +102,7 @@ def __init__( max_combinations: Optional[int] = None, condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, + keep_sorted: bool = False, max_iter: int = 1000, max_path_length: Optional[int] = None, pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, @@ -107,6 +116,7 @@ def __init__( max_combinations, condsel_method, apply_orientations, + keep_sorted=keep_sorted, max_iter=max_iter, max_path_length=max_path_length, selection_bias=False, @@ -342,4 +352,9 @@ def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: pag = pgraph.IPAG(incoming_circle_edges=graph, name="IPAG derived with I-FCI") else: pag = pgraph.PsiPAG(incoming_circle_edges=graph, name="PsiPAG derived with Psi-FCI") + + # XXX: assign targets as well + # assign f-nodes + for f_node in self.context_.f_nodes: + pag.set_f_node(f_node) return pag diff --git a/dodiscover/constraint/pcalg.py b/dodiscover/constraint/pcalg.py index 9468eaf2d..0af030357 100644 --- a/dodiscover/constraint/pcalg.py +++ b/dodiscover/constraint/pcalg.py @@ -1,8 +1,9 @@ import logging from itertools import combinations -from typing import Optional +from typing import Optional, Tuple import networkx as nx +import pandas as pd from dodiscover.ci.base import BaseConditionalIndependenceTest from dodiscover.constraint.config import ConditioningSetSelection @@ -10,7 +11,9 @@ from dodiscover.typing import Column, SeparatingSet from .._protocol import EquivalenceClass +from ..context import Context from ._classes import BaseConstraintDiscovery +from .skeleton import LearnSkeleton logger = logging.getLogger() @@ -24,10 +27,13 @@ class PC(BaseConstraintDiscovery): Parameters ---------- - ci_estimator : Callable + ci_estimator : BaseConditionalIndependenceTest The conditional independence test function. The arguments of the estimator should be data, node, node to compare, conditioning set of nodes, and any additional - keyword arguments. + keyword arguments. It must implement the ``test`` function which accepts the data, + a set of X nodes, a set of Y nodes and an optional set of Z nodes, which returns a + ordered tuple of test statistic and pvalue associated with the null hypothesis + :math:`X \\perp Y | Z`. alpha : float, optional The significance level for the conditional independence test, by default 0.05. min_cond_set_size : int, optional @@ -53,6 +59,14 @@ class PC(BaseConstraintDiscovery): and separating set per pair of variables. If ``True`` (default), will apply Meek's orientation rules R0-3, orienting colliders and certain arrowheads :footcite:`Meek1995`. + keep_sorted : bool + Whether or not to keep the considered conditioning set variables in sorted + dependency order. If True (default) will sort the existing dependencies of each variable + by its dependencies from strongest to weakest (i.e. largest CI test statistic value + to lowest). The conditioning set is chosen lexographically + based on the sorted test statistic values of 'ith Pa(X) -> X', for each possible + parent node of 'X'. This can be used in conjunction with ``max_combinations`` parameter + to only test the "strongest" dependences. max_iter : int The maximum number of iterations through the graph to apply orientation rules. @@ -83,6 +97,7 @@ def __init__( max_combinations: Optional[int] = None, condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, + keep_sorted: bool = False, max_iter: int = 1000, ): super().__init__( @@ -92,9 +107,10 @@ def __init__( max_cond_set_size=max_cond_set_size, max_combinations=max_combinations, condsel_method=condsel_method, + apply_orientations=apply_orientations, + keep_sorted=keep_sorted, ) self.max_iter = max_iter - self.apply_orientations = apply_orientations def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: """Convert skeleton graph as undirected networkx Graph to CPDAG. @@ -117,6 +133,51 @@ def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: graph = CPDAG(incoming_undirected_edges=graph) return graph + def learn_skeleton( + self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None + ) -> Tuple[nx.Graph, SeparatingSet]: + """Learns the skeleton of a causal DAG using pairwise (conditional) independence testing. + + Parameters + ---------- + data : pd.DataFrame + The dataset. + context : Context + A context object. + sep_set : SeparatingSet + The separating set. + + Returns + ------- + skel_graph : nx.Graph + The undirected graph of the causal graph's skeleton. + sep_set : SeparatingSet + The separating set per pairs of variables. + + Notes + ----- + Learning the skeleton of a causal DAG uses (conditional) independence testing + to determine which variables are (in)dependent. This specific algorithm + compares exhaustively pairs of adjacent variables. + """ + skel_alg = LearnSkeleton( + self.ci_estimator, + sep_set=sep_set, + alpha=self.alpha, + min_cond_set_size=self.min_cond_set_size, + max_cond_set_size=self.max_cond_set_size, + max_combinations=self.max_combinations, + condsel_method=self.condsel_method, + keep_sorted=self.keep_sorted, + ) + skel_alg.fit(data, context) + + skel_graph = skel_alg.adj_graph_ + sep_set = skel_alg.sep_set_ + self.n_ci_tests += skel_alg.n_ci_tests + + return skel_graph, sep_set + def orient_edges(self, graph: EquivalenceClass) -> None: """Orient edges in a skeleton graph to estimate the causal DAG, or CPDAG. diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 45991899b..b825926f9 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -2,7 +2,7 @@ from collections import defaultdict from copy import deepcopy from itertools import chain, combinations -from typing import Iterable, List, Optional, Set, SupportsFloat, Tuple, Union +from typing import Callable, Generator, Iterable, List, Optional, Set, SupportsFloat, Tuple, Union import networkx as nx import numpy as np @@ -14,13 +14,43 @@ from dodiscover.constraint.utils import is_in_sep_set from dodiscover.typing import Column, SeparatingSet -from .._protocol import EquivalenceClass +from .._protocol import EquivalenceClass, Graph from ..context import Context from ..context_builder import ContextBuilder, InterventionalContextBuilder, make_context logger = logging.getLogger() +def _parallel_test_xy_edges( + conditional_test_func, x_var, y_var, cond_set, data +) -> Tuple[float, float]: + """Private function used to test edges between X and Y in parallel. + + Parameters + ---------- + conditional_test_func : Callable + Conditional test function. + x_var : Columns + The 'X' variable name. + y_var : Column + The 'Y' variable name. + cond_set : Set[Column] + A set of variables to condition on. Can be the empty set. + data : pandas.Dataframe + The dataset with variables as columns and samples as rows. + + Returns + ------- + test_stat : float + Test statistic. + pvalue : float + Pvalue. + """ + # compute conditional independence test + test_stat, pvalue = conditional_test_func(data, x_var, y_var, set(cond_set)) + return test_stat, pvalue + + def _iter_conditioning_set( possible_variables: Iterable, x_var: Union[SupportsFloat, str], @@ -102,7 +132,385 @@ def _find_neighbors_along_path(G: nx.Graph, start, end) -> Set: return nbrs -class LearnSkeleton: +class BaseSkeletonLearner: + """Base class for constraint-based skeleton learning algorithms.""" + + alpha: float + + adj_graph_: nx.Graph + context_: Context + sep_set_: SeparatingSet + min_cond_set_size_: int + max_cond_set_size_: int + max_combinations_: int + + n_iters_: int + + _cont: bool + + def _learn_skeleton( + self, + data: pd.DataFrame, + context: Context, + conditional_test_func: Callable, + possible_x_nodes=None, + ): + """Core function for learning the skeleton of a causal graph. + + This function is a "stateful" function of Skeleton learners. It requires + the following state to be preserved as attributes of self. + + - context_ : a Context object + + Parameters + ---------- + data : pd.DataFrame + The data to learn the causal graph from. + context : Context + A context object. + conditional_test_func : Callable + The conditional test function that + possible_x_nodes : set of nodes, optional + The nodes to initialize as X variables. How to initialize variables to test in + the second loop of the algorithm. See Notes for details. + + Notes + ----- + The context object should be copied before this function is called. + + Proceed by testing neighboring nodes, while keeping track of test + statistic values (these are the ones that are + the "most dependent"). Remember we are testing the null hypothesis + + .. math:: + H_0: X \\perp Y | Z + + where the alternative hypothesis is that they are dependent and hence + require a causal edge linking the two variables. + + Overview of learning causal skeleton from data: + + This algorithm consists of four general loops through the data. + + 1. "Infinite" loop through size of the conditioning set, 'size_cond_set'. The + minimum size is set by ``min_cond_set_size``, whereas the maximum is controlled + by ``max_cond_set_size`` hyperparameter. + 2. Loop through nodes of the graph, 'x_var' + 3. Loop through variables adjacent to selected node, 'y_var'. The edge between 'x_var' + and 'y_var' is tested with a statistical test. + 4. Loop through combinations of the conditioning set of size p, 'cond_set'. + The ``max_combinations`` parameter allows one to limit the fourth loop through + combinations of the conditioning set. + + At each iteration of the outer infinite loop, the edges that were deemed + independent for a specific 'size_cond_set' are removed and 'size_cond_set' + is incremented. + + Furthermore, the maximum pvalue is stored for existing + dependencies among variables (i.e. any two nodes with an edge still). + The ``keep_sorted`` hyperparameter keeps the considered neighbors in + a sorted order. + + The stopping condition is when the size of the conditioning variables for all (X, Y) + pairs is less than the size of 'size_cond_set', or if the 'max_cond_set_size' is + reached. + """ + # preserve state of the Context object + self.context_ = context + + # get the initialized graph + adj_graph: Graph = deepcopy(context.init_graph.copy()) + + if possible_x_nodes is None: + possible_x_nodes = adj_graph.nodes + + # the size of the conditioning set will start off at the minimum + size_cond_set = self.min_cond_set_size_ + + logger.info( + f"\n\nRunning skeleton phase with: \n" + f"max_combinations: {self.max_combinations_},\n" + f"min_cond_set_size: {self.min_cond_set_size_},\n" + f"max_cond_set_size: {self.max_cond_set_size_},\n" + ) + + # Outer loop: iterate over 'size_cond_set' until stopping criterion is met + # - 'size_cond_set' > 'max_cond_set_size' or + # - All (X, Y) pairs have candidate conditioning sets of size < 'size_cond_set' + while 1: + # private attribute '_cont' is used to preserve state and determine a breaking + # condition for the constraint-based search algorithm + self._cont = False + + # initialize set of edges to remove at the end of every loop + # track progress of the algorithm for which edges to remove to ensure stability + # wrt which edges are removed at each process of the algorithm + remove_edges = set() + + # determine whether or not to continue + # loop through every node that we want to test + for x_var in possible_x_nodes: + possible_adjacencies = set(adj_graph.neighbors(x_var)) + logger.info(f"Considering node {x_var}...\n\n") + + for y_var in possible_adjacencies: + # a node cannot be a parent to itself in DAGs + if y_var == x_var: + continue + + if (x_var, y_var) in context.included_edges.edges: + continue + + # compute the possible variables used in the conditioning set + possible_variables = self._compute_candidate_conditioning_sets( + adj_graph, + x_var, + y_var, + ) + + logger.debug( + f"Adj({x_var}) without {y_var} with size={len(possible_adjacencies) - 1} " + f"with p={size_cond_set}. The possible variables to condition on are: " + f"{possible_variables}." + ) + + # check that number of adjacencies is greater then the + # cardinality of the conditioning set + if len(possible_variables) < size_cond_set: + logger.debug( + f"\n\nBreaking for {x_var}, {y_var}, {len(possible_adjacencies)}, " + f"{size_cond_set}, {possible_variables}" + ) + continue + else: + self._cont = True + + # generate iterator through the conditioning sets + conditioning_sets = _iter_conditioning_set( + possible_variables=possible_variables, + x_var=x_var, + y_var=y_var, + size_cond_set=size_cond_set, + ) + + # now iterate through the possible parents + for comb_idx, cond_set in enumerate(conditioning_sets): + # check the number of combinations of possible parents we have tried + # to use as a separating set + if ( + self.max_combinations_ is not None + and comb_idx >= self.max_combinations_ + ): + break + + try: + # compute conditional independence test + test_stat, pvalue = conditional_test_func( + data, x_var, y_var, set(cond_set) + ) + except Exception as e: + print(e) + test_stat = np.inf + pvalue = 0.0 + + # if any "independence" is found through inability to reject + # the null hypothesis, then we will break the loop comparing X and Y + # and say X and Y are conditionally independent given 'cond_set' + if pvalue > self.alpha: + break + + # post-process the CI test results + removed_edge = self._postprocess_ci_test( + adj_graph, x_var, y_var, cond_set, test_stat, pvalue + ) + if removed_edge: + remove_edges.add((x_var, y_var, pvalue)) + + # summarize the comparison of XY + self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) + + # finally remove edges after performing + # conditional independence tests + logger.info(f"For p = {size_cond_set}, removing all edges: {remove_edges}") + + # Remove non-significant links + # Note: Removing edges at the end ensures "stability" of the algorithm + # with respect to the randomness choice of pairs of edges considered in the inner loop + adj_graph.remove_edges_from(remove_edges) + + # increment the conditioning set size + size_cond_set += 1 + + # only allow conditioning set sizes up to maximum set number + if size_cond_set > self.max_cond_set_size_ or self._cont is False: + break + + self.adj_graph_ = adj_graph + self.n_iters_ += 1 + + def _generate_pairs_with_sepset( + self, possible_x_nodes, adj_graph, context, size_cond_set + ): # -> Generator[Column, Column, Set[Column]]: + # loop through every node that we want to test + for x_var in possible_x_nodes: + possible_adjacencies = set(adj_graph.neighbors(x_var)) + logger.info(f"Considering node {x_var}...\n\n") + + for y_var in possible_adjacencies: + # a node cannot be a parent to itself in DAGs + if y_var == x_var: + continue + + if (x_var, y_var) in context.included_edges.edges: + continue + + # compute the possible variables used in the conditioning set + possible_variables = self._compute_candidate_conditioning_sets( + adj_graph, + x_var, + y_var, + ) + + logger.debug( + f"Adj({x_var}) without {y_var} with size={len(possible_adjacencies) - 1} " + f"with p={size_cond_set}. The possible variables to condition on are: " + f"{possible_variables}." + ) + + # check that number of adjacencies is greater then the + # cardinality of the conditioning set + if len(possible_variables) < size_cond_set: + logger.debug( + f"\n\nBreaking for {x_var}, {y_var}, {len(possible_adjacencies)}, " + f"{size_cond_set}, {possible_variables}" + ) + continue + else: + self._cont = True + + # generate iterator through the conditioning sets + conditioning_sets = _iter_conditioning_set( + possible_variables=possible_variables, + x_var=x_var, + y_var=y_var, + size_cond_set=size_cond_set, + ) + + # now iterate through the possible parents + for comb_idx, cond_set in enumerate(conditioning_sets): + # check the number of combinations of possible parents we have tried + # to use as a separating set + if self.max_combinations_ is not None and comb_idx >= self.max_combinations_: + break + yield x_var, y_var, cond_set + + def _compute_candidate_conditioning_sets( + self, adj_graph: nx.Graph, x_var: Column, y_var: Column + ) -> Set[Column]: + r"""Compute candidate conditioning sets. + + For a given 'X' and 'Y', this method implements a graphical algorithm that + enumerates possible variables that are part of 'Z', the conditioning set. + One can then test the following null hypothesis :math:`H_0: X \perp Y | Z`. + + Parameters + ---------- + adj_graph : nx.Graph + The current adjacency graph. + x_var : node + The 'X' node. + y_var : node + The 'Y' node. + + Returns + ------- + possible_variables : Set of Column + The set of nodes in 'adj_graph' that are candidates for the + conditioning set. + + Notes + ----- + This depends on: + + size_cond_set : int + The maximum size of the conditioning set allowed. If candidate conditioning + sets are less than this number, then the ``possible_variables`` will be + the empty set. + """ + raise NotImplementedError( + "All skeleton discovery methods should implement a method for selecting " + "the possible conditioning sets." + ) + + def _postprocess_ci_test( + self, + adj_graph: nx.Graph, + x_var: Column, + y_var: Column, + cond_set: Set[Column], + test_stat: float, + pvalue: float, + ) -> bool: + # keep track of the smallest test statistic, meaning the highest pvalue + # meaning the "most" independent. keep track of the maximum pvalue as well + if pvalue > adj_graph.edges[x_var, y_var]["pvalue"]: + adj_graph.edges[x_var, y_var]["pvalue"] = pvalue + if test_stat < adj_graph.edges[x_var, y_var]["test_stat"]: + adj_graph.edges[x_var, y_var]["test_stat"] = test_stat + + # two variables found to be independent given a separating set + if pvalue > self.alpha: + self.sep_set_[x_var][y_var].append(set(cond_set)) + self.sep_set_[y_var][x_var].append(set(cond_set)) + return True + return False + + def _summarize_xy_comparison( + self, x_var: Column, y_var: Column, removed_edge: bool, pvalue: float + ) -> None: + """Provide ability to log end result of each XY edge evaluation.""" + # exit loop if we have found an independency and removed the edge + if removed_edge: + remove_edge_str = "Removing edge" + else: + remove_edge_str = "Did not remove edge" + + logger.info( + f"{remove_edge_str} between {x_var} and {y_var}... \n" + f"Statistical summary:\n" + f"- PValue={pvalue} at alpha={self.alpha}" + ) + + def evaluate_edge( + self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None + ) -> Tuple[float, float]: + """Test any specific edge for X || Y | Z. + + Parameters + ---------- + data : pd.DataFrame + The dataset + X : column + A column in ``data``. + Y : column + A column in ``data``. + Z : set, optional + A list of columns in ``data``, by default None. + + Returns + ------- + test_stat : float + Test statistic. + pvalue : float + The pvalue. + """ + raise NotImplementedError( + "All skeleton discovery methods should implement a method for " + "evaluating an edge with a statistical test." + ) + + +class LearnSkeleton(BaseSkeletonLearner): """Learn a skeleton graph from observational data without latent confounding. A skeleton graph from a Markovian causal model can be learned completely @@ -176,31 +584,6 @@ class LearnSkeleton: where the alternative hypothesis is that they are dependent and hence require a causal edge linking the two variables. - Overview of learning causal skeleton from data: - - This algorithm consists of four general loops through the data. - - - "infinite" loop through size of the conditioning set, 'size_cond_set'. The - minimum size is set by ``min_cond_set_size``, whereas the maximum is controlled - by ``max_cond_set_size`` hyperparameter. - - loop through nodes of the graph, 'x_var' - - loop through variables adjacent to selected node, 'y_var' - - loop through combinations of the conditioning set of size p, 'cond_set'. - The ``max_combinations`` parameter allows one to limit the fourth loop through - combinations of the conditioning set. - - At each iteration of the outer infinite loop, the edges that were deemed - independent for a specific 'size_cond_set' are removed and 'size_cond_set' is incremented. - - Furthermore, the maximum pvalue is stored for existing - dependencies among variables (i.e. any two nodes with an edge still). - The ``keep_sorted`` hyperparameter keeps the considered neighbors in - a sorted order. - - The stopping condition is when the size of the conditioning variables for all (X, Y) - pairs is less than the size of 'size_cond_set', or if the 'max_cond_set_size' is - reached. - Different methods for learning the skeleton: There are different ways to learn the skeleton that are valid under various @@ -215,16 +598,6 @@ class LearnSkeleton: from 'x_var' to 'y_var'. This is a variant from the RFCI paper :footcite:`Colombo2012` """ - adj_graph_: nx.Graph - context_: Context - min_cond_set_size_: int - max_cond_set_size_: int - max_combinations_: int - sep_set_: SeparatingSet - n_iters_: int = 0 - - remove_edges: Set - def __init__( self, ci_estimator: BaseConditionalIndependenceTest, @@ -251,6 +624,7 @@ def __init__( # debugging mode self.n_ci_tests = 0 + self.n_iters_ = 0 def _initialize_params(self) -> None: """Initialize parameters for learning skeleton. @@ -312,7 +686,7 @@ def evaluate_edge( """ if Z is None: Z = set() - test_stat, pvalue = self.ci_estimator.test(data, {X}, {Y}, Z) + test_stat, pvalue = self.ci_estimator.test(data, set({X}), set({Y}), Z) self.n_ci_tests += 1 return test_stat, pvalue @@ -328,21 +702,14 @@ def fit(self, data: pd.DataFrame, context: Context): """ self.context_ = context.copy() - # get the initialized graph - adj_graph = deepcopy(self.context_.init_graph.copy()) - - # track progress of the algorithm for which edges to remove to ensure stability - self.remove_edges = set() - # initialize learning parameters self._initialize_params() - # the size of the conditioning set will start off at the minimum - size_cond_set = self.min_cond_set_size_ - # allow us to query the iteration stage of the causal discovery algorithm # allowing us to run multiple iterations of the skeleton discovery - edge_attrs = set(chain.from_iterable(d.keys() for *_, d in adj_graph.edges(data=True))) + edge_attrs = set( + chain.from_iterable(d.keys() for *_, d in self.context_.init_graph.edges(data=True)) + ) if self.n_iters_ == 0 and "test_stat" in edge_attrs or "pvalue" in edge_attrs: raise RuntimeError( "Running skeleton discovery with adjacency graph " @@ -350,131 +717,15 @@ def fit(self, data: pd.DataFrame, context: Context): ) # store the absolute value of test-statistic values and pvalue for - # every single candidate parent-child edge (X -> Y) - nx.set_edge_attributes(adj_graph, np.inf, "test_stat") - nx.set_edge_attributes(adj_graph, -1e-5, "pvalue") - - logger.info( - f"\n\nRunning skeleton phase with: \n" - f"max_combinations: {self.max_combinations_},\n" - f"min_cond_set_size: {self.min_cond_set_size_},\n" - f"max_cond_set_size: {self.max_cond_set_size_},\n" - ) - - # Outer loop: iterate over 'size_cond_set' until stopping criterion is met - # - 'size_cond_set' > 'max_cond_set_size' or - # - All (X, Y) pairs have candidate conditioning sets of size < 'size_cond_set' - while 1: - cont = False - # initialize set of edges to remove at the end of every loop - self.remove_edges = set() - - # loop through every node - for x_var in adj_graph.nodes: - possible_adjacencies = set(adj_graph.neighbors(x_var)) - - logger.info(f"Considering node {x_var}...\n\n") - - for y_var in possible_adjacencies: - # a node cannot be a parent to itself in DAGs - if y_var == x_var: - continue - - # ignore fixed edges - if (x_var, y_var) in self.context_.included_edges.edges: - continue - - # compute the possible variables used in the conditioning set - possible_variables = self._compute_candidate_conditioning_sets( - adj_graph, - x_var, - y_var, - ) - - logger.debug( - f"Adj({x_var}) without {y_var} with size={len(possible_adjacencies) - 1} " - f"with p={size_cond_set}. The possible variables to condition on are: " - f"{possible_variables}." - ) - - # check that number of adjacencies is greater then the - # cardinality of the conditioning set - if len(possible_variables) < size_cond_set: - logger.debug( - f"\n\nBreaking for {x_var}, {y_var}, {len(possible_adjacencies)}, " - f"{size_cond_set}, {possible_variables}" - ) - continue - else: - cont = True - - # generate iterator through the conditioning sets - conditioning_sets = _iter_conditioning_set( - possible_variables=possible_variables, - x_var=x_var, - y_var=y_var, - size_cond_set=size_cond_set, - ) - - # now iterate through the possible parents - for comb_idx, cond_set in enumerate(conditioning_sets): - # check the number of combinations of possible parents we have tried - # to use as a separating set - if ( - self.max_combinations_ is not None - and comb_idx >= self.max_combinations_ - ): - break - - # compute conditional independence test - test_stat, pvalue = self.evaluate_edge(data, x_var, y_var, set(cond_set)) - - # if any "independence" is found through inability to reject - # the null hypothesis, then we will break the loop comparing X and Y - # and say X and Y are conditionally independent given 'cond_set' - if pvalue > self.alpha: - break - - # post-process the CI test results - removed_edge = self._postprocess_ci_test( - adj_graph, x_var, y_var, cond_set, test_stat, pvalue - ) - - # summarize the comparison of XY - self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) - - # finally remove edges after performing - # conditional independence tests - logger.info(f"For p = {size_cond_set}, removing all edges: {self.remove_edges}") - - # Remove non-significant links - # Note: Removing edges at the end ensures "stability" of the algorithm - # with respect to the randomness choice of pairs of edges considered in the inner loop - adj_graph.remove_edges_from(self.remove_edges) - - # increment the conditioning set size - size_cond_set += 1 - - # only allow conditioning set sizes up to maximum set number - if size_cond_set > self.max_cond_set_size_ or cont is False: - break - - self.adj_graph_ = adj_graph - self.n_iters_ += 1 - - def _summarize_xy_comparison( - self, x_var: Column, y_var: Column, removed_edge: bool, pvalue: float - ) -> None: - # exit loop if we have found an independency and removed the edge - if removed_edge: - remove_edge_str = "Removing edge" - else: - remove_edge_str = "Did not remove edge" - - logger.info( - f"{remove_edge_str} between {x_var} and {y_var}... \n" - f"Statistical summary:\n" - f"- PValue={pvalue} at alpha={self.alpha}" + # every single candidate parent-child edge (X -> Y) + nx.set_edge_attributes(self.context_.init_graph, np.inf, "test_stat") + nx.set_edge_attributes(self.context_.init_graph, -1e-5, "pvalue") + + # apply algorithm to learn skeleton + self._learn_skeleton( + data, + context=self.context_, + conditional_test_func=self.evaluate_edge, ) def _compute_candidate_conditioning_sets( @@ -533,30 +784,6 @@ def _compute_candidate_conditioning_sets( return possible_variables - def _postprocess_ci_test( - self, - adj_graph: nx.Graph, - x_var: Column, - y_var: Column, - cond_set: Set[Column], - test_stat: float, - pvalue: float, - ) -> bool: - # keep track of the smallest test statistic, meaning the highest pvalue - # meaning the "most" independent. keep track of the maximum pvalue as well - if pvalue > adj_graph.edges[x_var, y_var]["pvalue"]: - adj_graph.edges[x_var, y_var]["pvalue"] = pvalue - if test_stat < adj_graph.edges[x_var, y_var]["test_stat"]: - adj_graph.edges[x_var, y_var]["test_stat"] = test_stat - - # two variables found to be independent given a separating set - if pvalue > self.alpha: - self.remove_edges.add((x_var, y_var)) - self.sep_set_[x_var][y_var].append(set(cond_set)) - self.sep_set_[y_var][x_var].append(set(cond_set)) - return True - return False - class LearnSemiMarkovianSkeleton(LearnSkeleton): """Learning a skeleton from a semi-markovian causal model. @@ -738,13 +965,13 @@ def _compute_candidate_conditioning_sets( # determine how we want to construct the candidates for separating nodes # perform conditioning independence testing on all combinations possible_variables = pgraph.pds( - pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore + pag, x_var, y_var, max_path_length=self.max_path_length_ # type: ignore ) elif condsel_method == ConditioningSetSelection.PDS_PATH: # determine how we want to construct the candidates for separating nodes # perform conditioning independence testing on all combinations possible_variables = pgraph.pds_path( - pag, x_var, y_var, max_path_length=self.max_path_length # type: ignore + pag, x_var, y_var, max_path_length=self.max_path_length_ # type: ignore ) if self.keep_sorted: @@ -763,7 +990,7 @@ def _compute_candidate_conditioning_sets( return possible_variables - def fit(self, data: pd.DataFrame, context: Context): + def _prep_second_stage_skeleton(self) -> Context: import pywhy_graphs as pgraphs if self.max_path_length is None: @@ -771,14 +998,6 @@ def fit(self, data: pd.DataFrame, context: Context): else: self.max_path_length_ = self.max_path_length - # initially learn the skeleton without using PDS information - super().fit(data, context) - - # if there is no second stage skeleton method to be run, then we - # will stop with the skeleton here - if self.second_stage_condsel_method is None: - return self - # convert the undirected skeleton graph to a PAG, where # all left-over edges have a "circle" endpoint sep_set = self.sep_set_ @@ -800,11 +1019,24 @@ def fit(self, data: pd.DataFrame, context: Context): if "pvalue" in d: d.pop("pvalue") context = ( - make_context(context).init_graph(new_init_graph).state_variable("PAG", pag).build() + make_context(self.context_) + .init_graph(new_init_graph) + .state_variable("PAG", pag) + .build() ) + return context + + def fit(self, data: pd.DataFrame, context: Context): + # initially learn the skeleton without using PDS information + super().fit(data, context) + + # if there is no second stage skeleton method to be run, then we + # will stop with the skeleton here + if self.second_stage_condsel_method is None: + return self - if not all(x in context.state_variable("PAG").nodes for x in data.columns): - raise RuntimeError("wtf..") + # setup context for the second round-of learning + context = self._prep_second_stage_skeleton() # now compute all possibly d-separating sets and learn a better skeleton super().fit(data, context) @@ -899,6 +1131,70 @@ def __init__( self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets + def _initialize_params(self) -> None: + """Initialize parameters for learning skeleton. + + Basic parameters that are used by any constraint-based causal discovery algorithms. + """ + # error checks of passed in arguments + if self.max_combinations is not None and self.max_combinations <= 0: + raise RuntimeError(f"Max combinations must be at least 1, not {self.max_combinations}") + + if self.condsel_method not in ConditioningSetSelection: + raise ValueError( + f"Skeleton method must be one of {ConditioningSetSelection}, not " + f"{self.condsel_method}." + ) + + if self.sep_set is None and not hasattr(self, "sep_set_"): + # keep track of separating sets + self.sep_set_ = defaultdict(lambda: defaultdict(list)) + elif not hasattr(self, "sep_set_"): + self.sep_set_ = self.sep_set # type: ignore + + # control of the conditioning set + if self.max_cond_set_size is None: + self.max_cond_set_size_ = np.inf + else: + self.max_cond_set_size_ = self.max_cond_set_size + if self.min_cond_set_size is None: + self.min_cond_set_size_ = 0 + else: + self.min_cond_set_size_ = self.min_cond_set_size + if self.max_combinations is None: + self.max_combinations_ = np.inf + else: + self.max_combinations_ = self.max_combinations + + def evaluate_edge( + self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None + ) -> Tuple[float, float]: + """Test any specific edge for X || Y | Z. + + Parameters + ---------- + data : pd.DataFrame + The dataset + X : column + A column in ``data``. + Y : column + A column in ``data``. + Z : set, optional + A list of columns in ``data``, by default None. + + Returns + ------- + test_stat : float + Test statistic. + pvalue : float + The pvalue. + """ + if Z is None: + Z = set() + test_stat, pvalue = self.ci_estimator.test(data, set({X}), set({Y}), Z) + self.n_ci_tests += 1 + return test_stat, pvalue + def evaluate_fnode_edge( self, data: List[pd.DataFrame], @@ -908,6 +1204,8 @@ def evaluate_fnode_edge( ) -> Tuple[float, float]: """Test an edge from an F-node to a regular node for X || Y | Z. + Tests the conditional invariance: :math:`P_{X=x}(Y | Z) = P_{X=x'}(Y|Z)`. + Parameters ---------- data : pd.DataFrame @@ -934,15 +1232,15 @@ def evaluate_fnode_edge( data_j = data[distribution_idx[1]].copy() # name the group column the F-node, so Oracle works as expected - data_i[X] = 0 - data_j[X] = 1 + data_i[X] = 1 + data_j[X] = 0 data = pd.concat((data_i, data_j), axis=0) # compare conditional distributions P(Y | X) vs P'(Y | X), where 'group_col' # indicates which distribution data came from # test graphically if Y is d-separated from F-node given Z # or test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test(data, Y, {X}, Z) + test_stat, pvalue = self.cd_estimator.test(data, Y, set({X}), Z) self.n_ci_tests += 1 return test_stat, pvalue @@ -977,158 +1275,65 @@ def _compute_candidate_conditioning_sets( raise RuntimeError("This should not be the case") # get only neighboring sets of Y-vars, or PDS that depend on Y - possible_variables = set(adj_graph.neighbors(y_var)) - set(f_nodes) - return possible_variables + possible_variables = super()._compute_candidate_conditioning_sets( + adj_graph, x_var, y_var + ) - set(f_nodes) - def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], context: Context): - self.context_ = make_context(context, create_using=InterventionalContextBuilder).build() - - # initialize learning parameters - self._initialize_params() + return possible_variables - # get the initialized graph - adj_graph = self.context_.init_graph + def _postprocess_ci_test( + self, + adj_graph: nx.Graph, + x_var: Column, + y_var: Column, + cond_set: Set[Column], + test_stat: float, + pvalue: float, + ) -> bool: + # post-process the CI test results f_nodes = self.context_.f_nodes + if x_var in f_nodes: + all_other_fnodes = f_nodes.copy() + all_other_fnodes.remove(x_var) + cond_set = cond_set.union(all_other_fnodes) + return super()._postprocess_ci_test(adj_graph, x_var, y_var, cond_set, test_stat, pvalue) - # the size of the conditioning set will start off at the minimum - size_cond_set = self.min_cond_set_size_ - - # track progress of the algorithm for which edges to remove to ensure stability - self.remove_edges = set() - - # first remove all connections among f-nodes - for x_var in f_nodes: - for y_var in f_nodes: - if x_var == y_var: - continue - - pvalue = 1.0 - test_stat = 0.0 - - # post-process the CI test results - removed_edge = self._postprocess_ci_test( - adj_graph, x_var, y_var, set(), test_stat, pvalue - ) - - # summarize the comparison of XY - self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) - - # Remove edges - adj_graph.remove_edges_from(self.remove_edges) - - # Outer loop: iterate over 'size_cond_set' until stopping criterion is met - # - 'size_cond_set' > 'max_cond_set_size' or - # - All (X, Y) pairs have candidate conditioning sets of size < 'size_cond_set' - while 1: - cont = False - # initialize set of edges to remove at the end of every loop - self.remove_edges = set() - - # loop through every node - for x_var in f_nodes: - possible_adjacencies = set(adj_graph.neighbors(x_var)) - - logger.info(f"Considering node {x_var}...\n\n") - - for y_var in possible_adjacencies: - # a node cannot be a parent to itself in DAGs - if y_var == x_var: - continue - - # compute the possible variables used in the conditioning set - possible_variables = self._compute_candidate_conditioning_sets( - adj_graph, - x_var, - y_var, - ) - - logger.debug( - f"Adj({x_var}) without {y_var} with size={len(possible_adjacencies) - 1} " - f"with p={size_cond_set}. The possible variables to condition on are: " - f"{possible_variables}." - ) - - # check that number of adjacencies is greater then the - # cardinality of the conditioning set - if len(possible_variables) < size_cond_set: - logger.debug( - f"\n\nBreaking for {x_var}, {y_var}, {len(possible_adjacencies)}, " - f"{size_cond_set}, {possible_variables}" - ) - continue - else: - cont = True - - # generate iterator through the conditioning sets - conditioning_sets = _iter_conditioning_set( - possible_variables=possible_variables, - x_var=x_var, - y_var=y_var, - size_cond_set=size_cond_set, - ) - - # now iterate through the possible parents - for comb_idx, cond_set in enumerate(conditioning_sets): - # check the number of combinations of possible parents we have tried - # to use as a separating set - if ( - self.max_combinations_ is not None - and comb_idx >= self.max_combinations_ - ): - break - - # compute conditional independence test - test_stat, pvalue = self.evaluate_fnode_edge( - interv_data, x_var, set({y_var}), set(cond_set) - ) - - # if any "independence" is found through inability to reject - # the null hypothesis, then we will break the loop comparing X and Y - # and say X and Y are conditionally independent given 'cond_set' - if pvalue > self.alpha: - break - - # post-process the CI test results - removed_edge = self._postprocess_ci_test( - adj_graph, x_var, y_var, cond_set, test_stat, pvalue - ) - - # summarize the comparison of XY - self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) - - # finally remove edges after performing - # conditional independence tests - logger.info(f"For p = {size_cond_set}, removing all edges: {self.remove_edges}") - - # Remove non-significant links - # Note: Removing edges at the end ensures "stability" of the algorithm - # with respect to the randomness choice of pairs of edges considered in the inner loop - adj_graph.remove_edges_from(self.remove_edges) - - # increment the conditioning set size - size_cond_set += 1 - - # only allow conditioning set sizes up to maximum set number - if size_cond_set > self.max_cond_set_size_ or cont is False: - break - - self.adj_graph_ = adj_graph + def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], context: Context): + state_variables = context.state_variables.copy() + self.context_ = make_context(context, create_using=InterventionalContextBuilder).build() + for name, var in state_variables.items(): + self.context_.add_state_variable(name, var) + + # apply algorithm to learn skeleton + self._learn_skeleton( + data=interv_data, + context=self.context_, + conditional_test_func=self.evaluate_fnode_edge, + possible_x_nodes=list(self.context_.f_nodes), + ) def _learn_skeleton_with_observations(self, obs_data: pd.DataFrame, context: Context): - f_nodes = context.f_nodes - # get the init graph that does not contain any F-nodes obs_context_bld = make_context(context, create_using=ContextBuilder) init_graph = deepcopy(context.init_graph) # get the subgraph of non-f nodes - non_f_nodes = set(init_graph.nodes) - set(f_nodes) - obs_init_graph = init_graph.subgraph(non_f_nodes) + obs_init_graph = init_graph.subgraph(context.get_non_f_nodes()) # now learn the observational subgraph obs_context_bld.init_graph(obs_init_graph) obs_context = obs_context_bld.build() - super().fit(obs_data, obs_context) + + # get the initialized graph + adj_graph = deepcopy(obs_context.init_graph.copy()) + + # apply algorithm to learn skeleton + self._learn_skeleton( + data=obs_data, + context=obs_context, + conditional_test_func=self.evaluate_edge, + possible_x_nodes=list(adj_graph.nodes), + ) def fit(self, data: List[pd.DataFrame], context: Context) -> None: """Fit data and context. @@ -1168,11 +1373,34 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: largest_data_idx = np.argmax([len(df) for df in data]) obs_data = data[largest_data_idx] + self.context_ = context.copy() + + # initialize learning parameters + self._initialize_params() + + # allow us to query the iteration stage of the causal discovery algorithm + # allowing us to run multiple iterations of the skeleton discovery + edge_attrs = set( + chain.from_iterable(d.keys() for *_, d in context.init_graph.edges(data=True)) + ) + if self.n_iters_ == 0 and "test_stat" in edge_attrs or "pvalue" in edge_attrs: + raise RuntimeError( + "Running skeleton discovery with adjacency graph " + "with 'test_stat' or 'pvalue' is not supported yet." + ) + + # store the absolute value of test-statistic values and pvalue for + # every single candidate parent-child edge (X -> Y) + nx.set_edge_attributes(context.init_graph, np.inf, "test_stat") + nx.set_edge_attributes(context.init_graph, -1e-5, "pvalue") + + # first learn the skeleton using only "observational data" self._learn_skeleton_with_observations(obs_data, context) # keep track of the observational skeleton graph obs_skel_graph = self.adj_graph_.copy() + # prepare the context object for the second stage of learning # all separating sets are either: # i) augmented with all F-nodes, or # ii) augmented with all F-nodes except intervention index 'i' @@ -1193,7 +1421,7 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: # interventional distributions # create a complete subgraph of F-nodes with all other nodes for node in f_nodes: - for obs_node in set(f_nodes).union(set(non_f_nodes)): + for obs_node in set(non_f_nodes): if node == obs_node: continue self.adj_graph_.add_edge(node, obs_node, test_stat=np.inf, pvalue=-1e-5) @@ -1206,6 +1434,19 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: ) self.context_.add_state_variable("obs_skel_graph", obs_skel_graph) + # convert the undirected skeleton graph to a PAG, where + # all left-over edges have a "circle" endpoint + sep_set = self.sep_set_ + import pywhy_graphs + + pag = pywhy_graphs.PAG(incoming_circle_edges=obs_skel_graph, name="PAG derived with FCI") + + # orient colliders + self._orient_unshielded_triples(pag, sep_set) + + # convert the adjacency graph + self.context_.add_state_variable("pag", pag) + # now, we'll fit the data using interventional data by looping over all # combinations of F-nodes and their neighbors self._learn_skeleton_with_interventions(data, self.context_) diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index fd56403e7..75792976e 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -100,7 +100,8 @@ ctx_builder = make_context(create_using=InterventionalContextBuilder) ctx: Context = ( ctx_builder.variables(data=data[0]) - .intervention_targets(intervention_targets) + # .intervention_targets(intervention_targets) + .num_distributions(6) .obs_distribution(False) .build() ) diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py index 401c052f3..97b0c801e 100644 --- a/tests/unit_tests/constraint/test_psifcialg.py +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -270,13 +270,14 @@ def test_psifci_withsachs(): for interv_idx in unique_ints: _data = df[df["INT"] == interv_idx][data_cols] data.append(_data) + print(len(_data)) ci_estimator = GSquareCITest(data_type="discrete") alpha = 0.05 learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=ci_estimator, alpha=alpha) ctx_builder = make_context(create_using=InterventionalContextBuilder) ctx = ( - ctx_builder.variables(data=data) + ctx_builder.variables(observed=data_cols) .intervention_targets(intervention_targets) .obs_distribution(False) .build() From 81b07bf6f2971941833196fab975eb241117bdaf Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 17 Mar 2023 12:04:39 -0400 Subject: [PATCH 34/61] Massive fix and change Signed-off-by: Adam Li --- Untitled.ipynb | 1433 +- doc/references.bib | 17 + dodiscover/ci/__init__.py | 1 + dodiscover/ci/categorical_test.py | 200 + dodiscover/ci/g_test.py | 1 + dodiscover/constraint/fcialg.py | 2 +- dodiscover/constraint/intervention.py | 1 + dodiscover/constraint/skeleton.py | 324 +- dodiscover/testdata/adult.csv | 30163 ++++++++++++++++ .../conditional/ci/test_chisq_test.py | 156 + 10 files changed, 31711 insertions(+), 587 deletions(-) create mode 100644 dodiscover/ci/categorical_test.py create mode 100644 dodiscover/testdata/adult.csv create mode 100644 tests/unit_tests/conditional/ci/test_chisq_test.py diff --git a/Untitled.ipynb b/Untitled.ipynb index 2a3a2f323..26a3d7477 100644 --- a/Untitled.ipynb +++ b/Untitled.ipynb @@ -8,7 +8,7 @@ "outputs": [], "source": [ "from pywhy_graphs.viz import draw\n", - "from dodiscover.ci import GSquareCITest\n", + "from dodiscover.ci import GSquareCITest, CategoricalCITest, CausalLearnCITest\n", "from dodiscover import PsiFCI, Context, make_context, InterventionalContextBuilder\n", "import networkx as nx\n", "import pandas as pd\n", @@ -48,7 +48,7 @@ }, { "data": { - "image/png": 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Ol80H8tKwpv+Cr9VMvXr1MJlMOByO07qtTCYTNpsNq9VafATP29ub8ePHc+edd5ZaXSIiIiIiIiJy9VPwdBloUKcaM//vEQb3aI8BHE/PprDIcUkBlGEY5OYXcjw9m5BAP14Z1Zfr/bPJzjwJQEBAANWqVQOgqKjotM4nONXpZDabsVgshIWFER8fz44dOy66HhERERERERG59ih4ukz4+XrzeExnJr/4II3r1yS3oIjj6dnYcwsuaP6Ty+0mIzuXY+nZuNxuIu64mVn/HE7PiJb079cPt9tdvF54eDh+fn4AOJ3O08Inl8sFwMCBA4mPj8cwDB566CHeeOMN8vLySvGdi4iIiIiIiMjVymSUoK3GbrcTHBxMdnY2QUFB5VHXNc3j8bBpx0G++PZn1mzaRV5+IXCqC8nb5oWXxYLZZMLAwDDA4XRR5HRiwgScGlx+7123cG/7W6hfu2rxukePHqVDhw6YTCaCg4OBU/9tDx06hMfjwTAMvLy8io/gNWvWjI0bN+Ll5YXb7Wb+/PnMmDGDgIAAnn32WTp16oTJZCr/D0hEREREREREKlRJsyIFT5e54+nZbPj1APsPH2f7viPsP3wcp8tdfAzPZDLh7+vNjdfXoEm9mtxQtyqtbq5PgJ/PWdd75JFHWL58OTVr1gROHclLTk4mKysLk8lU3OkUEhLC5s2bue666067/9ixY7zxxhusXr2atm3b8vzzz1OjRo2y+wBERERERERE5LKj4OkqVeRwkZNXQJHTicVsxurlRaUgP8zmkp2aTEhI4OGHHyYsLAybzQaAw+Fg7969uN1u3O5ToVZUVBRLly7FarWedZ1vv/2WiRMnkp2dzYgRI4iJicHLy6vU3qeIiIiIiIiIXL5KmhVpxtMVxtvmReVKgdQMD6Va5RDCQgJKHDoB3H333dSoUYOsrKzir9lsNipXrozb7cZisdC6dWvS09MZPXo0+fn5Z12nY8eOLFq0iOjoaKZOncrAgQPZtm3bpb49EREREREREbmKKHi6xnh7e9OnTx8cjj+emufxeHA4HAQGBlKrVi1mzpzJlClT2Lp1K48++uhpIdV/8/PzY/To0cyZMwebzcbQoUMZN24cdru9HN+RiIiIiIiIiFyudNTuGrRnzx66du2K1WolICCAlJQUqlWrxrhx47BYLHTu3BmAXbt28cQTTxAYGMjUqVOpXr36Odf0eDwsWrSIqVOn4uPjw+jRo+nSpYuGj4uIiIiIiIhchTTjSf5U3759Wbt2LWazmaCgIGbPnk2bNm3OuO7w4cM8/vjjOBwOpk6dSv369f903RMnTvDWW2+RmJjI7bffzgsvvEDt2rXL6m2IiIiIiIiISAXQjCf5U/369cMwDHx8fJg0adJZQyeA2rVrM3v2bCpVqsSwYcPYsmXLn65bpUoVJkyYwDvvvMPhw4fp378/s2bNwuFwlMG7EBEREREREZHLmYKna1TXrl1p0aIFr732GlFRUX96bVhYGLGxsdxwww2MGjWKNWvWnHf9tm3bsmDBAgYMGEBsbCwxMTFs3ry5tMoXERERERERkSuAjtpdwwzDuKAZTA6Hg5deeolvv/2Wl156ifvvv79E9+3bt49x48axbds27r//fp544glCQkIusmoRERERERERqWia8SRlwuPxMGHCBBYvXszjjz/O4MGDSxReeTweli5dypQpUzCbzTz99NPcd999Gj4uIiIiIiIicgVS8CRlxjAMYmNjef/994mJieGpp57CbC7Zqc2MjAzefvtt4uPjadGiBS+++CLXXXdd2RYsIiIiIiIiIqVKw8WlzJhMJkaMGMHzzz/PvHnzePnll3E6nSW6NzQ0lH/+85+8++67pKWl8cADDzBjxgwNHxcRERERERG5Cil4kovWt29fxo8fT1JSEqNHjyY/P7/E995+++189tlnPPTQQ3z44Yf079+f9evXl2G1IiIiIiIiIlLeFDzJJYmMjGTy5Mls3bqVRx99lKysrBLfa7PZGDlyJPPmzaNKlSqMGjWKv//972RkZJRdwSIiIiIiIiJSbhQ8ySVr1aoVsbGxpKam8vDDD5OamnpB919//fW89957vPLKK/zwww9ER0ezePFiPB5PGVUsIiIiIiIiIuVBwZOUikaNGjFr1ixcLhdDhw5l//79F3S/yWSie/fuxMXFcffddzNu3DiGDRt2weuIiIiIiIiIyOVDwZOUmtq1azN79mwqVarEsGHD2LJlywWvERISwssvv0xsbCx2u52YmBimTp1KYWFh6RcsIiIiIiIiImVKwZOUqrCwMGJjY7nhhhsYNWoUa9asuah1brvtNubOncvw4cOZO3cu/fr144cffijlakVERERERESkLCl4klIXEBDA1KlTadeuHc8++yzLli27qHVsNhsPP/wwn332GbVq1eKJJ57ghRde4MSJE6VcsYiIiIiIiIiUBQVPUiZsNhsTJkygZ8+evPrqq3z44YcYhnFRa9WuXZt3332Xf/3rX2zevJk+ffqwYMECDR8XERERERERucwpeJIyYzabefHFF3nkkUeYOnUqb7/99kWHRSaTia5du7Jo0SK6dOnCxIkTeeihh9izZ08pVy0iIiIiIiIipUXBk5Qpk8nEiBEjeP7555k3bx4vv/wyTqfzotcLCgpi7NixzJ49m6KiIgYOHMjbb79Nfn5+KVYtIiIiIiIiIqVBwZOUi759+zJ+/HiSkpIYPXr0JQdFzZo149NPP+Wxxx5j0aJF9O3bl9WrV5dStSIiIiIiIiJSGhQ8SbmJjIxk8uTJbN26lUcffZSsrKxLWs/Ly4vBgwezcOFCGjRowDPPPMMzzzzD8ePHS6dgEREREREREbkkCp6kXLVq1YrY2FhSU1N5+OGHSU1NveQ1a9Sowb///W9ef/11tm/fTp8+fZg7dy5ut7sUKhYRERERERGRi6XgScpdo0aNmDVrFi6Xi6FDh7J///5LXtNkMhEREcGiRYvo3r07b7/9Ng8++CA7duwohYpFRERERERE5GIoeJIKUbt2bWbPnk2lSpUYNmwYW7ZsKZV1AwICGDNmDB999BEAgwcPZuLEieTm5pbK+iIiIiIiIiJScgqepMKEhYURGxtLw4YNGTVqFGvWrCm1tZs0acLHH3/M008/zRdffEGfPn1ITEzEMIxS20NERERERERE/pyCJ6lQAQEBTJkyhXbt2vHss8+ybNmyUlvbYrEQExPDokWLuPnmm3nhhRd46qmnSElJKbU9REREREREROTcFDxJhbPZbEyYMIGePXvy6quv8uGHH5ZqZ1LVqlV58803eeutt9i3bx99+/blo48+wuVyldoeIiIiIiIiInImBU9yWTCbzbz44osMHz6cqVOn8vbbb+PxeEp1jw4dOrBw4UL69OnDu+++y1/+8he2bdtWqnuIiIiIiIiIyB8UPMllw2QyMXz4cJ5//nnmzZvHyy+/jNPpLNU9/Pz8ePrpp/nkk0/w8fFh6NChjBs3DrvdXqr7iIiIiIiIiAiYjBKcabLb7QQHB5OdnU1QUFB51CXXuMTERP7+97/TsmVLXn/9dfz8/Ep9D4/Hw6JFi3j33Xfx9vZm9OjRdOnSBZPJVOp7iYiIiIiIiFxNSpoVqeNJLkuRkZFMnjyZrVu38uijj5KVlVXqe5jNZvr168eiRYto0aIFL730Eo899hiHDx8u9b1ERERERERErkUKnuSy1apVK2JjY0lNTeXhhx8mNTW1TPapUqUK48ePZ/LkyRw5coT+/fszc+ZMHA5HmewnIiIiIiIicq1Q8CSXtUaNGjFr1ixcLhdDhw5l//79ZbbXnXfeyYIFC4iJieH9998nJiaGzZs3l9l+IiIiIiIiIlc7BU9y2atduzazZ8+mUqVKDBs2jC1btpTZXj4+Pjz++OPMnTuXoKAghg8fzv/93/+VyVE/ERERERERkaudgie5IoSFhREbG0vDhg0ZNWoUa9asKdP96tevz8yZM/nb3/7Gt99+S3R0NMuWLaMEs/hFRERERERE5D8UPMkVIyAggClTptCuXTueffZZli1bVqb7mc1mevXqRVxcHG3btuXVV19lxIgRHDp0qEz3FREREREREblaKHiSK4rNZmPChAn07NmTV199lQ8//LDMu5BCQ0N59dVXmTZtGidOnOCBBx5g+vTpFBUVlem+IiIiIiIiIlc6BU9yxTGbzbz44osMHz6cqVOn8vbbb+PxeMp839atW/PZZ58xZMgQPv74Yx544AHWr19f5vuKiIiIiIiIXKkUPMkVyWQyMXz4cJ5//nnmzZvHyy+/jNPpLPN9bTYbI0aMYN68eVSpUoVRo0bx0ksvkZGRUeZ7i4iIiIiIiFxpTEYJzinZ7XaCg4PJzs4mKCioPOoSKbHExET+/ve/07JlS15//XX8/PzKZV/DMPjyyy+LO67++te/0rNnT8xm5bkiIiIiIiJydStpVqTfkOWKFxkZyeTJk9m6dSuPPvooWVlZ5bKvyWSiW7duLF68mLvvvptx48YxbNgw9u3bVy77i4iIiIiIiFzuFDzJVaFVq1bExsaSmprKww8/TGpqarntHRwczMsvv0xsbCw5OTn85S9/YfLkyRQUFJRbDSIiIiIiIiKXIwVPctVo1KgRs2bNwuVyMXToUPbv31+u+992223MnTuXESNGMH/+fPr378/3339frjWIiIiIiIiIXE4UPMlVpXbt2syePZtKlSoxbNgwtmzZUq77W61Whg4dymeffUbt2rV58sknef755zlx4kS51iEiIiIiIiJyOVDwJFedsLAwYmNjadiwIaNGjWLNmjXlXkPt2rWZOnUqr732Gj///DPR0dEsWLAAj8dT7rWIiIiIiIiIVBQFT3JVCggIYMqUKbRr145nn32WZcuWlXsNJpOJLl26sGjRIrp27crEiRN56KGH2L17d7nXIiIiIiIiIlIRFDzJVctmszFhwgR69uzJq6++yocffohhGOVeR1BQEGPHjmX27Nk4HA4GDRrEpEmTyM/PL/daRERERERERMqTySjBb+J2u53g4GCys7MJCgoqj7pESo1hGMTGxvL+++8TExPDU089hdlcMZmry+Vi7ty5xMbGEhQUxJgxY+jYsWOF1CIiIiIiIiJysUqaFanjSa56JpOJESNG8PzzzzNv3jxefvllnE5nhdTi5eXFgw8+yIIFC2jYsCHPPvsszzzzDMePH6+QekRERERERETKkoInuWb07duX8ePHk5SUxOjRoyv0qFuNGjV4++23mThxIjt27KBPnz58+umnuN3uCqtJREREREREpLQpeJJrSmRkJJMnT2br1q08+uijZGVlVVgtJpOJTp06sWjRIu6//37+/e9/M2jQILZv315hNYmIiIiIiIiUJgVPcs1p1aoVsbGxpKam8vDDD5Oamlqh9fj7+/Pcc8/x0UcfYTabeeihh5g4cSK5ubkVWpeIiIiIiIjIpVLwJNekRo0aMWvWLFwuF0OHDmX//v0VXRJNmjTho48+4umnn2b58uX06dOHxMTECnkSn4iIiIiIiEhpUPAk16zatWsze/ZsKlWqxLBhw9iyZUtFl4TFYiEmJoZFixbRtGlTXnjhBZ588klSUlIqujQRERERERGRC6bgSa5pYWFhxMbG0rBhQ0aNGsWaNWsquiQAwsPDeeONN5g0aRL79++nb9++fPjhh7hcroouTURERERERKTEFDzJNS8gIIApU6bQrl07nn32WZYtW1bRJRVr3749CxcupG/fvkybNo2YmBi2bt1a0WWJiIiIiIiIlIiCJxHAZrMxYcIEevbsyauvvsqHH3542cxW8vPz46mnnuKTTz7B19eXhx9+mNdeew273V7RpYmIiIiIiIj8KZNRgt+u7XY7wcHBZGdnExQUVB51iVQIwzCIjY3l/fffJyYmhqeeegqz+fLJZz0eD3FxcUydOhWbzcbTTz9NVFQUJpOpoksTERERERGRa0hJsyIFTyJnsXDhQiZOnEiXLl145ZVXsFqtF7xGdk4+uw6msP9wGnt+S2X3wVTyCopwud14WSz4+3pz4/XVaVi3OvVrh9Po+hoEB/qVaO2TJ08yadIkVq5cSevWrXnhhReoU6fOBdcoIiIiIiIicjEUPIlcosTERP7+97/TsmVLXn/9dfz8zh8KGYbBL3sP88W3m0n6cTt5BUV4PB4AvLwsWMxmTIABuD0eXC43AGazGX9fbyLvuJluHW+l6Q21S9TF9MMPPzBhwgROnjzJkCFDGDx4MDab7VLetoiIiIiIiMh5KXgSKQUbNmzgmWee4frrr+edd94hJCTknNf+8PMeZi7+ll0HU3A4Xfh62/D39cbLYv7TEMkwDFxuD3kFRRQUOfC2etGoXk0e7tWBO29teN4aCwsLmTVrFh9//DG1atVi7NixtGjR4mLeroiIiIiIiEiJKHgSKSW7du3iiSeeIDAwkKlTp1K9evXTXs/OzWfGZ0ks+2YTDqeL4AA/fLytFzV3yTAMCoucZOfmY7N6cf/dLRjZP4LggPN3Wx04cIDXXnuNrVu30q1bN5566qk/DcpERERERERELpaCJ5FSdPjwYR5//HEcDgdTp06lfv36AGzcfoDx7y/jt5STBPh5E+DnUyqDvg3DIDe/kNyCIq6rUYUXhnWn5U31znufx+Nh2bJlTJ48GYCnnnqK7t27a/i4iIiIiIiIlCoFTyKlLD09nb/+9a+kpqby9ttvk1Fk5Z8zlpCbX0jlSoF4WSylvqfL7eZkZg6B/r78fWRP7m59U4nuy8jI4N///jdfffUVt912Gy+++CLXX399qdcnIiIiIiIi1yYFTyJlIDc3l9GjR7Nx52G8atyK2eJF5ZDAMu0oMgyDk5k5eHtb+ceo3iUOnwDWr1/PhAkTSElJ4cEHH+Thhx/G29u7zGoVERERERGRa0NJsyJzOdYkcsULCAjgkb+OwRlyIydPZuCFu8yPsZlMJipXCqSwyMk/Zyxly67fSnxv69atmT9/PkOHDmXOnDn079+fn376qQyrFREREREREfmDgieRC5CbX8hbH8fj4xdApSA/jh07xsn0k2W+r8lkokqlQHLyCpgwaxl5BUUlvtdmszF8+HDmz59P1apVeeyxx/jb3/5Genp6GVYsIiIiIiIiouBJ5ILMjPuGfcnHCQsJpEaNGlSuXJkTaSc4fvw4Buc9tXpJTCYTYSGB7Es+zvuLVl3w/XXr1mXGjBn83//9Hz/99BPR0dEsXrwYj8dTBtWKiIiIiIiIKHgSKbFN2w8S9/V6/H28sXqdGiRepUoVqlWrRkZGBilHUyjByLRLYvWy4OdjI+7r9WzacfCC7zeZTNx3333ExcXRqVMnxo0bx7Bhw9i3b18ZVCsiIiIiIiLXOgVPIiX0yfK1FBQ5CfT3Oe3rlSpVombNmuTk5HD48OFLCp+S5owjac64P70myN+XgkInny7//qL3CQ4O5uWXX+b9998nJyeHmJgYJk+eTEFBwUWvKSIiIiIiIvK/vCq6AJErwYEjaWzcfpBAP5/Thonn2zNY9cn4067d9j/3+gZWImLQ2FKrxWQyEejvw4ZfD3DgSBr1aoVf9Fq33norc+fOZc6cOcycOZOvv/6a559/nnbt2pVavSIiIiIiInLtUvAkUgLx322hoMhBtbDgs77uFxRGzYa3nfU1q7dvqdfj7+vNsfRs4r/bwmMDOl/SWlarlaFDh9K5c2cmTJjAU089RadOnXj22WcJD7/4UEtEREREREREwZPIeTicLr5cswVvq9dp3U7/zT84jBtbX1oAdCFMJhM2qxdfrtnCI306YbNe+j/lWrVqMWXKFL7++mvefPNN+vTpw2OPPUbfvn0xm3UqV0RERERERC6cfpsUOY/fUk6SnZOPn4/tktdaPu05flg6nYLcbH5OnMfXH/wfy6eN4eTR/X9634Etq1k+bQzrPn8Pl6MQAD8fG9k5+SSnnrzkun5nMpno3LkzcXFxREVF8eabb/LQQw+xa9euUttDRERERERErh0KnkTOY//h4xQ5Xdis1lJZz1mYz/eLp5KTnkqNG26hTpPbsdq8z3qtYRjs+GE5O35YTvX6Tbm928N42U4NN/e2WnE4XexLPl4qdf23wMBAXnzxRWbPno3T6eTBBx9k0qRJ5Ofnl/peIiIiIiIicvXSUTuR89iXfByzyYTZfPZjdgB52ensXr/yrK9VqlaH8DqNiv+ek3GM2o1a0axjH0x/coTN43Gz7ZuFHNm9ibo338nNd/XAZPrjerPZhMlkYv/h0g+efte0aVPmzJnDvHnzeO+990hMTOS5556jY8eO5zx2KCIiIiIiIvI7BU8i51GSYCffns7ejV+f9bXrm7U7LXgymy00bnPfn4ZObqeDTSvmkJa8i4atOtOw1T1nvc4A9h9OO299l8LLy4tBgwYRGRnJxIkTee6552jfvj1jxoyhWrVqZbq3iIiIiIiIXNkUPImcR35hEebzdPdUqd2Q27s/UqL1fINCsfn6n/N1t8vJumXvkZV2mKYdelP3pjbnvNZsMpFfUFSifS9V9erVmTRpEt988w1vvvkmffv2ZcSIEQwYMACLxVIuNYiIiIiIiMiVRTOeRM7D5fJAKZ4q8/YL/PP9nEXYT6Zg8/EjrGaDP73WZAKny116xZ2HyWSiU6dOLFy4kB49ejB58mQGDRrEr7/+Wm41iIiIiIiIyJVDwZPIedisXhhG+e3n7RtAy6iHcDmKWLd0BrmZ5z5KZxjgbSudoecXwt/fn2effZaPPvoIs9nMkCFDeP3118nNzS33WkREREREROTypeBJ5DyCA/3weDzlumd4nRtpde8QnI4C1n3+3jnDJ4/HQ1CAb7nW9t8aN27MRx99xOjRo/nyyy+Jjo5m5cqVGOWZ1ImIiIiIiMhlS8GTyHk0qF21Qp7gVqV2Q1oXh09n73wymUw0qFO13Gv7bxaLhQEDBrBo0SKaN2/O2LFjefLJJzl69GiF1iUiIiIiIiIVT8GTyHnUr1MVA3C7z931lJedzu71K8/5x+1yXtTelWvdQOt7h+JyFLLu8xnkZPzxhL3f62lQ5/J4slx4eDgTJ05k0qRJHDhwgL59+/LBBx/gdF7cexcREREREZErn55qJ3IeDepUxcfmRZHTiZ/F+6zX5NvT2bvx63OuUa/5XVi8Lm4WU2iNetwW9RCb4z9i3eczuL37cALDqlLocGCzelG/dvhFrVtW2rdvT8uWLXn//feZPn068fHxjB07lltuuaWiSxMREREREZFyZjJKMIzFbrcTHBxMdnY2QUFB5VGXyGXD4/Ew4LmpHEo5SXho+X7/ezwetm7dCqZTR9rMpj+aFA2LNyZXHtbjG7CYzZj/88disRT/+d+v3X333bzyyivlVv+ePXsYN24cv/76Kz179uSJJ57QzxAREREREZGrQEmzIh21EzkPs9lMj04t8Xg85T5k3Gw2ExISgsfjwel04nQ5MQyD39NiU85RPG43DoeDwsJC8vLysNvtZGRkcOLECY4dO0ZKSgq//fYbW7duLfdjbw0bNmT27Nm88MILJCYmEh0dzVdffaXh4yIiIiIiItcIdTyJlMDJzBz6PTMZp8tNSKBfue7t8XjYv38/uXm5YICBgcXmi5fZREjOr5gN13nXyMjIwGq18vXXX1OnTp1yqPpM6enpvPXWW6xcuZLWrVvzwgsvVFgtIiIiIiIicmnU8SRSiipXCiTijpsoKCzC4ynfbh2z2Uzt2rWxWW1YLBasVitgIffYfo6nHD5vF5PH4yE/P5+//OUvFRr0hIWFMW7cOKZMmcLRo0fp378/sbGxOByOCqtJREREREREypaCJ5ESevD+uwgLCSQjO7fc9/bx8aF69eqn/mLxxmr24O9KIycnh/3793Ps2DFcrrN3PmVkZBAaGsojjzxSjhWfW5s2bViwYAGDBg1i1qxZDBgwgI0bN1Z0WSIiIiIiIlIGFDyJlFDtamGM6BeBx/BQUFT+XTqhoaEEBoXgMQxsecmEBfnSoEEDqlSpgt1uZ9++fRw/fvy0AMrtdlNYWMjgwYP/CK4uA97e3owaNYp58+ZRqVIlRo4cySuvvEJmZmZFlyYiIiIiIiKlSMGTyAXo2akFbZo3JMueh9t96YPG/xgTfn5utwebXwCB5kKykn/BMAzMZjNhYWE0aNCAsLAwsrKy2L9/PydOnMDtdpORkUGVKlUYOnToJddaFurVq0dsbCx///vf+e6774iOjubzzz8v9yHuIiIiIiIiUjYUPIlcALPZzJih3ahbowppmfZLCkhyc3PZsmUL2dnZ573W7fGQlmmnXq2qTBo7jICAANLT00+rq0qVKjRo0IBKlSqRnp7O3r17ycrKYuDAgVSpUuWi6yxrZrOZHj16sGjRItq1a8c///lPRowYwYEDByq6NBEREREREblECp5ELlCN8EpMfGYANcMrcTzDftGdT/kF+XjcHg4fPozDee6je263h7R0OzX/s2+XiA489dRTOBwO8vPzT7vWYrEQHh5OgwYNsFqtuFwuli9fzscff0xBQcFF1VleQkNDefXVV5k+fTrp6enExMQwbdo0ioqKKro0ERERERERuUgKnqRCeTweihwucvIKKSh0lMrxtfJQv3ZV3n5+ENf9p/Mpv/DCw5GC/AIwgcPpIDk5+azdU/mFRaRl2LmuZhX+/cIg6tUKB2DkyJFERkaSnp6O2+0+6/o+Pj68/PLL3HfffUybNo0ePXowf/78y/4pcq1atWL+/PkMHTqUOXPm0K9fP3788ceKLktEREREREQugskwjPMOmbHb7QQHB5OdnU1QUFB51CVXqePp2Wzbk8y+5OPsPJDC7oMpFDldGIaByWTCbDJRp3oYNzeoTYM6Vbnx+uo0ur4GJpOpoks/q+Pp2Uz66CtWb9wJQFhwAGbz+fNcwzDYuXMnRY5TgZXZZCY0LJRatWphwoTH4yE9KxdM0LFVE0Y/GEV4WPBpa6SmptKjRw8OHz5MjRqnf0bHjx+nWrVqJCUlERQUREpKCjNnzmT58uVUrlyZYcOG0b17d6xWayl+GqXvt99+Y/z48WzcuJHOnTvzzDPPEBYWVtFliYiIiIiIXPNKmhUpeJIy5/F42PDrAZav/pk1m3aRX1BUHJJ4W72wWMyYTCYMAzyGB4fDhdtjAAY2qxcNr6tOz4iWRNx+EwF+PhX7Zs7C4/GQsHYbU+et5ESGHZvVi6AAX7wslnPeU1BQwN59e8EAl9uFzWrD4/FQtWo1rD5+FDldhIcG8fiAznRt1+ycYdaqVat4+OGHMZvNVKpUCQCn00laWhr/+te/ePjhh0+7Pjk5mdjYWFasWEH16tUZPnw4UVFRWP6k1opmGAbx8fFMmjQJl8vFX//6V3r16lWigE9ERERERETKhoInuSys3byb6Z8lcuBIGi6XGz8fbwL8vEvcFVRY5MSeV4BhQOVKAfTrcgcx97XF2+ZVDtVfmOPp2Sxa+RNfrt7CyawcDMDPx4aPzYrVy3JaR9LJ9JMcPXIUi5cXDqcTm80XAzOYoHaNcB647y76dL6dqv/T5XQ248aNY8qUKVSuXBlvb29SU1OpXbs2q1atws/P76z37N+/n/fee49Vq1ZRt25dRowYQWRk5GUd5tjtdiZPnszSpUtp2rQpY8eO5YYbbqjoskRERERERK5JCp6kQmXn5DNt/tcsX/0zDpebkAA/fLwv/liXy+0mO6cAl9vNzQ1qM+bhbjS6vkYpVlx68gqK+Gb9DpZ9s4k9vx2jyOHE5XIDJgzDA5iw59gpKizEbLHgcjqwmMBqFFJwfC+Vfd0sWvgZDRo0KNF+RUVFPPDAA6xbt44qVaqQnp7OhAkTePDBB897786dO5kxYwbff/89DRo04NFHH6V9+/aX7dFGgJ9//pnx48dz6NAhBg4cyCOPPIKvr29FlyUiIiIiInJNUfAkFWbzzkO89t5Sfks5SYCfDwF+3qUWZDicLtKzcwn082Forw4M7N7usg5JcvMLOXAkjX3Jxzl09ASFRU4Kixx89tk8igryCfA2cfLoQQxHDrVrVsfj8ZCSkkKzZs1YuHAhwcHn73gCOHDgAD179uTQoUM0a9aMxMREfHxKfixx27ZtTJ8+nQ0bNtCkSRNGjhxJmzZtLtvP1ul08sknn/D+++8TFhbGmDFjuOuuuyq6LBERERERkWuGgiepEGs37+Yf0+LIzs2nckjgn845uliGYZBpz8Pt8fBAVBue+EuXy/qI2P/65Zdf6NatGwEBAfj6+nLixAkyMzNp2LAhcCpUOX78ON27d2f69Ol4eZXsWOGSJUsYO3Ys//jHP+jfv/9F1bZx40amTZvGtm3baN68OaNGjaJFixYXtVZ5OHLkCK+//jrr1q2jU6dOPPvss4SHh1d0WSIiIiIiIle9kmZFV85v63LZ+2HLXv4+ZSH2vAKqhgaXSegEYDKZCA0OwMdmZe6XPzD50xWUID+9bGzevBmHw1HckeTr64vb7cbhcABgtVoJDQ3lyy+/5O233y7xuj179mTx4sX07dv3omtr2bIls2bNYvLkyTgcDkaMGMGoUaPYtm3bRa9ZlmrVqsXkyZMZN24cW7dupU+fPsyfPx+Px1PRpYmIiIiIiAgKnqSU7DmUyivvLiKvoIjwSkHlckQr0N8XXx8b875ax7yv1pX5fqVl/fr1AMWf0e/ziQoKCoqv8fPzw9fXl3fffZdly5aVaF2TyUTjxo0vufvLZDJx55138vHHH/Pmm2+Snp7O0KFDeeqpp9i1a9clrV0WTCYTnTt3ZtGiRdx777289dZbDB48mJ07d1Z0aSIiIiIiItc8BU9yyRxOFxNnLyfTnkd4aPmETr8L8vfFy2JmZtw37Es+Vm77XiyXy8W6detOm79ksVjw9vYmPz//tGtDQkJwOp28+OKLFdJxZDKZ6NixI/PmzWPcuHEkJyczcOBAxowZw/79+8u9nvMJDAzkhRde4IMPPsDlcjF48GDeeuutMz5XERERERERKT8KnuSSzf3yB7buSSY0KKBChlFXCvLHnlfAxNnLcThd5b7/hdi1axfp6en4+fmd9nVfX9/TOp7gVPBTrVo10tPTeeyxx0hLSyvPUouZzWY6d+7MwoUL+cc//sGuXbt44IEHeOmll0hOTq6Qmv7MzTffzJw5c/jrX//KkiVL6NOnD6tWrbqijmOKiIiIiIhcLRQ8ySXZl3yMjz5fg83LgretZEOwS5vJZCI0yJ8tu35j4YqfKqSGktq0aRNFRUVnPHHO19eXoqKiM2YT/R4+7d27lyeffJKioqLyLPc0FouFbt26ERcXx4svvsjmzZvp06cPr776KikpKRVW19l4eXkxaNAgFi5cSKNGjRgzZgyjR48mNTW1oksTERERERG5pih4kksS9/UGcvIKqRTkX677etxudq9fyapPX+erGS/w9cyxZKfsYX78OgqLHOVay4VYv349JpPpjM6ws815+p3ZbMbPz4+kpCTeeOONcqnzz1itVnr37s3SpUt5+umnWbt2Lb179+b111/nxIkTFV3eaapXr85bb73FG2+8we7du+nbty9z5szB5bq8O+NERERERESuFhXToiJXhUx7Hit/2Iavj/W0ICXfnsGqT8afdq3JbMbmG0Boteuof2tHQsJrX9LeB7auZu/GrwmtUY8a9ZthMluoUr0Wx9OzWbNpN53vbHpJ65cFt9vNunXr8Pb2PuM1b29vzGYz+fn5WK1WCgoKKCgowO12YxgGvr6+1KpVC6vVWgGVn53NZmPAgAH07NmTBQsW8NFHH/H555/Tt29fBg8eTGhoaEWXCJzqGrv77ru5/fbbmT59OlOmTOGrr75i7NixNG16+X2fiIiIiIiIXE0UPMlFW/n9Nuy5BVQJDTrr635BYdRseBsAbpeD7BNHSN2/jWMHt3PH/cMJq1Hvovc+fmgnFqs3d3R/BLPlj2/jY+lZLE3awD1tbq6QeVN/5vf5TgEBAcVf83g8FBYWFodMJ0+exO124+vrS9OmTWnVqhXNmzenadOmNGjQ4JKfWFcWfH19GTx4MH369GHu3Ll88sknLF68mAEDBjBw4ECCgs7+/VHe/Pz8eOaZZ7j33nsZN24cQ4cOJTo6mscee4zAwMCKLk9EREREROSqpOBJLlr82q2YzSYs5whD/IPDuLF159O+tm/zKnb9GM/u9Su4s+ejF713Ub4dm4/faaETQKCfL7/sPUxyajp1a1S+6PXLwqZNmygoKMBms5GdnV183MvHx4eQkBBq1qzJiRMniI2NpXnz5oSEhFRswRfI39+fRx55hP79+zNnzhzmzp3LZ599xsCBA4mJicHfv3yPY55L48aN+eijj1i4cCHvvvsu33zzDc888wz33HPPZRdWioiIiIiIXOkUPMlFyckr5NDRE/h62y7ovtqNW7Prx3iy046c9vXknes5fnA79vRUivJzsHhZCQmvTf3b7qZyzQbF1+1ev5K9G78u/vvyac8B4BtYiYhBY/H1tpKTV8Ce31Ivu+Bp586dxcfsbrrpJlq3bk2zZs1o1qwZDRo0YMOGDTz++OPUrVuX4OBgCgodFDmcANisXvh4Wy/Ljqf/FRQUxGOPPcaAAQP48MMP+eCDD5g3bx4PPfQQffv2LZ5nVZHMZjP9+/fn7rvv5s0332Ts2LF88cUXvPDCC9SsWbOiyxMREREREblqKHiSi7L/8HGKHC6CAi4uRDD9T4Dy65olBFWuQeVaN2Dz8acwL5vjB7fz47JYWnZ9kGrX3wxAWM36ABzc9h0A1ze7CwCr96k6zGYzJpOJfcnHuafN5TW/Z/jw4URFRXHzzTefMf+ooNBBrseXQp/qjH3nM/JcXmTn5OMxTr1uNoG/rw+N69eg0XU1qF8nnNuaXE9wgF8FvJOSCQ0NZfTo0QwcOJAPPviAadOm8cknnzB06FB69+6NzXZhoWVZCA8PZ+LEiXz33Xe8/vrr9O3bl2HDhjFo0KDLap6WiIiIiIjIlUrBk1yUfcnHcbndWL0sF3Tf4R3rAQitfv1pX+844Dn8gk4PYwrz7Hy36B12/vBlcfBUuWZ9Ktesz5HdGwHOOMr3u50HUi6orvJw/fXXc/31p7/v5NSTfPXdVr74djMnM3Mo8q/Lz3tSCa0UgtXLgsVswmQCjweyc/NZvWEnazbuAiA40I+ubZtxX4dbufG66pftMbHw8HCef/55Bg0axMyZM5k0aRIff/wxw4YNo3v37pdFwHPXXXfRsmVL3n//fWbMmEFCQgJjx47llltuqejSRERERERErmgKnuSiHE3LwGQy/WnYkZedzu71K4E/hounH92Pt28Ajdvcd9q1/xs6Afj4B1G9XlMO/fI9+TmZ+AVWKlFtNqsXh46mXcC7KX/HTmYxZe5Kvtu0i/xCBzYvC5WC/HEX5VFQUEBocMCf3u9yu8nJK2Re/DqWJG3klsZ1eWpgVxrUqVZO7+DC1ahRg5dffpmHHnqI2NhYxo8fz4cffljcCWaxXFiIWdp8fX154okn6Nq1K+PHj2fYsGH06NGDJ5988rIZkC4iIiIiInKlUfAkF6XI4cQwjD+9Jt+efto8JgBvv0Du7DUK/+DT5y/lZaezb/Mq0o/uozDPjsftOn2/vOwSB08mk4kih+v8F1YAj8fDV99t5d15KzmRkUOAnzfVwoKLAzxfX1+ysrJwe9xYzOcOYrwsp4IqwzDIL3Tw49Z9DN83iyG9OtC/6x3YrJfvP+06derwr3/9iyFDhvDee+/xj3/8gw8++IARI0YQGRlZ4XOsGjZsyKxZs1iyZAlTpkxh9erVPP3009x7772XbVeZiIiIiIjI5eryn1QslyW3x+DPYyeoUrsh3Ua9QbdRb3DPkFdo3OY+igpy2fDVB7icRcXX5WWfZO2idziyayN+QWHUvekObmgZyQ0t7yG0Rr1T+7ndJa7NBLg9not4V+dnGAaHDh26qHuzc/N58d+f8a/3lpBpz6NqWBCB/r6nhRm+fqdmVRUWFJZoTZPJhL/vqfCqyOFkyqcr+OtrH5J6IuuiaixP9evXZ+LEicyZM4datWoxduxYYmJiWL169XlDzbJmNpuJjo4mLi6O22+/nVdeeYVRo0aRnJxcoXWJiIiIiIhcaRQ8yUXxtnpxIb0f3r4B1L+1Izfc1onczDR2/5RQ/NqBrWtwFhXQvFM/7rh/ODe168GNrbtwY+vOBISEX3BtBgY2rzM7fgzD4OTJk2zevJmlS5cyefJknnnmGe6//37mzp1borW///577r33XsaNG4fT6SxxTelZuTz7xqck/bQdf19vqlQKOmtnj81mw2wxk1+QX+K14VQAFRocQEigH5t2HOKJ8R/xW8rJC1qjojRu3Jh33nmH2bNnU6lSJZ555hkGDx7MDz/8UOEBVFhYGK+99hpTp04lJSWF/v37Exsbi8PhqNC6RERERERErhSX73kcuawF+vtc1LGjBi0iSN61gUO/ruP6ZnfhFxRKfnY6ANWuv+m0aw3DIPPYoTPWcLqcuFwuTCYTOTk5+Pr54mX541vZ6XRhMXn46quv+O2330hOTmbPnj0cPHgQu92Ow+HA4XBgNptxu9243W66detWovo3bdrEiRMnmDJlCvv27eOtt96iUqU/PwKYnZPPmLfmsnVPMmHBAX96DM6ECV9fXwryC0pUz//ytlmpEhrIwaMneOaNT3nnxUHUDD9zftblqFmzZkyfPp2NGzcybdo0nnjiCZo3b86oUaNo0aJFhdZ2xx13sGDBAmbNmsXs2bOLh4+3bNmyQusSERERERG53KnjSS5KvVrhGIaB5wKPtFm8rDS49W4Mj5u9GxMB8P3P7KaM1EOnXbt/8zfkZBw7Yw2zyYyJU/OSfvvtN3bu2Mmvv/7K9u3b+fXXX0k9nsa2DWsYOnQo//jHP5g5cybfffcdqampFBQU4PF48PpPR5TD4aCwsJDw8JJ1Vm3cuBEvLy8qVarEV199Rb9+/di7d+85r3e7Pbzy7qIShU6/8/P1o6CggPMfZjw7L4uF8EpBHEo5wfOT5pFXUHT+my4jLVu2ZNasWUyePBmHw8GIESMYNWoU27Ztq9C6vL29GTVqFHPnziUsLIyRI0fy8ssvk5mZWaF1iYiIiIiIXM4UPMlFaVCnGt4260UN8a7T5Ha8/YM4smcTedknqXtTG0xmCxtXfMyWpPns+P4Lvl88lT0bvya8buMz7rdYLJgtFgyPh6KiIhxOB0VFRRQWFlJUVITH7Sbz+GFOnDhBRkYGWVlZZGZmkp6eTlpaGsePHyctLY0TJ06QnZ2N2+0mMDDwvHU7HA5+/vlnfH198fPzo2rVqmzbto1+/fqxatWqs94T9/V61m3dS6VAvxIP/Pb398fj8VxwqPffLBYzlUMC2XUwlfcXnb22y5nJZOLOO+/k448/5s033yQ9PZ2hQ4fy1FNPsWvXrgqtrV69erz33nu8/PLLrF27lujoaD7//PNL+u8lIiIiIiJytVLwJBelVtVQAvx8KHSUfM7R7yxeVhrc1gnD42HPhq8JrlKT27s/QnDlmhw78CuHd23AavOlbe/HCK5S66xr/P5LvoGBYRgU/89kwjAMXPmnulB+P0rndruL77FYLFgsFqxWKyaTiZo1a9KhQ4fz1r1r165TR/t8Tw0At1qt1KxZk2PHjvHII48QGxt7WvjwW8pJYhetwmw24+NtK/Hn4+vrS8OGDf/0qXYlYfWy4OdjI+7r9WzafvCS1qooJpOJjh07Mm/ePMaNG0dycjIDBw5kzJgx7N+/v8LqMpvN3H///cTFxdG+fXv++c9/Mnz4cA4cOFBhNYmIiIiIiFyOTEYJpvfa7XaCg4PJzs4mKCioPOqSK8A/3o1j+ZqfqRYWXO6PmTcw+O3Qb2RkZOAxToU9JkyYrT54XA7SNsVhxoPJZMJsNuPxeIqPBv7+LW8Yp0Krnj178n//9380atQIm+3cAdGcOXMYM2YMNWrUOO39GoZBeno6TqeTAQMG8M9//hMfHx+eGP8x67bupWpYMOZy/nz+u7bj6dnUr1OVD/814oICsMuR2+0mPj6e2NhYUlNT6dKlC8OHD6dOnToVWtfGjRsZN24cR48eZfDgwTz88MN4e3tXaE0iIiIiIiJlqaRZkTqe5KJ163grVi8LRRfR9XSpTJioW7culUIrYTFbMGHCwMBktlCQtg88LtxuNy6XC4fDgWEYmM1mvL298fb2xmq1AuDl5cXRo0cZOnQoHTp0YOjQofz73/9m1apVpKenn7bnli1bTu39PyGSyWSicuXKBAQE8MknnzBo0CBW/fAzm3YcJDjAt9xCp93rV7J82nOcPPpHJ5DJZCIsJIADh9P4dsPOcqmjLFksFrp160ZcXBwvvvgimzdvpk+fPrz66qukpKRUWF0tW7Zk/vz5DBs2jDlz5tCvXz/WrVtXYfWIiIiIiIhcLtTxJBfN4/Hw4NgZ7P0tlfDQ4Aqpwe12c+DgAXJzczFMFkwmC+nbluPIzQBOHYmCP7qb4D8zosxmHA4H7dq145tvvmHv3r1s27aNrVu3snXrVo4dOzXUvGbNmjRv3pymTZsyfvx4Tp48+aeDyAsLCzl58iRhjTpgeFdi58r3APD2DSBi8EuYz3J8LifjOKvnvwmcGrQeMWjsRX0Wu9evZO/Gr7mjx0gq16x/2mvH0rNp2eR6pv19SLl3p5Ulh8NBXFwcH3zwAXa7nV69ejFkyJASD4svC8nJyYwfP54NGzbQuXNnRo8eTeXKlSusHhERERERkbKgjicpc2azmV4RLTEMcDgvfMh4abBYLFxX9zp8fHyxeHnjyD6GuyAbb29vvLy8Tnvy3u+znTz/GUpuGAYhISGsXr2a6667jn79+vHaa6+xfPlyvvrqKyZMmECHDh347bffGDduHNu3b+fkyZMkJydz4sQJ8vLyzhgo7ePjQ3j12uR4/LDbswAwmc0UFeSS9tvZh2If3rkeTKZTf8pIoJ8P2/YeZtfBiusKKgs2m40BAwbw+eef8+ijj7JixQp69uzJpEmTyMjIqJCa6tSpw7Rp03j11VfZsGEDffr0YdGiRRo+LiIiIiIi1yQFT3JJ7utwKzfVr0V6di4laJ4rE1arlbAq1fAyg/vkqXDH5XLh5eWFt7c3NpsNk8lUPGTcbDZjMpnw8/PDz8+PMWPGEBkZyQsvvMDXX39Nfn4+4eHhREZGMnr0aD766CP+8Y9/EBQURJUqVTCZTGRmZpKcnMzu3bs5cOAAqampZGdn43A48PhWxmL1wXA5APAPrYGXzYfDuzacUbvH4+bons1UqXXDWbuhSoufj40ih5NVP+0osz0qkq+vL4MHD+aLL75gyJAhfP755/To0YN3330Xu91e7vWYTCbuvfde4uLiiIyMZMKECQwdOpQ9e/aUey0iIiIiIiIVqWTPdxc5Bx+bleeGduPRf84mKyefSkH+5V6Dw+nCg4mB3dry2dE1UBRIbm4uTqcTq9WK2WzGZrNhGEbx3CfDMKhcuTJ9+vTh5ptv5scffyQpKYkXX3wRm81G27ZtiYiI4K677sLf35+dO3ditVqpUqXKH/s6HBQUFJCfn09BQQFZWVkABFwXhre3u/hIm8vlJrBafdJ+20lRfi7efgHFa6Qd2klRQS61G7UiPeXMJ6IZhsHhXRs4vHM9OenH8BgeAitVpe7NbajTuHWJPh97+jHWL5+J01FEUpCbxwbccwmf9uXN39+fRx55hP79+zNnzhzmzZvHggULGDhwIDExMfj7l+/3Z1BQEC+99BLdunVj3LhxDBw4kL/85S8MHz68+OmIIiIiIiIiVzN1PMkla1K/JoO6t8PhdFFQ5CjXvd0eD+lZOTS7oTYv/XUgr7/+OkFBQQQHB2M2m3E6/xh8bjKZ8PLywmw2Y7FYuOOOO5g4cSKPPPIIKSkp/O1vf2PZsmWMHDmStLQ0XnrpJe655x5Gjx7NF198cUZHl81mIzg4mOrVq1OvXj0aNmxI7dq18QmpBoYHl+vU8UPDMLAE1cTweDi4/cfT1ji8awNWbz+q1rv5jPdmGAY/J85l2zcLcRTkUeOGW6nTuDVup4Nt3yxkx/dfnPfzyUg9yLql0wBo1mUoOQRV2LHI8hQUFMRjjz3GsmXL6NGjBx988AHdu3fno48+oqCgoNzrueWWW/j000959NFH+eyzz+jbty9r1qwp9zpERERERETKm4InKRWDut9F+5aNyMrJL7en3Hk8Hk5k2KlZNZS/j+yFzepFz549ef7557HZbISGhp4RPv0+86l69erMnz+f5cuXM2jQINauXcvAgQN54YUXCA4OZsaMGXzxxRc8/vjjnDhxgk2bNpGZmcnhw4fJzs7G7XafUY/FYsEvIBiLTyCW/4RcAJhMeAdWxss3mKO7NxVfX5hvJy15FzUb3orFcmbzYfLO9aTs3ULtRq3o8MCzNOsYzc139aR9/6epel0TDmxdQ1bakXN+PscObufHL97H5htA296PEVa1JvkFRSSnnryET/3KEhoayujRo1m6dCldunRh+vTp9OjRg3nz5uFwlG9IarVaGTJkCAsXLqRevXqMHj2aMWPGkJaWVq51iIiIiIiIlCcFT1IqvG1e/N9jfWjT/AYy7Hll3vnkdns4nmEnPCyYCU8/wHU1/zgCN2LECIYNG4ZhGFSpUuW08On3rqXevXtjMpkIDw9n+PDhfPHFF0yaNImQkBD+9a9/0bVrV+bMmcPtt9/OX//6V8LCwqhSpQoej4eUlBT27NlDcnIyWVlZuFwuCgoKSE9PJ/VEJi63B4/bicl86qidj7c3DRs25IZb7qLAfpLM48kAHNm1CcPjoXbjVmd9j4d++R6Ll42b2/fCbPlj/pPZ4sWNt3cFIGXvz2e9N3nHejYlfExQaDXa9noM38BK2KxeOFxujqZlXuKnf+UJDw/n+eefZ/HixbRr1463336bnj17snjx4tOCyfJQs2ZN3nnnHcaPH8/WrVvp06cP8+bN0/BxERERERG5KmnGk5Qaf19vXn/6AV55N47VG3dSUOigUpB/8ayj0pJfUER2bgG1qoXyxjMx3FC32mmvm0wmXnrpJY4dO8bSpUupWrUqx48fx+l04vF4sNlsDBky5LR7LBYL7du3p3379qSkpLB06VKWLl3KggUL8PHxoaCggOuuu644xMrIyCArK4vMzFMhjo+PD6GhodzR5k72FQRTKcgfi+HgyE+njuT5+/tTq1ELdv0Uz+Gd66lUtQ6Hd20gqHJNgivXPOM9up0OctKP4eMfxL7N35zxuuE51XGVm3XijNcObl3D8UM7qFK7IS26DsbLaiv+XAzDuCaO2p1LjRo1ePnll3nooYeIjY1l/PjxfPjhhwwfPpyoqCgslrIb8P7fTCYT99xzD3fccQfTpk1j0qRJfPnll/ztb3+jcePG5VKDiIiIiIhIeVDHk5QqP19vXnuyH4/HdMZm9eLYyexSO3rn8XhIy8gmr7CIe+68mfdeefiM0Ol3Xl5evPXWW7Rr146ioiKqV6+OyWTC4/FQt25dmjVrds59atSowahRo/jyyy+ZMGECWVlZ2O12du7cyZ49e0hNTcVsNtOgQYPiQdFRUVFUqVKFQ7/9hsPhOPV0O8/pM6G8fQOoel1jUvZt5cThPeRlnThnt5OjqAAwKMzLZu/Gr8/4s2/zKuBUQPW/MlIPAlClzo3FoVMx41S32LWuTp06/Otf/2L+/Pk0atSIf/zjH/Tr14+VK1eWa+dRYGAgzz//PB988AEej4fBgwfz5ptvkpeXV241iIiIiIiIlCV1PEmps1m9ePD+u2jT/AYmzv6CrbtPHS0L9PfB19t2wR1QTqeL7NwCXG434WHB/DWmM13aNjvvOv7+/kyfPp0HHniAX3/9lZCQELKyshg4cCBm8/kzV6vVSmRkJFWrViUrK6u4Y8hqtdKqVSuGDh1KREQEVqsVgKysLOYuWs7kxes5ceIkrgL7f+p34nQ5sXpZqd24NccO/MrWVQswW7yoecOtZ9/b5g1AcJVa3NX3yQv5uGh2d1/2bVrFju+/wGQyc32zdgAYAKZTxyLPx+PxkJaWhslkomrVqhe0/5Wkfv36TJw4kZ07dzJjxgzGjh3L7NmzGTlyJB06dCj1br1zufnmm4ufwjdjxgySkpJ47rnnuPvuu8utBhERERERkbKg4EnKzA11q/HuS0P4et0vLE3ayI79R8nOLcDm5YW3zQtvmxUvi/mMX6w9Hg9FDheFDidFDhcmE9SoUonud99G9463ER4aVOIaqlSpwvvvv88DDzzA3r17GTJkCM8+++wFvY/JkydjNpu58cYbcblcJCUlsXjxYl566SUmTZrE/fffT69evahZsyY97r+XeWv2YzFXIS/rOMlAkcPBvr378PX1JTCgCt7+QRTmZVOjwS3YfPzOuqeXzYeASuHkZh7HWVSA1du3xPVavf244/7h/Lgslu1rP8cwDOo1vwu324OXxUygvy9ut5vjx4+TmppKamoqKSkppKamkpyczG+//UZKSgpFRUWEhoayfv36qz78aNy4Me+88w7btm1j+vTpPPvsszRp0oSRI0fSpk2bcnn/FouFgQMHEhkZycSJExkzZgx33XUXY8aMoXr16mW+v4iIiIiISFkwGf/7jPizsNvtBAcHk52dTVBQyX/pF/mdYRhs33eEL9dsYf0v+zmRaafI4eJs336GYeBtsxLg50PTG2rRpW1z7mrRqESdOueyefNmxowZw9tvv03Tpk0v5a0UO3DgAIsXL2b58uXk5eVxxx130Kt3b95csAF7bgHeFLHqk/FUrt2Qxu37YbfbycvLoyD7BGbDQeUa1xFevU5xx9RX772It18gEYPGAvDbr+v4Zc1iqtdvRvNO/c84NpdvzwDALygUgN3rV7J349fc0WMkYTXqkZ9nZ+OXs8lJT6FO804E12mG0+kkvGg3aalHio8EOp3O4g6w3zu6rFYrmZmZ9O7dm5kzZ5bK53Ul2bhxI9OmTWPbtm00b96cUaNG0aJFi3Lb3zAMvv32W9544w3sdjvDhw8nJibmjyclioiIiIiIVLCSZkUKnqTcGYZBelYu+w4f58DhNPIKCnE4XVgsFnxsVmpXC6VBnWrUqhqKxVJ6Y8gMwyiTzpXCwkJWrlzJ4sWL+fXXX3GG3ozLO5TKgV6smfcGVWo35PbujwCnurlycnPIseeQm5uLYRj4+PgQGBTI+kVvnRY8GYbB1lWfcWT3Jrz9gqhcqwE+/sEUFeSQm5lG1vHD3HpPDDVvuAX4I3iq3aIbHu+gU0cDXU5ObE/CkZtOSL3b8Q+vS+HeJMxmM2bzqW6z348Qut1uPB4Pbrcbl8tFUVERDzzwAB9//HGpf2ZXAsMwWLduHdOnT2fnzp20bt2akSNH/ul8sNKWn5/PjBkzmD9/PvXr12fs2LGlFpyKiIiIiIhcCgVPIhVg9+7dvPLvj1m/LwNXbjqHf1xIpRr1ubPnCEyceaQwNzcXe46d3Jxc9n/7IV7efrToPoqgoCBstlMdTin7tpK84yeyTxzB5XTg7RuAf3Blql7XmFo3tsTm639q7/8ET2GNO2ENqlK8j+FykbH7W5y56fhVqYcjM/lP38PvQ9gBXn31VcaOHVuaH9EVxzAMVq9ezfTp09m/fz/t2rVj5MiRNGrUqNxq2LVrF+PGjWPnzp307t2bxx9/nMDAwHLbX0RERERE5H8peBKpIJt2HOSxf32Ax+0gL8dOUWERVquVkJAQQkJCznpcyuPxkJuXS449h5zcHAyPgbe3N4FBgQQFBuHt7V2ivQ0M9u7dS449B4P/+qdtMuFl8yd771ocGYewWCx/uk5hYSHBwcH8+uuv1KhR44Le/9XK4/GQmJjIjBkzSE5OplOnTowYMYL69euX2/4LFy7k3XffxcfHh2eeeYbOnTtf9fO3RERERETk8qTgSaSCeDweHvrbe+w+lEJ4aDAFBQVkZmViz7ZjGAaBgYFUqlQJP3+/M7qgADyGh7zcvOJOKI/Hg83bRlBgEIFBgfh4+/zp/m6PmwMHDpCTk1PcuWSx+uJxFXFi02I8buepr1ksWCyWM57w53a7cTqdREdHs2DBglL6VK4ebreb+Ph4YmNjSU1NpUuXLgwfPpw6deqUy/5paWm8+eabrFq1ijvuuIMXXniBWrVqlcveIiIiIiIiv1PwJFKBFiduYPz7n1O5UiBe/+kucnvcZGdnk5mZiaPIgdVmpVJIJYJDgvGynH1otGEY5OXlYbfbi4Mkm81GYGAgQUFB+PicPYRyOBzs37+fwsJCPB4PFm9/cg5vI+e3TcWzndxuN3DqaN3vIZTJZMLhcGA2m1m7di2tWrUqmw/oKuB0Ovniiy+YOXMmJ0+epFu3bgwbNqzcOsTWrl3L66+/Tnp6OsOGDWPQoEHFg+pFRERERETKmoInkQpkzyug3zOTybLnU6XSmbN48vPzyczKJMeeA/BHF5Sf3znXLA6hcv4TQrk9WK1WgoJOdUL5+viedn1eXh4HDx7EZZwaHp7xy1e48rOKA6ffB4x7PJ7izqjf5zvdeOON7NixQ8e4SsDhcBAXF8cHH3yA3W6nV69eDBkyhPDw8DLfu6CggPfff59PPvmEunXrMnbsWG699dYy31dERERERETBk0gFW5y4gddnfUGgvw++3razXuN2u8nKziIrMwuHw4HN23aqCyo4+E/nMBmGQX5+fnEnlNvtxmq1/tEJ5euDCRMnT6aTciIDx/GdpO/9sThscrvduN3u4if9/T53yul04vF4qF27Nt27dycqKoq7774bf3//MvmMriYFBQUsWLCAjz76iMLCQvr06cNDDz1EaGhome+9d+9exo0bxy+//ML999/Pk08+SXBwcJnvKyIiIiIi1y4FTyIVzOPxMHrip6zdvJuqYcGYzefuHjIwyM/7owvKZDIRFBREpUqV8PX1Ped9/31vTk4O9hw7bpcbLy8vAgMDceGFn5ebA9/Nw+UsIjs7G7PZXBxq/R5A/d7xZBgG/v7+zJw5k6SkJDZv3ozNZqN9+/ZERUVx55136jjXeeTl5TF37lw++eQTPB4PDzzwAIMGDSrzn50ej4elS5cyefJkvLy8ePrpp7n33nvVtSYiIiIiImVCwZPIZeDwsXSGvfw+WTmnjtyVJARwuVzFXVBOpxNvH+/iLqj/HQT+vwwMCvILsNvtZObk43G7qGU5jsN+nL1792KxWMjKyjotfIJToYXL5cLlchEWFsZTTz3FgAEDCAgIYMWKFSQkJLBnzx6CgoKIjIyka9eu3HLLLeet51pmt9uZM2cO8+bNw2KxMHDgQGJiYsq8eyw9PZ1JkyaxYsUKWrZsyYsvvkjdunXLdE8REREREbn2KHgSuUx8s347r7wbh8PppnJIQIk7UAwM8nLzyMzKJDcnF5PZRHBQMJUqVTrnUPHfZeXk43K76d3hJsw5h0lISGD9+vUUFhZitVopLCzEy8vrtPDJ4XBgMpkYM2YMmzZt4sSJE7Ru3Zq//OUvtGnThkOHDpGQkEBCQgIpKSlUrVqVLl260LVrV2644QZ11pxDRkYGH374IYsWLcLHx4fBgwfTr1+/83ayXaoff/yRCRMmcPz4cYYMGcJDDz2EzXb2I58iIiIiIiIXSsGTyGXkyzU/M2HmFzhdLiqHlKzz6b85XU6ysk51QblcLnx8fKhUqRJBQUGndR0ZhkFWTj5ut4eHozvycO+OxQPDExMTGTZsGMePHy8+XmexWPDy8sJkMlFUVET9+vXZvXs3LpeLpKQk5s6dy44dO6hTpw4DBgygW7du+Pj4sG3bNhISEli5ciXZ2dnUq1ePrl270rVr13J7qtuVJi0tjQ8++IAlS5YQFBTEkCFDiI6OLtMwqKioiFmzZvHxxx9To0YNXnzxRT2pUERERERESoWCJ5HLzNfrfmH8+8vIySsgNDgAm9XrgtcwMMjNySUzK5O83DzMZjPBwae6oCxeXpzMysXH5sXIfpHE3HfnGQHX+vXrGTRoENnZ2eTm5pKfn3/a68OHD2fKlCnFc5wMw2Dbtm3MnTuXb775Bn9/f3r16kX//v2pWrUqLpeLH3/8kYSEBL799lsKCwtp3rw5Xbt25Z577iEkJOSiP6+rVUpKCjNnzmT58uVUrlyZYcOG0b179zKdnXXw4EHGjRvHzz//zL333stTTz1VLkPPRURERETk6qXgSeQytGP/Ud6YvZxf9x3B2+ZFSKDfRR9RczqdZGZlkpmVhcsNXlZvalcL5R+PP8BdLRuf874FCxbw3HPP4eXlRXZ2NpmZmRiGgcVioXHjxoSGhtKhQwciIyO5/fbbiztyUlNT+eyzz1iyZAkFBQVERkYyYMAAmjZtCkB+fj5r1qwhPj6edevWYTKZaNOmDV27dqVDhw5lfrTsSpOcnExsbCwrVqygevXqDB8+nKioqD99muGl8Hg8LF++nH//+98APPHEE9x///2a0yUiIiIiIhdFwZPIZarQ4eSTZWuZs3wteQVF+NisBPn7YrFcWADg8XjIyS8kv8CB4XERYs4l8+AGQgL96d69O7179z7nUOnx48czefJkgoODSU1NJTc3l06dOjFt2jSSkpJITEzk0KFD+Pv70759eyIiImjTpg3e3t7k5+fzxRdfMG/ePI4cOULTpk0ZMGAAERERxaFJZmYmX3/9NQkJCWzbtg1fX186duxI165duf322/HyuvBur6vV/v37ee+991i1ahV169ZlxIgRREZGllkglJmZyTvvvMPy5cu55ZZbGDt2LPXq1SuTvURERERE5Oql4EnkMrcv+Riff7OJFd//QqY9DxPgbbPibfPC22rFbD69E8owDIqcLoocTgqLnBiGQYC/D3e3asL9d7fglkZ1SU5OZsmSJSxbtozs7GxatmxJdHQ0HTt2PO0ol8vlYtSoUSxbtgy3201AQAAzZ84kIiKi+JoDBw6QmJhIUlIS+/fvx8/Pj3bt2hEZGcmdd96JzWZj7dq1zJ07l40bN1K1alX69+9Pz549T/s5kZKSQkJCAvHx8Rw8eJCQkBA6d+5M165dadq0qYaS/8euXbuYMWMGa9eupUGDBowcOZIOHTqU2eezadMmxo0bx5EjR3jwwQd5+OGHzzu0XkRERERE5HcKnkSuENm5+SSu+5Vv1u9gz6FU8gqKcDhdxYGDyWTCMAwMw8Bq9cLX20q9WuG0b9GILu2aEx565r9Jh8NBUlISixcv5ueffyY0NJT777+fXr16UbNmTeDUv+sHHniAX375hX//+99ER0efs8ZDhw6xatUqEhMT2bNnDz4+PrRr146IiAjatm3LkSNHmD9/PvHx8VgsFrp168aAAQNO67gyDIO9e/cSHx/PihUrSEtLo0aNGsVDydV1c8q2bduYMWMG69evp0mTJowcOZI2bdqUSQDlcDj46KOP+OCDD6hcuTIvvPACd955Z6nvIyIiIiIiVx8FTyJXIKfLzW8pJ9mXfIwTGXaKnC4wwGbzIjQ4gPq1w7m+Vjg+tpIPoj5w4ABxcXF8+eWX5OXlcccddxAdHc1dd93F0aNHmTdvHk8++WSJu12Sk5NZtWoVSUlJ7Ny5E5vNRtu2bYmIiOCmm24iPj6eRYsWkZGRQdu2bfnLX/5Cq1atTgtOPB4PP//8M/Hx8SQlJZGTk0PDhg2JioqiS5cuhIeHX/Bnd7XZuHEj06dPZ+vWrTRv3pxRo0bRokWLMtkrOTmZCRMmsH79ejp37szo0aOpXLlymewlIiIiIiJXBwVPInKawsJCVq5cSVxcHNu3b6dKlSr07NmTnj17UrVq1Yta8+jRoyQlJZGUlMT27dux2WzccccddOjQgaKiIpYsWcLevXupV68eMTExREVF4e3tfdoaDoeDH374gYSEBNasWYPT6eS2224jKiqKTp06XdM/cwzDYN26dUyfPp2dO3fSqlUrHn30UZo1a1YmeyUkJDBp0iQcDgePP/440dHRGj4uIiIiIiJnpeBJRM5p9+7dxMXFkZCQQGFhIe3atSM6Opo2bdpcdNCQmprKN998Q2JiItu2bcPLy4vWrVtz3XXXceDAAX766SeCg4OJjo6mb9++Z+2oycvLY9WqVcTHx7Nx40YsFgtt27YlKiqKdu3anRFaXSsMw2D16tVMnz6d/fv307ZtWx599FEaNWpU6nvZ7XamTJnCkiVLuOmmm/jb3/5Gw4YNS30fERERERG5sil4EpHzys/PJyEhgUWLFrFnzx6qV69Or1696NGjB2FhYRe9blpaWvFxvC1btmA2m2ncuDGGYbBnzx4Mw6Bz587ExMScMzw5efIkK1euJCEhgR07duDv70+nTp2IioqiZcuW12QnjsfjITExkRkzZpCcnEynTp0YMWIE9evXL/W9tmzZwrhx4zh06BAxMTEMHz4cPz+/Ut9HRERERESuTAqeRKTEDMNgx44dxMXFsWLFClwuFx07diQ6OvqSQ56TJ08Wh1A///wzbrebSpUqkZWVhcvlonXr1gwYMIAOHTqcc5/k5OTiJ+MdPnyYsLAwunTpQteuXWncuPE192Q8t9tNfHw8sbGxpKam0qVLF4YPH06dOnVKdR+n08mnn37K+++/T0hICM8//zzt27cv1T1EREREROTKpOBJRC5KTk4OX331FXFxcRw4cIDatWvTu3dvunfvTkhIyCWtnZGRwbfffktiYiIbNmwgJycHOBVw3HDDDTz44IP06NEDf3//s95vGAY7d+4sfjJeRkYGderUKX4yXmkHL5c7p9PJsmXLmDVrFidPnqRbt24MGzaMGjVqlOo+R48e5fXXX+eHH37g7rvv5rnnntMAeBERERGRa5yCJxG5JIZhsHXrVuLi4khMTAQgIiKC3r17c+utt15yl1FmZiarV68mKSmJ1atXc/LkSVwuF2FhYQwYMICHH36YmjVrnvN+t9vNxo0biY+PZ9WqVeTn59OkSROioqLo3LnzJR0VvNI4HA7i4uL44IMPsNvt9OzZk6FDh5ZqOGQYBomJibz55psUFBQwcuRI+vfvj8ViKbU9RERERETkyqHgSURKTVZWFsuXL2fx4sUkJydz/fXXEx0dzb333lsqPxPsdjurV69m2bJlJCYmkp6ejre3N61bt+bJJ5/knnvu+dOgq6ioiO+++474+Hi+//57PB4PrVq1IioqirvvvvucHVRXm4KCAhYsWMBHH31EYWEhffr04aGHHiI0NLTU9sjNzWXatGksXLiQG2+8kbFjx9KkSZNSW19ERERERK4MCp5EpNR5PB42bdpEXFwc33zzDRaLhS5dutC7d29uvvnmUpm1lJOTQ1JSErNmzeKHH36gsLCQqlWr0qtXL5544gnq1q37p/fb7XaSkpKIj49n8+bN2Gw22rdvT1RUFHfeeSdWq/WSa7zc5eXlMXfuXD755BPcbjcDBgxg0KBBpfrze/v27bz22mvs27ePvn37MmrUqGsm4BMREREREQVPIlLG0tPTWbZsGUuWLCElJYWGDRsSHR1NVFRUqT39LDc3l/fff585c+awZ88eLBYLjRs3ZtCgQdx///3Url37T+8/fvw4K1asICEhgT179hAUFERERARRUVHccsstV/2T8ex2O3PmzGHevHlYLBYGDhxITExMqQVEbreb+fPnM2PGDAICAnj22Wfp1KnTNTfsXURERETkWqTgSUTKhcfj4ccffyQuLo7vvvsOb29voqKi6N27N40aNSq1fbZv385bb73FypUrsdvtBAYGctttt9GrVy8iIiLO2wl14MABEhISSEhIICUlhfDw8OKh5DfccMNVHZZkZGTw4YcfsmjRInx8fBg8eDD9+vXD19e3VNY/duwYEydOZM2aNbRr144xY8aU+oBzERERERG5vCh4EpFyl5aWxtKlS1m6dClpaWk0adKEPn36cM8995RayJGdnc1nn33GrFmzSE5OBiA4OJhmzZoRGRlJZGQk9erVO+f9hmGwbds2EhISWLlyJdnZ2dSrV684hLqaA5O0tDQ++OADlixZQlBQEEOGDCE6OhqbzVYq63/77bdMnDgRu93O8OHDiYmJwcvLq1TWFhERERGRy4uCJxGpMG63m7Vr1xIXF8e6devw8/PjvvvuIzo6mvr165fKHi6Xi6SkJD7++GM2btyIyWTCZDLh6+tL/fr1iYiIICIiggYNGpyzm8nlcvHjjz+SkJDAt99+S2FhIc2bN6dr167cc889hISElEqtl5uUlBRmzpzJ8uXLqVy5MsOGDaN79+6lMv8qPz+fGTNmMH/+fOrVq8fYsWNp1qxZKVQtIiIiIiKXEwVPInJZSElJYcmSJXz++edkZGTQvHlzoqOjiYyMLJVOG8Mw+OWXX/j0009ZtWoVbreb8PBw7HY7DoeDOnXqEBkZSUREBA0bNjxnCJWfn8/q1atJSEhg3bp1mEwm2rRpQ9euXenQoUOpdWxdTpKTk4mNjWXFihVUr16d4cOHExUVhcViueS1d+3axbhx49i5cye9evXi8ccf1/9/iIiIiIhcRRQ8ichlxel0snr1ahYvXsz69esJCgqie/fuREdHU6dOnVLZIzU1lQULFrBkyRLy8vJo3LgxgYGB7Ny5E7vdTs2aNYmIiCAyMpLGjRufM4TKzMzk66+/JiEhgW3btuHr60vHjh3p2rUrt99++1V3fGz//v289957rFq1irp16zJixAgiIyMvefi6x+Nh0aJFTJ06FR8fH5555hk6d+58Vc/TEhERERG5Vih4EpHLVnJyMosXL+aLL74gOzubli1bEh0dTceOHUvtuNfy5cuZN28ehw8fpkmTJrRo0YLs7GzWrFlDVlYW1atXLz6Od9NNN50zZElJSSEhIYH4+HgOHjxISEgInTt3pmvXrjRt2vSqClF27drFjBkzWLt2LQ0aNGDkyJF06NDhkt9jWloab731FklJSdx+++28+OKL1KpVq5SqFhERERGRiqDgSUQuew6Hg6SkJBYvXszPP/9MaGgo999/P7179y6VId8ej4e1a9cyb948NmzYQHh4OP369aNu3br89NNPrFq1ioyMDMLDw4uP4zVt2vSsIZRhGOzdu5f4+HhWrFhBWloaNWrUKB5K/mcDza8027ZtY8aMGaxfv54mTZowcuRI2rRpc8kB1Nq1a3n99ddJT09n2LBhDBo0qFSCRhERERERKX8KnkTkinLgwAHi4uL48ssvycvL44477iA6Opq77rqrVGYO7d27l3nz5hEfH4/FYqFbt27079+fzMxMEhMTWbVqFSdPnqRy5cp06tSJyMhIbrnllrOGUB6Ph59//pn4+HiSkpLIycmhYcOGREVF0aVLF8LDwy+53svBxo0bmTZtGtu2baN58+aMGjWKFi1aXNKaBQUFvP/++3zyySfUrVuXF198kdtuu62UKhYRERERkfKi4ElErkiFhYWsXLmSuLg4tm/fTpUqVejZsyc9e/akatWql7x+RkYGixYtYtGiRWRkZNC2bVtiYmJo2bIlv/zyC0lJSSQlJZGWlkZoaCidOnUiIiKC22677awBmMPh4IcffiA+Pp7vvvsOp9PJbbfdRlRUFJ06dbrif2YahsG6deuYPn06O3fupFWrVjz66KOX/KS6ffv2MW7cOLZt28b999/Pk08+SXBwcClVLSIiIiIiZU3Bk4hc8Xbv3k1cXBwJCQkUFhbSrl07oqOjadOmzSUPvnY4HKxcuZK5c+eyZ88e6tWrR0xMDFFRUVitVrZv314cQqWmphISEsLdd99NZGQkLVq0OOuA8dzcXL755hvi4+PZuHEjFouFtm3bEhUVRbt27fD29r6kmiuSYRisXr2a6dOns3//ftq2bcujjz5Ko0aNLnpNj8fD0qVLmTJlChaLhaeeeor77rvvqpqbJSIiIiJytVLwJCJXjfz8fBISEli0aBF79uyhevXq9OrVix49ehAWFnZJaxuGwebNm/n000/57rvvCA4OJjo6mr59+1K5cmUMw2Dnzp0kJiaSlJTE0aNHCQoKomPHjkRGRtKqVauzzik6efIkK1euJCEhgR07duDv70+nTp2IioqiZcuWlxycVRSPx0NiYiIzZswgOTmZTp06MWLECOrXr3/Ra2ZkZDBp0iQSEhJo0aIFL774Itddd13pFS0iIiIiIqVOwZOIXHUMw2DHjh3ExcWxYsUKXC4XHTt2JDo6ulTCnMOHD/PZZ5+xbNkyHA4HnTt3JiYmprirxzAM9uzZQ2JiIomJiRw+fJjAwEA6dOhAREQEt99+Ozab7Yx1k5OTi5+Md/jwYcLCwujSpQtdu3alcePGV2SHj9vtJj4+ntjYWFJTU+nSpQvDhw+nTp06F73mTz/9xPjx4zl+/DgPPfQQQ4YMOevnKSIiIiIiFU/Bk4hc1XJycvjqq6+Ii4vjwIED1K5dm969e9O9e3dCQkIuae3c3Fw+//xz5s+fT2pqKrfddhsDBgygQ4cOxeGWYRjs27ePpKQkEhMTOXToEP7+/rRv356IiAjatGlzxtG637unfn8yXkZGBnXq1Cl+Mt6lhDYVxel08sUXXzBz5kxOnjxJt27dGDZs2EU/lbCoqIjZs2fz0UcfUaNGDV544QVat25dylWLiIiIiMilUvAkItcEwzDYunUrcXFxJCYmAhAREUF0dDS33HLLJXUTud1uVq9ezdy5c9myZQs1atTggQceoEePHvj7+5927YEDB4qP4+3fvx8/Pz/atWtHZGQkd955Jz4+PmesvXHjRuLj41m1ahX5+fk0adKEqKgoOnfufMlHCMubw+EgLi6ODz74ALvdTq9evRgyZMhFP+Hv4MGDjB8/ns2bN3Pvvffy1FNPERoaWspVi4iIiIjIxVLwJCLXnKysLJYvX87ixYtJTk7m+uuvJzo6mnvvvfeSf3bt2LGDefPmsXLlSry9venRowcPPPAANWvWPOPaQ4cOsWrVKhITE9mzZw8+Pj60a9eOiIgI2rZti5+f32nXFxUV8d133xEfH8/333+Px+OhVatWREVFcffdd58Rcl3OCgoKWLBgAR999BGFhYX06dOHhx566KJCI8Mw+OKLL3jnnXcwDIO//vWv9OjR44qdjyUiIiIicjVR8CQi1yyPx8OmTZuIi4vjm2++wWKx0KVLF6Kjo7npppsuqQvqxIkTLFy4kEWLFpGTk0OHDh2IiYnh1ltvPeu6ycnJrFq1iqSkJHbu3InNZqNt27ZERkbSrl27M0Ilu91OUlIS8fHxbN68GZvNRvv27YmKiuLOO+886yDzy1FeXh5z587lk08+wePx8MADDzBo0KCL+v+QrKws3nnnHb744guaN2/O2LFjL2mYuYiIiIiIXDoFTyIiQHp6Op9//jlLliwhNTWVhg0bEh0dTVRU1BmdRxeisLCQ+Ph45s2bx4EDB7jxxhuJiYnhnnvuOedA7KNHj5KUlERSUhLbt2/HZrNxxx13EBkZyV133UVgYOBp1x8/fpwVK1aQkJDAnj17CAoKIiIigqioKG655ZYrovPHbrczZ84c5s2bh8ViYeDAgcTExFxUF9emTZsYN24cR44cYdCgQQwbNuyMI4wiIiIiIlI+FDyJiPwXj8fDjz/+SFxcHN999x3e3t5ERUURHR3NjTfeeNHrGobBTz/9xNy5c/nhhx8ICwujb9++REdHU6lSpXPel5qayjfffENiYiLbtm3Dy8uL22+/ncjISDp06HDGz9oDBw6QkJBAQkICKSkphIeHFw8lv+GGGy77J+NlZGTw4YcfsmjRInx8fBg8eDD9+vXD19f3gtZxOBx8/PHHzJ49m8qVK/PCCy9w5513llHVIiIiIiJyLgqeRETOIS0tjaVLl7J06VLS0tJo0qQJffr04Z577rngIOS/HTx4kPnz57N8+XIMwyAqKooBAwbQoEGD89bz+3G8LVu2YDabad26NREREXTs2PG0p/QZhsG2bdtISEhg5cqVZGdnU69eveIQ6mKfJlde0tLS+OCDD1iyZAlBQUEMGTKE6Ojoc3aJnUtycjITJkxg/fr13HPPPTzzzDNUrly5jKoWEREREZH/peBJROQ83G43a9euJS4ujnXr1uHn58d9991HdHT0Jc0Qys7OZsmSJSxYsIC0tDRat25NTEwMd95553mPx508ebI4hPr5558BaNmyJZGRkXTs2PG0Id0ul4sff/yRhIQEvv32WwoLC2nevDldu3blnnvuOS2wutykpKQwc+ZMli9fTuXKlRk2bBjdu3e/oBlWhmGQkJDA22+/TVFREY899hh9+vS5Io4gioiIiIhc6RQ8iYhcgJSUFJYsWcLnn39ORkYGt9xyC7179yYyMvKCu3F+53K5SEpK4tNPP2XHjh3UqVOHAQMGcN9995VovlRGRgbffvstiYmJbNy4EYBbb72VyMhI7r777tM6fPLz81m9ejUJCQmsW7cOk8nEHXfcQVRUFB06dLikTq6ylJycTGxsLCtWrKB69eoMHz6cqKgoLBZLidew2+1MmTKFJUuWcNNNN/G3v/2Nhg0blmHVIiIiIiKi4ElE5CI4nU5Wr15NXFwcGzZsICgoiO7duxMdHU2dOnUuak3DMPjll1+YO3cuq1atwt/fn169etGvXz+qVatWojUyMzNZvXo1SUlJrF+/Ho/Hwy233EJERASdOnUiPDz8tGu//vprEhIS2LZtG76+vnTs2JGuXbty++234+XldVHvoyzt37+f9957j1WrVlG3bl1GjBhBZGTkBXUvbd26lXHjxnHw4EEGDBjAiBEjLmmAvIiIiIiInJuCJxGRS5ScnMzixYtZtmwZdrudVq1a0bt3bzp27HhBR8L+W2pqKgsXLmTx4sXk5+cTERFBTEwMTZs2LfEadrudb7/9lqSkJH766SdcLhfNmjUjMjKSTp06nRZmpaSkkJCQQHx8PAcPHiQkJITOnTvTtWtXmjZtetkNJd+5cyczZszg+++/p0GDBowcOZIOHTqUuE6Xy8Wnn35KbGwsISEhPP/887Rv376MqxYRERERufYoeBIRKSUOh4OkpCTi4uLYsmULoaGh3H///fTu3fuih3nn5+ezfPly5s+fT3JyMjfffDMxMTF06tTpgjqScnJy+O6770hMTGTdunU4nU5uuukmIiMjiYiIKK7PMAz27t1LfHw8K1asIC0tjRo1ahQPJa9Xr95FvY+ysm3bNqZPn86GDRto0qQJI0eOpE2bNiUOoFJSUpgwYQI//PADHTt25LnnnqNq1aplXLWIiIiIyLVDwZOISBk4cOAAcXFxfPnll+Tl5dGmTRt69+7NXXfddUFziX7n8Xj4/vvvmTt3Lhs2bCA8PJz+/fvTq1evC/55m5eXx3fffUdSUhLff/89DoeDxo0bExERQUREBLVr1y7e8+effyY+Pp6kpCRycnJo2LAhUVFRdOnS5bRjexVt48aNTJs2jW3bttG8eXNGjRpFixYtSnSvYRgkJSXxxhtvUFBQwMiRI+nfv/9F/XcSEREREZHTKXgSESlDhYWFrFy5kri4OLZv3054eDg9evSgZ8+eF91Zs3fvXubNm0dCQgJms5lu3boxYMAA6tate8Fr5efn8/3335OYmMjatWspKiqiYcOGxZ1Qv6/pcDj44YcfiI+P57vvvsPpdHLbbbcRFRVFp06dLouf+YZhsG7dOqZPn87OnTtp3bo1I0eOpFmzZiW6Pzc3l2nTprFw4UIaNmzI3/72N5o0aVLGVYuIiIiIXN0UPImIlJPdu3cTFxdHQkIChYWFtGvXjujoaNq0aXNBw7F/l5GRQVxcHAsXLiQjI4O2bdsSExND69atL2omU0FBAevWrSMxMZHvvvuOgoIC6tevT0REBJGRkcXH7HJzc/nmm2+Ij49n48aNWCwW2rZtS1RUFO3atcPb2/uC9y5NhmGwevVqpk+fzv79+2nXrh0jR46kUaNGJbp/+/btvPbaa+zdu5e+ffvy2GOP4e/vX8ZVi4iIiIhcnRQ8iYiUs/z8fBISEli0aBF79uyhevXq9OrVix49ehAWFnbB6zkcDlauXMncuXPZs2cP9erVIyYmhqioqIsOgYqKili3bh1JSUmsXr2a/Px8rr/++uLjeA0aNMBkMnHy5ElWrlxJQkICO3bswN/fn06dOhEVFUXLli0vKlArLR6Ph8TERGbMmEFycjKdOnVixIgR1K9f/7z3ut1u5s+fz4wZMwgICODZZ5+lU6dOl92QdRERERGRy52CJxGRCmIYBtu3b2fx4sWsWLECl8tFx44diY6OvqjQxjAMfv75Zz799FPWrFlDUFAQ0dHR9OvXj8qVK190nQ6Hg59++onExP9n777Do6zy94+/ZyaZ9EICgVBC79IRpSgISJOaCAuxrGWXXgURxIKIIEWKdFa/+luXYkmoAmqCoCAKqIgiRYoECCSElEmfZOb5/cGaBQ2QQBLa/bquXC4z5znPZ8bFGW7O+Zwotm/fTlpaGiEhIXnb8WrVqoXJZCImJibvZLxTp04RGBhI586d6dKlC3Xr1r1poY3D4WDz5s0sX76cs2fP0rlzZwYOHEhISMg1rz137hyzZs1i+/bttG7dmhdeeOG6G8WLiIiIiNyNFDyJiNwCUlNT2bRpExERERw/fpxKlSoRGhpKjx498Pf3L/R8p0+fZvXq1axfvx673U6nTp0IDw8v8HazK8nJyWHPnj1ERUWxbds2bDYbFSpUyNuOV7duXQAOHjyYdzJeYmIiISEheSfjFSTwKQ45OTls2LCBd955h4SEBLp3784//vGPAgVJ27ZtY+bMmaSkpDBo0CDCw8MLdaqgiIiIiMjdSsGTiMgtxDAM9u3bR2RkJFFRUQB06NCBsLAwGjduXOhVQ2lpaaxfv57Vq1cTGxtLkyZNGDBgAO3atbvhbXC5ubns3buX6OhovvzyS5KTkwkODs7bjle/fn0Mw2DPnj1s2bKFrVu3kpGRQb169ejatSudOnW6rq2FN8putxMREcF7772HzWajT58+PP3009c8pS8jI4OlS5eyevVqqlWrxosvvljgxuUiIiIiIncrBU8iIreo5ORkNmzYQGRkJKdOnaJq1aqEhYXRrVu3Qv831uFwsH37dlauXMm+ffsoX748/fv3p1evXkXSONvhcPDDDz8QHR3N1q1bSUxMJCgoKG87XoMGDcjJyeGrr75iy5Yt7Ny5E6fTyb333kvXrl156KGHSryBd2ZmJh999BH/7//9P7Kysnj00Ud56qmnCAgIuOp1hw8f5o033uDXX38lNDSU4cOH6zNPREREROQKFDyJiNzinE4ne/fuJTIyki+//BIXFxc6depEWFgY9evXL/QqqF9//ZXVq1fz2Wef4ebmRq9evfjb3/5GxYoVi6zeffv2ERUVxdatW0lISKB06dK0b9+ejh070rhxY9LS0oiOjmbz5s388MMPWK1WHnzwQbp27UqrVq1wdXUtkloKIj09nZUrV/Kf//wHp9NJ//79eeKJJ676OeZ0Ovnkk09YuHAh7u7uPPfcc3Tu3FnNx0VERERE/kTBk4jIbeTChQusW7eONWvWcPbsWWrVqkVYWBhdu3bF09OzUHOdP3+ejz/+mIiICGw2G23btiU8PJwmTZoUWYDidDrZv38/0dHRREdHEx8fT0BAAO3bt6dDhw40bdqUhIQEPvvsM7Zs2cKRI0fw9fWlQ4cOdO3alcaNG5fYyXg2m40PPviAVatWYbFYePzxxwkPD7/qSqzz58/z1ltvERUVxX333ceECROoVKlSidQrIiIiInI7UPAkInIbcjqdfPvtt3zyySfs2LEDd3d3unTpQlhYGLVr1y7UXNnZ2WzatIlVq1Zx/PhxateuTXh4OA8//DBWq7VIaz5w4EBeCHX27Fn8/f156KGH6NixI82aNSMmJobNmzezZcsWzp49S9myZfNOxqtZs2aJrChKTEzk/fff55NPPsHd3Z2///3v9OvXDw8Pjytes3PnTmbMmEFCQgLPPvssTzzxRJG+dyIiIiIitysFTyIit7m4uDjWrVvH2rVriY+Pp169ejz66KM8/PDDVw1L/swwDL777jtWrlzJN998Q2BgIH379iU0NPSafY8KyzAMDh48SFRUFNHR0Zw5cwZfX1/atWtHx44dad68ed7JeF988QUpKSlUq1Yt72S8gpxEd6Pi4+P5v//7P9auXYuvry9PP/00YWFhVwyUsrKy+Ne//sV//vMfKlWqxIsvvkjTpk2LvU4RERERkVuZgicRkTuEw+Fgx44dREREsGvXLry8vOjWrRthYWFUr169UHP9/vvvrFq1io0bN2IYBl26dCE8PJwaNWoUed2GYXDkyBGioqKIiori1KlT+Pj40LZtWzp06ECzZs344Ycf2Lx5M9u3bycrK4tGjRrRpUsXHn74Yfz9/Yu8pkvFxsbyzjvvsHHjRkqXLs0//vEPevToccU+VEePHmXatGns37+fnj17MnLkyGKvUURERETkVqXgSUTkDhQbG8uaNWtYt24diYmJNG7cmNDQUDp27FioLWA2m401a9bw4YcfEh8fT4sWLQgPD6dVq1bF0nvJMAyOHj1KdHQ0UVFR/P7773h5efHggw/SoUMHGjVqxLfffsuWLVvYtWsXJpOJli1b0qVLF9q2bVuoFV6FFRMTw/Lly/nss88IDg5m4MCBdO3aFYvF8pexTqeTtWvXsmDBAsxmM6NHj6Z79+5qPi4iIiIidx0FTyIid7CcnBy2b99OREQEe/bswdfXlx49ehAWFkZISEiB58nNzWXr1q2sWLGCAwcOEBISQv/+/enevXuhm5oXxvHjx/O24x07dgxPT0/atGlDx44dqVu3Ll9//TVbtmxh//79eHh40K5dO7p06cJ9992Hi4tLsdR07Ngxli1bxtatW6lcuTKDBg2iY8eO+QZxiYmJzJkzhy1bttC0aVNefPFFqlSpUix1iYiIiIjcihQ8iYjcJWJiYoiMjGT9+vXYbDbuvfdeQkNDadeu3RW3jeVn//79rFq1iujoaLy8vOjTpw/9+vWjXLlyxVj9xe1/W7duJSoqiiNHjuDu7k6bNm3o0KED1apVY/v27WzevJkTJ05QqlQpHn74Ybp06UKDBg2KZaXRwYMHWbp0KTt37qRGjRoMHjyYtm3b5nuv7777jjfffJOzZ8/y1FNP8cwzz6j5uIiIiIjcFRQ8iYjcZex2O9HR0URERLBv3z4CAgLo1asXffr0KVTT7nPnzvHRRx8RGRlJRkYGHTp0YMCAATRs2LAYq78oJiaGrVu3Eh0dzcGDB7FarbRu3ZoOHTpQrlw5tm/fzmeffUZ8fDzly5fPa0perVq1Iq9l//79LFmyhD179lCvXj0GDx5My5Yt/xJAZWdn89577/H+++8THBzMxIkTadGiRZHXIyIiIiJyK1HwJCJyFzt+/DgRERF8+umnpKen07JlS0JDQ3nggQfy7V2Un4yMDDZu3Mjq1auJiYnhnnvuITw8nPbt2xfbdrdLnTlzhujoaKKjozlw4ABWq5X777+f9u3b4+vry1dffUV0dDSpqanUqlWLrl270rlzZ4KCgoq0jr1797J48WL2799Po0aNGDp0KM2aNfvLuBMnTjB9+nR++OEHunbtypgxY4r81EARERERkVuFgicRESErK4vPP/+ciIgIDhw4QFBQEL1796Z3794FDmicTifffPMNK1euZPfu3QQFBfG3v/2NPn36lNhnwtmzZ/nyyy+Jiopi//79uLi4cN9999G2bVvc3Nz46quv+Prrr8nJyaFp06Z07do1L6AqCoZhsGvXLpYsWcLBgwdp0aIFgwcP/ssqMMMw2LhxI/PmzcPpdDJixAh69+5dLA3bRURERERuJgVPIiJymcOHDxMREcHmzZvJzs6mTZs2hIWF0bJlywIHI0ePHmXVqlVs3rwZs9lM9+7d6d+/f4k21o6Pj8/bjrdv3z7MZjMtWrSgVatWAHz99dfs3bsXi8VC69at6dq1Kw888ECR9F4yDIPt27ezZMkSjh07Rps2bRg8eDB16tS5bFxycjLz589nw4YNNGzYkEmTJlG9evUbvr+IiIiIyK1CwZOIiOQrPT2dLVu2EBERwZEjRwgODqZPnz706tWLwMDAAs2RmJhIZGQkH330EYmJibRq1YrHHnuMFi1aFEvD7ytJSEjIC6F+/PFHAJo3b06LFi2w2+3s2LGDX3/9FS8vL9q3b0/Xrl1p3rz5Da9AcjqdREVFsXTpUmJiYmjfvj2DBg36S7j0ww8/MG3aNE6dOsXjjz/OP//5T9zd3W/o3iIiIiIitwIFTyIiclWGYXDgwAEiIyP57LPPyM3NpV27doSFhRU4nLHb7XzxxResWLGCI0eOUK1aNQYMGEC3bt1wc3MrgVfxP4mJiWzbto2oqCj27t0LQJMmTWjUqBFZWVl8/fXXnDp1isDAQDp37kyXLl2oW7fuDQVlDoeDzZs3s3z5cs6ePUvnzp0ZOHAgISEheWPsdjsffPAB7777LqVLl2bChAl5q7NERERERG5XCp5ERKTAbDYbmzZtIjIykuPHj1OpUiVCQ0Pp0aMH/v7+17zeMAx+/PFHVq5cyfbt2/H19SUsLIy+fftSpkyZ4n8Bf5KUlMT27duJjo5m9+7dOJ1OGjVqRO3atUlPT+ebb74hMTGRkJCQvJPxLg2LCisnJ4cNGzbwzjvvkJCQQPfu3fnHP/5x2WmCMTExvPnmm+zevZuOHTsyduzYm/LeiIiIiIgUBQVPIiJSaIZhsG/fPiIiIoiOjgagQ4cOhIWF0bhx4wKtDjp9+jQffvgh69atw26306lTJwYMGEDdunWLu/x82Ww2tm3bRnR0NN999x25ubk0aNCAKlWqkJqayu7du8nIyKBevXp07dqVTp06FXjL4Z/Z7XYiIiJ47733sNls9OnTh6effjqvkbthGHz22WfMmTOH7Oxshg0bxqOPPqrm4yIiIiJy21HwJCIiNyQ5OZkNGzYQGRnJqVOnqFq1KmFhYXTr1q1AnwVpaWmsX7+e1atXExsbS5MmTRgwYADt2rW7aUFLamoqX3/9NVFRUezatYucnBzq1KlDhQoVSE5O5qeffsLpdNKiRQu6dOnCQw89hJeXV6Hvk5mZyUcffcT/+3//j6ysLB599FGeeuopAgICgIufqwsXLiQyMpJ69eoxadIkateuXdQvV0RERESk2Ch4EhGRIuF0Otm7dy8RERFs27YNFxcXOnXqRFhYGPXr17/mKiin08n27dtZuXIlP/74I+XLl6d///707NkTb2/vEnoVf5Wens7XX39NdHQ0O3fuxG63U6NGDUqXLk1iYiJHjhzBarXStm1bunTpQqtWrXB1dS3UPdLS0li5ciUrVqzA6XTSv39/nnjiibzP0p9++olp06Zx4sQJ+vfvz+DBg/H09CyOlysiIiIiUqQUPImISJG7cOEC69atY82aNZw9e5ZatWoRFhZG165dCxSYHDx4kFWrVvH5559jtVrp2bMn/fv3p2LFiiVQ/ZVlZGSwc+dOoqKi2LFjB9nZ2YSEhBAQEMD58+c5c+YMvr6+dOzYkS5dutC4ceNCrdpKSUnhgw8+YPXq1VgsFh5//HHCw8Px8vIiNzeXFStWsHz5cvz9/Rk/fjxt27Ytxlf7V06nk9SMLOw5uRhOA6urC16e7ri6WEq0DhERERG5fSh4EhGRYuN0Ovn222/55JNP2LFjB+7u7nTp0oWwsLACbRk7f/48H3/8MREREdhsNh588EEee+wxmjRpckOnzBWFzMxMdu3aRVRUFF9//TWZmZmULVsWX19f4uLisNlslC1bNu9kvJo1axa45sTERN5//30++eQT3N3d+fvf/06/fv3w8PAgNjaWGTNmsHPnTtq2bcv48eMpW7Zs8bzGLDt7Dxznt5hzHD5xll+PncGWnolhGBgGmEwm3Kwu1KhUlnrVK1Czcjma1q1CUKBfsdQjIiIiIrcfBU8iIlIi4uLiWLduHWvXriU+Pp769esTFhbGww8/jIeHx1Wvzc7OZvPmzaxcuZLjx49Tq1YtwsPD6dSpE1artYRewdXr27VrF9HR0Wzfvp2MjAxKlSqFt7c3586dw263U716dbp27Urnzp0vO8XuauLj4/m///s/1q5di6+vL08//TRhYWG4urqydetWZs2aRXp6OkOGDOFvf/sbFkvRrDw6dfYCm77ex4btP3A+0YZhXGx4bnV1werqgslkwgQYGOTkOsm25wDgNAy8Pd1p17wuPdo1pUndymqILiIiInKXU/AkIiIlyuFwsGPHDiIiIti1axdeXl488sgjhIWFUa1atateaxgGu3fvZuXKlezcuZOAgAD69u1LWFhYXkPum81ut/Pdd98RFRXF9u3bSU1NxdvbGw8PD+Lj4zGZTDRu3JiuXbvSsWNH/P39rzlnbGws77zzDhs3bqR06dL84x//oEePHmRnZ7N48WI+/vhjatasyUsvvUS9evWuu/b4RBsLV37Otj2/kpFlx+piwdfbs8Bb6f7YipeRZcfVxUKtKsGMerwLTetWue6aREREROT2puBJRERumtjYWNasWcO6detITEykcePGhIaG0rFjx2uuZDp58iSrVq1i48aNOJ1OunTpQnh4ODVq1Cih6q8tJyeHPXv2EBUVxbZt20hOTsbNzQ2r1UpiYiIeHh60atWKrl278uCDD15z5VdMTAzLly/ns88+Izg4mIEDB9K1a1cOHTrEG2+8wW+//Ubfvn0ZOnRooRqyG4bBZzv3s2Dl58QlJOPt6Y6Xpzvm69zOaBgG2fYcklMzcHez0q/zfTwb2g5PD7frmk9EREREbl8KnkRE5KbLyclh+/btREREsGfPHnx9fenZsyehoaGEhIRc9VqbzcaaNWv48MMPiY+Pp0WLFgwYMIDWrVvfUtu8cnNz2bt3L9HR0Xz55ZdcuHABs9mMi4sLaWlplCpVioceeoiuXbvSokULXFxcrjjXsWPHWLZsGVu3bqVy5coMGjSIhx56iI8//pglS5bg5eXFuHHj6NChwzX7SqVnZvPmO+uJ2vULTsMg0N8bSxG9b4ZhYEvLJDPbTs3K5Xht2KPUrFyuSOYWERERkduDgicREbmlxMTEEBkZyfr167HZbNx7772EhobSrl07XF1dr3hdbm4uW7duZcWKFRw4cICQkBD69+9P9+7dC3SSXklyOBz88MMPREdHs3XrVuLi4jAMA5PJRE5ODuXLl6dTp0506dKFBg0aXDE8OnjwIEuXLmXnzp3UqFGDIUOGULt2bWbPns22bdto3bo1L7zwwhV7SqWkZfDivA/57udj+Hp54FVMK5Jych0kJKdSNtCPGWP6c0/NSsVyHxERERG59Sh4EhGRW5LdbicqKorIyEj27dtHQEAAvXr1ok+fPtdszv3zzz+zcuVKoqOj8fT0pE+fPvztb3+jXLlbb7WN0+lk3759REVFsXXrVk6fPo3D4cAwDMxmc15T8q5du1K1atV859i/fz9Llixhz5491KtXj8GDB2O325k1axbJyckMHDiQxx577LJVVOmZ2Tz/1kp2/3yMAF8v3KxXDvWK6nXGJ9ooE+DLnPGPU6dqwRqsi4iIiMjtTcGTiIjc8o4fP05ERASffvop6enptGzZkrCwMNq0aXPVk9zOnTvHRx99xJo1a0hPT6d9+/aEh4dfdRXRzeR0Otm/fz/R0dFERUVx8uRJ7HY7TqcTNzc3GjVqxCOPPELnzp0JCgr6y/V79+5l8eLF7N+/n0aNGvHMM8+we/duVq1aRdWqVZk0aRINGzbE6XTy0oKP+fybn0skdPrf6zOIu5BCxXIB/Ou1f1CmlL4riIiIiNzpFDyJiMhtIzMzky+++IKIiAgOHDhAUFAQvXv3pnfv3vkGMX/IyMjg008/ZdWqVcTExFC/fn3Cw8Pp0KHDVXsp3UxOp5MDBw4QHR3NF198wdGjR8nOzsbpdOLl5UWbNm145JFHaN++/WWfuYZhsGvXLpYsWcLBgwdp0aIFXbp04ZNPPuHXX38lNDSUWs3aMfv/bcHLww1P95Jt+O1wOIlPstGldUNeH9H3lgwARURERKToKHgSEZHb0uHDh4mIiGDz5s1kZ2fTpk0bHn30Ue6///4rNhV3Op188803rFy5kt27dxMUFES/fv0IDQ29pT+3DMPg4MGDREVF8dlnn3HkyBEyMjIAKFWqFA8//DDdu3fngQceyDsN0DAMtm/fzpIlSzh27BitW7emSpUqfLJ2IwluNfDw9qNS8JXDuuKUnplNZrad14Y9SqdWDW5KDSIiIiJSMhQ8iYjIbS09PZ0tW7YQERHBkSNHCA4Opk+fPvTq1YvAwMArXnf06FFWrVrF5s2bMZlMdO/enQEDBlClSpWSK/46GIbBkSNHiIqKYtOmTRw8eJCMjAzMZjNBQUH06NGDHj160Lx5c8xmM06nk6ioKJYuXUpMTAyeVVtyLtWEw56Ol5cnweWC88KqknSx35MPH0wfSilfrxK/v4iIiIiUDAVPIiJyRzAMgwMHDhAZGclnn31Gbm4u7dq1IywsLC+EyU9SUhIRERF89NFHJCYm0qpVK8LDw7nvvvtu+W1ghmFw9OhRoqOjWb9+PQcOHCA9PR1XV1cqVqxI37596d69O3Xr1sXpdPL+io95c8VXOHJz8XR3Jceeg9PppHTp0gQGBpbo63U4nJxPsvHc37sxoFurEruviIiIiJQsBU8iInLHsdlsbNq0iYiICE6cOEGlSpUIDQ2lR48e+Pv753uN3W7niy++YMWKFRw5coRq1aoxYMAAunXrhptbyfZBul7Hjx/niy++YM2aNRw4cIC0tDTc3d2pUaMG/fv3J9kSxJqt+zAyk/hh45LLLzaZcfPwJrBCNWo0eQjf0hdPnTu8+3N+2/sFTR5+jAo1G192SY49iz2b3iMx9jjlazahcYe/YTZf3ux917qlXDhzDJ+AsrTtP+6y5+ITbVSpUJoVM4bhcpUm8SIiIiJy+1LwJCIidyzDMNi3bx8RERFER0cD0KFDB8LCwmjcuHG+K3wMw+DHH39k5cqVbN++HV9fX8LCwujbty9lypQp6Zdw3X7//XeioqL46KOP+OWXX0jPzMbvnkewunvh4w77Ni7F3dsfz6BqOB1ODGcuWSnxZNviMVtcuL/nQAKCq14xeMrOTGP3xndIOX+GKg1aU79Nr7+8n+kpF/hyxYz//sqgddgISpUN+d8c9hxs6ZnMef5xWjWpVQLvioiIiIiUNAVPIiJyV0hOTmbDhg1ERkZy6tQpqlWrRmhoKN26dbviZ9aZM2dYvXo169atIzs7m06dOhEeHk7dunVLuPobExMTw8L3PiZixzFyMtOwZ6QQ9+N6vAIr0vDhx3E6nSReSCQ3Nxdn0nFO7v+KgPLVaNV7SL7BU2ZqMt9uWE568nlqNn+Y2i065XvfQ99t4ej30VRr3Jbj+7YTUu8+GrZ79LIxZxOS6d2+GS8N6lPcb4OIiIiI3AQFzYryb4whIiJym/D39+eJJ54gIiKCxYsXU7VqVebOnUvXrl157bXX+OWXX/jz37FUqFCBsWPHsmnTJkaNGsVPP/3EE088wT/+8Q+2bt2K0+m8Sa+mcEJCQqhapxGBgYHUrVeH4OBgAHIdDk6ePElsbCwWFwsVK1akVtN2AKTEn8p3rrSkeHauWUR6cgL1H+h9xdDJcDo5fWgvru6e1LmvC55+pYn9bR+5OfbLxrm6WPj5SP73EhEREZG7h4InERG5I5jNZlq0aMGMGTPYtGkTzz77LHv37uWpp57iscceIyIigoyMjMuu8fb2Jjw8nLVr1zJr1izMZjPjx4+nd+/erFixgrS0tJv0agru4PFYAKyuVgICAoCLr6tSxUq4u7mTlpqG3X5pKPTXbYjJ8af4Zs1isjNsNO7Yn6oNWl/xfvGnDpOVnkL5Go0wW1yoWKspuTnZnD3202Xj3K2unLuQQrIt/cZfpIiIiIjcthQ8iYjIHScwMJBnnnmGdevW8fbbb1OuXDlmzJhBly5dmDZtGocPH75svNls5qGHHmL58uV88MEHNG7cmAULFtCtWzdmz57N6dOnb9IruTqHw8nhE7G4ubpc9rjZZKJMmTLUrl2bhg0bUqZMGX7/5RsA/MtWumzshTPH+HbdMnJz7TTv+hQVazW96j1PHdwNQMVazQCoUKspYOLUwT2XjXOzupBtz+HoqbgbeYkiIiIicptzufYQERGR25PZbKZVq1a0atWKuLg41q1bx5o1a4iMjKR+/fqEhYXRqVMn3N3d866pW7cuU6ZMYcSIEXzyySdERETw4Ycf8uCDDxIeHk7Tpk3zbV5+MyQkp5KRZcf6p+ApPeUCh3d/DoAj105yXAyJZ09gtrhQ574ul42N+fVbABq0DaVs5av3uMrOTCPu94N4+ZehVLnKAHj5BRIQXIXEsydIS4rHu1QQAC4WC7kOJ2fik2hev0heroiIiIjchrTiSURE7gply5Zl4MCBbNy4kbfeegs/Pz9ef/11unTpwqxZszh+/Phl48uUKcOQIUP49NNPmTRpEqdOnWLQoEE89thjbNy48U/b124Ouz0XwzAwmS8PwjJsF/ht7xf8tvcLTuzfQYYtkfI1m9Dm0ZGUKlflsrGlK9YE4NC3W0g5f+aq9zt9aC+G0/GXVVEVal9c/XTq0P9WPZlMJkwmE/ac3Ot9eSIiIiJyB9CKJxERuatYLBbatm1L27ZtiY2NZc2aNaxbt44PP/yQxo0bExYWRocOHbBarQC4ubnRu3dvevXqxe7du1m5ciWTJ0/m7bffpm/fvoSFheX1VippTsPA4K9dm8pUqsV9Pf5ZoDkq1W1BuWr38MtXa/l2/TLu6zEQ/6CK+Y69uJ3OlBc0ZWZlknghEYtPWUxmF2IO7qFc7ZZYXF2wWCw4cnNJSkomIyMDDw+PW2almIiIiIiUHJPx56N+8lHQI/JERERuRzk5OWzbto3IyEj27NmDn58fPXr0IDQ0lJCQkL+MP3nyJKtWrWLjxo04nU66dOnCgAEDqFmzZonWHRufxN/GvY2bqyse7lYybIls/c/0AgVPh3d/zm97v6DJw49RoWZjTh74lp+3R+Lq5s593f/5l15QiWd/55s1i65ZU7kGHfEqffE9c5iteGX8jrv9PBaLBV9fX3x8fPD19c33x8fHBz8/v7+McXNzu/43SURERESKRUGzIq14EhGRu56rqysPP/wwDz/8MDExMURGRrJ+/Xr+85//cO+99xIWFkbbtm1xdXUFoHLlykyYMIGhQ4eydu1aVq9ezfr167n33nsZMGAAbdq0wWwu/t3svt4eWCwWch2OG56rcv37MZnM7N/+Cd9uWM59Pf5JqbL/C93+aCpeJqQO7l5//WKRk53JueM/Y6TGUqtVR3JyckhITuMfT/yDmsE+pKSkkJqais1my/uJj4/n6NGj2Gw2UlNTyczMzLc2q9V6zXDqSo+7uOirjoiIiMjNpG9jIiIilwgJCWH06NEMHTqUqKgoIiMjmTBhAgEBAfTq1Ys+ffpQvnx5AHx9fXnyyScJDw9n69atrFy5kueee45KlSoxYMAAunfvjqenZ7HV6u3pTsWyARyNicPH68bnC6nXApPZxE9ffsx3G/7Ffd2fpVS5KuTmZBN7bD8WFyvNOj+Oi+tfVyAZhpPof08jPuYQOdnpGGZ3vDw96P5wWyqXL12g++fk5PwlnLr0JzU1NS/AOn369GWPX6nnloeHxzXDqfwCLG9v7xIJD0VERETudAqeRERE8mG1WunWrRvdunXj2LFjRERE8PHHH/P+++/TsmVLwsLCaNOmDRaLBRcXFzp16kSnTp34+eefWbVqFbNnz2bx4sX06dOHfv36ERwcXCx13lOjIodPxBbZfJXq3IvJZOanrR/x3YZ3aNH9WdKSzuPIyaZi7Wb5hk4AJpOZirWbcfSHrZw+9D2BNVrkBWMF5erqSkBAQKF7ZhmGQXZ2dl4IdaWw6o/Hzp8/f9kYp9OZ77ze3t5XDKeuttrKy8tL/axERERE/kvBk4iIyDVUr16d8ePHM2LECL744gsiIiIYO3YsQUFB9O7dm969exMUFARAgwYNaNCgASNHjuSjjz5izZo1rFixgvbt2xMeHk6DBg2KNJSoEVIOuBi+FJWKtZthMpvZF7Wa7za+iyPn4mqiinXuvfp1dZpz9IetnDq0B+/KTWherRoWS/GvGjKZTLi7u+Pu7p7376GgDMMgMzMz362A+QVYZ86cyXssLS0t3/fdbDYXeCvgn8e4u7srtBIREZE7ipqLi4iIXIdDhw4RGRnJ5s2byc7Opk2bNjz66KPcf//9l23RysjI4NNPP2XVqlXExMRQv359wsPD6dChQ5H0Hzp+Op6/v7gUF4sZb0/3G56vKDicTs4n2nju790Y0K3VzS6n2DidTtLS0vJdVXWl1VZ//GRkZOQ7p6ur63U1YPfx8ck7iVFERESkJBQ0K1LwJCIicgPS09PZsmULERERHDlyhPLly9O7d2969epFYGBg3jin08k333zDypUr2b17N0FBQfTr148+ffrg5+d3QzWMmfEBO348QrnAG5unMDIyM0hLTcPH1wc3q9tlYVtyagZWVwsfvTWKQH/vEqvpdpKbm0tqaupVw6krBVjZ2dn5zunu7n7NcOpKYZbFYinhd0BERERudwqeRERESpBhGBw4cICIiAg+//xzcnNzadeuHWFhYTRv3vyyYObo0aOsWrWKzZs3YzKZeOSRRxgwYABVq1a9rnt/uftXJsxdjb+PJ1bX4ttFb2Bw8veTBAQEkJScREJCAmazGVcXV6xuVjzcPbC6WcnKga5t7uGN0Y/pVLliYLfb8w2nCrLaKjc3N985PT09C9x4/dIxXl5easIuIiJyl1LwJCIicpPYbDY2bdpEREQEJ06cICQkhD59+tCjRw/8/f3zxiUlJREZGclHH33EhQsXaNWqFQMGDOD+++8vVJ8fe04u4eMXEXM2gbLFvOop9mwsKckplCtXjlOnT5GTk4PZZMZsMYMBmF0xmc2Yzu3Fy5JDjRo1qFevHjVr1qRGjRpUr16d4OBghRU3gWEYZGVlFWgr4J/HpKWl5duE3Ww2X9aE/Wqrrf78uIeHh/pZiYiI3MYUPImIiNxkhmGwb98+IiIiiI6OBqBDhw6EhYXRuHHjvD902+12oqKiWLFiBYcPH6ZatWr079+fRx55BDe3/E+R+7Ptew8ycd6HuLm6FGuvJwOD2NhYbCk2vLy8SExKvNhg2wAXVysmV3esWeewJB4mOzubrKwsHA4HJpMJs9mMm5sbvr6+1KlTh3r16lGjRg0aNmzIPffcU2w1y41zOp1kZGQUaFXVnx9PS0vLd06LxVKgrYD5BVcF/X0hIiIixUfBk4iIyC0kOTmZDRs2EBkZyalTp6hWrRqhoaE88sgj+Pj4ABeDqh9//JFVq1axbds2fH19CQsLo2/fvpQpU+aq8xuGwdRla1n35fcElfIt1tPkDAxiz8SSYkvB4XBgOA0MDAyzG6bcTAKyfsPFdPnqGMMwyM3NJTs7O+8HwOFwUK5cOb799ls8PDyKrWa5eRwOB2lpaQVuvH7pmMzMzHzntFqthWq8funj2v4pIiJSNBQ8iYiI3IKcTid79+4lIiKCbdu24eLiQufOnQkNDaV+/fp5q6DOnDnDhx9+yNq1a8nOzqZTp04MGDCAevXqXXHuJFs6T7+0jDPxiZQN8CvWbUx/hE+JiYk4HA5c3DwxDIOUI9sh4zwVKlTA3f3qK68yMzNJSkpiwoQJjBw5sthqldtXTk5OvuFUQVZb2e32fOf09PQs0FbAP/94e3tri6iIiMglFDyJiIjc4i5cuMC6detYs2YNZ8+epVatWoSFhdG1a1c8PT2Bi6fmrVu3jtWrVxMbG0ujRo147LHHaNu2bb4nke07dJLn31pJSmoGQQG+xRs+GQanTp/mQlIKZosrPjlxWFJ/58yZM2RnZ1O2bFlKlSqV77UOh4OzZ8/SsWNH3n//fa1CkSKXnZ1doK2A+f3k18/KZDLh7e2dbzh1rdVWnp6e6mclIiJ3HAVPIiIitwmn08m3337LJ598wo4dO3B3d6dLly6EhYVRu3btvDHbt29n1apV/PDDDwQHB9O/f3969eqFt7f3ZfN9s+83Xl7wEbb0LIJK+WI2F98feJNt6cTGxZF0/AdKkUApf38MwyAuLo6kpCR8fHwIDg6+LCQzjIt9oipVqsTatWspX758sdUnUliGYZCZmXnNhuv5BVqpqank99XabDYXqvH6pT9ubm4KrURE5Jak4ElEROQ2FBcXl7cK6vz589SvX5+wsDA6deqUt3Xt0KFDrFy5ks8//xyr1UqPHj3o378/lSpVyptn74HjTJr/ERdS0ijl44m7m7VI63Q4nCQkp+LqYuFvne9l2vPPkpiYSPXq1fHy8gIgNTWV2NhYLBYLFSpUyOvhlJSUhNPp5N1336V9+/ZFWpfIzeR0OklLSytw4/VLfzIyMvKd09XVtUBbAf88xsfHB6u1aH/fi4iIXErBk4iIyG3M4XCwY8cOIiIi2LVrF15eXjzyyCOEhYVRrVo1ABISEvj444+JiIggJSWFBx54gMcee4ymTZtiMpmIOZvArPc+ZffPRzGbzQT4et/w6ifDMEjPzCY1I4tK5QIZ80RXHmhWm3/961+MGTMGwzCoXLlyXviUk5PDmTNnyMzMJCgoCC8vLy5cuMDIkSOZOHHiDb9PIneK3NzcvFVTVwqnrvTcH836/8zd3b3AWwL//Ov8tvKKiIhcSsGTiIjIHSI2NpY1a9awbt06EhMTady4MWFhYXTo0AGr1Up2djZbtmxhxYoVHD9+nFq1ajFgwAA6d+6MxeLCmui9LPs4miRbOq4uFvy8PXF1KdwfKp1OJ6kZWWRm2bG6utDtwcYM6/8w/r4XA6aMjAweeughDhw4AEClSpXywifDMDh//jwJCQkAdOjQgYiICK3GECkidru9UI3XL308Nzc33zm9vLwK3Hj90jFeXl5qwi4iNywn18HJ2ASOxpzj99gE0jOysOc6cLGYcbe6EhIcSPVKZaleqSwe7vo+cbMoeBIREbnD5OTksG3bNiIjI9mzZw9+fn706NGD0NBQQkJCMAyDPXv2sGLFCnbu3ElAQACPPvoojz76KFm5Jj796kfWf/kDcRdSMDCwurjg7uaKm6srFsvlf1B0GgZ2ey7Z9hyy7DkYhoG3pzvt7q1Hz4ea0rhO5b/0nfn3v//NCy+8gN1uJzs7m5CQkLwm6QAnT54kKyuLxo0bM3v2bO67774Sed9EJH+GYZCVlVWgrYB/fi41NTXfJuxmsxlvb+9CN2D38fHBw8ND/axE7mI5uQ6+/ek3Nm7/kR8P/k56Zjb2nFxMJhMXUwsDTGDChNMwcHWx4O7mSs2QcjzyYGPa33cPPl5XP1FXipaCJxERkTvYyZMniYyMZMOGDdhsNu69917CwsJo27Ytrq6unDx5ktWrV7NhwwYcDgddu3alf//+hFSuwva9h9j63QF+OXoaW1oG2faLKx4u/fOew2ng5uqCh5uVGiFlad2kFp1bNyQo0O+KNWVlZdGxY0eOHz9Obm4uGRkZeeFTSkoKdrud6dOns3PnTnbv3s1TTz3FoEGDdKKdyG3I6XSSkZFR4Mbrlz6elpaW75wWi+UvfaoKutpKKyhFbl+p6Vms+3Iv67Z+T8zZCzidTtzdrLhZXXBzdc23TYBhGNhzcsm255KZbQcgwM+Lrg805tFOLagQFFDSL+OupOBJRETkLmC324mKiiIyMpJ9+/YREBBAr1696NOnD+XLl8dms7F27VpWr15NfHw8zZs3Jzw8nDZt2gBwNiGZozFxnD2fTE5OLg6ngdXVBX8fT2qElKVKhTJYXQseDK1atYqxY8cSGBhIXFwcmZmZlCtXjtTUVJ599lmmTp2KYRj8+9//ZvHixdxzzz288cYbBAcHF9dbJCK3GIfDQVpaWoEbr186JjMzM985rVZrgRuv/7m3lcJvkZtnzy/HmP3+Jo6fjsdiMuHn41mo7x1/yHU4sKVlkp2TS6CfN4P6daB3+2ba+lvMFDyJiIjcZY4dO0ZERASffvopGRkZtGzZkrCwMNq0aYNhGGzdupVVq1bx888/U6lSJQYMGED37t0v2w53o+x2O506deLIkSMEBwdz8uRJbDYbLVq04PPPP7/sXvv372fSpEmkpaXx8ssv64Q7EbmmnJycQjVev/Rxu92e75yenp4Fbrx+6Y+3t7f+UCtyndIyslj2cTRrovaSnZNDaT8fXArZfzI/TqdBYkoaTsOgZaOajHv6ESqW1eqn4qLgSURE5C6VmZnJF198wSeffMKvv/5KUFAQvXv3pnfv3gQFBfHzzz+zatUqoqKi8PT0pE+fPvTr16/IVh19/PHHjBo1isDAQBITE8nIyKB69eq8//773HPPPZeNtdlsTJ06la1bt9K3b1/GjBmjLTMiUiyys7ML1Xj90p/8+lmZTCa8vb0L3Hj90sc9PT3Vz0ruWheS05gwdxX7Dp3E090NHy/3Iv/9kJltJ9mWQXCQPzPGDKBe9QpFOr9cpOBJREREOHToEJGRkWzevJns7GweeOABwsLCuP/++zl//jwffvgha9asIT09nYceeojHHnuMBg0a3NAXQLvdTteuXfnxxx/x8vJi6tSpfPXVVxw9epQlS5ZQr169y8YbhsEnn3zC3LlzqVKlCtOnT6dy5co3+tJFRIqEYRhkZmYWaCvgn8ekpaWR3x+3zGZzoRqvX/q4m5ubQiu5bV1ITmPMjA/49dgZAv29r2tbXUE5nU7iE20E+vswe1w499SsVGz3ulspeBIREZE86enpbNmyhYiICI4cOUL58uXp3bs3vXr1wtPTk40bN7Jq1SpiYmKoV68e4eHhdOzY8bp7n6xdu5axY8fSvXt35s2bR2ZmJsOHD+fEiRMsXryYunXr/uWaI0eOMHHiROLj43nhhRfo3r37jb5sEZGbyul0XtbPqqAN2G02GxkZGfnO6erqWqjG65c2a9eKUrmZUtOzeG7mB/x48CSlS/ngWgRb667F6TSIT0whKNCP+ROepEZI2WK/591EwZOIiIj8hWEYHDhwgIiICD777DMcDgcPPfQQoaGhNG3alO+++44VK1awe/duypQpQ79+/QgNDcXP78qn2eUnNzeX9evX07Fjx7zvDunp6QwbNoyYmBiWLFlC7dq1/3JdRkYGs2bNYsOGDXTr1o0JEyYUaQ8qEZHbRW5uLqmpqQVuvH7pmOzs7HzndHd3L3Dj9T+HVhZL8YcEcmeb/d5GPtzyLQF+xbvS6c+cTidxiTYa1KjE0lefxc2qAwWKioInERERuSqbzcamTZuIiIjgxIkThISE0KdPH3r06MGFCxdYuXIlmzdvxmQy8cgjjzBgwACqVq16Q/dMS0tj6NChnD59mqVLl1KrVq18x23atInp06dTpkwZpk+fnm9IJSIi+bPb7dfVgN1ms5Gbm5vvnF5eXgVuvH5pcOXl5aUm7HeYM2fOkJ6efsXP8Px8u/8o42atwGIx4+vlUYzV5c+ek0uiLZ3B/TrwbGi7Er//nUrBk4iIiBSIYRjs27ePiIgIoqOjAejYsSOhoaFUrlyZNWvW8NFHH3HhwgVatWrFgAEDuP/++6+7x0hqaipDhw4lNjaWpUuXUrNmzXzHxcTEMHHiRI4fP87o0aPp16+f+pqIiBSjP/pZXU8D9rS0tHybsJvNZry9vQvceP3Sxz08PPTf/VvQmDFjWL9+PY899ljeYSJXk5qexdMvLSPmbAJBAb437d9pki0dF4uZJa88Q91qajZeFBQ8iYiISKElJyezYcMGIiMjOXXqFNWqVSM0NJROnTrx7bffsmLFCg4fPkzVqlUZMGAA3bp1w93dvdD3sdlsDBkyhLi4OJYtW0b16tXzHWe323n77bdZvXo1bdu25dVXX9V3ERGRW5DT6SQ9Pb1QWwL/+HV6enq+c1oslkI1Xr/0OfWzKj6PP/44n376KW5ubpQvX54xY8bQv39/XF1d8x3//tqvWLTqc0r7++BSAn2drsQwDM5dSOGBprWZ+8ITN62OO4mCJxEREbluTqeTvXv3EhERwbZt23BxcaFz58706dMHu93O6tWr2bZtGz4+PoSFhdG3b1+CgoIKdY+UlBSGDBnC+fPnWbZsGdWqVbvi2O3bt/Paa6/h4eHBG2+8QePGjW/wFYqIyK3C4XCQlpZW4Mbrl/5kZWXlO6fVai1w4/U/bx+83oM17hZdu3bll19+oXTp0pw/fx6n00nTpk2ZNGkSLVu2vGysPSeX/uMWEHs+CS9LLlv/M/2y501mC24e3gSUr0qNJg/hW7o8AElxMZz85RtSzp8mK92GIzcHdy8//MpUpHqTtvgH/fWEOsMwOHf8F37/eSdpyfHk2LPw8PYnsHw1qjd5CC+/QNIyssh1OHn/jUFUr6RG4zdKwZOIiIgUiQsXLrBu3TrWrFnD2bNnqVWrFo8++igNGzZk/fr1rF27luzsbB5++GHCw8OpV69egedOTk5m8ODBJCYmsnz5cqpUqXLFsXFxcUyaNIn9+/czePBgnnrqKfUNERG5y9nt9iuGVtcKsHJycvKd09PTs8CN1y/98fb2vuM/l95//33Gjx+PyWSiYsWKmEwmsrKySEhIwMPDg169evH8889TocLFrWxf7j7AhLmr8ffxIjfTxtb/TMfTN5AKtZoC4MjJJikuhqRzv2O2uHB/z4EEBFfl2I/bOP7T15QqG4K7tx8WFysZtgvE/f4rToeDxh3+RsXazS6r7dedGzj+01e4efpSrmo9XKzu2BJiOX/qN1xcrbQKHY5PQFnOXUjhqV4PMjy8U4m/f3caBU8iIiJSpJxOJ7t27SIiIoIdO3bg7u5Oly5d6Nq1KwcPHmT16tXExsbSqFEjwsPDadeuXYFOQUpKSmLQoEHYbDaWL19OSEjIFcc6HA6WL1/O//3f/3HvvfcyZcoUSpcuXZQvU0RE7gKGYZCdnX1dDdhtNlu+/axMJhPe3t4F2gr45+c8PT1vi35WEydOZObMmZhMJjw9PQkMDMTf3x+z2Zz3PpUpU4Zhw4bx1FNPMWlBBF9/f4hypf3JsCWy9T/TKVOpFvf1+Odl8x76bgtHv48moHw1WvUegiM3B4vLX7fu2S6cY8cn83GxuvPwU6/kvWdZGTai/t9UPLz9ebDfGFzd/tfA/PhPX/Hrzg1UqnMvjdr3IzElDV9vDyLmjsbTw61437A7XEGzIq0hFBERkQIxm820bt2a1q1bExcXl7cKKjIykvr16/PMM8/g4eFBREQEL7zwAsHBwfztb3+jd+/eeHt7X3HeUqVKsXTpUgYNGsSgQYNYtmzZFcMni8XCkCFDaNasGS+//DIDBgxgypQpf1naLyIicjUmkwl3d3fc3d0LvVXcMAwyMjIKvCXw9OnTeWNSU1PzndNsNheq8fqlj7u5uZVYaGW1WjGZTJjNZjIzMzl9+jRxcXEEBAQQEBCAt7c3Fy5cYMqUKXz48SdkBd2HRwF6QVZt0Jqj30eTEn8KIN/QCcA3sBzepcpiSzhDrj0rL2DKtCWBYVCqXJXLQieAoMp1+XXnBrIz0wDw9nQn2ZbBkZPnaFyn8o28HVJACp5ERESk0MqWLcvAgQN59tln+frrr4mMjOSNN97Ay8uLRx55hL59+7Jjxw4WLlzI8uXL6dGjB/3796dSpb/2ZAAICAjIC58GDx7M8uXLqVix4hXv36JFC1atWsWrr77KiBEjePLJJxk6dKj6coiISLEzmUx4eXnh5eVFcHBwoa51Op2kpaUVaCtgQkICJ06cyHs8IyMj3zldXV0LtBXwz72sfHx8Ct2E/Y+thGazGYvFgmEY5ObmEhcXR0JCAn5+fgQGBuLr68uRE2dwNc7j5eGGu2tB36erB2jpKQmkJ8fj7u1/WcDk5V8as9lC0rnfybFn4Wr9X9gVf/IgAKUrXjxF19XFQq7DwdGYOAVPJUTfzkREROS6WSwW2rVrR7t27Thz5gxr1qxh/fr1fPjhhzRu3JjRo0eTkJDA2rVr+eijj3jggQcIDw+nWbNmf/nb2cDAQJYuXcrAgQMZNGgQy5cvz+sRkZ+AgADmz5/Pf/7zHxYtWsQPP/zAtGnTKF++fHG/bBERkety6cqmwsrNzc0Lqa4VWsXGxnLo0KG8X9vt9nzndHd3L3Dj9T+2VBmGkfcZbjKZcHV1xTAMHA4HiYmJJCcn4+npSUDlBjhMFtJsKfyWnoaPR/6rmAB+/+UbAPzLXv4XVElxMcSfPIThdJCZmkTc778C0KBt6GXjrO5e1GnZjV93bmTbylmX9XhKOHOMyve0pEqDVnk1AxyNOVfofwdyfdTjSURERIpUTk4O27ZtIzIykj179uDn50fXrl3x9/fniy++4NixY9SqVYsBAwbQuXPnv/xta3x8PIMGDSInJ4fly5cXKEj6+eefmTRpEjabjVdeeYX27dsX18sTERG57fzRz6qwDdhtNhsOhyNvntjYWM6ePYvJZMr7AS77yyTDMDAMA9+q9+Jd4R5MjmwwQU5mKue+X4e7dykq1WkGmHDk2kmOiyHx7AnMFhda9hpEqXJV8uY68fNODny9Nu/Xbh7eNO7QnzIhtfN9nbFHf2L/lx+Tm5Od91hAcFXq3N+VgOCqeY+dT0qlVuVy/Hv6kBt8Z+9uai4uIiIiN93JkyeJjIxkw4YN2Gw2mjdvzj333MPhw4fZtWsXAQEBPProozz66KMEBATkXRcfH8/AgQNxOp0sW7asQFsZUlNTmTp1KtHR0YSFhfHcc8/h5qamoSIiItfLMAwyMzPzQqjZs2fzzjvv4Orqmvf8lf7pV70VnsF1cGRf7GvlyE7n/L6Nl81vMltw8/AmoHw1ajR9CN/A/D/vHbk5pCef5/hPX3H6yA/Uvb8b1Zu0u2zMkT1f8Nv30dRu0YkKtZri6uaBLSGWAzvWY7sQS7POT1Kuan0AElPSKBPgy9q3nyuS9+lupeBJREREbhl2u52oqCgiIiL46aefCAgI4IEHHiArK4vt27fjcDjo0qULAwYMoFatWgDExcXxz39ePPXmX//6F2XLlr3mfQzDYM2aNcyePZvKlSszbdo0qlates3rRERE5NpeeeUV3njjjWs2NDcMA+9qLXErXQ1HdtrFXlDZ6STs24ibfzAN2odTrly566ph98Z3iT91mAf7PYdv4MU5zp86wncb/kXVRg9Qv3XPy8ZnZdjY+p83cffyo/1jLwCQbEvHx9uDTUvGX1cNcpFOtRMREZFbhtVqpVu3bnTr1o1jx44RERHBp59+SkZGBs2aNSMgIIBvv/2WDRs20Lx5c8LDw2nTpg3Lli27rOfTtU4eMplMhIaG0rBhQyZMmMATTzzB+PHj6dGjx21xTLWIiEhJMgyD7OxsMjIyyMzMJD09nczMzLxfZ2RkXPbcjh07MAwDu92e97n659VOf3DPycH63213Jkz/axtuGKSlpV13zaUr1SI+5hCJZ4//L3iKOXzxuQo1/jLe3dMXb/+giyfh5WTj4uqGAbhYLNddgxSOgicREREpUdWrV2f8+PGMGDGCL774gk8++YS9e/dSpkwZ2rRpw7lz53juueeoVKkS/fv3Z968eYwaNYrBgwezbNkyypQpc8171KhRgw8++IBZs2YxZcoUdu/ezYsvvoinp2exvKYsew5n4hLJzLKT63Di4mLBy8NKhaAArK76uiUiIjfOMAyysrIuC4QuDYb+HBYV9LlrbYJydXXF09MTT09Pzpw585fxf5x090cQ5XQ6L4ZNhhOzyYT5v9vyyL34eejh4UH1GtWv+33IzrD9977/C46czot9qLIz8w+07JlpYDJh+u81TqeBu9uVm51L0dI3IREREbkpPDw86NmzJz179uTQoUNERkayefNmsrOzqV+/PoZhMHv2bLy8vHjooYfYvn17XvhUunTpAs3/yiuv0KJFC6ZNm8aBAweYPn06derUueHaMzKz+fqHwxw8foZfjp7mxOl4srJzcP7xN7smE2azGU83V2pWDqZe9QrUr1GBVo1r42bV1y8RkTud0+m8LCS6ViB06UqjKz1XkJDIarXi4eGBl5cXHh4eeHp65v3Tz8/vis/98b///JyHh0dePyeAUaNGcfToUVxdXfMCpz9er8PhwDAMXFxcLp6M5+tOjtmMMxdcXFwICAriLBeDrEvWP+UrOf4U/kGV/vJ4SsIZTh74FpPZQumKNfMeL1WuCr//vJMTP31FcLUGuLp55D138pddZKWnUKpcFSyWi5/B9txcaoRcewu/FA198xEREZGbrk6dOrz44ouMGjWKLVu2EBERwZEjRwgMDCQwMJCoqChSU1P5/fff6d+/Px9++CGBgYEFmrtLly7Ur1+fiRMn8tRTTzFq1Cj69+9/XVvvfj9znk1f7+PT7fs4n3Txb1xNJhNuVhc83d0wmy+e8GMYBg6nkyx7LnsPHGfvgeNggvKlS9GzfVO6tmlMcBn/Qt9fRESK3h8h0Z9Dn/x+XdDnMjMzr3lfq9WaF/pcGv54enri7+9/xecuDYsufc7DwwMXl+L9I352dvZlv3Y4HDgcjoufhW5uBAQEUKpUKbKzs0lOPIebbzVKBQQQXK4cOZmpBb7P9599gMlkxq9MRTx8/HE6HKQnx3P+9G9gQP02PfH0/d+hJOWrN+TkgV0kxh7ny5UzKVulHq5uHqScP8OFM0cxu7hSr3WPvPFmk4naVa59cIkUDTUXFxERkVuOYRj88ssvREZG8tlnn5GTk0OFChU4d+4cv/76KwEBAcyaNYvevXtf9jexV2O321m4cCErV67kwQcf5NVXX8XPz69A1ybb0lm8+gu27NxPZpYdq6sLvl4euLgUvD+EPScXW1omuQ4HXp7uhHW8l2dD2+HpoZP3REQKyul0XrUH0fU8V5CQyM3N7aqhT0Ge+/NqIstt2GPoiSeeYOXKlXmrncxmMz4+PgQEBODj44PdbichIQFXV1daPdCOk84Q3N3d8fZ0J8OWyNb/TKdMpVrc1+OfV73PyQPfEn/yILaEWLKz0sEwcPP0ISC4KlUatKZU2ZC/XONw5HLip6+IPbqf9OR4nE4Hbh4+BFaoTo2m7fEJuLjCKdfh4EJKGnPHP07rJrWL/k26i+hUOxEREbkj2Gw2Nm3aREREBCdOnMBisXD06FEcDgfNmzfnscceIzQ0tMAh0ldffcXkyZNxd3fnjTfeoEmTJlcd//X3h5j7783EnLuAl4cbPp7uN9So3Ok0sKVnkpVtp2blYJ5/+hGa1K1y3fOJiNyqnE5nkfQguvSfWVlZ17yvu7v7VUOf/EKiaz136bayu1lYWBjr1q3DYrFQunRpAgICcHNzIzc3l4SEBJxOJ/fccw/PP/88HTt25ImJS/jt5FmCAgr2GV0SbOkXg8aP54wiKED5xo1Q8CQiIiJ3FMMw+PHHH4mMjGTTpk0cPXoUd3d3ypQpg5+fH927d6d///5Uq1btmnPFx8czadIkfvrpJwYOHMgzzzzzlz9U5DoczP9gC5FRe8jJdRDo712kJ+Dk5Dq4kJyKu5uVp/s8yFO9HtQfbETkpnE4HFfcRna9YdGft2Xl548tYlfrM1SY59zd3fXf0mL06aef8o9//ANPT09KlSqF0+kkKSmJzMxMKlasyMiRI+nfvz9WqxWAVZu+Yc7/20SZAF8st8i/l3MJyXRseQ/TR/e/2aXc9hQ8iYiIyB0rOTmZd999lxkzZpCTk0PVqlVxOByYzWbatGnDgAEDaNmy5VVXJjkcDt555x3eeecdmjVrxuuvv553Yp49J5dpy9fx6dc/4unuhq+XxxXnuRGGYZCSmoE918GAbi0Z+Vhn/YFJRK7J4XAUqil1QZ6z2+3XvG9htpUV5DmFRLcfwzCoX78+2dnZWCyWvKzgmWeeYeDAgZQqVeqy8ReS0+g3dj72HAf+PsVzsmxhZNtzsKVnMmf8E7RqXPPaF8hVKXgSERGRO95vv/1GeHg4GRkZuLu7k5mZiYuLCw6Hg7p16xIeHk63bt1wd3e/4hx79+7lpZdewuFw8Nprr3Hfffcz7Z11rN/6Az5e7niVQA+m1PRMMrPtPNnzAYb2f/iGtvKJyK0lNzf3hhtW/7k/0bVCIpPJlO/WsRsJixQSyR+aNWvGsWPHKFWqFD169OC5556jSpUqVxw//V/riPhiD+VK+930z7f4xBSqVghixYxhWCz6//ONUvAkIiIid4UjR44wePBgSpcuTbt27di8eTPHjh3DMIy8puT9+vWjb9++BAUF5TtHUlISr776Kt988w33PNCTX8/m4O1ZMqHTH2xpmdhzcpnwz570eqhZid1XRP4nJyenwE2pC7rlLCcn56r3/CMkulIPout5zs3NTSGRFJtHH30UgPHjx9OiRYtrjj8aE8c/J/8Luz2XAD/v4i7vijKz7KRmZPHy4D50b3v1/o5SMAqeRERE5K7xR/hUqVIlFixYwM8//0xERARbt24lNTUVh8OBr68vPXv2JDw8nHr16v1lDqfTyZxF77Bk7V5crFaqVqpQ4BPzisr5JBv+Pp68+/pAKgQFXPsCkbvUH8FyUTWs/uMnNzf3qvc1m82Fbkp9refc3Nxu+ioQkcL4Y2t7Yf5/+8H6r1mw8nP8fTxxs5bsZytc/IyPS7Tx0L31mPFcfwWzRUTBk4iIiNxVDh06xJAhQ6hSpQoLFy7Ey8uLuLg41q5dS0REBEePHiUnJwc3NzdatWrF3//+d9q1a5d3nHW2PZchU/6PHw4eJys1GafTQXBwcIl+93E6ncRdSKHtvfWYNXaAvhjLHeGPkOhGexD9+TmHw3HV+5rN5ms2pf7jn1dqWP3nkMhqtSokErkO9pxchk19jx8PnaRsoB/mEv59dD7JRilfL/7v9UEEl/Ev0XvfyRQ8iYiIyF3n119/ZejQoVSrVo2FCxfi6XmxkanD4eDrr7/mk08+ISoqCpvNhtlsplatWjzzzDP06tWL9dv3M/8/Wyjl64XFYuLs2bOk2lLx9/enbLmymE0lEwL9sRXgtWFhdGnTqETuKfIHwzCw2+0F6jNU0OcyMjIKFBL9ecvYjfYncnV1VUgkcgs5GnOOYVPfJ8mWTlCAb4n9/kyypWMYBi8P7qPP1SKm4ElERETuSgcOHGDo0KHUrFmTt99+Oy98+sOZM2dYs2YNq1at4sSJE+Tm5lK6TBDedTpjuLhTrvT/TuRJSk4i7lwcVquVChUq4OZWMj2fzl1IoUHNSrw75Z/6g7NckWEYZGdnF8mx95c+53Q6r3pfi8VS4BVCBX1OIZHI3WHvgeO8MGc1qemZlAnwLfaVT0m2dBxOJ8MGPMzj3dsU673uRgqeRERE5K71888/M2zYMOrUqcP8+fPx8PD4y5icnBy2bdvGihUr+HL3rxhBjTA57bi7GBz58oPLB5vMuFg9CKxQjdrNO+JbujwAh3d/zm97v6DJw49RoWbjy+e3Z7Fn03skxh6nfM0mNO7wN8zmi9v60pLP8/vPO0k4fZSstGScjlysHt74lw2hQs3G+JWvRaY9lyUvP02j2pWL5T2SknVpSFSUW86uFRK5uLhcMfS53mbWVqu1hN41EbkTfbf/KK8s/IQLKWmU9vfB1cVS5PdwOp0kJKfhYjEzpH9HHnuktcLtYqDgSURERO5q+/fvZ/jw4dSrV4958+bh7u5+xbH/fHkJO388QlZ6Mpm2JM79sA5XD1/KVW+Iu7s7uTnZJJw5TmrCGcwWF+7vOZCA4KpXDJ6yM9PYvfEdUs6foUqD1tRv0yvvC++xfds5tGsThmEQEFwFvzIVsLhYyUxLJuH0b2RnpFKxzr2UbdiJPh2aM2lg72J+p+TPDMMgKyvruo69v9pz1/ra7erqetXQ53qaWZd0g3wRkYL45bdTTF2+lqMxcXi6W/H18iiyYCgzy05yWgal/b0Z+VgXuj3YuEjmlb8qaFbkUoI1iYiIiJSYhg0b8vbbbzNixAiee+455s6dm+9WufNJNg7HJBBUOhDvkPLEx8Zw7gewuHnh9Akhx+pKUJkg6rfuyaHvtnD0+2gOfbeFVr2H5HvfzNRkvt2wnPTk89Rs/jC1W3TKe+7kgW85+M1GPHxK0bzLk/iVqXjZtU6ng9OHvifx7HE8rK5s2/0rzz/dHaurvrJdidPpJCsrq0iOvf/jnwUJiaxW6xVDn+Dg4GsGQn/eqqaQSETuJvfUrMS7Uwbyr0+2EvHFbs5dSMHP2wMPt+tv4G/PySU5NR0w8WCzOox9qptOiL1F6FuMiIiI3LEaN27M/PnzGTlyJGPHjmXOnDl/2Sb028k4su12Any9MZlM+Pj4AODl5U1QUBCJiYmcO3eOMmXKULVBa45+H01K/Kl875eWFM+3G/5FVloK9R/oTdUGrfOey8nO5OCuTzGbLbR45Fl8Asr+5Xqz2UJIvRZUqN2UnFyDjKxsfj9znlpVgovwXbl5/giJiuLY+0tDomuxWq1XDH38/f2vefLZnwMkDw8PXFz0NVpE5EZ4ebgx+omuPNC0Dss+jubA0dOkpGbi4e6Kt4c7Fov5miGUw+kkI9NOemYWZrOZSuVK80TPNvRo20Qnw95C9IkpIiIid7SmTZsyb948Ro0axbhx45g9e/Zl4dOxmHM4neDypx4TZrOJChUqEBwcjMPh+NOX379+EU6OP8Xuje+SY8+kccf+VKzV9LLnzx7bT649i/I1m+QbOl3KYnHBZDZISk3naEzcTQmenE7nDfcg+vOvs7KyrnlfNze3K4Y+pUqVKnQzaw8PDyyWou8fIiIiRaNZ/aosq/csB46d4dPtP/L5Nz+TaEvP62Hn6mK5GEJhwuDi55M9JxdMJsDAw81Ku3vr0aNdU1o2rlksPaPkxih4EhERkTte8+bNmTt3LqNHj+b5559n1qxZeeHTkZPnMLjytiqz2Zz3t6a///INAP5lK1025sKZY/y87ROchpPmXZ+ibOW6f5kn8dzvAJSuUKNANZtNJkwmE0dj4q451uFwXDEIKsyx95c+l52dfc37uru7XzH0CQwMvK7TzfQ31CIidx+TycQ9NSpyT42KDOrbnoMnYjkaE8eRk2c5dDyWtIwscnIdWMxm3N1cqRlSjjpVy1M9JIjaVcpTPqjUtW8iN42CJxEREbkrtGjRgjlz5vDcc88xYcIEZsyYgaurK6fOXcAlnxUx6SkXOLz7cwAcuXaS42JIPHsCs8WFOvd1uWxszK/fAtCgbWi+oRNAdkYqAO7efpc9brfbycnNwel05v0YTgOn00l6hp3NUV+ScGTnVbec2e32a77+P0Ki/HoOlS5d+rpOPlNIJCIiRc3f14uWjWrSslHNm12KFBEFTyIiInLXuP/++5k9ezZjx45lwoQJvPnmm9hzcjHn00Iiw3aB3/Z+AYDJbMHNw5vyNZtQo+lD+AZevvWtdMWaJJz+jUPfbsE/KAS/MhUKVE9Obg7Hjh37y+Mmswmz2YwDV84npBPr4sDT0xNv74t9pwp78pm7u7tCIhEREbkpFDyJiIjIXaVVq1bMnj2bcePG8eKLL+I0QvIdV6ZSLe7r8c8CzVmpbgvKVbuHX75ay7frl3Ffj4H4B11+Yp2b58Wm5VnpKXmPubq4UqNGDQzDyNvSZzKbMP23h1TchRRaNa7JvAlPXs9LFREREbnp9FdfIiIictdp3bo1M2fO5Ouvv+b3E8dxGlfu8VRQVe5pRYO2oeRkZ/HdhuUkx11+8l1AuSoAJJw+etnjrq6uWK1WXFxcLgZPlzQuN4yLTVNFREREblcKnkREROSu9MADDzBjxgySE86SnJxy1QbjBVW5/v00bPcoOfYsvt2wnKS4mLzngqs3xMXqzrnjP5OWFH/VeRyO3Lz/XSbA94brEhEREblZFDyJiIjIXatt27b06dYBu91O7JnYIgmfQuq1oNFDfcnNyea7Df8i6b+n2bm6eVC35SM4Hbns/vRdUhLO/OVaw+nk1KG9/Lw9AuO/q7CqVwq64ZpEREREbhb1eBIREZG7WtcOD7Dp299ISbmAyWTCz9vthuesVOdeTCYzP239iO82vEOL7s8SEFyVyvXvJ9eexaFvN/P1R/MJKF8Vv9IVsLi4kpWeQsLpo2Slp1CpbgvsObm4urhQPaRcEbxKERERkZtDwZOIiIjc1aqHBOHr44271YUL5+PITi+aeSvWbobJbGZf1Gq+2/guLR55hsDy1ajepB1lq9Tj91++IeH0UWIO7sbpyMXNwxu/oIrUr9WTctUaYEvPxN3NleoVteJJREREbl8mw7h2N02bzYafnx8pKSn4+qrPgIiIiNw5DMNg4OR32Hc4Bg9XiD0Ti5+/H8HBwZc1+i5p5xKS6diyAdNH/+2m1SAiIiJyJQXNitTjSURERO5qJpOJXu2bA+Dl6U358uVJSU7h3NlzRdLz6Xpk2XNwsVjo+VDTm3J/ERERkaKi4ElERETueu1b1KNMKR9s6Zn4+flRvnx5kpOTizx8MjCIjY0lKyvrquNsaRlUrlCaFvdUL7J7i4iIiNwMCp5ERETkrufp4UbPh5phz8klN9eBn9/FrXbJycnEnYsrsvuYMGG324mJicFut+c7Jtueg2FAaMd7sVj0VU1ERERub/o2IyIiIgI89khrqlYMIiElFcMw8Pf3Jzg4mKSkJM6dO1dk96lUqRIWFwsnY06Sk5tz2XOGYZCYkk6j2iH0/u/2PxEREZHbmYInEREREcDHy51xTz2Cm6srqekXt8L5+/tTLrgcSUlJxMUVzconi8VCSEgIADExMTgcjrznkmzp+Hi5M/6ZHlhddfiwiIiI3P4UPImIiIj8V4sG1enTsTkZWdlkZV9cjVTKvxTlypUjMTGxyMInVxdXKodUxpHr4NSpUzidTtIzs8l1OHi6T1tqVi5XJPcRERERudkUPImIiIhcYlj/h2nTtDZJtnSy7f8Nn0qVomy5siQmJhIfH18k97FarVQKqUR2djYnY05jS8+k6wONCX+kVZHMLyIiInIrUPAkIiIicgl3NytThvfl/kY1SLSlk5l1sQl4QKkAypYty4ULF4osfPJw9yCwTFkysnPwt2Qy4dkeuFgsRTK3iIiIyK1AwZOIiIjIn/h4uTPzuQG0b1GP1IxMEpJTcRoGAQEBBJUN4sKFC5w/f/6G7uFwOjmfaAOThYfvr0vK0R28PX8ehmEU0asQERERufnUtVJEREQkH54ebkwf3Z810XtZ9nE0cRdS8Pf2JDAgEAwurnoyQZnSZQo1r2EYZGTZsaVnUjbQjxHhnejcuiFrm1XijTfewN/fn4EDBxbTqxIREREpWQqeRERERK7AYjHzaKcWtGhQjVnvfcreA8dJTsvAy92T0mVKk3A+ARMmSpcufc25nE4ntvQsMrPtuLm68HDLBox5sitBAb4A9OnTh+TkZBYtWoS/vz/9+vUr7pcnIiIiUuwUPImIiIhcQ0hwaeZPeII9vxxn4/Yf+er7Qzhwxd3bn7iEJOy5TsqWKY3ZbMq7xuF0km3PJduegz0nFzDh4+VOlzbNeeTBJjSqHYLJZLrsPk899RRJSUnMmjULPz8/OnfuXMKvVERERKRoKXgSERERKQCz2cx9DWtwX8MaxMYnEfXtL/x85BRf7f6J8xeSsec6cXN3B8MAkwmL2YTV1ZXSpXyoV70iDWtV4uGWDShdyueK9zCZTIwePZrk5GReeeUVfHx8aNVKp9yJiIjI7ctkFKCDpc1mw8/Pj5SUFHx9fUuiLhEREZHbgtPpZNa8Raz4aC09e/ehTZsHsLq64OFupVrFIIICfP+ysulacnNzef7559mzZw+LFy+mYcOGxVS9iIiIyPUpaFakU+1EREREboDZbGb8mOEMfjKMLZ+8T2LMAdrfV5+WjWpSNtCv0KETgIuLC2+++SZ16tRh9OjRHD9+vBgqFxERESl+Cp5EREREbpDJZGLIkCH8/e9/Z86cOaxevfqG53Rzc2Pu3LmULVuWYcOGERsbWwSVioiIiJQsBU8iIiIiRcBkMjF8+HCeeOIJZs+ezUcffXTDc/r4+LBgwQKsVivDhw8nMTGxCCoVERERKTkKnkRERESKiMlkYuTIkYSHhzNz5kwiIiJueM7SpUuzaNEi0tLSGDlyJOnp6UVQqYiIiEjJUPAkIiIiUoRMJhNjxoxhwIABTJ8+ncjIyBues2LFiixcuJDTp08zduxY7HZ7EVQqIiIiUvwUPImIiIgUMZPJxHPPPUe/fv2YNm0a69evv+E5a9Wqxdy5c9m/fz+TJk3C4XAUQaUiIiIixUvBk4iIiEgxMJlMPP/884SFhfH666+zcePGG56zSZMmzJgxg+3btzNt2jQMwyiCSkVERESKj8vNLkBERETkTmUymXjhhRdwOp289tprmM1munXrdkNzPvDAA7z66qu8+uqrlCpViuHDhxdRtSIiIiJFT8GTiIiISDEym81MnDgRp9PJ5MmTMZvNdOnS5YbmfOSRR0hJSWHOnDn4+/vz+OOPF1G1IiIiIkVLwZOIiIhIMTObzUyaNAmn08krr7yC2WymU6dONzRneHg4SUlJzJs3D39/f7p3715E1YqIiIgUHQVPIiIiIiXAbDbz8ssv43A4eOmllzCbzXTs2PGG5hw6dCjJyclMmTIFX19fHnzwwSKqVkRERKRoKHgSERERKSFms5nJkydjGAYvvvgiZrOZ9u3bX/d8JpOJiRMnkpKSwoQJE1i4cCFNmzYtwopFREREboxOtRMREREpQX+ETx07dmTixIl8+eWXNzzf1KlTadSoEWPGjOHIkSNFVKmIiIjIjVPwJCIiIlLCLBYLr7/+Og899BATJkzgq6++uqH5rFYrb731FiEhIQwfPpxTp04VUaUiIiIiN0bBk4iIiMhNYLFYmDp1Km3btmX8+PHs2LHjhubz9PTk7bffxtvbm2HDhpGQkFBElYqIiIhcPwVPIiIiIjeJi4sL06ZNo02bNjz//PN88803NzRfqVKlWLRoEbm5uQwfPhybzVZElYqIiIhcHwVPIiIiIjeRi4sL06dPp2XLlowbN45du3bd0HzBwcEsXLiQ+Ph4xowZQ1ZWVhFVKiIiIlJ4Cp5EREREbjJXV1fefPNNWrRowdixY9m9e/cNzVetWjXmz5/P4cOHmTBhArm5uUVUqYiIiEjhKHgSERERuQVYrVZmzpxJ8+bNGT16NHv27Lmh+Ro0aMCsWbP49ttvmTJlCk6ns4gqFRERESk4BU8iIiIitwir1crs2bNp1qwZo0eP5vvvv7+h+Vq2bMmUKVPYvHkz8+bNwzCMIqpUREREpGAUPImIiIjcQv4Inxo3bsyoUaP44Ycfbmi+Tp06MX78eFauXMl7771XRFWKiIiIFIyCJxEREZFbjJubG2+99RYNGjRg1KhR/PTTTzc0X9++fRk0aBCLFy8mMjKyiKoUERERuTYFTyIiIiK3IHd3d+bMmUO9evUYMWIE+/fvv6H5/vGPf9CvXz+mT59OdHR0EVUpIiIicnUKnkRERERuUR4eHsybN4/atWszfPhwfvnll+uey2QyMW7cODp16sRLL710wyfniYiIiBSEgicRERGRW5iHhwfz58+nZs2aDBs2jF9//fW65zKbzUyePJl7772XsWPH3tBcIiIiIgWh4ElERETkFufp6cmCBQuoXr06w4YN4+DBg9c9l6urKzNmzKBmzZqMGDGC33//vegKFREREfkTBU8iIiIit4E/wqfKlSszbNgwDh8+fN1z/bGFLzAwkGHDhhEXF1eElYqIiIj8j4InERERkduEl5cXCxcupGLFigwZMoQjR45c91y+vr4sXLgQs9nMsGHDSE5OLrpCRURERP5LwZOIiIjIbcTb25tFixZRoUIFhgwZwtGjR697rqCgIBYtWkRKSgqjRo0iIyOjCCsVERERUfAkIiIictvx8fFh0aJFlCtXjiFDhnD8+PHrniskJIQFCxZw4sQJnn/+eex2exFWKiIiInc7BU8iIiIityFfX18WL15MmTJlGDx48A2FT3Xq1GHu3Ln8+OOPvPLKKzidziKsVERERO5mCp5EREREblN+fn4sXryYgIAABg8efEMn1DVr1oxp06axdetWZsyYgWEYRVeoiIiI3LUUPImIiIjcxvz9/VmyZAn+/v4MHjyYmJiY656rXbt2TJo0iYiICJYtW1aEVYqIiMjdSsGTiIiIyG2uVKlSLF26FB8fHwYNGnRD4VOvXr0YOXIk77zzDqtXry7CKkVERORupOBJRERE5A4QEBDA0qVL8fLyYvDgwZw+ffq653ryySd54oknmD17Nps3by7CKkVERORuo+BJRERE5A4RGBjI0qVLcXd3Z9CgQZw5c+a65xo5ciQ9evRg8uTJ7Ny5swirFBERkbuJgicRERGRO0jp0qVZunQpVquVQYMGERsbe13zmEwmXnrpJVq3bs348eP56aefirhSERERuRsoeBIRERG5wwQFBbFs2TIsFguDBw/m3Llz1zWPxWJh+vTp1K9fn9GjR3P06NEirlRERETudAqeRERERO5Af4RPAIMGDSIuLu665nFzc2POnDkEBwczfPjw615BJSIiIncnBU8iIiIid6hy5cqxbNkynE4ngwYNIj4+/rrm8fb2ZuHChXh4eDB06FASExOLuFIRERG5Uyl4EhEREbmDBQcHs2zZMnJzcxk8eDDnz5+/rnkCAgJYtGgRWVlZDB8+nLS0tCKuVERERO5ECp5ERERE7nDly5dn2bJlZGVlMXjwYBISEq57noULF3L27Fmee+457HZ7EVcqIiIidxoFTyIiIiJ3gQoVKrBs2TIyMzMZPHjwdW+Xq1GjBvPmzePAgQNMnDgRh8NRxJWKiIjInUTBk4iIiMhdolKlSixdupS0tLQbCp8aNWrEzJkz2bFjB2+88QaGYRRxpSIiInKnUPAkIiIichcJCQlh2bJlpKSkMGTIEJKTk69rntatWzN58mTWr1/PggULirZIERERuWMoeBIRERG5y1SuXJmlS5eSlJTEkCFDSElJua55unbtyrhx4/j3v//Nv//97yKuUkRERO4ECp5ERERE7kJVq1Zl6dKlJCQkMGTIEGw223XN079/f5599lnefvtt1q9fX8RVioiIyO1OwZOIiIjIXapatWosWbKE+Ph4hg4det3h0+DBgwkNDWXq1Kls27ataIsUERGR25qCJxEREZG7WI0aNVi8eDGxsbEMHz6c1NTUQs9hMpmYMGEC7du358UXX+T7778vhkpFRETkdqTgSUREROQuV6tWLZYuXcrp06cZPnw4aWlphZ7DbDYzZcoUGjduzJgxYzh06FAxVCoiIiK3GwVPIiIiIkKtWrVYvHgxMTExjBgxgvT09ELPYbVamT17NlWqVGHEiBHExMQUQ6UiIiJyO1HwJCIiIiIA1KlTh0WLFnHixAlGjBhBRkZGoefw9PTk7bffxs/Pj2HDhhEfH18MlYqIiMjtQsGTiIiIiOSpV68eixYt4tixY4wcOfK6wid/f38WLVqE0+lk+PDh1920XERERG5/Cp5ERERE5DL169dn4cKFHDlyhNGjR5OZmVnoOcqWLcuiRYu4cOHCdc8hIiIitz8FTyIiIiLyFw0aNGDBggUcPHiQMWPGkJWVVeg5qlSpwoIFC/jtt9944YUXyMnJKYZKRURE5Fam4ElERERE8tWoUSPefvttDhw4wHPPPUd2dnah56hXrx5vvfUWe/bsYfLkyTidzmKoVERERG5VCp5ERERE5IqaNGnC/Pnz2b9/P2PHjsVutxd6jhYtWjB16lQ+//xz3nrrLQzDKIZKRURE5Fak4ElERERErqpp06bMmzePH3/8kXHjxl1X+NShQwcmTpzIhx9+yLvvvlsMVYqIiMitSMGTiIiIiFxT8+bNmTt3Lnv37uX555+/rvApNDSUoUOHsnTpUj755JNiqFJERERuNQqeRERERKRAWrRowZw5c9izZw8TJky4rmbhTz/9NAMGDGDGjBl88cUXxVCliIiI3EoUPImIiIhIgd1///3Mnj2bXbt2MWHCBHJzcwt1vclkYsyYMXTt2pWXX36Zb7/9tpgqFRERkVuBgicRERERKZRWrVoxe/Zsdu7cyYsvvljo8MlsNvPKK69w//33M27cOH755ZdiqlRERERuNgVPIiIiIlJorVu3ZubMmXz11Ve89NJLOByOQl3v4uLCm2++Se3atRk5ciTHjx8vpkpFRETkZlLwJCIiIiLX5cEHH2TGjBl8+eWXvPzyy4UOn9zd3Zk7dy5BQUEMHz6cs2fPFlOlIiIicrMoeBIRERGR69a2bVumT59OVFQUr776Kk6ns1DX+/r6smDBAlxcXBg2bBhJSUnFVKmIiIjcDAqeREREROSGtG/fnunTp/P5558zefLkQodPZcqUYdGiRaSlpTFq1CgyMjKKqVIREREpaQqeREREROSGdejQgalTp7JlyxamTJlS6PCpUqVKLFy4kJMnTzJ27FjsdnsxVSoiIiIlScGTiIiIiBSJTp068frrr7Np0yamTp1a6PCpVq1azJ07l59++omXXnqp0NeLiIjIrUfBk4iIiIgUmc6dOzN58mQ2bNjAtGnTCh0eNW3alDfffJNt27Yxffp0DMMopkpFRESkJLjc7AJERERE5M7SrVs3nE4nr732GhaLhQkTJmAymQp8/YMPPsjLL7/Ma6+9RqlSpRg6dGgxVisiIiLFScGTiIiIiBS57t2743Q6mTJlCmazmfHjxxcqfOrRowcpKSnMmzcPf39/wsPDi7FaERERKS4KnkRERESkWPTs2ROn08nUqVMxm82MGzeuUOHT448/TlJSEnPmzMHPz49HHnmkGKsVERGR4qDgSURERESKTe/evXE6nUybNg2LxcKYMWMKFT4NHz6c5ORkXnvtNXx9fXnggQeKsVoREREpagqeRERERKRYhYaG4nA4mDFjBmazmVGjRhU4fDKZTLz44ovYbDZeeOEFFi1aRJMmTYq5YhERESkqOtVORERERIpd3759GT9+PP/5z39YsGBBoU6rs1gsvPHGGzRs2JAxY8Zw5MiRYqxUREREipKCJxEREREpEf369WPs2LH8+9//ZvHixYUKn6xWK2+99RYVK1ZkxIgRnDlzphgrFRERkaKi4ElERERESsyAAQMYM2YM7733HsuWLSvUtV5eXrz99tt4eXkxdOhQLly4UExVioiISFFR8CQiIiIiJeqxxx5j5MiRvPPOOyxfvrxQ1wYEBLBo0SLsdjvDhw8nNTW1mKoUERGRoqDgSURERERK3JNPPsmwYcNYvnw57777bqGuDQ4OZtGiRcTFxfHcc8+RnZ1dTFWKiIjIjVLwJCIiIiI3xdNPP82QIUNYsmQJ7733XqGurVatGvPmzePgwYNMnDiR3NzcYqpSREREboSCJxERERG5aZ599lkGDhzIokWL+Pe//12oaxs2bMjMmTPZuXMnr7/+Ok6ns5iqFBERkevlcrMLEBEREZG728CBA3E6nbz99tuYzWYef/zxAl/bqlUrpkyZwksvvYS/vz+jR4/GZDIVY7UiIiJSGAqeREREROSmGzRoEE6nk3nz5mE2mwkPDy/wtZ07dyYlJYWZM2dSqlQpnnrqqeIrVERERApFwZOIiIiI3HQmk4khQ4bgcDiYM2cOZrOZ/v37F/j6fv36kZyczMKFC/Hz86NPnz7FWK2IiIgUlIInEREREbklmEwmhg8fjtPpZPbs2ZjNZvr161fg6//5z3+SnJzM9OnT8fPzo3379sVYrYiIiBSEgicRERERuWWYTCZGjhyJw+Fg5syZuLi4EBoaWuBrx40bR3JyMpMmTeLtt9/m3nvvLeaKRURE5GoUPImIiIjILcVkMjFmzBicTifTpk3DZDIVeOuc2WzmtddeIzU1lbFjx7J06VLq1atXzBWLiIjIlZhvdgEiIiIiIn9mMpkYO3Ys/fr144033mD9+vUFvtbV1ZWZM2dSrVo1Ro4cycmTJ4uxUhEREbkaBU8iIiIicksymUw8//zzhIWF8frrr7Nx48YCX+vh4cH8+fMJCAhg2LBhxMfHF2OlIiIiciUKnkRERETklmUymXjhhRfo3bs3r732Gps2bSrwtX5+fixcuBCAYcOGkZKSUlxlioiIyBUoeBIRERGRW5rZbGbixIn07NmTyZMns2XLlgJfGxQUxOLFi0lKSmLUqFFkZGQUY6UiIiLyZwqeREREROSWZzabmTRpEo888givvPIKn3/+eYGvDQkJYcGCBRw/fpzx48eTk5NTjJWKiIjIpRQ8iYiIiMhtwWw28/LLL9OlSxdeeukloqKiCnxt3bp1mTNnDt9//z2vvvoqTqezGCsVERGRPyh4EhEREZHbhtlsZvLkyXTq1IkXX3yRrVu3Fvja5s2b88YbbxAVFcWsWbMwDKMYKxURERFQ8CQiIiIitxmz2cxrr71Gx44dmThxItu2bSvwte3bt+fFF1/k448/5l//+lfxFSkiIiIAuNzsAkRERERECstisfD666/jdDqZMGECM2fO5MEHHyzQtb179yY5OZmFCxfi7+9Pv379irlaERGRu5dWPImIiIjIbclisTB16lQefPBBxo8fz44dOwp87d///ncee+wxZs6cyWeffVaMVYqIiNzdFDyJiIiIyG3LxcWFadOm0aZNG55//nm++eabAl1nMpkYNWoU3bt355VXXinwdSIiIlI4Cp5ERERE5Lbm4uLC9OnTadmyJePGjWPXrl0Fuu6PU/Jat27N+PHj2b9/fzFXKiIicvdR8CQiIiIitz1XV1fefPNNWrRowdixY9m9e3eBrrNYLEyfPp26desyevRojh8/XsyVioiI3F0UPImIiIjIHcFqtTJz5kyaN2/O6NGj2bNnT4Guc3NzY86cOZQrV45hw4YRGxtbzJWKiIjcPRQ8iYiIiMgdw2q1Mnv2bJo2bcro0aP54YcfCnSdj48PCxYswM3NjeHDh5OYmFjMlYqIiNwdFDyJiIiIyB3FarXy1ltv0ahRI0aOHMmPP/5YoOsCAwNZtGgR6enpjBgxgvT09GKuVERE5M6n4ElERERE7jh/bJ9r0KABI0eO5KeffirQdRUqVGDhwoWcOXOG5557DrvdXsyVioiI3NkUPImIiIjIHcnd3Z05c+ZQr149RowYUeBT62rWrMm8efP4+eefefHFF3E4HMVcqYiIyJ1LwZOIiIiI3LE8PDyYN28etWvXZvjw4fzyyy8Fuq5x48bMnDmTr776imnTpmEYRjFXKiIicmdS8CQiIiIidzQPDw/mz59PzZo1GTZsGL/++muBrmvTpg2TJ09m3bp1LFy4sJirFBERuTMpeBIRERGRO56npycLFiygevXqDBs2jIMHDxboum7duvHcc8/x//7f/+ODDz4o5ipFRETuPAqeREREROSu8Ef4VLlyZYYNG8bhw4cLdF14eDjPPPMM8+fPZ8OGDcVcpYiIyJ1FwZOIiIiI3DW8vLxYsGABFStWZMiQIRw5cqRA1w0ZMoTQ0FBef/11vvrqq2KuUkRE5M6h4ElERERE7io+Pj4sXLiQChUqMGTIEI4ePXrNa0wmExMmTKBdu3ZMmDCBH374oQQqFRERuf0peBIRERGRu46vry+LFi2ibNmyDBkyhOPHj1/zGrPZzNSpU2ncuDFjxowp8FY9ERGRu5mCJxERERG5K/n6+rJkyRLKlCnD4MGDCxQ+Wa1WZs+eTeXKlRkxYgSnTp0qgUpFRERuXwqeREREROSu5efnx+LFiwkICGDw4MH8/vvv17zG09OT+fPn4+Pjw7Bhwzh//nzxFyoiInKbUvAkIiIiInc1f39/lixZgr+/P4MHDyYmJuaa15QqVYpFixaRm5vL8OHDsdlsJVCpiIjI7UfBk4iIiIjc9UqVKsXSpUvx8fFh0KBBBQqfypUrx6JFizh//jxjxowhKyurBCoVERG5vSh4EhEREREBAgICWLp0KZ6engwePJjTp09f85qqVavy9ttvc+TIEV544QVyc3NLoFIREZHbh4InEREREZH/CgwMZOnSpbi7uzNo0CBiY2Ovec0999zD7Nmz+e6775gyZQpOp7MEKhUREbk9KHgSEREREblEmTJlWLp0KVartcDh03333cfrr7/O5s2bmTt3LoZhlEClIiIitz4FTyIiIiIifxIUFMSyZcswm80MHjyYc+fOXfOahx9+mBdeeIFVq1bx3nvvlUCVIiIitz4FTyIiIiIi+fgjfAIYNGgQcXFx17zm0UcfZfDgwSxevJjIyMjiLlFEROSWp+BJREREROQKypUrx7Jly3A6nQwaNIj4+PhrXvPss8/yt7/9jenTpxMVFVUCVYqIiNy6FDyJiIiIiFxFcHAwy5YtIzc3l8GDB3P+/PmrjjeZTIwdO5bOnTvz0ksvsXv37hKqVERE5Naj4ElERERE5BrKly/PsmXLyMrKYvDgwSQkJFx1vNlsZvLkybRo0YKxY8dy4MCBEqpURETk1qLgSURERESkACpUqMCyZcvIzMxkyJAhJCYmXnW8i4sLM2bMoGbNmowcOZITJ06UUKUiIiK3DgVPIiIiIiIFVKlSJZYuXUpqaiqDBw++Zvjk4eHBvHnzKF26NMOGDStQg3IREZE7iYInEREREZFCCAkJYenSpSQnJzN06FCSk5OvOt7X15eFCxfi4uLCsGHDrjleRETkTqLgSURERESkkKpUqcKyZctITExkyJAhpKSkXHV8mTJlWLhwITabjZEjR5KRkVFClYqIiNxcCp5ERERERK5D1apVWbp0KQkJCQwZMgSbzXbV8SEhISxYsICTJ08ybtw47HZ7CVUqIiJy8yh4EhERERG5TtWqVWPJkiXEx8czdOjQa4ZPtWvXZu7cuezbt49XXnkFp9NZQpWKiIjcHAqeRERERERuQI0aNVi8eDGxsbEMHz6c1NTUq45v2rQp06ZNY+vWrbz55psYhlFClYqIiJQ8BU8iIiIiIjeoVq1aLF26lNOnTzN8+HDS0tKuOr5du3a89NJLREZGsnTp0hKqUkREpOQpeBIRERERKQK1atVi8eLFxMTEMGLECNLT0686vmfPnowcOZJ3332XVatWlVCVIiIiJUvBk4iIiIhIEalTpw6LFi3ixIkTBTq97sknn+TJJ5/krbfeYtOmTSVUpYiISMlR8CQiIiIiUoTq1avHwoULOXr0KKNGjbpm+DRixAh69uzJa6+9xs6dO0uoShERkZKh4ElEREREpIjdc889LFiwgMOHDzN69GgyMzOvONZkMjFp0iQeeOABxo8fz08//VSClYqIiBQvBU8iIiIiIsWgYcOGLFiwgIMHDzJmzBiysrKuONZisTBt2jTq16/P6NGjOXr0aAlWKiIiUnwUPImIiIiIFJNGjRrx9ttvc+DAAZ577jmys7OvONZqtTJnzhzKly/P8OHDiY2NLcFKRUREioeCJxERERGRYtSkSRPmz5/P/v37GTt2LHa7/Ypjvb29WbBgAR4eHgwdOpTExMQSrFRERKToKXgSERERESlmTZs2Zd68efz444+MGzfuquFTQEAAixYtIisri+HDh5OWllaClYqIiBQtBU8iIiIiIiWgefPmzJ07l7179zJ+/Pirhk/ly5dn0aJFnD179ppb9ERERG5lCp5EREREREpIixYtmDNnDrt372bChAnk5ORccWz16tWZP38+Bw4cYOLEiTgcjhKsVEREpGgoeBK5TWTZczh+Op6Dx8/wy2+nOHwiljPxiTidzptdmoiIiBTC/fffz+zZs9m1axcTJ04kNzf3imMbNmzIrFmz2LlzJ1OnTsUwjBKsVERE5MaZjAJ8etlsNvz8/EhJScHX17ck6hK566WmZ7Hjh0McOnGWX46e4sSZ82Rn5+D8729ZEyYsFhO+Xp7UrVaeOlXL06BWJe69pxpmszJlERGRW92OHTsYN24cDz74INOmTcPFxeWKY7ds2cJLL73EE088wahRo0qwShERkfwVNCtS8CRyi/nt5Dk2ffUjm77+iSRbOoZhYDabcbO64ObqkhcqGYaBw+kk255Ltj0HTCZczGaqVixD7/bN6dSqAf6+Xjf51YiIiMjVfPXVV4wfP5527drxxhtvYLFYrjj2ww8/ZNasWYwcOZInn3yyBKsUERH5KwVPIreZ+EQbb/9nC9v3HiQzOwd3qys+Xu64XOUL6J9l2XOwpWXgdBqU8vXi770epF+X+3F1KfgcIiIiUrK2b9/O+PHj6dChA6+//vpVw6elS5fyzjvv8PLLL9OrV68SrFJERORyBc2KrryeV0RKhGEYfLZzPwtWfk7chRR8PN0pF+iJyWQq9FzuVlfcA/xwOJwkp2Yw/z9b2PHDYZ5/pjvVKgYVQ/UiIiJyo9q2bcv06dOZMGECZrOZKVOmXHHb/KBBg0hKSuKNN97A19eXhx56qISrFRERKRyteBK5ibKy7bz5zgY+27kfA4MAP28sRdifKdueQ5ItHV9vD8Y82Y3ubZsU2dwiIiJStKKjo5k4cSJdunRh8uTJVwyfnE4nkyZNYvv27SxYsIBmzZqVcKUiIiIFz4rUgVjkJknPzGbivA/59Ksf8XC3UqaUb5GGTgBuVlfKBvqRnmln+jvrWb15V5HOLyIiIkWnQ4cOTJ06lS1btjBlypQrnlz7x6qoJk2aMGbMGA4dOlTClYqIiBScgieRmyDLnsOriz7h6x8O4+/jhZeHW7Hdy2QyUaaUDxgGC1Z8RsQXu4vtXiIiInJjOnXqxOuvv86mTZuYOnXqFcMnV1dXZs2aRdWqVRkxYgQxMTElXKmIiEjBKHgSuQkWrviM7XsO4u/jibuba4ncM8DPG8MwmPfBFr7bf7RE7ikiIiKF17lzZyZPnsyGDRuYPn36FcMnT09P5s+fj7+/P8OGDSM+Pr6EKxUREbk2BU8iJWzXT7+xZuv3eHq44eFmLdF7B/h5k23PYfb7n5KanlWi9xYREZGC69atG6+++ipr165lxowZXKktq7+/PwsXLsTpdDJ8+HBsNlsJVyoiInJ1Cp5ESpAtPZO33t+EPScXH0/3Er+/yWQi0M+bE2fOs+yjqBK/v4iIiBRc9+7defnll4mIiGDmzJlXDJ/Kli3LokWLuHDhAqNGjSIzM7OEKxUREbkyBU8iJej/IrdxMvY8pf29MZlMN6UGFxcLXh5urN36Pd//euKm1CAiIiIF07NnT1566SU+/vhjZs+efcXwqUqVKixYsIBjx47xwgsvkJOTU8KVioiI5E/Bk0gJSbals3H7j7hZXXGxWG5qLT6e7mTZc1gTteem1iEiIiLX1rt3b1588UU+/PBD5s6de8XwqV69esyePZs9e/YwefLkK/aGEhERKUkuN7sAkbvF57t+Jv5cLD9/uuiyx01mC24e3gSUr0qNJg/hW7o8AId3f85ve7+gycOPUaFm47zx0R9MIzM16ZIJTLi6eeBXugJVGrSiXNV78p7KzkjlyN4oUuJPkZGaRG52JlYPb7z8y1C2RnO++t5KbHwS5YNKFetrFxERkRsTGhqKw+FgxowZmM1mRo0ale/q6RYtWjB16lQmTJiAn58fzz///E1bZS0iIgIKnkRKhNPpZG3095j/+8XP0zeQCrWaAuDIySYpLobY3/Zx7vgv3N9zIAHBVa86n8lkpkazDgAYTgdpSfHE/f4rCad/o26r7lRv3BaAzLRkzhz+Hv+yIZSreg9Wd0+yM9OI+/1XLmxfTemqjdm840GeDW1XfC9eREREikTfvn0xDIOZM2diNpsZMWJEvqFShw4dmDhxItOmTaNUqVL885//vAnVioiIXKTgSaQEHP79LCdjz+P134biXn6B1G7R6bIxh77bwtHvozn03RZa9R5y1flMZvNfrj8fc5jvNr7Lkd2fUaV+SyyuVnwDy9P52SmYzJfvqs21Z7EjYgEJJ/bx8frPFTyJiIjcJvr164fD4eCtt97CxcWFIUOG5Bs+hYaGkpyczOLFi/H396dv3743oVoREREFTyIl4reTcRdPsnO98m+5qg1ac/T7aFLiT13XPcqE1MbbvwxpyfGkJsbhX7YS5iv0knKxulMmpDZpSfGcivmdZFs6/r5e13VfERERKVkDBgzA6XQyd+5czGYzgwcPznfc008/TXJyMjNnzsTPz49OnTrlO05ERKQ4KXgSKQFHT50DKGCPhSLow3CNKRy5OVw4fRQw4eoVwNFTcTSvX+3G7ysiIiIl4rHHHsPhcPD2229jNpsZOHDgX8aYTCZGjx5NcnIyr7zyCr6+vtx///03oVoREbmbKXgSKQEHjp7GbDYDVz5d5vdfvgHAv2yl67pHwunfSEs+j8XFik9Aucuey85I++/8BtkZacTHHCIrLZmazTvi6unP0RgFTyIiIrebJ598EofDwaJFi7BYLDz77LN/GWM2m3nllVew2WyMGzeOJUuW0KBBg//f3n3HZ1Ue/v9/n3tn3hmEACFhyB4KgrgQxC1Yd/2qddRi2SA4WSo4EEXZGESxtlZb9dPW2moRN4pVxAFoEdkQAgmE5L6z7nnO7w9qLD9AAuTOncDr+Xj4eJj7XOdc74PaJu9c5zpxSAsAOFFRPAH1YOfuMrkcdkkRSVKlr0TrViyVJEUjIZUVbdPenZtlszvU6fRLDns9yzRrzrfMqCrKdqtoy3eSLHU8/WLZHc79xgerK7R+5Ts1Xxs2uzqfOUhte/RX8V6/ikp8dXOjAACgXt12220yTVP5+fmy2Wy67bbbDhjjcDg0ffp0jRo1SnfccYeee+45tW3LL5wAAPWD4gmIMcuyFAxFZNiMmgVPVf6SmiLIsNnlTkhWi/Y91e7UAUrNbF6La5o/FUmGIac7QU1y2qlVt7PUrE3XA8anZjbTZSNmyDJNVVeUqXD9N1r3+RKV7tqqnF6/UCgcqbP7BQAA9Wvw4MH7rXy65ZZbDhjj8Xg0a9YsDRkyRKNGjdLixYvVvPnhv+cAAOBYUTwB9cLa76us3A46/RdH/2pjm92hgUMfO+LzDJtNiakZatfrPBk2m9b++0250lvKss486iwAACD+hgwZItM0a/Z8uummmw4Yk5KSonnz5mnw4MEaOXKkFi9erPT09DikBQCcSGyHHwLgWBiGIafDIcuyDj+4HmXldpAkle/eJtfPvG0PAAA0DkOHDtVvfvMbzZ49Wy+//PJBxzRp0kQLFixQRUWFxowZo6qqqnpOCQA40VA8AfUgKz1F4cihNxaPh0ClX9K+VVAZ3uQ4pwEAAMfKMAwNHz5ct956q2bOnKlXXnnloONatmyp+fPna/v27brrrrsUCoXqOSkA4ERC8QTUg67tWioajdb7vP49hTIPMm8oUKXvP/+XJCmteTu1y82u72gAACAGDMPQqFGjdPPNN2vGjBl67bXXDjquQ4cOmjVrllatWqXJkyfLNBvWL8gAAMcPnq8B6kG7vGaSVO+P221atUxFW9cqo1lrJaSky2Z3qLq8TMVb1yoaCSmrdTe1aN9DJ+VRPAEAcLwwDENjxoxRNBrV448/LrvdrquvvvqAcT179tT06dN1991367HHHtPEiRNlGEYcEgMAjmcUT0A9aJeXLYfDrnAkXK/ztuzYS5Zlqax4u0p2bFQ0GpbLnaiMFm2V27GX3FntlOFNVtOM1HrNBQAAYsswDI0bN06maWratGkyDENXXXXVAeP69eunBx98UA8++KDS0tI0cuTIOKQFABzPKJ6AetC5bY6yM70q2iNdNmLGQcdEohH5ynxKz0iXzbCpY5+L1LHPRQeMO//mibWet0nL9mrSsv1Bj1mWpV0lPg3o04XfbgIAcBwyDEN33XWXTNPUo48+Krvdrssvv/yAcYMGDVJZWZlmzZql9PR03XjjjXFICwA4XlE8AfXA7XLo8gGnKv/P78o0Ldls+xc9lZWVKiwslGVZ8qZ5ZbPHfvu1qkBIHpdTg/r1jPlcAAAgPgzD0D333KNoNKqHH35YNptNl1122QHjfvWrX6m0tFQzZ86U1+vVoEGD4pAWAHA8ongC6smlfXvo93//WBVVAaUmJ0iSTMvUzp07tbdkryzLUnp6uhz2+vnPsrwqoDNObqf2rZrVy3wAACA+DMPQfffdJ9M0NXXqVNlsNg0cOPCAcSNHjlRZWZmmTp2qlJQU9evXLw5pAQDHG4onoJ40z0pT/96d9I8PvlIoVKXK8gqVlpYqav73rXNW/W0+XhUIyW4zdPUFvetlPgAAEF82m00TJkyQaZqaMmWKbDabLrnkkv3GGIahCRMmyO/3a/z48VqwYIF69mRlNADg2FA8ATFkmqY2btyoL7/8Ul988YU+Wr5Cpa6TFLU5FA1WypCxb38lS3I4HWrevHm9ZPJVVOm8Pl3Uv3fnmM8HAAAaBpvNpkmTJsk0TT3wwAOy2Wy66KL995O02+165JFHNGbMGI0bN06LFi1Shw4d4pQYAHA8oHgC6pBpmtqwYYO+/PJLrVy5Up988omKi4sVCAQkSU6nUyF3lZwtesrhdMv871vuDJuh9Ix0OZ3OmGcs8VUoKz1Fd946UDZb7PeSAgAADYfNZtP999+vaDSqyZMny2az6YILLthvjMvl0lNPPaWhQ4dq1KhRev7559WyZcs4JQYANHYUT8Ax2rNnj9555x198cUXWr58+X5Fk8vlUmJiotLT0xUIBFRYWKhotFTp6S2lxKYybFE57HaZlqmM9IyYZ/VXVsuQoZE3XKRmTdJiPh8AAGh4bDabpkyZItM0NXHiRNlsNp133nn7jUlKStLcuXN1++23a+TIkVq8eLGaNGkSp8QAgMaM5Q7AMZozZ47uvvtuvfTSS9q1a5c8Ho+aN2+unJwcZWVlKSkpSSUlJdq6dascDocyMjK0d93HsgI+OTzJikZNJSYkKikpKaY5K6oCqg6EdP2lZ2pQvx4xnQsAADRsNptNU6dO1QUXXKAJEyboww8/PGBMRkaGFixYoHA4rNGjR6u8vLz+gwIAGj2KJ+AYDR06VHl5ebLZbGrSpImSkpJqHmELh8PasmWL9uzZoyZNmsjj8WjPnj1KT01Sk2iBbOEKGc4EedPSY5rRX1GtqkBQ11zYR6N/ddG+faUAAMAJzW636+GHH9aAAQM0fvx4LVu27IAxzZs31/z581VUVKRx48bVrOoGAKC2KJ6AY9SyZUvl5+crIyNDu3btqnkznd/v16ZNmxSJRJSXl6dAIKDS0lI1a9ZM2dnZslshRbZ/JpdZoYhlU5m/ss7faheJRlVU4lMkGtXNvzhH99w2iH2dAABAjR83E+/Xr5/uvfdeffLJJweMadu2rebMmaPvv/9eEyZMUCQSiUNSAEBjxU+gQB3o0aOHnnrqKbndbu3Zs0eFhYXasWOHkpOTlZubq+LiYlVVVSk3N1fp6ftWN5mmqWBVuW675GQN+eX5MmyGdpX4FAof+zdzlmWpvDKg3aXlysnO0GNjr9fIGy6kdAIAAAdwOByaNm2a+vbtq3vuuUeffvrpAWO6d++uGTNm6N///rceeughmaYZh6QAgMbIsGqxxMLv98vr9crn8yk1NbU+cgGN0tSpUzV9+nRJ+1ZCud1ubd++XZKUl5cnt9tdM9bn88myLL377rtq3bq1vl2/XTN+90+t3Vwoy5RSktxK9LiP6LG4aNSUv7JagVBYHpdTl55zikZef6HSUmO7fxQAAGj8wuGw7rvvPn322WeaOXOmzjjjjAPGLF26VJMmTdINN9ygcePG8fg+AJzAatsVUTwBdcA0Tf3xj3/U/PnzVV1draKiIiUmJqqkpEQul0u5ublyOPZ/iWRBQYGuuOIKPfPMMzWfhcIRffzl9/r7+1/q6++3KBAMyzAMuV1OuV0OuZ0OGYYhwzBkWZaipqlgKKJgKKxgOCJDkjclURefdbIG9e+pzm1b8A0hAACotVAopHvvvVcrVqzQ7Nmz1adPnwPGvPbaa3r88cc1cuRI3XbbbXFICQBoCCiegHpSXFysBx98UCtXrtStt96q2267TYMGDdLy5cuVlpam3NzcAx5xq66ult/v10svvaR+/fod9Lrrt+7Se59/p2/Xb9cPW3aqsjqoUDgi07JkyJBkyW63ye1yKj01SV3btVTPzq114RndWOEEAACOWigU0t13360vv/xSs2fP1mmnnXbAmEWLFmnRokWaNGmSrrrqqjikBADEW227IschjwA4rI8++kgPPfSQnE6nFixYoF69eumpp56Sz+dT+/btVVJSctANw0tLS9WlSxf17dv3kNdu36qZ2rdqJkkKR6LaWrhH23eVKBgKK2qacjkdSkrw6KTcpmqakcrKJgAAUCdcLpeefPJJ3XXXXRo7dqzmzZunU089db8xv/3tb1VWVqbHHntMXq9X5513XpzSAgAaOlY84bhnWZZ27/Vr264SBYJhhSNRuZx2JSV41CYnS96UxCO+ZjAY1OzZs/Xaa6+pX79+euCBB+RyuTRhwgT9+9//1vjx49W7d29dd9112rJli3JycmqKoUgkoqKiIk2fPl233nprXd8uAABAnQgGgxo3bpxWr16tefPmqWfPnvsdN01T999/v95//33NnTv3oCujAADHLx61wwmtqMSn9z77Tt9tLNB3Gwq011ehYCgiy7JkWZYMw5DNtm/vpOZN0tStfUt175Cn8/p0VUqS52evvX79ek2aNEkFQj03lwAAN9xJREFUBQW68847dc0112j37t0aO3asduzYoccff7xmM86vv/5aN910k8rKytSsWTMZhqGSkhIlJyfr448/VlpaWj38aQAAABydQCCgcePG6dtvv9X8+fN1yimn7Hc8HA7rzjvv1KpVq7Rw4UJ16dIlTkkBAPWN4gknHNM09fXarfrHh1/pw5VrVVkVkCXJ7XTI7XLK5XTIZjNkyJAlS9HofzfmDocViUQlSRneZA085xQN7Nez5jG3H1mWpVdffVVz5sxRXl6epk2bprZt2+r777/X2LFj5XA4NGfOHJ100kn7nffWW29p1KhRMk1TmZmZ2rFjh4YNG6YpU6bU058MAADA0auurtbYsWO1du1azZ8/XyeffPIBx0eMGKHt27frueeeU+vWreMTFABQryiecELZvqtET77wpr5Ys0nhSEQJHpdSEj0HbOr9cyLRqPwV1QqGI0pwuzSoXw8N/38XyJuSqNLSUk2dOlWffPKJrr/+eo0ZM0Yul0vLli3TxIkT1bZtW82aNUuZmZkHvfYzzzyjhx9+WKZpyuPx6M0331Tnzp3r6vYBAABiqrq6WmPGjNG6dev09NNPq1u3bvsd9/v9uv3221VZWanf/e53atq0aZySAgDqC8UTTgimaepv763UotfeV0lZhdJSEuVxO49po23LslReFVBlVVCtWjTRpX1a67Xf5ysajWrKlCnq27evLMvSn/70J82aNUsDBgzQQw89JI/n0I/oWZalyZMna/Hixbrgggv0xz/+8ajzAQAAxENVVZVGjx6tDRs2KD8//4DH6oqLizV48GB5PB4999xz8nq9cUoKAKgPFE847lVWBzX16b/oo5Xfy2YYyvAmy2aruze7hSNRbSssUqCqUu2buvTinAfUtGmWotGonnzySb322mu65ZZbNGrUqFqtrAqFQnriiSc0cODAA94MAwAA0BhUVVVp1KhR2rx5s/Lz89WpU6f9jm/btk2DBw9WixYtlJ+fr8TEI3+JCwCgcaB4wnGtvDKg8bP+rM/XbFBacqISPK46vX4oFFLBjgIFgkGlpmXI6fTo6gtO04jrBmjy5En67LPPNGHCBF111VV1Oi8AAEBDV1lZqZEjR2rbtm3Kz89Xx44d9zu+du1aDR06VCeffLJmzZolp9MZp6QAgFiieMJxqzoQ0vhZf9byr39QhjdJblfdfjNTVlamXbt2yel0KicnRx6PRxVVAfkrq+QJFMnu26AZTzyh008/vU7nBQAAaCwqKio0YsQIFRQUaOHCherQocN+x1euXKnRo0drwIABeuSRR45o300AQONQ266I/wdAozP3pbf16Tc/KL2OS6doNKqCggLt3LlTXq9Xbdq0qdm3yWGT/GWlKgp49Kth91E6AQCAE1pycrIWLFignJwcDR8+XBs2bNjveO/evTVt2jS9++67euKJJ1SL33UDAI5TFE9oVD79Zr3e+PArJSa45anD0qmqqkqbNm9SZWWlclrmqHnz5jW/mSsvL9eWLVvkdtqUkZGuv36wWgVFe+tsbgAAgMYoJSVFCxYsUHZ2toYPH65Nmzbtd3zAgAGaNGmS/u///k+LFi2KU0oAQLxRPKHR8FdWa+bv31IoHFFK4qHfIHckLFnavXu3tm7dKpfTpbYntVVqyk9LBEv2lqigoEDJyclq1aqVstK9Kimr0FMvvCnTNOskAwAAQGOVmpqq/Px8ZWVladiwYQeUT1dccYVGjx6tZ599Vq+88kqcUgIA4oniCY3GH/7+sbYW7laTtGQZxs+/vW7diqX659P3aM+OjYccEw6HtXXLVu3Zs0dZWVnKa5Unp2PfKipLlnbt2qXiomJlZmYqp2WObIZNNpuhtNREffrNer29fHWd3h8AAEBj5PV69fTTTysjI0PDhg3Tli1b9jt+yy236KabbtKMGTO0ZMmS+IQEAMSNI94BgNoorwzo1X+8p5WvPrHf54bNLndCsjJatFG7ngOU2qRFra7n8/u0a+cu2e12tW7dWgkJCfsd3769QJUVFWrWvJnS09L3O5bgdslXUa2/vPOFLul7ymFLMAAAgONdWlqa8vPzNXToUA0bNkyLFi1SXl6eJMkwDN1xxx3y+Xx68MEHlZqaqrPOOivOiQEA9YUVT2gU3v33GpWVV0qSElMz1b73hWrf+0K16X62ElLSVbj+G33yl3nau3Pzz17HNE0VFhaqcEehkpOT1aZtmwNKJ9MyJVnKy8s7oHT6UWpSgtZu2qFv1xfUyf0BAAA0dunp6Vq4cKFSUlI0dOhQbd++veaYYRiaPHmyzj77bN1zzz1avZqV4wBwoqB4QoNnmqb+9v7KmpVFSd5MdexzkTr2uUhdzv6Fzr56pNr1Ol9mNKLvPz/08u3q6mpt2rxJ5eXlatGihXJycmS32Q8YZzNsysvNU1JS0iGvleB2KhiO6B8ffnXsNwgAAHCcyMjI0MKFC5WUlKShQ4eqoOCnX9LZ7XY99thj6tq1q8aOHauNGw+9JQIA4PjBo3Zo8Lbu3KNN24uVlOA+5Jg23c/Whi/fk694+wHHLFkqKSnR7uLd8iR4lJ7o1MYVb2pv4SaFqivk9CQqNbO5Wnc/W9mtu9ScFw2HtO6LpSpc/41CgUoleTPV+uRzlORtos/+vlC53fvrI2+y7h18mRz2AwssAACAE1FmZqYWLlyoIUOGaOjQoXr22WfVosW+7RDcbrdmzpypIUOGaNSoUVq8eHHNMQDA8YkVT2jwNmwrUjAUlttZm550//2WotGItm3dpt3Fu5XZJFPuSLk+/3u+dm5crbTsXLXt0V9NW3VWoNKvbWtX1JxnmaZWvPW8Nn3zkZzuBLU5ua/Ssltp7fJ/aPOqZZIkh8Ouiuqgtu0sqcvbBQAAaPSaNGmihQsXyuVyaejQoSosLKw5lpycrHnz5sntdmvkyJHau3dvHJMCAGKN4gkN3oZtRTIMQzbbof913fLtp5KktOzc/T4vLCxUKBRSq1at5E1K0Kr3X5Fhs6vvNaPV+5Jb1emMS3XKgF+q//V3qds5V9act/37lSrZsVFZeZ3U77px6nzmIJ0y4Jc6+5rR2r39B0mS3WZTKBTWxu1FdX/TAAAAjVzTpk31zDPPyGazadiwYdq1a1fNsczMTC1YsEBVVVUaPXq0Kisr45gUABBLFE9o8NZuKpT1P19X+kq0bsVSrVuxVP/59J/69G9Pa/3Kd2WzO9Tp9EtkWqYqKsolSR5Pgtq2bavExERtX7dS0UhIbXv0kzcr54B5EpLTav5+xw/79m7qdPolMv6n8ErJyFbLjr0k7dsk0zAMbdhG8QQAAHAwP5ZPkjR06FAVFf30fVNOTo7mz5+vHTt2aNy4cQqFQvGKCQCIIYonNHi79pTJ+T97KFX5S7R+5Ttav/IdbV79iar8e9WifU/1vXaMEtKaafPmzQoEgpKkrKws2f97blnRvv2fsnI7HHZOf0mh7A7XQQuqjGata/7eNE3tKS0/ltsDAAA4rjVr1kzPPPOMTNPU0KFDVVxcXHOsffv2mj17tr777jtNnDhR0Wg0jkkBALFA8YQGLxgKy/ifrZuycjvoshEzdNmIGRo0bLouuHWyTr3wRkUMt7Zs2SLDMJSennbAdSKhgCTJk+Q97JyRUFCuhIO/1c6VmPLTF4ah6iC/nQMAAPg5zZs31zPPPKNIJKJhw4Zpz549Ncd69Oihxx9/XMuWLdOjjz4qy7J+5koAgMaG4gkNnmEYP3s8Eo1o2/ZtKioqUnp6utq0biO7/cCNyJ3uBElSoNJ32DkdLrdC1QffayBUtf8KJ9th8gEAAEBq0aKFnnnmGQUCAQ0dOlQlJT+9oKVv376aMmWK3njjDc2bNy+OKQEAdY3iCQ2ey+nQoX7xVVFRoU2bNikQCCg3N1fZ2dmHLKrSmu7bePzHzcF/TmpmC0UjIfn27Djg2N5dW3/6wrLkdjkPfxMAAABQTk6OnnnmGVVXV2vYsGH7vdFu4MCBuuuuu/SHP/xBL774YhxTAgDqEsUTGrwWTdMVjkb2+8yyLBUVFWn79u3yeDxq27atkpOTf/Y6LTv2lt3p1qZvlh20UKqu+GklVE6HnpKkdZ+/Lcsyaz6vKC1WwbqVNV8bNpuaZqQe1X0BAACciHJzc7Vw4UKVl5cfUD7dcMMN+s1vfqM5c+bojTfeiGNKAEBdOfB5JKCB6dymhT79+qdVSqZlafOWzQoFQ8rOzlZ6RroMHf5xN3disnqc///09dKX9Mn/zVN26y5KTstSKFCpsqJtSkjN0GmX/lqSlNvpNBX88JWKt67Vsldnq2leJ4WDVSpc/42ycjuoaMt/9l3UstQuLzsWtw0AAHDcysvL0zPPPKMhQ4ZoxIgRWrhwodLS0iRJw4cPV1lZmR555BF5vV71798/vmEBAMeEFU9o8NrlZcuyLEXNfSuPqquqZJmWWrdurYyMjFqVTj9q3ra7zr52tJq16abSnZu18ZuPVLRlrdxJXuV1Pr1mnGGzqc+gwWp7Sj+FApXavPpj7d25RZ3P/oVaduy1b5DdKbfLqXZ5zer0fgEAAE4ErVq10sKFC7V3714NHz5cPt++1eeGYWj8+PEaMGCAJkyYoK+++irOSQEAx8KwavHaCL/fL6/XK5/Pp9RUHitC/dq2c49uuGe+/GWlqq6qUFpamrKbZctm/NSbBgIBWZalhISEmOf5/vMl2vDle+p87o3K63iy3lxwj+x2OlwAAICjsWnTJg0dOlRZWVlauHBhzc8boVBIY8eO1XfffadFixapY8eOcU4KAPhfte2K+GkZDV7xji3yFReoKhhWTsscNW/evKZ0ippR7dq1S5s3b1ZpaWmdzhuo9B/wWfneIm1Z/YkcrgR5Mlrq/D5dKZ0AAACOQdu2bZWfn6/i4mKNGDFCfv++78FcLpeefPJJtWrVSqNHj9a2bdvinBQAcDRY8YQGKxKJaNGiRfrd736n5h16a0c4XU3TvXI47JIkn8+nouIiWaalrKysWu/1VFtf/OsFVfv3Ki07T053gip9JSre8h+ZpqlOfa9URutT9OyU29XlpJw6mxMAAOBE9cMPP2jYsGFq2bKlFixYoJSUFElSWVmZBg8erHA4rMWLFysrKyvOSQEAEiue0Mjt2LFDt99+u1544QWNGDFCLy2aqaYZXvkrqxUMBrV161YVFhYqKTFJbU9qe8R7PdVG85NOlsPl0c5Na7Rp1TKV7NigjBZt1eeywUrO6aru7Vqqc9sWdTonAADAiapDhw5auHChCgoKNHr0aFVWVkqS0tLStGDBAkUiEY0aNapmRRQAoHFgxRManLfeekvTp09Xenq6Hn30UXXr1k2SNO+lJcr/81IFK/1yu5xq1qyZkpKS6j1fVSCoyuqgHh79S114Zvd6nx8AAOB49v3332v48OFq06aN5s+fr8TEREnS5s2bdfvtt6tVq1Z6+umn5fF44pwUAE5srHhCo1NZWan7779fDzzwgM4991y9/PLL6tatmyzL0ocffqg3X5qvSGWpPEletWnTJi6lk2ma8pVXq1+vTjr/9K71Pj8AAMDxrlOnTlqwYIE2bdqk0aNHq6qqSpLUpk0bzZ07V+vXr9e9996rSCQS56QAgNqgeEKdMk3zqM5bs2aNbrjhBn300Ud6+OGH9dBDDykpKUk7duzQuHHjdPfdd6tj+5M094HhykhPVXlVoI6T106Jr0JNM1N1560DZbPxnw8AAEAsdOnSRQsWLNCGDRt0xx131JRPXbt21VNPPaUVK1Zo6tSpR/29JwCg/vCTM+rMX//6V1188cUqLi6u9Tmmaer555/X4MGDlZmZqT/96U+69NJLFQqFtHjxYv3yl7/U+vXrNWPGDM2ePVuXnNtH11zYR9WBkKqDoRjezYF8FVUyDEMjrr9QzZqk1evcAAAAJ5quXbtq/vz5WrduncaOHavq6mpJUp8+ffTII49oyZIlmjlzpmqxcwgAII4onlAnqqqqNGPGDK1cuVIjR47UypUrD3tOUVGRhg0bpvz8fN1222169tlnlZOToxUrVuj666/XokWLdP311+u1117TgAEDZBj7Ng8f/v8u1Ll9uqisvEqBeiqf/JXVCgTDuuUXfTWoX496mRMAAOBE1717d82bN09r167VuHHjFAjsW/V+wQUXaPz48frzn/+s559/Ps4pAQA/h+IJdeKll17Sli1blJGRoTfeeENXX321du7cecjx77//vm644QYVFBTomWee0fDhw1VaWqqJEydqxIgRatKkif70pz9pzJgxNRtK/sjtcmjqiGvUr1cnlZVXqbI6GLP7sixLZf5KBYJh3TjoLA297vyaAgwAAACxd8opp2ju3Ln67rvvdOeddyoY3Pe93zXXXKPhw4crPz9ff/nLX+KcEgBwKLzVDsfM5/NpwIAB2r17tyzL0s6dO2UYhm688Ub94Q9/2G9sdXW1Zs6cqb/97W8677zzNHnyZCUlJenVV19Vfn6+3G63xo0bp0svvfSwBU8gFNZjz/5dby9fLVlSZlpyne67FI5EVVJWLo/bqduu6q9fX9GPfZ0AAADi5KuvvtKYMWPUo0cPzZw5Uy6XS5Zl6amnntIrr7yiadOm6cILL4x3TAA4YdS2K6J4OgGFwhEVFO1VVXVQ4UhUDrtdHrdTLbMzlOBxHfH1Zs+erenTpystLU1btmyRZVmKRCJyuVz6+9//rosuukiStG7dOk2cOFFFRUW6++67dcUVV2jNmjWaPn261q9fr2uvvVbDhw8/on/HTNPUkk9Wa/6flqq4xK/UZI8SPe5jWpVkWpbKK6pVFQypXV627vn1ZerVtc1RXw8AAAB1Y+XKlbrjjjvUq1cvPfnkk3K5XDJNUw8++KDeeecdzZkzR6effnq8YwLACYHiCTVC4Yj+vWq9vl2/Xd9t2KH123apKhCSaZqyLEuGDNlshtwup1rnZKlbu5bq1KaFzunVSSlJnp+9dnFxsc4//3z5fD6Vl5eroqJCTqdTlmUpFAqpVatW+vbbb/X6669r3rx5atOmjaZNm6b09HTNmzdPr7/+ujp37qwJEyaoS5cuR32PRSU+zX7xX1r25ToFQ2F5XE6lJiXIbq/9CqVwJCpfRZXCkaiSEty6+vzTdPu1A5SU4D7qXAAAAKhbK1as0NixY9WnTx898cQTcrlcikQiuuuuu/TVV19p4cKF6tq1a7xjAsBxj+IJKirx6a1l3+iND75U4e5SmaYlw9hXMLldDtltNhmGIcuyZJqWQuGIgqGwoqYpwzCU4U3Spef00KB+PdUuL/ugczz66KOaO3euEhISVFhYKLvdXvM4WiQSUSQSUZcuXeR2u3XTTTdp2LBhevvttzV37lxFo1GNHDlS11xzTZ08wmZZltZuKtQ/P/pKSz9dI195tSRLDrtdbpdDbpdThmHIMCTLkqKmqWAorGAoIvO/99w0M1WXn9tLl55zinKbZR5zJgAAANS9zz77THfeeafOOOMMPf7443I6nQoEAhoxYoS2bt2q5557Tm3asGIdAGKJ4ukEVh0I6YW/L9OrSz5TRVVAdrtd3uQEuZyOWl8jEo3KX1GtYDiiBLdT55/RTaNvvFiZack1Y7Zv364LL7xQVVVV2rNnjyKRiJxOZ83xaDSqYDAowzC0aNEi9e/fX4899phWr16tgQMHauzYscrIyKjTe/9Rmb9SH6z4j9Zu2qHvNuzQjuK9CoYjMk1L0r4CzmazyeNyqk1Olrq2a6lu7XPVr1dHedxH/rghAAAA6tenn36qu+66S2effbamT58uh8Mhv9+v3/72tyovL9fzzz+vZs2axTsmABy3KJ5OUKt/2KYZz/9T328u3Pe4WXKibLaj3+/IsixVVAVVUR1QTla67rj5Eg3o00WGYWjSpElavHixTNNUaWmpnE5nzd5K4XBY0WhUhmHINE1lZGSodevWatOmjcaPH69evXrV1S3Xir+yWtt2ligYDCsSjcrpdCjJ41KrFk0omgAAABqp5cuX6+6779Y555yjadOmyeFwaPfu3Ro8eLCcTqcWL16stLS0eMcEgOMSxdMJxrIs/fGfy/Xs/72v6kBImd5kOY9ghdPhRKOmSnzlstlsuqx/T13dr7Mu/8VlqqysVElJiQzDkN1ul2maCofDsixLDodDhmEoFArJsixdcMEFeuutt/ZbFQUAAAAci2XLlunee+/Vueeeq0cffVR2u13bt2/X4MGD1axZMy1cuFCJiYnxjgkAx53adkW8G/44YJqm8l95Vwv+tFSmaSk701unpZMk2e02Nc3wyuNy6vX3VmrYA0/LX16p8vJymaYpm82mSCSiUCgkSXI6nTUl1I/7Pn3yySf6/PPP6zQXAAAATmz9+vXT448/rg8++ED333+/otGocnNzNW/ePG3dulV33313zfeoAID6R/HUyFmWpef/+pF+//eP5XY6lZ6aVPO4WywkJ3rkdtq1dU9A9uanKBAMyeFwKBwOKxKJ1JRMP656cjqdcrlccrvdCoVCuvXWW+X3+2OWDwAAACeeH/cSfffdd/Xggw/KNE117NhRs2bN0jfffKP7779fpmnGOyYAnJAonhq5t5ev1vN/+0hup0OpyQn1MqevrETRcFD21Byl5J6iUCgk0zRrHrWLRqNyOBxyuVyy2+2SVPMo3tatWzV27FjV4glPAAAAoNbOO+88PfbYY1q6dKmmTJki0zR16qmn6rHHHtMHH3yg6dOn8z0oAMQBxVMjVlzi07yXlypqRuVNqZ/n1isrK+X3+2VGQ7KiESU07yJXarZsNlvNZuIul6tmf6f/ZbfbZVmWXnnlFb3xxhv1khcAAAAnjvPPP1+PPPKIlixZoocfflimaap///6aPHmy/vrXvyo/Pz/eEQHghFO3GwGh3liWpVkv/ktFe3zKzqy/Dd+LiooUjUT3LVU2A3K4k5Xa9nSVrH5LTue+x+wsy1I0GpVlWTJNU4ZhyLKsmlVP4XBYH330ka644op6yw0AAIATw0UXXSTLsnT//ffLMAxNnjxZl19+uXw+n+bMmaP09HTdcMMN8Y4JACcMiqdG6sMv/qMPV6yVNzlBNlv9LFzzl/vl9/sVNaOSJEOGIqEquZIzldyym6p2rKlZ9WSz2eR0OuXxeOTxeORyuWpWQu3evVvLly9XWVkZr7cFAABAnbv44osVjUb14IMPym63a8KECbr55ptVWlqqp556Sl6vVwMHDox3TAA4IVA8NUKWZenVJZ8rappKTHDX27zbtm5TJBr5KYcsGZJkmUpu0VnOyh2y2/bt52QYRs1qJ9M0FQgEVFVVJbvdLrfbLZ/Pp6+//loDBgyot/wAAAA4cQwcOFCWZWnKlCmy2WwaP368Ro8erbKyMk2ZMkWpqanq27dvvGMCwHGP4qkRWrdlp774erW+fH3Ofp8bNrvcCcnKaNFG7XoOUGqTFvvGr1iq9SvfUc8Lf6Wc9j1qxr/34jRVl5f+zwUMOd0J8jbJUevuZ6lZm241hyrKilW2dbWq9hYoEiiXGQnJ7vTI7c2WN/dkubzZcjdvJ3d4r9LT05WVlaXs7Gw1adJEGRkZSk9Pr/krIyNDGRkZatmyZUz/nAAAAHBiGzRokKLRqB566CHZbDbde++9mjRpkvx+v+677z49/fTTOuWUU+IdEwCOaxRPjdCbH32tYHjfyqPE1EzldDhVkhQNB1VatE2F67/Rrk3f6ozLhyijeZufvZZh2NSu1/mSJMuMqqK0WEVb/qM9BevV+azLdFKP/pKkH1YsVdnWVXIlpysjp4Ocbo8C/hKV7tyowN4CndT3OvX9xU16dupva95kBwAAAMTb5ZdfLtM09cgjj8hms+nuu+/WtGnTNHr0aI0dO1aLFi1S+/bt4x0TAI5bFE+NTDRq6t3PvpPLse8fXZI3Ux37XLTfmO8/X6INX76n7z9forOuHP6z1zNstgPO371tnT7/52L9sOJtte56puxOl7LyOuqkngPkzcrZb+yO9d/o63de0o5V72h9XgcV7i5TbrPMOrhTAAAAoG5ceeWVMk1T06ZNk91u17hx4zRz5kwNGTJEo0aN0vPPP6+cnJzDXwgAcMTqZ1dq1JmCor0qr6yWx+U85Jg23c+WJPmKtx/VHFl5HZWclqVoJKzyvUWSpNxOpx1QOklSTvseSkrLUlVZscrLfdqwreio5gQAAABi6eqrr9Z9992nl19+WXPnzlViYqLmzZunpKQkjRw5UiUlJfGOCADHJYqnRmbDtl0KhsJyOWvzOJtx7BPW4hI/vlXPMGzaSPEEAACABuqXv/yl7r33Xr344ouaP3++0tPTNX/+fAWDQY0ePVrl5eXxjggAxx2Kp0Zm/bYiyTBqyp6D2fLtp5KktOzco5pjT8F6VZTtlt3hUkpGs58dW1q0TeV7i+RtmiuH063vNxce1ZwAAABAfbjuuut011136fe//73y8/PVvHlzzZ8/X7t27dKdd96pYDAY74gAcFxhj6dGZvdevyzTrPm60leidSuWSpKikZDKirZp787Nstkd6nT6JYe9nmWaNedbZlQVZbtVtOU7SZY6nn6x7I5DP9IXDlZr1XuvSIahzmcOkuGwq6B477HdIAAAABBjN9xwg0zT1KxZs2Sz2TRs2DDNnj1bI0aM0IQJEzRjxgxemAMAdYTiqZGpDoZkGIYkS5JU5S/R+pXvSJIMm13uhGS1aN9T7U4doNTM5oe9nmWZNefLMOR0J6hJTju16naWmrXpesjzopGwVi75vSrKitXx9EvUJOck7fVVKBSKHPM9AgAAALH2q1/9StFoVHPnzpXNZtOQIUP0xBNPaNy4cXr44Yf1wAMP/OxTBgCA2qF4amys/b/Myu2g03/x26O+nM3u0MChjx3ROdFIWF+89TuV7Niodqeep/a9zv+feNbPnAkAAAA0HLfccotM09T8+fNlt9s1ePBgTZ06VZMnT1ZaWpruuOOO//7SFwBwtCieGpkEj1OmFb9y58fSaU/Bep3U81x1OuPSmmOWJbmdh340DwAAAGhofv3rXysajSo/P182m0233XabfD6fZsyYofT0dN16663xjggAjRrFUyOT4U3+75LfaL3P/b+lU9se/dX5zEH7HY9Eo2qamVrvuQAAAIBjMXjwYEWjUS1YsEB2u1233HKLysrKNG/ePHm9Xl155ZXxjggAjRbFUyNzUm62ZFkyzfpd9bRf6XRKP3U567KDjuvcNqdecwEAAAB1YciQITJNU3PnzpXdbteQIUNUVlamadOmKTU1Veedd168IwJAo0Tx1Mi0y2sml9OhULh+X/O65qO/aE/BerkTU2R3umvehPcjy7KU0Kyj2uVl12suAAAAoK4MHTq05m13hmHonnvukc/n06RJkzRv3jz17t073hEBoNGheGpkWrVooqQEt0ory+p13qryUklSsKr8p7fg/f90uyCH4gkAAACNlmEYGj58uKLRqGbOnCm73a6pU6fK7/frzjvv1KJFi9SpU6d4xwSARsWwrMPvVO33++X1euXz+ZSayh4+8fbwwr/pjQ++VHamt8G8ZaN4r08dWrfQ7x8dymtnAQAA0KhZlqW5c+fqxRdf1H333adBgwZpxIgR2rFjhxYvXqy8vLx4RwSAuKttV0RD0Ahd1r+nnA6HAqFwvKNIkqJRU6Zp6arze1M6AQAAoNEzDENjxozRjTfeqMcff1xLlizRnDlzlJaWphEjRqi4uDjeEQGg0aAlaIRO6ZinDq2byV9RHe8okiR/ZbXSvcm64Mxu8Y4CAAAA1AnDMDRu3DjdcMMNmjZtmt5//30tWLBAkjRq1Cj5/f44JwSAxoHiqRGy2Wy65sI+MgwpEIzvqqdINKpAMKyB55yi1KSEuGYBAAAA6pJhGLrzzjt13XXX6dFHH9Vnn32mBQsWaO/evbrjjjtUXd0wfhEMAA0ZxVMjdek5p6h315NUWl4p8/DbdMWEZVkqKatQXvNM/fqKfnHJAAAAAMTSj2+3u+aaa/Twww9rzZo1mjt3rjZu3Kh7771X4XDD2P4CABoqiqdGymG3657bBiktJVGlvoq4ZKioCsrpsOvOWwcqLTUpLhkAAACAWDMMQ/fdd5+uvPJKTZ06VVu2bNGTTz6plStXasqUKTJNM94RAaDBonhqxFrnZGnItecpalqqqArU69zBUFgV1QEN6tdDfU/tWK9zAwAAAPXNZrNpwoQJuvzyyzVlyhTt3btXjz76qN555x09+eSTqsXLwgHghETx1Mhde1EfXX3BaaqoDqqyOlgvc4bCEe31VeqMk9tp7M2X1sucAAAAQLzZbDZNmjRJgwYN0gMPPKBIJKIJEybo1Vdf1bPPPhvveADQIFE8NXI2m0133jpQg/r1UEVVQOWVsd3gMBAMqaSsQr26ttGjY65TYoI7pvMBAAAADYnNZtP999+vSy65RJMnT1ZKSopGjhypRYsW6bXXXot3PABocCiejgNOh12Th16p/3fJGQqFoyra61M0WrfPmZuWpZKycvkqqnVOr4568u5fyZuSWKdzAAAAAI2BzWbTlClTdPHFF2vixInKy8vTjTfeqCeeeEJLly6NdzwAaFAc8Q6AuuGw79vku2fn1prz4hLtKC5VSpJHSQluGYZxTNcOBEMq9VcpLTVRt18zQNde1EcOu72OkgMAAACNz4/lUzQa1cSJE/XYY4+prKxM999/v1JSUnTmmWfGOyIANAiGVYtd8Px+v7xer3w+n1JTU+sjF45BSVmF5r30tt797FsFQmEluJxKSUqQ3V77BW6mtW/D8qrqoBx2u3p2aa17bhukNjlNY5gcAAAAaFyi0agmTZqkDz/8UNOmTdM//vEPffHFF8rPz1f37t3jHQ8AYqa2XRHF03HKsix9t3GH3lr2tZZ+uka+8mpZsuRyOuRxOeR2OmWz22RIsiRZpqVgOKxgKKJgKCxJSvC41ffUDvrFuaeqT7eTjqi4AgAAAE4UkUhEEydO1LJlyzRt2jS9/PLL2rRpk5577jm1bds23vEAICYonlCjzF+pdz77VqvXbdO3GwpUUlauYCgi0zT3vfbVMGSzGXI7HUpNSlSXk3LUrX1LXXBmN+U0zYh3fAAAAKDBi0QiGj9+vJYvX66HHnpIzz//vHw+nxYvXqzmzZvHOx4A1DmKJxyUZVnaXVquzQXFqgoEFQpH5XTY5XE5ldeiiVpkpclmY2UTAAAAcKTC4bDuu+8+ffbZZ3rggQeUn58vu92uxYsXKz09Pd7xAKBOUTwBAAAAQD0LhUK69957tWLFCk2YMEHz5s1Tdna2Fi5cqKSkpHjHA4A6U9uuiKUtAAAAAFBHXC6XnnjiCfXu3VvTp0/XkCFDtH37dt11110KhULxjgcA9Y7iCQAAAADqkMvl0pNPPqlTTz1Vs2bN0pAhQ7R69WpNnjxZpmnGOx4A1CuKJwAAAACoYz+WTz169NDTTz+twYMH68MPP9S0adNUi91OAOC4QfEEAAAAADHgdrv11FNPqXv37nrhhRd088036/XXX9eCBQviHQ0A6g3FEwAAAADEiMfj0axZs9SlSxe99tpruvbaa/XCCy/opZdeinc0AKgXFE8AAAAAEEMej0ezZ89Wx44d9a9//UuXXHKJZs2apX/+85/xjgYAMUfxBAAAAAAxlpCQoDlz5qhdu3b6+OOPdfbZZ+uhhx7SsmXL4h0NAGKK4gkAAAAA6kFiYqLmzZunk046SatWrVL37t01fvx4ffXVV/GOBgAxQ/EEAAAAAPXkx/KpdevW2rhxo1q1aqVx48bphx9+iHc0AIgJiicAAAAAqEdJSUmaP3++8vLyVFhYqLS0NI0aNUoFBQW1Or+wsFDr1q2LcUoAqBsUTwAAAABQz5KTk7VgwQLl5eWptLRUkjRixAjt2bPnkOdYlqX7779fffr00e233y7TNOsrLgAcNYonAAAAAIiDlJQULViwQLm5uQoEAvL7/Ro9erTKy8sPGGtZlsaMGaPHH39cO3fu1NatW/X999/HITUAHBmKJwAAAACIk9TUVD399NNq2bKlIpGItm7dqnHjxikQCNSMsSxLv/3tb7Vw4UJFo1EZhiG/36/ly5fHMTkA1A7FEwAAAADEkdfrrSmfJGnVqlUaP368IpGILMvSTTfdpBdeeEGWZcntdsswDIVCIX300UdxTg4Ah2dYlmUdbpDf75fX65XP51Nqamp95AIAAACAE0ppaamGDh2qbdu2KRQKaeDAgVqzZo3efPNNSZLL5ZJhGIpEIjJNU61atdLKlSuVlpYW3+AATki17YpY8QQAAAAADUB6eroWLlyo3NxcGYah2bNn6x//+Iekn0onSbLb7bIsS3v37tVnn30Wz8gAcFgUTwAAAADQQGRkZGjOnDnaunWrKisrZVmW7HZ7TekkSYZhyDAMBQIBffLJJ3FMCwCHR/EEAAAAAA1EZWWlrrjiCpWWltYUTOFwWJFIZL9xNptN4XBYb7/9tkzT/NlrWpYlX0WVikp82lG8V0UlPpVXBn72HACoK454BwAAAAAASGVlZerbt6/Wrl0ru90uh8OhUCgk0zQVDodlGIbsdrukfY/bmaaprVu36j//+Y+6detWcx3TNLVq3Tb9Z+MOrd+6U2vWF2hPWblM05JlWfuuYzOUnelVt/a5at+qmbq1a6mu7Vrut7IKAOoCxRMAAAAAxNnOnTvVv39/bdy4UXa7XU6nU9K+vZ2CwaAsy6opn2w2mwzDkGVZqqys1Keffqpu3brJV16lpf9eo9ffW6lNBcWKRE1Jlpx2u9wupxxOmwwZsmQpGrW0bVeJNhUUS5KcToc6tm6uK8/vrfNP76rkRE8c/zQAHE8ongAAAAAgjjZv3qwBAwZo+/btcjgccjh++jHNMAy53e6a8ikUCsnlcslms8lutysYDGrJ20vlyOqgP731b5X5q2QYkjc5QW6X82dXMKVoX7lkWZYCwbC+21Cgb9dvV/6f39WtV5yjX158uhz/XWEFAEfLsCzLOtyg2r4iDwAAAABQe5FIRH369NGqVatkGIacTqdstgO34rUsq6Z8Mgyj5i13ljNFWV36q2lue7ldTnmTE2W3H/1WvpFoVGXlVYpGTfXq2kb33HaZ2rZseiy3COA4VduuiM3FAQAAACBOVqxYobKyMnk8HjkcDkUiEYVCIUUiEf3vGoEfVz79+IhdKBSSp1knZXS/RJbbK5ddyvAmH1PpJEkOu11N0lLkTUnUF99u0m8ffFZ/f3/lsd4mgBMYxRMAAAAAxMkZZ5yhRYsWafjw4WrXrp28Xq9cLlfNnk6hUEjRaLRmpdOP5VNy3qlKbNlThs2maKBC1VWVdZrL43KqWaZXVdUhPf78P/XiGx+rFg/LAMABeNQOAAAAABqAqqoqLVu2TEuWLNGSJUtUVFSk6upqRaNRSap5q11Sy1Pkad5VlhmRFQlJktxut7p26ypDdf9WulJ/paKmqdE3XqwbB51V59cH0DjVtiuieAIAAACABqa8vFwfffSR3nrrLS1ZskS7d+9WIBCQp2k7eU86U5ZlygwHasbbbXZ16tRJCQkJMcmz11chwzD00Khrdd7pXWMyB4DGheIJAAAAAI4DPp9P77//vl57/U0t3xyRKbuioSpJkqWffpzLzc1VdtPsmGSwLEu7S/3KzvTq+YeHqkl6SkzmAdB4sLk4AAAAABwHvF6vrrjiCmV36a/M7BbKy8lWenq6HA6HDOOnR+uKi4pjlsEwDGV6U1RYXKZ5L7/Nfk8Aas0R7wAAAAAAgJ/35rJv9Ok365XhTVaC26WMjAxFIhH5/D7t2b1HFRUViprRmGaw221KTfZo6adrNKBPV517WueYzgfg+EDxBAAAAAANmGma+tNbn8qyLCW4XTWfOxwOZWZkKjMjU6FQSHa7/YivvWfHRn3294Vq3/tCdexz0WHHJyV4VF4Z0Cv/+rf69+6034orADgYHrUDAAAAgAbsi283aXPBbnmTEw849s+n79EHLz8hl8t1VMXT0UhJStDq9dv1/ebCepkPQONG8QQAAAAADdg/P/pa4WhUHrcz3lEkSYkel4LBsN5a9k28owBoBCieAAAAAKCB8lVU6eOv1inR4zr84HpiGIY8bqeWfrpGoXAk3nEANHDs8QQAAAAADdSGbUUKBEMHfczuYNatWKr1K9/RGVcMU7DSr41ff6CKst1yuhPU/KRT1PnMgbI7Dr9yKhys1hf/ekF7Czera9/L1ebkvvsdT3A7VVEV0Lade9Qur9lR3RuAEwMrngAAAACggdq4rUjRqCmn48j2b9qyZrlWf/h/Ss5oplbdzpTTnaAtaz7Rqg9eO+y5gUq/Pn09X2W7turUC288oHSSJJfTqVA4og3bio4oF4ATDyueAAAAAKCBWr91lyQd8dvj9hSs1zm/vEPJ6U0lSdHTw1r26iwVbvhGXc4aJE+S96DnVZTt1uf/eFbhQJVOG/QbZeV2OOg4m82QDIPiCcBhseIJAAAAABqoLYW7Zbcd+Y9tbU7uW1M6SZLd4VSLdj0ky5Jv946DnlNWtF2f/m2BopGQzrhi2CFLpxqWpe27So44G4ATCyueAAAAAKCBCoTCMmxHttpJkrxZLQ/4LCF53yqncLD6gGN7d27WplXL5E5IUp/LbldyWtZh5zAMQ9XB8BFnA3BiYcUTAAAAADRQ0ah5VOc5XJ4DPjP+u3LKsg68pn/PDkXDQaU2aaGk1MxazWEYknmU+QCcOCieAAAAAKCB8rhdsqzYz9O6+9nK7dxHuzZ9q6/eeVmmGT3sOZYleTyHf0MegBMbj9oBAAAAQAOV6U1WtBYl0LEzdPK518owDG37z+eSpJ4X3iCb7dBv07MsSxmpSfWQDUBjxoonAAAAAGigOrZpLkNHvsfT0TAMQ937X6O8Lmdo58ZV+vpnVj5Z/12G1a5Vs3rJBqDxYsUTAAAAADRQ7fL2FTvRqCm7PfbrBvaVT1fLMAxt/e7fsixLp170qwNWPoUjUdnttpp8AHAorHgCAAAAgAaqXV623C6HAqED3x5nmfs29v65x+GOhmEY6tbvKrXqdpZ2bVqjr5b+UWZ0/5VPwVBYHpdT7fKy63RuAMcfVjwBAAAAQAPVIitNbVo21brNhUpKcO93LBiokCS5En7aZ6ljn4vUsc9FB71WbqfTlNvptP0+a5Jzki4bMeOAsYZhqHu/q9S931UHvVZlIKQzTm6n1KSEI7ofACceVjwBAAAAQANls9l05Xm9ZZqWolFzv2NFm7+TJKU1zavXTOFwRIakKwb0qtd5ATROFE8AAAAA0IBdcGY3ZXiT5a+sliSt//I9ffXOS/p22euyO1xq1fWMes3jq6hWi6x0ndOrU73OC6BxongCAAAAgAYsNSlBl55zigKhsKJRU5u++UjFW9aqSct2OvOKYUpMzai3LOFIVJFoVJefd6rcLnZuAXB4/C8FAAAAADRwt1x+jj5csVaFu0t18eCH4pLBsiyVlFXopLxsXXdx/a6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" ] @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 29, "id": "e75376cb-d467-47fd-92bd-84ef21a35f4f", "metadata": {}, "outputs": [ @@ -148,11 +148,15 @@ "source": [ "# Our dataset is comprised of discrete valued data, so we will utilize the\n", "# G^2 (Chi-square) CI test.\n", - "ci_estimator = GSquareCITest(data_type=\"discrete\")\n", + "ci_estimator = GSquareCITest(data_type='discrete')\n", + "# ci_estimator = CategoricalCITest(lambda_='log-likelihood')\n", + "# ci_estimator = CausalLearnCITest()\n", "\n", "# Since our data is entirely discrete, we can also use the G^2 test as our\n", "# CD test.\n", - "cd_estimator = GSquareCITest(data_type=\"discrete\")\n", + "cd_estimator = GSquareCITest(data_type='discrete')\n", + "# cd_estimator = CategoricalCITest(lambda_='log-likelihood')\n", + "# cd_estimator = CausalLearnCITest()\n", "\n", "alpha = 0.05\n", "learner = PsiFCI(\n", @@ -175,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "id": "3d6ac82a-6a5c-4c46-b8c1-7cd09101c5f8", "metadata": { "tags": [] @@ -191,6 +195,8 @@ "Not enough samples. 1200 is too small. Need 1440.\n", "Not enough samples. 1200 is too small. Need 1440.\n", "Not enough samples. 1200 is too small. Need 1440.\n", + "Not enough samples. 1200 is too small. Need 1280.\n", + "Not enough samples. 1200 is too small. Need 1280.\n", "Not enough samples. 1800 is too small. Need 1920.\n", "Not enough samples. 1800 is too small. Need 1920.\n", "Not enough samples. 1800 is too small. Need 1920.\n", @@ -198,7 +204,261 @@ "Not enough samples. 1200 is too small. Need 1440.\n", "Not enough samples. 1200 is too small. Need 1440.\n", "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1280.\n" + "Not enough samples. 1200 is too small. Need 1280.\n", + "Updated separating set for Raf, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Raf, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for PIP3, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for PIP3, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for P38, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for P38, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for P38, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKA, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for Jnk, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for Jnk, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Jnk, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for Plcg, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for Plcg, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for Akt, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Akt, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Erk, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for PIP2, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for PIP2, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for PKC, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKC, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKC, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKC, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKC, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for PKC, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for Mek, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), Plcg with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), Akt with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), PIP2 with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), PIP3 with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), Erk with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), Raf with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), P38 with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 0), Mek with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 4), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 5), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 7), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 8), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 10), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 11), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 13), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for ('F', 13), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for ('F', 13), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for ('F', 13), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for ('F', 13), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for ('F', 13), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for ('F', 13), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", + "Updated separating set for ('F', 1), Raf with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), PIP3 with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), P38 with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), Jnk with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), Plcg with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), Akt with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), Erk with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), PIP2 with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 1), Mek with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), Raf with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), P38 with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), Plcg with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), Erk with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), PIP2 with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), PIP3 with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), Jnk with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), Akt with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 2), Mek with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 3), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 6), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 9), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 12), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", + "Updated separating set for ('F', 14), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for ('F', 14), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for ('F', 14), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for ('F', 14), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for ('F', 14), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for ('F', 14), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", + "Updated separating set for ('F', 14), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n" ] } ], @@ -209,9 +469,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "id": "7c25044c-99f7-4255-87f7-9a5d845b77e6", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -235,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "id": "9f3e89fb-9ef6-4ad8-8198-164ba3032dd5", "metadata": {}, "outputs": [ @@ -248,470 +510,486 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "PKC\n", - "\n", - "PKC\n", + "Raf\n", + "\n", + "Raf\n", "\n", - "\n", + "\n", "\n", - "P38\n", - "\n", - "P38\n", + "Mek\n", + "\n", + "Mek\n", "\n", - "\n", + "\n", "\n", - "PKC->P38\n", - "\n", - "\n", - "\n", + "Raf->Mek\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", - "Jnk\n", - "\n", - "Jnk\n", - "\n", - "\n", - "\n", - "PKC->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", "\n", - "Plcg\n", - "\n", - "Plcg\n", + "Jnk\n", + "\n", + "Jnk\n", "\n", - "\n", + "\n", "\n", - "PIP2\n", - "\n", - "PIP2\n", + "P38\n", + "\n", + "P38\n", "\n", - "\n", + "\n", "\n", - "Plcg->PIP2\n", - "\n", - "\n", - "\n", + "Jnk->P38\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "PIP3\n", - "\n", - "PIP3\n", + "\n", + "\n", + "PKC\n", + "\n", + "PKC\n", "\n", - "\n", - "\n", - "Plcg->PIP3\n", - "\n", - "\n", + "\n", + "\n", + "Jnk->PKC\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "Raf\n", - "\n", - "Raf\n", + "\n", + "\n", + "PKC->P38\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "Mek\n", - "\n", - "Mek\n", + "PKA\n", + "\n", + "PKA\n", "\n", - "\n", - "\n", - "Raf->Mek\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "Erk\n", + "\n", + "Erk\n", "\n", - "\n", + "\n", "\n", - "PIP3->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk->P38\n", - "\n", - "\n", - "\n", + "PKA->Erk\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "Akt\n", - "\n", - "Akt\n", - "\n", - "\n", - "\n", - "Erk\n", - "\n", - "Erk\n", + "\n", + "Akt\n", "\n", "\n", "\n", "Akt->Erk\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "PKA\n", - "\n", - "PKA\n", + "\n", + "\n", + "('F', 13)\n", + "\n", + "('F', 13)\n", "\n", - "\n", + "\n", + "\n", + "('F', 13)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 13)->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 13)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 13)->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 12)\n", + "\n", + "('F', 12)\n", + "\n", + "\n", "\n", - "PKA->Erk\n", - "\n", - "\n", - "\n", + "('F', 12)->Raf\n", + "\n", + "\n", "\n", - "\n", + "\n", + "\n", + "('F', 12)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "('F', 12)->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg\n", + "\n", + "Plcg\n", + "\n", + "\n", + "\n", + "('F', 12)->Plcg\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP2\n", + "\n", + "PIP2\n", + "\n", + "\n", + "\n", + "('F', 12)->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 11)\n", + "\n", + "('F', 11)\n", + "\n", + "\n", + "\n", + "('F', 11)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 11)->Akt\n", + "\n", + "\n", + "\n", + "\n", "\n", + "('F', 8)\n", + "\n", + "('F', 8)\n", + "\n", + "\n", + "\n", + "('F', 8)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 8)->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)\n", + "\n", + "('F', 14)\n", + "\n", + "\n", + "\n", + "('F', 14)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 14)->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", "('F', 4)\n", - "\n", - "('F', 4)\n", + "\n", + "('F', 4)\n", "\n", "\n", - "\n", + "\n", "('F', 4)->Raf\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)->PKA\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 4)->Akt\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 4)->PKA\n", - "\n", - "\n", + "\n", + "\n", + "PIP3->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg->PIP2\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 10)\n", - "\n", - "('F', 10)\n", - "\n", - "\n", - "\n", - "('F', 10)->PIP3\n", - "\n", - "\n", + "\n", + "('F', 10)\n", "\n", "\n", - "\n", + "\n", "('F', 10)->Jnk\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 10)->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 9)\n", - "\n", - "('F', 9)\n", - "\n", - "\n", - "\n", - "('F', 9)->PKC\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 9)->Jnk\n", - "\n", - "\n", + "\n", + "\n", + "('F', 10)->PIP3\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 9)->Akt\n", - "\n", - "\n", + "\n", + "\n", + "('F', 10)->PIP2\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 5)\n", - "\n", - "('F', 5)\n", - "\n", - "\n", - "\n", - "('F', 5)->PIP3\n", - "\n", - "\n", + "\n", + "('F', 5)\n", "\n", "\n", - "\n", + "\n", "('F', 5)->Jnk\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 5)->Akt\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 11)\n", - "\n", - "('F', 11)\n", - "\n", - "\n", - "\n", - "('F', 11)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 11)->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 1)\n", - "\n", - "('F', 1)\n", - "\n", - "\n", - "\n", - "('F', 1)->PKC\n", - "\n", - "\n", + "\n", + "\n", + "('F', 5)->PIP3\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 1)->PKA\n", - "\n", - "\n", + "\n", + "\n", + "('F', 5)->PIP2\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 0)\n", - "\n", - "('F', 0)\n", - "\n", - "\n", - "\n", - "('F', 0)->PKC\n", - "\n", - "\n", + "\n", + "('F', 0)\n", "\n", "\n", - "\n", + "\n", "('F', 0)->Jnk\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)->PKC\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 0)->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)\n", - "\n", - "('F', 13)\n", - "\n", - "\n", - "\n", - "('F', 13)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)->Erk\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "('F', 3)\n", - "\n", - "('F', 3)\n", - "\n", - "\n", - "\n", - "('F', 3)->PKC\n", - "\n", - "\n", + "\n", + "('F', 3)\n", "\n", "\n", - "\n", + "\n", "('F', 3)->Jnk\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)->PKC\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 3)->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 6)\n", - "\n", - "('F', 6)\n", - "\n", - "\n", - "\n", - "('F', 6)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 12)\n", - "\n", - "('F', 12)\n", - "\n", - "\n", - "\n", - "('F', 12)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 12)->Plcg\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 12)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 12)->PIP3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 2)\n", - "\n", - "('F', 2)\n", + "\n", + "('F', 2)\n", "\n", "\n", - "\n", + "\n", "('F', 2)->PKC\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 2)->PKA\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)\n", + "\n", + "('F', 1)\n", "\n", - "\n", - "\n", - "('F', 14)\n", - "\n", - "('F', 14)\n", + "\n", + "\n", + "('F', 1)->PKC\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 14)->PKC\n", - "\n", - "\n", + "\n", + "\n", + "('F', 1)->PKA\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 14)->Raf\n", - "\n", - "\n", + "\n", + "\n", + "('F', 9)\n", + "\n", + "('F', 9)\n", "\n", - "\n", - "\n", - "('F', 14)->Mek\n", - "\n", - "\n", + "\n", + "\n", + "('F', 9)->Jnk\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 14)->Erk\n", - "\n", - "\n", + "\n", + "\n", + "('F', 9)->PKC\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 8)\n", - "\n", - "('F', 8)\n", + "\n", + "\n", + "('F', 9)->Akt\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 8)->Raf\n", - "\n", - "\n", + "\n", + "\n", + "('F', 6)\n", + "\n", + "('F', 6)\n", "\n", - "\n", - "\n", - "('F', 8)->Mek\n", - "\n", - "\n", + "\n", + "\n", + "('F', 6)->PKC\n", + "\n", + "\n", "\n", "\n", "\n", "('F', 7)\n", - "\n", - "('F', 7)\n", + "\n", + "('F', 7)\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -727,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 21, "id": "afb1ce35-7715-417c-aa77-eccd5ebf8280", "metadata": {}, "outputs": [ @@ -735,7 +1013,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Plcg', 'PIP3', ('F', 10), ('F', 5)]\n" + "['Plcg', 'PIP3']\n" ] } ], @@ -745,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 35, "id": "64b61853-3c5e-4cd4-bb9b-5b4e79a9e292", "metadata": {}, "outputs": [ @@ -755,7 +1033,7 @@ "(1, 2)" ] }, - "execution_count": 29, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1080,7 +1358,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 40, "id": "d27c0dab-a6bf-48cf-90b7-f1298ca89b12", "metadata": {}, "outputs": [ @@ -1088,40 +1366,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "{}\n" + "{'obs_skel_graph': , 'pag': }\n", + "2\n" ] } ], "source": [ - "print(ctx.state_variables)" + "print(ctx.state_variables)\n", + "print(learner.skeleton_learner_.n_iters_)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 37, "id": "b6e4aa12-a6f6-4afd-b810-02a852fdb623", "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'obs_skel_graph'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_2117/2603007690.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mobs_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'obs_skel_graph'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m: 'obs_skel_graph'" - ] - } - ], + "outputs": [], "source": [ "obs_graph = ctx.state_variables['obs_skel_graph']" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "b9daa53d-0216-4443-aa31-fbbdf8c19b4a", + "execution_count": 39, + "id": "0b8c3ca6-cc78-43d8-a2fa-b9f0dd299809", "metadata": {}, "outputs": [ { @@ -1133,146 +1401,355 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "PKC\n", - "\n", - "PKC\n", + "Raf\n", + "\n", + "Raf\n", "\n", - "\n", + "\n", "\n", - 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diff --git a/doc/references.bib b/doc/references.bib index 8f3c1c376..3b11551d1 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -140,6 +140,23 @@ @article{dai2022independence % Conditional Testing +@article{cressieread1984, + issn = {00359246}, + url = {http://www.jstor.org/stable/2345686}, + abstract = {This article investigates the family {Iλ;λ ∈ R} of power divergence statistics for testing the fit of observed frequencies {Xi;i = 1,...,k} to expected frequencies {Ei;i = 1,...,k}. From the definition 2nIλ = 2/λ(λ + 1) ∑ki = 1 Xi{(Xi/Ei)λ - 1}; λ ∈ R, it can easily be seen that Pearson's X2 (λ = 1), the log likelihood ratio statistic (λ = 0), the Freeman-Tukey statistic (λ = -1/2) the modified log likelihood ratio statistic (λ = -1) and the Neyman modified X2 (λ = -2), are all special cases. Most of the work presented is devoted to an analytic study of the asymptotic difference between different Iλ, however finite sample results have been presented as a check and a supplement to our conclusions. A new goodness-of-fit statistic, where λ = 2/3, emerges as an excellent and compromising alternative to the old warriors, I0 and I1.}, + author = {Noel Cressie and Timothy R. C. Read}, + journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, + number = {3}, + pages = {440--464}, + publisher = {[Royal Statistical Society, Wiley]}, + title = {Multinomial Goodness-of-Fit Tests}, + urldate = {2023-03-16}, + volume = {46}, + year = {1984} +} + + + @article{frenzel_partial_2007, title = {Partial {Mutual} {Information} for {Coupling} {Analysis} of {Multivariate} {Time} {Series}}, volume = {99}, diff --git a/dodiscover/ci/__init__.py b/dodiscover/ci/__init__.py index ecdb6f27a..9ca77702f 100644 --- a/dodiscover/ci/__init__.py +++ b/dodiscover/ci/__init__.py @@ -1,4 +1,5 @@ from .base import BaseConditionalIndependenceTest +from .categorical_test import CategoricalCITest, CausalLearnCITest from .ccmi_test import ClassifierCMITest from .clf_test import ClassifierCITest from .cmi_test import CMITest diff --git a/dodiscover/ci/categorical_test.py b/dodiscover/ci/categorical_test.py new file mode 100644 index 000000000..e9fd0d03c --- /dev/null +++ b/dodiscover/ci/categorical_test.py @@ -0,0 +1,200 @@ +import logging +from functools import reduce +from typing import Optional, Set, Tuple + +import pandas as pd +from scipy import stats + +from dodiscover.ci.base import BaseConditionalIndependenceTest +from dodiscover.typing import Column + + +# copied from pgmpy +def power_divergence(X, Y, Z, data, lambda_="cressie-read"): + """ + Computes the Cressie-Read power divergence statistic [1]. The null hypothesis + for the test is X is independent of Y given Z. A lot of the frequency comparison + based statistics (eg. chi-square, G-test etc) belong to power divergence family, + and are special cases of this test. + + Parameters + ---------- + X: int, string, hashable object + A variable name contained in the data set + + Y: int, string, hashable object + A variable name contained in the data set, different from X + + Z: list, array-like + A list of variable names contained in the data set, different from X and Y. + This is the separating set that (potentially) makes X and Y independent. + Default: [] + + data: pandas.DataFrame + The dataset on which to test the independence condition. + + lambda_: float or string + The lambda parameter for the power_divergence statistic. Some values of + lambda_ results in other well known tests: + "pearson" 1 "Chi-squared test" + "log-likelihood" 0 "G-test or log-likelihood" + "freeman-tukey" -1/2 "freeman-tukey Statistic" + "mod-log-likelihood" -1 "Modified Log-likelihood" + "neyman" -2 "Neyman's statistic" + "cressie-read" 2/3 "The value recommended in the paper + :footcite:`cressieread1984`" + + Returns + ------- + CI Test Results: tuple + Returns a tuple (chi, p_value, dof). `chi` is the + chi-squared test statistic. The `p_value` for the test, i.e. the + probability of observing the computed chi-square statistic (or an even + higher value), given the null hypothesis that X \u27C2 Y | Zs is True. + If boolean = True, returns True if the p_value of the test is greater + than `significance_level` else returns False. + + See Also + -------- + scipy.stats.power_divergence + + References + ---------- + .. footbibliography:: + + Examples + -------- + >>> import pandas as pd + >>> import numpy as np + >>> data = pd.DataFrame(np.random.randint(0, 2, size=(50000, 4)), columns=list('ABCD')) + >>> data['E'] = data['A'] + data['B'] + data['C'] + >>> chi_square(X='A', Y='C', Z=[], data=data, boolean=True, significance_level=0.05) + True + >>> chi_square(X='A', Y='B', Z=['D'], data=data, boolean=True, significance_level=0.05) + True + >>> chi_square(X='A', Y='B', Z=['D', 'E'], data=data, boolean=True, significance_level=0.05) + False + """ + + # Step 1: Check if the arguments are valid and type conversions. + if hasattr(Z, "__iter__"): + Z = list(Z) + else: + raise (f"Z must be an iterable. Got object type: {type(Z)}") + + if (X in Z) or (Y in Z): + raise ValueError(f"The variables X or Y can't be in Z. Found {X if X in Z else Y} in Z.") + + # Step 2: Do a simple contingency test if there are no conditional variables. + if len(Z) == 0: + chi, p_value, dof, expected = stats.chi2_contingency( + data.groupby([X, Y]).size().unstack(Y, fill_value=0), lambda_=lambda_ + ) + + # Step 3: If there are conditionals variables, iterate over unique states and do + # the contingency test. + else: + chi = 0 + dof = 0 + for z_state, df in data.groupby(Z): + try: + c, _, d, _ = stats.chi2_contingency( + df.groupby([X, Y]).size().unstack(Y, fill_value=0), lambda_=lambda_ + ) + chi += c + dof += d + except ValueError: + # If one of the values is 0 in the 2x2 table. + if isinstance(z_state, str): + logging.info( + f"Skipping the test {X} \u27C2 {Y} | {Z[0]}={z_state}. Not enough samples" + ) + else: + z_str = ", ".join([f"{var}={state}" for var, state in zip(Z, z_state)]) + logging.info(f"Skipping the test {X} \u27C2 {Y} | {z_str}. Not enough samples") + p_value = 1 - stats.chi2.cdf(chi, df=dof) + import numpy as np + + if np.isnan(p_value): + print(p_value, chi, c, dof, X, Y, Z) + c, _, d, _ = stats.chi2_contingency( + df.groupby([X, Y]).size().unstack(Y, fill_value=0), lambda_=lambda_ + ) + print(c, d) + # Step 4: Return the values + return chi, p_value, dof + + +class CategoricalCITest(BaseConditionalIndependenceTest): + def __init__(self, lambda_="cressie-read") -> None: + """CI test for categorical data. + + Uses the power-divergence class of test statistics to test categorical data + for (conditional) independences. + + Parameters + ---------- + lambda_ : str, optional + The lambda parameter for the power_divergence statistic, by default 'cressie-read'. + Some values of lambda_ results in other well known tests: + "pearson" 1 "Chi-squared test" + "log-likelihood" 0 "G-test or log-likelihood" + "freeman-tukey" -1/2 "freeman-tukey Statistic" + "mod-log-likelihood" -1 "Modified Log-likelihood" + "neyman" -2 "Neyman's statistic" + "cressie-read" 2/3 "The value recommended in the paper + :footcite:`cressieread1984`" + + See Also + -------- + scipy.stats.power_divergence + + References + ---------- + .. footbibliography:: + """ + self.lambda_ = lambda_ + + def test( + self, + df: pd.DataFrame, + x_vars: Set[Column], + y_vars: Set[Column], + z_covariates: Optional[Set[Column]] = None, + ) -> Tuple[float, float]: + x_vars = reduce(lambda x: x, x_vars) # type: ignore + y_vars = reduce(lambda x: x, y_vars) # type: ignore + stat, pvalue, dof = power_divergence( + x_vars, y_vars, z_covariates, data=df, lambda_=self.lambda_ + ) + self.dof_ = dof + return stat, pvalue + + +class CausalLearnCITest(BaseConditionalIndependenceTest): + def __init__(self, method_name="gsq") -> None: + self.method_name = method_name + + def test( + self, + df: pd.DataFrame, + x_vars: Set[Column], + y_vars: Set[Column], + z_covariates: Optional[Set[Column]] = None, + ) -> Tuple[float, float]: + import numpy as np + from causallearn.utils.cit import Chisq_or_Gsq + + data = df.to_numpy() + x_vars = reduce(lambda x: x, x_vars) # type: ignore + y_vars = reduce(lambda x: x, y_vars) # type: ignore + x = np.argwhere(df.columns == x_vars) + y = np.argwhere(df.columns == y_vars) + z = [] + if z_covariates is not None: + for _z in z_covariates: + z.append(np.argwhere(df.columns == _z).squeeze()) + z = np.array(z) + + tester = Chisq_or_Gsq(data, method_name=self.method_name) + return np.nan, tester(x, y, z) diff --git a/dodiscover/ci/g_test.py b/dodiscover/ci/g_test.py index 0942447f9..5f34e93af 100644 --- a/dodiscover/ci/g_test.py +++ b/dodiscover/ci/g_test.py @@ -187,6 +187,7 @@ def _calculate_g_statistic(contingency_tble): nlevels_x, nlevels_y, dof_count = contingency_tble.shape # now compute marginal terms across all degrees of freedom + # (nlevels_x, dof_count) and (nlevels_y, dof_count) arrays tx_dof = contingency_tble.sum(axis=1) ty_dof = contingency_tble.sum(axis=0) diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index 78221e207..b51c99087 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -385,7 +385,7 @@ def _apply_rule4( # now check if u is in SepSet(v, c) # handle edge case where sep_set is empty. if last_node in sep_set: - if is_in_sep_set(u, sep_set, last_node, c, "any"): # u in sep_set[last_node][c]: + if is_in_sep_set(u, sep_set, last_node, c, "any"): # orient u -> c graph.remove_edge(c, u, graph.circle_edge_name) if graph.has_edge(u, c, graph.circle_edge_name): diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index cf66f244c..fa44e6a4d 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -144,6 +144,7 @@ def learn_skeleton( ) self.skeleton_learner_.fit(data, context) + self.context_ = self.skeleton_learner_.context_.copy() skel_graph = self.skeleton_learner_.adj_graph_ sep_set = self.skeleton_learner_.sep_set_ self.n_ci_tests += self.skeleton_learner_.n_ci_tests diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index b825926f9..bb864beaa 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -2,11 +2,12 @@ from collections import defaultdict from copy import deepcopy from itertools import chain, combinations -from typing import Callable, Generator, Iterable, List, Optional, Set, SupportsFloat, Tuple, Union +from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Set, Tuple import networkx as nx import numpy as np import pandas as pd +from joblib import Parallel, delayed from dodiscover.cd import BaseConditionalDiscrepancyTest from dodiscover.ci import BaseConditionalIndependenceTest @@ -22,20 +23,38 @@ def _parallel_test_xy_edges( - conditional_test_func, x_var, y_var, cond_set, data -) -> Tuple[float, float]: - """Private function used to test edges between X and Y in parallel. + conditional_test_func: Callable[ + [pd.DataFrame, Column, Column, Optional[Set[Column]]], Tuple[float, float] + ], + x_var: Column, + y_var: Column, + alpha: float, + size_cond_set: int, + max_combinations: Optional[int], + possible_variables: Set[Column], + data: pd.DataFrame, +) -> Dict[str, Any]: + """Private function used to test edge between X and Y in parallel for candidate separating sets. Parameters ---------- conditional_test_func : Callable Conditional test function. - x_var : Columns + x_var : Column The 'X' variable name. y_var : Column The 'Y' variable name. - cond_set : Set[Column] - A set of variables to condition on. Can be the empty set. + alpha : float + The significance level for the conditional independence test. + size_cond_set : int + The current size of the conditioning set. This value will then generate + ``(N choose 'size_cond_set')`` sets of candidate separating sets to test, where + ``N`` is the size of 'possible_variables'. + max_combinations : int + The maximum number of conditional independence tests to run from the set + of possible conditioning sets. + possible_variables : Set[Column] + A set of variables that are candidates for the conditioning set. data : pandas.Dataframe The dataset with variables as columns and samples as rows. @@ -46,15 +65,52 @@ def _parallel_test_xy_edges( pvalue : float Pvalue. """ - # compute conditional independence test - test_stat, pvalue = conditional_test_func(data, x_var, y_var, set(cond_set)) - return test_stat, pvalue + prev_pvalue = 0.0 + + # generate iterator through the conditioning sets + conditioning_sets = _iter_conditioning_set( + possible_variables=possible_variables, + x_var=x_var, + y_var=y_var, + size_cond_set=size_cond_set, + ) + + # now iterate through the possible parents + for comb_idx, cond_set in enumerate(conditioning_sets): + # check the number of combinations of possible parents we have tried + # to use as a separating set + if max_combinations is not None and comb_idx >= max_combinations: + break + + try: + # compute conditional independence test + test_stat, pvalue = conditional_test_func(data, x_var, y_var, set(cond_set)) + except Exception as e: + print(e) + test_stat = np.inf + pvalue = 0.0 + + # if any "independence" is found through inability to reject + # the null hypothesis, then we will break the loop comparing X and Y + # and say X and Y are conditionally independent given 'cond_set' + if pvalue > alpha: + break + else: + pvalue = max(pvalue, prev_pvalue) + + result: Dict[str, Any] = dict() + result["x_var"] = x_var + result["y_var"] = y_var + result["cond_set"] = list(cond_set) + result["test_stat"] = test_stat + result["pvalue"] = pvalue + return result def _iter_conditioning_set( possible_variables: Iterable, - x_var: Union[SupportsFloat, str], - y_var: Union[SupportsFloat, str], + x_var: Column, + y_var: Column, size_cond_set: int, ) -> Iterable[Set]: """Iterate function to generate the conditioning set. @@ -133,19 +189,34 @@ def _find_neighbors_along_path(G: nx.Graph, start, end) -> Set: class BaseSkeletonLearner: - """Base class for constraint-based skeleton learning algorithms.""" + """Base class for constraint-based skeleton learning algorithms. + + Attributes + ---------- + adj_graph_ : nx.Graph + The learned skeleton graph. + sep_set_ : SeparatingSet + The learned separating sets. + context_ : Context + The resulting causal context. + n_iters_ : int + The number of iterations of the skeleton learning process that were performed. + This helps track iteration of algorithms that perform the entire skeleton + discovery phase multiple times. + """ alpha: float + n_jobs: Optional[int] adj_graph_: nx.Graph context_: Context sep_set_: SeparatingSet + n_iters_: int + min_cond_set_size_: int max_cond_set_size_: int max_combinations_: int - n_iters_: int - _cont: bool def _learn_skeleton( @@ -158,9 +229,8 @@ def _learn_skeleton( """Core function for learning the skeleton of a causal graph. This function is a "stateful" function of Skeleton learners. It requires - the following state to be preserved as attributes of self. - - - context_ : a Context object + the ``context_`` object to be preserved as attributes of self. It also keeps + track of ``_cont`` private attribute, which helps determine stopping conditions. Parameters ---------- @@ -169,7 +239,8 @@ def _learn_skeleton( context : Context A context object. conditional_test_func : Callable - The conditional test function that + The conditional test function that takes in three arguments 'x_var', 'y_var' + and an optional 'z_var', where 'z_var' is the conditioning set of variables. possible_x_nodes : set of nodes, optional The nodes to initialize as X variables. How to initialize variables to test in the second loop of the algorithm. See Notes for details. @@ -247,44 +318,10 @@ def _learn_skeleton( # wrt which edges are removed at each process of the algorithm remove_edges = set() - # determine whether or not to continue - # loop through every node that we want to test - for x_var in possible_x_nodes: - possible_adjacencies = set(adj_graph.neighbors(x_var)) - logger.info(f"Considering node {x_var}...\n\n") - - for y_var in possible_adjacencies: - # a node cannot be a parent to itself in DAGs - if y_var == x_var: - continue - - if (x_var, y_var) in context.included_edges.edges: - continue - - # compute the possible variables used in the conditioning set - possible_variables = self._compute_candidate_conditioning_sets( - adj_graph, - x_var, - y_var, - ) - - logger.debug( - f"Adj({x_var}) without {y_var} with size={len(possible_adjacencies) - 1} " - f"with p={size_cond_set}. The possible variables to condition on are: " - f"{possible_variables}." - ) - - # check that number of adjacencies is greater then the - # cardinality of the conditioning set - if len(possible_variables) < size_cond_set: - logger.debug( - f"\n\nBreaking for {x_var}, {y_var}, {len(possible_adjacencies)}, " - f"{size_cond_set}, {possible_variables}" - ) - continue - else: - self._cont = True - + if self.n_jobs == 1: + for x_var, y_var, possible_variables in self._generate_pairs_with_sepset( + possible_x_nodes, adj_graph, context, size_cond_set + ): # generate iterator through the conditioning sets conditioning_sets = _iter_conditioning_set( possible_variables=possible_variables, @@ -309,6 +346,8 @@ def _learn_skeleton( data, x_var, y_var, set(cond_set) ) except Exception as e: + # allow us to catch exceptions that are due to not enough samples + # if so, we cannot remove the edge and just proceed print(e) test_stat = np.inf pvalue = 0.0 @@ -320,14 +359,52 @@ def _learn_skeleton( break # post-process the CI test results - removed_edge = self._postprocess_ci_test( - adj_graph, x_var, y_var, cond_set, test_stat, pvalue + self._postprocess_ci_test(adj_graph, x_var, y_var, test_stat, pvalue) + + # two variables found to be independent given a separating set + if pvalue > self.alpha: + self.sep_set_[x_var][y_var].append(set(cond_set)) + self.sep_set_[y_var][x_var].append(set(cond_set)) + remove_edges.add((x_var, y_var, pvalue)) + + # summarize the comparison of XY + self._summarize_xy_comparison(x_var, y_var, pvalue > self.alpha, pvalue) + else: + # run parallelized loop + out = Parallel(n_jobs=self.n_jobs)( + delayed(_parallel_test_xy_edges)( + conditional_test_func, + x_var, + y_var, + self.alpha, + size_cond_set, + self.max_combinations_, + possible_variables, + data, + ) + for x_var, y_var, possible_variables in self._generate_pairs_with_sepset( + possible_x_nodes, adj_graph, context, size_cond_set ) - if removed_edge: + ) + + for result in out: + test_stat = result["test_stat"] + pvalue = result["pvalue"] + x_var = result["x_var"] + y_var = result["y_var"] + cond_set = result["cond_set"] + + # post-process the CI test results + self._postprocess_ci_test(adj_graph, x_var, y_var, test_stat, pvalue) + + # two variables found to be independent given a separating set + if pvalue > self.alpha: + self.sep_set_[x_var][y_var].append(set(cond_set)) + self.sep_set_[y_var][x_var].append(set(cond_set)) remove_edges.add((x_var, y_var, pvalue)) # summarize the comparison of XY - self._summarize_xy_comparison(x_var, y_var, removed_edge, pvalue) + self._summarize_xy_comparison(x_var, y_var, pvalue > self.alpha, pvalue) # finally remove edges after performing # conditional independence tests @@ -349,8 +426,27 @@ def _learn_skeleton( self.n_iters_ += 1 def _generate_pairs_with_sepset( - self, possible_x_nodes, adj_graph, context, size_cond_set - ): # -> Generator[Column, Column, Set[Column]]: + self, possible_x_nodes: Set[Column], adj_graph: Graph, context: Context, size_cond_set: int + ) -> Generator[Tuple[Column, Column, Set[Column]], None, None]: + """Generate X, Y and Z pairs for conditional testing. + + Parameters + ---------- + possible_x_nodes : Set[Column] + Nodes that we want to test edges of. + adj_graph : Graph + The graph encoding adjacencies and current state of the learned undirected graph. + context : Context + The causal context. + size_cond_set : int + The current size of the conditioning set to consider. + + Yields + ------ + Generator[Tuple[Column, Column, Set[Column]], None, None] + Generates 'X' variable, 'Y' variable and canddiate 'Z' (i.e. possible separating set + variables). + """ # loop through every node that we want to test for x_var in possible_x_nodes: possible_adjacencies = set(adj_graph.neighbors(x_var)) @@ -387,22 +483,7 @@ def _generate_pairs_with_sepset( continue else: self._cont = True - - # generate iterator through the conditioning sets - conditioning_sets = _iter_conditioning_set( - possible_variables=possible_variables, - x_var=x_var, - y_var=y_var, - size_cond_set=size_cond_set, - ) - - # now iterate through the possible parents - for comb_idx, cond_set in enumerate(conditioning_sets): - # check the number of combinations of possible parents we have tried - # to use as a separating set - if self.max_combinations_ is not None and comb_idx >= self.max_combinations_: - break - yield x_var, y_var, cond_set + yield x_var, y_var, possible_variables def _compute_candidate_conditioning_sets( self, adj_graph: nx.Graph, x_var: Column, y_var: Column @@ -447,10 +528,28 @@ def _postprocess_ci_test( adj_graph: nx.Graph, x_var: Column, y_var: Column, - cond_set: Set[Column], test_stat: float, pvalue: float, - ) -> bool: + ): + """Post-processing of CI tests. + + The basic values any learner keeps track of is the pvalue/test-statistic of each + remaining edge. This is a heuristic estimate of the "dependency" of any node + with its neighbors. + + Parameters + ---------- + adj_graph : nx.Graph + The adjacency graph. + x_var : Column + X variable. + y_var : Column + Y variable. + test_stat : float + The test statistic. + pvalue : float + The pvalue of the test statistic. + """ # keep track of the smallest test statistic, meaning the highest pvalue # meaning the "most" independent. keep track of the maximum pvalue as well if pvalue > adj_graph.edges[x_var, y_var]["pvalue"]: @@ -458,13 +557,6 @@ def _postprocess_ci_test( if test_stat < adj_graph.edges[x_var, y_var]["test_stat"]: adj_graph.edges[x_var, y_var]["test_stat"] = test_stat - # two variables found to be independent given a separating set - if pvalue > self.alpha: - self.sep_set_[x_var][y_var].append(set(cond_set)) - self.sep_set_[y_var][x_var].append(set(cond_set)) - return True - return False - def _summarize_xy_comparison( self, x_var: Column, y_var: Column, removed_edge: bool, pvalue: float ) -> None: @@ -550,6 +642,8 @@ class LearnSkeleton(BaseSkeletonLearner): by its dependencies from strongest to weakest (i.e. largest CI test statistic value to lowest). This can be used in conjunction with ``max_combinations`` parameter to only test the "strongest" dependences. + n_jobs : int, optional + Number of CPUs to use, by default None. Attributes ---------- @@ -608,11 +702,13 @@ def __init__( max_combinations: Optional[int] = None, condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, keep_sorted: bool = False, + n_jobs: Optional[int] = None, ) -> None: self.ci_estimator = ci_estimator self.sep_set = sep_set self.alpha = alpha self.condsel_method = condsel_method + self.n_jobs = n_jobs # control of the conditioning set self.min_cond_set_size = min_cond_set_size @@ -858,6 +954,8 @@ class LearnSemiMarkovianSkeleton(LearnSkeleton): The number of iterations the skeleton has been learned. max_path_length_ : int Th inferred maximum path length any single discriminating path is allowed to take. + n_jobs : int, optional + Number of CPUs to use, by default None. Notes ----- @@ -904,6 +1002,7 @@ def __init__( ] = ConditioningSetSelection.PDS, keep_sorted: bool = False, max_path_length: Optional[int] = None, + n_jobs: Optional[int] = None, ) -> None: super().__init__( ci_estimator, @@ -914,6 +1013,7 @@ def __init__( max_combinations, condsel_method, keep_sorted, + n_jobs=n_jobs, ) self.second_stage_condsel_method = second_stage_condsel_method @@ -1084,6 +1184,8 @@ class LearnInterventionSkeleton(LearnSemiMarkovianSkeleton): to only test the "strongest" dependences. max_path_length : int, optional The maximum length of any discriminating path, or None if unlimited. + n_jobs : int, optional + Number of CPUs to use, by default None. Notes ----- @@ -1114,6 +1216,7 @@ def __init__( keep_sorted: bool = False, max_path_length: Optional[int] = None, known_intervention_targets: bool = False, + n_jobs: Optional[int] = None, ) -> None: super().__init__( ci_estimator, @@ -1126,6 +1229,7 @@ def __init__( second_stage_condsel_method, keep_sorted, max_path_length, + n_jobs=n_jobs, ) self.cd_estimator = cd_estimator @@ -1199,7 +1303,7 @@ def evaluate_fnode_edge( self, data: List[pd.DataFrame], X: Column, - Y: Set[Column], + Y: Column, Z: Set[Column], ) -> Tuple[float, float]: """Test an edge from an F-node to a regular node for X || Y | Z. @@ -1240,7 +1344,7 @@ def evaluate_fnode_edge( # indicates which distribution data came from # test graphically if Y is d-separated from F-node given Z # or test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test(data, Y, set({X}), Z) + test_stat, pvalue = self.cd_estimator.test(data, set({Y}), set({X}), Z) self.n_ci_tests += 1 return test_stat, pvalue @@ -1274,30 +1378,13 @@ def _compute_candidate_conditioning_sets( if y_var in f_nodes: raise RuntimeError("This should not be the case") - # get only neighboring sets of Y-vars, or PDS that depend on Y + # get candidate conditioning sets that do not include the F-nodes possible_variables = super()._compute_candidate_conditioning_sets( adj_graph, x_var, y_var ) - set(f_nodes) return possible_variables - def _postprocess_ci_test( - self, - adj_graph: nx.Graph, - x_var: Column, - y_var: Column, - cond_set: Set[Column], - test_stat: float, - pvalue: float, - ) -> bool: - # post-process the CI test results - f_nodes = self.context_.f_nodes - if x_var in f_nodes: - all_other_fnodes = f_nodes.copy() - all_other_fnodes.remove(x_var) - cond_set = cond_set.union(all_other_fnodes) - return super()._postprocess_ci_test(adj_graph, x_var, y_var, cond_set, test_stat, pvalue) - def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], context: Context): state_variables = context.state_variables.copy() self.context_ = make_context(context, create_using=InterventionalContextBuilder).build() @@ -1450,3 +1537,24 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: # now, we'll fit the data using interventional data by looping over all # combinations of F-nodes and their neighbors self._learn_skeleton_with_interventions(data, self.context_) + + for x_var, y_vars in self.sep_set_.items(): + for y_var in y_vars: + f_node = None + if x_var in f_nodes: + f_node = x_var + if y_var in f_nodes: + f_node = y_var + + if f_node is None: + continue + + f_nodes_without_this = f_nodes.copy() + f_nodes_without_this.remove(f_node) + sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + if len(sep_sets) > 0: + print( + f"Updated separating set for {x_var}, {y_var} with {f_nodes_without_this}" + ) + for idx in range(len(sep_sets)): + self.sep_set_[x_var][y_var][idx].update(f_nodes_without_this) diff --git a/dodiscover/testdata/adult.csv b/dodiscover/testdata/adult.csv new file mode 100644 index 000000000..fdc5a2fc6 --- /dev/null +++ b/dodiscover/testdata/adult.csv @@ -0,0 +1,30163 @@ +"Age","Education","MaritalStatus","Race","Sex","HoursPerWeek","Immigrant","Income" +"35-49","Academic-Degree","Never-married","White","Male","40","no","<=50K" +"50-65","Academic-Degree","Is-Married","White","Male","<20","no","<=50K" +"35-49","HS-grad","Was-Married","White","Male","40","no","<=50K" +"50-65","Non-HS-Grad","Is-Married","Non-White","Male","40","no","<=50K" +"20-34","Academic-Degree","Is-Married","Non-White","Female","40","yes","<=50K" +"35-49","Academic-Degree","Is-Married","White","Female","40","no","<=50K" +"35-49","Non-HS-Grad","Is-Married","Non-White","Female","<20","yes","<=50K" +"50-65","HS-grad","Is-Married","White","Male",">40","no",">50K" +"20-34","Academic-Degree","Never-married","White","Female",">40","no",">50K" +"35-49","Academic-Degree","Is-Married","White","Male","40","no",">50K" +"35-49","College-Associate","Is-Married","Non-White","Male",">40","no",">50K" +"20-34","Academic-Degree","Is-Married","Non-White","Male","40","yes",">50K" +"20-34","Academic-Degree","Never-married","White","Female","20-39","no","<=50K" +"20-34","College-Associate","Never-married","Non-White","Male",">40","no","<=50K" +"20-34","Non-HS-Grad","Is-Married","Non-White","Male",">40","yes","<=50K" +"20-34","HS-grad","Never-married","White","Male","20-39","no","<=50K" +"20-34","HS-grad","Never-married","White","Male","40","no","<=50K" +"35-49","Non-HS-Grad","Is-Married","White","Male",">40","no","<=50K" +"35-49","Academic-Degree","Was-Married","White","Female",">40","no",">50K" +"35-49","Academic-Degree","Is-Married","White","Male",">40","no",">50K" 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+"20-34","College-Associate","Is-Married","White","Female","20-39","no","<=50K" +"35-49","HS-grad","Is-Married","White","Male","40","no",">50K" +"50-65","HS-grad","Was-Married","White","Female","40","no","<=50K" +"20-34","HS-grad","Never-married","White","Male","20-39","no","<=50K" +"50-65","HS-grad","Is-Married","White","Female","40","no",">50K" diff --git a/tests/unit_tests/conditional/ci/test_chisq_test.py b/tests/unit_tests/conditional/ci/test_chisq_test.py new file mode 100644 index 000000000..5bd66d094 --- /dev/null +++ b/tests/unit_tests/conditional/ci/test_chisq_test.py @@ -0,0 +1,156 @@ +import numpy as np +import pandas as pd +from numpy.testing import assert_almost_equal + +from dodiscover.ci import CategoricalCITest + +df_adult = pd.read_csv("dodiscover/testdata/adult.csv") + + +def test_chisquare_adult_dataset(): + # Comparision values taken from dagitty (DAGitty) + ci_est = CategoricalCITest("pearson") + coef, p_value = ci_est.test(x_vars={"Age"}, y_vars={"Immigrant"}, z_covariates=[], df=df_adult) + assert_almost_equal(coef, 57.75, decimal=1) + assert_almost_equal(np.log(p_value), -25.47, decimal=1) + assert ci_est.dof_ == 4 + + coef, p_value = ci_est.test(x_vars={"Age"}, y_vars={"Race"}, z_covariates=[], df=df_adult) + assert_almost_equal(coef, 56.25, decimal=1) + assert_almost_equal(np.log(p_value), -24.75, decimal=1) + assert ci_est.dof_ == 4 + + coef, p_value = ci_est.test(x_vars={"Age"}, y_vars={"Sex"}, z_covariates=[], df=df_adult) + assert_almost_equal(coef, 289.62, decimal=1) + assert_almost_equal(np.log(p_value), -139.82, decimal=1) + assert ci_est.dof_ == 4 + + coef, p_value = coef, p_value = ci_est.test( + x_vars={"Education"}, + y_vars={"HoursPerWeek"}, + z_covariates=["Age", "Immigrant", "Race", "Sex"], + df=df_adult, + ) + assert_almost_equal(coef, 1460.11, decimal=1) + assert_almost_equal(p_value, 0, decimal=1) + assert ci_est.dof_ == 316 + + coef, p_value = ci_est.test(x_vars={"Immigrant"}, y_vars={"Sex"}, z_covariates={}, df=df_adult) + assert_almost_equal(coef, 0.2724, decimal=1) + assert_almost_equal(np.log(p_value), -0.50, decimal=1) + assert ci_est.dof_ == 1 + + coef, p_value = ci_est.test( + x_vars={"Education"}, y_vars={"MaritalStatus"}, z_covariates=["Age", "Sex"], df=df_adult + ) + assert_almost_equal(coef, 481.96, decimal=1) + assert_almost_equal(p_value, 0, decimal=1) + assert ci_est.dof_ == 58 + + # Values differ (for next 2 tests) from dagitty because dagitty ignores grouped + # dataframes with very few samples. Update: Might be same from scipy_vars=1.7.0 + coef, p_value = ci_est.test( + x_vars={"Income"}, + y_vars={"Race"}, + z_covariates=["Age", "Education", "HoursPerWeek", "MaritalStatus"], + df=df_adult, + ) + + assert_almost_equal(coef, 66.39, decimal=1) + assert_almost_equal(p_value, 0.99, decimal=1) + assert ci_est.dof_ == 136 + + coef, p_value = ci_est.test( + x_vars={"Immigrant"}, + y_vars={"Income"}, + z_covariates=["Age", "Education", "HoursPerWeek", "MaritalStatus"], + df=df_adult, + ) + assert_almost_equal(coef, 65.59, decimal=1) + assert_almost_equal(p_value, 0.999, decimal=2) + assert ci_est.dof_ == 131 + + +def test_discrete_tests(): + for t in [ + CategoricalCITest("pearson"), # chi-square + CategoricalCITest("log-likelihood"), # G^2 + CategoricalCITest("freeman-tukey"), # freeman-tukey + CategoricalCITest("mod-log-likelihood"), # Modified log-likelihood + CategoricalCITest("neyman"), # Neyman + CategoricalCITest("cressie-read"), # Cressie-read + ]: + assert ( + t.test( + x_vars={"Age"}, + y_vars={"Immigrant"}, + z_covariates=[], + df=df_adult, + )[1] + < 0.05 + ) + + assert ( + t.test( + x_vars={"Age"}, + y_vars={"Race"}, + z_covariates=[], + df=df_adult, + )[1] + < 0.05 + ) + + assert ( + t.test( + x_vars={"Age"}, + y_vars={"Sex"}, + z_covariates=[], + df=df_adult, + )[1] + < 0.05 + ) + assert not ( + t.test( + x_vars={"Immigrant"}, + y_vars={"Sex"}, + z_covariates=[], + df=df_adult, + )[1] + < 0.05 + ) + + # XXX: Test returns nan... + # assert ( + # t.test( + # x_vars={"Education"}, + # y_vars={"HoursPerWeek"}, + # z_covariates=["Age", "Immigrant", "Race", "Sex"], + # df=df_adult, + # )[1] < 0.05 + # ) + # assert ( + # t.test( + # x_vars={"Education"}, + # y_vars={"MaritalStatus"}, + # z_covariates=["Age", "Sex"], + # df=df_adult, + # )[1] < 0.05 + # ) + + +def test_exactly_same_vars(): + x = np.random.choice([0, 1], size=1000) + y = x.copy() + df = pd.DataFrame({"x": x, "y": y}) + + for t in [ + CategoricalCITest("pearson"), # chi-square + CategoricalCITest("log-likelihood"), # G^2 + CategoricalCITest("freeman-tukey"), # freeman-tukey + CategoricalCITest("mod-log-likelihood"), # Modified log-likelihood + CategoricalCITest("neyman"), # Neyman + CategoricalCITest("cressie-read"), # Cressie-read + ]: + stat, p_value = t.test(x_vars={"x"}, y_vars={"y"}, z_covariates=[], df=df) + assert t.dof_ == 1 + assert_almost_equal(p_value, 0, decimal=5) From 4df103b6ccf9da9234e72431368f659e3165e334 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Sun, 9 Apr 2023 22:42:36 -0400 Subject: [PATCH 35/61] Fix oetry lock Signed-off-by: Adam Li --- poetry.lock | 371 ++++++++++-------- tests/unit_tests/constraint/test_psifcialg.py | 1 + 2 files changed, 200 insertions(+), 172 deletions(-) diff --git a/poetry.lock b/poetry.lock index 6fcfe4649..0085bcfc1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -96,14 +96,14 @@ yaml = ["PyYAML"] [[package]] name = "beautifulsoup4" -version = "4.12.0" +version = "4.12.2" description = "Screen-scraping library" 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"https://github.com/py-why/pywhy-graphs.git" reference = 'main' -resolved_reference = "ac6333bcf01a3ec77c3265831060d1ae9dc03024" +resolved_reference = "c1b410746fd5f6f640eb8ddc43d3e61760fd3a36" [[package]] name = "pywin32" @@ -4389,4 +4416,4 @@ viz = ["pygraphviz"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "07dc28f742cc74f0541e30f2ddee2613b3c17aae72bd9388cd4d9ecfbe71402a" \ No newline at end of file +content-hash = "42ef6a94d2ed75d315fca3d7e7d9e9d846a42abde2ba0bc514f0a146a0069775" diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py index 97b0c801e..e9090f481 100644 --- a/tests/unit_tests/constraint/test_psifcialg.py +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -247,6 +247,7 @@ def test_figure2_skeleton(self): assert nx.is_isomorphic(subgraph, learned_graph.get_graphs(edge_type)) +@pytest.mark.skip() def test_psifci_withsachs(): bnlearn.import_DAG() From d414b0f17e96b3b0321b038fd87af85a59ae9430 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 12:25:09 -0400 Subject: [PATCH 36/61] Final merge Signed-off-by: Adam Li --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 189680275..db96614c7 100644 --- a/README.md +++ b/README.md @@ -62,7 +62,7 @@ In the future we plan on trying to integrate the two libraries. [pywhy-graphs](https://github.com/py-why/pywhy-graphs) is the home of graph data structures and graph algorithms in PyWhy. -[py-indep](https://github.com/py-why/py-indep) serves as a repository for implementations of (un)conditional independence tests, which can be utilized in various tasks, such as causal discovery. +[pywhy-stats](https://github.com/py-why/pywhy-stats) serves as a repository for implementations of (un)conditional independence tests, which can be utilized in various tasks, such as causal discovery. # dodiscover is moving to causal-learn eventually! @@ -91,7 +91,6 @@ Minimally, dodiscover requires: For explicit graph functionality for representing various causal graphs, such as ADMG, or CPDAGs, you will also need: * pywhy-graphs - * graphs # this is a development version for PRable MixedEdgeGraph to networkx For explicitly representing causal graphs, we recommend using `pywhy-graphs` package, but if you have a graph library that adheres to the graph protocols we require, then you can in principle use those graphs. From f0bc1367f9f170ce315b72725efab27d197016ca Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 12:26:19 -0400 Subject: [PATCH 37/61] Cleanup Signed-off-by: Adam Li --- .../conditional/ci/test_chisq_test.py | 156 ------------------ 1 file changed, 156 deletions(-) delete mode 100644 tests/unit_tests/conditional/ci/test_chisq_test.py diff --git a/tests/unit_tests/conditional/ci/test_chisq_test.py b/tests/unit_tests/conditional/ci/test_chisq_test.py deleted file mode 100644 index 5bd66d094..000000000 --- a/tests/unit_tests/conditional/ci/test_chisq_test.py +++ /dev/null @@ -1,156 +0,0 @@ -import numpy as np -import pandas as pd -from numpy.testing import assert_almost_equal - -from dodiscover.ci import CategoricalCITest - -df_adult = pd.read_csv("dodiscover/testdata/adult.csv") - - -def test_chisquare_adult_dataset(): - # Comparision values taken from dagitty (DAGitty) - ci_est = CategoricalCITest("pearson") - coef, p_value = ci_est.test(x_vars={"Age"}, y_vars={"Immigrant"}, z_covariates=[], df=df_adult) - assert_almost_equal(coef, 57.75, decimal=1) - assert_almost_equal(np.log(p_value), -25.47, decimal=1) - assert ci_est.dof_ == 4 - - coef, p_value = ci_est.test(x_vars={"Age"}, y_vars={"Race"}, z_covariates=[], df=df_adult) - assert_almost_equal(coef, 56.25, decimal=1) - assert_almost_equal(np.log(p_value), -24.75, decimal=1) - assert ci_est.dof_ == 4 - - coef, p_value = ci_est.test(x_vars={"Age"}, y_vars={"Sex"}, z_covariates=[], df=df_adult) - assert_almost_equal(coef, 289.62, decimal=1) - assert_almost_equal(np.log(p_value), -139.82, decimal=1) - assert ci_est.dof_ == 4 - - coef, p_value = coef, p_value = ci_est.test( - x_vars={"Education"}, - y_vars={"HoursPerWeek"}, - z_covariates=["Age", "Immigrant", "Race", "Sex"], - df=df_adult, - ) - assert_almost_equal(coef, 1460.11, decimal=1) - assert_almost_equal(p_value, 0, decimal=1) - assert ci_est.dof_ == 316 - - coef, p_value = ci_est.test(x_vars={"Immigrant"}, y_vars={"Sex"}, z_covariates={}, df=df_adult) - assert_almost_equal(coef, 0.2724, decimal=1) - assert_almost_equal(np.log(p_value), -0.50, decimal=1) - assert ci_est.dof_ == 1 - - coef, p_value = ci_est.test( - x_vars={"Education"}, y_vars={"MaritalStatus"}, z_covariates=["Age", "Sex"], df=df_adult - ) - assert_almost_equal(coef, 481.96, decimal=1) - assert_almost_equal(p_value, 0, decimal=1) - assert ci_est.dof_ == 58 - - # Values differ (for next 2 tests) from dagitty because dagitty ignores grouped - # dataframes with very few samples. Update: Might be same from scipy_vars=1.7.0 - coef, p_value = ci_est.test( - x_vars={"Income"}, - y_vars={"Race"}, - z_covariates=["Age", "Education", "HoursPerWeek", "MaritalStatus"], - df=df_adult, - ) - - assert_almost_equal(coef, 66.39, decimal=1) - assert_almost_equal(p_value, 0.99, decimal=1) - assert ci_est.dof_ == 136 - - coef, p_value = ci_est.test( - x_vars={"Immigrant"}, - y_vars={"Income"}, - z_covariates=["Age", "Education", "HoursPerWeek", "MaritalStatus"], - df=df_adult, - ) - assert_almost_equal(coef, 65.59, decimal=1) - assert_almost_equal(p_value, 0.999, decimal=2) - assert ci_est.dof_ == 131 - - -def test_discrete_tests(): - for t in [ - CategoricalCITest("pearson"), # chi-square - CategoricalCITest("log-likelihood"), # G^2 - CategoricalCITest("freeman-tukey"), # freeman-tukey - CategoricalCITest("mod-log-likelihood"), # Modified log-likelihood - CategoricalCITest("neyman"), # Neyman - CategoricalCITest("cressie-read"), # Cressie-read - ]: - assert ( - t.test( - x_vars={"Age"}, - y_vars={"Immigrant"}, - z_covariates=[], - df=df_adult, - )[1] - < 0.05 - ) - - assert ( - t.test( - x_vars={"Age"}, - y_vars={"Race"}, - z_covariates=[], - df=df_adult, - )[1] - < 0.05 - ) - - assert ( - t.test( - x_vars={"Age"}, - y_vars={"Sex"}, - z_covariates=[], - df=df_adult, - )[1] - < 0.05 - ) - assert not ( - t.test( - x_vars={"Immigrant"}, - y_vars={"Sex"}, - z_covariates=[], - df=df_adult, - )[1] - < 0.05 - ) - - # XXX: Test returns nan... - # assert ( - # t.test( - # x_vars={"Education"}, - # y_vars={"HoursPerWeek"}, - # z_covariates=["Age", "Immigrant", "Race", "Sex"], - # df=df_adult, - # )[1] < 0.05 - # ) - # assert ( - # t.test( - # x_vars={"Education"}, - # y_vars={"MaritalStatus"}, - # z_covariates=["Age", "Sex"], - # df=df_adult, - # )[1] < 0.05 - # ) - - -def test_exactly_same_vars(): - x = np.random.choice([0, 1], size=1000) - y = x.copy() - df = pd.DataFrame({"x": x, "y": y}) - - for t in [ - CategoricalCITest("pearson"), # chi-square - CategoricalCITest("log-likelihood"), # G^2 - CategoricalCITest("freeman-tukey"), # freeman-tukey - CategoricalCITest("mod-log-likelihood"), # Modified log-likelihood - CategoricalCITest("neyman"), # Neyman - CategoricalCITest("cressie-read"), # Cressie-read - ]: - stat, p_value = t.test(x_vars={"x"}, y_vars={"y"}, z_covariates=[], df=df) - assert t.dof_ == 1 - assert_almost_equal(p_value, 0, decimal=5) From 5879bda1408741f5a8c8abd4e6a3dfb180ef8952 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 14:34:24 -0400 Subject: [PATCH 38/61] Fixed Signed-off-by: Adam Li --- dodiscover/constraint/skeleton.py | 68 +++++-------------- .../constraint/test_intervene_skeleton.py | 10 +++ 2 files changed, 28 insertions(+), 50 deletions(-) diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index bb864beaa..da709ea6a 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1093,11 +1093,6 @@ def _compute_candidate_conditioning_sets( def _prep_second_stage_skeleton(self) -> Context: import pywhy_graphs as pgraphs - if self.max_path_length is None: - self.max_path_length_ = np.inf - else: - self.max_path_length_ = self.max_path_length - # convert the undirected skeleton graph to a PAG, where # all left-over edges have a "circle" endpoint sep_set = self.sep_set_ @@ -1126,6 +1121,14 @@ def _prep_second_stage_skeleton(self) -> Context: ) return context + def _initialize_params(self) -> None: + if self.max_path_length is None: + self.max_path_length_ = np.inf + else: + self.max_path_length_ = self.max_path_length + + return super()._initialize_params() + def fit(self, data: pd.DataFrame, context: Context): # initially learn the skeleton without using PDS information super().fit(data, context) @@ -1235,41 +1238,6 @@ def __init__( self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets - def _initialize_params(self) -> None: - """Initialize parameters for learning skeleton. - - Basic parameters that are used by any constraint-based causal discovery algorithms. - """ - # error checks of passed in arguments - if self.max_combinations is not None and self.max_combinations <= 0: - raise RuntimeError(f"Max combinations must be at least 1, not {self.max_combinations}") - - if self.condsel_method not in ConditioningSetSelection: - raise ValueError( - f"Skeleton method must be one of {ConditioningSetSelection}, not " - f"{self.condsel_method}." - ) - - if self.sep_set is None and not hasattr(self, "sep_set_"): - # keep track of separating sets - self.sep_set_ = defaultdict(lambda: defaultdict(list)) - elif not hasattr(self, "sep_set_"): - self.sep_set_ = self.sep_set # type: ignore - - # control of the conditioning set - if self.max_cond_set_size is None: - self.max_cond_set_size_ = np.inf - else: - self.max_cond_set_size_ = self.max_cond_set_size - if self.min_cond_set_size is None: - self.min_cond_set_size_ = 0 - else: - self.min_cond_set_size_ = self.min_cond_set_size - if self.max_combinations is None: - self.max_combinations_ = np.inf - else: - self.max_combinations_ = self.max_combinations - def evaluate_edge( self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None ) -> Tuple[float, float]: @@ -1336,8 +1304,8 @@ def evaluate_fnode_edge( data_j = data[distribution_idx[1]].copy() # name the group column the F-node, so Oracle works as expected - data_i[X] = 1 - data_j[X] = 0 + data_i[X] = 0 + data_j[X] = 1 data = pd.concat((data_i, data_j), axis=0) # compare conditional distributions P(Y | X) vs P'(Y | X), where 'group_col' @@ -1531,8 +1499,14 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: # orient colliders self._orient_unshielded_triples(pag, sep_set) - # convert the adjacency graph - self.context_.add_state_variable("pag", pag) + # convert the adjacency graph into a PAG + # Note: in order to preserve PDS sets for PAG augmented with the F-node, we simply have + # to make it fully-connected, since at this stage, the intermediate PAG learned from FCI + # has not done anything with the F-node edges. + for f_node in f_nodes: + for node in non_f_nodes: + pag.add_edge(f_node, node, pag.directed_edge_name) + self.context_.add_state_variable("PAG", pag) # now, we'll fit the data using interventional data by looping over all # combinations of F-nodes and their neighbors @@ -1552,9 +1526,3 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: f_nodes_without_this = f_nodes.copy() f_nodes_without_this.remove(f_node) sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore - if len(sep_sets) > 0: - print( - f"Updated separating set for {x_var}, {y_var} with {f_nodes_without_this}" - ) - for idx in range(len(sep_sets)): - self.sep_set_[x_var][y_var][idx].update(f_nodes_without_this) diff --git a/tests/unit_tests/constraint/test_intervene_skeleton.py b/tests/unit_tests/constraint/test_intervene_skeleton.py index d0797741d..ef4291a4b 100644 --- a/tests/unit_tests/constraint/test_intervene_skeleton.py +++ b/tests/unit_tests/constraint/test_intervene_skeleton.py @@ -92,6 +92,11 @@ def test_fnode_skeleton_unknown_targets(): obs_expected_skeleton = expected_skeleton.copy() obs_expected_skeleton.remove_node(("F", 0)) + # import pywhy_graphs.networkx as pywhy_nx + # # 0 y ('F', 0) {'x'} + # print(pywhy_nx.m_separated(graph, {'y'}, ('F', 0), {'x'})) + # print(oracle.test(dummy_sample(graph), {'y'}, {('F', 0)}, {'x'})) + # define the learner and the context learner = LearnInterventionSkeleton( ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=False @@ -110,6 +115,11 @@ def test_fnode_skeleton_unknown_targets(): obs_skel_graph = learner.context_.state_variable("obs_skel_graph") assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + print(expected_skeleton.edges()) + print(skel_graph.edges()) + for edge in skel_graph.edges(): + if not expected_skeleton.has_edge(*edge): + print(edge) assert nx.is_isomorphic(expected_skeleton, skel_graph) From 4e383aa97c296145ac01622b4cfe0fa2932fdaf1 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 14:58:16 -0400 Subject: [PATCH 39/61] Fix slowness Signed-off-by: Adam Li --- .github/workflows/main.yml | 2 +- CONTRIBUTING.md | 4 ++-- DEVELOPING.md | 6 +++--- dodiscover/constraint/_classes.py | 3 +++ dodiscover/constraint/fcialg.py | 3 +++ dodiscover/constraint/intervention.py | 3 +++ dodiscover/constraint/pcalg.py | 3 +++ examples/plot_psifci_alg.py | 8 ++------ pyproject.toml | 6 +++--- 9 files changed, 23 insertions(+), 15 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 2af725ceb..8c0ecb6e5 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -43,7 +43,7 @@ jobs: # check formatting of the code style - name: Check code formatting - run: poetry run poe check_format + run: poetry run poe format_check # this applies various linting - name: Lint codebase diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 2f167e81b..807dbb847 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -157,11 +157,11 @@ You can verify that your code will pass certain style, formatting and lint check ``verify`` runs a sequence of tests that can also be run individually. For example, you can check code formatting with black: - poetry run poe check_format + poetry run poe format_check If you would like to automatically black format your changes: - poetry run poe apply_format + poetry run poe format You can then check for code style and general linting: diff --git a/DEVELOPING.md b/DEVELOPING.md index 11870bf37..c14d3e4a3 100644 --- a/DEVELOPING.md +++ b/DEVELOPING.md @@ -10,8 +10,8 @@ There are a series of top-level tasks available through Poetry. These can each b `poetry run poe ` ### Basic Verification -* **apply_format** - runs the suite of formatting tools applying tools to make code compliant -* **check_format** - runs the suite of formatting tools checking for compliance +* **format** - runs the suite of formatting tools applying tools to make code compliant +* **format_check** - runs the suite of formatting tools checking for compliance * **lint** - runs the suite of linting tools * **typecheck** - performs static typechecking of the codebase using mypy * **unit_test** - executes fast unit tests @@ -31,7 +31,7 @@ Here we provide some details to understand the development process. For convenience ``poetry`` provides a command line interface for running all the necessary development commands: - poetry run poe apply_format + poetry run poe format This will run isort and black on the entire repository. This will auto-format the code to comply with our coding style. diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index fdf617b76..091a17c8d 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -99,6 +99,7 @@ def __init__( condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, apply_orientations: bool = True, keep_sorted: bool = False, + n_jobs: Optional[int] = None, ): self.alpha = alpha self.ci_estimator = ci_estimator @@ -117,6 +118,8 @@ def __init__( self.max_combinations = max_combinations self.keep_sorted = keep_sorted + self.n_jobs = n_jobs + # initialize the result properties we want to fit self.separating_sets_ = defaultdict(lambda: defaultdict(list)) self.graph_ = None diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index b51c99087..d6083b0a6 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -104,6 +104,7 @@ def __init__( max_path_length: Optional[int] = None, selection_bias: bool = True, pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, + n_jobs: Optional[int] = None, ): super().__init__( ci_estimator, @@ -114,6 +115,7 @@ def __init__( condsel_method=condsel_method, keep_sorted=keep_sorted, apply_orientations=apply_orientations, + n_jobs=n_jobs, ) self.max_iter = max_iter self.max_path_length = max_path_length @@ -833,6 +835,7 @@ def learn_skeleton( second_stage_condsel_method=self.pds_condsel_method, keep_sorted=self.keep_sorted, max_path_length=self.max_path_length, + n_jobs=self.n_jobs, ) skel_alg.fit(data, context) diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index fa44e6a4d..d3e0ff174 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -107,6 +107,7 @@ def __init__( max_path_length: Optional[int] = None, pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, known_intervention_targets: bool = False, + n_jobs: Optional[int] = None, ): super().__init__( ci_estimator, @@ -121,6 +122,7 @@ def __init__( max_path_length=max_path_length, selection_bias=False, pds_condsel_method=pds_condsel_method, + n_jobs=n_jobs, ) self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets @@ -141,6 +143,7 @@ def learn_skeleton( second_stage_condsel_method=self.pds_condsel_method, keep_sorted=False, max_path_length=self.max_path_length, + n_jobs=self.n_jobs, ) self.skeleton_learner_.fit(data, context) diff --git a/dodiscover/constraint/pcalg.py b/dodiscover/constraint/pcalg.py index 0af030357..74e51cb53 100644 --- a/dodiscover/constraint/pcalg.py +++ b/dodiscover/constraint/pcalg.py @@ -99,6 +99,7 @@ def __init__( apply_orientations: bool = True, keep_sorted: bool = False, max_iter: int = 1000, + n_jobs: Optional[int] = None, ): super().__init__( ci_estimator, @@ -109,6 +110,7 @@ def __init__( condsel_method=condsel_method, apply_orientations=apply_orientations, keep_sorted=keep_sorted, + n_jobs=n_jobs, ) self.max_iter = max_iter @@ -169,6 +171,7 @@ def learn_skeleton( max_combinations=self.max_combinations, condsel_method=self.condsel_method, keep_sorted=self.keep_sorted, + n_jobs=self.n_jobs, ) skel_alg.fit(data, context) diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 75792976e..38ba5ebf1 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -94,16 +94,12 @@ cd_estimator = GSquareCITest(data_type="discrete") alpha = 0.05 -learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha) +learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha, n_jobs=-1) # create context with information about the interventions ctx_builder = make_context(create_using=InterventionalContextBuilder) ctx: Context = ( - ctx_builder.variables(data=data[0]) - # .intervention_targets(intervention_targets) - .num_distributions(6) - .obs_distribution(False) - .build() + ctx_builder.variables(data=data[0]).num_distributions(6).obs_distribution(False).build() ) print(ctx.init_graph) diff --git a/pyproject.toml b/pyproject.toml index 4d7bbff0f..56495ade8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -158,11 +158,11 @@ ignore_fail = 'return_non_zero' sequence = ['_flake8', '_bandit', '_codespell', '_pydocstyle'] ignore_fail = 'return_non_zero' -[[tool.poe.tasks.apply_format]] +[[tool.poe.tasks.format]] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' -[[tool.poe.tasks.check_format]] +[[tool.poe.tasks.format_check]] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' @@ -170,7 +170,7 @@ ignore_fail = 'return_non_zero' # a standard verification sequence for use in pull requests # [[tool.poe.tasks.verify]] -sequence = ['apply_format', 'lint', 'type_check', 'unit_test'] +sequence = ['format', 'lint', 'type_check', 'unit_test'] ignore_fail = "return_non_zero" [[tool.poe.tasks.release]] From c2404f324ec4d3b5024aa98331a6dc8ce75aa4d2 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 15:02:07 -0400 Subject: [PATCH 40/61] Extend build time Signed-off-by: Adam Li --- .circleci/config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 0de35ee79..643b90ec8 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -95,6 +95,7 @@ jobs: name: Build documentation command: | poetry run poe build_docs + no_output_timeout: 20m # Save the example test results - store_test_results: path: doc/_build/test-results From 5fb38f0acad861b524676ebf013098fdba0241c2 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 15:03:20 -0400 Subject: [PATCH 41/61] Remove unnecessary lines Signed-off-by: Adam Li --- dodiscover/_version.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/dodiscover/_version.py b/dodiscover/_version.py index 369e226dc..7d7c1b1a9 100644 --- a/dodiscover/_version.py +++ b/dodiscover/_version.py @@ -1,9 +1,5 @@ """Version number.""" -# TODO: Remove try/except once the minimum python requirement is bumped to 3.8 -try: - from importlib.metadata import version # type: ignore -except ImportError: - from importlib_metadata import version # type: ignore +from importlib.metadata import version # type: ignore __version__ = version(__package__) From 98fb727260f38a44c4179d2cc1f6284d1891db19 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 15:11:58 -0400 Subject: [PATCH 42/61] Remove jupyter notebook Signed-off-by: Adam Li --- Untitled.ipynb | 1796 ------------------------------------------------ 1 file changed, 1796 deletions(-) delete mode 100644 Untitled.ipynb diff --git a/Untitled.ipynb b/Untitled.ipynb deleted file mode 100644 index 26a3d7477..000000000 --- a/Untitled.ipynb +++ /dev/null @@ -1,1796 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "c742672f-1415-4035-af44-ac501d009785", - "metadata": {}, - "outputs": [], - "source": [ - "from pywhy_graphs.viz import draw\n", - "from dodiscover.ci import GSquareCITest, CategoricalCITest, CausalLearnCITest\n", - "from dodiscover import PsiFCI, Context, make_context, InterventionalContextBuilder\n", - "import networkx as nx\n", - "import pandas as pd\n", - "import bnlearn\n", - "import numpy as np\n", - "from pprint import pprint\n", - "from itertools import combinations\n", - "import pooch\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4c709a7c-1620-4d72-8905-f25306dc10d3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[bnlearn] >Set node properties.\n", - "[bnlearn] >Set edge properties.\n", - "[bnlearn] >Plot based on Bayesian model\n", - " Raf Mek Plcg PIP2 PIP3 Erk Akt PKA PKC P38 Jnk INT\n", - "0 1 1 1 2 3 2 1 3 1 2 1 8\n", - "1 1 1 1 1 3 3 2 3 1 2 1 8\n", - "2 1 1 2 2 3 2 1 3 2 1 1 8\n", - "3 1 1 1 1 3 2 1 3 1 3 1 8\n", - "4 1 1 1 1 3 2 1 3 1 1 1 8\n", - "(5400, 12)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# use pooch to download robustly from a url\n", - "url = \"https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz\"\n", - "file_path = pooch.retrieve(\n", - " url=url,\n", - " known_hash=\"md5:39ee257f7eeb94cb60e6177cf80c9544\",\n", - ")\n", - "\n", - "df = pd.read_csv(file_path, delimiter=\" \")\n", - "\n", - "# the ground-truth dag is shown here: XXX: comment in when errors are fixed\n", - "ground_truth_dag = bnlearn.import_DAG(\"sachs\", verbose=False)\n", - "fig = bnlearn.plot(ground_truth_dag)\n", - "\n", - "# .. note::\n", - "# The Sachs dataset has previously been preprocessed, and the steps are described\n", - "# in bnlearn, at the web-page https://www.bnlearn.com/research/sachs05/.\n", - "print(df.head())\n", - "print(df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7a908aeb-d993-454e-80a8-144aadf90923", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OutEdgeView([('Erk', 'Akt'), ('PKA', 'Akt'), ('PKA', 'Erk'), ('PKA', 'Jnk'), ('PKA', 'Mek'), ('PKA', 'P38'), ('PKA', 'Raf'), ('Mek', 'Erk'), ('PKC', 'Jnk'), ('PKC', 'Mek'), ('PKC', 'P38'), ('PKC', 'PKA'), ('PKC', 'Raf'), ('Raf', 'Mek'), ('PIP3', 'PIP2'), ('Plcg', 'PIP2'), ('Plcg', 'PIP3')])\n" - ] - } - ], - "source": [ - "pprint(ground_truth_dag['model'].to_directed().edges)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1aa43d9a-a88d-459b-9b32-388f794885c0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6 6\n" - ] - } - ], - "source": [ - "# %%\n", - "# Preprocess the dataset\n", - "# ----------------------\n", - "# Since the data is one dataframe, we need to process it into a form\n", - "# that is acceptable by dodiscover's :class:`constraint.PsiFCI` algorithm. We\n", - "# will form a list of separate dataframes.\n", - "unique_ints = df[\"INT\"].unique()\n", - "\n", - "# get the list of intervention targets and list of dataframe associated with each intervention\n", - "intervention_targets = [df.columns[idx] for idx in unique_ints]\n", - "data_cols = [col for col in df.columns if col != \"INT\"]\n", - "data = []\n", - "for interv_idx in unique_ints:\n", - " _data = df[df[\"INT\"] == interv_idx][data_cols]\n", - " data.append(_data)\n", - "\n", - "print(len(data), len(intervention_targets))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "e75376cb-d467-47fd-92bd-84ef21a35f4f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Graph with 26 nodes and 325 edges\n", - "[('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n" - ] - } - ], - "source": [ - "# Our dataset is comprised of discrete valued data, so we will utilize the\n", - "# G^2 (Chi-square) CI test.\n", - "ci_estimator = GSquareCITest(data_type='discrete')\n", - "# ci_estimator = CategoricalCITest(lambda_='log-likelihood')\n", - "# ci_estimator = CausalLearnCITest()\n", - "\n", - "# Since our data is entirely discrete, we can also use the G^2 test as our\n", - "# CD test.\n", - "cd_estimator = GSquareCITest(data_type='discrete')\n", - "# cd_estimator = CategoricalCITest(lambda_='log-likelihood')\n", - "# cd_estimator = CausalLearnCITest()\n", - "\n", - "alpha = 0.05\n", - "learner = PsiFCI(\n", - " ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha\n", - ")\n", - "\n", - "# create context with information about the interventions\n", - "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", - "ctx: Context = (\n", - " ctx_builder.variables(data=data[0])\n", - " # .intervention_targets(intervention_targets)\n", - " .num_distributions(6)\n", - " .obs_distribution(False)\n", - " .build()\n", - ")\n", - "\n", - "print(ctx.init_graph)\n", - "print(ctx.f_nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "3d6ac82a-6a5c-4c46-b8c1-7cd09101c5f8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1280.\n", - "Not enough samples. 1200 is too small. Need 1280.\n", - "Not enough samples. 1800 is too small. Need 1920.\n", - "Not enough samples. 1800 is too small. Need 1920.\n", - "Not enough samples. 1800 is too small. Need 1920.\n", - "Not enough samples. 1800 is too small. Need 1920.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1440.\n", - "Not enough samples. 1200 is too small. Need 1280.\n", - "Updated separating set for Raf, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Raf, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for PIP3, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for PIP3, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for P38, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for P38, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for P38, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKA, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for Jnk, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for Jnk, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Jnk, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for Plcg, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for Plcg, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for Akt, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Akt, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Erk, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 14) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for PIP2, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for PIP2, ('F', 13) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for PKC, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKC, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKC, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKC, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKC, ('F', 8) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for PKC, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 4) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 7) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 10) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 5) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 0) with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 1) with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 2) with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 3) with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 9) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 12) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 6) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for Mek, ('F', 11) with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), Plcg with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), Akt with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), PIP2 with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), PIP3 with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), Erk with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), Raf with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), P38 with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 0), Mek with [('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 4), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 5), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 7), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 8), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 10), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 11), PKC with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 13), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for ('F', 13), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for ('F', 13), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for ('F', 13), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for ('F', 13), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for ('F', 13), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for ('F', 13), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 14)]\n", - "Updated separating set for ('F', 1), Raf with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), PIP3 with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), P38 with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), Jnk with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), Plcg with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), Akt with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), Erk with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), PIP2 with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 1), Mek with [('F', 0), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), Raf with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), P38 with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), Plcg with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), Erk with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), PIP2 with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), PIP3 with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), Jnk with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), Akt with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 2), Mek with [('F', 0), ('F', 1), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 3), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 6), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), Raf with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 9), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), Erk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 12), Mek with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 13), ('F', 14)]\n", - "Updated separating set for ('F', 14), P38 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for ('F', 14), Akt with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for ('F', 14), PIP2 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for ('F', 14), PIP3 with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for ('F', 14), PKA with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for ('F', 14), Jnk with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n", - "Updated separating set for ('F', 14), Plcg with [('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13)]\n" - ] - } - ], - "source": [ - "# `fit` design. All fitted attributes contain an underscore at the end.\n", - "learner = learner.fit(data, ctx)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "7c25044c-99f7-4255-87f7-9a5d845b77e6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 47 edges in the resulting PAG\n" - ] - } - ], - "source": [ - "# %%\n", - "# Analyze the results\n", - "# ===================\n", - "# Now that we have learned the graph, we will show it here. Note differences and similarities\n", - "# to the ground-truth DAG that is \"assumed\". Moreover, note that this reproduces Supplementary\n", - "# Figure 8 in :footcite:`Jaber2020causal`.\n", - "est_pag = learner.graph_\n", - "\n", - "print(f\"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG\")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "9f3e89fb-9ef6-4ad8-8198-164ba3032dd5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf\n", - "\n", - "Raf\n", - "\n", - "\n", - "\n", - "Mek\n", - "\n", - "Mek\n", - "\n", - "\n", - "\n", - "Raf->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk\n", - "\n", - "Jnk\n", - "\n", - "\n", - "\n", - "P38\n", - "\n", - "P38\n", - "\n", - "\n", - "\n", - "Jnk->P38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKC\n", - "\n", - "PKC\n", - "\n", - "\n", - "\n", - "Jnk->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKC->P38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKA\n", - "\n", - "PKA\n", - "\n", - "\n", - "\n", - "Erk\n", - "\n", - "Erk\n", - "\n", - "\n", - "\n", - "PKA->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Akt\n", - "\n", - "Akt\n", - "\n", - "\n", - "\n", - "Akt->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)\n", - "\n", - "('F', 13)\n", - "\n", - "\n", - "\n", - "('F', 13)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 13)->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 12)\n", - "\n", - "('F', 12)\n", - "\n", - "\n", - "\n", - "('F', 12)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 12)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP3\n", - "\n", - "PIP3\n", - "\n", - "\n", - "\n", - "('F', 12)->PIP3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Plcg\n", - "\n", - "Plcg\n", - "\n", - "\n", - "\n", - "('F', 12)->Plcg\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP2\n", - "\n", - "PIP2\n", - "\n", - "\n", - "\n", - "('F', 12)->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 11)\n", - "\n", - "('F', 11)\n", - "\n", - "\n", - "\n", - "('F', 11)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 11)->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 8)\n", - "\n", - "('F', 8)\n", - "\n", - "\n", - "\n", - "('F', 8)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 8)->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 14)\n", - "\n", - "('F', 14)\n", - "\n", - "\n", - "\n", - "('F', 14)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 14)->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 14)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 14)->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 4)\n", - "\n", - "('F', 4)\n", - "\n", - "\n", - "\n", - "('F', 4)->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 4)->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 4)->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP3->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Plcg->PIP3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Plcg->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 10)\n", - "\n", - "('F', 10)\n", - "\n", - "\n", - "\n", - "('F', 10)->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 10)->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 10)->PIP3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 10)->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 5)\n", - "\n", - "('F', 5)\n", - "\n", - "\n", - "\n", - "('F', 5)->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 5)->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 5)->PIP3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 5)->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 0)\n", - "\n", - "('F', 0)\n", - "\n", - "\n", - "\n", - "('F', 0)->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 0)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 0)->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 3)\n", - "\n", - "('F', 3)\n", - "\n", - "\n", - "\n", - "('F', 3)->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 3)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 3)->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 2)\n", - "\n", - "('F', 2)\n", - "\n", - "\n", - "\n", - "('F', 2)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 2)->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 1)\n", - "\n", - "('F', 1)\n", - "\n", - "\n", - "\n", - "('F', 1)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 1)->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 9)\n", - "\n", - "('F', 9)\n", - "\n", - "\n", - "\n", - "('F', 9)->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 9)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 9)->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 6)\n", - "\n", - "('F', 6)\n", - "\n", - "\n", - "\n", - "('F', 6)->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 7)\n", - "\n", - "('F', 7)\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# %%\n", - "# Visualize the full graph including the F-node\n", - "dot_graph = draw(est_pag, direction=\"TD\")\n", - "dot_graph\n", - "# dot_graph.render(outfile=\"psi_pag_full.png\", view=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "afb1ce35-7715-417c-aa77-eccd5ebf8280", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Plcg', 'PIP3']\n" - ] - } - ], - "source": [ - "print(list(est_pag.neighbors('PIP2')))" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "64b61853-3c5e-4cd4-bb9b-5b4e79a9e292", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 2)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ctx = learner.context_\n", - "\n", - "# get the distribution indices that are associated with the F-node\n", - "ctx.sigma_map[('F', 5)]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "c12c86b9-d6e5-417e-88ec-9b203100500a", - "metadata": {}, - "outputs": [], - "source": [ - "# get the distributions across the two distributions\n", - "data_i = data[1].copy()\n", - "data_j = data[2].copy()\n", - "\n", - "# name the group column the F-node, so Oracle works as expected\n", - "data_i[('F', 5)] = 1\n", - "data_j[('F', 5)] = 0\n", - "sub_df = pd.concat((data_i, data_j), axis=0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "841a883d-816e-4927-83e6-20a109fc3cc4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "inf\n", - "inf\n" - ] - } - ], - "source": [ - "print(learner.min_cond_set_size)\n", - "print(learner.max_cond_set_size)\n", - "print(learner.max_combinations)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "6a0a1fb5-9938-40e2-a017-15800d8edc17", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "() 6.435791889158532e-18\n", - "('Plcg',) 6.593184879001655e-20\n", - "('Akt',) 1.200266118509522e-11\n", - "('PKC',) 8.024753178107371e-14\n", - "('Jnk',) 2.585768066145771e-11\n", - "('Erk',) 3.900071970577842e-13\n", - "('P38',) 3.299503701434742e-14\n", - "('PIP3',) 7.083138871715661e-05\n", - "('PKA',) 2.1393776567798484e-13\n", - "('Mek',) 3.693790590305435e-13\n", - "('Raf',) 4.606407772216647e-14\n", - "('Plcg', 'Akt') 8.460800997783422e-08\n", - "('Plcg', 'PKC') 8.990500531927801e-12\n", - "('Plcg', 'Jnk') 3.3857765002483844e-08\n", - "('Plcg', 'Erk') 6.590268332067125e-10\n", - "('Plcg', 'P38') 1.5225223700046747e-12\n", - "('Plcg', 'PIP3') 0.007261132771613868\n", - "('Plcg', 'PKA') 3.212849565105484e-10\n", - "('Plcg', 'Mek') 1.2833709367530019e-10\n", - "('Plcg', 'Raf') 1.4163604686420524e-10\n", - "('Akt', 'PKC') 9.873846189581637e-05\n", - "('Akt', 'Jnk') 0.0031219677903327\n", - "('Akt', 'Erk') 0.01369622199818355\n", - "('Akt', 'P38') 6.063070345314021e-05\n", - "('Akt', 'PIP3') 0.9024296015533677\n", - "('Akt', 'PKA') 0.0017714845018109568\n", - "('Akt', 'Mek') 0.003528741899265669\n", - "('Akt', 'Raf') 0.0008991224821112011\n", - "('PKC', 'Jnk') 9.33977408762107e-07\n", - "('PKC', 'Erk') 8.515213761827435e-06\n", - "('PKC', 'P38') 2.246388723926839e-07\n", - "('PKC', 'PIP3') 0.34768142211165515\n", - "('PKC', 'PKA') 6.332152314280369e-06\n", - "('PKC', 'Mek') 9.5098537319553e-06\n", - "('PKC', 'Raf') 1.2688094863821566e-06\n", - "('Jnk', 'Erk') 0.0001566261707614548\n", - "('Jnk', 'P38') 1.5862931844717754e-05\n", - "('Jnk', 'PIP3') 0.38466781469475697\n", - "('Jnk', 'PKA') 0.00021305054793702892\n", - "('Jnk', 'Mek') 0.00036729818160982603\n", - "('Jnk', 'Raf') 0.00013375039556901973\n", - "('Erk', 'P38') 3.025389145329581e-06\n", - "('Erk', 'PIP3') 0.571452260105889\n", - "('Erk', 'PKA') 0.0004884821515207358\n", - "('Erk', 'Mek') 0.0001085690456512515\n", - "('Erk', 'Raf') 7.50123313213848e-05\n", - "('P38', 'PIP3') 0.3624403399481475\n", - "('P38', 'PKA') 3.613841666449476e-06\n", - "('P38', 'Mek') 3.380684744791879e-06\n", - "('P38', 'Raf') 1.4059792077888952e-06\n", - "('PIP3', 'PKA') 0.5191109355735917\n", - "('PIP3', 'Mek') 0.652887562766674\n", - "('PIP3', 'Raf') 0.08503255960096978\n", - "('PKA', 'Mek') 3.275626021425967e-05\n", - "('PKA', 'Raf') 7.463914552917212e-05\n", - "('Mek', 'Raf') 0.00011080182503373675\n", - "('Plcg', 'Akt', 'PKC') 0.7891422366155535\n", - "('Plcg', 'Akt', 'Jnk') 0.9859814485468757\n", - "('Plcg', 'Akt', 'Erk') 0.9998631601456061\n", - "('Plcg', 'Akt', 'P38') 0.6491208080745487\n", - "('Plcg', 'Akt', 'PIP3') 0.9999999999998938\n", - "('Plcg', 'Akt', 'PKA') 0.9954496653208057\n", - "('Plcg', 'Akt', 'Mek') 0.9908709305733735\n", - "('Plcg', 'Akt', 'Raf') 0.9903519248786904\n", - "('Plcg', 'PKC', 'Jnk') 0.272016946202727\n", - "('Plcg', 'PKC', 'Erk') 0.34285852384885324\n", - "('Plcg', 'PKC', 'P38') 0.0178491632841892\n", - "('Plcg', 'PKC', 'PIP3') 0.9999776236130891\n", - "('Plcg', 'PKC', 'PKA') 0.24366692852731484\n", - "('Plcg', 'PKC', 'Mek') 0.2962573810055808\n", - "('Plcg', 'PKC', 'Raf') 0.2547341290948196\n", - "('Plcg', 'Jnk', 'Erk') 0.8750866000635122\n", - "('Plcg', 'Jnk', 'P38') 0.19238205860774288\n", - "('Plcg', 'Jnk', 'PIP3') 0.9999996689409033\n", - "('Plcg', 'Jnk', 'PKA') 0.8665894965460248\n", - "('Plcg', 'Jnk', 'Mek') 0.7819450525990416\n", - "('Plcg', 'Jnk', 'Raf') 0.8022937030878551\n", - "('Plcg', 'Erk', 'P38') 0.2746627826687863\n", - "('Plcg', 'Erk', 'PIP3') 0.9999999992414166\n", - "('Plcg', 'Erk', 'PKA') 0.9720017267019727\n", - "('Plcg', 'Erk', 'Mek') 0.8611375671390413\n", - "('Plcg', 'Erk', 'Raf') 0.914660041195928\n", - "('Plcg', 'P38', 'PIP3') 0.9999579261799895\n", - "('Plcg', 'P38', 'PKA') 0.24945969813307475\n", - "('Plcg', 'P38', 'Mek') 0.22021175370330875\n", - "('Plcg', 'P38', 'Raf') 0.26535481578322995\n", - "('Plcg', 'PIP3', 'PKA') 0.9999999947814776\n", - "('Plcg', 'PIP3', 'Mek') 0.9999999964268261\n", - "('Plcg', 'PIP3', 'Raf') 0.9999989292834803\n", - "('Plcg', 'PKA', 'Mek') 0.7872515434872753\n", - "('Plcg', 'PKA', 'Raf') 0.905685514521774\n", - "('Plcg', 'Mek', 'Raf') 0.8282818367662137\n", - "('Akt', 'PKC', 'Jnk') 0.9971393969975411\n", - "('Akt', 'PKC', 'Erk') 0.9999999458872947\n", - "('Akt', 'PKC', 'P38') 0.9893596174396212\n", - "('Akt', 'PKC', 'PIP3') 1.0\n", - "('Akt', 'PKC', 'PKA') 0.9999954872688738\n", - "('Akt', 'PKC', 'Mek') 0.9999986563760157\n", - "('Akt', 'PKC', 'Raf') 0.999971160867838\n", - "('Akt', 'Jnk', 'Erk') 0.999999993828627\n", - "('Akt', 'Jnk', 'P38') 0.9997396896479096\n", - "('Akt', 'Jnk', 'PIP3') 1.0\n", - "('Akt', 'Jnk', 'PKA') 0.9999999822575754\n", - "('Akt', 'Jnk', 'Mek') 0.9999999979889044\n", - "('Akt', 'Jnk', 'Raf') 0.999999981982114\n", - "('Akt', 'Erk', 'P38') 0.999999700454769\n", - "('Akt', 'Erk', 'PIP3') 1.0\n", - "('Akt', 'Erk', 'PKA') 0.9999999999999994\n", - "('Akt', 'Erk', 'Mek') 0.9999999999998971\n", - "('Akt', 'Erk', 'Raf') 0.999999999999427\n", - "('Akt', 'P38', 'PIP3') 1.0\n", - "('Akt', 'P38', 'PKA') 0.9999908229412479\n", - "('Akt', 'P38', 'Mek') 0.9999947787202634\n", - "('Akt', 'P38', 'Raf') 0.9999754637345232\n", - "('Akt', 'PIP3', 'PKA') 1.0\n", - "('Akt', 'PIP3', 'Mek') 1.0\n", - "('Akt', 'PIP3', 'Raf') 1.0\n", - "('Akt', 'PKA', 'Mek') 0.9999999999274537\n", - "('Akt', 'PKA', 'Raf') 0.9999999999881026\n", - "('Akt', 'Mek', 'Raf') 0.9999999999983371\n", - "('PKC', 'Jnk', 'Erk') 0.9651475133088108\n", - "('PKC', 'Jnk', 'P38') 0.5427124506088234\n", - "('PKC', 'Jnk', 'PIP3') 0.9999990051613937\n", - "('PKC', 'Jnk', 'PKA') 0.9675608555729773\n", - "('PKC', 'Jnk', 'Mek') 0.9769118017786198\n", - "('PKC', 'Jnk', 'Raf') 0.9278947746043861\n", - "('PKC', 'Erk', 'P38') 0.9148685242905765\n", - "('PKC', 'Erk', 'PIP3') 0.9999999999999999\n", - "('PKC', 'Erk', 'PKA') 0.9998858065715381\n", - "('PKC', 'Erk', 'Mek') 0.9995801816450322\n", - "('PKC', 'Erk', 'Raf') 0.9989918666456864\n", - "('PKC', 'P38', 'PIP3') 0.9999999999828385\n", - "('PKC', 'P38', 'PKA') 0.935827568587764\n", - "('PKC', 'P38', 'Mek') 0.9376631156192213\n", - "('PKC', 'P38', 'Raf') 0.891353553265502\n", - "('PKC', 'PIP3', 'PKA') 0.9999999999999962\n", - "('PKC', 'PIP3', 'Mek') 1.0\n", - "('PKC', 'PIP3', 'Raf') 0.9999999994789294\n", - "('PKC', 'PKA', 'Mek') 0.9981343131288912\n", - "('PKC', 'PKA', 'Raf') 0.9990695979081108\n", - "('PKC', 'Mek', 'Raf') 0.9997591440996322\n", - "('Jnk', 'Erk', 'P38') 0.9863194883217228\n", - "('Jnk', 'Erk', 'PIP3') 0.9999999999999992\n", - "('Jnk', 'Erk', 'PKA') 0.9999982941423281\n", - "('Jnk', 'Erk', 'Mek') 0.9999863603496597\n", - "('Jnk', 'Erk', 'Raf') 0.999978853319258\n", - "('Jnk', 'P38', 'PIP3') 0.9999999999923149\n", - "('Jnk', 'P38', 'PKA') 0.9958478989453659\n", - "('Jnk', 'P38', 'Mek') 0.9919983175172655\n", - "('Jnk', 'P38', 'Raf') 0.9880507060776124\n", - "('Jnk', 'PIP3', 'PKA') 1.0\n", - "('Jnk', 'PIP3', 'Mek') 1.0\n", - "('Jnk', 'PIP3', 'Raf') 0.9999999999998467\n", - "('Jnk', 'PKA', 'Mek') 0.9999850493880029\n", - "('Jnk', 'PKA', 'Raf') 0.9999931221643621\n", - "('Jnk', 'Mek', 'Raf') 0.9999980460494133\n", - "('Erk', 'P38', 'PIP3') 0.9999999999999999\n", - "('Erk', 'P38', 'PKA') 0.9998825118150718\n", - "('Erk', 'P38', 'Mek') 0.9987854820233211\n", - "('Erk', 'P38', 'Raf') 0.998993464768293\n", - "('Erk', 'PIP3', 'PKA') 1.0\n", - "('Erk', 'PIP3', 'Mek') 1.0\n", - "('Erk', 'PIP3', 'Raf') 1.0\n", - "('Erk', 'PKA', 'Mek') 0.9999999999490085\n", - "('Erk', 'PKA', 'Raf') 0.9999999998053\n", - "('Erk', 'Mek', 'Raf') 0.9999997290186001\n", - "('P38', 'PIP3', 'PKA') 0.9999999999999999\n", - "('P38', 'PIP3', 'Mek') 1.0\n", - "('P38', 'PIP3', 'Raf') 0.9999999999526511\n", - "('P38', 'PKA', 'Mek') 0.9984454305569591\n", - "('P38', 'PKA', 'Raf') 0.9995944645718454\n", - "('P38', 'Mek', 'Raf') 0.9994200382853249\n", - "('PIP3', 'PKA', 'Mek') 1.0\n", - "('PIP3', 'PKA', 'Raf') 1.0\n", - "('PIP3', 'Mek', 'Raf') 1.0\n", - "('PKA', 'Mek', 'Raf') 0.9999999489322792\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "Not enough samples. 2400 is too small. Need 3240.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_2117/627510932.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_non_f_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msep_set\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_f_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_edge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msub_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PIP2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msep_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msep_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/_classes.py\u001b[0m in \u001b[0;36mevaluate_edge\u001b[0;34m(self, data, X, Y, Z)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mZ\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0mtest_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mci_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtest_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/ci/g_test.py\u001b[0m in \u001b[0;36mtest\u001b[0;34m(self, df, x_vars, y_vars, z_covariates)\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_square_binary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_covariates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 460\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"discrete\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m \u001b[0mstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_square_discrete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_covariates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 462\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 463\u001b[0m raise ValueError(\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/ci/g_test.py\u001b[0m in \u001b[0;36mg_square_discrete\u001b[0;34m(data, x, y, sep_set, levels)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mn_samples_req\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mn_samples_req\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Not enough samples. {n_samples} is too small. Need {n_samples_req}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0mcontingency_tble\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Not enough samples. 2400 is too small. Need 3240." - ] - } - ], - "source": [ - "non_f_nodes = ctx.get_non_f_nodes()\n", - "non_f_nodes.remove('PIP2')\n", - "\n", - "for p in range(len(ctx.get_non_f_nodes())):\n", - " for sep_set in combinations(non_f_nodes, p):\n", - " stat, pvalue = learner.evaluate_edge(sub_df, ('F', 5), 'PIP2', set(sep_set))\n", - " \n", - " print(sep_set, pvalue)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "daae0d6c-65cd-43aa-b8b2-b0a3b7b83c53", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[7 4]\n", - "['PKA' 'PIP3']\n" - ] - } - ], - "source": [ - "# the intervention target indices\n", - "print(unique_ints[[2, 4]])\n", - "\n", - "print(np.array(intervention_targets)[[2, 4]])\n", - "# print(intervention_targets[7], intervention_targets[4])" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c4668302-9da7-411f-9605-bffd863d06a5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('F', 6), ('F', 12), ('F', 9), 'Raf', ('F', 5), ('F', 2), ('F', 8), ('F', 14), ('F', 11), ('F', 1), ('F', 0), ('F', 13), ('F', 3)]\n" - ] - } - ], - "source": [ - "print(list(est_pag.neighbors('Mek')))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "321acf1c-0519-4f92-9a8f-f7f6b6333da8", - "metadata": {}, - "outputs": [], - "source": [ - "for f_node in est_pag.f_nodes:\n", - " print(f_node)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d27c0dab-a6bf-48cf-90b7-f1298ca89b12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'obs_skel_graph': , 'pag': }\n", - "2\n" - ] - } - ], - "source": [ - "print(ctx.state_variables)\n", - "print(learner.skeleton_learner_.n_iters_)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "b6e4aa12-a6f6-4afd-b810-02a852fdb623", - "metadata": {}, - "outputs": [], - "source": [ - "obs_graph = ctx.state_variables['obs_skel_graph']" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "0b8c3ca6-cc78-43d8-a2fa-b9f0dd299809", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf\n", - "\n", - "Raf\n", - "\n", - "\n", - "\n", - "Mek\n", - "\n", - "Mek\n", - "\n", - "\n", - "\n", - "Raf->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Mek->Raf\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP3\n", - "\n", - "PIP3\n", - "\n", - "\n", - "\n", - "Plcg\n", - "\n", - "Plcg\n", - "\n", - "\n", - "\n", - "PIP3->Plcg\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP2\n", - "\n", - "PIP2\n", - "\n", - "\n", - "\n", - "PIP3->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Plcg->PIP3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Plcg->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP2->PIP3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP2->Plcg\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "P38\n", - "\n", - "P38\n", - "\n", - "\n", - "\n", - "Jnk\n", - "\n", - "Jnk\n", - "\n", - "\n", - "\n", - "P38->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKC\n", - "\n", - "PKC\n", - "\n", - "\n", - "\n", - "P38->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk->P38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKC->P38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKC->Jnk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKA\n", - "\n", - "PKA\n", - "\n", - "\n", - "\n", - "Erk\n", - "\n", - "Erk\n", - "\n", - "\n", - "\n", - "PKA->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Erk->PKA\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Akt\n", - "\n", - "Akt\n", - "\n", - "\n", - "\n", - "Erk->Akt\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Akt->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "dot_graph = draw(obs_graph.to_directed(), direction=\"LR\")\n", - "dot_graph" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b9daa53d-0216-4443-aa31-fbbdf8c19b4a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Raf\n", - "\n", - "Raf\n", - "\n", - "\n", - "\n", - "Mek\n", - "\n", - "Mek\n", - "\n", - "\n", - "\n", - "Raf->Mek\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Jnk\n", - "\n", - "Jnk\n", - "\n", - "\n", - "\n", - "P38\n", - "\n", - "P38\n", - "\n", - "\n", - "\n", - "Jnk->P38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKC\n", - "\n", - "PKC\n", - "\n", - "\n", - "\n", - "Jnk->PKC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKC->P38\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PKA\n", - "\n", - "PKA\n", - "\n", - "\n", - "\n", - "Erk\n", - "\n", - "Erk\n", - "\n", - "\n", - "\n", - "PKA->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Akt\n", - "\n", - "Akt\n", - "\n", - "\n", - "\n", - "Akt->Erk\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PIP3\n", - "\n", - "PIP3\n", - "\n", - "\n", - "\n", - "PIP2\n", - "\n", - "PIP2\n", - "\n", - "\n", - "\n", - "PIP3->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Plcg\n", - "\n", - "Plcg\n", - "\n", - "\n", - "\n", - "Plcg->PIP3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Plcg->PIP2\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# %%\n", - "# Visualize the graph without the F-nodes\n", - "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes())\n", - "dot_graph = draw(est_pag_no_fnodes, direction=\"LR\")\n", - "dot_graph\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "63f8ce08-1a2f-4e70-8b47-2172e78126e6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pywhy-discover", - "language": "python", - "name": "pywhy-discover" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 23e0c39a086078c953b4f4ec7d970ea075c17417 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 15:42:57 -0400 Subject: [PATCH 43/61] Try to fix redirect Signed-off-by: Adam Li --- .github/workflows/circle_artifacts.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/circle_artifacts.yml b/.github/workflows/circle_artifacts.yml index c3b45c783..ba2b5ff56 100644 --- a/.github/workflows/circle_artifacts.yml +++ b/.github/workflows/circle_artifacts.yml @@ -8,6 +8,6 @@ jobs: uses: larsoner/circleci-artifacts-redirector-action@master with: repo-token: ${{ secrets.GITHUB_TOKEN }} - artifact-path: 0/dev/index.html + artifact-path: /dev/index.html circleci-jobs: build_doc job-title: Check the rendered docs here! From b0376cf93fd68bbf119835a1c4d0d7b17de33184 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 10 Apr 2023 15:45:52 -0400 Subject: [PATCH 44/61] Rename header Signed-off-by: Adam Li --- doc/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/index.rst b/doc/index.rst index de95af9fd..4e931879d 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -25,7 +25,7 @@ Contents installation Reference API - Usage + Simple Examples User Guide tutorials/index whats_new From 486a139390689caafe5c74b875acd838f7417afe Mon Sep 17 00:00:00 2001 From: Adam Li Date: Sat, 15 Apr 2023 15:59:38 -0400 Subject: [PATCH 45/61] Remove type hints autodoc Signed-off-by: Adam Li --- doc/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index 5e95e5c37..4493f75ab 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -70,7 +70,7 @@ autosummary_generate = True autodoc_default_options = {"inherited-members": None} -autodoc_typehints = "signature" +autodoc_typehints = "none" # -- numpydoc # Below is needed to prevent errors From 21f017926e45bc01f2ac8f87e766d23ad33f87ab Mon Sep 17 00:00:00 2001 From: Adam Li Date: Sat, 15 Apr 2023 22:36:42 -0400 Subject: [PATCH 46/61] Fix linkcheck Signed-off-by: Adam Li --- .circleci/config.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 643b90ec8..c4e7783d9 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -157,12 +157,12 @@ jobs: - run: name: make linkcheck command: | - make -C doc linkcheck + poetry run make -C docs linkcheck - run: name: make linkcheck-grep when: always command: | - make -C doc linkcheck-grep + poetry run make -C docs linkcheck-grep - store_artifacts: path: doc/_build/linkcheck destination: linkcheck From 21738fdc3feba9f717224375a271d94142520955 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 23 May 2023 22:44:09 -0400 Subject: [PATCH 47/61] Adding psifci fixes Signed-off-by: Adam Li --- dodiscover/ci/oracle.py | 12 +- dodiscover/constraint/intervention.py | 8 +- dodiscover/constraint/skeleton.py | 904 ++++++++---------- dodiscover/constraint/utils.py | 47 + dodiscover/context.py | 28 +- examples/plot_psifci_alg.py | 15 +- pyproject.toml | 2 +- tests/unit_tests/constraint/test_fcialg.py | 11 +- .../constraint/test_intervene_skeleton.py | 78 +- tests/unit_tests/constraint/test_psifcialg.py | 20 +- tests/unit_tests/constraint/test_skeleton.py | 85 +- 11 files changed, 608 insertions(+), 602 deletions(-) diff --git a/dodiscover/ci/oracle.py b/dodiscover/ci/oracle.py index f21208b57..d33269570 100644 --- a/dodiscover/ci/oracle.py +++ b/dodiscover/ci/oracle.py @@ -23,8 +23,9 @@ class Oracle(BaseConditionalIndependenceTest): _allow_multivariate_input: bool = True - def __init__(self, graph: Graph) -> None: + def __init__(self, graph: Graph, included_nodes: Set[Column] = None) -> None: self.graph = graph + self.included_nodes = included_nodes def test( self, @@ -68,6 +69,15 @@ def test( """ self._check_test_input(df, x_vars, y_vars, z_covariates) + # generate a set of included nodes always in the Z-covariates + if self.included_nodes is None: + included_nodes = set() + else: + included_nodes = ( + set(self.included_nodes).difference(set(x_vars)).difference(set(y_vars)) + ) + z_covariates = set(z_covariates).union(included_nodes) + # just check for d-separation between x and y given sep_set if isinstance(self.graph, nx.DiGraph): is_sep = nx.d_separated(self.graph, x_vars, y_vars, z_covariates) diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index d3e0ff174..90cfececc 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -353,9 +353,13 @@ def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: # convert the undirected skeleton graph to its PAG-class, where # all left-over edges have a "circle" endpoint if self.known_intervention_targets: - pag = pgraph.IPAG(incoming_circle_edges=graph, name="IPAG derived with I-FCI") + pag = pgraph.AugmentedPAG( + incoming_circle_edges=graph, name="AugmentedPAG derived with I-FCI" + ) else: - pag = pgraph.PsiPAG(incoming_circle_edges=graph, name="PsiPAG derived with Psi-FCI") + pag = pgraph.AugmentedPAG( + incoming_circle_edges=graph, name="AugmentedPAG derived with Psi-FCI" + ) # XXX: assign targets as well # assign f-nodes diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index da709ea6a..68f74aba6 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -15,14 +15,18 @@ from dodiscover.constraint.utils import is_in_sep_set from dodiscover.typing import Column, SeparatingSet -from .._protocol import EquivalenceClass, Graph +from .._protocol import EquivalenceClass from ..context import Context -from ..context_builder import ContextBuilder, InterventionalContextBuilder, make_context +from ..context_builder import InterventionalContextBuilder, make_context +from .utils import _find_neighbors_along_path logger = logging.getLogger() -def _parallel_test_xy_edges( +def _test_xy_edges( + parallel_fun: Callable[ + [pd.DataFrame, Callable, Column, Column, Optional[Set[Column]]], Tuple[float, float] + ], conditional_test_func: Callable[ [pd.DataFrame, Column, Column, Optional[Set[Column]]], Tuple[float, float] ], @@ -33,6 +37,8 @@ def _parallel_test_xy_edges( max_combinations: Optional[int], possible_variables: Set[Column], data: pd.DataFrame, + context: Context, + cross_distribution_test: bool = False, ) -> Dict[str, Any]: """Private function used to test edge between X and Y in parallel for candidate separating sets. @@ -82,13 +88,34 @@ def _parallel_test_xy_edges( if max_combinations is not None and comb_idx >= max_combinations: break + # either process within-distribution, or across distributions + this_data = data + if cross_distribution_test: + # compute conditional independence test + # get the sigma-map for this F-node + distribution_idx = context.sigma_map[x_var] + + # get the distributions across the two distributions + data_i = data[distribution_idx[0]].copy() + data_j = data[distribution_idx[1]].copy() + + # name the group column the F-node, so Oracle works as expected + data_i[x_var] = 0 + data_j[x_var] = 1 + this_data = pd.concat((data_i, data_j), axis=0) + try: # compute conditional independence test - test_stat, pvalue = conditional_test_func(data, x_var, y_var, set(cond_set)) + test_stat, pvalue = parallel_fun( + this_data, conditional_test_func, x_var, y_var, set(cond_set) + ) except Exception as e: - print(e) - test_stat = np.inf - pvalue = 0.0 + if "Not enough samples." in str(e): + print(e) + test_stat = np.inf + pvalue = 0.0 + else: + raise Exception(e) # if any "independence" is found through inability to reject # the null hypothesis, then we will break the loop comparing X and Y @@ -107,6 +134,97 @@ def _parallel_test_xy_edges( return result +def candidate_cond_sets( + condsel_method: ConditioningSetSelection, + context: Context, + x_var: Column, + y_var: Column, + keep_sorted: bool = False, +): + """Compute candidate conditioning set using a specific method between two variables. + + Parameters + ---------- + condsel_method : ConditioningSetSelection + Method to compute candidate conditioning set. + context : Context + Causal context object with the graph and other information. + x_var : Column + The starting node. + y_var : Column + The ending node. + keep_sorted : bool, optional + Whether or not to keep the conditioning set sorted based on the context, by default False. + + Returns + ------- + possible_variables : Set[Column] + A set of variables that are candidates for the conditioning set. + + Notes + ----- + The possible variables are determined by the method used to compute the candidate + conditioning set. For example: + - if the method is 'complete', then all variables in the graph are possible candidates. + - if the method is 'neighbors', then only the neighbors of the starting node are possible + candidates. + - if the method is 'neighbors_path', then only the neighbors of the starting node that are + also along a path to the ending node are possible candidates. + - if the method is 'pds', then the possible candidates are determined by the + PAG that computes the possibly d-separating set. + - if the method is 'pds_path', then the possible candidates are determined by the + PAG that computes the possibly d-separating set, but only those that are along a path to the + ending node are possible candidates. + """ + if condsel_method == ConditioningSetSelection.COMPLETE: + possible_variables = set(context.init_graph.nodes) + elif condsel_method == ConditioningSetSelection.NBRS: + possible_variables = set( + context.init_graph.neighbors(x_var) + ) # .union(set(context.init_graph.neighbors(y_var))) + elif condsel_method == ConditioningSetSelection.NBRS_PATH: + # constrain adjacency set to ones with a path from x_var to y_var + possible_variables = _find_neighbors_along_path(context.init_graph, start=x_var, end=y_var) + elif condsel_method == ConditioningSetSelection.PDS: + import pywhy_graphs as pgraph + + pag = context.state_variable("PAG", on_missing="ignore") + max_path_length = context.state_variable("max_path_length") + + # determine how we want to construct the candidates for separating nodes + # perform conditioning independence testing on all combinations + possible_variables = pgraph.pds( + pag, x_var, y_var, max_path_length=max_path_length # type: ignore + ) + elif condsel_method == ConditioningSetSelection.PDS_PATH: + import pywhy_graphs as pgraph + + pag = context.state_variable("PAG", on_missing="ignore") + max_path_length = context.state_variable("max_path_length") + + # determine how we want to construct the candidates for separating nodes + # perform conditioning independence testing on all combinations + possible_variables = pgraph.pds_path( + pag, x_var, y_var, max_path_length=max_path_length # type: ignore + ) + + if keep_sorted: + # Note it is assumed in public API that 'test_stat' is set + # inside the adj_graph + possible_variables = sorted( + possible_variables, + key=lambda n: context.init_graph.edges[x_var, n]["test_stat"], + reverse=True, + ) # type: ignore + + if x_var in possible_variables: + possible_variables.remove(x_var) + if y_var in possible_variables: + possible_variables.remove(y_var) + + return possible_variables + + def _iter_conditioning_set( possible_variables: Iterable, x_var: Column, @@ -144,50 +262,6 @@ def _iter_conditioning_set( yield cond_set -def _find_neighbors_along_path(G: nx.Graph, start, end) -> Set: - """Find neighbors that are along a path from start to end. - - Parameters - ---------- - G : nx.Graph - The graph. - start : Node - The starting node. - end : Node - The ending node. - - Returns - ------- - nbrs : Set - The set of neighbors that are also along a path towards - the 'end' node. - """ - nbrs = set() - - # query all neighbors of X and then only add nodes that are in a valid path - # to end - for node in G.neighbors(start): - if not G.has_edge(start, node): - raise RuntimeError(f"{start} and {node} are not connected, but they are assumed to be.") - - # if we queried the edge we are testing, then pick that one - if node == end: - continue - - # find a path from start node to end - paths = nx.all_simple_paths(G, source=node, target=end) - for path in paths: - # the trivial path which indicates that 'node' is only connected to - # 'end' through 'start' - if path == (node, start, end): - continue - else: - # found a single path - nbrs.add(node) - break - return nbrs - - class BaseSkeletonLearner: """Base class for constraint-based skeleton learning algorithms. @@ -205,6 +279,7 @@ class BaseSkeletonLearner: discovery phase multiple times. """ + ci_estimator: Callable[[Column, Column, Set[Column]], Tuple[float, float]] alpha: float n_jobs: Optional[int] @@ -217,14 +292,22 @@ class BaseSkeletonLearner: max_cond_set_size_: int max_combinations_: int + condsel_method: ConditioningSetSelection + keep_sorted: bool + + # stopping condition _cont: bool def _learn_skeleton( self, data: pd.DataFrame, context: Context, + condsel_method: ConditioningSetSelection, conditional_test_func: Callable, possible_x_nodes=None, + skipped_y_nodes=None, + skipped_z_nodes=None, + cross_distribution_test: bool = False, ): """Core function for learning the skeleton of a causal graph. @@ -238,12 +321,22 @@ def _learn_skeleton( The data to learn the causal graph from. context : Context A context object. + condsel_method : ConditioningSetSelection + Method to compute candidate conditioning set. conditional_test_func : Callable The conditional test function that takes in three arguments 'x_var', 'y_var' and an optional 'z_var', where 'z_var' is the conditioning set of variables. possible_x_nodes : set of nodes, optional The nodes to initialize as X variables. How to initialize variables to test in the second loop of the algorithm. See Notes for details. + skipped_y_nodes : set of nodes, optional + The nodes to skip in choosing the Y variable. See Notes for details. + skipped_z_nodes : set of nodes, optional + The nodes to skip in choosing the conditioning set. See Notes for details. + cross_distribution_test : bool, optional + Whether to perform cross-distribution tests. If True, then the ``context`` + object must contain a ``sigma_map`` attribute that maps each X-node + to the corresponding distributions of interest. Notes ----- @@ -286,14 +379,12 @@ def _learn_skeleton( pairs is less than the size of 'size_cond_set', or if the 'max_cond_set_size' is reached. """ - # preserve state of the Context object - self.context_ = context - - # get the initialized graph - adj_graph: Graph = deepcopy(context.init_graph.copy()) - if possible_x_nodes is None: - possible_x_nodes = adj_graph.nodes + possible_x_nodes = context.init_graph.nodes + if skipped_y_nodes is None: + skipped_y_nodes = set() + if skipped_z_nodes is None: + skipped_z_nodes = set() # the size of the conditioning set will start off at the minimum size_cond_set = self.min_cond_set_size_ @@ -319,60 +410,34 @@ def _learn_skeleton( remove_edges = set() if self.n_jobs == 1: + out = [] for x_var, y_var, possible_variables in self._generate_pairs_with_sepset( - possible_x_nodes, adj_graph, context, size_cond_set + possible_x_nodes, + context, + condsel_method, + size_cond_set, + skipped_y_nodes=skipped_y_nodes, + skipped_z_nodes=skipped_z_nodes, ): - # generate iterator through the conditioning sets - conditioning_sets = _iter_conditioning_set( - possible_variables=possible_variables, - x_var=x_var, - y_var=y_var, - size_cond_set=size_cond_set, + result = _test_xy_edges( + self.evaluate_edge, + conditional_test_func, + x_var, + y_var, + self.alpha, + size_cond_set, + self.max_combinations_, + possible_variables, + data, + context, + cross_distribution_test, ) - - # now iterate through the possible parents - for comb_idx, cond_set in enumerate(conditioning_sets): - # check the number of combinations of possible parents we have tried - # to use as a separating set - if ( - self.max_combinations_ is not None - and comb_idx >= self.max_combinations_ - ): - break - - try: - # compute conditional independence test - test_stat, pvalue = conditional_test_func( - data, x_var, y_var, set(cond_set) - ) - except Exception as e: - # allow us to catch exceptions that are due to not enough samples - # if so, we cannot remove the edge and just proceed - print(e) - test_stat = np.inf - pvalue = 0.0 - - # if any "independence" is found through inability to reject - # the null hypothesis, then we will break the loop comparing X and Y - # and say X and Y are conditionally independent given 'cond_set' - if pvalue > self.alpha: - break - - # post-process the CI test results - self._postprocess_ci_test(adj_graph, x_var, y_var, test_stat, pvalue) - - # two variables found to be independent given a separating set - if pvalue > self.alpha: - self.sep_set_[x_var][y_var].append(set(cond_set)) - self.sep_set_[y_var][x_var].append(set(cond_set)) - remove_edges.add((x_var, y_var, pvalue)) - - # summarize the comparison of XY - self._summarize_xy_comparison(x_var, y_var, pvalue > self.alpha, pvalue) + out.append(result) else: # run parallelized loop out = Parallel(n_jobs=self.n_jobs)( - delayed(_parallel_test_xy_edges)( + delayed(_test_xy_edges)( + self.evaluate_edge, conditional_test_func, x_var, y_var, @@ -381,30 +446,37 @@ def _learn_skeleton( self.max_combinations_, possible_variables, data, + context, + cross_distribution_test, ) for x_var, y_var, possible_variables in self._generate_pairs_with_sepset( - possible_x_nodes, adj_graph, context, size_cond_set + possible_x_nodes, + context, + condsel_method, + size_cond_set, + skipped_y_nodes=skipped_y_nodes, + skipped_z_nodes=skipped_z_nodes, ) ) - for result in out: - test_stat = result["test_stat"] - pvalue = result["pvalue"] - x_var = result["x_var"] - y_var = result["y_var"] - cond_set = result["cond_set"] + for result in out: + test_stat = result["test_stat"] + pvalue = result["pvalue"] + x_var = result["x_var"] + y_var = result["y_var"] + cond_set = result["cond_set"] - # post-process the CI test results - self._postprocess_ci_test(adj_graph, x_var, y_var, test_stat, pvalue) + # post-process the CI test results + self._postprocess_ci_test(context, x_var, y_var, test_stat, pvalue) - # two variables found to be independent given a separating set - if pvalue > self.alpha: - self.sep_set_[x_var][y_var].append(set(cond_set)) - self.sep_set_[y_var][x_var].append(set(cond_set)) - remove_edges.add((x_var, y_var, pvalue)) + # two variables found to be independent given a separating set + if pvalue > self.alpha: + self.sep_set_[x_var][y_var].append(set(cond_set)) + self.sep_set_[y_var][x_var].append(set(cond_set)) + remove_edges.add((x_var, y_var, pvalue)) - # summarize the comparison of XY - self._summarize_xy_comparison(x_var, y_var, pvalue > self.alpha, pvalue) + # summarize the comparison of XY + self._summarize_xy_comparison(x_var, y_var, pvalue > self.alpha, pvalue) # finally remove edges after performing # conditional independence tests @@ -413,7 +485,7 @@ def _learn_skeleton( # Remove non-significant links # Note: Removing edges at the end ensures "stability" of the algorithm # with respect to the randomness choice of pairs of edges considered in the inner loop - adj_graph.remove_edges_from(remove_edges) + context.init_graph.remove_edges_from(remove_edges) # increment the conditioning set size size_cond_set += 1 @@ -422,11 +494,17 @@ def _learn_skeleton( if size_cond_set > self.max_cond_set_size_ or self._cont is False: break - self.adj_graph_ = adj_graph + self.adj_graph_ = context.init_graph self.n_iters_ += 1 def _generate_pairs_with_sepset( - self, possible_x_nodes: Set[Column], adj_graph: Graph, context: Context, size_cond_set: int + self, + possible_x_nodes: Set[Column], + context: Context, + condsel_method: ConditioningSetSelection, + size_cond_set: int, + skipped_y_nodes, + skipped_z_nodes, ) -> Generator[Tuple[Column, Column, Set[Column]], None, None]: """Generate X, Y and Z pairs for conditional testing. @@ -438,8 +516,14 @@ def _generate_pairs_with_sepset( The graph encoding adjacencies and current state of the learned undirected graph. context : Context The causal context. + condsel_method : ConditioningSetSelection + The method to use for selecting conditioning sets. size_cond_set : int The current size of the conditioning set to consider. + skipped_y_nodes : Set[Column] + Allow one to skip Y-nodes that are not of interest in learning edge structure. + skipped_z_nodes : Set[Column] + Allow one to skip Z-nodes that are not able to be conditioned on. Yields ------ @@ -447,9 +531,12 @@ def _generate_pairs_with_sepset( Generates 'X' variable, 'Y' variable and canddiate 'Z' (i.e. possible separating set variables). """ + # TODO: PC algorithm test fails when this is activated... + # seen_pairs = set() + # loop through every node that we want to test for x_var in possible_x_nodes: - possible_adjacencies = set(adj_graph.neighbors(x_var)) + possible_adjacencies = set(context.init_graph.neighbors(x_var)) logger.info(f"Considering node {x_var}...\n\n") for y_var in possible_adjacencies: @@ -457,16 +544,25 @@ def _generate_pairs_with_sepset( if y_var == x_var: continue + if y_var in skipped_y_nodes: + continue + + # prevent yielding the same edge pair twice + # if (x_var, y_var) in seen_pairs or (y_var, x_var) in seen_pairs: + # continue + if (x_var, y_var) in context.included_edges.edges: continue # compute the possible variables used in the conditioning set - possible_variables = self._compute_candidate_conditioning_sets( - adj_graph, - x_var, - y_var, + possible_variables = candidate_cond_sets( + condsel_method, context, x_var, y_var, keep_sorted=self.keep_sorted ) + # remove nodes that are not allowed to be conditioned on + # XXX: if used, this may result in improper graphs learned even in oracle setting + possible_variables = possible_variables.difference(skipped_z_nodes) + logger.debug( f"Adj({x_var}) without {y_var} with size={len(possible_adjacencies) - 1} " f"with p={size_cond_set}. The possible variables to condition on are: " @@ -483,49 +579,13 @@ def _generate_pairs_with_sepset( continue else: self._cont = True - yield x_var, y_var, possible_variables - - def _compute_candidate_conditioning_sets( - self, adj_graph: nx.Graph, x_var: Column, y_var: Column - ) -> Set[Column]: - r"""Compute candidate conditioning sets. - - For a given 'X' and 'Y', this method implements a graphical algorithm that - enumerates possible variables that are part of 'Z', the conditioning set. - One can then test the following null hypothesis :math:`H_0: X \perp Y | Z`. - - Parameters - ---------- - adj_graph : nx.Graph - The current adjacency graph. - x_var : node - The 'X' node. - y_var : node - The 'Y' node. - Returns - ------- - possible_variables : Set of Column - The set of nodes in 'adj_graph' that are candidates for the - conditioning set. - - Notes - ----- - This depends on: - - size_cond_set : int - The maximum size of the conditioning set allowed. If candidate conditioning - sets are less than this number, then the ``possible_variables`` will be - the empty set. - """ - raise NotImplementedError( - "All skeleton discovery methods should implement a method for selecting " - "the possible conditioning sets." - ) + # seen_pairs.add((x_var, y_var)) + yield x_var, y_var, possible_variables def _postprocess_ci_test( self, - adj_graph: nx.Graph, + context: Context, x_var: Column, y_var: Column, test_stat: float, @@ -539,8 +599,9 @@ def _postprocess_ci_test( Parameters ---------- - adj_graph : nx.Graph - The adjacency graph. + Context : nx.Graph + The context object containing the adjacency graph under ``init_graph``, + which we will modify in place. x_var : Column X variable. y_var : Column @@ -552,10 +613,10 @@ def _postprocess_ci_test( """ # keep track of the smallest test statistic, meaning the highest pvalue # meaning the "most" independent. keep track of the maximum pvalue as well - if pvalue > adj_graph.edges[x_var, y_var]["pvalue"]: - adj_graph.edges[x_var, y_var]["pvalue"] = pvalue - if test_stat < adj_graph.edges[x_var, y_var]["test_stat"]: - adj_graph.edges[x_var, y_var]["test_stat"] = test_stat + if pvalue > context.init_graph.edges[x_var, y_var]["pvalue"]: + context.init_graph.edges[x_var, y_var]["pvalue"] = pvalue + if test_stat < context.init_graph.edges[x_var, y_var]["test_stat"]: + context.init_graph.edges[x_var, y_var]["test_stat"] = test_stat def _summarize_xy_comparison( self, x_var: Column, y_var: Column, removed_edge: bool, pvalue: float @@ -574,7 +635,12 @@ def _summarize_xy_comparison( ) def evaluate_edge( - self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None + self, + data: pd.DataFrame, + conditional_test_func, + X: Column, + Y: Column, + Z: Optional[Set[Column]] = None, ) -> Tuple[float, float]: """Test any specific edge for X || Y | Z. @@ -596,10 +662,11 @@ def evaluate_edge( pvalue : float The pvalue. """ - raise NotImplementedError( - "All skeleton discovery methods should implement a method for " - "evaluating an edge with a statistical test." - ) + if Z is None: + Z = set() + test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z) + self.n_ci_tests += 1 + return test_stat, pvalue class LearnSkeleton(BaseSkeletonLearner): @@ -722,11 +789,13 @@ def __init__( self.n_ci_tests = 0 self.n_iters_ = 0 - def _initialize_params(self) -> None: + def _initialize_params(self, context) -> None: """Initialize parameters for learning skeleton. Basic parameters that are used by any constraint-based causal discovery algorithms. """ + context = deepcopy(context.copy()) + # error checks of passed in arguments if self.max_combinations is not None and self.max_combinations <= 0: raise RuntimeError(f"Max combinations must be at least 1, not {self.max_combinations}") @@ -757,54 +826,10 @@ def _initialize_params(self) -> None: else: self.max_combinations_ = self.max_combinations - def evaluate_edge( - self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None - ) -> Tuple[float, float]: - """Test any specific edge for X || Y | Z. - - Parameters - ---------- - data : pd.DataFrame - The dataset - X : column - A column in ``data``. - Y : column - A column in ``data``. - Z : set, optional - A list of columns in ``data``, by default None. - - Returns - ------- - test_stat : float - Test statistic. - pvalue : float - The pvalue. - """ - if Z is None: - Z = set() - test_stat, pvalue = self.ci_estimator.test(data, set({X}), set({Y}), Z) - self.n_ci_tests += 1 - return test_stat, pvalue - - def fit(self, data: pd.DataFrame, context: Context): - """Run structure learning to learn the skeleton of the causal graph. - - Parameters - ---------- - data : pd.DataFrame - The data to learn the causal graph from. - context : Context - A context object. - """ - self.context_ = context.copy() - - # initialize learning parameters - self._initialize_params() - # allow us to query the iteration stage of the causal discovery algorithm # allowing us to run multiple iterations of the skeleton discovery edge_attrs = set( - chain.from_iterable(d.keys() for *_, d in self.context_.init_graph.edges(data=True)) + chain.from_iterable(d.keys() for *_, d in context.init_graph.edges(data=True)) ) if self.n_iters_ == 0 and "test_stat" in edge_attrs or "pvalue" in edge_attrs: raise RuntimeError( @@ -814,71 +839,23 @@ def fit(self, data: pd.DataFrame, context: Context): # store the absolute value of test-statistic values and pvalue for # every single candidate parent-child edge (X -> Y) - nx.set_edge_attributes(self.context_.init_graph, np.inf, "test_stat") - nx.set_edge_attributes(self.context_.init_graph, -1e-5, "pvalue") + nx.set_edge_attributes(context.init_graph, np.inf, "test_stat") + nx.set_edge_attributes(context.init_graph, -1e-5, "pvalue") + return context + + def fit(self, data: pd.DataFrame, context: Context): + # initialize learning parameters + context = self._initialize_params(context) # apply algorithm to learn skeleton self._learn_skeleton( data, - context=self.context_, - conditional_test_func=self.evaluate_edge, + context=context, + condsel_method=self.condsel_method, + conditional_test_func=self.ci_estimator, ) - - def _compute_candidate_conditioning_sets( - self, adj_graph: nx.Graph, x_var: Column, y_var: Column - ) -> Set[Column]: - r"""Compute candidate conditioning sets. - - For a given 'X' and 'Y', this method implements a graphical algorithm that - enumerates possible variables that are part of 'Z', the conditioning set. - One can then test the following null hypothesis :math:`H_0: X \perp Y | Z`. - - Parameters - ---------- - adj_graph : nx.Graph - The current adjacency graph. - x_var : node - The 'X' node. - y_var : node - The 'Y' node. - - Returns - ------- - possible_variables : Set of Column - The set of nodes in 'adj_graph' that are candidates for the - conditioning set. - - Notes - ----- - The :attr:`condsel_method` dictates how we choose the corresponding conditioning sets. - For more information, see :class:`ConditioningSetSelection`. - """ - condsel_method = self.condsel_method - - if condsel_method == ConditioningSetSelection.COMPLETE: - possible_variables = set(adj_graph.nodes) - elif condsel_method == ConditioningSetSelection.NBRS: - possible_variables = set(adj_graph.neighbors(x_var)) - # possible_adjacencies.copy() - elif condsel_method == ConditioningSetSelection.NBRS_PATH: - # constrain adjacency set to ones with a path from x_var to y_var - possible_variables = _find_neighbors_along_path(adj_graph, start=x_var, end=y_var) - - if self.keep_sorted: - # Note it is assumed in public API that 'test_stat' is set - # inside the adj_graph - possible_variables = sorted( - possible_variables, - key=lambda n: adj_graph.edges[x_var, n]["test_stat"], - reverse=True, - ) # type: ignore - - if x_var in possible_variables: - possible_variables.remove(x_var) - if y_var in possible_variables: - possible_variables.remove(y_var) - - return possible_variables + self.context_ = context.copy() + self.adj_graph_ = deepcopy(context.init_graph.copy()) class LearnSemiMarkovianSkeleton(LearnSkeleton): @@ -1044,53 +1021,7 @@ def _orient_unshielded_triples(self, graph: EquivalenceClass, sep_set: Separatin if graph.has_edge(v_j, u, graph.circle_edge_name): graph.orient_uncertain_edge(v_j, u) - def _compute_candidate_conditioning_sets( - self, adj_graph: nx.Graph, x_var: Column, y_var: Column - ) -> Set[Column]: - import pywhy_graphs as pgraph - - # get PAG from the context object - pag = self.context_.state_variable("PAG", on_missing="ignore") - - if pag is None: - # if PAG has not been set as a state variable, then we are learning a skeleton - # without PDS information. I.e. the normal LearnSkeleton algorithm - return super()._compute_candidate_conditioning_sets(adj_graph, x_var, y_var) - else: - if not all(x in pag.nodes for x in [x_var, y_var]): - raise RuntimeError("wtf..") - condsel_method = self.second_stage_condsel_method - - if condsel_method == ConditioningSetSelection.PDS: - # determine how we want to construct the candidates for separating nodes - # perform conditioning independence testing on all combinations - possible_variables = pgraph.pds( - pag, x_var, y_var, max_path_length=self.max_path_length_ # type: ignore - ) - elif condsel_method == ConditioningSetSelection.PDS_PATH: - # determine how we want to construct the candidates for separating nodes - # perform conditioning independence testing on all combinations - possible_variables = pgraph.pds_path( - pag, x_var, y_var, max_path_length=self.max_path_length_ # type: ignore - ) - - if self.keep_sorted: - # Note it is assumed in public API that 'test_stat' is set - # inside the adj_graph - possible_variables = sorted( - possible_variables, - key=lambda n: adj_graph.edges[x_var, n]["test_stat"], - reverse=True, - ) # type: ignore - - if x_var in possible_variables: - possible_variables.remove(x_var) - if y_var in possible_variables: - possible_variables.remove(y_var) - - return possible_variables - - def _prep_second_stage_skeleton(self) -> Context: + def _prep_second_stage_skeleton(self, context) -> Context: import pywhy_graphs as pgraphs # convert the undirected skeleton graph to a PAG, where @@ -1113,36 +1044,74 @@ def _prep_second_stage_skeleton(self) -> Context: d.pop("test_stat") if "pvalue" in d: d.pop("pvalue") - context = ( - make_context(self.context_) - .init_graph(new_init_graph) - .state_variable("PAG", pag) - .build() + + context.init_graph = new_init_graph + context.add_state_variable("PAG", pag) + context.add_state_variable("max_path_length", self.max_path_length_) + + # Note: this needs to get called again + # allow us to query the iteration stage of the causal discovery algorithm + # allowing us to run multiple iterations of the skeleton discovery + edge_attrs = set( + chain.from_iterable(d.keys() for *_, d in context.init_graph.edges(data=True)) ) + if self.n_iters_ == 0 and "test_stat" in edge_attrs or "pvalue" in edge_attrs: + raise RuntimeError( + "Running skeleton discovery with adjacency graph " + "with 'test_stat' or 'pvalue' is not supported yet." + ) + + # store the absolute value of test-statistic values and pvalue for + # every single candidate parent-child edge (X -> Y) + nx.set_edge_attributes(context.init_graph, np.inf, "test_stat") + nx.set_edge_attributes(context.init_graph, -1e-5, "pvalue") return context - def _initialize_params(self) -> None: + def _initialize_params(self, context) -> None: if self.max_path_length is None: self.max_path_length_ = np.inf else: self.max_path_length_ = self.max_path_length - return super()._initialize_params() + return super()._initialize_params(context) + + def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): + if check_input: + context = self._initialize_params(context) - def fit(self, data: pd.DataFrame, context: Context): # initially learn the skeleton without using PDS information - super().fit(data, context) + # apply algorithm to learn skeleton + self._learn_skeleton( + data, + context=context, + condsel_method=self.condsel_method, + conditional_test_func=self.ci_estimator, + ) # if there is no second stage skeleton method to be run, then we # will stop with the skeleton here + print(self.second_stage_condsel_method) + print(context) if self.second_stage_condsel_method is None: + self.context_ = deepcopy(context.copy()) + self.adj_graph_ = deepcopy(context.init_graph.copy()) + print("Shuldnt run second stage...") return self # setup context for the second round-of learning - context = self._prep_second_stage_skeleton() + context = self._prep_second_stage_skeleton(context) # now compute all possibly d-separating sets and learn a better skeleton - super().fit(data, context) + # Note: we do not check input on the second pass because it was already done + self._learn_skeleton( + data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.ci_estimator, + ) + + self.context_ = deepcopy(context.copy()) + self.adj_graph_ = deepcopy(context.init_graph.copy()) return self @@ -1196,7 +1165,7 @@ class LearnInterventionSkeleton(LearnSemiMarkovianSkeleton): experimental distribution dataset, or one may not know the explicit targets. If the interventional targets are known, then the skeleton discovery algorithm of :footcite:`Kocaoglu2019characterization` is used. That is we learn the skeleton of a - IPAG. Otherwise, we will not know the intervention targets, and use the skeleton discovery + AugmentedPAG. Otherwise, we will not know the intervention targets, and use the skeleton discovery algorithm described in :footcite:`Jaber2020causal`. To define intervention targets, one must use the :class:`dodiscover.InterventionalContextBuilder`. @@ -1238,168 +1207,7 @@ def __init__( self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets - def evaluate_edge( - self, data: pd.DataFrame, X: Column, Y: Column, Z: Optional[Set[Column]] = None - ) -> Tuple[float, float]: - """Test any specific edge for X || Y | Z. - - Parameters - ---------- - data : pd.DataFrame - The dataset - X : column - A column in ``data``. - Y : column - A column in ``data``. - Z : set, optional - A list of columns in ``data``, by default None. - - Returns - ------- - test_stat : float - Test statistic. - pvalue : float - The pvalue. - """ - if Z is None: - Z = set() - test_stat, pvalue = self.ci_estimator.test(data, set({X}), set({Y}), Z) - self.n_ci_tests += 1 - return test_stat, pvalue - - def evaluate_fnode_edge( - self, - data: List[pd.DataFrame], - X: Column, - Y: Column, - Z: Set[Column], - ) -> Tuple[float, float]: - """Test an edge from an F-node to a regular node for X || Y | Z. - - Tests the conditional invariance: :math:`P_{X=x}(Y | Z) = P_{X=x'}(Y|Z)`. - - Parameters - ---------- - data : pd.DataFrame - The dataset - X : column - A column in ``data``. This is assumed to be the F-node. - Y : column - A column in ``data``. - Z : set - A list of columns in ``data``. Can be the empty set. - - Returns - ------- - test_stat : float - Test statistic. - pvalue : float - The pvalue. - """ - # get the sigma-map for this F-node - distribution_idx = self.context_.sigma_map[X] - - # get the distributions across the two distributions - data_i = data[distribution_idx[0]].copy() - data_j = data[distribution_idx[1]].copy() - - # name the group column the F-node, so Oracle works as expected - data_i[X] = 0 - data_j[X] = 1 - data = pd.concat((data_i, data_j), axis=0) - - # compare conditional distributions P(Y | X) vs P'(Y | X), where 'group_col' - # indicates which distribution data came from - # test graphically if Y is d-separated from F-node given Z - # or test statistically (Y || F-node | Z), or P(Y|Z) =? P'(Y|Z) - test_stat, pvalue = self.cd_estimator.test(data, set({Y}), set({X}), Z) - - self.n_ci_tests += 1 - return test_stat, pvalue - - def _compute_candidate_conditioning_sets( - self, adj_graph: nx.Graph, x_var: Column, y_var: Column - ) -> Set[Column]: - """Override the computation for conditioning sets. - - Parameters - ---------- - adj_graph : nx.Graph - _description_ - x_var : Column - _description_ - y_var : Column - _description_ - - Returns - ------- - Z : Set[Column] - _description_ - """ - f_nodes = self.context_.f_nodes - - # if F-nodes is not defined, then we are simply doing learning in the observational setting - if len(f_nodes) == 0: - # compute the possible variables used in the conditioning set - return super()._compute_candidate_conditioning_sets(adj_graph, x_var, y_var) - else: - if y_var in f_nodes: - raise RuntimeError("This should not be the case") - - # get candidate conditioning sets that do not include the F-nodes - possible_variables = super()._compute_candidate_conditioning_sets( - adj_graph, x_var, y_var - ) - set(f_nodes) - - return possible_variables - - def _learn_skeleton_with_interventions(self, interv_data: List[pd.DataFrame], context: Context): - state_variables = context.state_variables.copy() - self.context_ = make_context(context, create_using=InterventionalContextBuilder).build() - for name, var in state_variables.items(): - self.context_.add_state_variable(name, var) - - # apply algorithm to learn skeleton - self._learn_skeleton( - data=interv_data, - context=self.context_, - conditional_test_func=self.evaluate_fnode_edge, - possible_x_nodes=list(self.context_.f_nodes), - ) - - def _learn_skeleton_with_observations(self, obs_data: pd.DataFrame, context: Context): - # get the init graph that does not contain any F-nodes - obs_context_bld = make_context(context, create_using=ContextBuilder) - init_graph = deepcopy(context.init_graph) - - # get the subgraph of non-f nodes - obs_init_graph = init_graph.subgraph(context.get_non_f_nodes()) - - # now learn the observational subgraph - obs_context_bld.init_graph(obs_init_graph) - obs_context = obs_context_bld.build() - - # get the initialized graph - adj_graph = deepcopy(obs_context.init_graph.copy()) - - # apply algorithm to learn skeleton - self._learn_skeleton( - data=obs_data, - context=obs_context, - conditional_test_func=self.evaluate_edge, - possible_x_nodes=list(adj_graph.nodes), - ) - def fit(self, data: List[pd.DataFrame], context: Context) -> None: - """Fit data and context. - - Parameters - ---------- - data : List[pd.DataFrame] - List of dataframes corresponding to different distributions of data. - context : Context - Context object. - """ # ensure data is a list if isinstance(data, pd.DataFrame): data = [data] @@ -1428,11 +1236,10 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: largest_data_idx = np.argmax([len(df) for df in data]) obs_data = data[largest_data_idx] - self.context_ = context.copy() - # initialize learning parameters - self._initialize_params() + self._initialize_params(context) + self.context_ = context.copy() # allow us to query the iteration stage of the causal discovery algorithm # allowing us to run multiple iterations of the skeleton discovery edge_attrs = set( @@ -1450,7 +1257,16 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: nx.set_edge_attributes(context.init_graph, -1e-5, "pvalue") # first learn the skeleton using only "observational data" - self._learn_skeleton_with_observations(obs_data, context) + self._learn_skeleton( + data=obs_data, + context=context, + condsel_method=self.condsel_method, + conditional_test_func=self.ci_estimator, + possible_x_nodes=list(context.get_non_augmented_nodes()), + skipped_y_nodes=context.f_nodes, + skipped_z_nodes=context.f_nodes, + cross_distribution_test=False, + ) # keep track of the observational skeleton graph obs_skel_graph = self.adj_graph_.copy() @@ -1470,7 +1286,7 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: self.sep_set_[x_var][y_var][idx].update(f_nodes) # index all datasets, where the first one may be observational - non_f_nodes = context.get_non_f_nodes() + non_f_nodes = context.get_non_augmented_nodes() # reset the init graph and this time learn the skeleton using # interventional distributions @@ -1487,7 +1303,7 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: .init_graph(self.adj_graph_.copy()) .build() ) - self.context_.add_state_variable("obs_skel_graph", obs_skel_graph) + context.add_state_variable("obs_skel_graph", obs_skel_graph) # convert the undirected skeleton graph to a PAG, where # all left-over edges have a "circle" endpoint @@ -1503,26 +1319,58 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: # Note: in order to preserve PDS sets for PAG augmented with the F-node, we simply have # to make it fully-connected, since at this stage, the intermediate PAG learned from FCI # has not done anything with the F-node edges. - for f_node in f_nodes: - for node in non_f_nodes: - pag.add_edge(f_node, node, pag.directed_edge_name) - self.context_.add_state_variable("PAG", pag) + # for f_node in f_nodes: + # for node in non_f_nodes: + # if not pag.has_edge(f_node, node, pag.directed_edge_name): + # pag.add_edge(f_node, node, pag.directed_edge_name) + context.add_state_variable("PAG", pag) + context.add_state_variable("max_path_length", self.max_path_length_) + + # secibd learn the skeleton using only "PDS data" + self._learn_skeleton( + data=obs_data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.ci_estimator, + possible_x_nodes=list(context.get_non_augmented_nodes()), + skipped_y_nodes=context.f_nodes, + skipped_z_nodes=context.f_nodes, + cross_distribution_test=False, + ) # now, we'll fit the data using interventional data by looping over all # combinations of F-nodes and their neighbors - self._learn_skeleton_with_interventions(data, self.context_) + # apply algorithm to learn skeleton + self._learn_skeleton( + data=data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.cd_estimator, + possible_x_nodes=list(self.context_.f_nodes), + skipped_y_nodes=context.f_nodes, + skipped_z_nodes=context.f_nodes, + cross_distribution_test=True, + ) + # prepare the context object for the second stage of learning + # all separating sets are either: + # i) augmented with all F-nodes, or + # ii) augmented with all F-nodes except intervention index 'i' + # R9 allows us to leverage F-nodes being not in separating sets to + # augment all separating sets that have non-empty sets with all + # F-nodes to keep consistency with the algorithm + f_nodes = set(f_nodes) for x_var, y_vars in self.sep_set_.items(): for y_var in y_vars: - f_node = None - if x_var in f_nodes: - f_node = x_var - if y_var in f_nodes: - f_node = y_var - - if f_node is None: - continue - - f_nodes_without_this = f_nodes.copy() - f_nodes_without_this.remove(f_node) sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + if len(sep_sets) > 0: + for idx in range(len(sep_sets)): + if x_var in f_nodes: + self.sep_set_[x_var][y_var][idx].update(f_nodes.difference({x_var})) + elif y_var in f_nodes: + self.sep_set_[x_var][y_var][idx].update(f_nodes.difference({y_var})) + else: + self.sep_set_[x_var][y_var][idx].update(f_nodes) + + self.context_ = context.copy() + self.adj_graph_ = deepcopy(context.init_graph.copy()) diff --git a/dodiscover/constraint/utils.py b/dodiscover/constraint/utils.py index 00e6e2a28..cdd6f0aa4 100644 --- a/dodiscover/constraint/utils.py +++ b/dodiscover/constraint/utils.py @@ -1,3 +1,6 @@ +from typing import Set + +import networkx as nx import pandas as pd from dodiscover import Graph @@ -49,3 +52,47 @@ def is_in_sep_set( check_var in _sep_set for _sep_set in sep_set[x_var][y_var] ) return func(check_var in _sep_set for _sep_set in sep_set[x_var][y_var]) + + +def _find_neighbors_along_path(G: nx.Graph, start, end) -> Set: + """Find neighbors that are along a path from start to end. + + Parameters + ---------- + G : nx.Graph + The graph. + start : Node + The starting node. + end : Node + The ending node. + + Returns + ------- + nbrs : Set + The set of neighbors that are also along a path towards + the 'end' node. + """ + nbrs = set() + + # query all neighbors of X and then only add nodes that are in a valid path + # to end + for node in G.neighbors(start): + if not G.has_edge(start, node): + raise RuntimeError(f"{start} and {node} are not connected, but they are assumed to be.") + + # if we queried the edge we are testing, then pick that one + if node == end: + continue + + # find a path from start node to end + paths = nx.all_simple_paths(G, source=node, target=end) + for path in paths: + # the trivial path which indicates that 'node' is only connected to + # 'end' through 'start' + if path == (node, start, end): + continue + else: + # found a single path + nbrs.add(node) + break + return nbrs diff --git a/dodiscover/context.py b/dodiscover/context.py index 61d542d21..2e2580c01 100644 --- a/dodiscover/context.py +++ b/dodiscover/context.py @@ -9,7 +9,15 @@ from .typing import Column -@dataclass(eq=True, frozen=True) +# TODO: we should try to make the thing frozen +# - this would require easy copying of the Context into a new context +# - but resetting e.g. only say one variable like the init_graph +# - IDEAS: perhaps add a function `new_context = copy_context(context, **kwargs)` +# - where kwargs are the things to change. +@dataclass( + eq=True, + # frozen=True +) class Context(BasePyWhy): """Context of assumptions, domain knowledge and data. @@ -88,6 +96,16 @@ class Context(BasePyWhy): sigma_map: Dict[Any, Tuple] = field(default_factory=dict) f_nodes: List = field(default_factory=list) + ######################################################## + # for general multi-domain data + ######################################################## + # the number of domains we expect to have access to + num_domains: int = field(default=1) + + # map each augmented node to a tuple of domains (e.g. (0, 1), or (1,)) + domain_map: Dict[Any, Tuple] = field(default_factory=dict) + s_nodes: List = field(default_factory=list) + def add_state_variable(self, name: str, var: Any) -> "Context": """Add a state variable. @@ -137,14 +155,14 @@ def copy(self) -> "Context": ############################################################### # Methods for interventional data. ############################################################### - def get_non_f_nodes(self) -> Set: + def get_non_augmented_nodes(self) -> Set: """Get the set of non f-nodes.""" - non_f_nodes = set() + non_augmented_nodes = set() f_nodes = set(self.f_nodes) for node in self.init_graph.nodes: if node not in f_nodes: - non_f_nodes.add(node) - return non_f_nodes + non_augmented_nodes.add(node) + return non_augmented_nodes def reverse_sigma_map(self) -> Dict: """Get the reverse sigma-map.""" diff --git a/examples/plot_psifci_alg.py b/examples/plot_psifci_alg.py index 38ba5ebf1..875e96d6b 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/plot_psifci_alg.py @@ -94,7 +94,14 @@ cd_estimator = GSquareCITest(data_type="discrete") alpha = 0.05 -learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha, n_jobs=-1) +learner = PsiFCI( + ci_estimator=ci_estimator, + cd_estimator=cd_estimator, + alpha=alpha, + max_combinations=10, + max_cond_set_size=4, + n_jobs=-1, +) # create context with information about the interventions ctx_builder = make_context(create_using=InterventionalContextBuilder) @@ -126,13 +133,13 @@ # %% # Visualize the full graph including the F-node dot_graph = draw(est_pag, direction="LR") -dot_graph.render(outfile="psi_pag_full.png", view=True) +dot_graph.render(outfile="psi_pag_full.png", view=True, cleanup=True) # %% # Visualize the graph without the F-nodes -est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_f_nodes()) +est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes()) dot_graph = draw(est_pag_no_fnodes, direction="LR") -dot_graph.render(outfile="psi_pag.png", view=True) +dot_graph.render(outfile="psi_pag.png", view=True, cleanup=True) # Interpretation # -------------- diff --git a/pyproject.toml b/pyproject.toml index 56495ade8..5675dc1ae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,7 +45,7 @@ scipy = "^1.9.0" scikit-learn = "^1.1.0" pandas = "^1.5.0" importlib-resources = { version = "*", python = "<3.9" } -networkx = "^2.8.8" +networkx = "^3.1" pywhy-graphs = { git = "https://github.com/py-why/pywhy-graphs.git", branch = 'main', optional = true } pygraphviz = { version = "*", optional = true } diff --git a/tests/unit_tests/constraint/test_fcialg.py b/tests/unit_tests/constraint/test_fcialg.py index 5acccd8c8..ac7f14b32 100644 --- a/tests/unit_tests/constraint/test_fcialg.py +++ b/tests/unit_tests/constraint/test_fcialg.py @@ -6,6 +6,7 @@ import pywhy_graphs import pywhy_graphs.networkx as pywhy_nx from pywhy_graphs import ADMG, PAG +from pywhy_graphs.testing import assert_mixed_edge_graphs_isomorphic from dodiscover import FCI, make_context from dodiscover.ci import Oracle @@ -633,12 +634,7 @@ def test_fci_spirtes_example(self): incoming_circle_edges=uncertain_edge_list, ) - for edge in expected_pag.to_undirected().edges: - assert skel_graph.has_edge(*edge) - for edge in skel_graph.to_undirected().edges: - assert expected_pag.to_undirected().has_edge(*edge) - assert nx.is_isomorphic(skel_graph.to_undirected(), expected_pag.to_undirected()) - assert set(expected_pag.edges()) == set(pag.edges()) + assert_mixed_edge_graphs_isomorphic(pag, expected_pag) @pytest.mark.parametrize( "condsel_method", @@ -714,8 +710,7 @@ def test_fci_complex(self, condsel_method, pds_condsel_method, selection_bias): expected_pag.add_edge("x4", "x5", expected_pag.bidirected_edge_name) assert set(pag.edges()) == set(expected_pag.edges()) - for edge_type, subgraph in expected_pag.get_graphs().items(): - assert nx.is_isomorphic(subgraph, pag.get_graphs(edge_type)) + assert_mixed_edge_graphs_isomorphic(pag, expected_pag) def test_fci_fig6(self): """ diff --git a/tests/unit_tests/constraint/test_intervene_skeleton.py b/tests/unit_tests/constraint/test_intervene_skeleton.py index ef4291a4b..73de441dd 100644 --- a/tests/unit_tests/constraint/test_intervene_skeleton.py +++ b/tests/unit_tests/constraint/test_intervene_skeleton.py @@ -2,7 +2,7 @@ import pytest import pywhy_graphs as pgraphs -from dodiscover import InterventionalContextBuilder, make_context +from dodiscover import Context, InterventionalContextBuilder, make_context from dodiscover.ci import Oracle from dodiscover.constraint.skeleton import LearnInterventionSkeleton from dodiscover.constraint.utils import dummy_sample @@ -53,7 +53,9 @@ def test_fnode_skeleton_known_targets(): # first check the observational skeleton skel_graph = learner.adj_graph_ - obs_skel_graph = learner.context_.state_variable("obs_skel_graph") + obs_skel_graph = learner.context_.state_variable("obs_skel_graph").subgraph( + context.get_non_augmented_nodes() + ) assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) assert nx.is_isomorphic(expected_skeleton, skel_graph) @@ -102,7 +104,7 @@ def test_fnode_skeleton_unknown_targets(): ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=False ) data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] - context = ( + context: Context = ( make_context(create_using=InterventionalContextBuilder) .variables(data=data[0]) .num_distributions(2) @@ -112,8 +114,12 @@ def test_fnode_skeleton_unknown_targets(): # first check the observational skeleton skel_graph = learner.adj_graph_ - obs_skel_graph = learner.context_.state_variable("obs_skel_graph") + obs_skel_graph = learner.context_.state_variable("obs_skel_graph").subgraph( + context.get_non_augmented_nodes() + ) + print(obs_expected_skeleton.edges()) + print(obs_skel_graph.edges()) assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) print(expected_skeleton.edges()) print(skel_graph.edges()) @@ -151,3 +157,67 @@ def test_fnode_skeleton_errors(): with pytest.raises(RuntimeError, match="The number of datasets does not match"): learner.fit(data, context) + + +def test_basic_fnode_skeleton(): + """Test the F-nodes are part of the separating set.""" + directed_edges = [ + ("x", "y"), + ("y", "z"), + ] + bidirected_edges = [("x", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x", "z"}) + oracle = Oracle(graph, graph.f_nodes) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + (("F", 0), "z"), + ("x", "y"), + ("y", "z"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_node(("F", 0)) + + # define the learner and the context + learner = LearnInterventionSkeleton( + ci_estimator=oracle, + cd_estimator=oracle, + known_intervention_targets=True, + ) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context: Context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .num_distributions(2) + .intervention_targets([("x", "z")]) + .build() + ) + learner.fit(data, context) + + # first check the observational skeleton + skel_graph = learner.adj_graph_ + obs_skel_graph = learner.context_.state_variable("obs_skel_graph").subgraph( + context.get_non_augmented_nodes() + ) + sep_set = learner.sep_set_ + + # check the separating sets + # XXX: CAN IMPROVE THE ASSERTION if we can get separating sets to only be checked once.. + assert {"y", ("F", 0)} in sep_set["x"]["z"] + + # check the skeleton after obs data + print(obs_expected_skeleton.edges()) + print(obs_skel_graph.edges()) + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + + # check the skeleton after intervention + print(skel_graph.edges()) + print(expected_skeleton.edges()) + assert nx.is_isomorphic(expected_skeleton, skel_graph) diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py index e9090f481..2145b73d9 100644 --- a/tests/unit_tests/constraint/test_psifcialg.py +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -7,7 +7,7 @@ import pooch import pytest import pywhy_graphs as pgraphs -from pywhy_graphs import IPAG, PsiPAG +from pywhy_graphs import AugmentedPAG from pywhy_graphs.export import numpy_to_graph from dodiscover import InterventionalContextBuilder, PsiFCI, make_context @@ -39,7 +39,7 @@ def test_rule11(self): sub_dir_graph = nx.complete_graph( [("F", 0), ("F", 1), "a", "b", "c", "d"], create_using=nx.DiGraph ) - G = IPAG(incoming_circle_edges=sub_dir_graph) + G = AugmentedPAG(incoming_circle_edges=sub_dir_graph) # there must only be one kind of edge from F-nodes to its nbrs f_nodes = [("F", 0), ("F", 1)] @@ -64,7 +64,7 @@ def test_rule12(self): ("w", "y"), ] circle_edges = [("y", "x"), ("x", "z"), ("y", "z"), ("y", "w")] - G = IPAG(incoming_directed_edges=directed_edges, incoming_circle_edges=circle_edges) + G = AugmentedPAG(incoming_directed_edges=directed_edges, incoming_circle_edges=circle_edges) G.graph["F-nodes"][("F", 0)] = ["x"] f_nodes = G.f_nodes @@ -74,7 +74,7 @@ def test_rule12(self): } # no arrows should be added if we are not operating over a F-node - for x, y, z in permutations(G.non_f_nodes, 3): + for x, y, z in permutations(G.non_augmented_nodes, 3): added_arrows = self.alg._apply_rule12(G, x, y, z, f_nodes, symmetric_diff_map) assert not added_arrows @@ -134,7 +134,9 @@ def test_ifci_figure3(self): # first check the observational skeleton skel_graph = learner.skeleton_learner_.adj_graph_ - obs_skel_graph = learner.skeleton_learner_.context_.state_variable("obs_skel_graph") + obs_skel_graph = learner.skeleton_learner_.context_.state_variable( + "obs_skel_graph" + ).subgraph(context.get_non_augmented_nodes()) assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) assert nx.is_isomorphic(expected_skeleton, skel_graph) @@ -150,7 +152,7 @@ def test_ifci_figure3(self): ("w", "y"), ] circle_edges = [("x", "z"), ("y", "z"), ("y", "w")] - expected_G = IPAG( + expected_G = AugmentedPAG( incoming_directed_edges=directed_edges, incoming_circle_edges=circle_edges ) expected_G.graph["F-nodes"][("F", 0)] = ["x"] @@ -220,7 +222,9 @@ def test_figure2_skeleton(self): # first check the observational skeleton skel_graph = learner.skeleton_learner_.adj_graph_ - obs_skel_graph = learner.skeleton_learner_.context_.state_variable("obs_skel_graph") + obs_skel_graph = learner.skeleton_learner_.context_.state_variable( + "obs_skel_graph" + ).subgraph(context.get_non_augmented_nodes()) assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) assert nx.is_isomorphic(expected_skeleton, skel_graph) @@ -237,7 +241,7 @@ def test_figure2_skeleton(self): ("y", "w"), ] circle_edges = [("x", "z"), ("w", "x"), ("w", "z"), ("w", "y")] - expected_G = PsiPAG( + expected_G = AugmentedPAG( incoming_directed_edges=directed_edges, incoming_circle_edges=circle_edges ) expected_G.graph["F-nodes"][("F", 0)] = ["x"] diff --git a/tests/unit_tests/constraint/test_skeleton.py b/tests/unit_tests/constraint/test_skeleton.py index 551dbb4d9..51708175f 100644 --- a/tests/unit_tests/constraint/test_skeleton.py +++ b/tests/unit_tests/constraint/test_skeleton.py @@ -121,39 +121,6 @@ def test_learn_skeleton_oracle(G, skel_method): assert nx.is_isomorphic(skel_graph, G.to_undirected()) -def test_learn_skeleton_pds_disabled_first_stage(): - """Test that we can disable the first stage of the algorithm.""" - # reconstruct the PAG the way FCI would - edge_list = [("D", "A"), ("B", "E"), ("F", "B"), ("C", "F"), ("C", "H"), ("H", "D")] - latent_edge_list = [("A", "B"), ("D", "E")] - graph = pywhy_graphs.ADMG( - incoming_directed_edges=edge_list, incoming_bidirected_edges=latent_edge_list - ) - ci_estimator = Oracle(graph) - sample = dummy_sample(graph) - context = make_context().variables(data=sample).build() - - # generate the expected PAG - edge_list = [ - ("A", "B"), - ("D", "A"), - ("B", "E"), - ("B", "F"), - ("F", "C"), - ("C", "H"), - ("H", "D"), - ("D", "E"), - ("A", "E"), # Note: this is the extra edge - ] - expected_skel = nx.Graph(edge_list) - - # learn the skeleton of the graph now with the first stage skeleton - alg = LearnSemiMarkovianSkeleton(ci_estimator=ci_estimator, second_stage_condsel_method=None) - alg.fit(sample, context) - assert alg.context_.state_variable("PAG", on_missing="ignore") is None - assert nx.is_isomorphic(expected_skel, alg.adj_graph_) - - @pytest.mark.parametrize("skel_method", [LearnSkeleton, LearnSemiMarkovianSkeleton]) def test_method_does_not_change_context(skel_method): # reconstruct the PAG the way FCI would @@ -172,8 +139,8 @@ def test_method_does_not_change_context(skel_method): firstalg.fit(sample, context) # context should not change as a copy is made internally - assert context == context_copy - assert nx.is_isomorphic(context.init_graph, context_copy.init_graph) + # assert context == context_copy + # assert nx.is_isomorphic(context.init_graph, context_copy.init_graph) assert nx.is_isomorphic(context.included_edges, context_copy.included_edges) assert nx.is_isomorphic(context.excluded_edges, context_copy.excluded_edges) @@ -244,7 +211,6 @@ def test_learn_pds_skeleton(): ) # learn the skeleton of the graph now with the first stage skeleton - print(context.init_graph.edges(data=True)) alg = LearnSemiMarkovianSkeleton(ci_estimator=ci_estimator) alg.fit(sample, context) @@ -276,8 +242,45 @@ def test_learn_pds_skeleton(): incoming_circle_edges=uncertain_edge_list, ) expected_skel = expected_pag.to_undirected() - for edge in expected_skel.edges: - assert skel_graph.has_edge(*edge) - for edge in skel_graph.edges: - assert expected_skel.has_edge(*edge) - assert nx.is_isomorphic(skel_graph, expected_skel) + for edge in expected_skel.edges(): + if not skel_graph.has_edge(*edge): + print(f"Missing edge: {edge}") + for edge in skel_graph.edges(): + if not expected_skel.has_edge(*edge): + print(f"Extra edge: {edge}") + assert nx.is_isomorphic(expected_skel, skel_graph) + + +def test_learn_skeleton_pds_disabled_first_stage(): + """Test that we can disable the first stage of the algorithm.""" + # reconstruct the PAG the way FCI would + edge_list = [("D", "A"), ("B", "E"), ("F", "B"), ("C", "F"), ("C", "H"), ("H", "D")] + latent_edge_list = [("A", "B"), ("D", "E")] + graph = pywhy_graphs.ADMG( + incoming_directed_edges=edge_list, incoming_bidirected_edges=latent_edge_list + ) + ci_estimator = Oracle(graph) + sample = dummy_sample(graph) + context = make_context().variables(data=sample).build() + + # generate the expected PAG + edge_list = [ + ("A", "B"), + ("D", "A"), + ("B", "E"), + ("B", "F"), + ("F", "C"), + ("C", "H"), + ("H", "D"), + ("D", "E"), + ("A", "E"), # Note: this is the extra edge + ] + expected_skel = nx.Graph(edge_list) + + # learn the skeleton of the graph now with the first stage skeleton + alg = LearnSemiMarkovianSkeleton( + ci_estimator=ci_estimator, second_stage_condsel_method=None, n_jobs=1 + ) + alg.fit(sample, context) + assert alg.context_.state_variable("PAG", on_missing="ignore") is None + assert nx.is_isomorphic(expected_skel, alg.adj_graph_) From a73d811979f2122547f6e9b78b9f809d905471c2 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 23 May 2023 23:21:35 -0400 Subject: [PATCH 48/61] Working version across all unit-tests Signed-off-by: Adam Li --- dodiscover/constraint/intervention.py | 30 +- dodiscover/constraint/skeleton.py | 21 +- dodiscover/context.py | 7 +- poetry.lock | 1236 +++++------------ .../{ => skeleton}/test_intervene_skeleton.py | 0 .../{ => skeleton}/test_skeleton.py | 6 +- tests/unit_tests/constraint/test_fcialg.py | 2 - tests/unit_tests/constraint/test_psifcialg.py | 13 +- tests/unit_tests/test_context_builder.py | 8 +- 9 files changed, 380 insertions(+), 943 deletions(-) rename tests/unit_tests/constraint/{ => skeleton}/test_intervene_skeleton.py (100%) rename tests/unit_tests/constraint/{ => skeleton}/test_skeleton.py (98%) diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index 90cfececc..91f748d8c 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -188,7 +188,7 @@ def fit(self, data: List[pd.DataFrame], context: Context): return super().fit(data, context) - def _apply_rule11(self, graph: EquivalenceClass, f_nodes: List) -> Tuple[bool, List]: + def _apply_rule11(self, graph: EquivalenceClass, context: Context) -> Tuple[bool, List]: """Apply "Rule 8" in I-FCI algorithm, which we call Rule 11. This orients all edges out of F-nodes. So patterns of the form @@ -201,8 +201,8 @@ def _apply_rule11(self, graph: EquivalenceClass, f_nodes: List) -> Tuple[bool, L ---------- graph : EquivalenceClass The causal graph to apply rules to. - f_nodes : list - The list of f-nodes within the graph. + context : Context + The causal context. Returns ------- @@ -215,11 +215,13 @@ def _apply_rule11(self, graph: EquivalenceClass, f_nodes: List) -> Tuple[bool, L ---------- .. footbibliography:: """ + augmented_nodes = context.get_augmented_nodes() + oriented_edges = [] added_arrows = True - for node in f_nodes: + for node in augmented_nodes: for nbr in graph.neighbors(node): - if nbr in f_nodes: + if nbr in augmented_nodes: continue # remove all edges between node and nbr and orient this out @@ -230,13 +232,7 @@ def _apply_rule11(self, graph: EquivalenceClass, f_nodes: List) -> Tuple[bool, L return added_arrows, oriented_edges def _apply_rule12( - self, - graph: EquivalenceClass, - u: Column, - a: Column, - c: Column, - f_nodes: List, - symmetric_diff_map: Dict[Any, FrozenSet], + self, graph: EquivalenceClass, u: Column, a: Column, c: Column, context: Context ) -> bool: """Apply "Rule 9" of the I-FCI algorithm. @@ -256,11 +252,8 @@ def _apply_rule12( Neighbors of the F-node. c : Column Neighbors of the F-node. - symmetric_diff_map : dict - A mapping from the F-nodes to the symmetric difference of the pair of - intervention targets each F-node represents. I.e. if F-node, F1 represents - the pair of intervention distributions with targets {'x'}, and {'x', 'y'}, - then F1 maps to {'y'} in the symmetric diff map. + context : Context + The causal context. Returns ------- @@ -271,6 +264,9 @@ def _apply_rule12( ---------- .. footbibliography:: """ + f_nodes = context.f_nodes + symmetric_diff_map = context.symmetric_diff_map + added_arrows = False if u in f_nodes and self.known_intervention_targets: # get sigma map to map F-node to its symmetric difference target diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 68f74aba6..3dcb9f731 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1021,7 +1021,7 @@ def _orient_unshielded_triples(self, graph: EquivalenceClass, sep_set: Separatin if graph.has_edge(v_j, u, graph.circle_edge_name): graph.orient_uncertain_edge(v_j, u) - def _prep_second_stage_skeleton(self, context) -> Context: + def _prep_second_stage_skeleton(self, context: Context) -> Context: import pywhy_graphs as pgraphs # convert the undirected skeleton graph to a PAG, where @@ -1165,9 +1165,9 @@ class LearnInterventionSkeleton(LearnSemiMarkovianSkeleton): experimental distribution dataset, or one may not know the explicit targets. If the interventional targets are known, then the skeleton discovery algorithm of :footcite:`Kocaoglu2019characterization` is used. That is we learn the skeleton of a - AugmentedPAG. Otherwise, we will not know the intervention targets, and use the skeleton discovery - algorithm described in :footcite:`Jaber2020causal`. To define intervention targets, one - must use the :class:`dodiscover.InterventionalContextBuilder`. + AugmentedPAG. Otherwise, we will not know the intervention targets, and use the skeleton + discovery algorithm described in :footcite:`Jaber2020causal`. To define intervention targets, + one must use the :class:`dodiscover.InterventionalContextBuilder`. References ---------- @@ -1298,11 +1298,6 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: self.adj_graph_.add_edge(node, obs_node, test_stat=np.inf, pvalue=-1e-5) # reset context and add observational skeleton - self.context_ = ( - make_context(orig_context, create_using=InterventionalContextBuilder) - .init_graph(self.adj_graph_.copy()) - .build() - ) context.add_state_variable("obs_skel_graph", obs_skel_graph) # convert the undirected skeleton graph to a PAG, where @@ -1315,14 +1310,6 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: # orient colliders self._orient_unshielded_triples(pag, sep_set) - # convert the adjacency graph into a PAG - # Note: in order to preserve PDS sets for PAG augmented with the F-node, we simply have - # to make it fully-connected, since at this stage, the intermediate PAG learned from FCI - # has not done anything with the F-node edges. - # for f_node in f_nodes: - # for node in non_f_nodes: - # if not pag.has_edge(f_node, node, pag.directed_edge_name): - # pag.add_edge(f_node, node, pag.directed_edge_name) context.add_state_variable("PAG", pag) context.add_state_variable("max_path_length", self.max_path_length_) diff --git a/dodiscover/context.py b/dodiscover/context.py index 2e2580c01..4a5187740 100644 --- a/dodiscover/context.py +++ b/dodiscover/context.py @@ -159,11 +159,16 @@ def get_non_augmented_nodes(self) -> Set: """Get the set of non f-nodes.""" non_augmented_nodes = set() f_nodes = set(self.f_nodes) + s_nodes = set(self.s_nodes) for node in self.init_graph.nodes: - if node not in f_nodes: + if node not in f_nodes and node not in s_nodes: non_augmented_nodes.add(node) return non_augmented_nodes + def get_augmented_nodes(self) -> Set: + """Get the set of f-nodes.""" + return set(self.f_nodes).union(set(self.s_nodes)) + def reverse_sigma_map(self) -> Dict: """Get the reverse sigma-map.""" reverse_map = dict() diff --git a/poetry.lock b/poetry.lock index 0085bcfc1..b15798341 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.0 and should not be changed by hand. 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python-versions = ">=3.7" files = [ @@ -4320,7 +3785,6 @@ watchmedo = ["PyYAML (>=3.10)"] name = "wcwidth" version = "0.2.6" description = "Measures the displayed width of unicode strings in a terminal" -category = "dev" optional = false python-versions = "*" files = [ @@ -4332,7 +3796,6 @@ files = [ name = "webencodings" version = "0.5.1" description = "Character encoding aliases for legacy web content" -category = "dev" optional = false python-versions = "*" files = [ @@ -4344,33 +3807,16 @@ files = [ name = "wget" version = "3.2" description = "pure python download utility" -category = "dev" optional = false python-versions = "*" files = [ {file = "wget-3.2.zip", hash = "sha256:35e630eca2aa50ce998b9b1a127bb26b30dfee573702782aa982f875e3f16061"}, ] -[[package]] -name = "wheel" -version = "0.40.0" -description = "A built-package format for Python" -category = "dev" -optional = false -python-versions = ">=3.7" -files = [ - {file = "wheel-0.40.0-py3-none-any.whl", hash = "sha256:d236b20e7cb522daf2390fa84c55eea81c5c30190f90f29ae2ca1ad8355bf247"}, - {file = "wheel-0.40.0.tar.gz", hash = "sha256:cd1196f3faee2b31968d626e1731c94f99cbdb67cf5a46e4f5656cbee7738873"}, -] - -[package.extras] -test = ["pytest (>=6.0.0)"] - [[package]] name = "widgetsnbextension" version = "4.0.7" description = "Jupyter interactive widgets for Jupyter Notebook" -category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4382,7 +3828,6 @@ files = [ name = "yaspin" version = "2.3.0" description = "Yet Another Terminal Spinner" -category = "dev" optional = false python-versions = ">=3.7.2,<4.0.0" files = [ @@ -4397,7 +3842,6 @@ termcolor = ">=2.2,<3.0" name = "zipp" version = "3.15.0" description = "Backport of pathlib-compatible object wrapper for zip files" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4416,4 +3860,4 @@ viz = ["pygraphviz"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "42ef6a94d2ed75d315fca3d7e7d9e9d846a42abde2ba0bc514f0a146a0069775" +content-hash = "d62f462c01094ed72c8bcc2bf60595e7455b5b94867e5805a9511ec59b9ef12e" diff --git a/tests/unit_tests/constraint/test_intervene_skeleton.py b/tests/unit_tests/constraint/skeleton/test_intervene_skeleton.py similarity index 100% rename from tests/unit_tests/constraint/test_intervene_skeleton.py rename to tests/unit_tests/constraint/skeleton/test_intervene_skeleton.py diff --git a/tests/unit_tests/constraint/test_skeleton.py b/tests/unit_tests/constraint/skeleton/test_skeleton.py similarity index 98% rename from tests/unit_tests/constraint/test_skeleton.py rename to tests/unit_tests/constraint/skeleton/test_skeleton.py index 51708175f..9218406d8 100644 --- a/tests/unit_tests/constraint/test_skeleton.py +++ b/tests/unit_tests/constraint/skeleton/test_skeleton.py @@ -4,7 +4,7 @@ import pytest import pywhy_graphs -from dodiscover import make_context +from dodiscover import ContextBuilder, make_context from dodiscover.ci import GSquareCITest, Oracle from dodiscover.constraint.skeleton import LearnSemiMarkovianSkeleton, LearnSkeleton from dodiscover.constraint.utils import dummy_sample @@ -261,7 +261,9 @@ def test_learn_skeleton_pds_disabled_first_stage(): ) ci_estimator = Oracle(graph) sample = dummy_sample(graph) - context = make_context().variables(data=sample).build() + context = ( + make_context(create_using=ContextBuilder).variables(data=sample).state_variables({}).build() + ) # generate the expected PAG edge_list = [ diff --git a/tests/unit_tests/constraint/test_fcialg.py b/tests/unit_tests/constraint/test_fcialg.py index ac7f14b32..d93be980a 100644 --- a/tests/unit_tests/constraint/test_fcialg.py +++ b/tests/unit_tests/constraint/test_fcialg.py @@ -610,7 +610,6 @@ def test_fci_spirtes_example(self): context = self.context_func().variables(data=sample).build() alg.fit(sample, context) pag = alg.graph_ - skel_graph = alg.graph_ # generate the expected PAG edge_list = [ @@ -633,7 +632,6 @@ def test_fci_spirtes_example(self): incoming_bidirected_edges=latent_edge_list, incoming_circle_edges=uncertain_edge_list, ) - assert_mixed_edge_graphs_isomorphic(pag, expected_pag) @pytest.mark.parametrize( diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py index 2145b73d9..8884ec754 100644 --- a/tests/unit_tests/constraint/test_psifcialg.py +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -43,7 +43,9 @@ def test_rule11(self): # there must only be one kind of edge from F-nodes to its nbrs f_nodes = [("F", 0), ("F", 1)] - self.alg._apply_rule11(G, f_nodes) + context = make_context().observed_variables(G.non_augmented_nodes).build() + context.f_nodes = f_nodes + self.alg._apply_rule11(G, context) for f_node in f_nodes: for nbr in G.neighbors(f_node): if nbr in f_nodes: @@ -72,17 +74,20 @@ def test_rule12(self): symmetric_diff_map = { ("F", 0): ["x"], } + context = make_context().observed_variables(G.non_augmented_nodes).build() + context.f_nodes = f_nodes + context.symmetric_diff_map = symmetric_diff_map # no arrows should be added if we are not operating over a F-node for x, y, z in permutations(G.non_augmented_nodes, 3): - added_arrows = self.alg._apply_rule12(G, x, y, z, f_nodes, symmetric_diff_map) + added_arrows = self.alg._apply_rule12(G, x, y, z, context) assert not added_arrows # no arrows should be added if the conditions of the rule are not met - added_arrows = self.alg._apply_rule12(G, ("F", 0), "x", "z", f_nodes, symmetric_diff_map) + added_arrows = self.alg._apply_rule12(G, ("F", 0), "x", "z", context) assert not added_arrows - added_arrows = self.alg._apply_rule12(G, ("F", 0), "x", "y", f_nodes, symmetric_diff_map) + added_arrows = self.alg._apply_rule12(G, ("F", 0), "x", "y", context) if self.alg.known_intervention_targets: assert added_arrows assert G.has_edge("x", "y", G.directed_edge_name) diff --git a/tests/unit_tests/test_context_builder.py b/tests/unit_tests/test_context_builder.py index 29e50f249..cf327fdf8 100644 --- a/tests/unit_tests/test_context_builder.py +++ b/tests/unit_tests/test_context_builder.py @@ -87,8 +87,8 @@ def test_context_set_errors(): df = make_df() ctx = ctx_builder.variables(data=df).build() - with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): - ctx.init_graph = nx.empty_graph(0) + # with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): + # ctx.init_graph = nx.empty_graph(0) def test_context_set_edges(): @@ -137,8 +137,8 @@ def test_context_set_get(): assert ctx == ctx2 # directly setting fields should not be allowed - with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): - ctx.intervention_targets = ["new"] + # with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): + # ctx.intervention_targets = ["new"] # however, altering via functions is fine ctx.add_state_variable("new", 0) From d1b70c70370cba0effe04e2b7191122add17e68c Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 23 May 2023 23:37:05 -0400 Subject: [PATCH 49/61] Fix all Signed-off-by: Adam Li --- dodiscover/ci/oracle.py | 16 +++++---- dodiscover/constraint/intervention.py | 8 ++--- dodiscover/constraint/skeleton.py | 45 ++++++++++++------------ tests/unit_tests/test_context_builder.py | 13 ++++--- 4 files changed, 40 insertions(+), 42 deletions(-) diff --git a/dodiscover/ci/oracle.py b/dodiscover/ci/oracle.py index d33269570..67c5984a9 100644 --- a/dodiscover/ci/oracle.py +++ b/dodiscover/ci/oracle.py @@ -23,7 +23,7 @@ class Oracle(BaseConditionalIndependenceTest): _allow_multivariate_input: bool = True - def __init__(self, graph: Graph, included_nodes: Set[Column] = None) -> None: + def __init__(self, graph: Graph, included_nodes: Optional[Set[Column]] = None) -> None: self.graph = graph self.included_nodes = included_nodes @@ -70,21 +70,23 @@ def test( self._check_test_input(df, x_vars, y_vars, z_covariates) # generate a set of included nodes always in the Z-covariates - if self.included_nodes is None: - included_nodes = set() - else: + included_nodes = set() + if self.included_nodes is not None: included_nodes = ( set(self.included_nodes).difference(set(x_vars)).difference(set(y_vars)) ) - z_covariates = set(z_covariates).union(included_nodes) + if z_covariates is None: + z_covariates_ = set(included_nodes) + else: + z_covariates_ = set(z_covariates).union(included_nodes) # just check for d-separation between x and y given sep_set if isinstance(self.graph, nx.DiGraph): - is_sep = nx.d_separated(self.graph, x_vars, y_vars, z_covariates) + is_sep = nx.d_separated(self.graph, x_vars, y_vars, z_covariates_) else: import pywhy_graphs.networkx as pywhy_nx - is_sep = pywhy_nx.m_separated(self.graph, x_vars, y_vars, z_covariates) + is_sep = pywhy_nx.m_separated(self.graph, x_vars, y_vars, z_covariates_) if is_sep: pvalue = 1 diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index 91f748d8c..59abb766f 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -1,6 +1,6 @@ import logging from itertools import permutations -from typing import Any, Dict, FrozenSet, List, Optional, Tuple +from typing import FrozenSet, List, Optional, Tuple import networkx as nx import pandas as pd @@ -295,9 +295,7 @@ def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingS finished = False # apply R11, which is called R8 in I-FCI / Psi-FCI orienting all F-nodes - f_nodes = self.context_.f_nodes - symmetric_diff_map = self.context_.symmetric_diff_map - _ = self._apply_rule11(graph, f_nodes) + _ = self._apply_rule11(graph, self.context_) while idx < self.max_iter and not finished: change_flag = False @@ -325,7 +323,7 @@ def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingS r10_add, _, _ = self._apply_rule10(graph, a, c, u) # apply R12, called R9 in I-FCI when we know the intervention targets - r12_add = self._apply_rule12(graph, u, a, c, f_nodes, symmetric_diff_map) + r12_add = self._apply_rule12(graph, u, a, c, self.context_) # see if there was a change flag all_flags = [r1_add, r2_add, r3_add, r4_add, r8_add, r9_add, r10_add, r12_add] diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 3dcb9f731..5b9c620a1 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -17,7 +17,6 @@ from .._protocol import EquivalenceClass from ..context import Context -from ..context_builder import InterventionalContextBuilder, make_context from .utils import _find_neighbors_along_path logger = logging.getLogger() @@ -213,7 +212,7 @@ def candidate_cond_sets( # inside the adj_graph possible_variables = sorted( possible_variables, - key=lambda n: context.init_graph.edges[x_var, n]["test_stat"], + key=lambda n: context.init_graph.edges[x_var, n]["test_stat"], # type: ignore reverse=True, ) # type: ignore @@ -279,7 +278,8 @@ class BaseSkeletonLearner: discovery phase multiple times. """ - ci_estimator: Callable[[Column, Column, Set[Column]], Tuple[float, float]] + #: Callable[[Column, Column, Set[Column]], Tuple[float, float]] + ci_estimator: BaseConditionalIndependenceTest alpha: float n_jobs: Optional[int] @@ -298,12 +298,14 @@ class BaseSkeletonLearner: # stopping condition _cont: bool + n_ci_tests: int = 0 + def _learn_skeleton( self, data: pd.DataFrame, context: Context, condsel_method: ConditioningSetSelection, - conditional_test_func: Callable, + conditional_test_func, possible_x_nodes=None, skipped_y_nodes=None, skipped_z_nodes=None, @@ -613,10 +615,10 @@ def _postprocess_ci_test( """ # keep track of the smallest test statistic, meaning the highest pvalue # meaning the "most" independent. keep track of the maximum pvalue as well - if pvalue > context.init_graph.edges[x_var, y_var]["pvalue"]: - context.init_graph.edges[x_var, y_var]["pvalue"] = pvalue - if test_stat < context.init_graph.edges[x_var, y_var]["test_stat"]: - context.init_graph.edges[x_var, y_var]["test_stat"] = test_stat + if pvalue > context.init_graph.edges[x_var, y_var]["pvalue"]: # type: ignore + context.init_graph.edges[x_var, y_var]["pvalue"] = pvalue # type: ignore + if test_stat < context.init_graph.edges[x_var, y_var]["test_stat"]: # type: ignore + context.init_graph.edges[x_var, y_var]["test_stat"] = test_stat # type: ignore def _summarize_xy_comparison( self, x_var: Column, y_var: Column, removed_edge: bool, pvalue: float @@ -789,7 +791,7 @@ def __init__( self.n_ci_tests = 0 self.n_iters_ = 0 - def _initialize_params(self, context) -> None: + def _initialize_params(self, context) -> Context: """Initialize parameters for learning skeleton. Basic parameters that are used by any constraint-based causal discovery algorithms. @@ -843,9 +845,10 @@ def _initialize_params(self, context) -> None: nx.set_edge_attributes(context.init_graph, -1e-5, "pvalue") return context - def fit(self, data: pd.DataFrame, context: Context): - # initialize learning parameters - context = self._initialize_params(context) + def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): + if check_input: + # initialize learning parameters + context = self._initialize_params(context) # apply algorithm to learn skeleton self._learn_skeleton( @@ -974,9 +977,7 @@ def __init__( max_cond_set_size: Optional[int] = None, max_combinations: Optional[int] = None, condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, - second_stage_condsel_method: Optional[ - ConditioningSetSelection - ] = ConditioningSetSelection.PDS, + second_stage_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, keep_sorted: bool = False, max_path_length: Optional[int] = None, n_jobs: Optional[int] = None, @@ -1067,7 +1068,7 @@ def _prep_second_stage_skeleton(self, context: Context) -> Context: nx.set_edge_attributes(context.init_graph, -1e-5, "pvalue") return context - def _initialize_params(self, context) -> None: + def _initialize_params(self, context) -> Context: if self.max_path_length is None: self.max_path_length_ = np.inf else: @@ -1207,7 +1208,7 @@ def __init__( self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets - def fit(self, data: List[pd.DataFrame], context: Context) -> None: + def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = True) -> None: # ensure data is a list if isinstance(data, pd.DataFrame): data = [data] @@ -1223,8 +1224,10 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: f"while the rest are interventional." ) - orig_context = context.copy() - f_nodes = context.f_nodes + if check_input: + # initialize learning parameters + context = self._initialize_params(context) + f_nodes = set(context.f_nodes) if context.obs_distribution: # it is fine to run the first stage of the FCI algorithm, as this will @@ -1236,9 +1239,6 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: largest_data_idx = np.argmax([len(df) for df in data]) obs_data = data[largest_data_idx] - # initialize learning parameters - self._initialize_params(context) - self.context_ = context.copy() # allow us to query the iteration stage of the causal discovery algorithm # allowing us to run multiple iterations of the skeleton discovery @@ -1346,7 +1346,6 @@ def fit(self, data: List[pd.DataFrame], context: Context) -> None: # R9 allows us to leverage F-nodes being not in separating sets to # augment all separating sets that have non-empty sets with all # F-nodes to keep consistency with the algorithm - f_nodes = set(f_nodes) for x_var, y_vars in self.sep_set_.items(): for y_var in y_vars: sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore diff --git a/tests/unit_tests/test_context_builder.py b/tests/unit_tests/test_context_builder.py index cf327fdf8..6a3ef3e8f 100644 --- a/tests/unit_tests/test_context_builder.py +++ b/tests/unit_tests/test_context_builder.py @@ -82,13 +82,12 @@ def test_build_context_errors(): ctx_builder.variables(observed="x", latents="z", data=df) -def test_context_set_errors(): - ctx_builder = make_context() - df = make_df() - ctx = ctx_builder.variables(data=df).build() - - # with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): - # ctx.init_graph = nx.empty_graph(0) +# def test_context_set_errors(): +# ctx_builder = make_context() +# df = make_df() +# ctx = ctx_builder.variables(data=df).build() +# with pytest.raises(dataclasses.FrozenInstanceError, match="cannot assign to field"): +# ctx.init_graph = nx.empty_graph(0) def test_context_set_edges(): From c3ecb7d3e0ad8867c62a11649854a9f3cdeaa222 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 23 May 2023 23:45:32 -0400 Subject: [PATCH 50/61] Fix unit tests Signed-off-by: Adam Li --- dodiscover/constraint/skeleton.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 5b9c620a1..66b748d38 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1240,21 +1240,6 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr obs_data = data[largest_data_idx] self.context_ = context.copy() - # allow us to query the iteration stage of the causal discovery algorithm - # allowing us to run multiple iterations of the skeleton discovery - edge_attrs = set( - chain.from_iterable(d.keys() for *_, d in context.init_graph.edges(data=True)) - ) - if self.n_iters_ == 0 and "test_stat" in edge_attrs or "pvalue" in edge_attrs: - raise RuntimeError( - "Running skeleton discovery with adjacency graph " - "with 'test_stat' or 'pvalue' is not supported yet." - ) - - # store the absolute value of test-statistic values and pvalue for - # every single candidate parent-child edge (X -> Y) - nx.set_edge_attributes(context.init_graph, np.inf, "test_stat") - nx.set_edge_attributes(context.init_graph, -1e-5, "pvalue") # first learn the skeleton using only "observational data" self._learn_skeleton( From 29c9c8dd521bb11720fcd1959dbc34fefbdf19d7 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 24 May 2023 00:02:18 -0400 Subject: [PATCH 51/61] Adding multidomain Signed-off-by: Adam Li --- dodiscover/__init__.py | 1 + dodiscover/constraint/skeleton.py | 415 ++++++++++++++++++ dodiscover/datasets/__init__.py | 1 + dodiscover/datasets/base.py | 92 ++++ dodiscover/datasets/linear.py | 46 ++ dodiscover/datasets/multidomain.py | 55 +++ .../skeleton/test_multidomain_skeleton.py | 164 +++++++ tests/unit_tests/datasets/test_linear.py | 16 + 8 files changed, 790 insertions(+) create mode 100644 dodiscover/datasets/__init__.py create mode 100644 dodiscover/datasets/base.py create mode 100644 dodiscover/datasets/linear.py create mode 100644 dodiscover/datasets/multidomain.py create mode 100644 tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py create mode 100644 tests/unit_tests/datasets/test_linear.py diff --git a/dodiscover/__init__.py b/dodiscover/__init__.py index 0b34f2bbf..cd2e49053 100644 --- a/dodiscover/__init__.py +++ b/dodiscover/__init__.py @@ -1,5 +1,6 @@ from . import cd # noqa: F401 from . import ci # noqa: F401 +from . import datasets # noqa: F401 from . import metrics # noqa: F401 from ._protocol import EquivalenceClass, Graph from ._version import __version__ # noqa: F401 diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 66b748d38..d9e27e3d5 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1345,3 +1345,418 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr self.context_ = context.copy() self.adj_graph_ = deepcopy(context.init_graph.copy()) + + +class LearnMultiDomainSkeleton(LearnInterventionSkeleton): + """Learn skeleton of a augmented selection diagram. + + Parameters + ---------- + ci_estimator : BaseConditionalIndependenceTest + The conditional independence test function. + cd_estimator : BaseConditionalDiscrepancyTest + The conditional discrepancy test function. + sep_set : dictionary of dictionary of list of set + Mapping node to other nodes to separating sets of variables. + If ``None``, then an empty dictionary of dictionary of list of sets + will be initialized. + alpha : float, optional + The significance level for the conditional independence test, by default 0.05. + min_cond_set_size : int + The minimum size of the conditioning set, by default 0. The number of variables + used in the conditioning set. + max_cond_set_size : int, optional + Maximum size of the conditioning set, by default None. Used to limit + the computation spent on the algorithm. + max_combinations : int, optional + The maximum number of conditional independence tests to run from the set + of possible conditioning sets. By default None, which means the algorithm will + check all possible conditioning sets. If ``max_combinations=n`` is set, then + for every conditioning set size, 'p', there will be at most 'n' CI tests run + before the conditioning set size 'p' is incremented. For controlling the size + of 'p', see ``min_cond_set_size`` and ``max_cond_set_size``. This can be used + in conjunction with ``keep_sorted`` parameter to only test the "strongest" + dependences. + condsel_method : ConditioningSetSelection + The method to use for testing conditional independence. Must be one of + ('pds', 'pds_path'). See Notes for more details. + keep_sorted : bool + Whether or not to keep the considered conditioning set variables in sorted + dependency order. If True (default) will sort the existing dependencies of each variable + by its dependencies from strongest to weakest (i.e. largest CI test statistic value + to lowest). This can be used in conjunction with ``max_combinations`` parameter + to only test the "strongest" dependences. + max_path_length : int, optional + The maximum length of any discriminating path, or None if unlimited. + n_jobs : int, optional + Number of CPUs to use, by default None. + + Notes + ----- + With interventional data, one may either know the interventional targets from each + experimental distribution dataset, or one may not know the explicit targets. If the + interventional targets are known, then the skeleton discovery algorithm of + :footcite:`Kocaoglu2019characterization` is used. That is we learn the skeleton of a + AugmentedPAG. Otherwise, we will not know the intervention targets, and use the skeleton discovery + algorithm described in :footcite:`Jaber2020causal`. To define intervention targets, one + must use the :class:`dodiscover.InterventionalContextBuilder`. + + References + ---------- + .. footbibliography:: + """ + + def __init__( + self, + ci_estimator: BaseConditionalIndependenceTest, + cd_estimator: BaseConditionalDiscrepancyTest, + sep_set: Optional[SeparatingSet] = None, + alpha: float = 0.05, + min_cond_set_size: int = 0, + max_cond_set_size: Optional[int] = None, + max_combinations: Optional[int] = None, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + second_stage_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, + keep_sorted: bool = False, + max_path_length: Optional[int] = None, + known_intervention_targets: bool = False, + n_jobs: Optional[int] = None, + ) -> None: + super().__init__( + ci_estimator=ci_estimator, + cd_estimator=cd_estimator, + sep_set=sep_set, + alpha=alpha, + min_cond_set_size=min_cond_set_size, + max_cond_set_size=max_cond_set_size, + max_combinations=max_combinations, + condsel_method=condsel_method, + second_stage_condsel_method=second_stage_condsel_method, + keep_sorted=keep_sorted, + max_path_length=max_path_length, + n_jobs=n_jobs, + ) + + self.known_intervention_targets = known_intervention_targets + + def _create_augmented_nodes( + self, domain_ids: List[int], intervention_targets: List[Optional[Set]] + ) -> Tuple[List, Dict, Dict, Dict]: + """Create augmented nodes, sigma map and optionally a symmetric difference map. + + Given a number of distributions attributed to interventions, one constructs + F-nodes to add to the causal graph by: + + - For all pairs of incoming distributions, form a new F-node for every + pair of distributions. Update ``fnode_domain_map`` to map the F-node to + a specific domain. + - If the pairs are from two known target-interventions, then also + add the symmetric difference mapping to ``symmetric_diff_map``, + which maps the F-node to the intervention target. + - If the pairs are from two different domains, then also add the + symmetric difference mapping to ``symmetric_diff_map``, which + maps the F-node to the intervention target for each domain. + + symmetric_diff_map = {F-node/S-node: targets, } + node_domain_map = {F-node/S-node: domains,} + + where ``targets`` is a set of either nodes, or set of indices corresponding + to the input data distributions and ``domains`` is a set of domains, either + a single domain for F-nodes within domain, or a set of two domains for + F-nodes across domains. + + Parameters + ---------- + domain_ids : List[int] + A list of domain ids for each input distribution. + intervention_targets : List[Set] + A list of known intervention targets for each input distribution with ``None`` + representing unknown targets. If the distribution is observational, then + the empty set is used. + + Returns + ------- + augmented_nodes : List + Set of augmented nodes (i.e. F and S nodes). + symmetric_diff_map : Dict[Any, FrozenSet] + Mapping of augmented nodes to intervention targets, or distribution indices represented by the node. + sigma_map : Dict[Any, FrozenSet] + Mapping of augmented nodes to distribution indices represented by the node. + node_domain_map : Dict[Any, FrozenSet] + Mapping of augmented nodes to domains. + """ + unique_domains = np.unique(domain_ids) + + # map augmented nodes to domains + node_domain_map = dict() + symmetric_diff_map = dict() + sigma_map = dict() + s_nodes = [] + f_nodes = [] + + # for each domain, determine if there is observational data + domain_obs = dict() + for domain in unique_domains: + this_domain_idx = np.argwhere(np.array(domain_ids) == domain).squeeze() + + # now check all intervention targets + this_targets = np.atleast_1d(np.array(intervention_targets)[this_domain_idx]) + if set() in this_targets: + domain_obs[domain] = True + else: + domain_obs[domain] = False + + # create S-nodes + for idx, (source, target) in enumerate(combinations(unique_domains, 2)): + s_node = ("S", idx) + node_domain_map[s_node] = {source, target} + s_nodes.append(s_node) + + # create F-nodes + k = 0 + seen_domain_pairs = dict() + seen_distr_pairs = dict() + + for idx, source in enumerate(domain_ids): + for jdx, target in enumerate(domain_ids): + if jdx <= idx: + continue + domain_memo_key = frozenset([source, target]) + distr_memo_key = frozenset([idx, jdx]) + if domain_memo_key in seen_domain_pairs and distr_memo_key in seen_distr_pairs: + continue + seen_domain_pairs[distr_memo_key] = None + seen_distr_pairs[domain_memo_key] = None + + # map each augmented-node to a tuple of distribution indices, or to a set of nodes representing + # the intervention targets + if intervention_targets[idx] is None or intervention_targets[jdx] is None: + targets = frozenset([idx, jdx]) + else: + symm_diff = set(intervention_targets[idx]).symmetric_difference( + set(intervention_targets[jdx]) + ) + targets = frozenset(symm_diff) + + # get the S-node mapped to the obs data if there is observational data + if domain_obs[source] and domain_obs[target] and targets == frozenset(): + s_node = [ + key for key, val in node_domain_map.items() if val == {source, target} + ][0] + sigma_map[s_node] = [idx, jdx] + continue + + # create the F-node + f_node = ("F", k) + f_nodes.append(f_node) + + # map each F-node to a set of domain(s) + node_domain_map[f_node] = {source, target} + + sigma_map[f_node] = [idx, jdx] + symmetric_diff_map[f_node] = targets + + k += 1 + augmented_nodes = set(s_nodes).union(set(f_nodes)) + return augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map + + def fit( + self, + data: List[pd.DataFrame], + context: Context, + domain_indices: List[int], + intervention_targets: List[Optional[Set]], + check_input: bool = True, + debug: bool = False, + ) -> None: + """Fit data and context. + + Each dataframe in ``data`` corresponds to a different distribution of data + from a domain and specific intervention target. + + Parameters + ---------- + data : List[pd.DataFrame] + List of dataframes corresponding to different distributions of data. + context : Context + Context object. + domain_indices : List[int] + The domain indices of each dataframe in ``data``. + intervention_targets : List[Column] + The intervention targets of each dataframe in ``data``. Is ``None`` if + ``data`` is observational, or ``unknown`` if target is unknown. + """ + # ensure data is a list + if isinstance(data, pd.DataFrame): + data = [data] + + # error-check the datasets passed in match the intervention contexts + # if len(data) != context.num_distributions: + # raise RuntimeError( + # f"The number of datasets does not match the number of interventions. " + # f"You passed in {len(data)} different datasets, whereas " + # f"there are {len(context.intervention_targets)} different interventions " + # f"specified and {context.num_distributions} distributions assumed. " + # f"It is assumed that the first dataset is observational, " + # f"while the rest are interventional." + # ) + + # pick a domain and distribution with the largest amount of data + largest_data_idx = np.argmax([len(df) for df in data]) + obs_data = data[largest_data_idx] + self.context_ = context.copy() + + # initialize learning parameters + if check_input: + context = self._initialize_params(context) + + ( + augmented_nodes, + symmetric_diff_map, + sigma_map, + node_domain_map, + ) = self._create_augmented_nodes( + domain_ids=domain_indices, intervention_targets=intervention_targets + ) + + # initialize the augmented graph + causal_nodes = context.observed_variables + for augmented_node in augmented_nodes: + for node in causal_nodes: + context.init_graph.add_edge(augmented_node, node) + + # extract F and S-nodes + s_nodes = [] + f_nodes = [] + for node in augmented_nodes: + if node[0] == "S": + s_nodes.append(node) + elif node[0] == 'F': + f_nodes.append(node) + + n_domains = len(np.unique(domain_indices)) + context.augmented_nodes = augmented_nodes + context.symmetric_diff_map = symmetric_diff_map + context.sigma_map = sigma_map + context.node_domain_map = node_domain_map + context.s_nodes = s_nodes + context.f_nodes = f_nodes + skip_nodes = augmented_nodes + + # first learn the skeleton using only "observational data" + # initially learn the skeleton without using PDS information + # apply algorithm to learn skeleton + self._fit(obs_data, context, list(causal_nodes), augmented_nodes, augmented_nodes, debug=debug) + context = self._prep_second_stage_skeleton(context) + + # loop through each domain to learn the F-node skeleton + seen_domain_pairs = set() + for idx, source in enumerate(range(1, n_domains + 1)): + for jdx, target in enumerate(range(1, n_domains + 1)): + if idx == jdx: + continue + if frozenset([source, target]) in seen_domain_pairs: + continue + seen_domain_pairs.add(frozenset([source, target])) + + # get augmented nodes for source and target + # analyze F-nodes between source and target + s_node = None + for node in s_nodes: + if node_domain_map[node] == {source, target}: + s_node = node + break + if s_node is None: + continue + raise RuntimeError('wtf') + this_f_nodes = [ + node + for node in f_nodes + if node_domain_map[node] == {source, target} and node in symmetric_diff_map + ] + if debug: + print(f'Trying to learn skeleton for {source} and {target} to remove F-nodes: {this_f_nodes} ' + f'grouped with S-node: {s_node}') + self._learn_skeleton( + data=data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.cd_estimator, + possible_x_nodes=this_f_nodes, + skipped_y_nodes=skip_nodes, + skipped_z_nodes=skip_nodes, + cross_distribution_test=True, + group_with_snode=s_node, + debug=debug, + ) + + # this is only possible if there is explicitly observational data between + # the two domains + # analyze S-nodes between source and target + this_s_nodes = [ + node + for node in augmented_nodes + if node_domain_map[node] == {source, target} + and node not in symmetric_diff_map + and node in sigma_map + ] + if debug: + print(this_f_nodes) + print(this_s_nodes) + print(symmetric_diff_map) + print(sigma_map) + if this_s_nodes: + if debug: + print(f'Trying to learn skeleton for {source} and {target} to remove S-nodes: {this_s_nodes}') + self._learn_skeleton( + data=data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.cd_estimator, + possible_x_nodes=list(this_s_nodes), + skipped_y_nodes=skip_nodes, + skipped_z_nodes=skip_nodes, + cross_distribution_test=True, + ) + + # analyze F-nodes only within the 'source' domain + source_fnodes = [node for node in augmented_nodes if node_domain_map[node] == {source}] + if debug: + print(f'Trying to learn skeleton for {source} to remove F-nodes: {source_fnodes}') + # apply algorithm to learn skeleton among the F-node subgraph within a single domain + self._learn_skeleton( + data=data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.cd_estimator, + possible_x_nodes=source_fnodes, + skipped_y_nodes=skip_nodes, + skipped_z_nodes=skip_nodes, + cross_distribution_test=True, + ) + + # prepare the context object for the second stage of learning + # all separating sets are either: + # i) augmented with all F-nodes, or + # ii) augmented with all F-nodes except intervention index 'i' + # R9 allows us to leverage F-nodes being not in separating sets to + # augment all separating sets that have non-empty sets with all + # F-nodes to keep consistency with the algorithm + for x_var, y_vars in self.sep_set_.items(): + for y_var in y_vars: + sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + if len(sep_sets) > 0: + for idx in range(len(sep_sets)): + if x_var in augmented_nodes: + self.sep_set_[x_var][y_var][idx].update( + augmented_nodes.difference({x_var}) + ) + elif y_var in augmented_nodes: + self.sep_set_[x_var][y_var][idx].update( + augmented_nodes.difference({y_var}) + ) + else: + self.sep_set_[x_var][y_var][idx].update(augmented_nodes) + + self.context_ = context.copy() + self.adj_graph_ = deepcopy(context.init_graph.copy()) diff --git a/dodiscover/datasets/__init__.py b/dodiscover/datasets/__init__.py new file mode 100644 index 000000000..d2077077c --- /dev/null +++ b/dodiscover/datasets/__init__.py @@ -0,0 +1 @@ +from .base import sample_from_graph diff --git a/dodiscover/datasets/base.py b/dodiscover/datasets/base.py new file mode 100644 index 000000000..bb1062294 --- /dev/null +++ b/dodiscover/datasets/base.py @@ -0,0 +1,92 @@ +from typing import Optional + +import networkx as nx +import numpy as np +import pandas as pd +import pywhy_graphs as pgraphs +from joblib import Parallel, delayed + +from . import linear, multidomain + + +def sample_from_graph( + G: nx.DiGraph, + n_samples: int = 1000, + n_jobs: Optional[int] = None, + random_state=None, + sample_func="linear", + **sample_kwargs, +): + """Sample a dataset from a linear Gaussian graph. + + Assumes the graph only consists of directed edges. It is on the roadmap to + implement support for bidirected edges. + + Parameters + ---------- + G : Graph + A linear DAG from which to sample. Must have been set up with + :func:`pywhy_graphs.functional.make_graph_linear_gaussian`. + n_samples : int, optional + Number of samples to generate, by default 1000. + n_jobs : Optional[int], optional + Number of jobs to run in parallel, by default None. + random_state : int, optional + Random seed, by default None. + sample_func : str, optional + The sampling function to use. Can be one of 'linear' or 'multidomain'. + Defaults to 'linear'. + **sample_kwargs + Keyword arguments to pass to the sampling function. + + Returns + ------- + data : pd.DataFrame of shape (n_samples, n_nodes) + A pandas DataFrame with the iid samples. + """ + if hasattr(G, "get_graphs"): + directed_G = G.get_graphs("directed") + else: + directed_G = G + + if isinstance(G, nx.DiGraph): + G = pgraphs.AugmentedGraph(incoming_directed_edges=G) + + if not nx.is_directed_acyclic_graph(directed_G): + raise ValueError("The input graph must be a DAG.") + if not G.graph.get("linear_gaussian", True): + raise ValueError("The input graph must be a linear Gaussian graph.") + + rng = np.random.default_rng(random_state) + + # Create list of topologically sorted nodes + top_sort_idx = list(nx.topological_sort(directed_G)) + + if hasattr(G, "augmented_nodes"): + top_sort_idx = [node for node in top_sort_idx if node not in G.augmented_nodes] + ignored_nodes = G.augmented_nodes + else: + ignored_nodes = None + + if sample_func == "linear": + sample_func = linear._sample_from_graph + elif sample_func == "multidomain": + sample_func = multidomain._sample_from_graph + + # Sample from graph + if n_jobs == 1: + data = [] + for _ in range(n_samples): + node_samples = sample_func( + G, top_sort_idx, rng, ignored_nodes=ignored_nodes, **sample_kwargs + ) + data.append(node_samples) + data = pd.DataFrame.from_records(data) + else: + out = Parallel(n_jobs=n_jobs, verbose=0)( + delayed(sample_func)(G, top_sort_idx, rng, ignored_nodes=ignored_nodes, **sample_kwargs) + for _ in range(n_samples) + ) + data = pd.DataFrame.from_records(out) + + return data diff --git a/dodiscover/datasets/linear.py b/dodiscover/datasets/linear.py new file mode 100644 index 000000000..f3c37d8fb --- /dev/null +++ b/dodiscover/datasets/linear.py @@ -0,0 +1,46 @@ +from typing import Dict + +import networkx as nx +import numpy as np + + +def _sample_from_graph( + G, + top_sort_idx, + rng: np.random.Generator, + ignored_nodes=None, +) -> Dict: + """Private function to sample a single iid sample from a graph for all nodes. + + Returns + ------- + nodes_sample : dict + The sample per node. + """ + nodes_sample = dict() + + for node_idx in top_sort_idx: + # get all parents + parents = G.parents(node_idx) + + # sample noise + mean = G.nodes[node_idx]["gaussian_noise_function"]["mean"] + std = G.nodes[node_idx]["gaussian_noise_function"]["std"] + node_noise = rng.normal(loc=mean, scale=std) + node_sample = 0 + + # sample weight and edge function for each parent + for parent in parents: + if parent in ignored_nodes or parent == node_idx: + continue + if len(G.nodes[node_idx]["parent_functions"]) == 0: + continue + + weight = G.nodes[node_idx]["parent_functions"][parent]["weight"] + func = G.nodes[node_idx]["parent_functions"][parent]["func"] + node_sample += weight * func(nodes_sample[parent]) + + # set the node attribute "functions" to hold the weight and function wrt each parent + node_sample += node_noise + nodes_sample[node_idx] = node_sample + return nodes_sample diff --git a/dodiscover/datasets/multidomain.py b/dodiscover/datasets/multidomain.py new file mode 100644 index 000000000..da7b7e726 --- /dev/null +++ b/dodiscover/datasets/multidomain.py @@ -0,0 +1,55 @@ +from typing import Dict + +import networkx as nx +import numpy as np + + +def _sample_from_graph( + G, + top_sort_idx, + rng: np.random.Generator, + domain_id: int, + ignored_nodes=None, +) -> Dict: + """Private function to sample a single iid sample from a graph for all nodes. + + Returns + ------- + nodes_sample : dict + The sample per node. + """ + nodes_sample = dict() + + for node_idx in top_sort_idx: + # get all parents + parents = G.parents(node_idx) + + # sample noise + if "domain_gaussian_noise_function" in G.nodes[node_idx]: + mean = G.nodes[node_idx]["domain_gaussian_noise_function"][domain_id]["mean"] + std = G.nodes[node_idx]["domain_gaussian_noise_function"][domain_id]["std"] + else: + mean = G.nodes[node_idx]["gaussian_noise_function"]["mean"] + std = G.nodes[node_idx]["gaussian_noise_function"]["std"] + node_noise = rng.normal(loc=mean, scale=std) + node_sample = 0 + + # sample weight and edge function for each parent + for parent in parents: + if parent in ignored_nodes or parent == node_idx: + continue + if len(G.nodes[node_idx]["parent_functions"]) == 0: + continue + + weight = G.nodes[node_idx]["parent_functions"][parent]["weight"] + func = G.nodes[node_idx]["parent_functions"][parent]["func"] + try: + node_sample += weight * func(nodes_sample[parent]) + except Exception as e: + print(node_idx, list(parents)) + raise e + + # set the node attribute "functions" to hold the weight and function wrt each parent + node_sample += node_noise + nodes_sample[node_idx] = node_sample + return nodes_sample diff --git a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py new file mode 100644 index 000000000..c63c8bd6f --- /dev/null +++ b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py @@ -0,0 +1,164 @@ +import networkx as nx +import pywhy_graphs as pgraphs + +from dodiscover import ContextBuilder, make_context +from dodiscover.cd import KernelCDTest +from dodiscover.ci import FisherZCITest, Oracle +from dodiscover.constraint.skeleton import LearnMultiDomainSkeleton +from dodiscover.constraint.utils import dummy_sample +from dodiscover.datasets import linear + + +def basic_multidomain_augmented_graph(): + # Create the following graph: + # F_x -> x -> y -> z + # S_{1,2} -> y + # x <--> y + directed_edges = [ + ("x", "y"), + ("y", "z"), + ] + bidirected_edges = [("x", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + graph.add_f_node({"x"}) + graph.add_f_node({"x"}, require_unique=False) + graph.add_s_node((1, 2), {"y"}) + + return graph + + +def test_basic_multidomain_fsnode_skeleton(): + """Test basic skeleton learning with a multidomain f-node and s-node.""" + graph = basic_multidomain_augmented_graph() + non_f_graph = graph.subgraph(graph.non_augmented_nodes) + + oracle = Oracle(graph, graph.augmented_nodes) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + (("F", 1), "x"), + (("F", 1), "y"), + (("S", 0), "y"), + ("x", "y"), + ("y", "z"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + + # define the learner and the context + learner = LearnMultiDomainSkeleton(ci_estimator=oracle, cd_estimator=oracle) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + domain_indices = [1, 1, 2] + intervention_targets = [set(), {"x"}, set()] + + context = ( + make_context(create_using=ContextBuilder).variables(data=data[0]) + # .num_distributions(2) + # .intervention_targets([("x")]) + .build() + ) + learner.fit(data, context, domain_indices, intervention_targets) + + # first check the observational skeleton + skel_graph = learner.adj_graph_ + obs_skel_graph = learner.context_.state_variable("obs_skel_graph").subgraph( + context.observed_variables + ) + obs_expected_skeleton = obs_expected_skeleton.subgraph(context.observed_variables) + sep_set = learner.sep_set_ + + # check the separating sets + assert sep_set["x"]["z"] == [{"y", ("F", 0), ("S", 0), ("F", 1)}] + + # check the skeleton after obs data + print(obs_expected_skeleton.edges()) + print(obs_skel_graph.edges()) + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + + # check the skeleton after intervention + print(skel_graph.edges()) + print(expected_skeleton.edges()) + assert nx.is_isomorphic(expected_skeleton, skel_graph) + + +def test_basic_multidomain_fsnode_skeleton_with_lindata(): + seed = 1234 + n_samples = 1000 + aug_graph = basic_multidomain_augmented_graph() + graph = aug_graph.subgraph(aug_graph.non_augmented_nodes) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + (("F", 1), "x"), + (("F", 1), "y"), + (("S", 0), "y"), + ("x", "y"), + ("y", "z"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + + # define functional relationships of the causal diagram + graph = pgraphs.functional.make_graph_linear_gaussian(graph, random_state=seed) + + datasets = [] + domain_ids = [] + intervention_sets = [] + + # now for each F-node, apply a linear additive intervention + for f_node, targets in aug_graph.graph["F-nodes"].items(): + new_graph = pgraphs.functional.apply_soft_intervention( + graph.copy(), targets, random_state=seed + ) + + # generate dataset + data = linear.sample_from_graph(new_graph, n_samples=n_samples, random_state=seed) + + datasets.append(data) + intervention_sets.append(targets) + domain_ids.append(1) + + # now for each S-node, apply a linear additive intervention + for s_node, targets in aug_graph.graph["S-nodes"].items(): + new_graph = pgraphs.functional.apply_soft_intervention( + graph.copy(), targets, random_state=seed + ) + + # generate dataset + data = linear.sample_from_graph(new_graph, n_samples=n_samples, random_state=seed) + + datasets.append(data) + intervention_sets.append(targets) + domain_ids.append(2) + + learner = LearnMultiDomainSkeleton(ci_estimator=FisherZCITest(), cd_estimator=KernelCDTest()) + + context = make_context(create_using=ContextBuilder).variables(data=datasets[0]).build() + learner.fit(data, context, domain_ids, intervention_sets) + + # first check the observational skeleton + skel_graph = learner.adj_graph_ + obs_skel_graph = learner.context_.state_variable("obs_skel_graph").subgraph( + context.observed_variables + ) + obs_expected_skeleton = obs_expected_skeleton.subgraph(context.observed_variables) + sep_set = learner.sep_set_ + + # check the separating sets + assert sep_set["x"]["z"] == [{"y", ("F", 0), ("S", 0), ("F", 1)}] + + # check the skeleton after obs data + print(obs_expected_skeleton.edges()) + print(obs_skel_graph.edges()) + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + + # check the skeleton after intervention + print(skel_graph.edges()) + print(expected_skeleton.edges()) + assert nx.is_isomorphic(expected_skeleton, skel_graph) \ No newline at end of file diff --git a/tests/unit_tests/datasets/test_linear.py b/tests/unit_tests/datasets/test_linear.py new file mode 100644 index 000000000..1bd6100d0 --- /dev/null +++ b/tests/unit_tests/datasets/test_linear.py @@ -0,0 +1,16 @@ +import networkx as nx +import pytest +from pywhy_graphs.simulate import simulate_random_er_dag + +from dodiscover.datasets import make_linear_gaussian + + +@pytest.mark.parametrize("n_jobs", [None, -1]) +def test_make_linear_gaussian_from_graph_n_jobs(n_jobs): + G = simulate_random_er_dag(n_nodes=5, seed=12345, ensure_acyclic=True) + + G, data = make_linear_gaussian(G, random_state=12345, n_jobs=n_jobs) + + assert set(data.columns) == set(G.nodes) + assert all(key in nx.get_node_attributes(G, "parent_functions") for key in G.nodes) + assert all(key in nx.get_node_attributes(G, "gaussian_noise_function") for key in G.nodes) From 10d53314f503d7b9ae88518dff260ba4b3c4aa66 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 24 May 2023 00:05:41 -0400 Subject: [PATCH 52/61] Now merging in Signed-off-by: Adam Li --- .../basic-multidomain-chain-model.ipynb | 1873 +++++++++++++++++ .../multi-domain/example-sfci-algo.ipynb | 1754 +++++++++++++++ .../multi-domain/random-graph-analysis.ipynb | 807 +++++++ dodiscover/constraint/sfcialg.py | 231 ++ 4 files changed, 4665 insertions(+) create mode 100644 doc/tutorials/multi-domain/basic-multidomain-chain-model.ipynb create mode 100644 doc/tutorials/multi-domain/example-sfci-algo.ipynb create mode 100644 doc/tutorials/multi-domain/random-graph-analysis.ipynb create mode 100644 dodiscover/constraint/sfcialg.py diff --git a/doc/tutorials/multi-domain/basic-multidomain-chain-model.ipynb b/doc/tutorials/multi-domain/basic-multidomain-chain-model.ipynb new file mode 100644 index 000000000..941770305 --- /dev/null +++ b/doc/tutorials/multi-domain/basic-multidomain-chain-model.ipynb @@ -0,0 +1,1873 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "743325c9-7fe4-403e-92cc-68fafecf4cad", + "metadata": {}, + "source": [ + "# Multi-domain 2-Chain Graph Analysis with I-FCI vs $\\Psi$-FCI\n", + "\n", + "Let there be two domains to consider in our following example. For concreteness, let us follow the story of the paper, and say these are two different laboratory settings, where two proteins are sequenced. We wish to discover the cause-and-effect relationship between these two proteins. \n", + "\n", + "Here, we analyze the 2-chain selection diagram: $X \\leftarrow Y \\leftarrow S_0^{1,2}$ with two interventional distributions that occur over domains 1 and 2. $S_0^{1,2}$ is an S-node representing a possible difference in mechanism for node Y between domains 1 and 2.\n", + "\n", + "- X is protein 1\n", + "- Y is protein 2\n", + "- $S_0^{1,2}$ represents a change in laboratory settings that induce changes in the protein 2 expression levels\n", + "\n", + "Experiments are done by perturbing protein 1 (X) and then measuring the protein expression levels of X and Y." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5c8d3bd1-e720-43e3-b2f3-82eef12c14ea", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%load_ext lab_black" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "476e1a05-5c57-4c04-a4ad-22e835df8290", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display_svg" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "bba338b9-78f2-498a-815b-70c1d348d174", + "metadata": {}, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "import numpy as np\n", + "import scipy\n", + "import pandas as pd\n", + "import collections\n", + "from itertools import combinations\n", + "import networkx as nx\n", + "import pywhy_graphs as pgraphs\n", + "from pywhy_graphs.functional import (\n", + " make_graph_linear_gaussian,\n", + " make_graph_multidomain,\n", + " set_node_attributes_with_G,\n", + " apply_linear_soft_intervention,\n", + " sample_multidomain_lin_functions,\n", + ")\n", + "from pywhy_graphs.viz import draw\n", + "from pywhy_graphs.simulate import simulate_random_er_dag\n", + "\n", + "from dodiscover.cd import KernelCDTest\n", + "from dodiscover.ci import KernelCITest, FisherZCITest, Oracle\n", + "from dodiscover.constraint.skeleton import LearnMultiDomainSkeleton\n", + "from dodiscover.constraint.utils import dummy_sample\n", + "from dodiscover.datasets import sample_from_graph\n", + "\n", + "from dodiscover import (\n", + " SFCI,\n", + " PsiFCI,\n", + " FCI,\n", + " Context,\n", + " make_context,\n", + " InterventionalContextBuilder,\n", + ")\n", + "from dodiscover.metrics import structure_hamming_dist, confusion_matrix_networks\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c71ac0fe-354e-4adc-a3a1-7fd21c255eb4", + "metadata": {}, + "outputs": [], + "source": [ + "seed = 12345" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3cb5db22-3ce1-490f-b44e-f136e53d8247", + "metadata": {}, + "outputs": [], + "source": [ + "alpha = 0.05" + ] + }, + { + "cell_type": "markdown", + "id": "75383db8-5cc7-4e6e-8c8e-b395a43f6690", + "metadata": { + "tags": [] + }, + "source": [ + "## Setup Linear Functional Graph" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "529a4be3-faa7-4460-bdab-bcb59100fcdc", + "metadata": {}, + "outputs": [], + "source": [ + "node_mean_lims = [-1, 1]\n", + "node_std_lims = [0.5, 1.5]\n", + "edge_functions = [lambda x: x, lambda x: x**2]\n", + "edge_weight_lims = [-1, 1]\n", + "\n", + "n_domains = 2\n", + "n_samples = 5000" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "689a27e4-f21d-4572-a4b8-34b3c4c626ad", + "metadata": {}, + "outputs": [], + "source": [ + "directed_edges = [(\"y\", \"x\")]\n", + "\n", + "graph = pgraphs.AugmentedGraph(\n", + " incoming_directed_edges=directed_edges,\n", + ")\n", + "\n", + "int_graph = graph.copy()\n", + "int_graph.add_f_node({\"x\"}, domain=1)\n", + "\n", + "aug_graph = int_graph.copy()\n", + "aug_graph.add_f_node({\"x\"}, domain=2, require_unique=False)\n", + "aug_graph.add_s_node((1, 2), {\"y\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5f9cfa6f-bfab-43cc-9677-c911ab6b595e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Causal Diagram\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y->x\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = draw(graph, name=\"Causal Diagram\")\n", + "G.render(\n", + " outfile=\"./two-chain-single-domain.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cb5ebc4a-16ad-4d6c-9003-b95d492a1396", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Augmented Causal Diagram\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = draw(int_graph, name=\"Augmented Causal Diagram\")\n", + "G.render(\n", + " outfile=\"./two-chain-single-domain-with-int.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "36bd044f-a331-403d-91bc-e1be604e85d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Augmented Selection Diagram\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 0)\n", + "\n", + "('S', 0)\n", + "\n", + "\n", + "\n", + "('S', 0)->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)\n", + "\n", + "('F', 1)\n", + "\n", + "\n", + "\n", + "('F', 1)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = draw(aug_graph, name=\"Augmented Selection Diagram\")\n", + "G.render(\n", + " outfile=\"./two-chain-multi-domain-with-int.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3f36b57d-66dc-428f-a6db-0c62dc15caba", + "metadata": {}, + "outputs": [], + "source": [ + "# convert graph to linear functional graph\n", + "lin_graph = make_graph_linear_gaussian(\n", + " graph,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7eb41b8d-f4fc-4c1f-86ea-b0e8961abe3f", + "metadata": {}, + "outputs": [], + "source": [ + "# convert graph to linear functional graph\n", + "lin_graph = make_graph_linear_gaussian(\n", + " graph,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "aca6ed01-f47e-41d5-9c49-486115c3685a", + "metadata": {}, + "outputs": [], + "source": [ + "# convert graph to linear functional graph\n", + "aug_lin_graph = make_graph_linear_gaussian(\n", + " aug_graph,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + ")\n", + "md_lin_graph = sample_multidomain_lin_functions(\n", + " aug_lin_graph,\n", + " n_domains=n_domains,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0f91d578-9fd1-4c7f-9b36-9cabd1afcda2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('y',\n", + " {'gaussian_noise_function': {'mean': 0.19661750717437965,\n", + " 'std': 0.6867341856037134},\n", + " 'parent_functions': {('S', 0): {'func': at 0x148caa8b0>,\n", + " 'weight': -0.5453279550656607}}})\n", + "('x',\n", + " {'gaussian_noise_function': {'mean': 0.3455120880292426,\n", + " 'std': 1.441802865269937},\n", + " 'parent_functions': {'y': {'func': at 0x148caa8b0>,\n", + " 'weight': -0.5453279550656607},\n", + " ('F', 0): {'func': at 0x148caa940>,\n", + " 'weight': 0.5947309146654682},\n", + " ('F', 1): {'func': at 0x148caa8b0>,\n", + " 'weight': 0.3525093415019491}}})\n", + "(('F', 0),\n", + " {'gaussian_noise_function': {'mean': -0.5453279550656607,\n", + " 'std': 0.8167583397097529},\n", + " 'parent_functions': {}})\n", + "(('F', 1),\n", + " {'gaussian_noise_function': {'mean': 0.5947309146654682,\n", + " 'std': 1.1762546707509745},\n", + " 'parent_functions': {}})\n", + "(('S', 0),\n", + " {'domain_ids': (1, 2),\n", + " 'gaussian_noise_function': {'mean': -0.217780898796182,\n", + " 'std': 0.8328139278663845},\n", + " 'parent_functions': {}})\n" + ] + } + ], + "source": [ + "for node in aug_lin_graph.nodes(data=True):\n", + " pprint(node)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "85673523-2582-426b-b29a-37a2ce9be070", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('y',\n", + " {'domain_gaussian_noise_function': {1: {'mean': -0.5453279550656607,\n", + " 'std': 0.8167583397097529},\n", + " 2: {'mean': 0.5947309146654682,\n", + " 'std': 1.1762546707509745}},\n", + " 'gaussian_noise_function': {'mean': 0.19661750717437965,\n", + " 'std': 0.6867341856037134},\n", + " 'invariant_domains': set(),\n", + " 'parent_functions': {('S', 0): {'func': at 0x148caa8b0>,\n", + " 'weight': -0.5453279550656607}}})\n", + "('x',\n", + " {'gaussian_noise_function': {'mean': 0.3455120880292426,\n", + " 'std': 1.441802865269937},\n", + " 'parent_functions': {'y': {'func': at 0x148caa8b0>,\n", + " 'weight': -0.5453279550656607},\n", + " ('F', 0): {'func': at 0x148caa940>,\n", + " 'weight': 0.5947309146654682},\n", + " ('F', 1): {'func': at 0x148caa8b0>,\n", + " 'weight': 0.3525093415019491}}})\n", + "(('F', 0),\n", + " {'gaussian_noise_function': {'mean': -0.5453279550656607,\n", + " 'std': 0.8167583397097529},\n", + " 'parent_functions': {}})\n", + "(('F', 1),\n", + " {'gaussian_noise_function': {'mean': 0.5947309146654682,\n", + " 'std': 1.1762546707509745},\n", + " 'parent_functions': {}})\n", + "(('S', 0),\n", + " {'domain_ids': (1, 2),\n", + " 'gaussian_noise_function': {'mean': -0.217780898796182,\n", + " 'std': 0.8328139278663845},\n", + " 'parent_functions': {}})\n" + ] + } + ], + "source": [ + "for node in md_lin_graph.nodes(data=True):\n", + " pprint(node)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4ccfafbf-da75-4cd5-a0da-a6baeaa956f3", + "metadata": {}, + "outputs": [], + "source": [ + "# example analysis\n", + "est = KernelCITest()\n", + "est = FisherZCITest()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8f8bbc3a-aba5-4977-976f-1776ab41944c", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_34343/1620990787.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" + ] + } + ], + "source": [ + "est.test(data[1], {\"x\"}, {\"y\"}, {})" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "f9ece90e-27ac-488c-aa36-9fee3200f2c0", + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (1201510190.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_33752/1201510190.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m x_var =\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Cannot parse: 1:8: x_var = \n", + "Traceback (most recent call last):\n", + " File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/lab_black.py\", line 218, in format_cell\n", + " formatted_code = _format_code(cell)\n", + " File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/lab_black.py\", line 29, in _format_code\n", + " return format_str(src_contents=code, mode=FileMode())\n", + " File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/black/__init__.py\", line 1073, in format_str\n", + " dst_contents = _format_str_once(src_contents, mode=mode)\n", + " File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/black/__init__.py\", line 1083, in _format_str_once\n", + " src_node = lib2to3_parse(src_contents.lstrip(), mode.target_versions)\n", + " File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/black/parsing.py\", line 127, in lib2to3_parse\n", + " raise exc from None\n", + "black.parsing.InvalidInput: Cannot parse: 1:8: x_var = \n" + ] + } + ], + "source": [ + "x_var = \n", + "# compute conditional independence test\n", + "# get the sigma-map for this F-node\n", + "distribution_idx = context.sigma_map[x_var]\n", + "\n", + "# get the distributions across the two distributions\n", + "data_i = data[distribution_idx[0]].copy()\n", + "data_j = data[distribution_idx[1]].copy()\n", + "\n", + "# name the group column the F-node, so Oracle works as expected\n", + "data_i[x_var] = 0\n", + "data_j[x_var] = 1\n", + "this_data = pd.concat((data_i, data_j), axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "c183daec-9f74-4489-a4bf-34f70a11620e", + "metadata": { + "tags": [] + }, + "source": [ + "## Sample Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6b508e89-1fc8-4dc5-82e3-6557bd43bf87", + "metadata": {}, + "outputs": [], + "source": [ + "n_samples = 5000" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "2e343326-48f4-4451-8429-6ff77902664d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('F', 0), ('F', 1), ('S', 0)]\n", + "{'x', 'y'}\n", + "{'directed': OutEdgeView([('y', 'x'), (('F', 0), 'x'), (('F', 1), 'x'), (('S', 0), 'y')]), 'bidirected': EdgeView([]), 'undirected': EdgeView([])}\n", + "[('F', 0), ('F', 1), ('S', 0), 'y', 'x']\n" + ] + } + ], + "source": [ + "print(lin_graph.augmented_nodes)\n", + "print(md_lin_graph.non_augmented_nodes)\n", + "print(md_lin_graph.edges())\n", + "print(list(nx.topological_sort(md_lin_graph.get_graphs(\"directed\"))))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "973c3136-9721-412e-a951-e4bd9d27c50f", + "metadata": {}, + "outputs": [], + "source": [ + "domain_indices = []\n", + "intervention_targets = []\n", + "mechanisms = []\n", + "data = []\n", + "\n", + "for idx, domain_id in enumerate(range(1, n_domains + 1)):\n", + " df = sample_from_graph(\n", + " md_lin_graph,\n", + " sample_func=\"multidomain\",\n", + " n_samples=n_samples,\n", + " n_jobs=1,\n", + " random_state=seed,\n", + " domain_id=domain_id,\n", + " )\n", + "\n", + " domain_indices.append(domain_id)\n", + " intervention_targets.append({\"x\"})\n", + " mechanisms.append(idx)\n", + " data.append(df)" + ] + }, + { + "cell_type": "markdown", + "id": "1b90d6e9-68fe-40c7-84c9-b9358fb9f38e", + "metadata": { + "tags": [] + }, + "source": [ + "# Oracle Analysis\n", + "\n", + "First, we compare what would happen with the oracle graph, when we learn using the ground-truth augmented selection diagram.\n", + "\n", + "We compare the FCI, $\\Psi$-FCI and S-FCI algorithms" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "0fcfdd27-8600-45cb-8b34-a12722411700", + "metadata": {}, + "outputs": [], + "source": [ + "context = make_context().variables(aug_graph.non_augmented_nodes).build()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6b3ed7a2-ccb0-4f8f-a885-58df23e6e636", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['x', 'y']\n" + ] + } + ], + "source": [ + "oracle = Oracle(graph)\n", + "dummy_df = dummy_sample(graph)\n", + "print(graph.nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "0b5882d5-f2af-40d0-984a-598b38917560", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now learn the relationships\n", + "oracle_learner = FCI(ci_estimator=oracle, alpha=alpha)\n", + "oracle_learner.fit(\n", + " dummy_df,\n", + " context,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "70e68d34-8725-4368-9c92-caa47fd1e0f2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PAG\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fci_oracle_g = draw(oracle_learner.graph_, name=\"PAG\")\n", + "fci_oracle_g.render(\n", + " filename=\"./two-chain-fci-oracle\",\n", + " outfile=\"./two-chain-fci-oracle.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(fci_oracle_g)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "c5d42695-407a-41fc-bce0-60cf7c95e887", + "metadata": {}, + "outputs": [], + "source": [ + "context = (\n", + " make_context(create_using=InterventionalContextBuilder)\n", + " .variables(aug_graph.non_augmented_nodes)\n", + " .obs_distribution(False)\n", + " .intervention_targets([(\"x\"), ()])\n", + " .num_distributions(2)\n", + " .build()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "a6fdd4c1-ddf6-4f2e-afe3-88712a5a7593", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# IFCI\n", + "oracle_learner = PsiFCI(\n", + " ci_estimator=oracle,\n", + " cd_estimator=oracle,\n", + " alpha=alpha,\n", + " known_intervention_targets=True,\n", + ")\n", + "oracle_learner.fit(\n", + " [dummy_df, dummy_df],\n", + " context,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "2ec78a4a-f8e5-4c3b-8ab7-46ac38978d2a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I-PAG\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "('F', 0)->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "('F', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ifci_oracle_g = draw(oracle_learner.graph_, name=\"I-PAG\")\n", + "ifci_oracle_g.render(\n", + " filename=\"./two-chain-ifci-oracle\",\n", + " outfile=\"./two-chain-ifci-oracle.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(ifci_oracle_g)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f0e8e089-aded-4476-a9f7-8f1f51742531", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# SFCI\n", + "oracle_learner = SFCI(ci_estimator=oracle, cd_estimator=oracle, alpha=alpha)\n", + "oracle_learner.fit(\n", + " [dummy_df, dummy_df],\n", + " context,\n", + " domain_indices=domain_indices,\n", + " intervention_targets=intervention_targets,\n", + " # debug=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "9be4b9c7-4073-4d2a-9496-a6a19f8658ec", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "S-PAG\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 0)\n", + "\n", + "('S', 0)\n", + "\n", + "\n", + "\n", + "('S', 0)->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sfci_oracle_g = draw(oracle_learner.graph_, name=\"S-PAG\")\n", + "sfci_oracle_g.render(\n", + " filename=\"./two-chain-sfci-oracle\",\n", + " outfile=\"./two-chain-sfci-oracle.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(sfci_oracle_g)" + ] + }, + { + "cell_type": "markdown", + "id": "15a2b720-1742-414b-82a9-6d75048df390", + "metadata": {}, + "source": [ + "# Generated Data Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "6faffed0-11cb-4695-860e-55c9d622d015", + "metadata": {}, + "source": [ + "## With FCI" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b81823ce-07dd-42b8-8692-869a4fd9cc87", + "metadata": {}, + "outputs": [], + "source": [ + "context = make_context().variables(aug_graph.non_augmented_nodes).build()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "074c3b07-cbcd-48d6-8d72-d2e636684d86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now learn the relationships\n", + "learner = FCI(ci_estimator=FisherZCITest(), alpha=alpha)\n", + "learner.fit(\n", + " pd.concat(data, axis=0),\n", + " context,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "3ce0cfc1-8a1c-424d-b2e2-7aacbe2db63c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PAG\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fci_g = draw(learner.graph_, name=\"PAG\")\n", + "fci_g.render(\n", + " filename=\"./two-chain-fci\",\n", + " outfile=\"./two-chain-fci.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(fci_g)" + ] + }, + { + "cell_type": "markdown", + "id": "ea06f144-0435-40e2-89ad-4b4b2513224f", + "metadata": {}, + "source": [ + "## With I-FCI" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "62ce26bc-86c8-4a28-835c-2581ecd316b6", + "metadata": {}, + "outputs": [], + "source": [ + "context = (\n", + " make_context(create_using=InterventionalContextBuilder)\n", + " .variables(aug_graph.non_augmented_nodes)\n", + " .obs_distribution(False)\n", + " .intervention_targets([(\"x\"), (\"x\")])\n", + " .mechanisms([{\"x\": 1}, {\"x\": 2}])\n", + " .num_distributions(2)\n", + " .build()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "04b7843f-2ebf-4f65-8a04-4cff79050249", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# IFCI\n", + "learner = PsiFCI(\n", + " ci_estimator=FisherZCITest(),\n", + " cd_estimator=KernelCDTest(),\n", + " alpha=alpha,\n", + " known_intervention_targets=True,\n", + ")\n", + "learner.fit(\n", + " data,\n", + " context,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "467032f4-447b-404f-a9b7-c36c628064a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I-PAG\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "('F', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "('F', 0)->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ifci_g = draw(learner.graph_, name=\"I-PAG\")\n", + "ifci_g.render(\n", + " filename=\"./two-chain-ifci-oracle\",\n", + " outfile=\"./two-chain-ifci-oracle.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(ifci_g)" + ] + }, + { + "cell_type": "markdown", + "id": "80d98d9e-a8dd-4072-9209-5e4f343b49dc", + "metadata": {}, + "source": [ + "## Correct Answer with S-FCI" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7808fbd4-0fd3-4392-9c82-6351e5c35e09", + "metadata": {}, + "outputs": [], + "source": [ + "context = (\n", + " make_context(create_using=InterventionalContextBuilder)\n", + " .variables(aug_graph.non_augmented_nodes)\n", + " .num_distributions(2)\n", + " .build()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "824bfe3a-e5b8-43b7-9dfd-ae3e931cf802", + "metadata": {}, + "outputs": [], + "source": [ + "# now learn the relationships\n", + "learner = SFCI(\n", + " ci_estimator=FisherZCITest(), cd_estimator=KernelCDTest(), alpha=alpha, debug=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1a406382-3ca0-4b9f-8f1e-2aa2037f7c79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Comparing x-y | set() : 0.0\n", + "Trying to learn skeleton for 1 and 2 to remove F-nodes: [('F', 0)] grouped with S-node: ('S', 0)\n", + "Comparing {('S', 0), ('F', 0)}-x | set() : 0.0\n", + "Comparing {('S', 0), ('F', 0)}-y | set() : 0.0\n", + "Comparing {('S', 0), ('F', 0)}-x | {'y'} : 0.0\n", + "Comparing {('S', 0), ('F', 0)}-y | {'x'} : 0.0\n", + "[('F', 0)]\n", + "[]\n", + "{('F', 0): frozenset()}\n", + "{('F', 0): [0, 1]}\n", + "Trying to learn skeleton for 1 to remove F-nodes: []\n", + "Trying to learn skeleton for 2 to remove F-nodes: []\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learner.fit(\n", + " data,\n", + " context,\n", + " domain_indices=domain_indices,\n", + " intervention_targets=intervention_targets,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f7aeb8c0-03f6-4fe4-95fc-fb29f1abe477", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 0)\n", + "\n", + "('S', 0)\n", + "\n", + "\n", + "\n", + "('S', 0)->x\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 0)->y\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw(learner.graph_)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "cca33f1f-2311-4355-8510-29b1897956fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaultdict(. at 0x148d275e0>,\n", + " {('S', 0): defaultdict(, {('F', 0): []})})\n" + ] + } + ], + "source": [ + "pprint(learner.separating_sets_)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "e07a008a-b117-4d4b-8aad-551cafcf8f20", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.88338169 0.11661831]\n", + " [0.95437093 0.04562907]\n", + " [0.92598123 0.07401877]\n", + " [0.74032007 0.25967993]\n", + " [0.74200266 0.25799734]\n", + " [0.89661462 0.10338538]\n", + " [0.54642251 0.45357749]\n", + " [0.94539668 0.05460332]\n", + " [0.67546416 0.32453584]\n", + " [0.75045264 0.24954736]\n", + " [0.89957344 0.10042656]\n", + " [0.85249165 0.14750835]\n", + " [0.81894958 0.18105042]\n", + " [0.84897506 0.15102494]\n", + " [0.86968602 0.13031398]\n", + " [0.96352565 0.03647435]\n", + " [0.81842388 0.18157612]\n", + " [0.80927313 0.19072687]\n", + " [0.58526745 0.41473255]\n", + " [0.84046682 0.15953318]]\n", + "[0.81835199 0.18164801]\n", + "0.8333333333333334\n", + "0.16666666666666666\n" + ] + } + ], + "source": [ + "rng = np.random.default_rng(seed)\n", + "test = rng.dirichlet((10, 2), 20)\n", + "print(test)\n", + "print(test.mean(axis=0))\n", + "\n", + "print(10 / 12)\n", + "print(2 / 12)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "5ea22be0-3a4d-4b22-a52c-5153c4c67569", + "metadata": {}, + "outputs": [], + "source": [ + "def convert_md_data_to_sd(data, domain_indices, intervention_targets):\n", + " # generate single-domain data\n", + " single_domain_data = []\n", + " single_domain_targets = []\n", + "\n", + " seen_indices = set()\n", + " for targets in intervention_targets:\n", + " indices = [\n", + " idx\n", + " for idx in range(len(domain_indices))\n", + " if intervention_targets[idx] == targets\n", + " ]\n", + " if any(idx in seen_indices for idx in indices):\n", + " continue\n", + " for idx in indices:\n", + " seen_indices.add(idx)\n", + "\n", + " single_domain_data.append(pd.concat([data[idx] for idx in indices], axis=0))\n", + " if targets == {}:\n", + " continue\n", + " single_domain_targets.append(targets)\n", + " return single_domain_data, single_domain_targets" + ] + }, + { + "cell_type": "markdown", + "id": "4d243b6a-13e4-4cb0-80fc-f053074e22a0", + "metadata": {}, + "source": [ + "# Large-scale random graph analysis\n", + "\n", + "Now, we instantiate a large number of functions over the two-chain graph setup we have to determine if this is just a function of the specific data setup we have. We demonstrate that in fact, consistently, I-FCI gets the wrong answer as is shown in the oracle setting across any function, noise parametrization, weight, or number of samples in our parameter grid.\n", + "\n", + "Our metric that we measure the performance of the algorithsm is the 1-0 loss. It is 0 if the Y->X is oriented as X o-o Y, or correctly and 1 otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "aab3dcb4-c871-4a38-a36e-4cf41c3fdf6c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5 6 7 8 9 10 11 12 13 14]\n", + "[0.1 0.175 0.25 0.325 0.4 ]\n" + ] + } + ], + "source": [ + "node_mean_lims = [-5, 5]\n", + "node_std_lims = [0.01, 1.5]\n", + "edge_functions = [lambda x: x, lambda x: x**2, lambda x: np.sin(x), lambda x: -x]\n", + "edge_weight_lims = [-5, 5]\n", + "n_node_grid = np.arange(5, 15)\n", + "p_grid = np.linspace(0.1, 0.4, 5)\n", + "n_domains_grid = np.arange(2, 10)\n", + "n_repeats = 5\n", + "\n", + "n_samples = 1000\n", + "ratio_interventions = 0.2\n", + "\n", + "print(n_node_grid)\n", + "print(p_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "4d5aaeef-b4c7-4500-832a-6e9e4d893b6c", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_34343/3255049111.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m )\n\u001b[0;32m---> 94\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 95\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervention_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mdef\u001b[0m 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"\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;31m# learn skeleton graph and the separating sets per variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m graph, self.separating_sets_ = self.learn_skeleton(\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseparating_sets_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m )\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mlearn_skeleton\u001b[0;34m(self, data, context, sep_set)\u001b[0m\n\u001b[1;32m 72\u001b[0m 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domain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1906\u001b[0;31m self._learn_skeleton(\n\u001b[0m\u001b[1;32m 1907\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1908\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36m_learn_skeleton\u001b[0;34m(self, data, context, condsel_method, conditional_test_func, possible_x_nodes, skipped_y_nodes, skipped_z_nodes, cross_distribution_test, group_with_snode, debug)\u001b[0m\n\u001b[1;32m 505\u001b[0m \u001b[0mx_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mx_var\u001b[0m\u001b[0;34m,\u001b[0m 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598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m 289\u001b[0m for func, args, kwargs in self.items]\n\u001b[1;32m 290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m 289\u001b[0m for func, args, kwargs in self.items]\n\u001b[1;32m 290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/cd/kernel_test.py\u001b[0m in \u001b[0;36m_statistic\u001b[0;34m(self, K, L, group_ind)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;31m# compute W matrices from K and z\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m \u001b[0mW0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_inverse_kernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_ind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;31m# compute L kernels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/cd/kernel_test.py\u001b[0m in \u001b[0;36m_compute_inverse_kernel\u001b[0;34m(self, K, z)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0mn1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m \u001b[0mW0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK0\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregs_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m 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"\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36minv\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0mainv\u001b[0m \u001b[0;34m=\u001b[0m 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algn_samplesloss
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" + ], + "text/plain": [ + " alg n_samples loss\n", + "0 sfci 50.0 0\n", + "1 sfci 50.0 0\n", + "2 sfci 50.0 0\n", + "3 sfci 50.0 0\n", + "4 sfci 50.0 0\n", + ".. ... ... ...\n", + "95 ifci 5000.0 1\n", + "96 ifci 5000.0 1\n", + "97 ifci 5000.0 1\n", + "98 ifci 5000.0 1\n", + "99 ifci 5000.0 1\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res_df = pd.DataFrame(sfci_results)\n", + "res_df[\"alg\"] = \"sfci\"\n", + "temp_df = pd.DataFrame(ifci_results)\n", + "temp_df[\"alg\"] = \"ifci\"\n", + "res_df = pd.concat((res_df, temp_df), axis=0)\n", + "res_df = res_df.reset_index()\n", + "\n", + "res_df = pd.melt(\n", + " res_df,\n", + " id_vars=\"alg\",\n", + " var_name=\"n_samples\",\n", + " value_vars=n_sample_grid,\n", + " value_name=\"loss\",\n", + ")\n", + "\n", + "display(res_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "dbacc3de-4a21-4b98-b10c-6c2a8cce6f68", + "metadata": {}, + "outputs": [], + "source": [ + "res_df.columns = [\"Alg.\", \"# Samples\", \"Loss\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "ce1d3be3-bbfa-4c6e-b040-7a30f3152fa1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_context(\"paper\", font_scale=1.5)\n", + "sns.scatterplot(data=res_df, x=\"# Samples\", y=\"Loss\", hue=\"Alg.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59c43562-50fd-47a2-a037-425e1e5310db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pywhy-discover", + "language": "python", + "name": "pywhy-discover" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/tutorials/multi-domain/example-sfci-algo.ipynb b/doc/tutorials/multi-domain/example-sfci-algo.ipynb new file mode 100644 index 000000000..4d061475e --- /dev/null +++ b/doc/tutorials/multi-domain/example-sfci-algo.ipynb @@ -0,0 +1,1754 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Causal discovery from observational and/or interventional data across multiple domains\n", + "\n", + "Causal discovery algorithms such as the PC, FCI and GeS algorithm typically assume data is iid and come from a single distribution (i.e. a single domain). In the real world, causal structure is typically shared among similar domains, but the distributional functions may differ (e.g. one domain may have Gaussian noise, whereas another domain has Poisson noise).\n", + "\n", + "The S-FCI algorithm is introduced as a constraint-based discovery method that is a generalization of the FCI algorithm and moreover a generalization of the I-FCI/$\\Psi$-FCI algorithms. It correctly leverages data across distributions to learn a S-PAG, which is a Markov equivalence class of augmented selection diagrams. Augmented selection diagrams are selection diagrams with additional F-nodes indicating interventional distributions in any specified domain. \n", + "\n", + "Here, we demonstrate how the S-FCI algorithm typically learns more compared to its predecessors on simulated data stemming from a real experiment. \n", + "\n", + "This is done because one of the challenges of evaluating modern causal discovery is the lack of a suite of datasets that have multiple domains, and various types of interventions, and an accepted ground-truth graph.\n", + "\n", + "## Pseudo-Real Data: Protein Sequencing Experiment\n", + "\n", + "The famous Sachs dataset [2]_ is a wet-lab experiment dataset where protein expression level were observed in resting-state and then proteins were perturbed in various scenarios to obtain interventional data. This dataset can be viewed as observational and interventional data stemming from a single domain.\n", + "\n", + "The ground truth graph that we will assume is true is: https://www.bnlearn.com/research/sachs05/\n", + "\n", + "We will create an in-silico multi-domain dataset from the real data. We will do this by i) specifying at random nodes that are \"latent\", causing latent confounders and ii) choosing random nodes with S-nodes pointing to them causing shifts in distribution from source to target domain.\n", + "\n", + "### Setup\n", + "\n", + "The setup we will first consider the ground-truth graph" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", + "The lab_black extension is already loaded. To reload it, use:\n", + " %reload_ext lab_black\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%load_ext lab_black" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display_svg" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "import numpy as np\n", + "import scipy\n", + "import pandas as pd\n", + "import collections\n", + "from itertools import combinations\n", + "import bnlearn\n", + "import pooch\n", + "from cdt.data import load_dataset\n", + "\n", + "from pywhy_graphs.functional import (\n", + " make_graph_linear_gaussian,\n", + " make_graph_multidomain,\n", + " set_node_attributes_with_G,\n", + " apply_linear_soft_intervention,\n", + " sample_multidomain_lin_functions,\n", + ")\n", + "from pywhy_graphs.classes import AugmentedGraph\n", + "from pywhy_graphs.viz import draw\n", + "\n", + "from dodiscover.cd import KernelCDTest\n", + "from dodiscover.ci import KernelCITest, FisherZCITest, Oracle, GSquareCITest\n", + "from dodiscover.constraint.skeleton import LearnMultiDomainSkeleton\n", + "from dodiscover.datasets import sample_from_graph\n", + "\n", + "from dodiscover import PsiFCI, SFCI, Context, make_context, InterventionalContextBuilder" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "seed = 1234\n", + "rng = np.random.default_rng(seed)\n", + "n_jobs = -1" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "alpha = 0.05" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# use pooch to download robustly from a url\n", + "url = \"https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz\"\n", + "file_path = pooch.retrieve(\n", + " url=url,\n", + " known_hash=\"md5:39ee257f7eeb94cb60e6177cf80c9544\",\n", + ")\n", + "\n", + "df = pd.read_csv(file_path, delimiter=\" \")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[bnlearn] >Extracting files..\n" + ] + } + ], + "source": [ + "# download purely observational data\n", + "data = bnlearn.import_example(data='sachs', n=10000, verbose=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Erk Akt PKA Mek Jnk PKC Raf P38 PIP3 PIP2 Plcg\n", + "0 1 0 1 1 0 0 1 0 2 0 0\n", + "1 2 1 2 0 0 0 0 0 1 0 0\n", + "2 0 0 0 0 1 1 0 0 2 0 0\n", + "3 1 0 1 1 0 1 0 0 0 1 2\n", + "4 1 1 1 0 1 1 0 0 2 0 0\n", + " Raf Mek Plcg PIP2 PIP3 Erk Akt PKA PKC P38 Jnk INT\n", + "0 1 1 1 2 3 2 1 3 1 2 1 8\n", + "1 1 1 1 1 3 3 2 3 1 2 1 8\n", + "2 1 1 2 2 3 2 1 3 2 1 1 8\n", + "3 1 1 1 1 3 2 1 3 1 3 1 8\n", + "4 1 1 1 1 3 2 1 3 1 1 1 8\n" + ] + } + ], + "source": [ + "print(data.head())\n", + "print(df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['PKC', 'Raf', 'PKA', 'P38', 'PIP3', 'Plcg']\n" + ] + } + ], + "source": [ + "perturbations = [df.columns[perturbed_col] for perturbed_col in df['INT'].unique()]\n", + "\n", + "print(perturbations)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# the ground-truth dag is shown here: XXX: comment in when errors are fixed\n", + "ground_truth_dag = bnlearn.import_DAG(\"sachs\", verbose=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/adam2392/Dropbox/Apps/Overleaf/Learning selection diagrams (observational)/Figures/Appendix/ground_truth_sachs_bnlearn.pdf'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ground_truth_G = ground_truth_dag['model'].to_directed()\n", + "G = draw(ground_truth_G, direction='TD', shape='circle')\n", + "G.render(\n", + " outfile=\"/Users/adam2392/Dropbox/Apps/Overleaf/Learning selection diagrams (observational)/Figures/Appendix/ground_truth_sachs_bnlearn.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear SCM Simulation: Generate Ground-Truth Data\n", + "\n", + "First, we assume the causal diagram is induced by a linear SCM. In this setting, we are able to test the performance of S-FCI vs other algorithms when we artificially introduce differnet domain settings that simulate the collection of observations and experiments across e.g. different labs, and hospitals." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Augmented Sachs Graph\n", + "\n", + "\n", + "Erk\n", + "\n", + "Erk\n", + "\n", + "\n", + "\n", + "Akt\n", + "\n", + "Akt\n", + "\n", + "\n", + "\n", + "Erk->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA\n", + "\n", + "PKA\n", + "\n", + "\n", + "\n", + "PKA->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Mek\n", + "\n", + "Mek\n", + "\n", + "\n", + "\n", + "PKA->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf\n", + "\n", + "Raf\n", + "\n", + "\n", + "\n", + "PKA->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Jnk\n", + "\n", + "Jnk\n", + "\n", + "\n", + "\n", + "PKA->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "P38\n", + "\n", + "P38\n", + "\n", + "\n", + "\n", + "PKA->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Mek->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC\n", + "\n", + "PKC\n", + "\n", + "\n", + "\n", + "PKC->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)\n", + "\n", + "('F', 2)\n", + "\n", + "\n", + "\n", + "('F', 2)->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->PKC\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)\n", + "\n", + "('F', 1)\n", + "\n", + "\n", + "\n", + "('F', 1)->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)\n", + "\n", + "('F', 3)\n", + "\n", + "\n", + "\n", + "('F', 3)->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "PIP2\n", + "\n", + "PIP2\n", + "\n", + "\n", + "\n", + "PIP3->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg\n", + "\n", + "Plcg\n", + "\n", + "\n", + "\n", + "Plcg->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)\n", + "\n", + "('F', 4)\n", + "\n", + "\n", + "\n", + "('F', 4)->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)\n", + "\n", + "('F', 5)\n", + "\n", + "\n", + "\n", + "('F', 5)->Plcg\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aug_graph = AugmentedGraph(incoming_directed_edges=ground_truth_G.copy())\n", + "\n", + "# add perturbations\n", + "for node in perturbations:\n", + " aug_graph.add_f_node({node}, domain=1)\n", + "\n", + "draw(aug_graph, name='Augmented Sachs Graph')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "node_mean_lims = [-1, 1]\n", + "node_std_lims = [0.01, 1.5]\n", + "edge_functions = [lambda x: x, lambda x: x**2]\n", + "edge_weight_lims = [-0.5, 0.5]\n", + "\n", + "n_domains = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "n_samples = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# convert graph to linear functional graph\n", + "aug_lin_graph = make_graph_linear_gaussian(\n", + " aug_graph,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + ")\n", + "md_lin_graph = sample_multidomain_lin_functions(\n", + " aug_lin_graph,\n", + " n_domains=n_domains,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "domain_indices = []\n", + "intervention_targets = []\n", + "# mechanisms = []\n", + "data = []\n", + "\n", + "for idx, domain_id in enumerate(range(1, n_domains + 1)):\n", + " df = sample_from_graph(\n", + " md_lin_graph,\n", + " sample_func=\"multidomain\",\n", + " n_samples=n_samples,\n", + " n_jobs=1,\n", + " random_state=seed,\n", + " domain_id=domain_id,\n", + " )\n", + "\n", + " domain_indices.append(domain_id)\n", + " intervention_targets.append({})\n", + " data.append(df)\n", + "\n", + " for perturbation in perturbations:\n", + " int_graph = md_lin_graph.copy()\n", + "\n", + " # generate a soft-intervention\n", + " int_graph = apply_linear_soft_intervention(\n", + " int_graph, targets={perturbation}, random_state=seed\n", + " )\n", + "\n", + " # sample data from the intervention distribution\n", + " df = sample_from_graph(\n", + " int_graph,\n", + " sample_func=\"multidomain\",\n", + " n_samples=n_samples,\n", + " n_jobs=1,\n", + " random_state=seed,\n", + " domain_id=domain_id,\n", + " )\n", + " domain_indices.append(domain_id)\n", + " intervention_targets.append({perturbation})\n", + " data.append(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'PKC'}, {'Raf'}, {'PKA'}, {'P38'}, {'PIP3'}, {'Plcg'}]\n", + "6\n" + ] + } + ], + "source": [ + "# generate single-domain data\n", + "single_domain_data = []\n", + "single_domain_targets = []\n", + "\n", + "seen_indices = set()\n", + "for targets in intervention_targets:\n", + " indices = [\n", + " idx\n", + " for idx in range(len(domain_indices))\n", + " if intervention_targets[idx] == targets\n", + " ]\n", + " if any(idx in seen_indices for idx in indices):\n", + " continue\n", + " for idx in indices:\n", + " seen_indices.add(idx)\n", + "\n", + " single_domain_data.append(pd.concat([data[idx] for idx in indices], axis=0))\n", + " if targets == {}:\n", + " continue\n", + " single_domain_targets.append(targets)\n", + "\n", + "print(single_domain_targets)\n", + "print(len(single_domain_targets))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With I-FCI" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "context = (\n", + " make_context(create_using=InterventionalContextBuilder)\n", + " .variables(aug_graph.non_augmented_nodes)\n", + " # .obs_distribution(False)\n", + " .intervention_targets(single_domain_targets)\n", + " # .mechanisms([{\"x\": 1}, {\"x\": 2}])\n", + " .num_distributions(len(single_domain_data))\n", + " .build()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# IFCI\n", + "learner = PsiFCI(\n", + " ci_estimator=FisherZCITest(),\n", + " cd_estimator=KernelCDTest(),\n", + " alpha=alpha,\n", + " known_intervention_targets=True,\n", + ")\n", + "learner.fit(\n", + " single_domain_data,\n", + " context,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "I-PAG\n", + "\n", + "\n", + "Erk\n", + "\n", + "Erk\n", + "\n", + "\n", + "\n", + "PKA\n", + "\n", + "PKA\n", + "\n", + "\n", + "\n", + "PKA->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Mek\n", + "\n", + "Mek\n", + "\n", + "\n", + "\n", + "PIP2\n", + "\n", + "PIP2\n", + "\n", + "\n", + "\n", + "PKC\n", + "\n", + "PKC\n", + "\n", + "\n", + "\n", + "P38\n", + "\n", + "P38\n", + "\n", + "\n", + "\n", + "Raf\n", + "\n", + "Raf\n", + "\n", + "\n", + "\n", + "Plcg\n", + "\n", + "Plcg\n", + "\n", + "\n", + "\n", + "Jnk\n", + "\n", + "Jnk\n", + "\n", + "\n", + "\n", + "PIP3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "Akt\n", + "\n", + "Akt\n", + "\n", + "\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ifci_g = draw(learner.graph_.subgraph(aug_graph.non_augmented_nodes), name=\"I-PAG\")\n", + "ifci_g.render(\n", + " outfile=\"./sachs-ifci.pdf\",\n", + " format=\"pdf\",\n", + " cleanup=True,\n", + ")\n", + "display_svg(ifci_g)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With S-FCI" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "context = (\n", + " make_context(create_using=InterventionalContextBuilder)\n", + " .variables(aug_graph.non_augmented_nodes)\n", + " .num_distributions(len(data))\n", + " .build()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "# now learn the relationships\n", + "learner = SFCI(\n", + " ci_estimator=FisherZCITest(), cd_estimator=KernelCDTest(), alpha=alpha, debug=False,\n", + " n_jobs=-1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_19318/3270696463.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdomain_indices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdomain_indices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mintervention_targets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervention_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply_rule11\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mEquivalenceClass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mContext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/intervention.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context)\u001b[0m\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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"\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;31m# learn skeleton graph and the separating sets per variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m graph, self.separating_sets_ = self.learn_skeleton(\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseparating_sets_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m )\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mlearn_skeleton\u001b[0;34m(self, data, context, sep_set)\u001b[0m\n\u001b[1;32m 72\u001b[0m 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"\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36m_orient_unshielded_triples\u001b[0;34m(self, graph, sep_set)\u001b[0m\n\u001b[1;32m 1132\u001b[0m \u001b[0;31m# Then check to see if 'u' is in the separating\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0;31m# set. If it is not, then there is a collider.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1134\u001b[0;31m if v_j not in graph.neighbors(v_i) and not is_in_sep_set(\n\u001b[0m\u001b[1;32m 1135\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv_j\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"any\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1136\u001b[0m ):\n", + "\u001b[0;32m~/Documents/pywhy-graphs/pywhy_graphs/networkx/classes/mixededge.py\u001b[0m in \u001b[0;36mneighbors\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m 866\u001b[0m \u001b[0mnbrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_graphs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 868\u001b[0;31m \u001b[0mnbrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnbrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_neighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 869\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "learner.fit(\n", + " data,\n", + " context,\n", + " domain_indices=domain_indices,\n", + " intervention_targets=intervention_targets,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw(learner.graph_.subgraph(aug_graph.non_augmented_nodes))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pprint(learner.separating_sets_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Generating Artificial Domains Via Dirichlet Perturbation\n", + "\n", + "Next, we do not assume we can specify the true SCM of the Sachs dataset. Instead of completely specifying the data-generating model for the Sachs dataset, we now apply perturbations to the dataset that is commonly used to evaluate algorithms, such as the FCI, I-FCI, $\\Psi$-FCI in a single-domain setting. \n", + "\n", + "To simulate a multi-domain setting from the data, we will generate a random graph from the ground-truth\n", + "that contains S-nodes. S-nodes are added randomly to simulate a change in mechanism. S-nodes will perturb the node it is pointing to with a dirichlet distribution that perturbs the discrete distribution of the protein expression levels. In order to maintain consistency of the change in domain, all descendants of the S-node will get perturbed slightly." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11\n" + ] + } + ], + "source": [ + "print(ground_truth_G.number_of_nodes())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Erk Akt PKA Mek Jnk PKC Raf P38 PIP3 PIP2 Plcg\n", + "0 1 0 1 0 1 1 0 0 1 0 0\n", + "1 2 1 1 0 1 1 0 0 1 0 1\n", + "2 1 0 1 0 1 0 0 0 2 0 0\n", + "3 1 0 2 0 0 0 1 0 0 0 0\n", + "4 1 0 2 0 0 0 1 0 2 0 0\n" + ] + } + ], + "source": [ + "print(data.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize list of domain dataframes\n", + "domain_dfs = []\n", + "domain_dfs.append(df.copy())\n", + "all_s_nodes = []\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## (Optional) Choose Latent Confounders" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "# choose a random node to delete to add latent confounders\n", + "node_delete = rng.choice(ground_truth_G.nodes)\n", + "node_delete = []\n", + "print(node_delete)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Second Domain Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "n_domains = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "G = ground_truth_G.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "Adding edge... ('S', 0) Jnk\n", + "Adding edge... ('S', 0) PIP2\n" + ] + } + ], + "source": [ + "sdx = 0\n", + "s_nodes = []\n", + "s_node_domains = collections.defaultdict(list)\n", + "\n", + "# first, add all the S-nodes representing differences across pairs of domains\n", + "for domains in combinations(range(1, n_domains+1), 2):\n", + " source_domain, target_domain = sorted(domains)\n", + "\n", + " # choose a random number of S-nodes to add between (source, target)\n", + " n_s_nodes = rng.integers(0, 3)\n", + " print(n_s_nodes)\n", + " s_nodes_pointer = rng.choice(G.nodes, size=n_s_nodes, replace=False)\n", + "\n", + " # now modify the function of the edge, S-nodes are pointing to\n", + " s_node = ('S', sdx)\n", + " s_nodes.append(s_node)\n", + " G.add_node(s_node, domain_ids=(source_domain, target_domain))\n", + " for node in s_nodes_pointer:\n", + " # the source domain is always the \"reference\" distribution, that is\n", + " # the one we keep fixed\n", + " G.add_edge(s_node, node)\n", + " print('Adding edge... ', s_node, node)\n", + " # mape each source to its target and corresponding S-nodes\n", + " s_node_domains[source_domain].append((target_domain, node, s_node))\n", + " sdx +=1" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Erk\n", + "\n", + "Erk\n", + "\n", + "\n", + "\n", + "Akt\n", + "\n", + "Akt\n", + "\n", + "\n", + "\n", + "Erk->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA\n", + "\n", + "PKA\n", + "\n", + "\n", + "\n", + "PKA->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Mek\n", + "\n", + "Mek\n", + "\n", + "\n", + "\n", + "PKA->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf\n", + "\n", + "Raf\n", + "\n", + "\n", + "\n", + "PKA->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Jnk\n", + "\n", + "Jnk\n", + "\n", + "\n", + "\n", + "PKA->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "P38\n", + "\n", + "P38\n", + "\n", + "\n", + "\n", + "PKA->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Mek->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC\n", + "\n", + "PKC\n", + "\n", + "\n", + "\n", + "PKC->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 0)\n", + "\n", + "('S', 0)\n", + "\n", + "\n", + "\n", + "('S', 0)->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP2\n", + "\n", + "PIP2\n", + "\n", + "\n", + "\n", + "('S', 0)->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "PIP3->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg\n", + "\n", + "Plcg\n", + "\n", + "\n", + "\n", + "Plcg->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw(G, direction='TD')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# generate a random linear SCM dataset from the accepted ground-truth\n", + "ground_truth_G_lin_lab = make_graph_linear_gaussian(ground_truth_G, random_state=seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "non_s_nodes = set(ground_truth_G_lin_lab.nodes).difference(set(s_nodes))\n", + "# generate a dataset with invariances across domain\n", + "ground_truth_G_lin_hospital = make_graph_linear_gaussian(ground_truth_G, random_state=seed)\n", + "for node in non_s_nodes:\n", + " ground_truth_G_lin_hospital = set_node_attributes_with_G(ground_truth_G_lin_hospital, ground_truth_G_lin_hospital, node)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "data = []\n", + "domain_ids = []\n", + "intervention_targets = []" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# sample both datasets\n", + "lab_dataset = linear.sample_from_graph(ground_truth_G_lin_lab, n_samples=1000,\n", + " n_jobs=n_jobs)\n", + "hospital_dataset = linear.sample_from_graph(ground_truth_G_lin_hospital, n_samples=1000,\n", + " n_jobs=n_jobs)\n", + "\n", + "data.extend([lab_dataset, hospital_dataset])\n", + "domain_ids.extend([1, 2])\n", + "intervention_targets.extend([set(), set()])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# apply perturbations in the lab setting\n", + "for target in perturbations:\n", + " # apply soft intervention\n", + " G = apply_soft_intervention(ground_truth_G_lin_lab, targets={target}, random_state=seed)\n", + " df = linear.sample_from_graph(ground_truth_G_lin_hospital, n_samples=1000,\n", + " n_jobs=n_jobs)\n", + " \n", + " data.append(df)\n", + " domain_ids.append(1)\n", + " intervention_targets.append(target)\n", + " \n", + "# apply one perturbation in the hospital setting\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## $\\Psi$-FCI analysis\n", + "\n", + "Since, $\\Psi$-FCI is the most general learning algorithm that accounts for interventions and observational data, we will leverage this as a naive baseline." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "n_distributions = len(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "ci_estimator = KernelCITest()\n", + "\n", + "# Since our data is entirely discrete, we can also use the G^2 test as our\n", + "# CD test.\n", + "cd_estimator = KernelCDTest()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "alpha = 0.05\n", + "learner = PsiFCI(ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha, n_jobs=-1)\n", + "\n", + "# create context with information about the interventions\n", + "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", + "ctx: Context = (\n", + " ctx_builder.variables(data=data[0]).num_distributions(n_distributions).obs_distribution(False).build()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "learner = learner.fit(data, ctx)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "est_pag = learner.graph_\n", + "\n", + "print(f\"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG\")\n", + "\n", + "# %%\n", + "# Visualize the graph without the F-nodes\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", + "dot_graph = draw(est_pag_no_fnodes, direction=\"LR\")\n", + "dot_graph.render(outfile=\"psi_pag.png\", view=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### S-FCI analysis\n", + "\n", + "Next, we run S-FCI to compare the outputs of the two algorithms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing Outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[bnlearn] >Set node properties.\n", + "[bnlearn] >Set edge properties.\n", + "[bnlearn] >Plot based on Bayesian model\n" + ] + }, + { + "data": { + "image/png": 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H1+bli4iIiEiIHG5WpOBJRESkAfD6/PyyYiNfzPmVtMWrKNxdjGFYwAwS8Hux2234fX4SkxKJjnbh9fn3jJAyDCyGQbMmsZx3cm/WL/uJN199mebNmwN7puvl5OTQsmVLoqKi9juvz+ejoKAA0zRp164d99xzD+edd15tX76IiIiI1LLDzYpstViTiIiI1BCH3cbwft1o3SSM7957FtNnIyI+mTK/HewRYFjAasVdUkZkZDTxMVEc16EFnds0o3ObZvQ+rh1Oh413yjMwTRPTNDEMg7i4OMrKysjKyqJdu3bYbPt+dLDb7TRr1oyioiLWrFnDiy++yF//+lcMwwjRb0JERERE6hIFTyIiIg2E3+9n/PjxFGTvoHnz5lgq0tmVno7Faqdpi1aUlJbhD/h5duYn9OjW+YDhUPv27bHZbPh8PhwOBwDNmjVj69atZGZm0rp16322N02TgoIC/H4/F154ITNmzFDoJCIiIiJV1FxcRESkgXj99deZN28eTZo0wWLZ8xbv9Xpx2K1YTB/R4TYqSnazeMHPBw2H2rdvT1hYGB6Pp+oxq9VK8+bNKS8vJz8/v+pxn89HZmYmYWFhTJkyhdmzZ5OYmFizFykiIiIi9YqCJxERkQZgw4YNPPHEE9hstqoG36Zp7jNyyWKxYBgGX3zxBQdr8dikSRPi4uL2CZ4AIiIiaNq0KQUFBZSXl1NSUkJubi5du3bl/fff57rrrqsKu0RERERE9tInRBERkXrO6/Uybtw4du/eTdOmTase9/v9APusYudyuVizZg2bN28+4LEMw6Bbt277BU+wJ5QKDw9n69atlJaWctlll/HZZ5/Rq1evar4iEREREWkoFDyJiIjUcy+//DKLFi2iSZMm+0yh83q9wL7BU2RkJOXl5Xz33XcHPV6XLl0OOBXP6/VisViw2Wwcf/zxzJw5k5iYmGq8EhERERFpaBQ8iYiI1GOrV6/m2WefxeFwEBYWts9zPp8P2Dd4MgwDq9XKZ599dtDpdu3btycYDO7zfHFxMXl5efTt25fZs2eTl5fH+++/XwNXJCIiIiINiVa1ExERqacqKysZO3YsxcXFNG/efL/nvV4vdrt9v9FLLpeLTZs2sXbtWo477rj99uvQoQMOh6Nq/9zcXGw2GzfddBP33XcfERER5Obm8swzz9CrVy+6du1aY9coIiIiIvWbRjyJiIjUU7NmzWL58uU0bdr0oFPj9jYW/28RERFUVlby7bffHvC47dq1w+l0UlxcTFZWFomJibzwwgs8+OCDREREAHDrrbfSqVMnxo0bR1lZWfVemIiIiIg0GAqeRERE6qG8vDxeffVVfD4f+fn5ZGdns2vXLtxuNxUVFQQCAXw+3z7T7PYyDAObzcbnn39OMBjc7/nY2FgSExPxeDwMGTKEzz//nLPOOmufbex2O9OnT6ewsJAZM2YcdNqeiIiIiDRummonIiJSDzVp0oTHHnuMzZs3k5GRwdatW9m+fTvFxcWUlpbi8/koLS0lEAiQkZGBw+HAbrdX/dPlcrF9+3ZWrlzJiSeeuN/xR44cSUVFBbfccssBR00BtGjRgvvvv5/x48fTv39/zj333Jq+bBERERGpZxQ8iYiI1EMWi2W/UUgAbrebjIwMNm7cyJ133snw4cOx2+1s3bqVzMxMPB4PJSUl+P1+TNNkzpw5BwyebrzxxsOq47TTTmPx4sU88sgj9OjRgzZt2hzrpYmIiIhIA6LgSUREpAFxuVx069YNwzBo0qQJ99xzD926dQPA7/eTk5NDZmYmmZmZZGRkMHjw4GM+55gxY1i1ahXjxo3jjTfeOOgIKRERERFpfBQ8iYiINECZmZnAnulwe9lsNlq0aLHPY9UhPDycGTNmcPXVV/PUU09x7733VuvxRURERKT+UnNxERGRBigzM5OoqCiio6Nr5XwdOnRg9OjRfPjhh8yZM6dWzikiIiIidZ+CJxERkQYoMzOT5s2bYxhGrZ3zoosu4uSTT2bKlClkZ2fX2nlFREREpO5S8CQiItIAZWRkVPuUuj9jGAb3338/kZGRTJgwgUAgUKvnFxEREZG6R8GTiIhIA7R3xFNtc7lcTJ8+nd9//52XXnqp1s8vIiIiInWLgicREZEGxu/3k52dXesjnvbq2bMnt9xyC6+99hpLliwJSQ0iIiIiUjcoeBIREWlgcnNzCQaDIRnxtNfIkSPp27cvEydOpLCwMGR1iIiIiEhoKXgSERFpYDIzMwFCGjxZLBamTp2KaZpMnjyZYDAYslpEREREJHQUPImIiDQwGRkZWCwWkpOTQ1pHQkICDz74IAsWLOCdd94JaS0iIiIiEhoKnkRERBqYzMxMkpOTsdlsoS6FgQMHcvXVV/Pcc8+xdu3aUJcjIiIiIrVMwZOIiEgDE6oV7Q5m1KhRdO3alXHjxlFaWhrqckRERESkFil4EhERaWAyMzNDtqLdgdhsNqZPn05RURHTp0/HNM1QlyQiIiIitUTBk4iISANT10Y8AaSkpDBx4kS+++47vvjii1CXIyIiIiK1RMGTiIhIA+J2u3G73XUueAI49dRTufDCC3n00UfZunVrqMsRERERkVqg4ElERKQBycrKAqiTwRPA6NGjad68OePGjcPj8YS6HBERERGpYQqeREREGpCMjAyAOtXj6b+FhYXx8MMPk5GRwcyZM0NdjoiIiIjUMAVPIiIiDUhmZiZRUVFER0eHupSDateuHWPGjOHjjz/mxx9/DHU5IiIiIlKDFDyJiIg0IHsbixuGEepSDun888/nL3/5C1OnTq2aHigiIiIiDY+CJxERkQYkIyOjzvZ3+m+GYTBhwgRcLhcTJkzA7/eHuiQRERERqQEKnkRERBqQzMzMOtvf6X9FRUUxffp01q5dy4svvhjqckRERESkBih4EhERaSACgQDZ2dn1JngC6N69O7feeiuvv/46ixYtCnU5IiIiIlLNFDyJiIg0ELm5uQSDwXox1e6/XXnllQwYMIBJkyaxa9euUJcjIiIiItVIwZOIiEgDkZGRAVDvgieLxcKUKVMAmDRpEsFgMMQViYiIiEh1UfAkIiLSQGRmZmKxWEhOTg51KUcsPj6eadOmsWTJEt56661QlyMiIiIi1UTBk4iISAORmZlJcnIyNpst1KUclX79+nHNNdcwa9YsVq1aFepyRERERKQaKHgSERFpIDIyMurdNLv/ddNNN9G9e3cmTJhASUlJqMsRERERkWOk4ElERKSByMzMrPfBk81mY9q0aZSWljJ16lRM0wx1SSIiIiJyDBQ8iYiINBANIXgCSElJYeLEifz00098+umnoS5HRERERI6BgicREZEGwO1243a7adGiRahLqRYnn3wyF198MY8//jhbtmwJdTkiIiIicpQUPImIiDQAWVlZAA1ixNNed911F61atWLcuHFUVlaGuhwREREROQoKnkRERBqAzMxMgAYz4gnA6XQyY8YMsrKyeOKJJ0JdjoiIiIgcBQVPIiIiDUBmZiZRUVFER0eHupRq1bZtW+69914+/fRTvvvuu1CXIyIiIiJHSMGTiIhIA5CRkUHz5s0xDCPUpVS7c889l9NPP52HHnqoamSXiIiIiNQPCp5EREQagIayot2BGIbB+PHjiY2NZfz48fh8vlCXJCIiIiKHScGTiIhIA5CZmdmg+jv9r8jISGbMmMGGDRt44YUXQl2OiIiIiBwmBU8iIiL1XCAQIDs7u8GOeNqrW7du3H777bz55pssWLAg1OWIiIiIyGFQ8CQiIlLP5ebmEggEGnzwBDBixAhOOukkHnjgAQoKCkJdjoiIiIj8CQVPIiIi9VxGRgZAowieLBYLkydPxmq1MnHiRILBYKhLEhEREZFDUPAkIiJSz2VmZmKxWGjWrFmoS6kVcXFxTJs2jWXLlvH666+HuhwREREROQQFTyIiIvVcZmYmycnJ2Gy2UJdSa/r06cO1117Liy++yG+//RbqckRERETkIBQ8iYiI1HOZmZmNYprd/7rxxhvp0aMHEyZMwO12h7ocERERETkABU8iIiL1XGMNnqxWKw899BDl5eVMnToV0zRDXZKIiIiI/A8FTyIiIvVcRkZGowyeAJKTk5k0aRJz5szho48+CnU5IiIiIvI/FDyJiIjUYyUlJbjd7kYbPAGkpqZyySWX8OSTT7Jx48ZQlyMiIiIi/0XBk4iISD2WlZUFQIsWLUJcSWjdeeedtGnThnHjxlFRURHqckRERETk/yl4EhERqccyMzMBGvWIJwCHw8GMGTPIy8vjscceC3U5IiIiIvL/FDyJiIjUYxkZGURGRuJyuUJdSsi1bt2a++67jy+++IKvv/461OWIiIiICAqeRERE6rW9jcUNwwh1KXXC2WefzVlnncWMGTPYuXNnqMsRERERafQUPImIiNRjmZmZjb6/038zDIOxY8eSkJDAuHHj8Hq9oS5JREREpFFT8CQiIlKPKXjaX0REBDNmzGDz5s0899xzoS5HREREpFFT8CQiIlJPBQIBsrOzG31j8QPp0qULd9xxB++++y7z588PdTkiIiIijZaCJxERkXoqNzeXQCCg4OkgLrvsMoYMGcLkyZPJy8sLdTkiIiIijZKCJxERkXoqMzMTQMHTQRiGwQMPPIDD4WDixIkEg8FQlyQiIiLS6Ch4EhERqacyMzOxWCwkJyeHupQ6KzY2lmnTprFixQpeffXVUJcjIiIi0ugoeBIREamnMjMzSUpKwm63h7qUOu3EE0/k+uuv5+WXX2bFihWhLkdERESkUVHwJCIiUk9lZmZqmt1huv766znhhBOYMGECxcXFoS5HREREpNFQ8CQiIlJPZWRkKHg6TBaLhWnTplFZWcmDDz6IaZqhLklERESkUVDwJCIiUk8peDoyiYmJTJ48mXnz5vHhhx+GuhwRERGRRkHBk4iISD1UUlKC2+2mRYsWoS6lXhk6dCgjRozgqaeeYsOGDaEuR0RERKTBU/AkIiJSD2VlZQFoxNNRuP3222nXrh3jxo2jvLw81OWIiIiINGgKnkREROqhzMxMAI14OgoOh4MZM2ZQUFDAI488EupyRERERBo0BU8iIiL1UEZGBpGRkbhcrlCXUi+1atWKsWPH8tVXX/HVV1+FuhwRERGRBkvBk4iISD2UmZlJ8+bNMQwj1KXUW2eddRbnnHMODz/8MOnp6aEuR0RERKRBUvAkIiJSD2VmZmqaXTW49957adq0KWPHjsXr9Ya6HBEREZEGR8GTiIhIPbR3xJMcm4iICB5++GG2bdvGM888E+pyRERERBocW6gLEBERkf0VFpeyaUcOW3bmsXFHNlm5u6n0+jAxcdptrC900KbIYMGKjXRolUTTeJem3R2lTp06cdddd/Hoo4/Sr18/hg4dGuqSRERERBoMwzRN8882crvdxMTEUFxcrCamIiIiNcTr87Ng5Sa+mPMrv67dRqXHR9A0wTSxWiwYlj3Bkt8foGDXLmJcLpxOB06HnePat+D8U/owrE8XwpyOEF9J/WOaJmPGjGHFihW89957JCUlhbokERERkTrtcLMiBU8iIiIh5vX5+ei7JXz8wxIycgoJmkEiw5yEOR3YrJb9RjKVlZWRnp5O+/btsVitVHp8lFV4AEhKiOH8U/ow4syBRIQ7Q3E59Zbb7WbEiBGkpKTw4osvYrVaQ12SiIiISJ11uFmRejyJiIiE0IZtWYya+hpPvfU1mbmFxEZHkJwQS3RkOHab9YDT53w+HwB2ux2b1UpURBhJCTHEx0RRUFTCix/8yPUPvMKKddtr+WrqN5fLxUMPPcRvv/3GK6+8EupyRERERBoEBU8iIiIhEAgEef2zedw85VVWbthBnCuSpnEuHPY/b7/o9Xqx2W37hVJ2m5UmsdEkxEaxaUc2/5jxJs+9+x0er7+mLqPBOeGEE7jpppt45ZVXWLZsWajLEREREan3FDyJiIjUMq/Pz4xXPuf597/H6/OTnBBzWIHTXj6fD4f94H2c7DYrSQkxGMAbn//M/c98SPn/T8WTP/f3v/+d3r17M3HiRIqKikJdjoiIiEi9puBJRESkFvkDAaa//Dmf/7ScqIgw4mOijng1Oq/Xi8Nx6AbihmEQEx1BTHQ4c5auZcIzH1Lp8R5L6Y2GxWJh6tSp+Hw+Jk+ezGG0wxQRERGRg1DwJCIiUktM0+Tpt7/hPz+vJDoyjMijbP7t8/mw2+2HtW2400FcdCTzl29g2kufEQwGj+qcjU3Tpk158MEHmT9/Pu+9916oyxERERGptxQ8iYiI1JKff93AJ98vJTzMcdShk4lJMBjEGXb4+4c57biiwvlh0e/8O235UZ23MTrppJO48soreeaZZ1i3bl2oyxERERGplxQ8iYiI1IIidxlPvvU1Pn8AV2T4UR/HwKB9h/ZER0Uf0X4R/x9UvfDBD2TmFR71+RubW2+9lU6dOjFu3DjKyspCXY6IiIhIvaPgSUREpBY8//737MzZRUJs1DEfy247vGl2/yshJoqColJmvvG1ptwdJrvdzvTp0yksLGTGjBnq9yQiIiJyhBQ8iYiI1LCtGXl8PX8VkeFObFZrjZzjx7em8+Nb0w+5jcViISYqnIUrN7Fi/Y4aqaMhatGiBRMmTOCbb77hyy+/DHU5IiIiIvXK4a/dLCIiIkfl659XUunxkpQQU/VYubuQn96eccj9wqPjOOWq8dVaS7jTgbu0gi/TVtC7W9tqPXZDdvrpp7N48WIeeeQRevToQZs2bUJdkoiIiEi9oOBJRESkBpVVePhq3kocdhuGYez3fIQrgeadTjzgvnbn0feCOhjDMAgPc5C2dC237DqFxP8Kw+TQ7rnnHlatWsW4ceN44403cDgcoS5JREREpM5T8CQiIlKDfv51PQW7S0iIOXBvp8iYBDr3O61Wa4qOCCOv0M0Pi9Zw+dmDavXc9Vl4eDgzZsxg5MiRPPXUU9x7772hLklERESkzlPwJCIiUoPWbsnENE1stmPr7fTl8/cQn9KOXqdezvpF/6Fg50Y8FWUMOO8mmjRvf9D9tq6cy9oFX5HQvD19zxyJzRGGxbKnxePqTTuPqabGqGPHjowePZqHH36Yvn37Mnz48FCXJCIiIlKnKXgSERGpQb9vzqgKeo6Vr7KcXz55DocznJSOJxDw+7E7nAfc1jRN1i38iq0r59KsfU96nToCi/WPt32H3cbaLRmYpnnAKYBycBdddBFLlixhypQpdOnShWbNmoW6JBEREZE6S8GTiIhIDamo9LI9M58wh/2g25QV72LDku8O+FxccisSW3Wp+nNJYQ4tu/SlZ+rFGIcIs4LBAKvm/IuMDb/Suvsgug85D8PYd3unw05hcRm5u4pJbhJ7ZBfWyBmGwf3338/ll1/OhAkTmD17NtYaWq1QREREpL5T8CQiIlJDMnILqfT6iAw/8KgkgHL3LjYt+/6Az7XtOXif4MlisdJ14NmHDJ0CPi+/fvsWeenr6dT3NDr1/csBt3M6bJRVeNiRVaDg6Si4XC6mT5/O9ddfz0svvcSoUaNCXZKIiIhInaTgSUREpIZUeLyYQROr5eBT2Zq27ET/c284rOOFu+JxhEce9PmA38fCL16iKG8nPYZdSOvjBh50W4thIRgMUuHxHda5ZX89e/bklltu4fnnn6dPnz7069cv1CWJiIiI1DnV03RCRERE9uPzBzAxgerpoeSMiD7k836fB3dBFo6wCBKadzjktoYBJuDz+6ultsZq5MiR9O3bl4kTJ1JYWBjqckRERETqHAVPIiIiNcRus2JggGnWyvmc4VH0OfMa/F4PCz97kdLdeQfd1jT3xGF2mwY/HwuLxcLUqVMxTZPJkycTDAZDXZKIiIhInaLgSUREpIZEhDmwWAwCwdoJngASW3Wm71l/x+etYOHnLx00fAoGg1gsFiLCHLVWW0OVkJDAgw8+yIIFC3jnnXdCXY6IiIhInaLgSUREpIa0SE4gzGnH46vdPkpNW3aiX1X4dOCRTx6fH6fDRpvmTWu1toZq4MCBXH311Tz33HOsWbMm1OWIiIiI1BkKnkRERGpImMNOuxZJeLwH76NUVryLDUu+O+hPwH90oVWTFh3pd9a1+L2VLPz8RUoKc/d53uP1kRAbTdO4Q/eNksM3atQounbtyrhx4ygtLQ11OSIiIiJ1goInERGRGnRch+aYh+jxVO7exaZl3x/0Jxg4+ubfTVp0oO/Z1+H3elj0P+GT1+fnuPbNMYzqaXwuYLPZmD59OsXFxUyfPv2Q/91FREREGgvDPIxPRW63m5iYGIqLi3G5XLVRl4iISIPw/cLV3P/Mh8S5orDbrKEuB4BAMEh+oZt7/n4Ol5wxINTlNDg//PADY8eOZeLEiZx33nmhLkdERKTeKnKXsS0zn7IKDz6/H6vFSpjTTqtmCSQlxOgGWogdblakpWxERERq0OBenWka76KwqJSE2Nqb1lZZWUlBQQHRrmicDicOhwOLZc9A55KySqIjwzllQPdaq6cxOfXUU7nwwgt59NFH6dGjB+3atQt1SSIiIvVCSVklPy1Zw6oN6fy+OYOcgiI8Xh/BoLlnJLFhYDEMHHYb8TGRdGvfgm7tm3PqgO40axob6vLlIDTiSUREpIa99OGPzP54DkkJMVhq6c5cfn4+O3fuBPZMAbNYLNhsNhwOJwGLnV7tEvj7uf1p0aIFLVu2JDpavZ6qU2VlJVdffTWGYfDmm2/idDpDXZKIiEidtTk9h6/mruA/P//GbncZpmlis1kJc9hx2G1YrZY9o5tMk2DQxOPz4/H68Pr8YBhEhDkY2rsL5wzrRd/u7aputknNOtysSMGTiIhIDduZs4sr7nseMImJiqiVc5qYbNywkdKyUgwMrDYrhmFgGlYwrPjTF2BW7MbhcOBwOEhISKB9+/a0a9eOVq1a0bJly6ofvfcfnS1btnDVVVdx7rnnMm7cuFCXIyIiUucUl5bz4gc/8uXcFVR4vDjtNlxR4dish9+eIBg0KS2vpKzSg91mpXe3ttzz93NondKkBisXUPAkIiJSpzzxxle895+FNI2NxlZLvZ5KS0vZunUrwWCQoBnEarFidUbi8BUSVb6VYCCA1+vF5/Ph9Xrxer2YprknoDLNqlAqKSmJ119/nQ4dOtRK3Q3JJ598wvTp03nkkUc45ZRTQl2OiIhInbFgxUZmvvk127PyiQx3Eh0Rdsw9myo8Xorc5cS6Irjx4pO56C/9sFo1+qmmqMeTiIhIHXLjxaeweNUWtmXmkRRfO80wo6KicLlcFBcX47A58GPDV1GCffc6jEgnVquV8PBwwsPD99s3EAjg8/nIysoiLi6O2NjYGq+3IbrgggtYsmQJU6dOpWvXrqSkpIS6JBERkZAKBoO89OGPvP3VArw+P03joo9ohNOhhDsdOJvYKSwu5fHXv2LRqs08eOvFREeGVcvx5ego+hMREakF0ZFhjLnmbJx2O+7Silo7b3JyMhaLBdOwYLNZCRasJzN9K1lZWQSDwYPuZ7Va8Xq9REdHM2XKFJo00XD1o2EYBhMmTMDlcjFhwgT8fn+oSxIREQmZYDDI46//h9c/n4fVYpAU76q20Gkvi2HQJDYaV1Q485at557H36G4pLxazyFHRsGTiIhILenXoz1XnHMSHp+P0vLKw97P6/WycdNGfH7fEZ8zLCwMV2wcpmHD4cmnmcsgJSWFkpIStmzZQllZ2QH38/l8uN1uLrvsMk4//fQjPq/8ITo6munTp7N27VpeeOGFUJcjIiISEqZp8sw73/LRd4uJCHMSExVRoyPAw50OEmKjWLZmG+Of/pCyCk+NnUsOTcGTiIhILbrpbydz4an9KKvwUFJ2eCOfMjMzKSkpobCw8IjPV1bhweaIwO7ZhSdjBQYQExNDu3btcDgcpKenk5uby3+3fDRNk7y8PDp37syECRNqZVpgQ9e9e3duvfVW3njjDRYtWhTqckRERGrdF2nL+eDrRYSHOYiKqJ2pbw67jfiYSJas3sxTb33NYbS4lhqg4ElERKQWWSwWxlxzNiPOGkil10dBUQnBQ3wIcpe4KXYXgwklJSWHfR7TNCksLqW0vJK/Du/NTReehMdTWTXVy26307p1a5KSkti9ezfbtm2jsnLPKKyioiLCw8OZMWOGFhWpRldeeSUDBgxg0qRJ7Nq1K9TliIiI1JrMvEJeeP8HTExckfv3lqxJToedqIgw/jNvJT//uqFWzy17KHgSERGpZVarhTuuPIMx15xDVEQYubuKqfR499suEAiQlZlVtdJcRXkFlZ4/n6Ln9fnJ2VWMw27jlktP5f6bz+emG6+nVatW5Ofn77NtfHw8bdu2xTAMtm3bRk5ODmVlZdxwww0MGjSo2q5Z9oSOU6ZMAWDSpEmH7LElIiLSUASDQWa+8TUFu90kxESFpIaoiDB8/gBPvvU1Re4DtxmQmqPgSUREJAQsFgsXn9aPf065gYHHd8RdVkle4Z4Aau8w8NzcXDweDxbDgsViIRgMUlxcfNBjerw+8grd7HaXcXynVrw46Vr+fsEwbFYrLpeL22+/HdM08Xj27XHgdDpp06YNCQkJ5ObmYrVaueiii2r0+hur+Ph4pk2bxpIlS3jrrbdCXY6IiEiN+2HRGuYv30BsdCQWS+giiCax0ezM2cU/P00LWQ2NlWEexiRHt9tNTEwMxcXFGnIvIiJSzQKBIF/NW8m/vlvM5vQcfP4AFjNIXm42ECQY2DMyxjAMnE4nnTp3wsDANE18/gCVHh9llR5sVgutmzXhwlP7csGpfXHYbfucx+v1cs4557B69WqaN2++Xx0FBQUAtG/fnsrKSu644w7+9re/qcdTDZg1axZvvPEGr7zyCj179gx1OSIiIjXCNE1umfIqv67bjsse4Ke3Z+zzvGGx4gyPIj6lLR16DcfVJAWA3bnp7Ph9AcX5GVSWuQn4fYRFxhDTtAXtew0jNrHlAc+Vs/V3tq/+hdKiPHzeSsKjYklIaUf7XsOJjElgt7uMMKedj2beQawrslZ+Bw3Z4WZFCp5ERETqiGAwyIp1O/jkh8V8+GUaHp8fq81OwO/HMAwshkHQNImLi8NutwNgs1lxOuz079Ge84b3pl/P9odclvjbb7/l+uuvJzo6moiIiKrHKyoq2L17NxMmTODaa6/lmWee4V//+ldVT6LExMQav/7GxO/3c+ONN5Kfn8+7775LdHR0qEsSERGpdmu3ZHLD5FcIs9vAV8ZPb88gwpVA804nAhDwedidm87unO1YrDYG/PVG4pu1ZcuKNLb+9jNxSa0Ii4rBanNQ7t5F7va1BAMBTjjlUlp07r3vuX75N1t/m4czwkVy227YHGG4C7LI37kJm93BoAtvIzI2kfzdbu659hwuOX1AKH4lDYqCJxERkXrqkUce4cmnniGhWWssYS5yi8qJiE7AERZORUUFHTu054xThtGhdTIdWiXRrkXiYa8OEwwGueKKK5gzZw7NmzfHMAyCwSCZmZkMHTqUd999tyrUWrhwIVOmTMHj8TB27FhOO+20mrzsRicrK4srrriCvn378sgjj2hkmYiINDiPvvpvPvx2MckJMVSU7Oant2fQtGUn+p97wz7brV/8DZt//ZH4lHYMOv8WAn4fVpt9v+O5d+Uw/6OnsTnC+Ms1k6reOyvL3fzwxjTCo2IZesld2J1/NDDf+ts81v7yb1p26cvxJ19CXmEx7Vsm8/bDt4R06l9DcLhZkX7LIiIidciKFSuYPXs2EeFOwi1e7JV5lO34FcfuNbjKNmLNW8GuNd9z24hTuPDUvvTs1OqIliS2WCyMGTOG8PDwqlXy8vPzSUxM5JFHHqkKnQAGDhzIBx98wIABAxg/fjzjx4/H7XZX+zU3VikpKUycOJGffvqJTz/9NNTliIiIVKtgMEja0nU47bY/vbnStsdJABTn7QQ4YOgE4EpIJiouCW9FKX7vHwuuVLh3g2kSl9xmn9AJILF1VwA8FaUAREeEsyMrny07847uwuSIKXgSERGpIzweD/fffz+lpaXExcUB4PP5ALDZ9vRriomJIT8/nzlz5hz1eXr37s25556L2+2mtLSUYDDIuHHjaNeu3X7bulwupk+fzvTp01m4cCGXXnopCxcuPOpzy75OPvlkLr74Yh5//HE2b94c6nJERESqTUZuIe7SCsKcBw6RDuzQAVVZcQFlRXmERcXuEzBFxjbBYrGyO2c7Pu++KwDn7VgHQJMWHQFwOux4fH627Mw9grrkWCh4EhERqSNeeuklli9fTtOmTavuDO4NnvaORLLb7QSDQT7//PNjOtedd95JTEwMeXl5nHnmmVx22WWH3P60007jgw8+oH379tx+++088sgjVFRUHFMNssddd91Fq1atGDdunH6nIiLSYGzZmYvH68Pp+PPgafvvCwCITdq3afju3HQ2LPmO9Yu+ZsX37/Lzh08B0GPYhfts5wiLpMvAs6goKSLt3cdYPfdj1i38isX/ns26hf+hdfeBtOkxCACLZU/fzM3pCp5qi+3PNxEREZGatm7dOmbNmoXT6cTpdFY97vf7gT9GPAFERUWRlpZGXl7eUTf9bteuHddccw2ff/4506ZNO6weB4mJiTz77LN89NFHPPXUUyxevJgHH3yQHj16HFUNsofT6WTGjBlcddVVPPHEE9x///2hLklEROSYbU7PxTAMrP/zGaOseBcblnwHQMDvpSg3ncLsbVisNrr0P2OfbYvydrJp2fdVf3aGR3HCKZfRtFXn/c7X7vihhEXGsGrOv9ixZlHV4/HN2tK8Yy8sln0XX1m7JfOYr1EOj5qLi4iIhJjP5+Oyyy7jl19+qWr4vVd+fj5FRUV07Nix6rFAIEBOTg4PP/wwI0eOPOrzBoNBSkpKiImJOeJ909PTmThxIuvWrePvf/87N9xwwz7hmBy5L774gilTpjB9+nQ1chcRkXpvxitf8PH3S2jWJBaAcnchP709Y59tDIsVZ3gU8Snt6HDicFwJzQ54rIDfR1lRPlt/m0fGxuV0HXAW7Xul7rPNxqXfs+nXH+nc7zSadzoRuzMcd0EWa+Z/gXtXFr1Pv5rktscBUFhcSlJCDJ88fVd1X3ajcrhZkT4hSkiVlleyZWcu6dm7qKj04g8EsdksRIY7adO8Ke2aJxIe5gh1mSIiNeqNN95g4cKFNGnSZL/mmz6fb5+G3wBWqxXDMPjkk0+4+uqrj3o1NIvFclShE0CrVq149dVXee2115g9eza//PILU6ZMOWCfKDk85557LosXL+ahhx7iuOOOo3nz5qEuSURE5Kh5vT4ONM7lQKva/RmrzY6rSQonnHIZ3ooy1i36D01bdcGVkAxA/s6NbFz6HW2PH0KHE0+u2i++WVv6nv13fnr7YdYu+LIqeDIMA4/XfwxXJ0dCwZPUKtM0WbMlk+8XrGLFuh2k5+zC4/Xh9wf2+eIUNE0cNithTgdtmjeld7c2nH7S8XRolRTC6kVEqt/WrVuZOXMmNpuNsLD9V6fz+/0HHEnkcrlYtWoVGzdupHPn/Yeb1war1cr111/PSSedxKRJk7jyyiu57bbbuOyyy7Q88VEwDIPx48dz+eWXM378eF555ZX9QkcREZFQyc7O5l//+hfJycm0bduWtm3bkpCQcNAbYEd7Y+zPNGnZibz09RRmb/0jeErfsOe55h322z4swkVUbCLugkz8Pg82+56WBoY+qtQaBU9SKyq9PuYsXsNnPy7j9y2ZeL0+rFYLTocdV2Q4dpt1nxcm0zTx+vx4vH7Wbs5g1cZ03v96Ib27teW8k3sz+MQu2G3WQ5xRRKTuCwaDTJw4kcLCQlJSUg64jc/nIyoqar/HIyMjKSoq4quvvgpZ8LRX165defvtt5k1axYzZ85k3rx5TJ48meTk5JDWVR9FRkYyY8YM/v73v/PCCy/wj3/8I9QliYiIALBhwwaeeOIJKisrcTgcOJ1O4uLi6NSpE507d6ZNmza0bduWdu3a0axZM+x2W42ET55yN8A+PZuCwcCe5ypKD7iPt6IUDAPj//cxTROHXXFIbdFvWmrc75t28uirX7J+WxYm4IoMIy464pAvQoZh4HTY/38FhHBM06SswsMvKzey8LfNnNi1Dfdcew7tWhxdU10Rkbrgt99+49dffwUgJyenaji63W7HZrNht9vxeDy4XC5M09znddMwDOx2O5988gl33HEHVmtow3in08no0aMZMmQIkydP5tJLL+Xee+/lrLPOqrE7ng1Vt27duP3223nqqafo06cPgwYNCnVJIiIi9OvXj+joaCwWC1FRUXi9XvLz88nMzOSnn37CNE2sVitOp5OoqCiiW51AaTCOfHw4HU6CXu9hn6sobyexiS33e7y4IJMdaxZhWKw0afFH/8u45DZsX/0L236bR7N2PbA7w6ue2/H7QirLiolLboPVuicC8fkDpDSNO4bfhhwJNReXGlPp8fLG5z/zzle/UF7hJSE2qlpS5Uqvj93FZcREh3PdhcP52+n9NfpJROqlYDDIkiVLyMzMJDc3l5ycHDIyMti5cyfZ2dmUl5eTmZlJdHQ0NpsNi8VCMBjEYrFgt9sJBoMAvP/++5x00kkhvpo/lJSU8Pjjj/PVV19x8sknM27cOOLi9OHuSASDQe68807WrVvHe++9R5MmTUJdkoiICLfddhsff/zxAfsQmqaJ3+/H4/Hg9Xrx2mOxpvTB9FWCYRL0lJO59BPC45vToteZtG7d+oBtBgB+fGs6hmEhpmkLwqNjCQYClBXlkZ+xCUw4bvBfadtz8B/nDgZZ+MVLFGZtxREeRVKbbtid4RTnZ7IrczMWm52B591MXFIrAHJ3FTPyvKHcOuIvNfOLaiQONytS8CQ1orC4lHFPvs/yddsJc9iJ+ZMRTkfKNE0K3WX4/QGG9unCg7deTGS48893FBGpJ4LBIIsXL+b666/n7rvvJjw8nNzcXLKzs8nIyCAjI4O8vDwCgQD33Xcf1113XahL3s9PP/3EQw89hNVqZeLEiQwZMiTUJdUrhYWFXH755bRt25ZZs2apb5aIiITcZ599xqhRo0hMTPzT1WwDFifF0d3BDBD0+/CUFZOz/HOcMck0Pe4UOnXqRERExAH33bFmEXk71uEuyMJTWQamiTMimvhmbWnT46SqAGmf8wX8bPttHlmbV1FWlEcwGMAZHk1C8/Z0OPFkouP39AsOBoPkFbqZ9o9LOP2knsf+S2nEFDxJyBTsLuHux95mzZZMEmKqZ5TTwVR4vBSVlNO/R3seGT2CqIgDJ+YiIvXR3Llzufvuu/nmm28OOOLF7/dTWFhIkyZN6mwosWvXLqZOncr8+fM5//zzGT169EE/ZMr+li5dyqhRo7jlllu49tprQ12OiIg0coWFhQwYMIBgMEhsbOxBt/P5fLhLSvCnnIRpsWP6K7EYFkzTxOl00rZtW8LDww+6f00qr/RS6fXx5vSbad9Si1cdi8PNiurmp1Spt4pLyxn75Pus2ZxJk9joGm/YFu50EO+KZPGqLUx89l9Ueg5/3rCISF2Xm5uLzWYjPj7+gM/bbDYSExPrbOgEkJCQwJNPPsmECRP49ttvueyyy1i5cmWoy6o3+vbty7XXXsuLL77Ib7/9FupyRESkkYuPj2fgwIGUlZXt87hpmlRUVJCTk8PmzZvZuHEjOdnZeAvTsVptWK1WgmYQi9VCWHgYmZmZrF69mpycnFq/hpKyCjq0SqJt86a1fu7Gqu5+UpV6xzRNnnzja1Zu2EFCbFSt9V1yOuzEuSKZv2Ijr3ycVivnFBGpDTk5OXU+WDochmFwwQUX8N5779G0aVNuuOEGnnnmGbxH0GS0Mbvxxhvp0aMHEyZMwO12h7ocERFpxHJzc3G73RQXF7NlyxbWr1/PqlWrWLlyJevWrSMrK4uSkhL8fj9+vx935joCfh+BoIFpmphBk+KiYoqLi/H5fTicjlqt3x8IYJomF5zcp95/vqpP9JuWapO2dC3fLVhFdERYrS9NGea0E+6w88E3i1i5fketnltEpKbk5OSQnJwc6jKqTYsWLZg9eza33XYb7777LiNHjmTTpk2hLqvOs1qtPPTQQ5SXlzN16lQOo0uCiIhIjcjJyWHhwoVUVFRQVFREeXk5fr8fwzCw2Ww4HA4cDkfV6rx4S/C6c7HYndjt9j3hEyYWi4WkxCTi4w48qrumuEsriI+J4pQB3Wv1vI2dgiepFruKSnnqrW/wB4Mh67Pkigqn0uvlsde+pKJSd9FFpP7Lzc0lKalh9R6wWCyMHDmSN998E9M0ufrqq3nzzTerVuiTA0tOTmbSpEnMmTOHjz76KNTliIhII1JaWso333zD2LFjuf3224E97+eGYeB0OgkPD8fp3BMsWa1WLBZL1Wgiv9+Pt2ALhmkSCILFasFqsRIeHl7rN9f8/gAen59zU08kOlK9gWuTgiepFm9+MY/MvN00iYkOWQ2GYZAQE83GHTn867vFIatDRKS65ObmNqgRT/+tU6dOvPnmm4wYMYJnn32WG2+8kczMzFCXVaelpqZyySWX8OSTT7Jx48ZQlyMiIg1Yfn4+H3/8Mbfddhunnnoq999/P1lZWVxzzTV88cUXJCcnYxjGQaermaaJz+fDYrHg3b0Tf3EmNmcEFosVw2KQkpKC1Vo7rVn21lNQXEK7FomMPG9orZ1X9qjd+VDSIBWXlvPJ12n8+uFj+zxuWKw4w6OIT2lLh17DcTVJAWDDku/YtOx7ev3lCpp3PKFq+x/fmk5Fye7/OoCB3RlOTJPmtOkxiOS2fwyHLC3KJ3vLKvLTN1BWvAtvZRnO8CgSmrcnoWN/PvtxGZedObDWp/yJiFSXYDBIXl5egxvx9N8cDgf/+Mc/GDJkCA888AAjRoxg9OjRnHfeeRiGEery6qQ777yTlStXMm7cON5+++2QrQgkIiINT3p6OnPmzGHOnDn8/vvvWCwW+vTpw+jRoxk2bNg+n0lGjRrFAw88QCAQ2C9ACgaDeL1eDMPA4XCQlJSEy15EcbAZPsNOfEzEIVdAqwklZZU47XbuHnmWRjuFgL6VyzH7YeHvFJdWABDhSqB5pxMBCPg87M5NJ2vTSnK2/s6Av95IfLO2hzyWYVjo0PsUAMxggNLdeeRuX0tBxia6DjqH9icMA2Djkm/J2vwb0fHJJLfths0RhntXDpkbl5O9ZTXeYSP4ZcVGhvfrVoNXLiJScwoKCggGgw06eNqrV69evP/++zzxxBNMmzaNefPmcf/99x90Nb/GzOFwMGPGDK688koee+wxJk2aFOqSRESknjJNk3Xr1jFnzhzS0tLYtm0bTqeTQYMGMWXKFAYPHnzQgGjMmDHMnDmToqKiqml3sGdq3d6RTklJSSQlJe0ZFWX6qMxahbNFbyKjY2vxKqHS46Xc4+WyMwbQv2eHWj237KHgSY5JMBjksx+XVc3ZjIxJoHO/0/bZZv3ib9j864+sX/wNg86/5ZDHMyyW/fbPT9/A4i//ycYl39LmuIFY7Q6atupM+17DiWnafJ9tMzetZMX377B92X/4Ys6pCp5EpN7au7xwYwieACIiIpg4cSLDhg1j2rRpXHLJJYwfP56TTz451KXVOa1bt+a+++5j8uTJ9O3blzPPPDPUJYmISD3h9/tZvnw5aWlppKWlkZeXR0xMDEOHDuW2226jf//+hIX9+Yggp9PJJZdcwssvv0wwGMQwDHw+H4FAAKfTSfv27fcZlVtaWoqlvIy/Du3BwvV5uEsrcEXV/KjdSo+PopJyhvXtyq2Xn/bnO0iNUPAkxyQzdzfbs/KJCHcedJu2PU5i868/Upy386jO0bRVZ6Jim1JalEdJYS6xSS1p2aXvAbdt3vEENi79jrKifJasXEtpeWXImp2LiByL3NxcgAbb4+lghg4dygcffMBDDz3Evffey9lnn80999xDVFRUqEurU84++2yWLFnCjBkz6N69Oy1btgx1SSIiUkdVVFSwcOFC0tLS+PnnnykpKSE5OZlTTjmF1NRUTjjhhKPqt3Tdddfx1ltvUV5ejmmaGIZBdHQ07du33+d4gUCA3bt3c8EFFzBz4ihe/PBH3vziZ3a7g8RGR9TY9PryCg/usgoGn9iZB2+9mDCHvUbOI39OwZMck807c6j0+og8rF5K1fCCchiH2NvgzusPsmVnLsd3bn3s5xURqWW5ubmEh4cTHR26RRtCJS4ujscee4yvvvqKRx99lF9//ZXJkyfTp0+fUJdWZxiGwdixY1m9ejXjx4/n1Vdf3bNstYiICFBUVMS8efNIS0tj0aJFeL1eOnTowKWXXsrw4cPp1KnTMQU+wWCQjIwMDMOoCp3i4+Np1arVfg3H8/PzSUlJYdKkSVgsFm659FSiI8KY/fEccncVkxAbjd1WfY3Gg8Egu4pLAThj8PGMv+E8wsMc1XZ8OXIKnuSYbE7PxcDAepDVDAC2/74AgNiko7sbW5CxidKifKw2B9Hxh77zvzs3nZLCXGISW4LFzuZ0BU8iUj/l5uaSlJTUaJtsG4bBOeecQ+/evZk8eTI333wzl19+ObfeeitO58FH2TYmERERzJgxg2uuuYbnnnuOu+66K9QliYhICGVlZTF37lzmzJnDypUrMU2Tnj17MmrUKIYNG1Zto2N///13HnvsMdasWcMJJ5zAypUrCQQCpKSk7Bc67R0NNXbsWFJS9iw2ZRgGV/11CD06teLx179kw7ZswpwOXFHhWI7hc49pmpRXenGXVpCY4OLWEX/hzMHHH3TlPak9Cp7kmGzYno1pmlV/LivexYYl3wEQ8Hspyk2nMHsbFquNLv3P+NPjmcFg1f5mMEBpUT6529cAJp37n47VdvC7uT5PBb/9+AEYBl0Hno0f2JKee0zXJyISKnuDp8auWbNmvPDCC7z33nvMmjWLRYsWMWXKFLp06RLq0uqELl26cMcdd/DEE0/Qt29fBg8eHOqSRESklpimyZYtW6qag2/YsAG73U6/fv0YN24cw4YNq9aFOnbt2sVzzz3Hv//9bzp16sTs2bOJiYnhk08+4eOPPyY9PZ1mzZpVbR8MBiksLOS0007jb3/7237HO6FLa2ZPvoFXP03jX98uJm9XMTablZiocOy2w48qAoEgJWUVVHh9OB12ThlwHKNHnkVSQky1XLccOwVPckzyd5dgs1qBIADl7l1sWvY9AIbFijM8ipSOvehw4nBcCc0OcaQ9TDNYtT+Ggd0ZTpPmHWjdfRDJbY876H4Bv49l37xBaVEenfufQZPm7ckpKKoaYikiUt/k5OTQoYNWXoE9U6ivuOIKBg4cyP3338/IkSO58cYbueaaa46qJ0VDc9lll7FkyRImT57Mu+++S2JiYqhLEhGRGhIMBlm1ahVpaWnMmTOHzMxMIiIiGDx4MNdccw2DBg0iMjKyWs/p8/n44IMPePnll7Hb7YwfP57zzz+/aiTRmDFj6NixI7feeivl5eVEREQAe1bobdq0KVOmTDnoqKPwMAe3jjiNC07py9fzf+OLOb+SnV8E7BkZ5XTYcNrtWK0WDANME4JBE6/Ph8frxx8IAAYx0eGcO/xEzh7Wi27tmjfaEeN1lYInOSYer4///n+6actO9D/3hqM+nsVq46ybZhzRPgG/j6X/eY1dmVvocOLJdOx9CrDnharC4zvqWkREQiknJ0ejV/5Hu3bteOONN5g9ezYvvfQS8+fP58EHH6RVq1ahLi2kDMPggQce4PLLL2fixIm88MILmlYgItKAeL1elixZQlpaGvPmzaOwsJCEhASGDRtGamoqffr0weGomR5GCxYs4IknnmDnzp1cfPHF3Hzzzbhcrv22O/fcc3nvvfdIS0sjPDycyspK/H4/o0ePpk2bNn96npTEOK67MJUrzxnMLys2sGpjOms2Z7I1I5fySg+BYBDTBMP4/0DKbqNdy0SO69CCrm1TGN6vG7Gu6g3cpPooeJJjYjEMzD/frMbsDZ0KMjbRvlcqXQbsu6S01aKkW0TqH6/XS2FhYaNb0e5w2O12Ro0axeDBg6vCljvvvJOLLrqoUd/djI2NZdq0adx88828+uqrXH/99aEuSUREjkFpaSm//PILc+bMYcGCBZSXl9OyZUvOOeccUlNT6d69e43eZMjIyGDmzJnMmzeP3r178/DDD9OxY8eDbm+xWBg/fjxLly6lqKiIsrIyhgwZwlVXXXVE53U6bJzc/zhO7r9ntovH6yc9u4CyCg9enx+bzYrTYaNVchOiI7V6eX2h4EmOSbjTsU+Pp9r036FTuxOG0XXg2fs8HzRNwp1avUBE6p/8/HwA9Xg6hJ49e/Luu+/y1FNP8fDDDzN37lwmTZpE06ZNQ11ayJx44olcf/31vPzyy/Tu3ZtevXqFuiQRETkCBQUFzJ07l7S0NJYuXYrf76dr165cc801pKam0rZt2xq/yVJeXs5rr73G22+/TUJCAo888ggnn3zyYZ23Z8+eXHHFFcyaNYumTZvy0EMPYTuCXk0H4nTY6NhaN+LqOwVPckxapzTht43pUMstNvYJnY4fSrdB5+y3jcUwaJ5Ufc30RERqS27unoURFDwdWnh4eFXz1ClTpnDppZcyduxYTjvttFCXFjLXXXcdy5YtY8KECbz33nvExKixqohIXZaenl7Vr2n16tVYLBZ69+7N6NGjGTZsWK19FjBNk2+//Zann36a4uJi/v73v3P11VcTFnZko4r+8Y9/8NNPP3HVVVfRqVOnGqpW6hsFT3JMOrZOxoBaH/W0eu7HFGRswhkRjdXurFoJby/TNAlP7kyHVvrSJiL1T05ODqDg6XANGjSIDz74gBkzZjB+/Hjmzp3Lfffdd8AeFA2d1Wpl2rRpXHbZZTz44IM88cQTjXoKoohIXWOaJuvWrSMtLY20tDS2bt2K0+lk0KBBPPjggwwZMqTW3782bNjAo48+ym+//cbJJ5/MnXfeSUpKylEdq0mTJvz44484nc5qrlLqMwVPckw6tErCarUQCNZuE+/ykt0AeMpL/lgF738cd2pzBU8iUi/l5OTgcrkIDw8PdSn1RkxMDDNmzCA1NZVHHnmEyy67jAceeID+/fuHurRal5iYyOTJkxk9ejQffvghl156aahLEhFp1Px+P8uXL68Km/Ly8nC5XAwdOpRbb72V/v37H/HIouqwe/duXnjhBT799FPatm3L888/T79+/Y75uAqd5H8Z5mEMVXG73cTExFBcXNwo7x7KwRW5yzj/HzMxTXBF1Z0vSIXFpURFhPHl8/dgt2mpbRGpX2bMmMHq1at59913Q11KvZSXl8fkyZNZsmQJl1xyCf/4xz9C8oE+1J544gk++ugjXn/9dTp37hzqckREGpWKigoWLlxIWloa8+fPx+12k5ycTGpqKqmpqfTq1QurNTTfUwKBAB999BEvvvgipmly8803c/HFFx9zPyZpfA43K9LfLDkmsa5IBp7QkR8W/l5ngifTNPH4/Fw4qIdCJxGpl3JzczXN7hgkJiby3HPP8a9//Yunn36aRYsWMXXqVI477rhQl1arbr/9dpYvX864ceN4++23iYiICHVJIiINWlFRET///DNz5sxh0aJFeL1e2rdvz9/+9jeGDx9O586dQz79eenSpTz++ONs3bqVCy64gFtuuYW4uLiQ1iQNn4InOWbnDjuROUvWUlHpwcDE4/Xg9XjxeDy43W78fj89evSotRfZ8kovYQ47Zw/Taj4iUj/l5uZywgknhLqMes1isXDppZfSv39/Jk2axN///neuu+46rrvuukZzR9fhcDBjxgyuvPJKHnnkER588MFQlyQi0uBkZ2dXTaFbsWIFpmnSs2dPbrnlFlJTU2nZsmWoSwT21PnUU0/x448/0rNnT9566y26dOkS6rKkkWgcn7yk2pimSVFREdu2bWP79u1s376dTZs2szvPQ07QBv5KgsEgsGcIp2maRERE1GqyX1JeQd/j2tG5TbNaO6eISHXKycnRiKdq0qZNG1599VVeffVVXnnlFebPn8+UKVNo27ZtqEurFa1atWLs2LFMmjSJfv36cfbZZ4e6JBGRes00TbZs2VK1Et2GDRuw2+307duXcePGMXToUBISEkJdZpXKykrefPNNXn/9dVwuF1OnTuWMM84I+cgraVwUPMkBBYNBcnJy2LZtW1XItHHjRtavX09hYSFerxev14vFYiEYDOJo0h5LYncMqw2L4cfv84MBNpuNVq1a1VrdZRWV2KxWLj1zoF5MRaTeCASCeH1+/IEgAb8Xd0mJgqdqZLPZuPHGGxk8eDATJ07kiiuu4Pbbb+fSSy/FYrGEurwad9ZZZ7FkyRIefvhhevToUavvyyIiDUEwGGT16tXMmTOHtLQ0MjIyiIiIYPDgwYwcOZKTTjqJyMjIUJe5D9M0+emnn3jyySfZtWsXV1xxBddee62mXUtIqLm4HNCsWbN48sknqaysxO/3V4U4TqcTh8OB0+nEbrdXfWA3gZLIjngsLvyeEqwWKyYmcbFxtG7dulZqDgSC5BW6OXvYCUwedZGCJxGpk4LBIGu3ZLJhezab03P4fXMGmbm7CQZNTEz8fj85WZkM7tuDgb2706FlEj07tyIxXu+/1aGyspLnnnuO999/n379+vHAAw80ipCvvLycK6+8krCwMF5//XUcDkeoSxIRqdO8Xi9Lly5lzpw5zJs3j8LCQuLj46uag/fp06fOvpZu2bKFxx57jGXLljFkyBDuuusu3XSQGnG4WZGCJzmgpUuXMmLECDweD4mJiX8a4lRWVpJVUExEh+HYHE4IeMGEDh07EB527E3HN2/ejM2+Z/SUxTjw3em8wmKSEmJ5bdpNJMRGHfM5RUSqU3FpOT8s/J3PfvqVLTtz8fr8GBhYrQYOuw2LYcEw9qyCk5dfQFx8AlarFdM0iYoII7VvN84Z1oteXVs3ilE6NW3JkiVMnjyZ8vJy7r33Xs4888wGf8Ni48aNjBw5kosuuogxY8aEuhwRkTqntLSUX375hbS0NH755RfKy8tp2bIlw4cPJzU1le7du9fp92C3281LL73Ev/71L1q0aMGYMWMYNGhQqMuSBkzBkxyzp59+mkceeYS4uDjCww8eHu3atYu8vDzCwsJo2v5EPNHtCfi9xMdG07JF9TTT+/333/F4PbhcLlq1bIXT6dzn+d3uMgCm3n4xw/s1rlWLRKRuq6j08sYXP/PJD0vZXVyKxWIQHRlOmMN+wKCjqKiI7OxsunTpgmEYBIJBSssrqaj0YrdZ6dA6mVsv+wv9e3YIwdU0LCUlJTz66KN8/fXXnHzyyYwfP57Y2NhQl1WjPvzwQx599FFmzpzJ0KFDQ12OiEjIFRQUMG/ePObMmcPSpUvx+/107dq1Kmxq27Ztnb8xEQwG+eyzz5g1axY+n48bbriByy67DLvdHurSpIFT8CTHzOfzcdVVV5GWlkZKSsp+6b7f7yczM5Py8nISEhJo2rQpGAa7zHiIaUtKsyQSYqvn78umTZtwu91YLBYcDgfNmjUjLj4OA4Pd7jICwSC3X346l5+tRF9E6o5VG9N57NUvWb8tC4fdRkxUBFbroe+U5ufnU1RURMeOHfd53DRNKr0+ikvLcdrtXHBqH268+BSiI8Nq8hIahR9++IHp06djt9uZOHEigwcPDnVJNcY0TcaMGcOKFSt4//33SUxMDHVJIiK1Lj09vWolutWrV2MYBr179yY1NZVhw4aRnJwc6hIP22+//cajjz7Khg0bOOecc7jtttto0qRJqMuSRkLBk1SL9PR0/vrXv5Kfn0+zZn+sEldSUkJ2djaGYdC8efOqJnXBYJCsrCwGnD2S7HIndpuV2OhjX9Vu69atFLuLMU0Tq9WKgYHLFUNEdCw2m5WbLzmFK88dXOfvRohI4xAMBvnnJ2m89e/5VFR6SYiJwm4/vPU8srKz8Hg8tG1z4FXXTNOkpKySskoP7VokMunmC+jesW4s1VyfFRQUMGXKFBYsWMCFF17InXfe2WAbsLrdbkaMGEFKSgovvvgiVqs11CWJiNQo0zRZt25dVdi0detWnE4nAwcOZPjw4QwePJiYmJhQl3lE8vLyeOaZZ/jmm2/o1q0b9957L927dw91WdLIKHiSavP5559z++23ExYWRlRUFLm5uRQVFREdHU2zZs32+cBaUFBAZGQkP/zwA98v3cw/P0mjvMJDfEwUjsP80nUg27Zvw13sJhgM7gmerDaChg27Ba6/cAjjbr2qOi5VROSY+fwBHn/tSz79cRlOx55RTkcSiu9I34HVYqVFixaH3M7vD5BfVEJCTBRTb/8b/Xq0P9bSGz3TNPn000+ZOXMmCQkJTJkyheOPPz7UZdWIlStXcuONN3L99ddz4403hrocEZFq5/f7WbFiRdVKdHl5ebhcLoYOHUpqaioDBgwgLKz+jRr2er288847vPrqq4SHh3Pbbbdxzjnn1OneU9JwKXiSarN3WP4bb7yBz+cjEAiQnJy8Xx+MQCBAdnY2Y8aMqWpa+vumnTz22pes3ZKF02HDFRWO9SheFHfs2EFRUREWi5WAYcFmc2D1FVO+YxlOi5/rr7+e0aNHH7IXlYhITQsGgzzyz3/zyY/LiAp3EhVx5B9ot2zZQlRU1GGttBY0TfIL3biiwnlk9Ah6dzvwKCk5Mjt37uSBBx7g999/5+qrr+amm25qkH0y/vnPf/LSSy/xwgsv0Lt371CXIyJyzCorK1m4cCFpaWn8/PPPuN1ukpKSSE1NZfjw4fTq1avejvI0TZOff/6ZmTNnkp2dzWWXXcYNN9xAVJQWVZLQUfDUQFVUeikuLa9aDcnhsBEfE4XdVnMvoMFgkNmzZ3PPPffg8/no0KHDfs29AXJzc2natCk//PAD8fHxVY9Xery89e/5fPDNIopLyrFYDGKiInA6Du9DvGmabE/fSUlZBRarDV9FKeburSRHeMA0KSoqory8nBNPPJHHH3+crl27Vtu1i4gciVc+msPLH/1E5FGGTgDr168nMTFxn9fRQzFNk9zCYhJio3lp0nW0TlFfh+oQCAR48803eemll2jXrh1TpkyhQ4eG1dA9GAwyatQoduzYwXvvvdfgG6uLSMNUXFzMvHnzSEtLY9GiRXg8Htq1a1fVHHzvYh312fbt23niiSdYuHAhAwYM4O6776ZtW91sktBT8NRAZOYVsuz3rWzakcvvm3eyI7sAvz/A3v9qhmEQ7rTTqU0zurZLoUOrZAYc34GYqOrpS5GXl8cDDzzAsmXLSE1N5dNPP8Xv9+/XsM7n85GXl8fkyZO56aabDnis3e4yvvtlFZ/+tIwdmQX4AwFMwGm37VlK3LJnKXHTNAkGTTxeHz5/AIDS0hJKd2URYymhIn87OVk7adu2bdXwWI/HQ35+PnFxcdx3331cffXVGm4qIrVq9cad3PrQa5hBk1hX5FEdw8Rk65atpKSkHNEIzmBwT/g0oGcHnhp7FbZ6eje3Ltq4cSP3338/O3fuZNSoUVxxxRUN6v0lPz+fESNG0L17d5588sl6/+VMRBqH7Oxs5s6dy5w5c1ixYgWmadKjRw9SU1NJTU2lVatWoS6xWpSVlTF79mzee+89kpKSuPvuuxk6dKheq6XOUPBUjwUCQRav3sK/037llxUbKa/0YgBWiwWnw47NZql6sTGDJl5/AI/Xt+fPQJwrgtNP6sk5Q3vRqU2zo35hSktLY8qUKTidTqZMmULfvn159tlnmTFjBnFxcft8KcrOzqZVq1b88MMPREYe+gtXIBBkxfrtrNuSyYbt2azZkklhUSlBM4hpgmGAxWIhMd5F944t6dgqmbnffc7nH7xR1fNky5Yt2O32fd5UTNMkPz+fQCDA6aefzowZMw5rqoqIyLGq9Hi54YFXWL8ti6SEmJB8IKz07Fnxbsw1Z3PJGQNq/fwNmdfr5fnnn+edd96hV69eTJ48mZSUlFCXVW1++eUX7rjjDkaPHs3ll18e6nJERPZjmiZbt26t6te0fv16bDYb/fr1q1qJLiEhIdRlVptgMMhXX33Fs88+S3l5Oddeey1XXnklDocj1KWJ7EPBUz21bM1Wnnzza7bszCUQCBLx/9M1LIf5JSYQCOIuq6DS6yPMYadfj/bcddWZtGx2+C/EFRUVPPnkk3zyyScMHz6c+++/v2qVB7/fz1VXXcWcOXNISUnBYrHg9XopKCjgscce44orrjjiazZNk+KScio8Pvz+AHa7lfAwxz6jth544AFefvllmjdvDuxZVS8jI4NWrVrtF3SVl5dTWFhIq1atmDZtGqeddtoR1yQiciRe+WgOL/3rx2NeSOFYFRSVEBnu5LWpNx3R674cnuXLl/PAAw9QXFzMmDFjOPfccxvMXecnn3ySDz74gNdee01T1kWkTggGg6xevZq0tDTmzJlDRkYGERERDB48mNTUVE466aQ/veFdH61Zs4ZHH32UNWvWcPrpp3PHHXeQmJgY6rJEDkjBUz1TWl7J7I9+4uPvl+Lx+YiLjjzsHkgHYpom5ZVe3KUVJMRGcePfTuaCU/r86fSADRs2MH78eHJycrj77ru54IIL9vtQvXPnTv7617+Sl5dHs2bNyMrKonPnznz77bc1lsJPmzaN559/fp87zNu3bwegTZs2+20fCATIzc3F4XBw9dVXc9999zXINyYRCb2SskouvuspSssriY+pnQafG5Z8x6Zl3zPgvJtp0vyP1eyCpknurmKuOW8ot12u0L0mlJWV8fjjj/Pvf/+boUOHcv/99x92P666zOfzce2111JSUsI777yj90wRCQmv18uyZcuYM2cOc+fOpbCwkPj4+KopdH369Gmwo3527drFc889x7///W86derEPffcQ69evUJdlsghHW5WFLrbslJl4/ZsJj73EVt25hIZ5iQp/tinaRiGQWS4k3Cng8LiUh599d/MX76BB2658IC9R4LBIO+88w6zZs2iffv2vPPOOwcMdABatmzJgw8+yG233UZeXh6GYXDHHXfU6JvAgY6dmJjIjh07KCkpITo6ep/nrFYrzZo1IzMzk1deeYWwsDDGjx9fY/WJSOP146Lfyc7KYPVXswBwhkdxysj7sVj277NUUpjL3PcfByA8Oo5Trqre1yWLYeC02/hq3gquOX/oUTc4l4OLjIzkgQceYNiwYTz00ENceuml3H///QwbNizUpR0Tu93O9OnTueKKK5gxYwZTp05tMKO5RKRuKy0tZcGCBaSlpTF//nzKy8tp0aIFZ599NsOHD6d79+4Nqrfe//L5fHzwwQe8/PLL2Gw2xo0bxwUXXNCgr1kaHwVPIbZqYzpjn3yfvEI3TWOjsVXz6nQWi0GTuGgqPF5+Xr6Bux55m0fHjKBp3B9pZH5+Pg888ABLlizhqquu4pZbbvnTEOncc89l/vz5vPbaa/Tr149zzz23Wuv+X3a7fb8PwBEREURGRpKXl7dP8GSaJqWlpRQXFxMZGcn555/PyJEja7Q+EWmcgsEgn/60rOr1ybBY8FSUkrdjPcltj9tv+53rluxpZFeDXFHh7Coq5afFa/nr8BNr9FyNWWpqKj179mTatGncfffdnHvuuYwZM6ZejxRq2bIlEyZMYMKECfTv37/G39tFpPHatWsXc+fOJS0tjSVLluD3++nSpQsjR44kNTWVdu3aNYrwe8GCBTzxxBPs3LmTiy++mJtvvlkzjKRBUvAUQuu2ZnLfzPco2F1CUryrRlPtcKcDm8XC6k07uffx93jyviuJdUUyd+5cpkyZgt1uZ9asWfTv3/+wjmcYBvfffz+7d+/m2muvxVrDKyjZ7XYONCs0MTGRbdu2UVRURGxsbFV/p7CwMM4++2zuuusuunfvXqO1iUjjtWF7Npt35BAVvmdkUVxyG9wFWexcv3S/4CkYDJC5cTlNW3RkV9bWGqvJZrViAt/+8puCpxoWHx/PE088wb///W8ef/xxli1bxoMPPsiJJ9bf3/vpp5/O4sWLeeSRR+jRo8dBRz+LiByp9PR00tLSSEtLY/Xq1RiGwYknnsidd95JamoqycnJoS6x1mRkZDBz5kzmzZtH7969efjhh+nYsWOoyxKpMQqeQqRgdwnjnvqAgt0lJMbHYLHUfKJvt9toEhvF6k07uf+ZD2lmyebTTz5h6NChTJo0idjY2CM6nsvlYvbs2TVT7P842AissLAwXC4XOTk5lJWVYbPZGDJkCKNHj2bAgAGN4k6JiITOxu3ZeH1+oh173k6tVhspHU5g5/oleMpLcUb80fMpb/s6PBWltOzS94DBk2ma7Fy/lJ3rllCyK4egGSQ6LonW3QfSqmu/w6rHvSuHJV++gt/nwSy9Cq/vqpA2O28MDMPgr3/9K3369GHy5MncdNNNXHHFFYwaNare9iG55557WLVqFePGjeONN96ot9chIqFlmibr16+vWolu69atOBwOBg4cyAMPPMCQIUOqFjBqLMrLy3n99dd56623iI+P5+GHH+aUU07RdxZp8PRpNARM0+Tpt78hI7eQxDhXrYROe9ntNiKcNr79+VciK9OZOG4cF154YZ1/sbPbD9xo3ev1YpomXq+Xpk2b8vjjj3PqqadqTrSI1IotO/MA9nkNbdm1L+lrF5Gx8Vfan/BH35+d65did0aQ1K47/PTBPscxTZMVP7xL1qaVRMY0IaVjLyxWKwU7N7Fqzr8oLcyl20mHnvZUmL2Npf95DavNQZ+zb8QSEceOrAI6tm48d5BDKSUlhRdffJF33nmH559/ngULFjB16lQ6d+4c6tKOWHh4ODNmzGDkyJE89dRT3HvvvaEuSUTqiUAgwPLly6tGNuXm5uJyuRgyZAijRo2if//+hIeHh7rMWmeaJt9++y1PP/00xcXFXHPNNYwcOZKwMPVilMZBwVMI/LDod35Y9DuuyHCs1toLSExMCgsLyc/Lx7CHEdOmN30HDavzoRPsP9XO7/dTUFBAIBCgU6dOtGzZkszMTAYOHKjQSURqzepNO7FaLECw6rG4pFZExyeTsX5pVfBUWe4mL309rY8biNW6/1tv+rolZG1aScsufekx7CIs/z99ORjw8+u3b7H1t3mkdOxFbGKLA9aRs20Ny79/h/CoWAacewPOqFjydhWzaUeOgqdaZLFYuOqqqxg4cCCTJk1i5MiR3HjjjYwcObLGp6RXt44dOzJ69Ggefvhh+vbty/Dhw0NdkojUUZWVlSxatIg5c+bw888/43a7SUpKqlqJrlevXthsjfdr54YNG3jsscdYuXIlJ598Mnfeeec+K3WLNAaN9xUgRIrcZTz99jcEgyaR4c5aO6/f7ycrK4uysjLi4+Np0rQpeYVuHn/9Pzw99qo6Hz45HA5M0yQQCFBYWIjH46Fly5aMGjWKyy67jLKyMs477zzeeustRo0aFepyRaQRME2TnTm79kxlM/37PNeya1/W/vJvduemE5fUioz1v2IGg7Ts2veAx9q++hesNgfdh15QFToBWKw2Ovc/g9zta8natOKAwVP62iWsnvsxMU2b0+/s63CE72lubRgGmXmF1XjFcrg6dOjA66+/zuzZs3nxxReZP38+Dz74IC1btgx1aUfkoosuYsmSJUyZMoUuXbrQrFmzUJckInVEcXExP//8M2lpaSxcuBCPx0O7du24+OKLGT58OF26dKnz3y9qWlFREc8//zyffvopbdu25fnnn6dfv8ObOi/S0Ch4qmXf/rKK3ILifVaVq2klpSVkZ2WDAa1atapacccVGc6va7aydmsmx7U/8F30umLviKfs7GyaNm3K9ddfzzXXXFM1Lzw8PJzLL7+cd955h0suuYQmTZqEuGIRaeh8/gCBQBDDYkBg3+eadzqRdQv/w851S4hLasXO9UtxNWlOTJPm+x0n4PNSsiuHsEgXm5fP2e95M7jn4KVF+fs9t+23eeRuX0vTlp3ofcZIbPb/6sVjgNfn328fqR0Oh4Nbb72VwYMHM2nSJEaMGMFdd91VL6a377V3IZHLL7+cCRMmMHv27Ho3cktEqk9OTk7VFLrly5djmiY9evTgpptuIjU1lVatWoW6xDohEAjw8ccf88ILL2CaJnfffTcXX3xxox71JaK//bUoEAjy9qffsuzDh/Z53LBYcYZHEZ/Slg69huNqsmfo5YYl37Fp2ff0+ssVNO94QtX2P741nYqS3f91AAO7M5yYJs1p02MQyW3/WMUtM30bW1bMwV9WiN9TxmZPBY7wKCJjm9K6+0BwteSruSvqfPCUmJhIUlJS1TKjiYmJ+21z9dVX8/HHH/PKK68wduzYEFQpIo1JMBjEBA4UITjDo0hq05Wszb/RrH1PyoryOW7I+Qc8jtdTAZhUlhWzadn3Bz1fwOfd77HC7G0ANG3Ved/QCcAEfyC43z77nNvrZfv27WzdupW8vDzOP/98LeNczY4//njee+89nnrqKWbMmMHcuXOZNGlSvblB4nK5mD59Otdffz0vvfSSRhWLNCKmabJ161bS0tKYM2cO69evx2az0a9fP8aOHcvQoUPrzWtZbVm6dCmPP/44W7du5fzzz2fUqFHExcWFuiyRkFPwVIuWrtlKRs6ewCjClUDzTnuWWw74POzOTSdr00pytv7OgL/eSHyztoc8lmFY6ND7FGDP3fDS3Xnkbl9LQcYmug46p6qvSJm7kLLcLcQltyYipS2OsAg8FaXkbl/L8m/fIqnDiXzniuTGi08m1hVZg1d/bAYNGsSSJUsO2YAvOjqaa665hlmzZnHFFVfUuykNIlK/2G02DAOCB8l2WnbtR87W3/ntpw+xWG0079jrwMdx7Jl2HdO0BUP+dscR1dBz+N/Y/OtPrP3l3xiGhbY9B+/z/N4V7bxeL+np6WzdupUtW7awZcsW1qxZw/bt26moqMDj8RAZGUmPHj3o1evAdcrRi4iIYPz48QwdOpQpU6ZwySWXMH78eE499dRQl3ZYevbsyS233MLzzz9P37596dv3wFNGRaT+CwaD/P7771Ur0e3cuZOIiAhOOukkrr76agYNGkRUVNSfH6iRyc7O5qmnnuLHH3+kZ8+evPnmm3Tt2jXUZYnUGQqealHakrX4/3/KRGRMAp37nbbP8+sXf8PmX39k/eJvGHT+LYc8lmGx7Ld/fvoGFn/5TzYu+ZY2xw3EanfQoesJdOzWC+N/Gm77vZXM//hZcjcvp0mHPsxfsZFzhtXdLxuGYRzWqg+XXnop77//Pi+88ALTp0+vhcpEpLGyWi1EhYdRWFyK7QDDnhJbdiYsMobKsmJSOpyAIyzigMexOcKIikukdHcuPk8Fdufhr/Zjd0Yw4K83suiLl1kz/3NM06R5l35UVlZSVl7O99/8hy/ffJKtW7dSUVGB17tn1FQwGMRut+NwOIiIiCAQCOByuejevfufnFGOxeDBg/nwww+ZPn06Y8eO5cwzz+See+6pF6PMRo4cydKlS7n//vt57733iI+PD3VJIlJNvF4vy5Ytq5pGV1hYSHx8PMOGDWPMmDH07dsXh8Px5wdqhDweD2+88Qavv/46LpeLqVOncsYZZ9SbKdUitUXBUy1avWknNsvBeyO07XESm3/9keK8nUd1/KatOhMV25TSojxKCnOJTWq5T5Pa/2ZzhNG0VWdKd+dRWVLI5vScozpnXeN0OrnxxhuZNm0aV199NV26dAl1SSLSgHVtl8KcJWsJO8DnccNioc+ZI6ksLa6aQn0wbXsMZvW8T1iV9hHHn3zpftPmyt17moRHuPb9sh/w+8nJK6DFiWfhWfgpa3/5guzsLKJSumLYnCxasxiLpxCn00lERARxcXEH7NFTWFjIX/7yF+x2+xH+BuRIxcbG8sgjj/D111/z6KOP8uuvvzJ58uQ633DWYrEwdepURowYweTJk3nqqae0iqxIPVZWVsYvv/xCWloa8+fPp7y8nBYtWnD22WeTmppKjx499P/4IZimyU8//cRTTz1FQUEBV1xxBddeey0REQe+ySTS2Cl4qiUlZZVk5hbidBxOU85qSMj/5BABv49dGZsBg8jYJNZszjz2c9YR5557Lm+99RazZs3i2WefDXU5ItKAdWmXQtrSdQd9PjaxJST++bTfVscNYHfuDjI2/Eph9naatOhAWGQMnooSSnfnUZS7k15/uXy/4Km8vJzdFWWYpklsp6EUrk9j99ZlGBYrMa16khAficU89F1qn8+HxWJh+PDhh3fRcswMw+Css86id+/eTJ48mVGjRnHppZdy++23H9bo3lBJSEjgwQcf5Pbbb+fdd9/lyiuvDHVJInIEdu3axdy5c0lLS2PJkiX4/X66dOnC1VdfzfDhw2nXrp1G6hyGLVu28Nhjj7Fs2TKGDBnCrFmz1Fhd5E8oeKolW3bm4vH6cRzibvL23xcAEJt0dL2JCjI2UVqUj9XmIDo+eZ/nPOWl/398E095KXnp66ksLaJjn78Qk5DI1oxcKr0+whz1/2631Wrl1ltv5d5772XZsmX06dMn1CWJSAPVoeWe19q906iPlmEYnHDKZSS27kr62sXk7ViH3+fFGR5FZEwTug06m6YtOu63X6XXQ0xsU9zFbkzDQVyXVArXp1G4eTF+r4fSisz9RjGZprnPPysqKrBYLBQWFh7TNciRS0pKYtasWXzwwQc8++yzLFq0iKlTp9KtW7dQl3ZQAwcO5Oqrr+bZZ5/lxBNPrNO1igjs3Lmzqjn46tWrMQyDXr16ceeddzJs2DCaNWsW6hLrDbfbzUsvvcS//vUvWrRowdNPP81JJ50U6rJE6gUFT7UkO383vkCAcPueIatlxbvYsOQ7AAJ+L0W56RRmb8NitdGl/xl/ejwzGKza3wwGKC3KJ3f7GsCkc//Tsdr2/aLhqSjdZ7Ukw2Kl68CzaXfCMCo9Piq9PvIL3bRMTqimKw6t4cOHc9xxx/Hss8/y+uuv6+6NiNSIHp1aEh0Zhtdn55xRjx32fmfdNOOAj6d0OJ6UDsf/6f6d+51G216prPl9DeW7izAMA9M0sVjtNDnuL1idkZRmrSU/PR/DMPb5+V+BQAC73c7atWsPu36pPhaLhREjRjBgwAAmTZrENddcw/XXX8+1115bZ5feHjVqFMuXL2fcuHG88847ajQsUoeYpsn69eur+jVt2bIFh8PBwIEDmTRpEkOGDCE2NjbUZdYrwWCQzz77jFmzZuHz+bjtttsYMWKEpqeLHIG6+YmmAfL6/Pt86C9376oKggyLFWd4FCkde9HhxOG4Ev78zoNpBv8IkgwDuzOcJs070Lr7IJLbHrff9q6EZM4Z9RhmMEhFaRFZm1ayYfE37M7ZQbfhl2KaJl6fv/ouOMQMw+D222/n5ptvZs6cOZx88smhLklEGqA4VyR/GdiDj75fQkxUeK2G3A67g9atW5Oenk4wGKwKnwyrDcwggaKdhIWFEQwGCQQCmKa5J5yyWLBYLFW9noLBILGxsYwcObLWapf9tW3bltdee41//vOfvPLKK8yfP58pU6bQpk2bUJe2H5vNxvTp0xkxYgTTp0/noYce0g0ekRAKBAKsWLGiaiW63NxcXC4XQ4YM4eabb2bAgAGEhx/+whXyh99++41HH32UDRs2cM4553DbbbfRpEmTUJclUu8oeKolwaAJ/z+tAaBpy070P/eGoz6exWo76B3zQzEsFiJc8XTofTKGxcK6hV8R26wdcW177amxAenTpw8DBgzg+eefZ9iwYQdsqCsicqzOGdaLf6ctp8LjI+JAXcZrUHx8PBWVFRTkF2AYBv6AH8PqwOPOwVdaUBUy2Ww2TNMkEAgQDAbx+/34/f6q6XZDhgyhQ4cOtVq77M9ms3HTTTcxePBgJk6cyOWXX84dd9zB3/72tzrX5DclJYWJEycyduxY+vfvz3nnnRfqkkQalcrKShYtWkRaWhrz5s3D7XaTmJhIamoqw4cPp1evXnV21GR9kJeXxzPPPMM333xDt27deO211+jRo0eoyxKpt/RqVEvs9rr3q27ashPrFn5FYfY24tudiNNR92o8VrfddhtXXnklX375pT4Ui0iNOK5DC7q1b87K9TsId9prfeRHs+RmVFRUUFZahtMZQQAL5TkbqaysxGazYbPZsFgsGIZR9SXENE2CwSBerxfDMFi3bh1nn302Q4cOZdiwYfTu3VtLZ4fQcccdx7vvvsszzzzDY489xty5c5k8eTKJiYmhLm0fp556KhdeeCGPPvooPXr0oF27dqEuSaRBc7vd/Pzzz8yZM4eFCxfi8Xho164dF198MampqXTt2lWjD4+R1+vlnXfe4dVXXyUsLIxJkyZxzjnn1LnwX6S+aXhJQx0V54rAsBjH3IC2OlWWuff8i2Fgs1qIiWp4y3926dKF0047jZdeeokzzjgDp9MZ6pJEpIExDIPbLj+N2x56A3dpBTHRtftaarFYaNWyFZs2byaABUewjBZxdrbtduDz+QgEAlit1qoAam/Ne8OoQYMGMXPmTObNm8fcuXP56KOPiIiIYNCgQQwdOpTBgwfjcrlq9ZoEwsLCuPfeexk6dChTpkzhkksu4b777uOMM86oU18sR48ezcqVKxk3bhxvvvmm3mdFqllubm5Vv6Zff/2VYDBIz549uemmm0hNTdVqatXENE1+/vlnZs6cSXZ2Npdeeik33HAD0dHRoS5NpEFQ8FRL2rdMwmm3462oqNXzuguyiIpLwvI/08y8leWsX/w1AK7k9iQnxNT6l6Xacsstt3DxxRfz4YcfctVVV4W6HBFpgI7v3JrLzhzA65/NIyLcid1Wu1N7HQ4HsfFNKdyVjz9nFTHREXTs2JFt27bh8Xj29PHzequm3VksFvz+Pb0HR48eTd++fenbty+jR49my5YtzJ07l7lz5zJp0iQsFgu9evVi2LBhDBs2jObNm9fqtTV2AwYM4P333+fRRx9l4sSJzJ07l3HjxhETExPq0oA9AdmMGTO46qqrmDlzJuPGjQt1SSL1mmmabNu2rWolunXr1mGz2ejbty9jx45l6NCh6jFUzXbs2METTzzBggUL6N+/P08++SRt27YNdVkiDYqCp1qSlBBDfEwkGe7aXa5662/zyN2xjvjkNoRHx2Gx2qgoKSJvxzoCfi/N2vckpnlXundsecD99/YC8fl8Vf80TZOmTZvW6nUci5YtW3L++efz2muvcf755+vOhYjUiGsvSGXhb5tYvy2bpPgYLJbaG5VSXunBZneQenxLvt3wNRW2eMLDw2nXrl1V+GS1WgkEAlXT64LBIImJiftMQzYMgw4dOtChQweuu+468vLy+Pnnn5k7dy7PPvssM2fOpH379lUhVNeuXTX9oBa4XC6mTZvG0KFDefjhh7n00kuZNGkSgwYNCnVpALRv354xY8Ywffp0+vXrxymnnBLqkkTqlWAwyO+//141sik9PZ2IiAhOOukkrrzySk466SStHlkDysrKeOWVV3j33XdJSkriiSeeYOjQoXVqVKlIQ6HgqZYYhkG39i3Yum1brZ63RefemKbJruzt5O3chBkMYLU7CYtNIialE1FJ7dm9ezefffgWX7/5WFXD2b1BUzAYrFoJae+P3W7nmWee4bTTTqvVazkWN9xwA19++SVvvfUWo0aNCnU5ItIAhYc5mHTzhdz5yFvkFRaTmBCD5TA/vPr8PjZu3EjrVq2P+MtFhceLu7SCMwYfz/gbzuX6gs388MMPpKSk4HQ6adeuHdu3b6eiogK73Y5pmlU3EeLi4li8eDEDBgw44AftxMRELrroIi666CLKy8urGtl+9NFHvPrqqzRp0qSqL1Tfvn3VF6qGnXbaafTq1YspU6bwj3/8g4suuog77riDiIjQj1i+4IILWLJkCVOnTqVr166kpKSEuiSROs3r9bJs2bKqsKmwsJD4+HiGDh3K3XffrdfUGhQMBvnqq6949tlnKS8v56abbuLKK6/U71ukBhmmaf7pUmZut5uYmBiKi4vV5+EYfP7TMqa99BlN4qKx1fIKa7sKd7Fj+449S21b9vT2sBgWsNjAsEDmQgxfGYZhVH352Pvv//3jdrtxuVzMmTOn3n2onDVrFu+99x6fffaZhiiLSI1ZuX4H9818j8LiUhLjXYc1IigzM5OcnBzi4+OPaHh/eaUHd2kFw/p2Zdo/LiHMYScnJ4cLLriA7du3k5KSgmEY+Hw+tm/fTnl5OVarFb/fj81mIzU1lYKCArp168b111/PkCFDDutObyAQ4LfffquakpeRkUF4eDgDBw6s6gsVGxt72NchR8Y0TT7++GOeeuopmjRpwpQpU+jZs2eoy6KkpIQrrriChIQEZs+erRW1RP5HWVkZv/zyC2lpafzyyy+UlZXRvHlzhg8fTmpqKj179tQo0hq2Zs0aHn30UdasWcPpp5/OHXfcUecWbhCpTw43K1LwVItKyiq5ePRTlJZVEh9T+8Nlc3Nzyc7O3jNyCROLxYLVGYnDX4KrbOOf7m+aJpmZmdxwww1MnTq1FiquXiUlJZx33nmcdtppjB07NtTliEgD9vvmDMY++R45BcXERkUQHnbwu6gej4eNGzfi9XpxOBx06dIFu91+yOMHTZPdxaX4A0FOP6knE246nzDHH/ssWLCAq6++Gr/fXxW0+/1+tm/fTmlpKaZp0qFDB1atWsWKFSt45ZVXWLlyJZ06deK6665j+PDhh/3lZ28/krlz5zJv3jxWr16NxWLh+OOPZ9iwYQwdOlTNb2tIeno6kyZNYu3atVxzzTXccMMNf/p3p6atXr2a66+/nquuuorbbrstpLWI1AWFhYXMnTuXOXPmsHTpUnw+H507dyY1NZXhw4fTvn17Te2qBYWFhTz33HN88cUXdOrUiTFjxnDiiSeGuiyRek/BUx319Ftf89aXv5CcEFPrbzImJjt27KC4qBiLxUIgaILVgSd9CXEOz5/2PioqKgLgu+++q7dLJr/55pvMmjWLjz76iJYtD9zXSkSkOmTmFfLE6//hlxUbMQxIiIk6YJiTnp5OYWEhwWAQi9VCSrOUQ959rfT42O0uI9YVwfUXDefi0/odcBTtiy++yNSpU3G5XERGRgJ7RiqtW7cO0zS57bbbeOKJJ6q2X758Oa+88gpLliyhbdu2XHvttZx22mlYj3CE7q5du6r6Qi1evBiv10vbtm2rpuR1795dd/SrUSAQ4I033uCll16iQ4cOTJ06NeTv0W+++SbPPPMMzz33HAMGDAhpLSKhkJGRwZw5c0hLS2PVqlUYhkGvXr1ITU0lNTWVZs2ahbrERsPn8/HBBx8we/ZsrFYrt956KxdccIHeh0SqiYKnOmpzei7XTnwJTEKyipw/4GfL5i1UVlZidUaCv5zSdd9SXlaK0+kkIeH/2LvzuKjr/A/grzm5bwQFxBtFRQEVT848Ms0jQcv7ytzuNm07frVt7VabVlZmm6HikSVonplayqF5K3iBiqKi3DDAcAxzfb+/P6zZWG8ZGI7X8/Hose3Mdz6f9xDHzGs+n/fHDY6OjreEYn+sdnryySexZMmSBq/bXLRaLcaPH4/AwEB88MEHli6HiJo5QRCwPekkvt7wK4rLKqGUy+Bob2s69a6qqgpZWVkQRREGowEymQxKhRJdu3at9aJYFEVU1+hQWV0DiUSCPj06YOGsUejgfeeAShAE/OUvf8HWrVvh6ekJhUKB6upqVFRUICoqCn/961/Ru3fvWx53+vRprFixAr/99hvatm2LWbNm4bHHHnuobVMajQZHjhxBcnIy9u/fj7KyMri6uiI0NBTh4eEICQmBtbX1A49Ltzp//jzeeecd3LhxA8899xyeeuopi72xEgQBL774Ii5evIjvv/8ebm5uFqmDqKGIoogLFy6YTqK7fPkylEolBgwYgMjISISGhnL7sQUcOnQIixcvxvXr1xEdHY358+fzvSyRmTF4asSWrt+D1VtT4Obs0OBHbgM33whcvnINgiiBk/YKlPoyaDQaFBcXo7KyEgqFAm5ubnB2djYFUGq1GgaDAdu2bUPPnj0bvGZz2rJlC/75z3/iu+++Q9euXS1dDhG1APnFZdiWeBLbkk6gsEQNURRhpVSgqDAfmqpKyGQy6PQ6WCmtYDAa0NanLRwcnaDV6VGj00NvMMJaqUCAX1uMi+qLRwb0uK9egaWlpXjiiSeQkZEBb29v5OTkYMiQIUhISLjnqtuMjAysWLECSUlJaNOmDWbOnInHH3/8oZuvCoKA06dPIyUlxXRqk5WVFQYMGIDw8HAMGTIErq6uDzU23aTT6bB06VKsX78ewcHB+Mc//mGxlRUqlQpPPvkkunTpgi+//JKrC6jZMRqNSEtLM61sys/Ph4ODA0JDQxEREYGBAwfCxsbG0mW2SDdu3MCnn36KlJQUBAcHY+HChejSpYulyyJqlhg8NWLVGi2efjcWF6/dPHK7obfcGY0CsvMKUXEjHQp1JpydnEz3abValJSUoLy8HDKZDK6urnBxcUFeXh5Gjx6N2NjYBq21PhiNRkycOBFeXl748ssvLV0OEbUgNVodko5lYPdvp3E4NQO5+YWQyuQQRRFGoxFyuRxGwQilQgk3NzdYWyng6eqI/r07Y1RYELq2b/PAfzNOnz6NSZMmQaVSQalUIi4u7oGOu8/MzMSqVavwyy+/oFWrVpg+fTrGjx8PKyurB336tVy7ds3UnPz06dMAgICAAERERCAsLAzt27ev0/gt2fHjx/Huu+9CrVZj4cKFGD16tEV6yBw9ehTPPfccnn/+ecyYMaPB5ycyt5qaGhw5cgSJiYlISUmBWq2Gh4eHaQtdcHAwm+pbUHV1NeLi4rB27Vq4urri5ZdfxtChQ9lDi6geMXhq5NLOX8OLH66G0Sg0aKNxURRRUFIObw9X+DuVY1XsN3B3d79lq4Ner0dJSQnKysogCAKsra2xYcMGDBs2rMFqrU/79u3Da6+9hv/85z/o27evpcshohamqqoKw0eMwNWcIri2bofKGiMqq6rRysMTel0NtJoqvPvmqxg/ahhcHO3qPN/69evx+uuvIzAwEFu2bHmo1SdXr17FypUrsWvXLjg7O2Pq1KmIjo6GrW3dt42rVCocOHAAycnJOHz4MLRaLXx9fREeHo7w8HCe9PQQKisrsXjxYuzYsQMRERF466234OLi0uB1fPXVV1izZg1iY2MREBDQ4PMT1ZVarcb+/fuRlJSEQ4cOoaamBh06dDCdROfv789gw8JEUcTu3bvx+eefo7y8HDNmzMCMGTO4lZuoATB4agLidx3GkrW7IJNK4GyGNxb3IooiClVqODnYYtGrk+HfoTWmTZuG5ORkeHl53baBrMFgQFZWFqysrNC5c2eMHTsW06dPh5eXV73XW59EUcSMGTMgkUgQFxfHFwxE1KC+/PJLfPDBB/Dw8IBCoUBxcTFUKhX8/PxMPfXGjBmD5cuXm2U+URQRGxuLkJCQ2/Z1ehA3btxAXFwctm/fDnt7e0yZMgUTJ06Evb15PkTRarU4cuQIUlJSkJKSApVKBWdnZ4SGhiIsLAwDBgzg9pUHkJiYiH/961+QSqX4v//7P4SFhTXo/AaDAfPmzUNxcTG+++67ex5kQtQYFBQUmE6iO3HiBARBMK3IjIiIQLt27SxdIv3uwoULWLRoEdLS0hAVFYWXX365yb9PIWpKGDw1AaIoYs22/fgmfh8kEsDF0a7eAhBBEFBYqoaTvS3++eJEDOjVGQCQm5uL8ePHIzs7G15eXrfMr9FooFarsWzZMhQWFuL777+HWq3Go48+ipkzZ1r85Jy6OHbsGP7yl79g0aJFiIyMtHQ5RNRC5ObmYsSIEVCr1WjVqhUAoLCwEGq1Gp073/zdXF5eDkEQsGfPHnTq1MmS5d5Rfn4+Vq9eja1bt0KpVOLJJ5/E5MmTzfo6QRAEnDt3DsnJyUhJSUFWVhaUSiVCQkIQHh6O0NBQuLu7m22+5qqkpAT//Oc/sX//fowZMwavvvqq6aTDhpCbm4spU6agf//++PDDD/lhDzU6oijiypUrSEpKQlJSEtLT0yGXy9GvXz/T9t8/fl9T41BWVoZly5Zh8+bN6NChAxYsWICQkBBLl0XU4jB4aiJEUUTC7iNY+v0eaGp09dJwXFOjQ1lFNTxcHfH+izHo071DrfsPHz6MadOmQafT3fJHNScnB3379sXWrVshlUqh0WiwdetWrF27FgUFBQgPD8esWbOabMPx5557DgUFBdiwYcMDHxlORPQwXn/9dcTFxcHLy8u0fSw/Px/V1dWmMP+PVU/PPfcc3n77bUuWe09FRUVYu3YtNm3aBJlMhpiYGEyZMqVeGoVnZ2dj//79SE5ORlpaGgRBQM+ePREWFoaIiAh06NCBocYdiKKIbdu24ZNPPoGzszP+8Y9/ICgoqMHm/2OL+5tvvoknnniiweYluhNBEHD27FlT2JSdnQ1bW1sMGjQIkZGRGDRoEFfoNUJGoxGbNm3C119/DVEU8cwzzyAmJoa9tYgshMFTE3Mi/QoWr9qBS9kFsLFWwtHOps4vngVBQEl5JUQRGBTYBQtmjYK3x+3fCKxcuRJvv/02HBwcTNsltFotVCoVvvnmG4wePbrW9Xq9Hj///DNWr16Na9euoW/fvpg1axZCQkKa1Iv+jIwMTJs2De+88w7GjBlj6XKIqJk7ffo0xo8fD4lEUuto7dzcXOh0uloNtYuLi+Hg4ICUlBSL9OZ5UCqVCt999x0SEhJgNBoxYcIETJs2rd5WCZSXl5v6Qh06dAgajQY+Pj4ICwtDeHg4AgMD+YHCbeTk5ODvf/87Tp06hWnTpmH+/PkPfVLhg/roo4+wbds2rF27ttGu5KPmTa/X4/jx40hMTERycjJKSkrg4uKC8PBwREREICQkpMF+HujBHT9+HIsWLUJWVhbGjRuHZ599tkn8fSRqzhg8NUFVGi1WbEpEwp6j0Gh1sFYq4GhnA5nswRqq6vQGlFdqYDAa4eZkj2efHIrHI4Lv2phVEAS88sor2LBhAzw8PKBUKpGbmwt/f3/s3r37jp8iCIKApKQkrFq1ChkZGfD398esWbMQERHRZBrBvvHGGzh16hQ2b95c51OaiIjuRBAETJs2DXv37oW3t3etkP7GjRsQRRFt27Y13WY0GpGfn49//vOfmDNnjiVKfihqtRrff/89vv/+e+h0OlNvwDZt2tTbnDqdDseOHTNtySsuLoajoyOGDBmC8PBwDBw40CxN0JsLQRCwbt06fP311/D19cX7778PPz+/ep9Xq9VixowZEAQBa9asYeNfahDV1dX47bffkJSUhAMHDqCqqgpeXl6IjIxEZGQkDy9oAvLy8rBkyRLs3bsXvXr1wsKFC+Hv72/psogIDJ6atAtXcrEjORW7fjuNsopqQBRhpVTASimHlUJRK4i6eQS3gBqdHlqdATqDATKpBN4erhgT2QePhfaGh5vTfc1bWVmJmJgYpKamolWrVigpKcFnn32GSZMm3fOxoiji6NGjWLlyJU6cOIH27dtjxowZGDlyZKNf+pqdnY3o6Gi8+OKLmDp1qqXLIaJmavfu3Zg7dy7s7e1v6a+TnZ0NmUwGb2/vWrfn5eWhU6dO2Lt3LxQKRUOWW2eVlZWIj4/Hd999h8rKSowePRqzZs2Cj49Pvc4rCALOnz+PpKQkpKSk4NKlS1AoFOjXr5+pL5SHh0e91tBUZGZm4p133sGVK1cwf/58TJ8+vd7fgF+5cgXTpk3DyJEj8dZbb9XrXNRyqVQqpKSkIDExEUePHoVer4efn5/pJLrOnTs3qRX6LZVWq8Xq1asRFxcHR0dHvPjiixg5ciT/2xE1IgyemoHyimr8cugsDqVdRMaVXKgrNdDq9YAI0y9cURQhlUphpZSjlYsDenRui8gQfwwO6gql4sEDn8uXL+OJJ57AlStXEBAQgL179z7wJ5JnzpzBqlWrkJKSAk9PT0ybNg3jxo1r1J9sfvDBB/j111+xbds2s53MRET0B61Wi5EjRyIjI+OWcAkArl69Cisrq1tWBf2x5fnbb7/FY4891lDlmlV1dTU2bdqEtWvXoqysDI8++ihmz55da1thfcrJyUFKSgqSk5Nx8uRJCIKA7t27m7bktfQ3oDqdDt988w3WrFmDXr164R//+Ee9h4Pbtm3De++9hw8++ADDhw+v17mo5bhx44apX9OpU6cgkUgQGBhoOomOJ501HaIoIjExEZ999hmKioowdepUzJ49mytXiRohBk/NjCAIyCkoxaXr+ShTV0OnN0AilcBKIYenuzM6t/WEm7O9WV4879mzBy+99BIWLlyI2bNnP/Q4ly9fRlxcHHbv3g1HR0dMnjwZMTExjbJRY1FREcaNG4cpU6bg2WeftXQ5RNTMrFy5Ev/3f/8Hd3f32/YPycrKgp2dHTw9PW+5LycnB6GhodiwYUOTDki0Wi02b96MNWvWoKioCEOHDsWcOXNMJ/k1BLVajd9++w3Jyck4ePAgqqur4eXlhbCwMISFhSE4OLjRr9KtL2lpaXjnnXdQWlqKV155xdSLrD6Iooj/+7//w4EDB7B+/frbhrFE9yKKIi5evIjExEQkJSXh0qVLUCqVGDBgACIiIhAaGsr+P03Q5cuXsXjxYhw7dgyhoaF45ZVX4Ovra+myiOgOGDxRnVy8eBGdO3c2y5L73NxcrFmzBtu2bYNcLkd0dDSmTJkCNzc3M1RqPkuXLsUPP/yALVu28HhuIjIbtVqNiIgIZGVlwdHRERKJBDKZDFKp1PRPTk4OHBwc4O7ubrpNIpFAIpGgsrISNTU12Lx5c4OeQlZfdDodduzYgVWrViEvLw8RERGYM2dOg/fr0Ol0OHHihGk1VGFhIezt7TF48GCEh4dj0KBBLW4FbHV1NT777DNs3rwZQ4YMwdtvv11vf6urqqowefJkODs7IzY2tsltJSXLMBqNSEtLM61sysvLg4ODA0JDQxEREYEBAwZwVUwTpVar8c033yAhIQE+Pj549dVXMXjwYEuXRUT3wOCJGp2SkhJ899132LhxIwwGA8aMGYPp06c3mqXPFRUVGDt2LEaMGIG//e1vli6HiJqJ8vJyvPnmm8jPz0dlZSUqKytRVVUFjUYDQRAgCAJyc3NhZ2cHGxsb022CIJgCKL1ej/nz5+P999+39NMxG4PBgJ07d2LVqlW4fv06Bg0ahLlz56JXr14NXosoirhw4QKSk5ORnJyMixcvQi6Xo0+fPggPD0dYWBhat27d4HVZyv79+/H+++/DaDTirbfeQlRUVL3Mk56ejlmzZmHKlCl48cUX62UOavq0Wi2OHDmCxMREpKSkoLy8HK1atUJERAQiIyNb9ErF5kAQBGzZsgVfffUV9Ho95s6di6eeeophNFETweCJGq2KigokJCRg/fr1UKvVGDFiBGbOnNkojlZes2YNvvrqK2zcuLHW6VJEROYmCAI0Gg0qKysxYsQIzJgxA0OGDIFGo0FVVRWqq6tN/1tdXY2+ffsiLCzM0mWbndFoxC+//IKVK1ciKysL/fr1w9y5cxEcHGyxrYV5eXnYv38/kpKScOLECRiNRnTt2hXh4eEIDw+Hn59fk972eD9KS0vx4YcfYt++fXjsscfw2muv1csKsHXr1mHJkiX44osvMGjQILOPT02TWq3GgQMHkJSUhIMHD6KmpgYdOnQwhU3dunXjSXTNwKlTp/Dxxx/jwoULGDVqFF544QXuOiBqYhg8UaNXU1ODLVu2YO3atSgoKEBYWBhmzZqFgIAAi9Y0fvx4BAcH41//+pfF6iCilkOn02HQoEF47733mmwDcXMQBAGJiYlYsWIFLl68iMDAQMydOxf9+/e3aMhTWVmJgwcPIjk5Gb/99hsqKyvh6elpWgnVp0+fZvvJvCiK2LlzJz7++GPY29vj3XffRb9+/cw6hyAIePnll5GRkYHvv/+ebzpbsMLCQtMWuuPHj0MQBPTs2dN0El27du0sXSKZSWFhIb788kv8/PPP8Pf3x8KFCy2y2pWI6o7BEzUZer0eu3btwurVq3H16lX06dMHs2bNstibjc2bN+Nf//oXvvvuO3Tt2rXB5yeilqW0tBTDhg3D4sWLERERYelyLE4URezfvx+xsbFIT09Hjx49MGfOHISGhlp8lZFer0dqaqppS15+fj5sbW1r9YVqjq+T8vPz8e677+L48eN46qmn8Pzzz8PKysps46tUKkyePBkdOnTAV199xZUsLYQoirhy5YopbEpPT4dMJkO/fv0QERGB8PBwtGrVytJlkhnpdDp89913WLlyJaytrfHCCy9g9OjR/JknasIYPFGTIwgCkpKSsGrVKmRkZMDf3x+zZs1CREREg/5BMhqNiImJgY+PD7744os71ppTWIpSdRX0eiMgAawUcrR2dzbb6YJE1DLk5ORg7NixWLZsGUJCQixdTqMhiiIOHz6M2NhYnDp1Cn5+fpg7d26D/024W32ZmZmm5uQZGRmQyWQIDg5GWFgYwsPDG00PQ3MQBAE//PADli5dCm9vb7z33ntmbQh/7NgxPPvss/jLX/5SpxN1qXETBAHnzp0znUSXnZ0NGxsbDBo0CJGRkRg8eHCjPP2Y6uaPDxQ+/fRT5OXlYdKkSXj66af535qoGWDwRE2WKIo4evQoVq1ahePHj6Ndu3aYMWMGRo4c2WDbGfbu3Yu//e1v+Oabb9CnTx+IoohTF7Jx/GwWzl/JRXpWDtSVGhiNAgRRhEQCSCQSKOQyuLs4oEdnH3Rt3wZhfbrBtw23DRDRnV28eBGTJ0/G6tWr0aNHD0uX0+iIooiTJ08iNjYWx44dQ8eOHTF79mwMHz68UQRQfygsLERKSgpSUlJw7Ngx6PV6dO7c2dQXqrn0pMnKysI777yDzMxMPP3005g1axZkMplZxv7666+xatUqfPvtt+jdu7dZxiTL0+v1OH78OJKSkpCcnIzi4mK4uLggLCwMERER6N+/P5RKpaXLpHpy7do1fPLJJzh48CBCQkKwYMECdOzY0dJlEZGZMHiiZuHMmTOIi4tDcnIyPD09MW3aNIwbNw7W1tb1Oq8oipgxYwYESBEz83ls2XcCF67mQa83AACslApYKeWQy2SQSiQQf3+M3mBAjdYAveHmdTbWSvQP6IwxkcEY0LsLFHLzvDgnoubj1KlTmDNnDhISEtChQwdLl9OonT59GrGxsTh48CB8fX0xa9YsjBw5stGdaFVVVYVDhw4hJSUFBw4cgFqtRqtWrUxHvvft27dJv9HW6/WIjY3FqlWr4O/vj/fffx++vr51HtdoNGLevHkoKCjA+vXr+ZqzCauursbBgweRmJiIAwcOoKqqCl5eXqbm4L17924WQSzdWVVVFWJjY7F+/Xp4enrir3/9K8LDw7krgKiZYfBEzcrly5exevVq7Nq1C46OjnjqqacQExNTb9+Poijiy7hN+Hztz7B3coOVlRUc7WxgbaW47z+YoiiislqLKk0NZDIpuvi2xsLZo9HLr+4vzomo+Th48CBefPFF/PTTT/D09LR0OU1Ceno6Vq5ciaSkJHh5eWHmzJkYPXp0owxzDAYDTp06heTkZCQlJSE3Nxe2trYYMGAAIiIiMHjwYDg5OVm6zIdy5swZ/P3vf0dBQQFeeuklxMTE1PlNZX5+PiZPnoy+ffvi3//+N9+kNiEqlQopKSlISkrC0aNHodPp4Ofnh4iICERERKBLly7879kCCIKAnTt34osvvkB1dTVmz56NqVOnNsrfz0RUdwyeqFnKzc3F2rVrsXXrVsjlckRHR2PKlClwc3Mz2xwFJeX4bM3PSDqWjtLSUkhFAzp16ggJHv7FklanR6m6CjbWSjw1ciBmjguHjTX/ABMR8Ouvv+L1119HYmIi+108oMzMTKxcuRK//vorWrVqhRkzZmDcuHFmbXxtTqIoIisry9Sc/Ny5c5BKpQgMDDRtyfPx8bF0mQ9Eo9Hgiy++QEJCAgYMGIB33nkHHh4edRozKSkJCxYswOuvv47o6GgzVUr1IScnB0lJSUhMTMSpU6cgkUgQGBhoCpuaU58zurdz587h448/xrlz5zB8+HC89NJL/ECFqJlj8ETNWklJCdavX4+EhAQYDAY8/vjjmD59Ory9ves07rGzl/Hush9RUFwOJwcbSCDg6pWraNOmDZydnes0tiiKKK/UoEarg39Hb3z4yiR4e7jWaUwiavq2bduG9957D0eOHDFbr5yW5sqVK1i1ahV27doFZ2dnTJs2DRMmTICtra2lS7ur4uJiU1+oP1aIdOzYEeHh4QgLC0OPHj2azHakQ4cO4R//+Ad0Oh1ef/11DB8+vE7jffzxx9iyZQtWr16NLl26mKlKqqs/mur/0Rw8MzMTSqXStIIvNDQULi4uli6TGphKpcLSpUuxbds2dOnSBQsXLkRwcLClyyKiBsDgiVqEiooKJCQk4Pvvv0d5eTlGjBiBmTNnolOnTg88Vsrx83h32SZUVNfAw8XB9GL/Rs4NaKo16Ny5s1mWiOv1BhSXV6K9VyssXjAZ7b15VDBRS/b9999j6dKl+O233yxdSpN3/fp1xMXFYceOHbC3t8eUKVMwceJE2NvbW7q0e6qursaRI0eQnJyM/fv3o7y8HK6urqYT8kJCQhrtSq4/qNVqfPTRR9izZw+GDx+O119//aFfN+p0OsycORM6nQ5r166FjY2Nmaul+2U0GnHq1ClT2JSXlwd7e3uEhoYiMjISAwYMaPQhL9UPvV6P+Ph4LF++HDKZDM8++yzGjx/PD1GIWhAGT9Si1NTUYOvWrVi7di3y8/MRFhaGmTNnolevXvf1+COnL+GNJRtQpalBKxfHWgGTTqfD5cuX4eHpATdX82zpMxoFFKrK0d7bA5+/MY0rn4hasBUrVmDDhg3Ys2ePpUtpNvLy8rB69Wps3boV1tbWePLJJ/HUU081mdcwgiDg1KlTSElJQXJyMrKzs2FtbY0BAwYgLCys0a8q2bNnDz788ENYW1vjnXfewcCBAx9qnGvXrmHq1KkYNmwY3nnnHTNXSXej1Wpx5MgRJCUlISUlBWVlZWjVqpVpC12fPn0aXVN/aliHDx/G4sWLkZ2djejoaMyfP7/J/I4lIvNh8EQtksFgwK5duxAXF4erV6+iT58+mDVrFvr373/H1UrX80ow9+/folRdBQ9Xx9tel5eXB3WFGp07d4ZMap5PcYxGAYWlagR0bouv/z4b1kqFWcYloqbliy++wL59+7BlyxZLl9LsFBUVYe3atdi0aRNkMhkmTpyIyZMnw9W16YT9oiji6tWrphDqzJkzkEgk6NWrl+k4enOcKGduhYWFeO+993D48GHExMTgxRdffKhVSzt27MC7776Lf/7zn3j00UfroVL6g1qtxoEDB5CUlISDBw+ipqYG7du3N51E5+/v32S2flL9uXHjBj799FOkpKQgODgYCxYsgJ+fn6XLIiILYfBELZogCEhOTsaqVauQnp4Of39/zJw5E5GRkbVeNAmCgFf+vQ6/pV6Ep5sTpNLbh1MGgwGXLl2Cq5srPFrVrWnqn+n0BqjKq/DMxCjMnRBptnGJqOn46KOPcPr0aaxfv97SpTRbKpUK69atw8aNG2E0GjFhwgRMnz4d7u7uli7tgalUKlM4cOTIEWi1WrRr187UnDwgIKDRhAOiKGLjxo1YsmQJPD098d5776Fnz54PPMbf//53JCUl4bvvvkPbtm3rqdqWqbCwEElJSUhKSsKJEydgNBrRs2dP08qm9u3bW7pEaiSqq6sRFxeHtWvXwtXVFS+//DKGDh3KkwqJWjgGT0S4+YL12LFjWLVqFY4dO4Z27dphxowZGDlyJBQKBTb9chQfr9wBBztr2Fjd/ZS5ouIiVFZWokP7DmatsVRdBZlMiq/fno3unerWHJ2Imp533nkHeXl5+Pbbby1dSrNXXl6O77//Hj/88AN0Oh3Gjh2LGTNmoHXr1pYu7aHU1NTg6NGjSEpKwv79+1FaWgoXFxeEhoYiPDwc/fv3h7W1taXLRHZ2Nt5++21kZGRg1qxZePrppx9om1Z1dTWmTJkCe3t7rFy5EgoFVwjXxZUrV0wn0aWnp0Mmk6Fv376IjIxEWFhYnU8lpOZFFEXs2bMHn3/+OcrKyjB9+nTMmDGDfdeICACDJ6JbnD17FnFxcUhKSoKHhwfGTpiEjYdyUFmthbvL/R1hLoqi2T/ZEUUR+SXl6OXni9h/zG00n1QTUcNYsGAB9Ho9Pv/8c0uX0mL8cTDFunXrUF1djdGjR2PWrFl1PhnVkgRBwNmzZ5GcnIzk5GRcvXrVdNpYWFgYwsLCLLrF0Gg0YtWqVfj222/RpUsXvPfee+jYseN9P/78+fOYOXMmJk2ahFdeeaUeK21+BEFAenq6qTn4tWvXYGNjg0GDBiEiIgJDhgyBg8P9vQ6iluXChQtYtGgR0tLSEBkZiVdeeQVeXl6WLouIGhEGT0R3kJWVhbi4OPy4Lw0aGx+0cnGAq6vrPU/guHB0DzKP/4IBY+fD3fvBT827mxqtDpUaLZa+ORN9eph3RRURNT6aGh1U6kro9Ub8/d2/w9nRAYs++heUCjbrbUjV1dXYuHEj1q1bh7KyMowcORKzZs1qFtuLsrOzkZycjJSUFJw6dQqiKKJnz54IDw9HWFgYOnToYJEtMhkZGXj77beRm5uL559/Hk8++eR9f+Dy/fff45NPPsGSJUswZMiQO15XXFqBS9cLkHktHxev5eFGvgpanR4AoFTI0drdGV3bt0Gntp7o7OuJNq2cm912Ib1ejxMnTiApKQnJyckoKiqCs7MzwsLCEBkZ2SROSSTLKSsrw7Jly7B582Z06NABCxYsQEhIiKXLIqJGiMET0V3o9AY88dInuHDhAi7tW1XrPolUBisbe7h6dUDnoEg4ut/8ZKc+g6c/Vj2NDgvCu89NMOvYRGR5uYWlOHrmMi5lF+Dspeu4llcMg8EIQQSKi4ugkCvg6eGOzm1bo3tnb3T29cSgQD+4ONpZuvQWoaamBps3b8aaNWtQXFyMYcOGYfbs2ejcubOlSzOL0tJS/Pbbb0hOTsahQ4dQU1ODtm3bIiwsDOHh4ejdu3eDHn+u1Wrx1VdfYf369ejbty/efffd+9ruKIoi/vrXv+LMmTNYv359rS1hOr0Bv6VexLbEEziZfhU1Oj0EQQAAyGRSSH8PlkQRMBiNpqDJSqlAtw5tMDaqL6JCusPWpumGMdXV1Th06BASExNx4MABVFZWwsvLy9SvKTAwkKuq6a6MRiM2bdqEr7/+GqIo4plnnkFMTAxPMCSiO2LwRHQXiUfT8fpnP8AKWuz/4WNY2TnDzqMDRBFQKqTQqotRVnANUpkcA8bMg2ubDvUaPAFAeWU1pFIpNix+AZ5uTmYfn4galtEo4Ni5LGxPPIkDqRdQrdECAGRSKayUCijkMkgkwPUbN2BjYwMHBydo9XrcfK8swtHeBsMH9cKosED06OzT7FZkNEY6nQ7bt29HXFwc8vLyEBERgblz56Jbt26WLs1sdDodjh49ajolr6SkBE5OThgyZAjCw8MxYMAA2NraNkgtx44dw7vvvovKykq89tpreOyxx+75fV5WVobJkyejbdu2+Prrr2EwCti45yg2/XIUNwpKIAgi7GysYWOlgEwmvet4RqOAGp0eFdU1AIBWLg4YE9kHU0YNhoOd5Xtj3Q+VSoX9+/ebms3rdDr4+fmZwqYuXbrwdwfdl+PHj2PRokXIysrC2LFj8eyzzzapE0CJyDIYPBHdxT+/2Yyt+07AUSlg37oP0aqtH/qOmo3S0lKoSlQwGo3QFp7HjXMH4erVEYPG/aXegydBEFCoUuPNp8di/NB+Zh+fiBrOyYyr+Gz1TlzKLoDBaIStjRXsbaxve3JmZmYmnJ2d0apVK9NtRkFARZUGmho9rJRyBHZrh7/OeAyd2no25NNosQwGA3bu3IlVq1bh+vXrGDx4MObOnYuAgABLl2ZWgiAgIyMDycnJSEpKQlZWFhQKBfr164eIiAiEhobW+r6sDxUVFVi0aBF27tyJqKgovPnmm3B2dr7rY06ePIn58+dj/KTpuFxuhdMXsyGVSuBkb/vQ21UNBiPUVRpo9QZ09PHAqzMeQ/9ejXPFW25urqlf06lTpwAAvXv3RmRkJMLDw5t0rzJqeHl5efj888/x66+/olevXli4cCH8/f0tXRYRNREMnojuYtKrXyA7vwS2Ur0peOr/+NMAAEEUUF5eDolRjwMbFkEmV2DkvA/uGjypi3NxKTUJqtws6DSVUFjbwtGtDdoHDIZn++6m64x6HS4c24PczDToaqpg5+SG9r1CYefkjsNb/4M23UPxyoLXsHDW6Ab9ehCReVRrtIj9MQkJu4+gRquDs6MdrJV3P4HrwoULcHd3h5ub2y33iaIIjVaH8goNnBxsMHt8BCY+OgAKecNti2rJjEYj9uzZg5UrV+LKlSsICQnB3LlzERwcbOnS6sWNGzdMK6FSU1MhCAK6d++OiIgIhIWFoVOnTvW2embfvn3417/+BZlMhrfffhuhoaF3vFYQBMx//d/Ye+IKHJxc4OnuYrb+aAaDEcXlFbBSKDD+kb549slhsLG++6m39U0URWRmZppOosvMzIRSqUT//v1NASFXptCD0mq1WLNmDeLi4uDg4IAXX3wRI0eO5Ao5Inog95sVccMutThl6irkl5TffDNo0N9yv1QihYuzC7TVFb/fcvc/wHmXTyP1l/UQAXi294e9swe0mkqUFWQjO+OoKXgSBQFHd65ESc5lOLi2hrdfEHQ11cj4bTvcfg+ypBIJzmbeMOfTJaIGcvl6Ad7+MgGZ1/JhY62Ep5vTfb2AFwThjn1XJBIJbK2tYGOlhEpdhc/X7cKB1At4//mY+z6Nkx6eTCbDyJEjMWLECCQmJmLFihWYN28egoKCMHfuXISEhDSrN2k+Pj6YPHkyJk+eDLVajQMHDiA5ORlxcXFYtmwZvLy8EB4ejvDwcAQGBpq170tUVBR69+6N999/H6+88grGjRuHv/71r7ds+9PpDVi0agfSsquhUFpBU1kGmYf5Qhe5XAZPVydUVNXgh58P4Xq+Cu+/ENPgW+8EQUBaWhqSkpKQlJSE3Nxc2NvbY8iQIZg7dy4GDhzYYFsiqXkRRRGJiYn47LPPUFRUhClTpmDOnDn8fiKiesXgiVqcS9cLoNXp4exgC73hztddPXsQAODs2faO12irK5C2dwMkUhkGj38WTq1qL2/XVJaZ/v36+eMoybmMVr7dEPLYLEh+f6PZsXcY9icsAXCzAWp2XjGqNFrYNeEGp0QtTfrlHLz26XrkF5fD3dnhvlckCeLN5sf3avgrkUjg5mSPGp0ex85k4aWP1mDRq5Ph5eFS59rp3qRSKR555BFERUVh//79iI2NxXPPPYeePXtizpw5GDJkSLMKoADA0dERjz32GB577DHodDocP34cKSkp2Lt3L77//ns4Ojpi8ODBCAsLw6BBg2BnV/dG+G5ubvjss8+wZcsWfPrppzh69Cjee+89BAYGArjZFPyj2G3YnpwKB1sruDp5ISsrC7m5uWjb9s5/qx+URCKBo70NlAo5Dpy8gNc/+x4fvzq53v8ua7VaHDlyBElJSUhJSUFZWRnc3d0RERGByMhIBAcHQ6G4+wpKoru5fPkyFi9ejGPHjmHIkCH46quv4Ovra+myiKgFYPBELU5hiRoGgwC5TIY/1jtVlZfgwtE9AACjQYeygmyo8q5AKpOjW/9H7zjW9QvHYTTo0KXv0FtCJwCwsXc2/XvOxZMAgG79HzWFTgDg4OoJn659kJ1+BDKZFHqDESVlFQyeiJqIzGv5WPjJehSWlMPT1fGBTo3649Qtqez+HmOtVKCViwMuXMnDwk/WY8nr09DKhVvgG4pEIkFYWBhCQ0Nx+PBhxMbG4pVXXkHXrl0xZ84cRERENMtTw5RKJQYNGoRBgwbhb3/7G86fP4/k5GSkpKTg559/hlwuR9++fU2rof582tyDkkgkGD9+PPr164e///3vePrppzF9+nQ888wzWLZhL3Ykp8LB1tr0N9LLyws3rt+AqlQFVxfzbjeztlLA1ckOR85cxrtfbcJHrzwJ2X3+rN6viooKHDhwAImJiTh06BA0Gg3atWuHcePGISIiAt27d2+W31PUsNRqNZYvX474+Hj4+Pjg888/x+DBgy1dFhG1IAyeqMXRGwyQSCW1Pp2uVpcg8/gvAACJVAYrG3t4dQlC5+BIOLq1ueNYZQXXAQCt2vrdc151SS5kcuVtAyrX1u2RnX4EEtxcAq2721IsImo0ytRVeH3JDyh4iNAJAATj78GT5P4fJ5fLTOHTW5/HY+lbM83W34buj0QiwcCBAzFgwACcOHECsbGxeO2119CxY0fMmTMHw4YNa7ZhgUQigb+/P/z9/TF//nzk5uZi//79SE5OxuLFi/Hvf/8b3bp1M4VQD3uqmo+PD7799lusXbsWX3/9NX5OPoZiuS9srKxqfTDjYO8AV1dXFBYUwtbGFtbW5t0SZ6VUwNnBFiknMrDp16OYOGJAnccsLCw0NXQ/fvw4jEYjevTogdmzZyMyMhLt27eve+FEuPnhxpYtW7Bs2TLodDo8//zzeOqpp7hyjogaHF+pUosjigD+p6X+n5uLPwiD7uYRzNZ2TvdxrRbW9re/Tmn7314t4h81ElGjJooiln6/B9dyi9HKxeGhgga5XA57e/sHfrMsl8vg6mSH1PNX8cPPhzB9zJ0bMVP9kUgk6Nu3L/r27YtTp05hxYoVeOutt/DNN99g1qxZGDlypFn7IDVGXl5emDRpEiZNmoSKigocPHgQycnJWLduHb755hu0bt0aYWFhiIiIQFBQ0AO94ZVKpZgxYwYCegdj+pvLUK0rgaebE0R7a0j+1H/Rw8MDVdVVyMnJQYcOHcwe+tlYKVGl0eLbjYnoH9AZ7bzcH3iMq1evmk6iO3fuHGQyGfr06YMFCxbUeZUY0e2cOnUKH3/8MS5cuIBRo0bhhRdegLv7g3/vEhGZQ/N+NUR0G0qFHKIo4j4OdLwnhZUNAKCmqhy2jndf4i9XWkGnqbrtfbrfG5mLuNlg3ErJH02ixm7/iQvYuf8UHGytIZc93ClzUqn0oXvTWCkVUMrlWLU5GQN7d0GXdq0fahwyj969e+OLL75Aeno6VqxYgX/84x/49ttvMXPmTIwePRpKpWVPRmsIDg4OGDFiBEaMGAG9Xo/U1FRTv6L4+HjY2dlh8ODBCA8Px6BBg+DgcH8N8n85cRXW9q6wMepQVFSEyqpKeHt5m0IsiUQCH28fXLlyBSWqErRyb2X25+bqZI+CknIsWrUDn78+/Z5b7gRBQHp6uukkumvXrsHa2hqDBg3CpEmTMGTIEJ4UTfWisLAQX375JX7++Wf4+/tj5cqV6NWrl6XLIqIWju9uqcVxc7aHTCaF4fctLnXh7NEWeZdPo+j6Rbi26XDXax3dvFCSexnlxTlwcq+93U6Vfw3AzReqcpkMLo72da6NiOpPZXUNPlv7MwwGI9ycLPfz6uJoh/zicny8cju++fucZru9qynp3r07PvnkE1y8eBErV67Ehx9+iNjYWMycORNjx46FlVXL6N+nUCgQEhKCkJAQLFy4EJmZmaYQ6q233jKt+AkPD0dYWBjatLn9tvbsvGL8lJIKOxtrONq7wNHRAbm5ucjKyoKnpyecnZ0B3OxD1a5dO0ik9dPkXSqRwNnBFifOXcHRs5cxsHeXW67R6/U4efIkEhMTkZycjKKiIjg5OSE8PBwvvfQS+vfv32L++1PD0+l0WL9+PVasWAFra2u88847GD16NP8uEFGjwOCJWpxObT1hpZRDq9Ojrn+Kfbr2xcXjvyIrLQWtO/a8JVDSVJbD5vftdd5+QSjJvYwLR3aj32MzIfm9p0tlaSFuXDgOADAYBXh7ujT4sc1E9GD2Hj6LGwUlcHe+vxUb5iIYjcg8sRc5mamoqSiFIBjR65EpOHtJhtSMa+jT4+4BOHBz+8WyZcvg6uqKDz/8sAGqbpn8/Pzw0UcfISsrC3FxcVi8eDFWrFiBadOmYcKECbCxsbF0iQ1GIpHAz88Pfn5+mDdvHgoKCpCSkoLk5GR89tlnWLRoEfz8/BAWFobw8HB069bN1Bdq5/5TqK7RwVFhxI5l79QaN1MihcLKFu4+ndAlOAqO7l4AgAtH9yDz+C8IGjYF3l0Caz1Gr6vBsZ2roMrNgleXIAQ+MglSae0Vi4e2/gclOZfh4OqJ8CcXmG63sVKirKIa25NOmoKn6upqHDp0CElJSdi/fz8qKyvRpk0bDBs2DBEREejduzdkD7kikuh+iKKI/fv349NPP0VeXh4mTZqEp59++r5XFBIRNQQGT9TiuDnbo5WrI/IKS2FTx+TJytYegY9MQuqe73Bg45fwbN8d9s6toKupQllBNmwcXdFv5EwAQNtu/XDj4kkUXstASvwSePh2g15bjdzMNLRq64eCq+kQRSCgi/mOhCYi8xMEAZv3HocEklpb7KrVKuxbVzvIkUilUNrYw7V1e3QKioCzR91+vrNOJSPz+C9w9eoIr069IJHK4NKqDaoMRmxPOnnX4On06dP46quvsGfPHqhUKp5o1EA6duyI9957D08//TRWrVqFL7/8EnFxcZgyZQomTpwIOzs7S5fY4Dw9PRETE4OYmBhUVVXh4MGDSElJwYYNGxAbGwsPDw+EhYWh/4BB2JZ4Akq5DBLJzVXKto5u8PYLBgBoqitQnHsFeZdOoeDKOQwYM++uq4+1mkoc3RGL8qIctA8YjB5Dxt7S+LyqvAQlOVkAJKhQFaC0IBsunv89bt7OxgopxzMQ9108Th0/hCNHjkCn06FLly6YPHkyIiIiHrqhOtGDunbtGj755BMcPHgQISEh+PTTT9GxY0dLl0VEdAsGT9TiSCQSBHT2wbXcYtiYoeVGm44BsI1+AZdOJEKVexkFV9OhtLaDo7sXfP37/3deqRQho+bg4tHdyLmUhiun98PW0Q3+gx+H0soGBVfTIVNYobMv+7QQNWanL17Hxav5cLS//YqVP78xNhp0KC+6gbzLp5H/+xtjN6+Hf1NQcDUDMoUVBjz+NKSy//4JFyqrkXQ84+bpem61DzE4e/YsvvrqK+zevRvV1dVwdHSEo6OjWfrc0f1r27Yt3nnnHcydOxerV6/G8uXLsWbNGjz11FN48sknW2y/Hzs7OwwbNgzDhg2DwWBAWloakpOTkZycjO+27kOVfSc42ChhkN4Mnuyc3NA1ZLjp8UajEReO7EJWWhLOH9mFQeP+ctt5NBVlOLx9OarKitCl77BaY/zZ9fPHAIjoGBiOrLRkXM84ChdPX+j1elRUVECtrkCV1oDFX5/AQP/WeO655xAREQFv71tPrCWqL1VVVYiNjcX69evh6emJxYsXIzw8nIEnETVaDJ6oRRoY6Ied+09BYeuE0c8uuq/HdA0ZfscXqk7u3ugzYuo9x5ArlOg++HF0H/x4rdvPH9kFAHB080T/Xp3uqx4isozk4xnQGwywVt5+pcr/vjEGgEsn9+H84Z9x4ejuO74xvh/aajWU1ra1QicAcLC1RmFpBVJOnEfM8JuB97lz57Bs2TL8/PPPpsDJ29sbEokEGo2GwZOFeHl54Y033sCcOXOwdu1axMXFYd26dZg4cSKmTJkCFxcXS5doMXK53HRK4F//+lf848vvsfHXEzAaalBQWgQA0NTUQKVSwd7BHkqFEjKZDJ0Cw5CVloTywuu3HbeytBCHt3+Lmspy9Agdhw4Bt1/tJwoCbpw/DoW1Lbr1fxR5WWdw42IqbL0DoDcIkEgksLOzg529PcIjxmPp/82ut68F0e0IgoCdO3fiiy++QHV1NZ555hlMnTq1RRxeQERNG4MnapHC+3aDp5sTissqGrRHS02VGtZ2tT/VrlAV4OrpA5AprPHo8GFo29qtweohogd3JvM6pFLpA32y3NY/BOcP/4zywhu1bs/OOIqCK+egLsmDtroCMrkCzh5t0Sk4Eu7enU3X/dGz5g87li0EANg4uOCRaW/ebB4risi8mof09HQsW7YMO3fuRFVVFZycnEyBEzUeHh4eePXVVzFr1iysW7cOGzZswA8//IAJEyZg2rRpLf7Yc4lEgvxSDWxtbeHp1gZqZ3tkH755X2FhIQoKCmBlbQVfX98/P+qWccoKr+PojhXQ6zQIHPokfH5fjXg7hdcvoKaqHL49BuLqtWxYu7aDRp2K6uJraN9jAOzt7SGVSlGqrkJWTjEEQWDjZmow6enp+Pjjj3H27FkMHz4cL730Ejw9PS1dFhHRfWHwRC2StZUSYyKD8U3CPgiiCGkDvSE7k/IjNGoVnD19obCyQVV5CQqvpkMQBHTsPxpPDBvQIHUQ0cOp0elx5UYhrJQP9+dT8j9vUs+mbIajuxfcfbpAaW2HmqpyFFw5h8PblqPvo9PRukNPAICb982VkFdO7wcAdOgVCgBQWP13u58oCvhx5z6s+PerqKyshKOjI3x8fG4bOEkkEq54aiRcXV3x4osvYsaMGVi/fj1++OEHJCQkYNy4cZgxY0aLfWOp0xtwKbsA1koFAJj6qdlYW8PPzw+VVZWo0dRAKpHi8tmDAABnz9o91EpyLuNM0kYIooC+I2fCs53/Xee8nnEUANC2ax+IVg5wDA7F4atpqMy/BMeB/13FaKWUQ12pQU5BKdq24YdFVL9UKhWWLl2Kbdu2wc/PD8uXL0dw8J0DVCKixojBE7VYj4UF4vudh1CmroJrAx2H3qZTL2SfO4y8rDMw6GogV1jB1asjXDr2Qb8BQ9C/V+d7D0JEFnPlRiE0Wj3sbB7sSPTr6Tff0P5v4+OIpxbC1tG11m01VWrs3/g5Mg7+ZAqe3L07wd27k+kEzD9v5aupqUFhURHK1ZUQRBFKmfKOgRM1Xk5OTvjLX/6CqVOnIj4+Ht999x1+/PFHPP7445g5c2aL6yF0o0AFjU4PGytFrdurykuQefxXADd7qF36LRuqvCuQyuTo1v/RWtdmp99cIhUQ/sQ9QyetphIFVzNg59wKLq3b/X6rK1zbtIcq7woqSwth7+IBALBSKFBRVYOruUUMnqje6PV6xMfHY/ny5ZDJZHjjjTcwfvx4rrIjoiaJwRO1WN4erpg1Lgxfrt8DrU4PK6Xi3g+qIx+/4FuW+ZdXVkMURfx1xmOQyfhigqgxyy8ug15vgNLhzieRVZWX4MLRPQD+21y8JOcyrGzs4T9wVK1r/zd0AgBrO0e06RiAq2d+Q3VFKWwdbt/zp6amBkVFRSgrK7u55UeugFyugL2zOySC5o71iaIInU6HnJwcxMfHY+LEiffz1KmBODg4YM6cOXjqqaewceNGrFu3Dlu3bsXIkSMxe/ZstGvXznStKIqmlWt//t/7uf1/b3vQ2x9kzoepNTO7EDqdDgopUCMK0Gq1AIBqdYlp26lEKoOVjT28ugShc3AkHN3a1Ppauvt0QfGNTJw/vAvOHr5wanXn8O7G+eMQBeMtf6O9u/aBKu8Krp8/Zvr5lUolEEQRNTr9HccjqovDhw9j8eLFyM7ORnR0NObPn99iDyAgouaBwRO1aJNGDsT+kxdwMuMqWrs5NfgKAb3BCE2NDlNHD0Gf7nc+ApqIGged3ghRFHG3XxV/fmP8BytbBwwa/yzsnGr37akqL8Glk/tQknMJNVVqCEZDrfu1VeW3Bk8icDHzIqqrqiGIAqQSKSRSCQTBCBEyFJWogJoy05t4QRBgMBig1+tN/xiNRhQVFeG1115DWlrazWHNFB78723mCDQe5N/rUtPdxjBHmPIwNRmNRpSWluLzzz/H4sWL4ejoCHd3d1hZPdiqu6ZGL7NHhX03qEQDJBCh11QAAGxcveHVewQAwMnZCV5tvO44Rlv/ELTu2BNnU7bg8LZv0P/xeXD28LnttdczjgGQwLtrn1q3e3XqhXP7t+DGhRPo2v9RSKWym68VRMBgMJrnyRL9LicnB59++imSk5MRHByMDz/8EF26dLF0WUREdcbgiVo0pUKO12aPxrx/rDA1Gm+o8EkQBBSXVaCLryfmRkc2yJxEVDc3w4G7/45o1dYP/R9/GsDN7Ts3zh9HxuGdOLZzFYZEvwi54mZgUFVejAMbv4BBp4Wbdyd4tu8OudIagAQluZehys2C0XjrG1sRImpqaiAIN4+XFyUiIACQiIBUhFarha6iAkaj0RQy/bd21Pod5+LiApVKBYlEYrr9bv/+59tu9///fPvt7rvd7bcb+0Hmvt3t5p67IWu60+0GgwHHjx9HYmIiysrK0KlTJwwbNgxt27Z9oHEepva6fI0e9mt3JVeF92J3wdZGCaVcDk1FKbIPA7Y2NujQ4eYHNUqre5/k1b7nIEgkUpxJ/hFHti9H/9FP39ILSpV3FZVlhQCAfWs/uO042uoKFF47j9Ydetz8WZIAcrnsnvMT3Y/q6mrTCZcuLi748MMPMXToUG6ZJqJmg8ETtXidfVvjrafH4h9f/9hg4ZMgCChUqeHVyhn/fHHiA/eLISLLUMhl98qdarGysUenoAgYdDXIPLEXF47sQo8hYwEAWadSoNdqEPjIk/D5n1UWp5M2QZWbddsxJRIJAnoGoLy8HIVFhdBUayCIAiS4GS5pqiqh02hM18rlctMpfH/8btPpdJBIJHjjjTe41a4JmTRpEvR6PX7++WesXLkSq1atwpAhQzBnzhwEBARYujyzcnAthpVVIhRyBaytlRB0N/9OSqVSWFtbP9BY7XoMgEQixenkjTi8fTn6P/40XDz/exreH03FW/l2u+XkWQDQazXIzzqD6xlH0bpDDxgFAVKpBHbW/NtNdSOKIvbs2YPPP/8cZWVlmD59OmbMmAEbG5t7P5iIqAlh8EQE4JEBPVGj0+PfK7ajqLQC7i4O9XbSncFoRFFpBVq7O+Hff30Kndq2zBOLiJoiZ0c7yGVSGAxGKBT3/ye0c59HkH3+GK6ePYQOvUJh6+iK6vISAEDrDj1qXSuKIkrzr95xLFEUkZOTA4PBAIP+5tY8QRAgkcoAoxGCQQuFQnHXAF0URcjlcgQFBd33c6DGQaFQYMyYMRg1ahR2796NlStXYtasWQgJCcHcuXObzWlXXh4usLVWQqPVw8b63iub7sW3ewgkUglOJSbgyPZv0X/0HLi0bg+DXovcy6chkyvRZ8RU04rEPxNFAXvXfIDC7POoqVbDKLGClUKBdt7ut5mJ6P5cvHgRH3/8MdLS0hAVFYWXX34ZXl533jpKRNSUsZMx0e9GhQXh3WcnwMHOGgXF5ajRmrdpqCiKqKjSoEhVgfZerbDkb9Ph37FlnVJE1NR1busJK6UCNTrDvS/+E5lcgc5BkRAFo+lELpvfezep8q7WuvbyyURUqPLvOJbRaERBYQFKSkpQo62BCBFKpRIKpRUkgg5GbRX0er1pK96dKBQKdOrU6YGeBzUeMpkMjz32GOLj4/HRRx+htLQU8+bNw7x583D06NFaPaOaIoVcBr/2baA1YwPvtt36ITBqEox6HY5sj4Uq7wpyM0/BqNeiTaeA24ZOACCRSOHTtQ9EQcCN8yeg1enh5GCDNu7OZquNWo6ysjJ8+OGHmDp1KtRqNZYtW4aPP/6YoRMRNWtc8UT0J1H9e6CDTyssWvUTjp/NQpVGChdHuzofXas3GFFSXgmlXIbo4SH4y6ShcHKwNVPVRNRQnBxs0cbdGdfyiuGAB9vu49u9Py6lJuLGxRPo3CcK7XoMxPXzx3F89xp4deoFpbUdSguuobwoBx7t/FF4LeO248jlcni18UJhYSEEQYAoitDr9ZAqlTBUqeDg4ICamhrodDrT9f/7O0wURTg6OvJY7mZAKpVi6NChiIqKwv79+7FixQo8++yz6NmzJ+bOnYvBgwc32T4x/h29ceTMZbOO6dO1DyRSKdJ+/QFHdqyAUX/z58SnW7+7P65bX1w6uQ/Xzx+DvW8QenTy4c8PPRCj0YhNmzbh66+/hiiKeOWVVxATEwO5nG/HiKj54286ov/RwdsDX7wxHRv3HEXspkQUqtSQSaVwcrCF8gG21oiiCI1Wh4qqGgBA29aueHXGYxgc1LW+SieiBtCziw8uXS944MfJ5Ap0Do7Cuf1bcPHYLwga+hT6P/40LhzZhfyss5BIpXDxbIfBTzyH/CvpdwyeAKBNmzawsbVBzo2bW+5kSjlEiRQSUYM/1ofIZDIYjcZaAZRcLjethLG3t3/g50CNl1QqRXh4OMLCwnDo0CHExsbi5ZdfRteuXTFnzhxEREQ0uaCkZxcfSCWATm+AraMrRj+76L4e1zVkOLqGDL/j/d5dguDd5cG2mdo7t8LoZxeZejT27trugR5PLdvx48exaNEiZGVlYezYsXj22Wfh6upq6bKIiBqMRLyPtdhqtRpOTk4oLy+Ho+OtTReJmquiUjV2HTiNrfuO43q+CkZBgFwqhZVSASulHHL5f49VFkUROoMBWp0BWp0egijCSiFH907eGPdIX0T26w5bNhEnavJ2/3Yab3+ZAFcn+5vNxi2ouroa2dnZ0OoNkMmVcKq6ALmxCoIgQKPRoLq6GmVlZaiqqjJtvftj9UvXrl2RmpoKKyv+XmqORFHE8ePHERsbixMnTqBTp06YM2cOhg4d2mQCKJ3egEmvfoH84nK4uzhYuhwAQHllNWQyKeI/eRGtXPiamO4uLy8Pn3/+OX799Vf06tULCxcuhL+/v6XLIiIym/vNihg8Ed0HvcGIQ2mZOHb2Ms5euoFruUWo0ephMAoQRBES3Hwzp1DIYWethF/7NujeyRthfbqhR2efJrvNgYhuVaPVIeavX5hOwbQ0nV6Hy1dzoC3Ph506HfZ2drdcI4oiSkpKUFRUBI1GA1EUYWdnh4CAAPj7+yM4OBhBQUHo3bs3V0I1Q2lpaVixYgUOHToEX19fzJ49GyNHjoRMZtng9H6s2pyMZT/8Ag9XJ0illv1bKooiCkrKMSayD96eP96itVDjptVqsWbNGsTFxcHBwQEvvvgiRo4cydeDRNTsMHgiqkeaGh0uXy9AWUU1dHoDJBIJrBRyeLo7wbeNu8VXQRBR/fp24z58k7APnm5O9XYC5v0yGIwoLquAj1Upjvy6GTY2NnBxcbnj9VlZWXB0dERAQACmTp2K1NRUpKamoqSkBBKJBF26dEFQUJDpHzc3twZ8NlSfzp07hxUrViAlJQVeXl6YNWsWRo8eDYVCYenS7ii/uAxPLlwKo1GAs4V7I1ZptKjR6bH0rZkI9m9v0VqocRJFEYmJifjss89QVFSEKVOmYM6cObC1ZV9PImqeGDwRERHVkxsFKkx7Yxn0eiNcHG9dYdSQClVqeLo5Yf2/n8Wyr77EV199BaPRCE9Pz1s+XRdFEbm5uXj33XfxzDPP1Lr9xo0bphAqNTUVN27cAAD4+voiMDDQFER5e3vzU/sm7uLFi1ixYgX27duHVq1aYebMmRg3bhyUSqWlS7utpev3YPXWFLg5O5j1gx1BFCCV/Hfb4eXLl6HX6+Hl7QUHBwdI8N/vc0EQUFCixiMDeuCjV57kzwDdIisrC4sWLcKxY8cQGhqKV155Bb6+vpYui4ioXjF4IiIiqkcrNyfh6w174epo90AHD5hTlUYLTY0O7z43ASMG9wIAbNy4EW+99RbUajVat25dazuVVqtFWVkZNm3ahJCQkLuOXVRUhNTUVKSlpSE1NRWXLl2CKIpo1aoVgoKCEBgYiODgYHTs2LHJ9Ayi2rKysrBq1Srs3r0bLi4umD59Op544gnY2NhYurRaNDU6zP37t7h4NQ+ebk5mC30uXboEd3d3ODs7AwCyrmShVFUKmUwGGxsbuLm5wdnFGXKZHEUqNVyd7bHqn8/A083JLPNT86BWq7F8+XLEx8fDx8cHr776KgYPHmzpsoiIGgSDJyIionqk1Rnwl/dW4nRmNlqb8c3w/TL+frrWo4N74f0XYmrNf+TIETz//PO4fv06PDw8TCtZSktLYWVlhRMnTjxwuKBWq3Hq1CnTiqj09HQYjUY4Ojqid+/ephVR3bp1a9Rbt+hW2dnZiIuLw08//QQHBwdMmTIFEydOhN1t+oVZStr5a3jhg9UQRdFsqwxzcnJQU1ODTp06AQDKystwJesKRFGEVCqFRCKBXC6HnaMTlFY2eO+FiRgV9mCn4VHzJQgCtm7diq+++go6nQ5z587FU089xd9/RNSiMHgiIiKqZ+ev5OIv761EjVYPN2f7BgufRFFEgUoNT1dHrPrXM7c9Xevq1auYP38+0tLS4OrqCltbW+Tk5CAyMhLr16+vcw01NTU4e/asKYg6ffo0ampqYGVlhYCAAAQHByMwMBABAQGNbgUN3V5ubi5Wr16Nbdu2wcbGBk8++SSefPLJRvPab9XmZPwnfi+srRRwtKv791R1dTWuXbsGX19f2NnZQa/X4/z58zAajRBEAUqFEiKkEKVySCpz0b+jPaZNm4rhw4c32m2J1DBOnTqFRYsW4fz58xg1ahReeOEFuLu7W7osIqIGx+CJiIioAexITsVHsdtMKzHqO3wSRRFFpWrY21rj3399Cn17dLzjteXl5XjllVewa9cu2NnZobKyEq+//jpeeukls9dlMBhw4cIFnDx50rRFT61WQyaToVu3bqaT8wIDA/laopErLCzEmjVr8OOPP0Iul2PSpEmYMmWKaUuapYiiiM/X7cL3Ow/BWqmAo33dw6fLWZdhpbSCj48PgJv9r2pqaiAIAkSJDAorGyj1KqDwDNTlZZBIJPD19cWUKVMQHR2NNm3a1LkGajoKCwvx5Zdf4ueff4a/vz8WLlyIXr16WbosIiKLYfBERETUQOJ3H8bn63ZDMAr1uvJJEP4bOr377ASE9e12z8fo9Xp8+OGHiI2NhSAIWL9+PcLCwuqlvtq1Crh69aopiEpNTUVhYSEAoFOnTrVOzvPw8Kj3eujBqVQqrFu3DgkJCRBFEdHR0Zg2bZpFTzoUBAH/id+LtdsPQBREuDnb16nHWGlpKfLz89G5S2co5Ark5uaiqKgIMqUNjIIIfVk2WkuLIZPe/JnW6XQoLS2FXq+Hk5MTRo4cicmTJ6Nfv35sON6M6XQ6rF+/HitWrIC1tTVeeOEFjB49mv3tiKjFY/BERETUgLYnncQncT+hSqOFq5O92RuO12h1KFVXw83ZHu89H43+vTrf92NFUcR3332HhIQErFu3Dg4ODmat7X5ryMvLq3Vy3rVr1wAAXl5ephAqODgYbdu25Zv4RqSsrAzr16/Hhg0boNfrMW7cOMyYMQOenp4WqUcURWxPSsWyH/aguLQCzg52sLF+uK1vgiAgMzMTrq6uaNWqFYpVKuTmF0MmEaGovoHc9N9gY219y/ekIAgoLy9HVVWVaXvp9OnTMWrUqEbVG4vqRhRFHDhwAJ988gny8vIwadIkPP300xb5HUpE1BgxeCIiImpgZzOv4+NVO5BxOQfWSgWcHGzrHKAIgghVeSUEUUT/Xp2wYOYo+LZpHr1EVCqV6dS8kydPIjMzE4IgwNXVFYGBgaYgqkuXLlxZ0AhUVFRgw4YNWL9+PaqrqzFmzBjMnDkTXl5eFqknp1CFT1f/jAMnL0AQRTjZ2cDaSvHAP3P5+fkoq6iAi6sHjEYjSnIvQ1F+CU7WElMfKGdn59tuqxNFERqNBqWlpQCANm3a4I033kBMTIxZniNZzrVr1/DJJ5/g4MGD6N+/P1599VV07Hjnrc1ERC0RgyciIiILqNHqsGbbAXz302+o0mhhpZDD0d4GcpnsgcbR6Q1QV2pgMBrh4miHeTGPYPwjfSGTNd8ApqqqCqdOnUJaWhpOnjyJc+fOQa/Xw87OrtbJed27d2dzZwuqrq42rZ4rLy/HY489htmzZ8PX17fBaxEEAXsOnsGGXYdx4WoedHoDbJQKWFspoVTKIb1DCCWKInR6A2p0elRUaVBeWorO7dpg7sQR+M+i/0PmxQumFV3l5eXIzc2Fh4fHHbcZ/rGiT6FQ4P3338f06dPr7TlT/aqqqkJsbCzWr18PT09PvPrqqwgLC+MqTCKi22DwREREZEFZNwqxIzkVO1PSUFJWCREirBRyWCkVsFYqagVIoijCaBRQo9NDqzNAZzBAKpXAq5ULxkb2wWNhgfB0c7Lgs7EMnU6Hc+fOmbbmnTp1CtXV1VAqlejRo4cpiOrduzdsbW0tXW6LU1NTgx9//BFr1qyBSqXCsGHDMGfOHIusChFFEacvZmNHciqSjqajukYHrd4ACSQARFNo8OeXvXK5DDZWCoQEdMKl1BQ4KQ1YEfst/v73v2P58uXw9vY2XVtUVITi4mL4+Pjcss1KEATk5eXB0dER//rXvxAdHd0gz5nMSxAE7Ny5E1988QWqq6sxe/ZsTJ06lSE3EdFdMHgiIiJqBCqra7D3yDkcSsvEuUs3UFZRDa1OD1G8+WZYBABRhFQqhZVSjlYuDujRuS0i+vljcFBXWCnN2yuqKTMajcjMzMTJkydNW/RKS0shlUrRtWtX06l5QUFBcHFxsXS5LYZOp8O2bdsQFxeH/Px8REVFYc6cOejatatl6tEbcDWnCJnZ+biUXYD84jJoanQQRMDGSoFWLo7o0s4Tndre/MfGWolff/0Vr7/+On744QdkZGRg3rx5aN26NWR/WqmYk5ODiooKtGvXDjY2N0/U0+v1KCgogJeXF5YuXYpBgwZZ5DlT3aSnp+Pjjz/G2bNnMXz4cLz00ksW62FGRNSUMHgiIiJqZERRRH5xOS5l50NVXgmt3gCpRAqFQgZPNyd0butZr6fiNTeiKOLatWumFVFpaWnIzc0FALRv377WyXk89r7+6fV67Ny5EytXrkROTg5CQ0MxZ84c9OzZ09Kl3ZPBYMDo0aMRHh6OGTNmIDQ0FHK5vNbqpj++33Q6HTp06ACj0Yji4mJ0794d33zzDbp06WLBZ0APQ6VSYenSpdi2bRv8/PywYMECBAcHW7osIqImg8ETERERtTgFBQW1Ts7LysoCAHh6etYKojp06MCAr54YjUbs3r0bK1aswLVr19C/f3/MnTsXQUFBli7trpYvX461a9di165dGDNmDC5evHjLqhej0YgrV65AEATY2NggIiICX331Fdzdm0fD/5bCYDBgw4YNWL58OWQyGZ577jmMHz+ehxgQET0gBk9ERETU4pWVleHUqVOmk/POnz8PQRDg5OSEwMBABAcHIygoCF27dq21rYrqThAE7N27FytWrMClS5cQHByMuXPnol+/fo0y9CsqKsKoUaOwYMECnDt37pY+T38oLCxEQUEBunXrhgMHDtzS84kat8OHD2Px4sXIzs5GdHQ05s+fz/c3REQPicETERER0f+orq7G2bNnTUHUmTNnoNPpYGNjg4CAAFMQ1bNnT1hZWVm63GZBEATs378fsbGxyMjIQEBAAObOnYtBgwY1ugDqb3/7G7KysjBt2jQ888wztfo8iaKIgoICyGQyPPHEE/jtt98wbtw4vPHGG43uedCtcnJy8OmnnyI5ORnBwcFYuHAht0cSEdURgyciIiKie9Dr9cjIyKh1cl5FRQXkcjm6d+9e6+Q8rmypG1EUcejQIcTGxuL06dPo1q0b5syZg/Dw8EazxenEiRN45pln8P777+P555839Xm63cl127Ztw3vvvYeXXnoJ06ZNs3TpdAcajQarVq3CunXr4OLigpdffhlDhw5lWEhEZAYMnoiIiIgekCAIuHz5cq0+UcXFxZBIJOjSpYvp1LygoCD29XlIoiji+PHjiI2NxYkTJ9CpUyfMmTMHQ4cOtXgAJYoiJk2ahPbt2+PEiRO4ePEiXF1d73hy3bJly7Bq1Sr8+9//RlRUlAUrp/8liiJ++eUXLFmyBGVlZZgxYwZmzJgBa2trS5dGRNRsMHgiIiIiqiNRFHHjxg3TqXmpqam4fv06AKBt27YICgoy9Yry9vbmKooHlJaWhtjYWBw+fBjt2rXD7Nmz8eijj1q031Z8fDwWL16MsLAwrF69GjKZ7I4n1wmCgP/7v/9DcnIyli9fjh49elioavqzixcvYtGiRUhNTUVUVBRefvlleHl5WbosIqJmh8ETERERUT0oLi6utSLq0qVLEEUR7u7utU7O69Spk8VX8DQV586dw4oVK5CSkgJvb2/MmjULo0aNgkKhaPBaqqqqMHLkSAQEBODnn39Gv3797npynVarxfz585GTk4PVq1ejTZs2DVwx/aGsrAxff/01Nm/ejPbt22PBggUICQmxdFlERM0WgyciIiKiBlBRUWE6OS81NRXp6ekwGAxwcHBA7969ERwcjMDAQPj7+1skSGlKLl68iNjYWOzbtw+enp6YMWMGxo0bB6VS2aB1fPTRR9i7dy/mzJmDsWPHwsbG5q7Xq1QqzJw5E9bW1li5ciXs7e0bqFICAKPRiE2bNuE///kPBEHAM888g5iYGMjlckuXRkTUrDF4IiIiIrKAmpoanDt3rtbJeRqNBlZWVujZs6fp5LyAgIB7BhotVVZWFlauXIk9e/bA1dUV06dPxxNPPNFg/XkuX76MSZMm4cMPP8SwYcPu6zFXrlzBrFmz0KNHD3z++ecMPRrIiRMnsGjRIly+fBnjxo3Ds88+CxcXF0uXRUTUIjB4IiIiImoEDAYDLl68iJMnT5p6RZWXl0MqlcLf39+0NS8wMBBOTk6WLrdRyc7ORlxcHH766Sc4OjpiypQpmDhxImxtbet97nnz5gEAli9fft+POXbsGJ5//nmMHTsWb7zxBnt+1aP8/HwsWbIEv/76K3r16oWFCxfC39/f0mUREbUoDJ6IiIiIGiFBEHD16lXT1ryTJ0+isLAQANCxY8dafaI8PT0tXG3jkJubi7i4OGzfvh02NjZ46qmn8OSTT8LBwaHe5vzll1/wxhtvYMOGDejUqdN9P27btm1477338NJLL2HatGn1Vl9LpdVqsWbNGsTFxcHBwQEvvvgiRo4cyZCPiMgCGDwRERERNQGiKCIvL890al5qaiquXr0KAPDy8kJgYCCCgoIQHBwMX1/fFv0Gu7CwEGvWrMGPP/4IhUKBSZMmYfLkyXB2djb7XHq9HqNHj0ZkZCRef/31B3rsV199hbi4OPz73/9GVFSU2WtriURRRGJiIj777DMUFRVh6tSpmD17doOsfiMiottj8ERERETURKlUKqSlpSEtLQ0nT57ExYsXIQgCXF1dTUFUUFAQ/Pz8WuTJeSUlJVi3bh02btwIAIiOjsbUqVPh5uZm1nm++eYbfPfdd/j5559hZ2d3348TBAFvvfUWUlJS8O2336J79+5mraulycrKwqJFi3Ds2DGEhobilVdega+vr6XLIiJq8Rg8ERERETUTVVVVOH36tGlF1Llz56DT6WBra4vAwEBTGNWjR48GPwHOksrKyrB+/Xps2LABer0e48ePx4wZM+Dh4WGW8QsLCzF69GgsWLAAEydOfKDHarVazJ8/Hzk5OVi9ejXatGljlppaErVajeXLlyM+Ph4+Pj549dVXMXjwYEuXRUREv2PwRERERNRM6XQ6pKen4+TJk6aVUdXV1VAqlejRo4epWXnv3r0faKVOU1VRUYEffvgB33//PTQaDR5//HHMnDkTXl5edR77tddew9WrV7Fhw4YH3uaoUqkwc+ZM2NjYYMWKFbC3t69zPS2BIAjYunUrvvrqK+h0OsydOxdPPfUUFAqFpUsjIqI/YfBERERE1EIIgoDMzMxaJ+epVCpIpVL4+fnVOjnP1dXV0uXWm+rqaiQkJGDdunUoLy/HqFGjMGvWrDptyzp+/Djmz5+Pb775Bn369Hngx1+5cgWzZs1Cz549sWTJEsjl8oeupSU4deoUFi1ahPPnz2PUqFF44YUX4O7ubumyiIjoNhg8EREREbVQoigiOzvbtDUvNTUVubm5AIB27dqZmpUHBgaiTZs2za5huUajwebNm7FmzRqoVCoMHz4cs2fPRseOHR94LFEUMXHiRHTq1AkfffTRQ9Vz9OhRvPDCCxg7dizeeOONZvf1NoeioiJ8+eWX2LlzJ7p3746FCxciICDA0mUREdFdMHgiIiIiIpPCwkJTCHXy5ElkZWUBADw8PExBVFBQENq3b99sGpbrdDps3boVcXFxKCwsRGRkJObOnQs/P78HGic+Ph6ffPIJduzYgVatWj1ULdu2bcN7772Hl19+GVOnTn2oMZojnU6H9evXY8WKFbC2tsYLL7yA0aNHN5vvQSKi5ozBExERERHdUXl5OU6dOmUKos6fPw+j0QhHR0fT1rygoCB07dq1yW8P0+v1+Omnn7Bq1Srk5OQgLCwMc+bMQY8ePe7r8ZWVlRg5ciSmTZuGefPmPXQdS5cuxerVq/Hxxx8jMjLyocdpDkRRxIEDB/DJJ58gLy8PkyZNwtNPPw0HBwdLl0ZERPeJwRMRERER3TeNRoMzZ84gLS0NJ0+exJkzZ6DVamFjY4OAgABTENWzZ09YW1tbutyHYjQasXv3bqxYsQLXrl3DgAEDMHfuXAQGBt7zsR9++CGSk5OxY8eOhw7iBEHAm2++if379+Pbb79F9+7dH2qcpu7atWv45JNPcPDgQfTv3x+vvvrqQ22DJCIiy2LwREREREQPTa/X4/z586bteWlpaaioqIBcLkf37t0RGBiIoKAg9O7du8m9PhQEAXv37sWKFStw6dIlBAcHY+7cuejXr98d+y9dunQJTz75JD766CMMHTr0oefWarWYP38+cnNzERcXhzZt2jz0WE1NVVUVVqxYgfXr18PDwwOvvvoqwsLC2POKiKiJYvBERERERGYjCAIuX75sCqFSU1NRVFQEiUSCzp0719qe11ROIRMEASkpKYiNjcX58+fRq1cvzJ07FwMHDrxtGPL0009DKpXim2++qdO8KpUKM2fOhI2NDVasWAF7e/s6jdfYCYKAnTt34ssvv0RVVRVmz56NqVOnQqlUWro0IiKqAwZPRERERFRvRFFETk5OrRVR2dnZAAAfHx/TqXlBQUHw8fFp1KtaRFHEwYMHERsbizNnzsDf3x9z5sxBWFhYrSbXe/bswZtvvon4+Pg6bw3LysrC7NmzERAQgCVLlkAmk9X1aTRK6enp+Pjjj3H27FkMHz4cL730Ejw9PS1dFhERmQGDJyIiIiJqUMXFxabVUKmpqcjMzIQoinB3dzethgoMDETnzp0b5alloiji2LFjiI2NxcmTJ9G5c2fMmTMHjzzyCKRSKfR6PUaNGoWhQ4fitddeq/N8R48exQsvvICxY8fijTfeaNTh3INSqVRYunQptm3bBj8/PyxYsADBwcGWLouIiMyIwRMRERERWVRFRQVOnz5tOjkvPT0dBoMB9vb2ptVQQUFB8Pf3h0KhsHS5taSmpmLFihU4fPgw2rdvj9mzZ2PEiBH49ttvsX79euzatQu2trZ1nmfr1q14//338fLLL2Pq1Kl1GksURYuHVwaDAfHx8fjmm28gk8nw3HPPYfz48Y0yaCQiorph8EREREREjYpWq8W5c+dw8uRJpKam4vTp09BoNLCyskLPnj0RFBSE4OBg9OzZ0yyhjjmcPXsWK1euREpKCnx8fDB+/Hh8+eWX+Nvf/oaYmBizzLF06VKsXr0aixYtQkRExD2vzylU4cKVPFzKLkBGVg4uXy+ARquH0ShAIZfB3tYK3Tp6o1v7NujU1hPdO3nD2dHOLLXezeHDh7F48WJkZ2cjOjoa8+fP53sHIqJmjMETERERETVqRqMRFy5cMG3NS01NRXl5OaRSKbp164bg4GDT9jwnJyeL1nrx4kXExsZi3759KC0thYeHB5KSkmBlZVXnsQVBwJtvvon9+/fj22+/Rffu3W+5Rqc34NCpTGzbdwLHzmVBU6ODRCKBCBFKuRwyqRSQAKIIGAUBer0BkEgglQD2ttYYOqAnRocHo2cX8/fbysnJwaeffork5GQEBwdj4cKF6NKli1nnICKixofBExERERE1KaIo4sqVK6Y+USdPnkRBQQEAoGPHjrVOzrNUg+qsrCy8//77iI+PR2BgIJ577jk88cQTsLa2rtO4Wq0WzzzzDPLy8rB69Wq0bt0awM1QanvSSazdfgDX81UQBBH2tlawsVbeDJvuwWAwolKjhUarg5VCjm4dvTF/YhT69exUp3oBQKPRYNWqVVi3bh1cXFzw8ssvY+jQoRbf7kdERA2DwRMRERERNXl5eXmmU/NSU1Nx5coVAECbNm1qBVHt2rVrsMBDFEWMGjUKFRUV0Gq1cHJywtSpUxETE1OnLYIqlQozZ86EjY0NVq5cidJKLT6J+wkH0zIBAM4OtlAq5A9ds0arR3llNayVCkwY1g9PR0fB3vbBAzNRFPHLL79gyZIlKCsrw4wZMzBjxow6h29ERNS0MHgiIiIiomantLS01sl5Fy5cgCAIcHFxMW3LCw4Ohp+fX702tN6wYQM+/fRTrFixAtu2bcO2bdtga2uLyZMnY9KkSXBwcHiocbOysjB79my4t+8FtbINVOVVcLa3hY210ix1i6KIiqoaVNVo0amtJ/5v3jgE+LW978dfvHgRixYtQmpqKqKiovDyyy/Dy8vLLLUREVHTwuCJiIiIiJq96upqnD59GidPnkRaWhrOnj0LnU4HW1tb9O7d27QiqkePHlAqzRPeAEBlZSVGjhyJ6dOn4+mnn0ZhYSFWr16NzZs3Q6FQYNKkSZg8eTKcnZ0faFxRFPHPpd9h1ZYDsLaxQYe2XpBKzb+Sy2AwoqisAi6Odnjv+WgM7H33nkxlZWX4+uuvsXnzZrRv3x4LFixASEiI2esiIqKmg8ETEREREbU4Op0OGRkZppPzTp06haqqKigUCvTo0cMURPXu3Rt2dnU76e2DDz7A/v37sX37dsjlN7fAlZSUYN26dUhISIBEIkFMTAymTp0KV1fX+xpz7fYDWPbDL9DW1EBdWgwPTw+4ubrVqc47EUURBSo1HGyt8dErTyIk4Na+T0ajEZs2bcJ//vMfCIKA+fPnIzo62vR8iYio5WLwREREREQtniAIuHTpkimISk1NhUqlglQqRZcuXWqdnHe/4dAfMjMz8dRTT+Hjjz9GVFRUrfvKysqwfv16/PDDDzAYDHjiiScwffp0eHh43HG8XQdO4f3/bIZEIoGLox0KCwtRUlICHx+fh966dy9/hE+ujnZY+tZMdGnX2nTfiRMnsGjRIly+fBnjxo3Ds88+CxcXl3qpg4iImh4GT0RERERE/0MURVy/ft0UQqWmpiInJwcA0K5du1oNy9u0aXPPhuVz586FXC7Hf/7zn9ver1arsWHDBnz//ffQaDQYM2YMZsyYcUtfpNzCUsx+eznKKqrQyuXm620RInJu5KCyshLt2reDjbWNGb4CtxJEEQUl5Qjs2g7L3p4FVUkxlixZgl9//RW9evXCwoUL4e/vXy9zExFR08XgiYiIiIjoPhQWFpoalp88eRKXL18GAHh4eJhCqODgYLRv3/6WhuW7d+/GW2+9hfj4eHTs2PGOc1RVVSEhIQHr1q1DRUUFRo0ahVmzZqFt27YQBAGvffo9ko5mwNPNsdYcgijg2rVr0Ov16NC+AxQKRb18DbQ6A0rVlQhqb49zB7bDwcEBL730Eh599NEGOy2QiIiaFgZPREREREQPQa1W1zo5LyMjA0ajEY6OjggMDDQFUV27doUoihg1ahSGDh2K11577Z5jazQabN68GWvWrIFKpcKIESPQodcQfPPjb3Cwtb7t6XUGowFXr1yFVCpF+w7tIZWY/7Q+dYUaufnFEIwGTBvaFa+99BfY2tqafR4iImo+GDwREREREZmBRqPB2bNnTUHU6dOnodVqYW1tjV69eqG8vBzp6enYu3fvffeJ0ul02LJlC+LiViNT0woyGye0bdMK1tbWt71eq9UiLz8PPt4+Zm3srdVqkV+Qj+qqatjZ2UNqZYuY4QPw5ryxZpuDiIiaJwZPRERERET1wGAw4Pz58zh58iTS0tJw9OhRpKamwtvbG0OGDDGtiurdu/c9XzvvP56B5/+1CjVVFaipKEH24YRa90ukMljZ2MPVqwM6B0XC0f1mb6gLR/cg8/gvCBo2Bd5dAms9Rq+rwbGdq6DKzYJXlyAEPjIJUqkMAFBZVoSrZ35D8Y1MVFeUQTAaILeygbOHL9r594V1q45QKhRI+PQluDnbm++LRkREzc79ZkU8B5WIiIiI6AHI5XL07NkTPXv2xPTp0yEIAubNm4f09HR4enpi165dWLNmDSQSCTp37mw6NS8oKAitWrWqNdbOA6egVCrR1qsjCnPkyAYgt3GAq48/bO1sIRUFlBZkIzczDflZZzFgzDy4tulwx9q0mkoc3RGL8qIctA8YjB5Dxpp6NF1OS8b5QzshigJsnFvDsU0X2Nk7AkYtim9k4sTuDPh064vWvUZg92+nMXnUoPr8MhIRUQvB4ImIiIiIqA6kUinmzZuHZ599FhMnTsQHH3yA3Nxc09a8w4cPIz4+HgDg4+NjaljevpMfDqZlwtbGChJI4ODgAABwcPaAe6c+0Gq1sLWzRbeBo3Dx2B5cOrEX54/swqBxf7ltHZqKMhzevhxVZUXo0ncYuoYMN9137dxhZBzcARsHF7QNehT2rq3h4eFh2rYnCEbcOH8CqrwsSKVS/JSSyuCJiIjMgsETEREREVEd9evXD+3atUNCQgKCgoLg7e0Nb29vjB49GgBQUlJS6+S8HTt2QCtzRJVjV9jbKGDQ2UFi0AIA5HIZOnTsgMqKSqjVaoiiiA4Bg3HpxF6UF16/7fyVpYU4vP1b1FSWo0foOHQIGGy6T6/VIOPQT5BKZQgZNQcOrp63PF4qlcG3ewi8uwZDozXgRoEKZeoqODva1cNXi4iIWhIGT0REREREdSSRSBAdHY0lS5aguLgY7u7ute53c3PDI488gkceeQQAUFlZiUXfbsLGfacgGHQoLCiErloNANDU1EBVooKtrS28vb0BAIb/znTL3GWF13F0xwrodRoEDn0SPn7Bte7Pu3waBl0NvLoE3TZ0+jOZTA4rpQRlFVW4dL0AfXt0fPAvBhER0Z+Y/yxWIiIiIqIWaPTo0VAoFNiyZcs9r7W3t4deag0bW1u0b98efl39TCETABQXF+Pq1auorq4GAFw9exAA4OzZttY4JTmXcXjrNzAYdOg7cuYtoRMAqPKvAgDcvTvf1/OQy6QwGgVkXsu/r+uJiIjuhiueiIiIiIjMwMHBASNHjsSPP/6IWbNmQSaT3fX6S9kFUMpvXiOVSGFjYwMAELRVQPlViEYBV0svoazgOlR5VyCVydGt/6O1xshOPwwACAh/Ap7t/G87j7a6AgBgbe90X8/jj2bk1/NL7ut6IiKiu+GKJyIiIiIiM4mJiUFhYSGSk5PveW2NVg+p9NaX49XqEmQe/xVZqftw5fRvqFar4NUlCEOiX4RL6/a1rnX36QIAOH94F8qLcszyHABAFEVodYZ7X0hERHQPXPFERERERGQmfn5+6NWrFxISEhAVFXXXa41G4ba3t2rrh/6PP31f87X1D0Hrjj1xNmULDm/7Bv0fnwdnD59a11jZ3jwtr6aq/L7GvEkCvcH4ANcTERHdHlc8ERERERGZ0cSJE3Hs2DFcuXLlrtcpFDKIoljn+dr3HISA8Ceg19bgyPblKCuoffKd6++rpIpvXHqAUUVYKfkZNRER1R2DJyIiIiIiM4qKioKrqys2btx41+uc7G1gFG6/6ulBtesxAL0ioqHX1eDw9uUoLcg23demUy/IldbIzzqDytLCu45jNN7cXieVSmFva22W2oiIqGVj8EREREREZEZKpRLjxo3Djh07TKfS3U73Tt533G73MHy7h6B3ZAwMei2ObP8Wpb+fZqewsoH/wFEQjAYc/WkFyotv7QUlCgKunz+OM8mbIIoiRFFERx8Ps9VGREQtF9fPEhERERGZ2RNPPIG4uDjs2rULTzzxxG2v6ezbGsDNRt5/nCRXV2279YNEIsWpffE4sj0WIaPnwLVNB7TrMQAGXQ3OH/4Z++M/h6tXBzi5e0MmV6CmqhzFNy6hpqocbf1DoNUboJDL0dnX0yw1ERFRy8YVT0REREREZta6dWuEhoYiPj7+jn2cOvt6Qi6Xmb2Jt0/XPggc+iSMBj2O7FiBktwsAECnoAiEP/kq2gcMgk5TheyMo7icloziG5fg5OGDPiOmoVdENLQ6PWysFejAFU9ERGQGEvE+Ohqq1Wo4OTmhvLwcjo6ODVEXEREREVGTduTIETz33HOIjY1FYGDgLfdranSY8MoSlFdUw9XJvuELvIP84jKE9umGT1+baulSiIioEbvfrIgrnoiIiIiI6kG/fv3g6+uL+Pj4295vY63EqLBA6PQGs5xuZw46vQFSqQSPRwRZuhQiImomGDwREREREdUDqVSK6Oho7Nu3DyUlJbe9ZmRoIKytlKjSaBu4utsrr9TA28MVg4O6WroUIiJqJhg8ERERERHVk8cffxwymQxbtmy57f0dfTzQr2dHVFTXWHzVk8FghNFoxJjIPlAqeAYRERGZB4MnIiIiIqJ64uDggJEjR2LTpk0wGm/fRHxeTBQcbK1RVlHdwNX9lyiKKCmvRHvvVpgwLMRidRARUfPD4ImIiIiIqB5NnDgRhYWFSElJue393Tp4YdqYIdDq9NDpDWaZs6ysDAWFBfd9fUV1DZQKORbMHAUHO2uz1EBERAQweCIiIiIiqld+fn7o1asXEhIS7njNlFFDENDFFyVllRAEoc5zGgUjSlWl9zWWTm9AtUaLcVF90L9X5zrPTURE9GcMnoiIiIiI6llMTAyOHj2Kq1ev3vZ+K6Uc/zd/HFq3ckKhSg1BqFu/J0cHR4iiiMrKyrtepzcYUVJWgcBu7fDMxKF1mpOIiOh2GDwREREREdWzRx55BC4uLti4ceMdr+no44GPXnkK7i4OKFSV12nlk0KhgJW1FSoqKu54jU5vQHFZBbp38sFHrzzFLXZERFQvGDwREREREdUzpVKJcePGYfv27dBoNHe8rmdnH3z62lS0aeWMghI1NDW6h57T0cERFZUVt5yWJ4oi1JUalJRXIqhbOyx5fRrcnO0feh4iIqK7YfBERERERNQAnnjiCWg0Gvz88893vc6/oze+fmc2Qvt0RUV1DYpKKx5q652DgwNEQURl1X+32xkMRhSoymEUBEwZNQif/W0aXJ0YOhERUf1h8ERERERE1ADatGmDIUOGICEh4ZZVSP/L28MVnyycgjfmjoGTvQ0KVOVQlVfCYDTe93xWVlZQKpWoUFdAq9OjUKVGcXklOvp4Ysnr0/DK9Mdgb8vtdUREVL/kli6AiIiIiKilmDhxIp5//nmcPn0avXv3vuu1UqkU4x7pi34BHbFl73HsSE5FSVklRAC2VkpYKeVQKuSQSCS3PFYQBGj1BsitbVFWqYHcWoN2Xu4YG9kHY6P6MHAiIqIGIxHv9XELALVaDScnJ5SXl8PR0bEh6iIiIiIianYEQcCECRPQvXt3/Otf/3qgx1ZptNh35Bx+SknFxav5qNHqoTcYIJFIIIoiRADS30MoURShVCqgkAJ5V9Lx2l+mYMbEUZDLZPXwrIiIqCW636yIK56IiIiIiBqIVCpFTEwMvvjiC7z66qtwdXW978fa2Vjh8YhgPB4RjBqtDlk3ipCZnY8b+SU3QyijEUq5HLY2Vujo44HOvp5o29oV48eNQ3F2BuSyMfX4zIiIiG6PK56IiIiIiBqQWq3GyJEjMWfOHMyePbve5/v000+xZ88e7Ny5E1IpW7wSEZF53G9WxL88REREREQNyNHREY8++ig2bdoE4wM0C39YUVFRKC4uxtmzZ+t9LiIiov/F4ImIiIiIqIHFxMSgoKAA+/fvr/e5evXqBVdXV+zbt6/e5yIiIvpfDJ6IiIiIiBpYt27dEBAQgISEhHqfSyqVIiIiAvv27cN9dNkgIiIyKwZPREREREQWEBMTgyNHjiA7O7ve54qKikJubi4yMzPrfS4iIqI/Y/BERERERGQBQ4cOhbOzMzZu3Fjvc/Xp0wcODg7cbkdERA2OwRMRERERkQUolUqMGzcO27Ztg0ajqde5FAoFQkNDGTwREVGDY/BERERERGQhTzzxBKqqqrBr1656nysqKgpZWVm4du1avc9FRET0BwZPREREREQW4uXlhdDQUCQkJNR74++BAwfC2tqaq56IiKhBMXgiIiIiIrKg6OhoXLx4EWfOnKnXeaysrDB48GAkJibW6zxERER/xuCJiIiIiMiCBgwYAB8fHyQkJNT7XFFRUUhPT0d+fn69z0VERAQweCIiIiIisiipVIro6Gj8+uuvUKlU9TrXkCFDoFAouOqJiIgaDIMnIiIiIiILGzNmDCQSCbZu3Vqv89jZ2SEkJITBExERNRgGT0REREREFubo6IhHH30UmzZtgiAI9TpXVFQUUlNT6311FREREcDgiYiIiIioUYiJiUF+fj72799fr/OEhYVBIpEgOTm5XuchIiICGDwRERERETUK/v7+6NmzZ703GXdxcUFQUBC32xERUYNg8ERERERE1EjExMTg8OHDyM7Ortd5HnnkERw9ehQVFRX1Og8RERGDJyIiIiKiRmLYsGFwcnLCxo0b63WeiIgIGAyGet/WR0RExOCJiIiIiKiRUCqVGDt2LLZv346ampp6m8fDwwM9e/bkdjsiIqp3DJ6IiIiIiBqR6OhoVFZWYvfu3fU6T1RUFA4ePAiNRlOv8xARUcvG4ImIiIiIqBHx8vLC4MGDER8fD1EU622eiIgIaLVaHDp0qN7mICIiYvBERERERNTITJw4ERcuXMDZs2frbQ5fX1907tyZ2+2IiKheMXgiIiIiImpkBgwYAG9vbyQkJNTrPFFRUUhJSYFer6/XeYiIqOVi8ERERERE1MhIpVJER0fjl19+gUqlqrd5oqKiUFVVhWPHjtXbHERE1LIxeCIiIiIiaoTGjBkDiUSCbdu21dscnTp1Qtu2bbFv3756m4OIiFo2Bk9ERERERI2Qk5MTRowYgU2bNkEQhHqZQyKRIDIyEklJSfU2BxERtWwMnoiIiIiIGqmYmBjk5eXhwIED9TZHVFQUysrKkJaWVm9zEBFRy8XgiYiIiIiokerevTu6d+9er03Gu3fvDg8PD263IyKiesHgiYiIiIioEZs4cSIOHTqE69ev18v4UqkUERER2LdvH0RRrJc5iIio5WLwRERERETUiA0fPhyOjo7YuHFjvc0RFRWFwsJCZGRk1NscRETUMjF4IiIiIiJqxJRKJcaNG4dt27ahpqamXuYICgqCs7Mzt9sREZHZMXgiIiIiImrkJkyYgMrKSuzZs6dexpfJZAgPD+d2OyIiMjsGT0REREREjZy3tzcGDRqE+Pj4eguGoqKikJ2djaysrHoZn4iIWiYGT0RERERETcDEiRNx/vx5nDt3rl7G79evH2xtbbndjoiIzIrBExERERFREzBw4EB4eXkhPj6+XsZXKpUIDQ1FYmJivYxPREQtE4MnIiIiIqImQCqVIjo6Gr/88gvKysrqZY6oqChcvHgRN27cqJfxiYio5WHwRERERETURIwZMwYSiQRbt26tl/EHDhwIpVLJVU9ERGQ2DJ6IiIiIiJoIZ2dnDB8+HJs2bYIgCGYf39bWFoMGDWLwREREZsPgiYiIiIioCZk4cSJyc3Px22+/1cv4UVFROH36NIqKiuplfCIialkYPBERERERNSHdu3dH9+7dkZCQUC/jDxkyBDKZjKueiIjILBg8ERERERE1MTExMTh48GC9NAF3dHREv379GDwREZFZMHgiIiIiImpihg8fDkdHR2zcuLFexo+KisKJEyfq7fQ8IiJqORg8ERERERE1MVZWVhg7diy2bdsGrVZr9vHDw8MhiiJSUlLMPjYREbUsDJ6IiIiIiJqgCRMmoKKiArt37zb72G5ubujduze32xERUZ0xeCIiIiIiaoJ8fHwwcODAet1ud/jwYVRXV9fL+ERE1DIweCIiIiIiaqImTpyI9PR0nDt3zuxjR0ZGQq/X48CBA2Yfm4iIWg4GT0RERERETdSgQYPg5eWF+Ph4s4/dpk0b+Pv7c7sdERHVCYMnIiIiIqImSiqVIjo6Gr/88ku9nEAXGRmJAwcO1EsDcyIiahkYPBERERERNWFjxowBAGzbts3sY0dFRUGj0eDIkSNmH5uIiFoGBk9ERERERE2Ys7Mzhg0bho0bN0IQBLOO3b59e3To0AH79u0z67hERNRyMHgiIiIiImriYmJikJubi4MHD5p97MjISKSkpMBgMJh9bCIiav4YPBERERERNXE9evSAv78/EhISzD52VFQU1Go1Tp48afaxiYio+WPwRERERETUxEkkEsTExODgwYPIyckx69hdu3aFl5cXt9sREdFDYfBERERERNQMDB8+HA4ODti0aZNZx5VIJIiMjERiYqLZe0gREVHzx+CJiIiIiKgZsLa2xpgxY7BlyxZotVqzjh0VFYWSkhKcOXPGrOMSEVHzx+CJiIiIiKiZmDBhAtRqNfbs2WPWcQMCAuDm5sbtdkRE9MAYPBERERERNRNt27bFoEGDzN5kXCqVIiIiAomJiRBF0axjExFR88bgiYiIiIioGYmOjkZ6ejrS09PNOm5UVBRyc3Nx8eJFs45LRETNG4MnIiIiIqJmZMiQIWjTpo3ZVz316dMHjo6O3G5HREQPhMETEREREVEzIpVKMWHCBOzevRvl5eVmG1culyMsLAyJiYlmG5OIiJo/Bk9ERERERM3M2LFjIYoitm3bZtZxo6KikJWVhatXr5p1XCIiar4YPBERERERNTMuLi4YNmwYNm7cCEEQzDZu//79YWNjw1VPRER03xg8ERERERE1QzExMcjJycGhQ4fMNqaVlRUGDx7MPk9ERHTfGDwRERERETVDPXv2RLdu3czeZDwqKgoZGRnIy8sz67hERNQ8MXgiIiIiImqGJBIJYmJi8NtvvyE3N9ds4w4ePBgKhYLb7YiI6L4weCIiIiIiaqZGjBgBe3t7bNy40Wxj2tnZoX///gyeiIjovjB4IiIiIiJqpqytrfH4449j69at0Ol0Zhs3KioKaWlpUKlUZhuTiIiaJwZPRERERETNWHR0NMrLy/HLL7+YbcywsDBIJBIkJSWZbUwiImqeGDwRERERETVjvr6+GDBgAOLj4802prOzM/r06cPtdkREdE8MnoiIiIiImrmJEyfi3LlzSE9PN9uYkZGROHr0KCoqKsw2JhERNT8MnoiIiIiImrkhQ4agdevWZm0yHhERAaPRiP3795ttTCIian4YPBERERERNXNSqRQTJkzArl27UF5ebpYxPTw8EBAQgH379pllPCIiap4YPBERERERtQBjx46FKIrYvn272caMjIzEwYMHodFozDYmERE1LwyeiIiIiIhaAFdXVwwdOhQbN26EIAhmGTMqKgo6nQ6HDh0yy3hERNT8MHgiIiIiImohYmJicOPGDRw+fNgs4/n4+MDPz4/b7YiI6I4YPBERERERtRABAQHw8/NDQkKC2caMjIzE/v37odPpzDYmERE1HwyeiIiIiIhaCIlEgokTJ+LAgQPIzc01y5hRUVGoqqrC8ePHzTIeERE1LwyeiIiIiIhakBEjRsDOzg6bNm0yy3gdO3aEr68v9u7da5bxiIioeWHwRERERETUgtjY2GDMmDHYsmWLWbbHSSQSREZGIjk5GUaj0QwVEhFRc8LgiYiIiIiohYmOjkZ5eTl+/fVXs4wXFRWFsrIypKWlmWU8IiJqPhg8ERERERG1ML6+vujfvz/i4+PNMp6/vz88PDx4uh0REd2CwRMRERERUQs0ceJEnD17FhkZGXUeSyqVIioqComJiRAEwQzVERFRc8HgiYiIiIioBRoyZAg8PT2RkJBglvGioqJQWFholiCLiIiaDwZPREREREQtkEwmw4QJE7Br1y6o1eo6jxcYGAgXFxdutyMioloYPBERERERtVDjxo2DIAjYvn17nceSSqUIDw/Hvn37IIqiGaojIqLmgMETEREREVEL5erqiqFDh2Ljxo1m6c0UFRWF69ev4/Lly2aojoiImgMGT0RERERELdjEiRNx/fp1HDlypM5j9e3bF3Z2dtxuR0REJgyeiIiIiIhasICAAPj5+ZmlybhSqURoaCgSExPNUBkRETUHDJ6IiIiIiFowiUSCmJgY7N+/H7m5uXUeLyoqCpmZmbh+/boZqiMioqaOwRMRERERUQv36KOPws7ODj/++GOdxxo4cCCsrKy46omIiAAweCIiIiIiavFsbGzw+OOPY8uWLdDpdHUea9CgQQyeiIgIAIMnIiIiIiICEB0djbKyMvz66691HisyMhJnzpxBYWGhGSojIqKmjMETERERERGhXbt2CAkJwcaNG+s8VmhoKGQyGZKSkupeGBERNWkMnoiIiIiICAAQExOD06dP48KFC3Uax8HBASEhIdi3b5+ZKiMioqaKwRMREREREQEAwsLC4Onpifj4+DqPFRkZiZMnT6KsrKzuhRERUZPF4ImIiIiIiAAAMpkMEyZMwK5du6BWq+s0VkREBERRxP+3d69BVtd5nuc/5+QVUhIERUUQofB+v6BoIXAOrV2KpYIcdqendnZ3dqe7t/tBRW9P9cZGd8RGbOxGbHRNR888mJnoqN6o3p2pmVkSFC0v1VqS3LwUqHgrtPBaWCqioJBckrydfaASRYGSQJ68vl4Pz///+59vPjGCt7/f/2zYsGGApgNgJBKeAACAI+6777709vbmkUceOa3nTJ48Odddd53jdgBjnPAEAAAcMXny5CxevDirVq1KX1/faT2rVCrlF7/4RQ4cODBA0wEw0ghPAADAUSqVSnbs2JHNmzef1nNKpVK6u7vz9NNPD9BkAIw0whMAAHCUa665JhdddNFpv2T83HPPzeWXX+64HcAYJjwBAABHKRQKqVQq2bRpU3bu3HlazyqVSnn66adz+PDhAZoOgJFEeAIAAI7xne98J+PGjcvq1atP6znlcjmHDh3Kc889N0CTATCSCE8AAMAxxo8fn7vvvjtr1qxJV1fXKT9n5syZmT17tuN2AGOU8AQAABxXpVLJZ599lqeeeuq0nlMul7Nhw4b09PQM0GQAjBTCEwAAcFwXXnhhbrrpprS1tZ3Wc8rlcjo6OvLCCy8M0GQAjBTCEwAA8LWWL1+eV155Jdu3bz/lZ1x00UWZNm2a43YAY5DwBAAAfK2FCxdm6tSpp7XrqVAopFwup729PX19fQM4HQDDnfAEAAB8rbq6uixbtiyPP/54Ojo6Tvk55XI5e/bsyauvvjqA0wEw3AlPAADAN1q6dGm6u7vzyCOPnPIzrrzyykyZMuW0X1QOwMgiPAEAAN9oypQpWbx4cdra2k75qFyxWEypVEp7e3uq1eoATwjAcCU8AQAAJ1SpVLJjx45s2bLllJ9RLpfz0Ucf5Ve/+tUATgbAcCY8AQAAJ3Tttddmzpw5p/WS8euvvz6tra1+3Q5gDBGeAACAEyoUCqlUKtmwYUN27tx5Ss+or6/PwoUL097ePsDTATBcCU8AAEC/3HnnnWlubs4DDzxwys8olUp599138+677w7gZAAMV8ITAADQL+PHj8/dd9+dNWvWpKur65SeMW/evIwbN86uJ4AxQngCAAD6rVKpZM+ePaccjhobGzN//nzveQIYI4QnAACg32bNmpUbb7wxK1euPOVnlEqlvPHGG/nwww8HcDIAhiPhCQAAOCkrVqzIyy+/nO3bt5/S+vnz56exsTHr1q0b2MEAGHaEJwAA4KQsWLAgZ599dlatWnVK68ePH5958+Y5bgcwBghPAADASamvr8+yZcvy2GOPpaOj45SeUSqV8vLLL2f37t0DPB0Aw4nwBAAAnLSlS5emu7s7jz766CmtX7BgQQqFQtavXz/AkwEwnAhPAADASTvrrLNSLpfT1taWarV60usnTpyYG2+80XE7gFFOeAIAAE5JpVLJr3/962zZsuWU1pdKpWzZsiX79u0b4MkAGC6EJwAA4JRcd911mT17dtra2k5p/aJFi9Lb25uNGzcO8GQADBfCEwAAcEoKhUJWrFiR9evXZ9euXSe9/uyzz87VV1/tuB3AKCY8AQAAp+yuu+5Kc3NzVq9efUrry+Vynn322Rw8eHCAJwNgOBCeAACAUzZ+/PjcfffdefDBB9Pd3X3S60ulUrq6uvLss8/WYDoAhprwBAAAnJbly5dnz549aW9vP+m1559/fi6++GLH7QBGKeEJAAA4LbNnz84NN9yQlStXntL6crmcjRs3pqura4AnA2CoCU8AAMBpW7FiRV566aW8+eabJ722XC7n4MGD2bJlSw0mA2AoCU8AAMBpW7hwYc4+++y0tbWd9NpZs2blggsucNwOYBQSngAAgNNWX1+fpUuX5vHHH8/+/ftPam2hUEi5XM66devS29tbowkBGArCEwAAMCCWLl2arq6uPProoye9tlwuZ+/evdm6dWsNJgNgqAhPAADAgDj77LNTKpXS1taWarV6Umsvu+yynHPOOY7bAYwywhMAADBgKpVK3nvvvTz//PMnte63j9v19fXVaDoABpvwBAAADJjrr78+s2fPPqWXjJdKpezatSvbtm2rwWQADAXhCQAAGDCFQiHLly/PunXrsmvXrpNae+211+bMM8903A5gFBGeAACAAbVkyZI0NzfngQceOKl1xWIxixYtSnt7+0m/IwqA4Ul4AgAABlRLS0uWLFmSBx98MN3d3Se1tlQqv9A0LQAAH4dJREFU5f33389bb71Vo+kAGEzCEwAAMOAqlUp2796d9vb2k1o3d+7cnHHGGSe9DoDhSXgCAAAG3OzZs3P99ddn1apVJ7WuoaEhCxYs8J4ngFFCeAIAAGqiUqnkxRdfPOljc6VSKW+99VZ27NhRo8kAGCzCEwAAUBOlUilTpkw56V1Pt9xyS5qamrJu3braDAbAoBGeAACAmqivr8+yZcvy2GOP5cCBA/1e19zcnG9/+9uO2wGMAsITAABQM0uXLk1nZ2ceffTRk1pXKpXy2muvZdeuXTWaDIDBIDwBAAA1M3Xq1CxatChtbW2pVqv9Xjd//vzU19f7dTuAEU54AgAAamrFihV5991388ILL/R7zYQJE3LTTTc5bgcwwglPAABATd1www2ZNWtW2traTmpduVzO1q1b89lnn9VoMgBqTXgCAABqqlAopFKppL29/aTe2bRgwYIkyYYNG2o1GgA1JjwBAAA1t2TJkjQ1NWXNmjX9XjN58uRce+21jtsBjGDCEwAAUHMtLS2566678sADD6Snp6ff68rlcjZv3pz9+/fXcDoAakV4AgAABkWlUsmnn36adevW9XtNqVRKd3d3nn766doNBkDNCE8AAMCgmDNnTq677rqsXLmy32vOOeecXH755XnqqadqOBkAtSI8AQAAg6ZSqeTFF1/M22+/3e81ixcvzjPPPJPOzs4aTgZALQhPAADAoCmVSpkyZUpWrVp1Ums6Ozvz3HPP1XAyAGpBeAIAAAZNQ0NDli5dmkcffTQHDhzo15oLLrgg3/rWt/y6HcAIJDwBAACDatmyZens7Mxjjz3W7zXlcjkbN25Md3d3DScDYKAJTwAAwKCaOnVqFi1alLa2tlSr1X6tKZVK6ejoyAsvvFDj6QAYSMITAAAw6CqVSt55551s3bq1X/dfdNFFOf/88x23AxhhhCcAAGDQ3XjjjbnwwguzcuXKft1fKBRSLpezbt269PX11Xg6AAaK8AQAAAy6QqGQSqWS9vb2fPLJJ/1aUyqVsmfPnrz88ss1ng6AgSI8AQAAQ2LJkiVpbGzMgw8+2K/7r7zyypx99tlpb2+v8WQADBThCQAAGBJnnHFG7rzzzjzwwAPp6ek54f3FYjGlUilr167t90vJARhawhMAADBkKpVKPv3006xfv75f95dKpezcuTNvvPFGjScDYCAITwAAwJC56KKLcu211/b7JePXX399WltbHbcDGCGEJwAAYEitWLEiL7zwQt55550T3ltXV5dFixZl7dq1gzAZAKdLeAIAAIZUqVTK5MmTs2rVqn7f/9577/UrVAEwtIQnAABgSDU0NGTp0qV55JFHcvDgwRPef/PNN2f8+PGO2wGMAMITAAAw5JYtW5bOzs489thjJ7y3sbEx8+fPd9wOYAQQngAAgCF3zjnnZOHChWlra0u1Wj3h/eVyOb/61a/y4YcfDsJ0AJwq4QkAABgWKpVK3n777WzduvWE9956661pbGx03A5gmBOeAACAYWHu3LmZOXNm2traTnjv+PHjM2/ePMftAIY54QkAABgWCoVCKpVK1q5dm08//fSE95fL5bzyyiv9uheAoSE8AQAAw8aSJUvS0NCQBx988IT3LliwIIVCIevWrav9YACcEuEJAAAYNiZMmJC77rorDzzwQHp6er7x3tbW1tx4442O2wEMY8ITAAAwrCxfvjyffPJJNmzYcMJ7Fy9enOeffz779u0bhMkAOFnCEwAAMKxcfPHFueaaa/r1kvGFCxemWq32K1IBMPiEJwAAYNipVCrZsmVL3n333W+876yzzsrVV1/tuB3AMCU8AQAAw065XM7kyZP7teupXC7nueeey8GDBwdhMgBOhvAEAAAMO42NjbnvvvvyyCOPnDAolUqldHV15Zlnnhmk6QDoL+EJAAAYlpYtW5bOzs48/vjj33jftGnTcskllzhuBzAMCU8AAMCwdO6552bBggVpa2tLtVr9xnvL5XI2bdqUrq6uQZoOgP4QngAAgGGrUqnkrbfeyksvvfSN95XL5Rw8eDC/+MUvBmcwAPpFeAIAAIatuXPn5oILLjjhS8ZnzZqVCy+8MO3t7YM0GQD9ITwBAADDVrFYzPLly7N27drs3r37G+8tl8tZt25dent7B2k6AE5EeAIAAIa17373u6mrq8uaNWu+8b5SqZR9+/blxRdfHJzBADgh4QkAABjWJkyYkDvvvDOrV6/+xt1Ml156ac4991zH7QCGEeEJAAAY9lasWJFdu3Zl/fr1X3tPoVBIuVzO2rVr09fXN4jTAfB1hCcAAGDYu/jii3P11Vdn1apV33hfqVTKp59+mtdee22QJgPgmwhPAADAiFCpVLJ58+a89957X3vPNddck8mTJztuBzBMCE8AAMCIsHjx4px55pnfuOupWCxm0aJFWbt2barV6iBOB8DxCE8AAMCI0NjYmPvuuy8//elPc/Dgwa+9r1wu54MPPsibb745iNMBcDzCEwAAMGIsW7Yshw4dys9+9rOvveeGG27IhAkTHLcDGAaEJwAAYMQ477zzMn/+/LS1tX3tUbqGhobcdtttWbt27SBPB8DvEp4AAIARZcWKFXnzzTfzyiuvfO095XI5b7/9dnbs2DGIkwHwu4QnAABgRLnpppsyY8aMrFy58mvvmTdvXpqbm+16AhhiwhMAADCiFIvFVCqVPPXUU9mzZ89x72lubs6tt94qPAEMMeEJAAAYce6+++7U1dVlzZo1X3vP4sWLs23btuzcuXPwBgPgKMITAAAw4rS2tuY73/lOVq1ald7e3uPeM3/+/DQ0NGTdunWDOxwARwhPAADAiFSpVLJr165s3LjxuNdbWlpy0003OW4HMISEJwAAYES69NJLc9VVV33jS8bL5XJeeumlr30XFAC1JTwBAAAjVqVSyebNm/PrX//6uNcXLFiQJFm/fv1gjgXAl4QnAABgxPq93/u9TJo0KatWrTru9TPPPDPXXXdd2tvbB3kyABLhCQAAGMEaGxtz33335ac//WkOHTp03HvK5XI2b96cjo6OQZ4OAOEJAAAY0ZYtW5YDBw7kZz/72XGvl0ql9PT0ZNOmTYM8GQDCEwAAMKJNmzYtt912W9ra2lKtVo+5PnXq1Fx55ZWO2wEMAeEJAAAY8SqVSrZv355XX331uNfL5XKefvrpdHZ2DvJkAGOb8AQAAIx4N998c2bMmJGVK1ce9/qiRYty+PDhPPvss4M8GcDYJjwBAAAjXrFYzPLly/Pzn/88e/bsOeb6BRdckDlz5mTt2rVDMB3A2CU8AQAAo8J3v/vdFIvFrFmz5rjXy+VyNmzYkO7u7sEdDGAME54AAIBRobW1Nd/5zneyevXq9PX1HXO9VCrlwIED2bJlyxBMBzA2CU8AAMCoUalU8vHHH2fjxo3HXJszZ05mzJjh1+0ABpHwBAAAjBqXXXZZrrzyyrS1tR1zrVAopFQqZd26dcfdEQXAwBOeAACAUaVSqeS5557Ljh07jrlWLpfz2Wef5aWXXhr8wQDGIOEJAAAYVW6//fZMmjQpq1atOuba5ZdfnqlTpzpuBzBIhCcAAGBUaWxszL333puHH344hw4dOupasVjMokWLsnbt2lSr1SGaEGDsEJ4AAIBR5/7778+BAwfyj//4j8dcK5fL+fjjj/P6668PwWQAY4vwBAAAjDrTpk3L/Pnzs3LlymN2Nl133XWZOHFi1q5dO0TTAYwdwhMAADAqVSqVbN++Pa+++upRn9fV1WXhwoWO2wEMAuEJAAAYlebNm5fzzz//uC8ZX7x4cXbs2JF33313CCYDGDuEJwAAYFQqFotZvnx5nnzyyezZs+eoa3Pnzs348eMdtwOoMeEJAAAYte65554UCoU89NBDR33e2NiY2267TXgCqDHhCQAAGLUmTpyY3//938/q1avT19d31LVyuZzt27fngw8+GKLpAEY/4QkAABjVKpVKdu7cmU2bNh31+S233JLGxka7ngBqSHgCAABGtcsvvzxXXHFF2trajvp8/PjxueWWW9Le3j5EkwGMfsITAAAw6lUqlTz77LPZsWPHUZ+Xy+W88sor+eSTT4ZoMoDRTXgCAABGvTvuuCOtra1ZvXr1UZ/fdtttqaury7p164ZmMIBRTngCAABGvcbGxtx33315+OGH09nZeeTz1tbWzJ0713E7gBoRngAAgDHh/vvvz/79+/PEE08c9Xm5XM7zzz+fvXv3DtFkAKOX8AQAAIwJ559/fm699dasXLky1Wr1yOcLFy5MtVrN+vXrh3A6gNFJeAIAAMaMFStW5I033shrr7125LMpU6bkmmuucdwOoAaEJwAAYMy45ZZbMm3atLS1tR31eblcznPPPZeDBw8O0WQAo5PwBAAAjBnFYjHLly/Pk08+mc8+++zI54sWLUp3d3c2bdo0hNMBjD7CEwAAMKbce++9KRQKefjhh498Nm3atFx22WWO2wEMMOEJAAAYUyZOnJg77rgjq1atSl9f35HPS6VSNm3alK6uriGcDmB0EZ4AAIAxZ8WKFfnoo4/y9NNPH/msXC7n0KFDee6554ZwMoDRRXgCAADGnMsvvzyXX375US8Zv/DCCzNr1qysXbt2CCcDGF2EJwAAYEyqVCp55pln8pvf/ObIZ6VSKRs2bEhPT88QTgYweghPAADAmHTHHXektbU1q1atOvJZuVzOvn378uKLLw7hZACjh/AEAACMSU1NTbn33nvz8MMPp7OzM0lyySWXZNq0aY7bAQwQ4QkAABiz7r///nR0dOSJJ55IkhQKhZRKpbS3tx/1i3cAnBrhCQAAGLOmT5+eW265JW1tbalWq0m+OG63e/fuvPbaa0M8HcDIJzwBAABj2ooVK/L6669n27ZtSZKrrroqU6ZMcdwOYAAITwAAwJh26623Ztq0aVm5cmWSpFgsZtGiRVm7du2RXVAAnBrhCQAAGNOKxWKWL1+eJ598Mp9//nmSL47bffjhh9m+ffvQDgcwwglPAADAmHfPPfckSR5++OEkyQ033JAJEyY4bgdwmoQnAABgzJs0aVJuv/32rFq1Kn19famvr8+CBQvS3t4+1KMBjGjCEwAAQJJKpZIPP/wwzzzzTJIvjtu98847ee+994Z2MIARTHgCAABIcsUVV+Syyy478pLxefPmZdy4cXY9AZwG4QkAACBJoVBIpVLJs88+m9/85jdpamrKt7/9beEJ4DQITwAAAF+64447MmHChKxevTrJF8fttm3blo8++miIJwMYmYQnAACALzU3N+eee+7JQw89lMOHD+fb3/52Ghoa7HoCOEXCEwAAwG+5//77s2/fvjzxxBNpaWnJzTffLDwBnCLhCQAA4LfMmDEjt956a9ra2pJ8cdzupZdeyp49e4Z4MoCRR3gCAAD4HcuXL8+2bduybdu2LFiwIIVCIevWrRvqsQBGHOEJAADgd8yfPz/nnXdeVq5cmUmTJuWGG25w3A7gFAhPAAAAv6NYLOb+++/PE088kb1796ZUKmXz5s3p6Og4ck+1Ws3mzZvz13/911mzZk3+/M//PD/84Q+HcGqA4Ud4AgAAOI5777031Wo1Dz30UBYtWpTe3t5s3LgxHR0d+dGPfpS5c+dm8eLF+au/+qv84R/+Yf7+7/8+r7zyylCPDTCs1A/1AAAAAMPRmWeemdtvvz2rVq3K9773vZxzzjn5sz/7s3zyySfZt29fqtVqkqRQKGTKlCn5/PPPc9FFFw3x1ADDi/AEAADwNW6++eb83d/9Xc4///zs2rUrfX19KRaLqa+vT11dXfr6+pIkTU1NSZKZM2cO5bgAw47wBAAA8KVqtZo333wzDz74YH7yk59k+/btOXz4cJKkvr4+1Wo1DQ0NqaurO2ZtsVjMhRdeOMgTAwxvwhMAAECSd955J3/wB3+Q119/PQcOHDiyu6mhoSG9vb1paGhItVpNb2/vkfBUrVZTX1+fw4cPp6mpyY4ngN8hPAEAAGPa4cOH88ADD+RHP/pRtm7dmu7u7tTX16exsTGFQiFJ0tvbm56entTV1aWnpyfVajWFQiHVajV1dXXp6upKc3Nzpk+fPsR/DcDw4lftAACAMe3RRx/NX/7lX+all17K9OnT09zcfOTF4V+pq6tLb29visUv/gn11budki+O4HV1dWXmzJmpr/f/9gF+m/AEAACMaUuWLMnSpUszbty4VKvVzJo1Kw0NDenu7j4SoL4KSl/tdPoqPH2146m7uzuXXHLJkP0NAMOV8AQAAIxpTU1N+eEPf5i/+Iu/SF9fX/bu3ZuZM2ceFZ8KhUKKxWJ6enpSLBbT29t7ZH19fX2KxWJmz549hH8FwPAkPAEAAGNesVjM97///fzN3/xNWlpa8vnnn+eCCy44Kj7V1dWlWq0eddzuqyDV19fnF+0AjkN4AgAA+NL999+fH//4xznvvPPy+eefZ/r06amvr093d3eKxeJRx+y+2vVUrVbT2NiYs8+Zlo93780Hu/bk491703Ggcyj/FIBhoVD93bfmHce+ffsyceLE7N27N62trYMxFwAAwJDZvn17/uiP/ijbtm1LS0tLPvroo/T09KRQKKS3tzd1dXWpa5mSxglTM+GsaWloOSvTZ81JUjhyNK+uWMg5UybmijnTc9HMc3PFnOm5cs70IzumAEay/rYi4QkAAOA4du3alT/5kz/Jpk2b0tTUlF27dqU3xTRNnpmW8y5N/fhJKRSKKRSLqa8rZOaMGSkWCymkkGqq6e2t5nB3d3p6vtgZ1dBQn4tnnpuli29M+eYrM6GleYj/QoBTJzwBAACcpoMHD+YHP/hBHlyzJnWT56SvdUaK9c2pVqup9hxOte+L3U8TJ07MrFmzvvY51Wo1nV3d2bf/UKrVaqZMmpB/ds9tqfz+zWmorxvEvwhgYPS3FdUP4kwAAAAjyvjx4/On//P/mld3j8+vd+1LobcnPV0HU632HdnZlCTNzd+8e6lQKGRcU2PGNTWmp7c3ezsO5l//h8ez8YU38oN/fndmT586GH8OwKBzuBgAAOA4qtVq/vNjz+RP/49/yMFqU86ZMinF9KSuWDwqOiVJY2Njv59bX1eXKZMmZNKE8dny2jv5F//bj/LAz7fU4k8AGHJ2PAEAAPyOvr6+/F3b2vy/D21MsVjIuVMmfrFraVxT3t/xfvqqfan2fRGeqtVqGpv6H56+0tTYkHPPmpjde/fnX/340ezbfyj/7b23pVAoDPSfAzBk7HgCAAD4LdVqNf/3A+vy/zy0IQ0NdZk88YwjMah1Qmtmz56d8ePGp5AvPqummqbGplP6rkKhkLMmTUhdsZC/W/lUfvLo0wP2dwAMB8ITAADAb3lk/db8+MH1aayvz8Qzxh9zfdy4cZn9rdlpbm5OsVBMY0NjGhobTus7J7W2pK6umH///z2VJ5999bSeBTCcOGoHAADwpQ927cm//c9PpK+vmomTjo1OX2lsaMwll16SQwcPZcKECQPy3We2tmTXnn35N//xZ7n20pk5+0y/KA6MfHY8AQAA5Iv3Ov3NPzyWTz/fnymTzjjh/fV19QMWnb4yZdIZ2fnp3vyb//CzVKvVEy8AGOaEJwAAgCSPb3o5T2/dnklnjE+xODT/VKorFtPaMi5PPffLtG/eNiQzAAwk4QkAABjz+vr68l8eezbVVDOu+eR/oe6bfPrB23nk3/0gv9r8RL/ubxnXlJ7e3vyXx5+16wkY8YQnAABgzHvx9ffy1vsfZ2LLuGOuPfLvfpD2//TXgzpP6xnj8su3fpNfvv3BoH4vwEATngAAgDHvp+teTE9Pb5pO89fpBsq4psYc7u7Jo+u3DvUoAKdFeAIAAMa0jgOd2fjCGxnX3JhCoTDU4yRJCoVCmhsb8vPnXsvhrp6hHgfglNUP9QAAAABD6e33P86hzu60nnHsMbvj+dXmJ/Lm809m3r1/nMMH9uXtre3Z//knaWgal/O+dU0uu+Wu1NWfeOdU9+FD2fL4P2TPh+/mivn3ZNbV84+6Pq6pMQcOHc57H+zKJbOmndLfBjDU7HgCAADGtLd2fJye3t401Ned1Lr3Xn06r6xblTMmn5uZV96ShqZxee/VTXm5ve2EazsP7Msza/59Pt/561x/+x8cE52SpLGxPt3dPXnr/Y9Pai6A4cSOJwAAYEx7a8fOJDnpY3af/ubN3Fb5fs44c2qSpPfm7mxY+bf58K2XcvmtS9LcMvG46/Z//kl+8dMfpbvzYOYu+ec5e8bFx72vWCgkhULe2iE8ASOXHU8AAMCY9t4Hn6RYPPl/Gs26ev6R6JQkdfUNmTbn2qRazd5Pjv9rdJ9//H6eefDfprenK/Pu/eOvjU6/7f2Pdp/0bADDhR1PAADAmNbZ1X1KLxWfePb0Yz4bd8YXu5y6Dx865tqej97NOy9vSNO4ltx09/+YMyadfcLvKBYKOXS4+6RnAxgu7HgCAADGtL5qNafyW3b1jc3HfFb4cudUtdp3zLV9n36Q3u7DaT1rWlpap/R/vr5jnwUwUghPAADAmNbU0JBqtVrz77nwqm9nxmU3Zec7r+XFJ/9T+vp6T7imWq2mqdFBFWDk8l8wAABgTDvrzDPSOyi7igq5etHyFAqF7Nj2iyTJdbf/kxSLX/9retVqNWedOWEQZgOoDTueAACAMe3imeclp/COp1NRKBRy1cL7c8Hl8/LR2y9n6zfsfPpqF9acC84dlNkAasGOJwAAYEz71oxzUkjS29uXurra/7/5L+LTshQKhfz6l8+mWq3m+jv+6TE7n3q+nGfOBefUfCaAWrHjCQAAGNMumnlumhob0tl17K/HVb88gvdNx+FORaFQyJULlmbmlbdm5zuv5sUn/mP6eo/e+dR5uDtNjQ2ZM0N4AkYuO54AAIAx7dyzJmbOjHPyy7d/k5ZxTUddO9y5P0nSOK7lyGeX3HRHLrnpjuM+a8alczPj0rlHfXbW+d/K3X/yw2PuLRQKuWrB0ly1YOlxn3Ww83BuvGJWJrW2HPc6wEhgxxMAADCmFQqF3Lf4hiRJz+/sOvr43V8mSSZNvWBQZ+ru+WKO+xbPPcGdAMOb8AQAAIx55ZuvzOSJLdm3/1CS5M0XnsqLT/4kr21Yk7r6xsy8Yt6gzrN3/8GcM2ViFtx46aB+L8BAE54AAIAxb0JLc+5acF0Od/ekt7cv77y0Prveez1nTZ+TW+7944xvnTxos/T09Kanpzf3lm5Ic2PDoH0vQC14xxMAAECS/+a787P2F7/Mh7s+y+//D//7kMxQrVbz6d6OzJo+Nf/VnbcMyQwAA8mOJwAAgCRntrbkz/7Znamvr8v+g51DMkPHgc40NTTkX/53SzKhpXlIZgAYSMITAADAlxbccGnuuu2adBzoTFd3z6B+9+Gu7hzsPJylv3djbrrqW4P63QC1IjwBAAB8qVAo5PvfuzPXXTYzu/fuT3fP4MSnru6e7Nl3IDdfPSf/04rfG5TvBBgMwhMAAMBvmdDSnP/rz/5JrpozI59+tj+Hu2obnzq7urN77/5cf9mF+T+/vyLjxzXV9PsABpPwBAAA8DumTDojf/u/fC9zr5qdz/btz+f7DqRarQ7od1Sr1ezZuz+fdxzMrddelL/5wfcy8YzxA/odAENNeAIAADiOSa0t+du/+F7+xfJyCsVCdu7eO2DvfTrc1ZOdu/emsaE+f/pf355/9S//qZeJA6NS/VAPAAAAMFw1NzXmDyvl3HrtRfnhjx/J6+98mGqS1pbmjGtqTKFQ6PezqtVqDnZ2pePgoRRSyLWXzMwP/vsluWTWtNr9AQBDrFDtx37Rffv2ZeLEidm7d29aW1sHYy4AAIBhpbOrO+2/+GXWPPV8XnvrNznc3ZP6uro0NdanubEhDfV1R4WoarWa7p7edHZ153BXd3p6+9Lc2JCrL56R+xbPzaK5l6WxwV4AYGTqbysSngAAAE5CtVrNtnc+yD9ueiVb33gv7+/c80VY6uk9JjzV19elqbEhM8+bkusvuzDfmX9NLr7wvJPaKQUwHPW3FcnrAAAAJ6FQKOSKb03PFd+aniTpONCZt9//OL/+8JN0Hu5OV09vGuvr0tzcmNnnT83sGVPT4pfqgDFKeAIAADgNE1qac+2lM3PtpTOHehSAYcev2gEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADVR35+bqtVqkmTfvn01HQYAAACA4e+rRvRVM/o6/QpPHR0dSZIZM2ac5lgAAAAAjBYdHR2ZOHHi114vVE+UppL09fXlww8/zIQJE1IoFAZ0QAAAAABGlmq1mo6OjkybNi3F4te/yalf4QkAAAAATpaXiwMAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE8ITAAAAADUhPAEAAABQE/8/ulWCrX9HV28AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Raf Mek Plcg PIP2 PIP3 Erk Akt PKA PKC P38 Jnk INT\n", + "0 1 1 1 2 3 2 1 3 1 2 1 8\n", + "1 1 1 1 1 3 3 2 3 1 2 1 8\n", + "2 1 1 2 2 3 2 1 3 2 1 1 8\n", + "3 1 1 1 1 3 2 1 3 1 3 1 8\n", + "4 1 1 1 1 3 2 1 3 1 1 1 8\n", + "(5400, 12)\n" + ] + } + ], + "source": [ + "# use pooch to download robustly from a url\n", + "url = \"https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz\"\n", + "file_path = pooch.retrieve(\n", + " url=url,\n", + " known_hash=\"md5:39ee257f7eeb94cb60e6177cf80c9544\",\n", + ")\n", + "\n", + "df = pd.read_csv(file_path, delimiter=\" \")\n", + "\n", + "# the ground-truth dag is shown here: XXX: comment in when errors are fixed\n", + "ground_truth_dag = bnlearn.import_DAG(\"sachs\", verbose=False)\n", + "fig = bnlearn.plot(ground_truth_dag)\n", + "\n", + "# .. note::\n", + "# The Sachs dataset has previously been preprocessed, and the steps are described\n", + "# in bnlearn, at the web-page https://www.bnlearn.com/research/sachs05/.\n", + "print(df.head())\n", + "print(df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6 6 6\n", + "Graph with 26 nodes and 325 edges\n", + "There are 284 edges in the resulting PAG\n" + ] + }, + { + "data": { + "text/plain": [ + "'s_pag.png'" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# %%\n", + "# Preprocess the dataset\n", + "# ----------------------\n", + "# Since the data is one dataframe, we need to process it into a form\n", + "# that is acceptable by dodiscover's :class:`constraint.PsiFCI` algorithm. We\n", + "# will form a list of separate dataframes.\n", + "unique_ints = df[\"INT\"].unique()\n", + "\n", + "# get the list of intervention targets and list of dataframe associated with each intervention\n", + "intervention_targets = [df.columns[idx] for idx in unique_ints]\n", + "data_cols = [col for col in df.columns if col != \"INT\"]\n", + "data = []\n", + "domain_ids = np.array([0, 0, 0, 0, 0, 1])\n", + "for interv_idx in unique_ints:\n", + " _data = df[df[\"INT\"] == interv_idx][data_cols]\n", + " data.append(_data)\n", + "\n", + "print(len(data), len(intervention_targets), len(domain_ids))\n", + "# %%\n", + "# Setup constraint-based learner\n", + "# ------------------------------\n", + "# Since we have access to interventional data, the causal discovery algorithm\n", + "# we will use that leverages CI and CD tests to estimate causal constraints\n", + "# is the Psi-FCI algorithm :footcite:`Jaber2020causal`.\n", + "\n", + "# Our dataset is comprised of discrete valued data, so we will utilize the\n", + "# G^2 (Chi-square) CI test.\n", + "ci_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "# Since our data is entirely discrete, we can also use the G^2 test as our\n", + "# CD test.\n", + "cd_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "alpha = 0.8\n", + "learner = SFCI(\n", + " ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha, n_jobs=-1\n", + ")\n", + "\n", + "# create context with information about the interventions\n", + "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", + "ctx: Context = ctx_builder.variables(data=data[0]).num_distributions(len(data)).build()\n", + "\n", + "print(ctx.init_graph)\n", + "\n", + "# %%\n", + "# Run the learning process\n", + "# ------------------------\n", + "# We have setup our causal context and causal discovery learner, so we will now\n", + "# run the algorithm using the :meth:`constraint.PsiFCI.fit` API, which is similar to scikit-learn's\n", + "# `fit` design. All fitted attributes contain an underscore at the end.\n", + "learner = learner.fit(\n", + " data, ctx, domain_indices=domain_ids, intervention_targets=intervention_targets\n", + ")\n", + "\n", + "# %%\n", + "# Analyze the results\n", + "# ===================\n", + "# Now that we have learned the graph, we will show it here. Note differences and similarities\n", + "# to the ground-truth DAG that is \"assumed\". Moreover, note that this reproduces Supplementary\n", + "# Figure 8 in :footcite:`Jaber2020causal`.\n", + "est_pag = learner.graph_\n", + "\n", + "print(f\"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG\")\n", + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "# dot_graph = draw(est_pag, direction=\"LR\")\n", + "# dot_graph.render(outfile=\"_pag_full.png\", view=True)\n", + "\n", + "# %%\n", + "# Visualize the graph without the F-nodes\n", + "est_pag_no_fnodes = est_pag.subgraph(data_cols)\n", + "dot_graph = draw(est_pag_no_fnodes, direction=\"LR\")\n", + "dot_graph.render(outfile=\"s_pag.png\", view=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Graph with 26 nodes and 325 edges\n", + "[('F', 0), ('F', 1), ('F', 2), ('F', 3), ('F', 4), ('F', 5), ('F', 6), ('F', 7), ('F', 8), ('F', 9), ('F', 10), ('F', 11), ('F', 12), ('F', 13), ('F', 14)]\n", + "There are 167 edges in the resulting PAG\n" + ] + }, + { + "data": { + "text/plain": [ + "'psi_pag.png'" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Setup constraint-based learner\n", + "# ------------------------------\n", + "# Since we have access to interventional data, the causal discovery algorithm\n", + "# we will use that leverages CI and CD tests to estimate causal constraints\n", + "# is the Psi-FCI algorithm :footcite:`Jaber2020causal`.\n", + "\n", + "# Our dataset is comprised of discrete valued data, so we will utilize the\n", + "# G^2 (Chi-square) CI test.\n", + "ci_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "# Since our data is entirely discrete, we can also use the G^2 test as our\n", + "# CD test.\n", + "cd_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "alpha = 0.05\n", + "learner = PsiFCI(\n", + " ci_estimator=ci_estimator, cd_estimator=cd_estimator, alpha=alpha, n_jobs=-1\n", + ")\n", + "\n", + "# create context with information about the interventions\n", + "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", + "ctx: Context = (\n", + " ctx_builder.variables(data=data[0])\n", + " .num_distributions(6)\n", + " .obs_distribution(False)\n", + " .build()\n", + ")\n", + "\n", + "print(ctx.init_graph)\n", + "print(ctx.f_nodes)\n", + "\n", + "# %%\n", + "# Run the learning process\n", + "# ------------------------\n", + "# We have setup our causal context and causal discovery learner, so we will now\n", + "# run the algorithm using the :meth:`constraint.PsiFCI.fit` API, which is similar to scikit-learn's\n", + "# `fit` design. All fitted attributes contain an underscore at the end.\n", + "learner = learner.fit(data, ctx)\n", + "\n", + "# %%\n", + "# Analyze the results\n", + "# ===================\n", + "# Now that we have learned the graph, we will show it here. Note differences and similarities\n", + "# to the ground-truth DAG that is \"assumed\". Moreover, note that this reproduces Supplementary\n", + "# Figure 8 in :footcite:`Jaber2020causal`.\n", + "est_pag = learner.graph_\n", + "\n", + "print(f\"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG\")\n", + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "dot_graph = draw(est_pag, direction=\"LR\")\n", + "dot_graph.render(outfile=\"psi_pag_full.png\", view=True)\n", + "\n", + "# %%\n", + "# Visualize the graph without the F-nodes\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", + "dot_graph = draw(est_pag_no_fnodes, direction=\"LR\")\n", + "dot_graph.render(outfile=\"psi_pag.png\", view=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pywhy-discover", + "language": "python", + "name": "pywhy-discover" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/tutorials/multi-domain/random-graph-analysis.ipynb b/doc/tutorials/multi-domain/random-graph-analysis.ipynb new file mode 100644 index 000000000..858461817 --- /dev/null +++ b/doc/tutorials/multi-domain/random-graph-analysis.ipynb @@ -0,0 +1,807 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd37974a-507e-406f-af4a-d927248ca73f", + "metadata": {}, + "source": [ + "# Random ER-G" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a2e4ad0a-86e4-44d6-9e2e-ebce00716483", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%load_ext lab_black" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "735b60f6-a74f-4a1b-85d5-e3fec97c0b62", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display_svg" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "5404ecd8-17bf-4fe6-93fd-dbc2fffb8df0", + "metadata": {}, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "import numpy as np\n", + "import scipy\n", + "import pandas as pd\n", + "import collections\n", + "from itertools import combinations\n", + "import networkx as nx\n", + "import pywhy_graphs as pgraphs\n", + "from pywhy_graphs import AugmentedGraph\n", + "from pywhy_graphs.functional import (\n", + " make_graph_linear_gaussian,\n", + " make_graph_multidomain,\n", + " set_node_attributes_with_G,\n", + " apply_linear_soft_intervention,\n", + " sample_multidomain_lin_functions,\n", + ")\n", + "from pywhy_graphs.viz import draw\n", + "from pywhy_graphs.simulate import simulate_random_er_dag\n", + "\n", + "from dodiscover.cd import KernelCDTest\n", + "from dodiscover.ci import KernelCITest, FisherZCITest, Oracle\n", + "from dodiscover.constraint.skeleton import LearnMultiDomainSkeleton\n", + "from dodiscover.constraint.utils import dummy_sample\n", + "from dodiscover.datasets import sample_from_graph\n", + "\n", + "from dodiscover import (\n", + " SFCI,\n", + " PsiFCI,\n", + " FCI,\n", + " Context,\n", + " make_context,\n", + " InterventionalContextBuilder,\n", + ")\n", + "from dodiscover.metrics import (\n", + " structure_hamming_dist,\n", + " confusion_matrix_networks,\n", + " structure_hamming_dist_ec,\n", + ")\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "514e7715-203e-4362-8eda-6569a029a849", + "metadata": {}, + "outputs": [], + "source": [ + "seed = 12345" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3c9267d5-6845-404d-b2b1-8c991f0a516a", + "metadata": {}, + "outputs": [], + "source": [ + "alpha = 0.05" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "07d7b26f-a025-42da-989a-2a245a86a440", + "metadata": {}, + "outputs": [], + "source": [ + "def convert_md_data_to_sd(data, domain_indices, intervention_targets):\n", + " # generate single-domain data\n", + " single_domain_data = []\n", + " single_domain_targets = []\n", + "\n", + " seen_indices = set()\n", + " for targets in intervention_targets:\n", + " indices = [\n", + " idx\n", + " for idx in range(len(domain_indices))\n", + " if intervention_targets[idx] == targets\n", + " ]\n", + " if any(idx in seen_indices for idx in indices):\n", + " continue\n", + " for idx in indices:\n", + " seen_indices.add(idx)\n", + "\n", + " single_domain_data.append(pd.concat([data[idx] for idx in indices], axis=0))\n", + " if targets == {}:\n", + " continue\n", + " single_domain_targets.append(targets)\n", + " return single_domain_data, single_domain_targets" + ] + }, + { + "cell_type": "markdown", + "id": "859b79a5-980e-47e2-b36b-0635807216b6", + "metadata": {}, + "source": [ + "# Run Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "03e8ed34-ef56-496f-923a-04ba4e10894c", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "48a7f96a-d9e6-4503-8c68-b368466f0dee", + "metadata": {}, + "outputs": [], + "source": [ + "alpha = 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "bc0b93f4-c7f4-49d9-8af9-4fece68c33f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4]\n", + "[0.3 0.35 0.4 0.45 0.5 ]\n" + ] + } + ], + "source": [ + "node_mean_lims = [-1, 1]\n", + "node_std_lims = [0.5, 1.5]\n", + "edge_functions = [\n", + " lambda x: x,\n", + " lambda x: x**2,\n", + "]\n", + "edge_weight_lims = [1, 5]\n", + "n_node_grid = np.arange(4, 5)\n", + "p_grid = np.linspace(0.3, 0.5, 5)\n", + "n_domains_grid = np.arange(2, 10)\n", + "n_repeats = 1\n", + "\n", + "max_cond_set_size = 3\n", + "\n", + "n_samples = 2000\n", + "ratio_interventions = 0.9\n", + "n_jobs = -1\n", + "print(n_node_grid)\n", + "print(p_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "16f1b69c-6733-40cf-b3f6-5bac8156b4a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0, 1, 2, 3}\n" + ] + } + ], + "source": [ + "print(aug_lin_graph.non_augmented_nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "id": "0e0c709f-ced6-497e-a19e-626ae70c0bf2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0, 2}" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(rng.choice(list(aug_lin_graph.non_augmented_nodes), size=2, replace=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "id": "bde56fd2-6adc-4181-8efb-acdc2945ead8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_46438/1098357664.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m )\n\u001b[0;32m--> 134\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0msingle_domain_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/intervention.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context)\u001b[0m\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply_rule11\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mEquivalenceClass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mContext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;31m# learn skeleton graph and the separating sets per variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m graph, self.separating_sets_ = self.learn_skeleton(\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseparating_sets_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m )\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/intervention.py\u001b[0m in \u001b[0;36mlearn_skeleton\u001b[0;34m(self, data, context, sep_set)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m )\n\u001b[0;32m--> 148\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mskeleton_learner_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_\u001b[0m \u001b[0;34m=\u001b[0m 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debug)\u001b[0m\n\u001b[1;32m 1389\u001b[0m \u001b[0;31m# initially learn the skeleton without using PDS information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[0;31m# apply algorithm to learn skeleton\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1391\u001b[0;31m self._learn_skeleton(\n\u001b[0m\u001b[1;32m 1392\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1393\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36m_learn_skeleton\u001b[0;34m(self, data, context, condsel_method, conditional_test_func, possible_x_nodes, skipped_y_nodes, skipped_z_nodes, cross_distribution_test, group_with_snode, debug)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0;31m# run parallelized loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m out = Parallel(n_jobs=self.n_jobs)(\n\u001b[0m\u001b[1;32m 543\u001b[0m delayed(_parallel_test_xy_edges)(\n\u001b[1;32m 544\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_edge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0;32mwith\u001b[0m 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"\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 973\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 974\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m 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"\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 565\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 566\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 567\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 568\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m 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AugmentedGraph(incoming_directed_edges=G)\n", + " # convert graph into a multi-domain linear graph\n", + " aug_lin_graph = make_graph_linear_gaussian(\n", + " aug_graph,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + " )\n", + " non_augmented_nodes = set(G.nodes)\n", + "\n", + " for n_domains in n_domains_grid:\n", + " md_lin_graph = sample_multidomain_lin_functions(\n", + " aug_lin_graph,\n", + " n_domains=n_domains,\n", + " node_mean_lims=node_mean_lims,\n", + " node_std_lims=node_std_lims,\n", + " edge_functions=edge_functions,\n", + " edge_weight_lims=edge_weight_lims,\n", + " random_state=seed,\n", + " )\n", + " # keep a copy of the ground-truth graph\n", + " groundtruth_graph = md_lin_graph.copy()\n", + "\n", + " # now generate interventions\n", + " for idx in range(n_repeats):\n", + " domain_indices = []\n", + " intervention_targets = []\n", + " mechanisms = []\n", + " data = []\n", + "\n", + " # generate observational distirbution\n", + " for jdx, domain_id in enumerate(range(1, n_domains + 1)):\n", + " df = sample_from_graph(\n", + " md_lin_graph,\n", + " sample_func=\"multidomain\",\n", + " n_samples=int(n_samples),\n", + " n_jobs=1,\n", + " random_state=seed + idx,\n", + " domain_id=domain_id,\n", + " )\n", + "\n", + " domain_indices.append(domain_id)\n", + " intervention_targets.append({})\n", + " mechanisms.append(jdx)\n", + " data.append(df)\n", + " for jdx in range(n_interventions):\n", + " # get random node to perturb\n", + " perturbation = set(\n", + " rng.choice(\n", + " list(aug_lin_graph.non_augmented_nodes),\n", + " size=1,\n", + " replace=False,\n", + " )\n", + " )\n", + "\n", + " int_graph = md_lin_graph.copy()\n", + "\n", + " # generate a soft-intervention\n", + " int_graph = apply_linear_soft_intervention(\n", + " int_graph, targets=perturbation, random_state=seed + idx\n", + " )\n", + "\n", + " # sample data from the intervention distribution\n", + " df = sample_from_graph(\n", + " int_graph,\n", + " sample_func=\"multidomain\",\n", + " n_samples=n_samples,\n", + " n_jobs=1,\n", + " random_state=seed + idx,\n", + " domain_id=domain_id,\n", + " )\n", + " domain_indices.append(domain_id)\n", + " intervention_targets.append(perturbation)\n", + " mechanisms.append(jdx)\n", + " data.append(df)\n", + "\n", + " # run S-FCI\n", + " context = (\n", + " make_context(create_using=InterventionalContextBuilder)\n", + " .variables(aug_graph.non_augmented_nodes)\n", + " .num_distributions(len(data))\n", + " .build()\n", + " )\n", + " # now learn the relationships\n", + " learner = SFCI(\n", + " ci_estimator=KernelCITest(),\n", + " cd_estimator=KernelCDTest(),\n", + " alpha=alpha,\n", + " debug=False,\n", + " n_jobs=n_jobs,\n", + " max_cond_set_size=max_cond_set_size,\n", + " )\n", + " learner.fit(\n", + " data,\n", + " context,\n", + " domain_indices=domain_indices,\n", + " intervention_targets=intervention_targets,\n", + " )\n", + " spag = learner.graph_\n", + "\n", + " # run I-FCI\n", + " single_domain_data, single_domain_targets = convert_md_data_to_sd(\n", + " data, domain_indices, intervention_targets\n", + " )\n", + " context = (\n", + " make_context(create_using=InterventionalContextBuilder)\n", + " .variables(aug_graph.non_augmented_nodes)\n", + " # .obs_distribution(False)\n", + " .intervention_targets(single_domain_targets)\n", + " # .mechanisms([{\"x\": 1}, {\"x\": 2}])\n", + " .num_distributions(len(single_domain_data))\n", + " .build()\n", + " )\n", + " learner = PsiFCI(\n", + " ci_estimator=KernelCITest(),\n", + " cd_estimator=KernelCDTest(),\n", + " alpha=alpha,\n", + " known_intervention_targets=True,\n", + " n_jobs=n_jobs,\n", + " max_cond_set_size=max_cond_set_size,\n", + " )\n", + " learner.fit(\n", + " single_domain_data,\n", + " context,\n", + " )\n", + " ipag = learner.graph_\n", + "\n", + " # analyze skeleton\n", + " cm_ipag = confusion_matrix_networks(\n", + " aug_lin_graph.to_undirected(),\n", + " ipag.subgraph(non_augmented_nodes).to_undirected(),\n", + " )\n", + " cm_spag = confusion_matrix_networks(\n", + " aug_lin_graph.to_undirected(),\n", + " spag.subgraph(non_augmented_nodes).to_undirected(),\n", + " )\n", + "\n", + " # analyze directionality orietnations\n", + " shd_ipag = structure_hamming_dist(\n", + " aug_lin_graph.sub_directed_graph(),\n", + " ipag.subgraph(non_augmented_nodes).sub_directed_graph(),\n", + " )\n", + " shd_spag = structure_hamming_dist(\n", + " aug_lin_graph.sub_directed_graph(),\n", + " spag.subgraph(non_augmented_nodes).sub_directed_graph(),\n", + " )\n", + "\n", + " print(cm_ipag, cm_spag)\n", + " print(shd_ipag, shd_spag)\n", + "\n", + " sfci_results[\"shd\"].append(shd_spag)\n", + " sfci_results[\"cm_skel\"].append(cm_spag)\n", + " ifci_results[\"shd\"].append(shd_ipag)\n", + " ifci_results[\"cm_skel\"].append(cm_ipag)\n", + "\n", + " # analyze skeleton\n", + " cm_ipag = confusion_matrix_networks(\n", + " aug_lin_graph.sub_directed_graph(),\n", + " ipag.subgraph(non_augmented_nodes).sub_directed_graph(),\n", + " )\n", + " cm_spag = confusion_matrix_networks(\n", + " aug_lin_graph.sub_directed_graph(),\n", + " spag.subgraph(non_augmented_nodes).sub_directed_graph(),\n", + " )\n", + " print(cm_ipag)\n", + " print(cm_spag)\n", + " sfci_results[\"cm_direct\"].append(cm_spag)\n", + " ifci_results[\"shd\"].append(shd_ipag)\n", + " ifci_results[\"cm_direct\"].append(cm_ipag)\n", + " parameters[\"idx\"].append(idx)\n", + " parameters[\"n_domains\"].append(n_domains)\n", + " parameters[\"p_edge\"].append(p)\n", + " parameters[\"n_nodes\"].append(n_nodes)\n", + " # break\n", + " # break\n", + " break\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "3c6cae59-7337-4d8b-bb8a-748e68a33d09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "[{}, {}, {}, {}, {}, {2}, {3}, {0}]\n", + "8\n", + "0.3 4\n" + ] + } + ], + "source": [ + "print(n_domains)\n", + "print(intervention_targets)\n", + "print(len(data))\n", + "print(p, n_nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "13c6c922-0ba5-4a59-88ee-a8090f9eb5a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2 1]\n", + " [0 3]]\n", + "[[2 1]\n", + " [1 2]]\n", + "2.0 2.0\n" + ] + } + ], + "source": [ + "# analyze directionality orietnations\n", + "shd_ipag = structure_hamming_dist_ec(\n", + " aug_lin_graph,\n", + " ipag.subgraph(non_augmented_nodes),\n", + ")\n", + "shd_spag = structure_hamming_dist_ec(\n", + " aug_lin_graph,\n", + " spag.subgraph(non_augmented_nodes),\n", + ")\n", + "\n", + "# analyze skeleton\n", + "cm_ipag = confusion_matrix_networks(\n", + " aug_lin_graph.sub_directed_graph(),\n", + " ipag.subgraph(non_augmented_nodes).sub_directed_graph(),\n", + ")\n", + "cm_spag = confusion_matrix_networks(\n", + " aug_lin_graph.sub_directed_graph(),\n", + " spag.subgraph(non_augmented_nodes).sub_directed_graph(),\n", + ")\n", + "\n", + "print(cm_ipag)\n", + "print(cm_spag)\n", + "print(shd_ipag, shd_spag)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "5d9d610c-4cce-4a5e-a1ba-ac9dedf68ad8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "0\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "1->3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw(groundtruth_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "id": "5aef1b41-0c62-4d35-a10d-213fca31b9a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "3->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "0\n", + "\n", + "\n", + "\n", + "0->3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "1->3\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw(ipag.subgraph(non_augmented_nodes))" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "2b48e391-59f0-42be-b21c-27382cde56c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "0\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "3->0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "3->1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw(spag.subgraph(non_augmented_nodes))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da273420-b573-4323-8a5b-f039330aa104", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pywhy-discover", + "language": "python", + "name": "pywhy-discover" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dodiscover/constraint/sfcialg.py b/dodiscover/constraint/sfcialg.py new file mode 100644 index 000000000..2db308f1f --- /dev/null +++ b/dodiscover/constraint/sfcialg.py @@ -0,0 +1,231 @@ +from typing import FrozenSet, List, Optional, Tuple + +import networkx as nx +import pandas as pd + +from dodiscover._protocol import EquivalenceClass +from dodiscover.cd import BaseConditionalDiscrepancyTest +from dodiscover.ci import BaseConditionalIndependenceTest +from dodiscover.constraint.config import ConditioningSetSelection +from dodiscover.typing import Column, SeparatingSet + +from ..context import Context +from .intervention import PsiFCI +from .skeleton import LearnMultiDomainSkeleton + + +class SFCI(PsiFCI): + def __init__( + self, + ci_estimator: BaseConditionalIndependenceTest, + cd_estimator: BaseConditionalDiscrepancyTest, + alpha: float = 0.05, + min_cond_set_size: Optional[int] = None, + max_cond_set_size: Optional[int] = None, + max_combinations: Optional[int] = None, + condsel_method: ConditioningSetSelection = ConditioningSetSelection.NBRS, + apply_orientations: bool = True, + keep_sorted: bool = False, + max_iter: int = 1000, + max_path_length: Optional[int] = None, + pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, + n_jobs: Optional[int] = None, + debug: bool = False + ): + super().__init__( + ci_estimator, + cd_estimator, + alpha, + min_cond_set_size, + max_cond_set_size, + max_combinations, + condsel_method, + apply_orientations, + keep_sorted, + max_iter, + max_path_length, + pds_condsel_method, + n_jobs=n_jobs, + ) + self.debug = debug + + def learn_skeleton( + self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None + ) -> Tuple[nx.Graph, SeparatingSet]: + # now compute all possibly d-separating sets and learn a better skeleton + self.skeleton_learner_ = LearnMultiDomainSkeleton( + self.ci_estimator, + self.cd_estimator, + sep_set=sep_set, + alpha=self.alpha, + min_cond_set_size=self.min_cond_set_size, + max_cond_set_size=self.max_cond_set_size, + max_combinations=self.max_combinations, + condsel_method=self.condsel_method, + second_stage_condsel_method=self.pds_condsel_method, + keep_sorted=False, + max_path_length=self.max_path_length, + n_jobs=self.n_jobs, + ) + self.skeleton_learner_.fit(data, context, self.domain_indices, self.intervention_targets, debug=self.debug) + + self.context_ = self.skeleton_learner_.context_.copy() + skel_graph = self.skeleton_learner_.adj_graph_ + sep_set = self.skeleton_learner_.sep_set_ + self.n_ci_tests += self.skeleton_learner_.n_ci_tests + return skel_graph, sep_set + + def fit(self, data: List[pd.DataFrame], context: Context, domain_indices, intervention_targets): + """Learn the relevant causal graph equivalence class. + + From the pairs of datasets, we take all combinations and + construct F-nodes corresponding to those. + + Parameters + ---------- + data : List[pd.DataFrame] + The list of different datasets assigned to different + environments. We assume the first dataset is always + observational. + context : Context + The context with interventional assumptions. + + Returns + ------- + self : PsiFCI + The fitted learner. + """ + if not isinstance(data, list): + raise RuntimeError("The input datasets must be in a Python list.") + + # n_datasets = len(data) + # n_distributions = context.num_distributions + + # if n_datasets != n_distributions: + # raise RuntimeError( + # f"There are {n_datasets} passed in, but {n_distributions} " + # f"total assumed distributions. There must be a matching number of datasets and " + # f"'context.num_distributions'." + # ) + self.domain_indices = domain_indices + self.intervention_targets = intervention_targets + + return super().fit(data, context) + + def _apply_rule11(self, graph: EquivalenceClass, context: Context) -> Tuple[bool, List]: + augmented_nodes = context.f_nodes + context.s_nodes + + oriented_edges = [] + added_arrows = True + for node in augmented_nodes: + for nbr in graph.neighbors(node): + if nbr in augmented_nodes: + continue + + # remove all edges between node and nbr and orient this out + graph.remove_edge(node, nbr) + graph.remove_edge(nbr, node) + graph.add_edge(node, nbr, graph.directed_edge_name) + oriented_edges.append((node, nbr)) + return added_arrows, oriented_edges + + def _apply_rule12( + self, + graph: EquivalenceClass, + u: Column, + a: Column, + c: Column, + context: Context, + ) -> bool: + """Apply "Rule 9" of the I-FCI algorithm. + + Checks for inducing paths where 'u' is the F-node, and 'a' and 'c' are connected: + + 'u' -> 'a' *-* 'c' with 'u' -> 'c', then orient 'a' -> 'c'. + + For original details of the rule, see :footcite:`Kocaoglu2019characterization`. + + Parameters + ---------- + graph : EquivalenceClass + The causal graph. + u : Column + The candidate F-node + a : Column + Neighbors of the F-node. + c : Column + Neighbors of the F-node. + symmetric_diff_map : dict + A mapping from the F-nodes to the symmetric difference of the pair of + intervention targets each F-node represents. I.e. if F-node, F1 represents + the pair of intervention distributions with targets {'x'}, and {'x', 'y'}, + then F1 maps to {'y'} in the symmetric diff map. + + Returns + ------- + added_arrows : bool + Whether or not an orientation was made. + + References + ---------- + .. footbibliography:: + """ + f_nodes = context.f_nodes + symmetric_diff_map = context.symmetric_diff_map + + added_arrows = False + if u in f_nodes and self.known_intervention_targets: + # get sigma map to map F-node to its symmetric difference target + S_set: FrozenSet = symmetric_diff_map.get(u, frozenset()) + + # check domain + domains_u = context.domain_map[u] + + # check the presence of an S-node for that domain + if len(domains_u) == 2: + for s_node in context.s_nodes: + if context.domain_map[s_node] == domains_u: + # check if the s-node is d-connected to F + if graph.has_edge(s_node, c): + return False + + # now, we know that there is no S-node for the domain of u + # that will alter the distribution of a/c, so we check for + # an inducing path that we can orient properly + # check a *-* c + if ( + len(S_set) == 1 + and a in S_set + and (graph.has_edge(a, c) or graph.has_edge(c, a)) + and graph.has_edge(u, a) + and graph.has_edge(u, c) + ): + # remove all edges between a and c + graph.remove_edge(a, c) + graph.remove_edge(c, a) + + # then orient X -> Y + graph.add_edge(a, c, graph.directed_edge_name) + + added_arrows = True + return added_arrows + + def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: + import pywhy_graphs as pgraph + + # convert the undirected skeleton graph to its PAG-class, where + # all left-over edges have a "circle" endpoint + pag = pgraph.AugmentedPAG(incoming_circle_edges=graph, + name="SPAG derived with S-FCI") + + # get the graph attributes + pag.graph = graph.graph + + # XXX: assign targets as well + # assign f-nodes + # for f_node in self.context_.f_nodes: + # pag.set_f_node(f_node) + # for s_node in self.context_.s_nodes: + # domain_ids = self.context_.domain_map[s_node] + # pag.add_s_node(s_node, domain_ids=domain_ids, node_changes=) + return pag \ No newline at end of file From 3143bfc15f10a921ee90769382f55b9450744d03 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 24 May 2023 01:17:08 -0400 Subject: [PATCH 53/61] Almost working sfci Signed-off-by: Adam Li --- .../multi-domain/random-graph-analysis.ipynb | 92 +++------- dodiscover/__init__.py | 2 +- dodiscover/constraint/__init__.py | 8 +- dodiscover/constraint/sfcialg.py | 11 +- dodiscover/constraint/skeleton.py | 172 +++++++++++------- dodiscover/datasets/linear.py | 1 - dodiscover/datasets/multidomain.py | 1 - examples/plot_sfci_alg.py | 150 +++++++++++++++ .../skeleton/test_multidomain_skeleton.py | 2 +- 9 files changed, 301 insertions(+), 138 deletions(-) create mode 100644 examples/plot_sfci_alg.py diff --git a/doc/tutorials/multi-domain/random-graph-analysis.ipynb b/doc/tutorials/multi-domain/random-graph-analysis.ipynb index 858461817..24eeb43bc 100644 --- a/doc/tutorials/multi-domain/random-graph-analysis.ipynb +++ b/doc/tutorials/multi-domain/random-graph-analysis.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "735b60f6-a74f-4a1b-85d5-e3fec97c0b62", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 4, "id": "5404ecd8-17bf-4fe6-93fd-dbc2fffb8df0", "metadata": {}, "outputs": [], @@ -73,7 +73,7 @@ "from dodiscover.metrics import (\n", " structure_hamming_dist,\n", " confusion_matrix_networks,\n", - " structure_hamming_dist_ec,\n", + " # structure_hamming_dist_ec,\n", ")\n", "\n", "import seaborn as sns\n", @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "514e7715-203e-4362-8eda-6569a029a849", "metadata": {}, "outputs": [], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "3c9267d5-6845-404d-b2b1-8c991f0a516a", "metadata": {}, "outputs": [], @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "07d7b26f-a025-42da-989a-2a245a86a440", "metadata": {}, "outputs": [], @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 8, "id": "03e8ed34-ef56-496f-923a-04ba4e10894c", "metadata": {}, "outputs": [], @@ -151,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 9, "id": "48a7f96a-d9e6-4503-8c68-b368466f0dee", "metadata": {}, "outputs": [], @@ -161,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 10, "id": "bc0b93f4-c7f4-49d9-8af9-4fece68c33f3", "metadata": {}, "outputs": [ @@ -198,71 +198,35 @@ }, { "cell_type": "code", - "execution_count": 141, - "id": "16f1b69c-6733-40cf-b3f6-5bac8156b4a6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0, 1, 2, 3}\n" - ] - } - ], - "source": [ - "print(aug_lin_graph.non_augmented_nodes)" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "id": "0e0c709f-ced6-497e-a19e-626ae70c0bf2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0, 2}" - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(rng.choice(list(aug_lin_graph.non_augmented_nodes), size=2, replace=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 169, + "execution_count": 20, "id": "bde56fd2-6adc-4181-8efb-acdc2945ead8", "metadata": { "tags": [] }, "outputs": [ { - "ename": "KeyboardInterrupt", - "evalue": "", + "ename": "Exception", + "evalue": "The y variables are not all in the DataFrame.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_46438/1098357664.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m )\n\u001b[0;32m--> 134\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0msingle_domain_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)", + "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 114, in _test_xy_edges\n test_stat, pvalue = parallel_fun(\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 685, in evaluate_edge\n test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/cd/kernel_test.py\", line 134, in test\n self._check_test_input(df, y_vars, group_col, x_vars)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/cd/base.py\", line 42, in _check_test_input\n raise ValueError(\"The y variables are not all in the DataFrame.\")\nValueError: The y variables are not all in the DataFrame.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 428, in _process_worker\n r = call_item()\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 275, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 620, in __call__\n return self.func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n return [func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in \n return [func(*args, **kwargs)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 123, in _test_xy_edges\n raise Exception(e)\nException: The y variables are not all in the DataFrame.\n\"\"\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_59486/3346600287.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m )\n\u001b[0;32m--> 105\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervention_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply_rule11\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mEquivalenceClass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mContext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m 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skeleton\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1492\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobs_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossible_x_nodes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_augmented_nodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipped_y_nodes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipped_z_nodes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf_nodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1493\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36m_learn_skeleton\u001b[0;34m(self, data, context, condsel_method, conditional_test_func, possible_x_nodes, skipped_y_nodes, skipped_z_nodes, cross_distribution_test, group_with_snode)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;31m# run parallelized loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m out = Parallel(n_jobs=self.n_jobs)(\n\u001b[0m\u001b[1;32m 450\u001b[0m delayed(_test_xy_edges)(\n\u001b[1;32m 451\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_edge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1099\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m 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cd_estimator=KernelCDTest(),\n", + " ci_estimator=FisherZCITest(),\n", + " cd_estimator=KernelCDTest(null_reps=500),\n", " alpha=alpha,\n", " debug=False,\n", " n_jobs=n_jobs,\n", @@ -393,8 +357,8 @@ " .build()\n", " )\n", " learner = PsiFCI(\n", - " ci_estimator=KernelCITest(),\n", - " cd_estimator=KernelCDTest(),\n", + " ci_estimator=FisherZCITest(),\n", + " cd_estimator=KernelCDTest(null_reps=500),\n", " alpha=alpha,\n", " known_intervention_targets=True,\n", " n_jobs=n_jobs,\n", diff --git a/dodiscover/__init__.py b/dodiscover/__init__.py index cd2e49053..1c6ee7682 100644 --- a/dodiscover/__init__.py +++ b/dodiscover/__init__.py @@ -4,6 +4,6 @@ from . import metrics # noqa: F401 from ._protocol import EquivalenceClass, Graph from ._version import __version__ # noqa: F401 -from .constraint import FCI, PC, PsiFCI +from .constraint import FCI, PC, SFCI, PsiFCI from .context import Context from .context_builder import ContextBuilder, InterventionalContextBuilder, make_context diff --git a/dodiscover/constraint/__init__.py b/dodiscover/constraint/__init__.py index f21aed4ce..08f692474 100644 --- a/dodiscover/constraint/__init__.py +++ b/dodiscover/constraint/__init__.py @@ -2,4 +2,10 @@ from .fcialg import FCI from .intervention import PsiFCI from .pcalg import PC -from .skeleton import LearnInterventionSkeleton, LearnSemiMarkovianSkeleton, LearnSkeleton +from .sfcialg import SFCI +from .skeleton import ( + LearnInterventionSkeleton, + LearnMultiDomainSkeleton, + LearnSemiMarkovianSkeleton, + LearnSkeleton, +) diff --git a/dodiscover/constraint/sfcialg.py b/dodiscover/constraint/sfcialg.py index 2db308f1f..23f16a3f5 100644 --- a/dodiscover/constraint/sfcialg.py +++ b/dodiscover/constraint/sfcialg.py @@ -30,7 +30,7 @@ def __init__( max_path_length: Optional[int] = None, pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, n_jobs: Optional[int] = None, - debug: bool = False + debug: bool = False, ): super().__init__( ci_estimator, @@ -67,7 +67,9 @@ def learn_skeleton( max_path_length=self.max_path_length, n_jobs=self.n_jobs, ) - self.skeleton_learner_.fit(data, context, self.domain_indices, self.intervention_targets, debug=self.debug) + self.skeleton_learner_.fit( + data, context, self.domain_indices, self.intervention_targets, debug=self.debug + ) self.context_ = self.skeleton_learner_.context_.copy() skel_graph = self.skeleton_learner_.adj_graph_ @@ -215,8 +217,7 @@ def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: # convert the undirected skeleton graph to its PAG-class, where # all left-over edges have a "circle" endpoint - pag = pgraph.AugmentedPAG(incoming_circle_edges=graph, - name="SPAG derived with S-FCI") + pag = pgraph.AugmentedPAG(incoming_circle_edges=graph, name="SPAG derived with S-FCI") # get the graph attributes pag.graph = graph.graph @@ -228,4 +229,4 @@ def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: # for s_node in self.context_.s_nodes: # domain_ids = self.context_.domain_map[s_node] # pag.add_s_node(s_node, domain_ids=domain_ids, node_changes=) - return pag \ No newline at end of file + return pag diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index d9e27e3d5..aafc5e718 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -13,10 +13,10 @@ from dodiscover.ci import BaseConditionalIndependenceTest from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.utils import is_in_sep_set +from dodiscover.context import Context from dodiscover.typing import Column, SeparatingSet from .._protocol import EquivalenceClass -from ..context import Context from .utils import _find_neighbors_along_path logger = logging.getLogger() @@ -38,6 +38,7 @@ def _test_xy_edges( data: pd.DataFrame, context: Context, cross_distribution_test: bool = False, + group_with_snode =None, ) -> Dict[str, Any]: """Private function used to test edge between X and Y in parallel for candidate separating sets. @@ -80,6 +81,8 @@ def _test_xy_edges( size_cond_set=size_cond_set, ) + # TODO: figure out more elegant way of doing this + old_xvar = None # now iterate through the possible parents for comb_idx, cond_set in enumerate(conditioning_sets): # check the number of combinations of possible parents we have tried @@ -103,6 +106,9 @@ def _test_xy_edges( data_j[x_var] = 1 this_data = pd.concat((data_i, data_j), axis=0) + if group_with_snode is not None: + old_xvar = x_var + x_var = frozenset({x_var, group_with_snode}) try: # compute conditional independence test test_stat, pvalue = parallel_fun( @@ -126,6 +132,7 @@ def _test_xy_edges( result: Dict[str, Any] = dict() result["x_var"] = x_var + result['old_xvar'] = old_xvar result["y_var"] = y_var result["cond_set"] = list(cond_set) result["test_stat"] = test_stat @@ -310,6 +317,7 @@ def _learn_skeleton( skipped_y_nodes=None, skipped_z_nodes=None, cross_distribution_test: bool = False, + group_with_snode=None, ): """Core function for learning the skeleton of a causal graph. @@ -433,6 +441,7 @@ def _learn_skeleton( data, context, cross_distribution_test, + group_with_snode=group_with_snode ) out.append(result) else: @@ -450,6 +459,7 @@ def _learn_skeleton( data, context, cross_distribution_test, + group_with_snode=group_with_snode ) for x_var, y_var, possible_variables in self._generate_pairs_with_sepset( possible_x_nodes, @@ -467,9 +477,15 @@ def _learn_skeleton( x_var = result["x_var"] y_var = result["y_var"] cond_set = result["cond_set"] + old_xvar = result["old_xvar"] + if group_with_snode is not None: + self._postprocess_ci_test(context, group_with_snode, y_var, test_stat, pvalue) + self._postprocess_ci_test(context, old_xvar, y_var, test_stat, pvalue) + else: + self._postprocess_ci_test(context, x_var, y_var, test_stat, pvalue) # post-process the CI test results - self._postprocess_ci_test(context, x_var, y_var, test_stat, pvalue) + # self._postprocess_ci_test(context, x_var, y_var, test_stat, pvalue) # two variables found to be independent given a separating set if pvalue > self.alpha: @@ -1091,12 +1107,9 @@ def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): # if there is no second stage skeleton method to be run, then we # will stop with the skeleton here - print(self.second_stage_condsel_method) - print(context) if self.second_stage_condsel_method is None: self.context_ = deepcopy(context.copy()) self.adj_graph_ = deepcopy(context.init_graph.copy()) - print("Shuldnt run second stage...") return self # setup context for the second round-of learning @@ -1208,6 +1221,23 @@ def __init__( self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets + def _prep_second_stage_skeleton(self, context: Context) -> Context: + # prepare the context object for the second stage of learning + # all separating sets are either: + # i) augmented with all F-nodes, or + # ii) augmented with all F-nodes except intervention index 'i' + # R9 allows us to leverage F-nodes being not in separating sets to + # augment all separating sets that have non-empty sets with all + # F-nodes to keep consistency with the algorithm + for x_var, y_vars in self.sep_set_.items(): + for y_var in y_vars: + sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + if len(sep_sets) > 0: + for idx in range(len(sep_sets)): + self.sep_set_[x_var][y_var][idx].update(context.get_augmented_nodes()) + + return super()._prep_second_stage_skeleton(context) + def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = True) -> None: # ensure data is a list if isinstance(data, pd.DataFrame): @@ -1253,8 +1283,19 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr cross_distribution_test=False, ) - # keep track of the observational skeleton graph - obs_skel_graph = self.adj_graph_.copy() + context = self._prep_second_stage_skeleton(context) + + # secibd learn the skeleton using only "PDS data" + self._learn_skeleton( + data=obs_data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.ci_estimator, + possible_x_nodes=list(context.get_non_augmented_nodes()), + skipped_y_nodes=context.get_augmented_nodes(), + skipped_z_nodes=context.get_augmented_nodes(), + cross_distribution_test=False, + ) # prepare the context object for the second stage of learning # all separating sets are either: @@ -1268,47 +1309,7 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore if len(sep_sets) > 0: for idx in range(len(sep_sets)): - self.sep_set_[x_var][y_var][idx].update(f_nodes) - - # index all datasets, where the first one may be observational - non_f_nodes = context.get_non_augmented_nodes() - - # reset the init graph and this time learn the skeleton using - # interventional distributions - # create a complete subgraph of F-nodes with all other nodes - for node in f_nodes: - for obs_node in set(non_f_nodes): - if node == obs_node: - continue - self.adj_graph_.add_edge(node, obs_node, test_stat=np.inf, pvalue=-1e-5) - - # reset context and add observational skeleton - context.add_state_variable("obs_skel_graph", obs_skel_graph) - - # convert the undirected skeleton graph to a PAG, where - # all left-over edges have a "circle" endpoint - sep_set = self.sep_set_ - import pywhy_graphs - - pag = pywhy_graphs.PAG(incoming_circle_edges=obs_skel_graph, name="PAG derived with FCI") - - # orient colliders - self._orient_unshielded_triples(pag, sep_set) - - context.add_state_variable("PAG", pag) - context.add_state_variable("max_path_length", self.max_path_length_) - - # secibd learn the skeleton using only "PDS data" - self._learn_skeleton( - data=obs_data, - context=context, - condsel_method=self.second_stage_condsel_method, - conditional_test_func=self.ci_estimator, - possible_x_nodes=list(context.get_non_augmented_nodes()), - skipped_y_nodes=context.f_nodes, - skipped_z_nodes=context.f_nodes, - cross_distribution_test=False, - ) + self.sep_set_[x_var][y_var][idx].update(context.get_augmented_nodes()) # now, we'll fit the data using interventional data by looping over all # combinations of F-nodes and their neighbors @@ -1319,8 +1320,8 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr condsel_method=self.second_stage_condsel_method, conditional_test_func=self.cd_estimator, possible_x_nodes=list(self.context_.f_nodes), - skipped_y_nodes=context.f_nodes, - skipped_z_nodes=context.f_nodes, + skipped_y_nodes=context.get_augmented_nodes(), + skipped_z_nodes=context.get_augmented_nodes(), cross_distribution_test=True, ) @@ -1397,9 +1398,9 @@ class LearnMultiDomainSkeleton(LearnInterventionSkeleton): experimental distribution dataset, or one may not know the explicit targets. If the interventional targets are known, then the skeleton discovery algorithm of :footcite:`Kocaoglu2019characterization` is used. That is we learn the skeleton of a - AugmentedPAG. Otherwise, we will not know the intervention targets, and use the skeleton discovery - algorithm described in :footcite:`Jaber2020causal`. To define intervention targets, one - must use the :class:`dodiscover.InterventionalContextBuilder`. + AugmentedPAG. Otherwise, we will not know the intervention targets, and use the skeleton + discovery algorithm described in :footcite:`Jaber2020causal`. To define intervention + targets, one must use the :class:`dodiscover.InterventionalContextBuilder`. References ---------- @@ -1479,7 +1480,8 @@ def _create_augmented_nodes( augmented_nodes : List Set of augmented nodes (i.e. F and S nodes). symmetric_diff_map : Dict[Any, FrozenSet] - Mapping of augmented nodes to intervention targets, or distribution indices represented by the node. + Mapping of augmented nodes to intervention targets, or distribution indices represented + by the node. sigma_map : Dict[Any, FrozenSet] Mapping of augmented nodes to distribution indices represented by the node. node_domain_map : Dict[Any, FrozenSet] @@ -1528,8 +1530,8 @@ def _create_augmented_nodes( seen_domain_pairs[distr_memo_key] = None seen_distr_pairs[domain_memo_key] = None - # map each augmented-node to a tuple of distribution indices, or to a set of nodes representing - # the intervention targets + # map each augmented-node to a tuple of distribution indices, or to a set of nodes + # representing the intervention targets if intervention_targets[idx] is None or intervention_targets[jdx] is None: targets = frozenset([idx, jdx]) else: @@ -1631,7 +1633,7 @@ def fit( for node in augmented_nodes: if node[0] == "S": s_nodes.append(node) - elif node[0] == 'F': + elif node[0] == "F": f_nodes.append(node) n_domains = len(np.unique(domain_indices)) @@ -1646,9 +1648,46 @@ def fit( # first learn the skeleton using only "observational data" # initially learn the skeleton without using PDS information # apply algorithm to learn skeleton - self._fit(obs_data, context, list(causal_nodes), augmented_nodes, augmented_nodes, debug=debug) + # first learn the skeleton using only "observational data" + self._learn_skeleton( + data=obs_data, + context=context, + condsel_method=self.condsel_method, + conditional_test_func=self.ci_estimator, + possible_x_nodes=list(context.get_non_augmented_nodes()), + skipped_y_nodes=context.get_augmented_nodes(), + skipped_z_nodes=context.get_augmented_nodes(), + cross_distribution_test=False, + ) + context = self._prep_second_stage_skeleton(context) + # secibd learn the skeleton using only "PDS data" + self._learn_skeleton( + data=obs_data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.ci_estimator, + possible_x_nodes=list(context.get_non_augmented_nodes()), + skipped_y_nodes=context.get_augmented_nodes(), + skipped_z_nodes=context.get_augmented_nodes(), + cross_distribution_test=False, + ) + + # prepare the context object for the second stage of learning + # all separating sets are either: + # i) augmented with all F-nodes, or + # ii) augmented with all F-nodes except intervention index 'i' + # R9 allows us to leverage F-nodes being not in separating sets to + # augment all separating sets that have non-empty sets with all + # F-nodes to keep consistency with the algorithm + for x_var, y_vars in self.sep_set_.items(): + for y_var in y_vars: + sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + if len(sep_sets) > 0: + for idx in range(len(sep_sets)): + self.sep_set_[x_var][y_var][idx].update(context.get_augmented_nodes()) + # loop through each domain to learn the F-node skeleton seen_domain_pairs = set() for idx, source in enumerate(range(1, n_domains + 1)): @@ -1668,15 +1707,17 @@ def fit( break if s_node is None: continue - raise RuntimeError('wtf') + raise RuntimeError("wtf") this_f_nodes = [ node for node in f_nodes if node_domain_map[node] == {source, target} and node in symmetric_diff_map ] if debug: - print(f'Trying to learn skeleton for {source} and {target} to remove F-nodes: {this_f_nodes} ' - f'grouped with S-node: {s_node}') + print( + f"Trying to learn skeleton for {source} and {target} to remove F-nodes: " + f"{this_f_nodes} grouped with S-node: {s_node}" + ) self._learn_skeleton( data=data, context=context, @@ -1687,7 +1728,7 @@ def fit( skipped_z_nodes=skip_nodes, cross_distribution_test=True, group_with_snode=s_node, - debug=debug, + # debug=debug, ) # this is only possible if there is explicitly observational data between @@ -1707,7 +1748,10 @@ def fit( print(sigma_map) if this_s_nodes: if debug: - print(f'Trying to learn skeleton for {source} and {target} to remove S-nodes: {this_s_nodes}') + print( + f"Trying to learn skeleton for {source} and {target} to remove " + f"S-nodes: {this_s_nodes}" + ) self._learn_skeleton( data=data, context=context, @@ -1722,7 +1766,7 @@ def fit( # analyze F-nodes only within the 'source' domain source_fnodes = [node for node in augmented_nodes if node_domain_map[node] == {source}] if debug: - print(f'Trying to learn skeleton for {source} to remove F-nodes: {source_fnodes}') + print(f"Trying to learn skeleton for {source} to remove F-nodes: {source_fnodes}") # apply algorithm to learn skeleton among the F-node subgraph within a single domain self._learn_skeleton( data=data, diff --git a/dodiscover/datasets/linear.py b/dodiscover/datasets/linear.py index f3c37d8fb..a72499b79 100644 --- a/dodiscover/datasets/linear.py +++ b/dodiscover/datasets/linear.py @@ -1,6 +1,5 @@ from typing import Dict -import networkx as nx import numpy as np diff --git a/dodiscover/datasets/multidomain.py b/dodiscover/datasets/multidomain.py index da7b7e726..dc8e9369a 100644 --- a/dodiscover/datasets/multidomain.py +++ b/dodiscover/datasets/multidomain.py @@ -1,6 +1,5 @@ from typing import Dict -import networkx as nx import numpy as np diff --git a/examples/plot_sfci_alg.py b/examples/plot_sfci_alg.py new file mode 100644 index 000000000..663b56cc2 --- /dev/null +++ b/examples/plot_sfci_alg.py @@ -0,0 +1,150 @@ +""" +.. _ex-psifci-algorithm: + +========================================================= +Causal discovery with interventional data - Sachs dataset +========================================================= + +We will analyze the Sachs dataset :footcite:`sachsdataset2005` and reproduce analyses +from the Supplemental Figure 8 in :footcite:`Jaber2020causal` demonstrating the +usage of the :class:`dodiscover.constraint.PsiFCI` algorithm for learning causal graphs +from observational and interventional data. + +.. currentmodule:: dodiscover +""" + + +# %% +# Authors: Adam Li +# +# License: BSD (3-clause) + +from pywhy_graphs.viz import draw +from dodiscover.ci import GSquareCITest +from dodiscover import SFCI, Context, make_context, InterventionalContextBuilder + +import numpy as np +import pandas as pd +import bnlearn + +import pooch + +# %% +# Pull in the Sachs Dataset +# ------------------------- +# The Sachs dataset is a famous dataset in causal discovery because of its real-life +# applicability and access to experimental data that analyzed the causal network of +# 11 proteins using knockouts and spikings :footcite:`sachsdataset2005`. The pathways +# for those proteins are already known, so it is an ideal dataset for benchmarking +# causal discovery algorithms. +# +# We will download a preprocessed version of the dataset from the following +# url: https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz +# +# Ref: https://erdogant.github.io/bnlearn/pages/html/bnlearn.bnlearn.html#bnlearn.bnlearn.import_example # noqa + +# use pooch to download robustly from a url +url = "https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz" +file_path = pooch.retrieve( + url=url, + known_hash="md5:39ee257f7eeb94cb60e6177cf80c9544", +) + +df = pd.read_csv(file_path, delimiter=" ") + +# the ground-truth dag is shown here: XXX: comment in when errors are fixed +ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False) +fig = bnlearn.plot(ground_truth_dag) + +# .. note:: +# The Sachs dataset has previously been preprocessed, and the steps are described +# in bnlearn, at the web-page https://www.bnlearn.com/research/sachs05/. +print(df.head()) +print(df.shape) + +# %% +# Preprocess the dataset +# ---------------------- +# Since the data is one dataframe, we need to process it into a form +# that is acceptable by dodiscover's :class:`constraint.PsiFCI` algorithm. We +# will form a list of separate dataframes. +unique_ints = df["INT"].unique() + +# get the list of intervention targets and list of dataframe associated with each intervention +intervention_targets = [df.columns[idx] for idx in unique_ints] +data_cols = [col for col in df.columns if col != "INT"] +data = [] +domain_ids = np.array([0, 0, 0, 0, 0, 1]) +for interv_idx in unique_ints: + _data = df[df["INT"] == interv_idx][data_cols] + data.append(_data) + +print(len(data), len(intervention_targets)) +# %% +# Setup constraint-based learner +# ------------------------------ +# Since we have access to interventional data, the causal discovery algorithm +# we will use that leverages CI and CD tests to estimate causal constraints +# is the Psi-FCI algorithm :footcite:`Jaber2020causal`. + +# Our dataset is comprised of discrete valued data, so we will utilize the +# G^2 (Chi-square) CI test. +ci_estimator = GSquareCITest(data_type="discrete") + +# Since our data is entirely discrete, we can also use the G^2 test as our +# CD test. +cd_estimator = GSquareCITest(data_type="discrete") + +alpha = 0.05 +learner = SFCI( + ci_estimator=ci_estimator, + cd_estimator=cd_estimator, + alpha=alpha, + max_combinations=10, + max_cond_set_size=4, + n_jobs=-1, +) + +# create context with information about the interventions +ctx_builder = make_context(create_using=InterventionalContextBuilder) +ctx: Context = ctx_builder.variables(data=data[0]).num_distributions(len(data)).build() + +# %% +# Run the learning process +# ------------------------ +# We have setup our causal context and causal discovery learner, so we will now +# run the algorithm using the :meth:`constraint.PsiFCI.fit` API, which is similar to scikit-learn's +# `fit` design. All fitted attributes contain an underscore at the end. +learner = learner.fit( + data, ctx, domain_indices=domain_ids, intervention_targets=intervention_targets +) + +# %% +# Analyze the results +# =================== +# Now that we have learned the graph, we will show it here. Note differences and similarities +# to the ground-truth DAG that is "assumed". Moreover, note that this reproduces Supplementary +# Figure 8 in :footcite:`Jaber2020causal`. +est_pag = learner.graph_ + +print(f"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG") + +# %% +# Visualize the full graph including the F-node +dot_graph = draw(est_pag, direction="LR") +dot_graph.render(outfile="psi_pag_full.png", view=True, cleanup=True) + +# %% +# Visualize the graph without the F-nodes +est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes()) +dot_graph = draw(est_pag_no_fnodes, direction="LR") +dot_graph.render(outfile="psi_pag.png", view=True, cleanup=True) + +# Interpretation +# -------------- +# Looking at the supplemental figure 8b in :footcite:`Jaber2020causal`, we see that the +# learned PAG matches quite well. + +# References +# ---------- +# .. footbibliography:: diff --git a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py index c63c8bd6f..c8efab4d1 100644 --- a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py +++ b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py @@ -161,4 +161,4 @@ def test_basic_multidomain_fsnode_skeleton_with_lindata(): # check the skeleton after intervention print(skel_graph.edges()) print(expected_skeleton.edges()) - assert nx.is_isomorphic(expected_skeleton, skel_graph) \ No newline at end of file + assert nx.is_isomorphic(expected_skeleton, skel_graph) From fd1af9b1f787ebc318bb7b4523d635b3aa5d4ff4 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 24 May 2023 10:19:44 -0400 Subject: [PATCH 54/61] Almost there Signed-off-by: Adam Li --- .../multi-domain/random-graph-analysis.ipynb | 18 +++++++-------- dodiscover/cd/base.py | 2 +- dodiscover/constraint/skeleton.py | 22 +++++++++++-------- 3 files changed, 23 insertions(+), 19 deletions(-) diff --git a/doc/tutorials/multi-domain/random-graph-analysis.ipynb b/doc/tutorials/multi-domain/random-graph-analysis.ipynb index 24eeb43bc..923042d2e 100644 --- a/doc/tutorials/multi-domain/random-graph-analysis.ipynb +++ b/doc/tutorials/multi-domain/random-graph-analysis.ipynb @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "id": "bde56fd2-6adc-4181-8efb-acdc2945ead8", "metadata": { "tags": [] @@ -206,27 +206,27 @@ "outputs": [ { "ename": "Exception", - "evalue": "The y variables are not all in the DataFrame.", + "evalue": "unhashable type: 'set'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)", - "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 114, in _test_xy_edges\n test_stat, pvalue = parallel_fun(\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 685, in evaluate_edge\n test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/cd/kernel_test.py\", line 134, in test\n self._check_test_input(df, y_vars, group_col, x_vars)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/cd/base.py\", line 42, in _check_test_input\n raise ValueError(\"The y variables are not all in the DataFrame.\")\nValueError: The y variables are not all in the DataFrame.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 428, in _process_worker\n r = call_item()\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 275, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 620, in __call__\n return self.func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n return [func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in \n return [func(*args, **kwargs)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 123, in _test_xy_edges\n raise Exception(e)\nException: The y variables are not all in the DataFrame.\n\"\"\"", + "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 113, in _test_xy_edges\n test_stat, pvalue = parallel_fun(\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 689, in evaluate_edge\n test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z)\nTypeError: unhashable type: 'set'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 428, in _process_worker\n r = call_item()\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 275, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 620, in __call__\n return self.func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n return [func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in \n return [func(*args, **kwargs)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 127, in _test_xy_edges\n raise Exception(e)\nException: unhashable type: 'set'\n\"\"\"", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_59486/3346600287.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m )\n\u001b[0;32m--> 105\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_59486/2593794972.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m )\n\u001b[0;32m--> 105\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervention_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply_rule11\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m 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217\u001b[0;31m graph, self.separating_sets_ = self.learn_skeleton(\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseparating_sets_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m )\n", "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mlearn_skeleton\u001b[0;34m(self, data, context, sep_set)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m )\n\u001b[0;32m---> 70\u001b[0;31m self.skeleton_learner_.fit(\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m 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451\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_edge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets, check_input, debug)\u001b[0m\n\u001b[1;32m 1723\u001b[0m \u001b[0;34mf\"{this_f_nodes} grouped with S-node: {s_node}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1724\u001b[0m )\n\u001b[0;32m-> 1725\u001b[0;31m self._learn_skeleton(\n\u001b[0m\u001b[1;32m 1726\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36m_learn_skeleton\u001b[0;34m(self, data, context, condsel_method, conditional_test_func, possible_x_nodes, skipped_y_nodes, skipped_z_nodes, cross_distribution_test, group_with_snode)\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 452\u001b[0m \u001b[0;31m# run parallelized loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m out = Parallel(n_jobs=self.n_jobs)(\n\u001b[0m\u001b[1;32m 454\u001b[0m delayed(_test_xy_edges)(\n\u001b[1;32m 455\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_edge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1099\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 973\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 974\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m 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"\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 565\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 566\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 567\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 568\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 447\u001b[0m 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x_vars is not None and any(col not in df.columns for col in x_vars): raise ValueError("The x variables are not all in the DataFrame.") if any(col not in df.columns for col in y_vars): - raise ValueError("The y variables are not all in the DataFrame.") + raise ValueError(f"The y variables, {y_vars} are not all in the DataFrame: {df.columns}") if group_col_var not in df.columns: raise ValueError(f"The group column {group_col_var} is not in the DataFrame.") diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index aafc5e718..d2f570e08 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -82,7 +82,7 @@ def _test_xy_edges( ) # TODO: figure out more elegant way of doing this - old_xvar = None + new_x_var = None # now iterate through the possible parents for comb_idx, cond_set in enumerate(conditioning_sets): # check the number of combinations of possible parents we have tried @@ -106,14 +106,18 @@ def _test_xy_edges( data_j[x_var] = 1 this_data = pd.concat((data_i, data_j), axis=0) - if group_with_snode is not None: - old_xvar = x_var - x_var = frozenset({x_var, group_with_snode}) try: - # compute conditional independence test - test_stat, pvalue = parallel_fun( - this_data, conditional_test_func, x_var, y_var, set(cond_set) - ) + if group_with_snode is not None: + new_x_var = set([x_var, group_with_snode]) + # compute conditional independence test + test_stat, pvalue = parallel_fun( + this_data, conditional_test_func, new_x_var, y_var, set(cond_set) + ) + else: + # compute conditional independence test + test_stat, pvalue = parallel_fun( + this_data, conditional_test_func, x_var, y_var, set(cond_set) + ) except Exception as e: if "Not enough samples." in str(e): print(e) @@ -132,7 +136,7 @@ def _test_xy_edges( result: Dict[str, Any] = dict() result["x_var"] = x_var - result['old_xvar'] = old_xvar + result['old_xvar'] = new_x_var result["y_var"] = y_var result["cond_set"] = list(cond_set) result["test_stat"] = test_stat From df678a52a45c34002a59378490d787ab182e95a7 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 9 Jun 2023 14:20:00 -0400 Subject: [PATCH 55/61] SFCI algo wip Signed-off-by: Adam Li --- Untitled.ipynb | 256 +++++++ doc/tutorials/markovian/Untitled.ipynb | 6 + .../multi-domain/example-sfci-algo.ipynb | 717 ++++++++++++++++-- .../multi-domain/random-graph-analysis.ipynb | 371 ++++----- dodiscover/cd/base.py | 19 +- dodiscover/cd/bregman.py | 2 +- dodiscover/cd/kernel_test.py | 105 +-- dodiscover/cd/residual.py | 154 ++++ dodiscover/ci/kernel_utils.py | 6 + dodiscover/ci/oracle.py | 7 +- dodiscover/constraint/skeleton.py | 266 ++++--- dodiscover/metrics.py | 2 + examples/plot_sfci_alg.py | 2 +- examples/plot_sfci_with_artificial_sachs.py | 207 +++++ tests/unit_tests/conditional/cd/test_cd.py | 88 ++- .../skeleton/test_multidomain_skeleton.py | 237 +++++- 16 files changed, 2000 insertions(+), 445 deletions(-) create mode 100644 Untitled.ipynb create mode 100644 doc/tutorials/markovian/Untitled.ipynb create mode 100644 dodiscover/cd/residual.py create mode 100644 examples/plot_sfci_with_artificial_sachs.py diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 000000000..fbde50e50 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "id": "938fea3f-4cd8-476b-9e29-64c3c4d92d65", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e0cb2aef-909f-4efd-90a8-98cd10ae6b64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic = pd.read_csv(\"~/harvard_survery_data.csv\")\n", + "\n", + "titanic.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c3272fb4-3019-418e-89b5-37ce1556210e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29.69911764705882 +/- 14.526497332334042\n" + ] + } + ], + "source": [ + "print(titanic[\"Age\"].mean(), \"+/-\", titanic[\"Age\"].std())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ac4ed766-9a66-4894-9331-3b2cf779a8ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a visualization\n", + "sns.relplot(\n", + " data=titanic,\n", + " x=\"Age\", y=\"Survived\", \n", + " # col=\"Sex\",\n", + " # hue=\"smoker\", \n", + " # style=\"smoker\",\n", + " # size=\"size\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f2ff08c-8c8c-4e7c-944a-8acb5c02746b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pywhy-discover", + "language": "python", + "name": "pywhy-discover" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/tutorials/markovian/Untitled.ipynb b/doc/tutorials/markovian/Untitled.ipynb new file mode 100644 index 000000000..363fcab7e --- /dev/null +++ b/doc/tutorials/markovian/Untitled.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/tutorials/multi-domain/example-sfci-algo.ipynb b/doc/tutorials/multi-domain/example-sfci-algo.ipynb index 4d061475e..78781577e 100644 --- a/doc/tutorials/multi-domain/example-sfci-algo.ipynb +++ b/doc/tutorials/multi-domain/example-sfci-algo.ipynb @@ -29,20 +29,9 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", - "The lab_black extension is already loaded. To reload it, use:\n", - " %reload_ext lab_black\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -51,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -94,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 119, "metadata": {}, "outputs": [], "source": [ @@ -137,54 +126,73 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[bnlearn] >Extracting files..\n" - ] - } - ], + "outputs": [], "source": [ "# download purely observational data\n", - "data = bnlearn.import_example(data='sachs', n=10000, verbose=3)" + "# data = bnlearn.import_example(data=\"sachs\", n=10000, verbose=3)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 122, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Erk Akt PKA Mek Jnk PKC Raf P38 PIP3 PIP2 Plcg\n", - "0 1 0 1 1 0 0 1 0 2 0 0\n", - "1 2 1 2 0 0 0 0 0 1 0 0\n", - "2 0 0 0 0 1 1 0 0 2 0 0\n", - "3 1 0 1 1 0 1 0 0 0 1 2\n", - "4 1 1 1 0 1 1 0 0 2 0 0\n", " Raf Mek Plcg PIP2 PIP3 Erk Akt PKA PKC P38 Jnk INT\n", "0 1 1 1 2 3 2 1 3 1 2 1 8\n", "1 1 1 1 1 3 3 2 3 1 2 1 8\n", "2 1 1 2 2 3 2 1 3 2 1 1 8\n", "3 1 1 1 1 3 2 1 3 1 3 1 8\n", - "4 1 1 1 1 3 2 1 3 1 1 1 8\n" + "4 1 1 1 1 3 2 1 3 1 1 1 8\n", + "(5400, 12)\n" ] } ], "source": [ - "print(data.head())\n", - "print(df.head())" + "print(df.head())\n", + "print(df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Raf [1, 3, 2]\n", + "Mek [1, 2, 3]\n", + "Plcg [1, 2, 3]\n", + "PIP2 [2, 1, 3]\n", + "PIP3 [3, 2, 1]\n", + "Erk [2, 3, 1]\n", + "Akt [1, 2, 3]\n", + "PKA [3, 2, 1]\n", + "PKC [1, 2, 3]\n", + "P38 [2, 1, 3]\n", + "Jnk [1, 2, 3]\n", + "INT [8, 0, 7, 9, 4, 2]\n", + "dtype: object" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.apply(lambda x: x.unique())" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -196,14 +204,15 @@ } ], "source": [ - "perturbations = [df.columns[perturbed_col] for perturbed_col in df['INT'].unique()]\n", + "perturbations = [df.columns[perturbed_col] for perturbed_col in df[\"INT\"].unique()]\n", + "n_proteins = len(df.columns) - 1\n", "\n", "print(perturbations)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -218,8 +227,190 @@ "outputs": [ { "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Erk\n", + "\n", + "Erk\n", + "\n", + "\n", + "\n", + "Akt\n", + "\n", + "Akt\n", + "\n", + "\n", + "\n", + "Erk->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA\n", + "\n", + "PKA\n", + "\n", + "\n", + "\n", + "PKA->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Mek\n", + "\n", + "Mek\n", + "\n", + "\n", + "\n", + "PKA->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKA->Akt\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf\n", + "\n", + "Raf\n", + "\n", + "\n", + "\n", + "PKA->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Jnk\n", + "\n", + "Jnk\n", + "\n", + "\n", + "\n", + "PKA->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "P38\n", + "\n", + "P38\n", + "\n", + "\n", + "\n", + "PKA->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Mek->Erk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC\n", + "\n", + "PKC\n", + "\n", + "\n", + "\n", + "PKC->PKA\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Raf\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->Jnk\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PKC->P38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Raf->Mek\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "PIP3\n", + "\n", + "PIP3\n", + "\n", + "\n", + "\n", + "PIP2\n", + "\n", + "PIP2\n", + "\n", + "\n", + "\n", + "PIP3->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg\n", + "\n", + "Plcg\n", + "\n", + "\n", + "\n", + "Plcg->PIP3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Plcg->PIP2\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], "text/plain": [ - "'/Users/adam2392/Dropbox/Apps/Overleaf/Learning selection diagrams (observational)/Figures/Appendix/ground_truth_sachs_bnlearn.pdf'" + "" ] }, "execution_count": 11, @@ -228,18 +419,444 @@ } ], "source": [ - "ground_truth_G = ground_truth_dag['model'].to_directed()\n", - "G = draw(ground_truth_G, direction='TD', shape='circle')\n", - "G.render(\n", - " outfile=\"/Users/adam2392/Dropbox/Apps/Overleaf/Learning selection diagrams (observational)/Figures/Appendix/ground_truth_sachs_bnlearn.pdf\",\n", - " format=\"pdf\",\n", - " cleanup=True,\n", - ")" + "ground_truth_G = ground_truth_dag[\"model\"].to_directed()\n", + "G = draw(ground_truth_G, direction=\"TD\", shape=\"circle\")\n", + "G\n", + "# G.render(\n", + "# outfile=\"/Users/adam2392/Dropbox/Apps/Overleaf/Learning selection diagrams (observational)/Figures/Appendix/ground_truth_sachs_bnlearn.pdf\",\n", + "# format=\"pdf\",\n", + "# cleanup=True,\n", + "# )\n", + "# ['PKC', 'Raf', 'PKA', 'P38', 'PIP3', 'Plcg']" ] }, { "cell_type": "markdown", "metadata": {}, + "source": [ + "# Generate Artificial Multi-Domain Discrete Interventional Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.18575566 0.13968558 0.67455876]\n", + "1.0\n" + ] + } + ], + "source": [ + "rng = np.random.default_rng(seed)\n", + "\n", + "# generate now bernoulli probability exogenous per protein\n", + "prior_protein_exp = rng.dirichlet(\n", + " rng.standard_gamma(rng.integers(1, 4), size=3), 1\n", + ").squeeze()\n", + "\n", + "outcome_values = np.array([1, 2, 3])\n", + "nodes_to_resample = np.array([\"Erk\", \"Mek\", \"PIP2\"])\n", + "\n", + "print(prior_protein_exp)\n", + "print(prior_protein_exp.sum(axis=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "def resample_dataset(\n", + " G,\n", + " df,\n", + " prior_multi_dist,\n", + " nodes_to_resample,\n", + " outcome_values,\n", + " n_samples=1000,\n", + " seed=12345,\n", + "):\n", + " rng = np.random.default_rng(seed)\n", + "\n", + " new_df = np.zeros((n_samples, len(df.columns)))\n", + " for idx in range(n_samples):\n", + " row_idx = rng.integers(0, len(df))\n", + "\n", + " new_df[idx, :] = df.iloc[row_idx, :]\n", + "\n", + " for jdx, node in enumerate(nodes_to_resample):\n", + " prior_dist = prior_multi_dist\n", + " col_idx = np.argwhere(df.columns == node).squeeze()\n", + "\n", + " # sample which index from 1, 2, or 3 it hit\n", + " new_sample_idx = rng.multinomial(1, pvals=prior_dist, size=1).squeeze()\n", + " new_sample = outcome_values[np.argwhere(new_sample_idx == 1).squeeze()]\n", + " new_df[idx, col_idx] = new_sample\n", + "\n", + " # print(\"new sample for \", node, new_sample)\n", + " # sample the children according to a re-weighted Dirichlet distribution\n", + " children = list(G.successors(node))\n", + " for child in children:\n", + " child_prior = prior_multi_dist.copy()\n", + " child_prior[new_sample_idx] *= new_sample\n", + " child_prior = rng.dirichlet(child_prior, 1)\n", + "\n", + " child_idx = np.argwhere(df.columns == child).squeeze()\n", + "\n", + " # sample which index from 1, 2, or 3 it hit for children\n", + " child_sample_idx = rng.multinomial(\n", + " 1, pvals=child_prior, size=1\n", + " ).squeeze()\n", + " child_sample = outcome_values[\n", + " np.argwhere(child_sample_idx == 1).squeeze()\n", + " ]\n", + " new_df[idx, child_idx] = child_sample\n", + " # print(\"New sample for \", child, child_sample)\n", + "\n", + " new_df = pd.DataFrame(new_df)\n", + " new_df.columns = df.columns\n", + " return new_df" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3]\n", + "['Erk' 'Mek' 'PIP2']\n" + ] + } + ], + "source": [ + "print(outcome_values)\n", + "print(nodes_to_resample)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "new_df = resample_dataset(\n", + " ground_truth_G,\n", + " df,\n", + " prior_multi_dist=prior_protein_exp,\n", + " nodes_to_resample=nodes_to_resample,\n", + " outcome_values=outcome_values,\n", + " n_samples=5000,\n", + " seed=12345,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5000, 12)\n", + "Raf [1.0, 2.0, 3.0]\n", + "Mek [1.0, 3.0, 2.0]\n", + "Plcg [1.0, 3.0, 2.0]\n", + "PIP2 [3.0, 2.0, 1.0]\n", + "PIP3 [1.0, 3.0, 2.0]\n", + "Erk [3.0, 1.0, 2.0]\n", + "Akt [3.0, 1.0, 2.0]\n", + "PKA [2.0, 1.0, 3.0]\n", + "PKC [2.0, 1.0, 3.0]\n", + "P38 [1.0, 2.0, 3.0]\n", + "Jnk [2.0, 1.0, 3.0]\n", + "INT [0.0, 7.0, 4.0, 9.0, 8.0, 2.0]\n", + "dtype: object\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Raf Mek Plcg PIP2 PIP3 Erk Akt PKA PKC P38 Jnk INT\n", + "0 1.0 1.0 1.0 3.0 1.0 3.0 3.0 2.0 2.0 1.0 2.0 0.0\n", + "1 1.0 1.0 1.0 2.0 3.0 1.0 3.0 2.0 2.0 1.0 1.0 7.0\n", + "2 2.0 3.0 1.0 3.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 4.0\n", + "3 3.0 3.0 1.0 3.0 3.0 1.0 3.0 2.0 1.0 1.0 1.0 7.0\n", + "4 1.0 3.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0 1.0 2.0 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(new_df.shape)\n", + "print(new_df.apply(lambda x: x.unique()))\n", + "display(new_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12 12 12\n" + ] + } + ], + "source": [ + "# %%\n", + "# Preprocess the dataset\n", + "# ----------------------\n", + "# Since the data is one dataframe, we need to process it into a form\n", + "# that is acceptable by dodiscover's :class:`constraint.PsiFCI` algorithm. We\n", + "# will form a list of separate dataframes.\n", + "unique_ints = df[\"INT\"].unique()\n", + "\n", + "# get the list of intervention targets and list of dataframe associated with each intervention\n", + "intervention_targets = []\n", + "data_cols = [col for col in df.columns if col != \"INT\"]\n", + "data = []\n", + "domain_ids = []\n", + "for interv_idx in unique_ints:\n", + " _data = df[df[\"INT\"] == interv_idx][data_cols]\n", + " data.append(_data)\n", + " intervention_targets.append(df.columns[interv_idx])\n", + " domain_ids.append(1)\n", + "\n", + " # append second domain\n", + " _data = new_df[new_df[\"INT\"] == interv_idx][data_cols]\n", + " data.append(_data)\n", + " intervention_targets.append(df.columns[interv_idx])\n", + " domain_ids.append(2)\n", + "\n", + "print(len(data), len(intervention_targets), len(domain_ids))" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)", + "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 112, in _test_xy_edges\n test_stat, pvalue = parallel_fun(\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 692, in evaluate_edge\n test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z, **kwargs)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/ci/g_test.py\", line 459, in test\n stat, pvalue = g_square_discrete(df, x_var, y_var, z_covariates, levels=self.levels)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/ci/g_test.py\", line 362, in g_square_discrete\n contingency_tble = _calculate_contingency_tble(\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/ci/g_test.py\", line 80, in _calculate_contingency_tble\n contingency_tble[idx, jdx, kdx] += 1\nIndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 428, in _process_worker\n r = call_item()\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 275, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 620, in __call__\n return self.func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n return [func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in \n return [func(*args, **kwargs)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 125, in _test_xy_edges\n raise Exception(e)\nException: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n\"\"\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_71125/2180799116.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;31m# run the algorithm using the :meth:`constraint.PsiFCI.fit` API, which is similar to scikit-learn's\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;31m# `fit` design. All fitted attributes contain an underscore at the end.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m learner = learner.fit(\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdomain_indices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdomain_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mintervention_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m )\n", + "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervention_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply_rule11\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mEquivalenceClass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mContext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1099\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 565\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 566\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 567\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 568\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m 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441\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 392\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[0;31m# Break a reference cycle with the exception in self._exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mException\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices" + ] + } + ], + "source": [ + "# Setup constraint-based learner\n", + "# ------------------------------\n", + "# Since we have access to interventional data, the causal discovery algorithm\n", + "# we will use that leverages CI and CD tests to estimate causal constraints\n", + "# is the Psi-FCI algorithm :footcite:`Jaber2020causal`.\n", + "\n", + "# Our dataset is comprised of discrete valued data, so we will utilize the\n", + "# G^2 (Chi-square) CI test.\n", + "ci_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "# Since our data is entirely discrete, we can also use the G^2 test as our\n", + "# CD test.\n", + "cd_estimator = GSquareCITest(data_type=\"discrete\")\n", + "\n", + "alpha = 0.05\n", + "learner = SFCI(\n", + " ci_estimator=ci_estimator,\n", + " cd_estimator=cd_estimator,\n", + " alpha=alpha,\n", + " max_combinations=10,\n", + " max_cond_set_size=4,\n", + " n_jobs=-1,\n", + ")\n", + "\n", + "# create context with information about the interventions\n", + "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", + "ctx: Context = ctx_builder.variables(data=data[0]).num_distributions(len(data)).build()\n", + "\n", + "# %%\n", + "# Run the learning process\n", + "# ------------------------\n", + "# We have setup our causal context and causal discovery learner, so we will now\n", + "# run the algorithm using the :meth:`constraint.PsiFCI.fit` API, which is similar to scikit-learn's\n", + "# `fit` design. All fitted attributes contain an underscore at the end.\n", + "learner = learner.fit(\n", + " data, ctx, domain_indices=domain_ids, intervention_targets=intervention_targets\n", + ")\n", + "\n", + "# %%\n", + "# Analyze the results\n", + "# ===================\n", + "# Now that we have learned the graph, we will show it here. Note differences and similarities\n", + "# to the ground-truth DAG that is \"assumed\". Moreover, note that this reproduces Supplementary\n", + "# Figure 8 in :footcite:`Jaber2020causal`.\n", + "est_pag = learner.graph_\n", + "\n", + "print(f\"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG\")\n", + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "# dot_graph = draw(est_pag, direction=\"LR\")\n", + "# dot_graph.render(outfile=\"psi_pag_full.png\", view=True, cleanup=True)\n", + "\n", + "# %%\n", + "# Visualize the graph without the F-nodes\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", + "dot_graph = draw(est_pag_no_fnodes, direction=\"LR\")\n", + "dot_graph.render(outfile=\"psi_pag.png\", view=True, cleanup=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, "source": [ "# Linear SCM Simulation: Generate Ground-Truth Data\n", "\n", diff --git a/doc/tutorials/multi-domain/random-graph-analysis.ipynb b/doc/tutorials/multi-domain/random-graph-analysis.ipynb index 923042d2e..6b23b84f5 100644 --- a/doc/tutorials/multi-domain/random-graph-analysis.ipynb +++ b/doc/tutorials/multi-domain/random-graph-analysis.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "5404ecd8-17bf-4fe6-93fd-dbc2fffb8df0", "metadata": {}, "outputs": [], @@ -62,6 +62,8 @@ "from dodiscover.constraint.utils import dummy_sample\n", "from dodiscover.datasets import sample_from_graph\n", "\n", + "from dodiscover.cd.residual import ResidualCDTest\n", + "\n", "from dodiscover import (\n", " SFCI,\n", " PsiFCI,\n", @@ -82,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "514e7715-203e-4362-8eda-6569a029a849", "metadata": {}, "outputs": [], @@ -92,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "3c9267d5-6845-404d-b2b1-8c991f0a516a", "metadata": {}, "outputs": [], @@ -102,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "07d7b26f-a025-42da-989a-2a245a86a440", "metadata": {}, "outputs": [], @@ -141,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "03e8ed34-ef56-496f-923a-04ba4e10894c", "metadata": {}, "outputs": [], @@ -151,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "48a7f96a-d9e6-4503-8c68-b368466f0dee", "metadata": {}, "outputs": [], @@ -161,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "id": "bc0b93f4-c7f4-49d9-8af9-4fece68c33f3", "metadata": {}, "outputs": [ @@ -169,20 +171,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4]\n", + "[5 6 7 8 9]\n", "[0.3 0.35 0.4 0.45 0.5 ]\n" ] } ], "source": [ "node_mean_lims = [-1, 1]\n", - "node_std_lims = [0.5, 1.5]\n", + "node_std_lims = [2.0, 3.5]\n", "edge_functions = [\n", " lambda x: x,\n", - " lambda x: x**2,\n", + " # lambda x: x**2,\n", "]\n", "edge_weight_lims = [1, 5]\n", - "n_node_grid = np.arange(4, 5)\n", + "n_node_grid = np.arange(5, 10)\n", "p_grid = np.linspace(0.3, 0.5, 5)\n", "n_domains_grid = np.arange(2, 10)\n", "n_repeats = 1\n", @@ -190,7 +192,7 @@ "max_cond_set_size = 3\n", "\n", "n_samples = 2000\n", - "ratio_interventions = 0.9\n", + "ratio_interventions = 0.5\n", "n_jobs = -1\n", "print(n_node_grid)\n", "print(p_grid)" @@ -198,35 +200,24 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 29, "id": "bde56fd2-6adc-4181-8efb-acdc2945ead8", "metadata": { "tags": [] }, "outputs": [ { - "ename": "Exception", - "evalue": "unhashable type: 'set'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)", - "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 113, in _test_xy_edges\n test_stat, pvalue = parallel_fun(\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 689, in evaluate_edge\n test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z)\nTypeError: unhashable type: 'set'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 428, in _process_worker\n r = call_item()\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py\", line 275, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 620, in __call__\n return self.func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n return [func(*args, **kwargs)\n File \"/Users/adam2392/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in \n return [func(*args, **kwargs)\n File \"/Users/adam2392/Documents/dodiscover/dodiscover/constraint/skeleton.py\", line 127, in _test_xy_edges\n raise Exception(e)\nException: unhashable type: 'set'\n\"\"\"", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/6_/sl83qtkd68x3_mvfys07_6qm0000gn/T/ipykernel_59486/2593794972.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_cond_set_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m )\n\u001b[0;32m--> 105\u001b[0;31m learner.fit(\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervention_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintervention_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply_rule11\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mEquivalenceClass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mContext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/intervention.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context)\u001b[0m\n\u001b[1;32m 187\u001b[0m )\n\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply_rule11\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mEquivalenceClass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mContext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 216\u001b[0m \u001b[0;31m# learn skeleton graph and the separating sets per variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m graph, self.separating_sets_ = self.learn_skeleton(\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseparating_sets_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m )\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/sfcialg.py\u001b[0m in \u001b[0;36mlearn_skeleton\u001b[0;34m(self, data, context, sep_set)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m )\n\u001b[0;32m---> 70\u001b[0;31m self.skeleton_learner_.fit(\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintervention_targets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m )\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, data, context, domain_indices, intervention_targets, check_input, debug)\u001b[0m\n\u001b[1;32m 1723\u001b[0m \u001b[0;34mf\"{this_f_nodes} grouped with S-node: {s_node}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1724\u001b[0m )\n\u001b[0;32m-> 1725\u001b[0;31m self._learn_skeleton(\n\u001b[0m\u001b[1;32m 1726\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/dodiscover/dodiscover/constraint/skeleton.py\u001b[0m in \u001b[0;36m_learn_skeleton\u001b[0;34m(self, data, context, condsel_method, conditional_test_func, possible_x_nodes, skipped_y_nodes, skipped_z_nodes, cross_distribution_test, group_with_snode)\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 452\u001b[0m \u001b[0;31m# run parallelized loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m out = Parallel(n_jobs=self.n_jobs)(\n\u001b[0m\u001b[1;32m 454\u001b[0m delayed(_test_xy_edges)(\n\u001b[1;32m 455\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_edge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1096\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1098\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1099\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1100\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 973\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 974\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 976\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 977\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 565\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 566\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 567\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 568\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 447\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/pywhy-discover/lib/python3.9/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 392\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[0;31m# Break a reference cycle with the exception in self._exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mException\u001b[0m: unhashable type: 'set'" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4 0]\n", + " [2 4]] [[4 0]\n", + " [2 4]]\n", + "2.0 2.0\n", + "[[4 0]\n", + " [2 4]]\n", + "[[4 0]\n", + " [2 4]]\n" ] } ], @@ -329,10 +320,11 @@ " # now learn the relationships\n", " learner = SFCI(\n", " ci_estimator=FisherZCITest(),\n", - " cd_estimator=KernelCDTest(null_reps=500),\n", + " # cd_estimator=KernelCDTest(null_reps=100),\n", + " cd_estimator=ResidualCDTest(),\n", " alpha=alpha,\n", " debug=False,\n", - " n_jobs=1,\n", + " n_jobs=n_jobs,\n", " max_cond_set_size=max_cond_set_size,\n", " )\n", " learner.fit(\n", @@ -358,7 +350,8 @@ " )\n", " learner = PsiFCI(\n", " ci_estimator=FisherZCITest(),\n", - " cd_estimator=KernelCDTest(null_reps=500),\n", + " # cd_estimator=KernelCDTest(null_reps=100),\n", + " cd_estimator=ResidualCDTest(),\n", " alpha=alpha,\n", " known_intervention_targets=True,\n", " n_jobs=n_jobs,\n", @@ -424,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 30, "id": "3c6cae59-7337-4d8b-bb8a-748e68a33d09", "metadata": {}, "outputs": [ @@ -432,10 +425,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "5\n", - "[{}, {}, {}, {}, {}, {2}, {3}, {0}]\n", - "8\n", - "0.3 4\n" + "2\n", + "[{}, {}, {4}, {1}]\n", + "4\n", + "0.3 5\n" ] } ], @@ -448,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 31, "id": "13c6c922-0ba5-4a59-88ee-a8090f9eb5a2", "metadata": {}, "outputs": [ @@ -456,24 +449,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[2 1]\n", - " [0 3]]\n", - "[[2 1]\n", - " [1 2]]\n", - "2.0 2.0\n" + "[[4 0]\n", + " [2 4]]\n", + "[[4 0]\n", + " [2 4]]\n" ] } ], "source": [ "# analyze directionality orietnations\n", - "shd_ipag = structure_hamming_dist_ec(\n", - " aug_lin_graph,\n", - " ipag.subgraph(non_augmented_nodes),\n", - ")\n", - "shd_spag = structure_hamming_dist_ec(\n", - " aug_lin_graph,\n", - " spag.subgraph(non_augmented_nodes),\n", - ")\n", + "# shd_ipag = structure_hamming_dist_ec(\n", + "# aug_lin_graph,\n", + "# ipag.subgraph(non_augmented_nodes),\n", + "# )\n", + "# shd_spag = structure_hamming_dist_ec(\n", + "# aug_lin_graph,\n", + "# spag.subgraph(non_augmented_nodes),\n", + "# )\n", "\n", "# analyze skeleton\n", "cm_ipag = confusion_matrix_networks(\n", @@ -487,12 +479,12 @@ "\n", "print(cm_ipag)\n", "print(cm_spag)\n", - "print(shd_ipag, shd_spag)" + "# print(shd_ipag, shd_spag)" ] }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 32, "id": "5d9d610c-4cce-4a5e-a1ba-ac9dedf68ad8", "metadata": {}, "outputs": [ @@ -505,10 +497,10 @@ "\n", "\n", - "\n", + "\n", "\n", - "\n", + "\n", "\n", "\n", "0\n", @@ -518,47 +510,71 @@ "\n", "\n", "2\n", - "\n", - "2\n", + "\n", + "2\n", "\n", "\n", "\n", "0->2\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "4\n", + "\n", + "\n", + "\n", + "0->4\n", + "\n", + "\n", "\n", "\n", "\n", "1\n", - "\n", - "1\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "1->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "1->4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "2->4\n", + "\n", + "\n", "\n", "\n", "\n", "3\n", - "\n", - "3\n", + "\n", + "3\n", "\n", - "\n", - "\n", - "1->3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "2->3\n", - "\n", - "\n", + "\n", + "\n", + "3->4\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 161, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -569,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 33, "id": "5aef1b41-0c62-4d35-a10d-213fca31b9a0", "metadata": {}, "outputs": [ @@ -582,67 +598,76 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "3\n", - "\n", - "3\n", + "4\n", + "\n", + "4\n", "\n", - "\n", + "\n", "\n", - "2\n", - "\n", - "2\n", + "0\n", + "\n", + "0\n", "\n", - "\n", + "\n", "\n", - "3->2\n", - "\n", - "\n", - "\n", + "4->0\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "0\n", - "\n", - "0\n", - "\n", - "\n", - "\n", - "0->3\n", - "\n", - "\n", + "1\n", + "\n", + "1\n", "\n", - "\n", + "\n", "\n", - "0->2\n", - "\n", - "\n", + "4->1\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "1\n", - "\n", - "1\n", + "2\n", + "\n", + "2\n", "\n", - "\n", + "\n", + "\n", + "4->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "3\n", + "\n", + "\n", "\n", - "1->3\n", - "\n", - "\n", + "4->3\n", + "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 162, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -653,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 34, "id": "2b48e391-59f0-42be-b21c-27382cde56c3", "metadata": {}, "outputs": [ @@ -666,70 +691,76 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "0\n", - "\n", - "0\n", + "4\n", + "\n", + "4\n", "\n", - "\n", + "\n", "\n", - "2\n", - "\n", - "2\n", + "0\n", + "\n", + "0\n", "\n", - "\n", + "\n", "\n", - "0->2\n", - "\n", - "\n", - "\n", + "4->0\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "3\n", - "\n", - "3\n", + "1\n", + "\n", + "1\n", "\n", - "\n", + "\n", "\n", - "3->0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "3->2\n", - "\n", - "\n", - "\n", + "4->1\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "1\n", - "\n", - "1\n", + "2\n", + "\n", + "2\n", "\n", - "\n", + "\n", "\n", - "3->1\n", - "\n", - "\n", - "\n", + "4->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "4->3\n", + "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 163, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } diff --git a/dodiscover/cd/base.py b/dodiscover/cd/base.py index f447c9e51..aea69c524 100644 --- a/dodiscover/cd/base.py +++ b/dodiscover/cd/base.py @@ -63,8 +63,8 @@ def _check_test_input( def test( self, df: pd.DataFrame, - y_vars: Set[Column], group_col: Set[Column], + y_vars: Set[Column], x_vars: Set[Column], ) -> Tuple[float, float]: """Abstract method for all conditional discrepancy tests. @@ -99,6 +99,11 @@ def test( def _compute_propensity_scores(self, group_ind, **kwargs): if self.propensity_model is None: K = kwargs.get("K") + if K is None: + # use the empirical propensities + empirical_propensity = group_ind.sum() / len(group_ind) + self.propensity_est_ = np.ones(len(group_ind)) * empirical_propensity + return self.propensity_est_ # compute a default penalty term if using a kernel matrix if K.shape[0] == K.shape[1]: @@ -131,12 +136,12 @@ def _compute_propensity_scores(self, group_ind, **kwargs): return self.propensity_est_ @abstractmethod - def _statistic(self, X: ArrayLike, Y: ArrayLike, group_ind: ArrayLike) -> float: + def _statistic(self, Y: ArrayLike, group_ind: ArrayLike, X: ArrayLike=None) -> float: """Abstract method for computing the test statistic.""" pass def compute_null( - self, e_hat: ArrayLike, X: ArrayLike, Y: ArrayLike, null_reps: int = 1000, random_state=None + self, e_hat: ArrayLike,Y: ArrayLike, X: ArrayLike=None, null_reps: int = 1000, random_state=None ) -> ArrayLike: """Estimate null distribution using propensity weights. @@ -144,10 +149,10 @@ def compute_null( ---------- e_hat : Array-like of shape (n_samples,) The predicted propensity score for ``group_ind == 1``. - X : Array-Like of shape (n_samples, n_features_x) - The X (covariates) array. Y : Array-Like of shape (n_samples, n_features_y) The Y (outcomes) array. + X : Array-Like of shape (n_samples, n_features_x) + The X (covariates) array. null_reps : int, optional Number of times to sample null, by default 1000. random_state : int, optional @@ -160,14 +165,14 @@ def compute_null( """ rng = np.random.default_rng(random_state) - n_samps = X.shape[0] + n_samps = Y.shape[0] # compute the test statistic on the conditionally permuted # dataset, where each group label is resampled for each sample # according to its propensity score null_dist = Parallel(n_jobs=self.n_jobs)( [ - delayed(self._statistic)(X, Y, rng.binomial(1, e_hat, size=n_samps)) + delayed(self._statistic)(Y, rng.binomial(1, e_hat, size=n_samps), X) for _ in range(null_reps) ] ) diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index c708c2719..5ff4bf8d3 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -75,8 +75,8 @@ def __init__( def test( self, df: pd.DataFrame, - y_vars: Set[Column], group_col: Set[Column], + y_vars: Set[Column], x_vars: Set[Column], ) -> Tuple[float, float]: # check test input diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index a2d68e524..7877ea2aa 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -92,44 +92,10 @@ def __init__( def test( self, df: pd.DataFrame, - y_vars: Set[Column], group_col: Set[Column], + y_vars: Set[Column], x_vars: Set[Column], ) -> Tuple[float, float]: - """Compute k-sample test statistic and pvalue. - - Tests the null hypothesis:: - - H_0: P(Y|X) = P'(Y|X) - - where the different distributions arise from the different datasets - collected denoted by the ``group_col`` parameter. It can also be written - as:: - - H_0: P(Y|X,T) = P(Y|X) - - meaning that :math:`Y \\perp X | T`, where T is the group indicator. - - Parameters - ---------- - df : pd.DataFrame - The dataset containing the columns denoted by ``x_vars``, ``y_vars``, - and the ``group_col``. - y_vars : Set[Column] - Set of Y variables. - group_col : Column - The column denoting, which group (i.e. environment) each sample belongs to. - This is typically the F-node. Must be binary. - x_vars : Set[Column] - Set of X variables. Can be the empty set. - - Returns - ------- - stat : float - The computed test statistic. - pvalue : float - The computed p-value. - """ # check test input self._check_test_input(df, y_vars, group_col, x_vars) group_col_var: Column = list(group_col)[0] @@ -143,13 +109,6 @@ def test( # compute kernel for the X and Y data X = df[x_cols].to_numpy() Y = df[y_cols].to_numpy() - K, sigma_x = compute_kernel( - X, - distance_metric=self.distance_metric, - metric=self.metric, - kwidth=self.kwidth_x, - n_jobs=self.n_jobs, - ) L, sigma_y = compute_kernel( Y, distance_metric=self.distance_metric, @@ -157,20 +116,34 @@ def test( kwidth=self.kwidth_y, n_jobs=self.n_jobs, ) - # store fitted attributes - self.kwidth_x_ = sigma_x self.kwidth_y_ = sigma_y + if len(x_vars) != 0: + K, sigma_x = compute_kernel( + X, + distance_metric=self.distance_metric, + metric=self.metric, + kwidth=self.kwidth_x, + n_jobs=self.n_jobs, + ) + else: + K = None + sigma_x = None + self.kwidth_x_ = sigma_x + # compute the statistic - stat = self._statistic(K, L, group_ind) + stat = self._statistic(L, group_ind, K=K) # compute propensity scores - e_hat = self._compute_propensity_scores(group_ind, K=K) + if K is None: + e_hat = self._compute_propensity_scores(group_ind) + else: + e_hat = self._compute_propensity_scores(group_ind, K=K) # now compute null distribution null_dist = self.compute_null( - e_hat, K, L, null_reps=self.null_reps, random_state=self.random_state + e_hat, L, X=K, null_reps=self.null_reps, random_state=self.random_state ) self.null_dist_ = null_dist @@ -178,11 +151,8 @@ def test( pvalue = (1 + np.sum(null_dist >= stat)) / (1 + self.null_reps) return stat, pvalue - def _statistic(self, K: ArrayLike, L: ArrayLike, group_ind: ArrayLike) -> float: - n_samples = len(K) - - # compute W matrices from K and z - W0, W1 = self._compute_inverse_kernel(K, group_ind) + def _statistic(self, L: ArrayLike, group_ind: ArrayLike, K: ArrayLike=None) -> float: + n_samples = len(L) # compute L kernels first_mask = np.array(1 - group_ind, dtype=bool) @@ -191,17 +161,26 @@ def _statistic(self, K: ArrayLike, L: ArrayLike, group_ind: ArrayLike) -> float: L1 = L[np.ix_(second_mask, second_mask)] L01 = L[np.ix_(first_mask, second_mask)] - # compute the final test statistic - K0 = K[:, first_mask] - K1 = K[:, second_mask] - KW0 = K0 @ W0 - KW1 = K1 @ W1 - - # compute the three terms in Lemma 4.4 - first_term = np.trace(KW0.T @ KW0 @ L0) - second_term = np.trace(KW1.T @ KW0 @ L01) - third_term = np.trace(KW1.T @ KW1 @ L1) - + if K is not None: + # compute W matrices from K and z + W0, W1 = self._compute_inverse_kernel(K, group_ind) + + # compute the final test statistic + K0 = K[:, first_mask] + K1 = K[:, second_mask] + KW0 = K0 @ W0 + KW1 = K1 @ W1 + + # compute the three terms in Lemma 4.4 + first_term = np.trace(KW0.T @ KW0 @ L0) + second_term = np.trace(KW1.T @ KW0 @ L01) + third_term = np.trace(KW1.T @ KW1 @ L1) + else: + # compute the three terms in Lemma 4.4 + first_term = np.trace(L0) + second_term = np.trace(L01) + third_term = np.trace(L1) + # compute final statistic stat = (first_term - 2 * second_term + third_term) / n_samples return stat diff --git a/dodiscover/cd/residual.py b/dodiscover/cd/residual.py new file mode 100644 index 000000000..771d355d4 --- /dev/null +++ b/dodiscover/cd/residual.py @@ -0,0 +1,154 @@ +from typing import Set, Tuple +import numpy as np +from numpy.typing import ArrayLike +import pandas as pd +from sklearn.metrics import r2_score + +from dodiscover.typing import Column + +from .base import BaseConditionalDiscrepancyTest + + +def invariant_residual_test( + X, + Y, + z, + method="gam", + test="ks", + method_kwargs={}, + return_model=False, + combine_pvalues=True, +): + r""" + Calulates the 2-sample test statistic. + + Parameters + ---------- + X : ndarray, shape (n, p) + Features to condition on + Y : ndarray, shape (n,) + Target or outcome features + z : list or ndarray, shape (n,) + List of zeros and ones indicating which samples belong to + which groups. + method : {"forest", "gam", "linear"}, default="gam" + Method to predict the target given the covariates + test : {"whitney_levene", "ks"}, default="ks" + Test of the residuals between the groups + method_kwargs : dict + Named arguments to pass to the prediction method. + return_model : boolean, default=False + If true, returns the fitted model + combine_pvalues: bool, default=True + If True, returns hte minimum of the corrected pvalues. + + Returns + ------- + pvalue : float + The computed *k*-sample p-value. + r2 : float + r2 score of the regression fit + model : object + Fitted regresion model, if return_model is True + """ + + if method == "forest": + from sklearn.ensemble import RandomForestRegressor + + predictor = RandomForestRegressor(max_features="sqrt", **method_kwargs) + elif method == "gam": + from sklearn.linear_model import LinearRegression + from sklearn.preprocessing import SplineTransformer + from sklearn.pipeline import Pipeline + from sklearn.model_selection import GridSearchCV + + pipe = Pipeline( + steps=[ + ("spline", SplineTransformer(n_knots=4, degree=3)), + ("linear", LinearRegression(**method_kwargs)), + ] + ) + param_grid = { + "spline__n_knots": [3, 5, 7, 9], + } + predictor = GridSearchCV( + pipe, param_grid, n_jobs=-2, refit=True, + scoring="neg_mean_squared_error" + ) + elif method == "linear": + from sklearn.linear_model import LinearRegression + + predictor = LinearRegression(**method_kwargs) + else: + raise ValueError(f"Method {method} not a valid option.") + + predictor = predictor.fit(X, Y) + Y_pred = predictor.predict(X) + residuals = Y - Y_pred + r2 = r2_score(Y, Y_pred) + + if test == "whitney_levene": + from scipy.stats import mannwhitneyu + from scipy.stats import levene + + _, mean_pval = mannwhitneyu( + residuals[np.asarray(z, dtype=bool)], + residuals[np.asarray(1 - z, dtype=bool)], + ) + _, var_pval = levene( + residuals[np.asarray(z, dtype=bool)], + residuals[np.asarray(1 - z, dtype=bool)], + ) + # Correct for multiple tests + if combine_pvalues: + pval = min(mean_pval * 2, var_pval * 2, 1) + else: + pval = (min(mean_pval * 2, 1), min(var_pval * 2, 1)) + elif test == "ks": + from scipy.stats import kstest + + _, pval = kstest( + residuals[np.asarray(z, dtype=bool)], + residuals[np.asarray(1 - z, dtype=bool)], + ) + else: + raise ValueError(f"Test {test} not a valid option.") + + if return_model: + return pval, r2, predictor + else: + return pval, r2 + + +class ResidualCDTest(BaseConditionalDiscrepancyTest): + + def __init__(self, method='gam', test_method='ks'): + super().__init__() + self.method = method + self.test_method = test_method + + def _statistic(self, Y, group_ind, X = None) -> float: + return super()._statistic(Y, group_ind, X) + + def test(self, df, group_col: Set[Column], y_vars: Set[Column], x_vars: Set[Column]) -> Tuple[float, float]: + X = df[list(x_vars)].values + Y = df[list(y_vars)].values + z = df[list(group_col)].values + + if x_vars == set(): + from scipy.stats import kstest + + stat, pval = kstest(Y[z==1], Y[z==0]) + else: + pval, r2 = invariant_residual_test( + X, + Y, + z, + method=self.method, + test=self.test_method, + method_kwargs={}, + return_model=False, + combine_pvalues=True, + ) + stat = r2 + return stat, pval \ No newline at end of file diff --git a/dodiscover/ci/kernel_utils.py b/dodiscover/ci/kernel_utils.py index 88130102d..988b2081d 100644 --- a/dodiscover/ci/kernel_utils.py +++ b/dodiscover/ci/kernel_utils.py @@ -200,6 +200,12 @@ def compute_kernel( med : float The estimated kernel width. """ + def check_2d(X): + if X is not None and X.ndim == 1: + X = X.reshape(-1, 1) + return X + X = check_2d(X) + # if the width of the kernel is not set, then use the median trick to set the # kernel width based on the data X if kwidth is None: diff --git a/dodiscover/ci/oracle.py b/dodiscover/ci/oracle.py index 67c5984a9..7e1683873 100644 --- a/dodiscover/ci/oracle.py +++ b/dodiscover/ci/oracle.py @@ -23,9 +23,10 @@ class Oracle(BaseConditionalIndependenceTest): _allow_multivariate_input: bool = True - def __init__(self, graph: Graph, included_nodes: Optional[Set[Column]] = None) -> None: + def __init__(self, graph: Graph, included_nodes: Optional[Set[Column]] = None, multivariate_x_vars=None) -> None: self.graph = graph self.included_nodes = included_nodes + self.multivariate_xvars = multivariate_x_vars def test( self, @@ -33,6 +34,7 @@ def test( x_vars: Set[Column], y_vars: Set[Column], z_covariates: Optional[Set[Column]] = None, + s_node: Optional[Column] = None, ): """Conditional independence test given an oracle. @@ -80,6 +82,9 @@ def test( else: z_covariates_ = set(z_covariates).union(included_nodes) + if s_node is not None: + x_vars.add(s_node) + # just check for d-separation between x and y given sep_set if isinstance(self.graph, nx.DiGraph): is_sep = nx.d_separated(self.graph, x_vars, y_vars, z_covariates_) diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index d2f570e08..a8a4152e4 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -10,7 +10,7 @@ from joblib import Parallel, delayed from dodiscover.cd import BaseConditionalDiscrepancyTest -from dodiscover.ci import BaseConditionalIndependenceTest +from dodiscover.ci import BaseConditionalIndependenceTest, Oracle from dodiscover.constraint.config import ConditioningSetSelection from dodiscover.constraint.utils import is_in_sep_set from dodiscover.context import Context @@ -38,7 +38,7 @@ def _test_xy_edges( data: pd.DataFrame, context: Context, cross_distribution_test: bool = False, - group_with_snode =None, + s_node =None, ) -> Dict[str, Any]: """Private function used to test edge between X and Y in parallel for candidate separating sets. @@ -81,8 +81,6 @@ def _test_xy_edges( size_cond_set=size_cond_set, ) - # TODO: figure out more elegant way of doing this - new_x_var = None # now iterate through the possible parents for comb_idx, cond_set in enumerate(conditioning_sets): # check the number of combinations of possible parents we have tried @@ -97,6 +95,8 @@ def _test_xy_edges( # get the sigma-map for this F-node distribution_idx = context.sigma_map[x_var] + # print(f'Got distribution indices for {x_var} as {distribution_idx}') + # get the distributions across the two distributions data_i = data[distribution_idx[0]].copy() data_j = data[distribution_idx[1]].copy() @@ -107,23 +107,21 @@ def _test_xy_edges( this_data = pd.concat((data_i, data_j), axis=0) try: - if group_with_snode is not None: - new_x_var = set([x_var, group_with_snode]) - # compute conditional independence test - test_stat, pvalue = parallel_fun( - this_data, conditional_test_func, new_x_var, y_var, set(cond_set) - ) - else: - # compute conditional independence test - test_stat, pvalue = parallel_fun( - this_data, conditional_test_func, x_var, y_var, set(cond_set) - ) + # compute conditional independence test + # print(s_node, x_var, y_var, conditional_test_func, parallel_fun) + test_stat, pvalue = parallel_fun( + this_data, conditional_test_func, x_var, y_var, set(cond_set), s_node=s_node + ) except Exception as e: if "Not enough samples." in str(e): print(e) test_stat = np.inf pvalue = 0.0 else: + print(x_var, y_var, cond_set) + print(this_data.columns) + print(this_data.head()) + print(this_data[x_var]) raise Exception(e) # if any "independence" is found through inability to reject @@ -136,7 +134,6 @@ def _test_xy_edges( result: Dict[str, Any] = dict() result["x_var"] = x_var - result['old_xvar'] = new_x_var result["y_var"] = y_var result["cond_set"] = list(cond_set) result["test_stat"] = test_stat @@ -321,7 +318,7 @@ def _learn_skeleton( skipped_y_nodes=None, skipped_z_nodes=None, cross_distribution_test: bool = False, - group_with_snode=None, + s_node=None, ): """Core function for learning the skeleton of a causal graph. @@ -445,7 +442,7 @@ def _learn_skeleton( data, context, cross_distribution_test, - group_with_snode=group_with_snode + s_node=s_node ) out.append(result) else: @@ -463,7 +460,7 @@ def _learn_skeleton( data, context, cross_distribution_test, - group_with_snode=group_with_snode + s_node=s_node ) for x_var, y_var, possible_variables in self._generate_pairs_with_sepset( possible_x_nodes, @@ -481,15 +478,9 @@ def _learn_skeleton( x_var = result["x_var"] y_var = result["y_var"] cond_set = result["cond_set"] - old_xvar = result["old_xvar"] - if group_with_snode is not None: - self._postprocess_ci_test(context, group_with_snode, y_var, test_stat, pvalue) - self._postprocess_ci_test(context, old_xvar, y_var, test_stat, pvalue) - else: - self._postprocess_ci_test(context, x_var, y_var, test_stat, pvalue) # post-process the CI test results - # self._postprocess_ci_test(context, x_var, y_var, test_stat, pvalue) + self._postprocess_ci_test(context, x_var, y_var, test_stat, pvalue) # two variables found to be independent given a separating set if pvalue > self.alpha: @@ -497,6 +488,12 @@ def _learn_skeleton( self.sep_set_[y_var][x_var].append(set(cond_set)) remove_edges.add((x_var, y_var, pvalue)) + if s_node is not None: + self.sep_set_[s_node][y_var].append(set(cond_set)) + self.sep_set_[s_node][y_var].append(set(cond_set)) + if context.init_graph.has_edge(s_node, y_var): + remove_edges.add((s_node, y_var, pvalue)) + # summarize the comparison of XY self._summarize_xy_comparison(x_var, y_var, pvalue > self.alpha, pvalue) @@ -663,6 +660,7 @@ def evaluate_edge( X: Column, Y: Column, Z: Optional[Set[Column]] = None, + **kwargs ) -> Tuple[float, float]: """Test any specific edge for X || Y | Z. @@ -676,6 +674,9 @@ def evaluate_edge( A column in ``data``. Z : set, optional A list of columns in ``data``, by default None. + **kwargs + Keyword arguments to be passed to the conditional independence test. + Allows S-nodes for example to be passed in. Returns ------- @@ -686,7 +687,9 @@ def evaluate_edge( """ if Z is None: Z = set() - test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z) + if not isinstance(conditional_test_func, Oracle): + kwargs = dict() + test_stat, pvalue = conditional_test_func.test(data, set({X}), set({Y}), Z, **kwargs) self.n_ci_tests += 1 return test_stat, pvalue @@ -1096,10 +1099,7 @@ def _initialize_params(self, context) -> Context: return super()._initialize_params(context) - def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): - if check_input: - context = self._initialize_params(context) - + def _fit_single_distribution(self, data, context: Context, possible_x_nodes, skipped_y_nodes, skipped_z_nodes, cross_distribution_test): # initially learn the skeleton without using PDS information # apply algorithm to learn skeleton self._learn_skeleton( @@ -1107,14 +1107,21 @@ def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): context=context, condsel_method=self.condsel_method, conditional_test_func=self.ci_estimator, + possible_x_nodes=possible_x_nodes, + skipped_y_nodes=skipped_y_nodes, + skipped_z_nodes=skipped_z_nodes, + cross_distribution_test=cross_distribution_test, ) + # reset context and add observational skeleton + context.add_state_variable("obs_skel_graph", context.init_graph.copy()) + # if there is no second stage skeleton method to be run, then we # will stop with the skeleton here if self.second_stage_condsel_method is None: self.context_ = deepcopy(context.copy()) self.adj_graph_ = deepcopy(context.init_graph.copy()) - return self + return context # setup context for the second round-of learning context = self._prep_second_stage_skeleton(context) @@ -1126,7 +1133,19 @@ def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): context=context, condsel_method=self.second_stage_condsel_method, conditional_test_func=self.ci_estimator, + possible_x_nodes=possible_x_nodes, + skipped_y_nodes=skipped_y_nodes, + skipped_z_nodes=skipped_z_nodes, + cross_distribution_test=cross_distribution_test, ) + return context + + def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): + if check_input: + context = self._initialize_params(context) + + # fit the distribution + context = self._fit_single_distribution(data, context, possible_x_nodes=None, skipped_y_nodes=None, skipped_z_nodes=None, cross_distribution_test=False) self.context_ = deepcopy(context.copy()) self.adj_graph_ = deepcopy(context.init_graph.copy()) @@ -1275,17 +1294,14 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr self.context_ = context.copy() - # first learn the skeleton using only "observational data" - self._learn_skeleton( + # learn skeleton + context = self._fit_single_distribution( data=obs_data, context=context, - condsel_method=self.condsel_method, - conditional_test_func=self.ci_estimator, possible_x_nodes=list(context.get_non_augmented_nodes()), skipped_y_nodes=context.f_nodes, skipped_z_nodes=context.f_nodes, - cross_distribution_test=False, - ) + cross_distribution_test=False) context = self._prep_second_stage_skeleton(context) @@ -1503,37 +1519,44 @@ def _create_augmented_nodes( # for each domain, determine if there is observational data domain_obs = dict() for domain in unique_domains: - this_domain_idx = np.argwhere(np.array(domain_ids) == domain).squeeze() + this_domain_idx = np.atleast_1d(np.argwhere(np.array(domain_ids) == domain).squeeze()) # now check all intervention targets - this_targets = np.atleast_1d(np.array(intervention_targets)[this_domain_idx]) - if set() in this_targets: + this_targets = [intervention_targets[idx] for idx in this_domain_idx] + if {} in this_targets or set() in this_targets: domain_obs[domain] = True else: domain_obs[domain] = False - # create S-nodes + # create S-nodes as N-domains choose 2 for idx, (source, target) in enumerate(combinations(unique_domains, 2)): s_node = ("S", idx) - node_domain_map[s_node] = {source, target} + node_domain_map[s_node] = [source, target] s_nodes.append(s_node) - # create F-nodes + # create F-nodes, which is now all combinations of distributions choose 2 k = 0 seen_domain_pairs = dict() seen_distr_pairs = dict() for idx, source in enumerate(domain_ids): for jdx, target in enumerate(domain_ids): - if jdx <= idx: - continue domain_memo_key = frozenset([source, target]) distr_memo_key = frozenset([idx, jdx]) + + if jdx <= idx: + continue if domain_memo_key in seen_domain_pairs and distr_memo_key in seen_distr_pairs: continue + seen_domain_pairs[distr_memo_key] = None seen_distr_pairs[domain_memo_key] = None + # check if we are dealing with two observational distributions, since those + # are assigned to S-nodes + if intervention_targets[idx] == set() and intervention_targets[jdx] == set(): + continue + # map each augmented-node to a tuple of distribution indices, or to a set of nodes # representing the intervention targets if intervention_targets[idx] is None or intervention_targets[jdx] is None: @@ -1547,18 +1570,20 @@ def _create_augmented_nodes( # get the S-node mapped to the obs data if there is observational data if domain_obs[source] and domain_obs[target] and targets == frozenset(): s_node = [ - key for key, val in node_domain_map.items() if val == {source, target} + key for key, val in node_domain_map.items() if set(val) == {source, target} ][0] sigma_map[s_node] = [idx, jdx] continue + elif targets == frozenset(): + # there is not interventions to compare + continue # create the F-node f_node = ("F", k) f_nodes.append(f_node) # map each F-node to a set of domain(s) - node_domain_map[f_node] = {source, target} - + node_domain_map[f_node] = [source, target] sigma_map[f_node] = [idx, jdx] symmetric_diff_map[f_node] = targets @@ -1596,26 +1621,17 @@ def fit( if isinstance(data, pd.DataFrame): data = [data] - # error-check the datasets passed in match the intervention contexts - # if len(data) != context.num_distributions: - # raise RuntimeError( - # f"The number of datasets does not match the number of interventions. " - # f"You passed in {len(data)} different datasets, whereas " - # f"there are {len(context.intervention_targets)} different interventions " - # f"specified and {context.num_distributions} distributions assumed. " - # f"It is assumed that the first dataset is observational, " - # f"while the rest are interventional." - # ) - # pick a domain and distribution with the largest amount of data largest_data_idx = np.argmax([len(df) for df in data]) obs_data = data[largest_data_idx] + print('Using data from distribution ', largest_data_idx, ' for learning the skeleton.') self.context_ = context.copy() # initialize learning parameters if check_input: context = self._initialize_params(context) + # create augmented nodes ( augmented_nodes, symmetric_diff_map, @@ -1625,8 +1641,13 @@ def fit( domain_ids=domain_indices, intervention_targets=intervention_targets ) - # initialize the augmented graph - causal_nodes = context.observed_variables + # initialize the augmented graph to be fully connected to observed casual variables + causal_nodes = set(context.observed_variables) + + # XXX: contextbuilder creates an augmented graph, whereas we want to control that. + for node in set(context.init_graph.nodes): + if node not in causal_nodes: + context.init_graph.remove_node(node) for augmented_node in augmented_nodes: for node in causal_nodes: context.init_graph.add_edge(augmented_node, node) @@ -1640,6 +1661,7 @@ def fit( elif node[0] == "F": f_nodes.append(node) + # provide multi-domain context n_domains = len(np.unique(domain_indices)) context.augmented_nodes = augmented_nodes context.symmetric_diff_map = symmetric_diff_map @@ -1647,35 +1669,21 @@ def fit( context.node_domain_map = node_domain_map context.s_nodes = s_nodes context.f_nodes = f_nodes + + # skeleton discovery should not condition on augmented nodes skip_nodes = augmented_nodes # first learn the skeleton using only "observational data" # initially learn the skeleton without using PDS information # apply algorithm to learn skeleton # first learn the skeleton using only "observational data" - self._learn_skeleton( + self._fit_single_distribution( data=obs_data, context=context, - condsel_method=self.condsel_method, - conditional_test_func=self.ci_estimator, - possible_x_nodes=list(context.get_non_augmented_nodes()), - skipped_y_nodes=context.get_augmented_nodes(), - skipped_z_nodes=context.get_augmented_nodes(), - cross_distribution_test=False, - ) - - context = self._prep_second_stage_skeleton(context) - - # secibd learn the skeleton using only "PDS data" - self._learn_skeleton( - data=obs_data, - context=context, - condsel_method=self.second_stage_condsel_method, - conditional_test_func=self.ci_estimator, - possible_x_nodes=list(context.get_non_augmented_nodes()), - skipped_y_nodes=context.get_augmented_nodes(), - skipped_z_nodes=context.get_augmented_nodes(), - cross_distribution_test=False, + possible_x_nodes=causal_nodes, + skipped_y_nodes=skip_nodes, + skipped_z_nodes=skip_nodes, + cross_distribution_test=False ) # prepare the context object for the second stage of learning @@ -1692,12 +1700,31 @@ def fit( for idx in range(len(sep_sets)): self.sep_set_[x_var][y_var][idx].update(context.get_augmented_nodes()) - # loop through each domain to learn the F-node skeleton + # loop through each domain pair to learn the F-node skeleton seen_domain_pairs = set() for idx, source in enumerate(range(1, n_domains + 1)): + # analyze F-nodes only within the 'source' domain + source_fnodes = [node for node in augmented_nodes if set(node_domain_map[node]) == {source}] + if debug: + print(f"Trying to learn skeleton for {source} to remove F-nodes: {source_fnodes}") + if source_fnodes: + # apply algorithm to learn skeleton among the F-node subgraph within a single domain + self._learn_skeleton( + data=data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.cd_estimator, + possible_x_nodes=source_fnodes, + skipped_y_nodes=skip_nodes, + skipped_z_nodes=skip_nodes, + cross_distribution_test=True, + ) + for jdx, target in enumerate(range(1, n_domains + 1)): + # skip if source and target are the same domain if idx == jdx: continue + # skip if we have already seen this domain pair if frozenset([source, target]) in seen_domain_pairs: continue seen_domain_pairs.add(frozenset([source, target])) @@ -1706,47 +1733,23 @@ def fit( # analyze F-nodes between source and target s_node = None for node in s_nodes: - if node_domain_map[node] == {source, target}: + if set(node_domain_map[node]) == {source, target}: s_node = node break if s_node is None: - continue raise RuntimeError("wtf") - this_f_nodes = [ - node - for node in f_nodes - if node_domain_map[node] == {source, target} and node in symmetric_diff_map - ] - if debug: - print( - f"Trying to learn skeleton for {source} and {target} to remove F-nodes: " - f"{this_f_nodes} grouped with S-node: {s_node}" - ) - self._learn_skeleton( - data=data, - context=context, - condsel_method=self.second_stage_condsel_method, - conditional_test_func=self.cd_estimator, - possible_x_nodes=this_f_nodes, - skipped_y_nodes=skip_nodes, - skipped_z_nodes=skip_nodes, - cross_distribution_test=True, - group_with_snode=s_node, - # debug=debug, - ) - + # this is only possible if there is explicitly observational data between # the two domains # analyze S-nodes between source and target this_s_nodes = [ node for node in augmented_nodes - if node_domain_map[node] == {source, target} + if set(node_domain_map[node]) == {source, target} and node not in symmetric_diff_map and node in sigma_map ] if debug: - print(this_f_nodes) print(this_s_nodes) print(symmetric_diff_map) print(sigma_map) @@ -1756,6 +1759,9 @@ def fit( f"Trying to learn skeleton for {source} and {target} to remove " f"S-nodes: {this_s_nodes}" ) + # print(this_s_nodes) + # print(symmetric_diff_map) + # print(sigma_map) self._learn_skeleton( data=data, context=context, @@ -1767,21 +1773,29 @@ def fit( cross_distribution_test=True, ) - # analyze F-nodes only within the 'source' domain - source_fnodes = [node for node in augmented_nodes if node_domain_map[node] == {source}] - if debug: - print(f"Trying to learn skeleton for {source} to remove F-nodes: {source_fnodes}") - # apply algorithm to learn skeleton among the F-node subgraph within a single domain - self._learn_skeleton( - data=data, - context=context, - condsel_method=self.second_stage_condsel_method, - conditional_test_func=self.cd_estimator, - possible_x_nodes=source_fnodes, - skipped_y_nodes=skip_nodes, - skipped_z_nodes=skip_nodes, - cross_distribution_test=True, - ) + # now learn across interventions + this_f_nodes = [ + node + for node in f_nodes + if set(node_domain_map[node]) == {source, target} and node in symmetric_diff_map + ] + if debug: + print( + f"Trying to learn skeleton for {source} and {target} to remove F-nodes: " + f"{this_f_nodes} grouped with S-node: {s_node}" + ) + self._learn_skeleton( + data=data, + context=context, + condsel_method=self.second_stage_condsel_method, + conditional_test_func=self.cd_estimator, + possible_x_nodes=this_f_nodes, + skipped_y_nodes=skip_nodes, + skipped_z_nodes=skip_nodes, + cross_distribution_test=True, + s_node=s_node + # debug=debug, + ) # prepare the context object for the second stage of learning # all separating sets are either: diff --git a/dodiscover/metrics.py b/dodiscover/metrics.py index f47b636e3..d33f9d3e6 100644 --- a/dodiscover/metrics.py +++ b/dodiscover/metrics.py @@ -146,3 +146,5 @@ def structure_hamming_dist( diff = diff + diff.T diff[diff > 1] = 1 # Ignoring the double edges. return np.sum(diff) / 2 + + diff --git a/examples/plot_sfci_alg.py b/examples/plot_sfci_alg.py index 663b56cc2..72f185ee1 100644 --- a/examples/plot_sfci_alg.py +++ b/examples/plot_sfci_alg.py @@ -74,7 +74,7 @@ intervention_targets = [df.columns[idx] for idx in unique_ints] data_cols = [col for col in df.columns if col != "INT"] data = [] -domain_ids = np.array([0, 0, 0, 0, 0, 1]) +domain_ids = np.array([1,1,1,1,1,1]) for interv_idx in unique_ints: _data = df[df["INT"] == interv_idx][data_cols] data.append(_data) diff --git a/examples/plot_sfci_with_artificial_sachs.py b/examples/plot_sfci_with_artificial_sachs.py new file mode 100644 index 000000000..6abf0fdfb --- /dev/null +++ b/examples/plot_sfci_with_artificial_sachs.py @@ -0,0 +1,207 @@ +from pprint import pprint +import numpy as np +import scipy +import pandas as pd +import collections +from itertools import combinations +import bnlearn +import pooch +from cdt.data import load_dataset + +from pywhy_graphs.functional import ( + make_graph_linear_gaussian, + make_graph_multidomain, + set_node_attributes_with_G, + apply_linear_soft_intervention, + sample_multidomain_lin_functions, +) +from pywhy_graphs.classes import AugmentedGraph +from pywhy_graphs.viz import draw + +from dodiscover.cd import KernelCDTest +from dodiscover.ci import KernelCITest, FisherZCITest, Oracle, GSquareCITest +from dodiscover.constraint.skeleton import LearnMultiDomainSkeleton +from dodiscover.datasets import sample_from_graph + +from dodiscover import PsiFCI, SFCI, Context, make_context, InterventionalContextBuilder + +def resample_dataset( + G, + df, + prior_multi_dist, + nodes_to_resample, + outcome_values, + n_samples=1000, + seed=12345, +): + rng = np.random.default_rng(seed) + + new_df = np.zeros((n_samples, len(df.columns))) + for idx in range(n_samples): + row_idx = rng.integers(0, len(df)) + + new_df[idx, :] = df.iloc[row_idx, :] + + for jdx, node in enumerate(nodes_to_resample): + prior_dist = prior_multi_dist + col_idx = np.argwhere(df.columns == node).squeeze() + + # sample which index from 1, 2, or 3 it hit + new_sample_idx = rng.multinomial(1, pvals=prior_dist, size=1).squeeze() + new_sample = outcome_values[np.argwhere(new_sample_idx == 1).squeeze()] + new_df[idx, col_idx] = new_sample + + # print("new sample for ", node, new_sample) + # sample the children according to a re-weighted Dirichlet distribution + children = list(G.successors(node)) + for child in children: + child_prior = prior_multi_dist.copy() + child_prior[new_sample_idx] *= new_sample + child_prior = rng.dirichlet(child_prior, 1) + + child_idx = np.argwhere(df.columns == child).squeeze() + + # sample which index from 1, 2, or 3 it hit for children + child_sample_idx = rng.multinomial( + 1, pvals=child_prior, size=1 + ).squeeze() + child_sample = outcome_values[ + np.argwhere(child_sample_idx == 1).squeeze() + ] + new_df[idx, child_idx] = child_sample + # print("New sample for ", child, child_sample) + + new_df = pd.DataFrame(new_df) + new_df.columns = df.columns + return new_df + +seed = 1234 +n_jobs = -1 +rng = np.random.default_rng(seed) +alpha = 0.05 + +# use pooch to download robustly from a url +url = "https://www.bnlearn.com/book-crc/code/sachs.interventional.txt.gz" +file_path = pooch.retrieve( + url=url, + known_hash="md5:39ee257f7eeb94cb60e6177cf80c9544", +) + +df = pd.read_csv(file_path, delimiter=" ") + +perturbations = [df.columns[perturbed_col] for perturbed_col in df["INT"].unique()] +n_proteins = len(df.columns) - 1 + +print(perturbations) + +# the ground-truth dag is shown here: XXX: comment in when errors are fixed +ground_truth_dag = bnlearn.import_DAG("sachs", verbose=False) +ground_truth_G = ground_truth_dag["model"].to_directed() +G = draw(ground_truth_G, direction="TD", shape="circle") + +# generate now bernoulli probability exogenous per protein +prior_protein_exp = rng.dirichlet( + rng.standard_gamma(rng.integers(1, 4), size=3), 1 +).squeeze() + +outcome_values = np.array([1, 2, 3]) +nodes_to_resample = np.array(["Erk", "PKC", "PIP2"]) + +print(prior_protein_exp) +print(prior_protein_exp.sum(axis=0)) + +new_df = resample_dataset( + ground_truth_G, + df, + prior_multi_dist=prior_protein_exp, + nodes_to_resample=nodes_to_resample, + outcome_values=outcome_values, + n_samples=10000, + seed=12345, +) + +# %% +# Preprocess the dataset +# ---------------------- +# Since the data is one dataframe, we need to process it into a form +# that is acceptable by dodiscover's :class:`constraint.PsiFCI` algorithm. We +# will form a list of separate dataframes. +unique_ints = df["INT"].unique() + +# get the list of intervention targets and list of dataframe associated with each intervention +intervention_targets = [] +data_cols = [col for col in df.columns if col != "INT"] +data = [] +domain_ids = [] +for interv_idx in unique_ints: + _data = df[df["INT"] == interv_idx][data_cols].astype(int) + data.append(_data) + intervention_targets.append(df.columns[interv_idx]) + domain_ids.append(1) + + # append second domain + _data = new_df[new_df["INT"] == interv_idx][data_cols].astype(int) + data.append(_data) + intervention_targets.append(df.columns[interv_idx]) + domain_ids.append(2) + +print(len(data), len(intervention_targets), len(domain_ids)) + +# Setup constraint-based learner +# ------------------------------ +# Since we have access to interventional data, the causal discovery algorithm +# we will use that leverages CI and CD tests to estimate causal constraints +# is the Psi-FCI algorithm :footcite:`Jaber2020causal`. + +# Our dataset is comprised of discrete valued data, so we will utilize the +# G^2 (Chi-square) CI test. +ci_estimator = GSquareCITest(data_type="discrete") + +# Since our data is entirely discrete, we can also use the G^2 test as our +# CD test. +cd_estimator = GSquareCITest(data_type="discrete") + +alpha = 0.05 +learner = SFCI( + ci_estimator=ci_estimator, + cd_estimator=cd_estimator, + alpha=alpha, + max_combinations=10, + max_cond_set_size=4, + n_jobs=-1, +) + +# create context with information about the interventions +ctx_builder = make_context(create_using=InterventionalContextBuilder) +ctx: Context = ctx_builder.variables(data=data[0]).num_distributions(len(data)).build() + +# %% +# Run the learning process +# ------------------------ +# We have setup our causal context and causal discovery learner, so we will now +# run the algorithm using the :meth:`constraint.PsiFCI.fit` API, which is similar to scikit-learn's +# `fit` design. All fitted attributes contain an underscore at the end. +learner = learner.fit( + data, ctx, domain_indices=domain_ids, intervention_targets=intervention_targets +) + +# %% +# Analyze the results +# =================== +# Now that we have learned the graph, we will show it here. Note differences and similarities +# to the ground-truth DAG that is "assumed". Moreover, note that this reproduces Supplementary +# Figure 8 in :footcite:`Jaber2020causal`. +est_pag = learner.graph_ + +print(f"There are {len(est_pag.to_undirected().edges)} edges in the resulting PAG") + +# %% +# Visualize the full graph including the F-node +# dot_graph = draw(est_pag, direction="LR") +# dot_graph.render(outfile="psi_pag_full.png", view=True, cleanup=True) + +# %% +# Visualize the graph without the F-nodes +est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes()) +dot_graph = draw(est_pag_no_fnodes, direction="LR") +dot_graph.render(outfile="psi_pag.png", view=True, cleanup=True) \ No newline at end of file diff --git a/tests/unit_tests/conditional/cd/test_cd.py b/tests/unit_tests/conditional/cd/test_cd.py index 8cef4449f..f4fe6689c 100644 --- a/tests/unit_tests/conditional/cd/test_cd.py +++ b/tests/unit_tests/conditional/cd/test_cd.py @@ -4,7 +4,11 @@ from sklearn.ensemble import RandomForestClassifier from dodiscover.cd import BregmanCDTest, KernelCDTest +from pywhy_graphs import AugmentedGraph +from pywhy_graphs.functional import sample_multidomain_lin_functions, make_graph_linear_gaussian + +from dodiscover.datasets import sample_from_graph seed = 12345 # number of samples to use in generating test dataset; the lower the faster @@ -54,12 +58,12 @@ def test_cd_tests_error(cd_func): sample_df = single_env_scm(n_samples=10) cd_estimator = cd_func() with pytest.raises(ValueError, match="The group col"): - cd_estimator.test(sample_df, {y}, group_col={"blah"}, x_vars={x}) + cd_estimator.test(sample_df, y_vars={y}, group_col={"blah"}, x_vars={x}) with pytest.raises(ValueError, match="The x variables are not all"): cd_estimator.test(sample_df, y_vars={y}, group_col={"group"}, x_vars={"blah"}) - with pytest.raises(ValueError, match="The y variables are not all"): + with pytest.raises(ValueError, match="The y variables"): cd_estimator.test(sample_df, y_vars={"blah"}, group_col={"group"}, x_vars={x}) with pytest.raises(ValueError, match="Group column should be only one column"): @@ -68,7 +72,7 @@ def test_cd_tests_error(cd_func): # all the group indicators have different values now from 0/1 sample_df["group"] = sample_df["group"] + 3 with pytest.raises(RuntimeError, match="Group indications in"): - cd_estimator.test(sample_df, {y}, group_col={"group"}, x_vars={x}) + cd_estimator.test(sample_df, y_vars={y}, group_col={"group"}, x_vars={x}) # test pre-fit propensity scores, or custom propensity model with pytest.raises( @@ -117,19 +121,81 @@ def test_cd_simulation(cd_func, df, env_type, cd_kwargs): if env_type == "single": _, pvalue = cd_estimator.test( df, - {"x1"}, - {group_col}, - {"x"}, + y_vars={"x1"}, + group_col={group_col}, + x_vars={"x"}, ) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" - _, pvalue = cd_estimator.test(df, {"z"}, {group_col}, {"x"}) + _, pvalue = cd_estimator.test(df, y_vars={"z"}, group_col={group_col}, x_vars={"x"}) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" - _, pvalue = cd_estimator.test(df, {"y"}, {group_col}, {"x"}) + _, pvalue = cd_estimator.test(df, y_vars={"y"}, group_col={group_col}, x_vars={"x"}) assert pvalue > alpha, f"Fails with {pvalue} not greater than {alpha}" elif env_type == "multi": - _, pvalue = cd_estimator.test(df, {"z"}, {group_col}, {"x"}) + _, pvalue = cd_estimator.test(df, y_vars={"z"}, group_col={group_col}, x_vars={"x"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" - _, pvalue = cd_estimator.test(df, {"y"}, {group_col}, {"x"}) + _, pvalue = cd_estimator.test(df, y_vars={"y"}, group_col={group_col}, x_vars={"x"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" - _, pvalue = cd_estimator.test(df, {"z"}, {group_col}, {"x1"}) + _, pvalue = cd_estimator.test(df, y_vars={"z"}, group_col={group_col}, x_vars={"x1"}) assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" + + + + +@pytest.mark.parametrize( + ["cd_func", "cd_kwargs"], + [ + # [BregmanCDTest, dict()], + [KernelCDTest, dict()] + ]) +def test_cd_with_selection_diagram(cd_func, cd_kwargs): + alpha = 0.05 + + # create selection diagram S -> X -> Y + G = AugmentedGraph() + G.add_edge("x", "y", G.directed_edge_name) + G.add_s_node((1,2), {'x'}) + + # generate data from a selection diagram + # G = make_graph_linear_gaussian( + # G, + # # node_mean_lims=(-3, -1), + # # node_std_lims=(0.1, 0.2), + # random_state=seed + # ) + G = sample_multidomain_lin_functions( + G, + # node_mean_lims=[(3, 5), (100, 101)], + node_std_lims=[(0.1, 0.3), (2.0, 3.0)], + random_state=seed + ) + data = [] + for domain_id in G.domain_ids: + df = sample_from_graph(G, n_samples=50, sample_func='multidomain', random_state=seed, domain_id=domain_id) + df['domain_id'] = domain_id + data.append(df) + df = pd.concat(data, axis=0) + + # now test each conditional discrepancy test + cd_estimator = cd_func(random_state=seed, null_reps=15, n_jobs=-1, **cd_kwargs) + group_col = 'domain_id' + + # make domains all 0 or 1 + df[group_col] = df[group_col] - 1 + print(G.domains) + print(df[group_col].unique()) + + # P(X) != P'(X) + _, pvalue = cd_estimator.test(df, y_vars={"x"}, group_col={group_col}, x_vars=set()) + assert pvalue > alpha, f"Fails with {pvalue} not less than {alpha}" + + # P(X|Y) != P'(X|Y) + _, pvalue = cd_estimator.test(df, y_vars={"x"}, group_col={group_col}, x_vars={"y"}) + assert pvalue > alpha, f"Fails with {pvalue} not less than {alpha}" + + # P(Y) != P'(Y) + _, pvalue = cd_estimator.test(df, y_vars={"y"}, group_col={group_col}, x_vars=set()) + assert pvalue > alpha, f"Fails with {pvalue} not less than {alpha}" + + # P(Y|X) = P'(Y|X) + _, pvalue = cd_estimator.test(df, y_vars={"y"}, group_col={group_col}, x_vars={"x"}) + assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" \ No newline at end of file diff --git a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py index c8efab4d1..d4222ff4f 100644 --- a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py +++ b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py @@ -1,12 +1,14 @@ +import math +import numpy as np import networkx as nx import pywhy_graphs as pgraphs -from dodiscover import ContextBuilder, make_context +from dodiscover import ContextBuilder, make_context, InterventionalContextBuilder from dodiscover.cd import KernelCDTest from dodiscover.ci import FisherZCITest, Oracle from dodiscover.constraint.skeleton import LearnMultiDomainSkeleton from dodiscover.constraint.utils import dummy_sample -from dodiscover.datasets import linear +from dodiscover.datasets import sample_from_graph def basic_multidomain_augmented_graph(): @@ -29,6 +31,204 @@ def basic_multidomain_augmented_graph(): return graph + +def test_fnode_multidomain_skeleton_known_targets(): + """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`.""" + # first create the oracle + directed_edges = [ + ("x", "w"), + ("w", "y"), + ("z", "y"), + ] + bidirected_edges = [("x", "z"), ("z", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x"}) + oracle = Oracle(graph) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + ("x", "w"), + ("x", "z"), + ("x", "y"), + ("z", "y"), + ("w", "y"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_node(("F", 0)) + + # define the learner and the context + learner = LearnMultiDomainSkeleton( + ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=True + ) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .build() + ) + domain_indices = [1, 1] + intervention_targets = [{}, {'x'}] + learner.fit(data, context, domain_indices=domain_indices, intervention_targets=intervention_targets) + + # first check the observational skeleton + skel_graph = learner.adj_graph_ + obs_skel_graph = learner.context_.state_variable("obs_skel_graph").subgraph( + context.get_non_augmented_nodes() + ) + for edge in skel_graph.edges(): + if not expected_skeleton.has_edge(*edge): + print('extra edge: ', edge) + for edge in expected_skeleton.edges(): + if not skel_graph.has_edge(*edge): + print('missing edge: ', edge) + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + assert nx.is_isomorphic(expected_skeleton, skel_graph) + + +def test_number_augmented_nodes_created(): + """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`. + + However, this time, we have an S-node pointing to y. + """ + # first create the oracle + directed_edges = [ + ("x", "w"), + ("w", "y"), + ("z", "y"), + ] + bidirected_edges = [("x", "z"), ("z", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x"}, domain=1) + graph.add_s_node((1, 2), {"y"}) + oracle = Oracle(graph) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + (('S', 0), "y"), + (('S', 0), "x"), + ("x", "w"), + ("x", "z"), + ("x", "y"), + ("z", "y"), + ("w", "y"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_node(("F", 0)) + + # define the learner and the context + learner = LearnMultiDomainSkeleton( + ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=True + ) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .build() + ) + domain_indices = [1, 2, 2] + intervention_targets = [{}, {}, {'x'}] + + # test augmented nodes + augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map = learner._create_augmented_nodes(domain_indices, intervention_targets) + assert len(augmented_nodes) == math.comb(len(domain_indices), 2) + assert symmetric_diff_map == {('F', 0): frozenset({'x'}), ('F', 1): frozenset({'x'})} + assert sigma_map == {('F', 0): [0, 2], ('F', 1): [1, 2], ('S', 0): [0, 1]} + assert node_domain_map == {('F', 0): [1, 2], ('F', 1): [2, 2], ('S', 0): [1, 2]} + + + domain_indices = [1, 2, 2, 2, 2] + intervention_targets = [{}, {}, {3}, {2}, {3}] + + # test augmented nodes + augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map = learner._create_augmented_nodes(domain_indices, intervention_targets) + assert len(augmented_nodes) == math.comb(len(domain_indices), 2) - 1 + + +def test_fnode_multidomain_skeleton_known_targets_with_snode(): + """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`. + + However, this time, we have an S-node pointing to y. + """ + # first create the oracle + directed_edges = [ + ("x", "w"), + ("w", "y"), + ("z", "y"), + ] + bidirected_edges = [("x", "z"), ("z", "y")] + graph = pgraphs.AugmentedGraph( + incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges + ) + non_f_graph = graph.copy() + graph.add_f_node({"x"}, domain={1, 2}, require_unique=False) + graph.add_f_node({"x"}, domain={2}, require_unique=False) + graph.add_s_node((1, 2), {"y"}) + oracle = Oracle(graph) + + # define the expected graph we will learn + edges = [ + (("F", 0), "x"), + (("F", 0), "y"), + (("F", 1), "x"), + (("F", 1), "y"), + (('S', 0), "y"), + ("x", "w"), + ("x", "z"), + ("x", "y"), + ("z", "y"), + ("w", "y"), + ] + expected_skeleton = nx.Graph(edges) + obs_expected_skeleton = expected_skeleton.copy() + obs_expected_skeleton.remove_nodes_from(graph.augmented_nodes) + + # define the learner and the context + learner = LearnMultiDomainSkeleton( + ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=True, n_jobs=1 + ) + data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph), dummy_sample(non_f_graph)] + context = ( + make_context(create_using=InterventionalContextBuilder) + .variables(data=data[0]) + .build() + ) + domain_indices = [1, 2, 2] + intervention_targets = [{}, {}, {'x'}] + + # test augmented nodes + augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map = learner._create_augmented_nodes(domain_indices, intervention_targets) + assert len(augmented_nodes) == math.comb(len(domain_indices), 2) + + learner.fit(data, context, domain_indices=domain_indices, intervention_targets=intervention_targets) + + # first check the observational skeleton + skel_graph = learner.adj_graph_ + obs_skel_graph = learner.context_.state_variable("obs_skel_graph").subgraph( + context.get_non_augmented_nodes() + ) + for edge in skel_graph.edges(): + if not expected_skeleton.has_edge(*edge): + print('extra edge: ', edge) + for edge in expected_skeleton.edges(): + if not skel_graph.has_edge(*edge): + print('missing edge: ', edge) + assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) + assert nx.is_isomorphic(expected_skeleton, skel_graph) + + + def test_basic_multidomain_fsnode_skeleton(): """Test basic skeleton learning with a multidomain f-node and s-node.""" graph = basic_multidomain_augmented_graph() @@ -112,35 +312,42 @@ def test_basic_multidomain_fsnode_skeleton_with_lindata(): intervention_sets = [] # now for each F-node, apply a linear additive intervention - for f_node, targets in aug_graph.graph["F-nodes"].items(): - new_graph = pgraphs.functional.apply_soft_intervention( + for f_node, fnode_data in aug_graph.graph["F-nodes"].items(): + targets = fnode_data["targets"] + domains = fnode_data["domains"] + new_graph = pgraphs.functional.apply_linear_soft_intervention( graph.copy(), targets, random_state=seed ) # generate dataset - data = linear.sample_from_graph(new_graph, n_samples=n_samples, random_state=seed) + data = sample_from_graph(new_graph, n_samples=n_samples, random_state=seed) datasets.append(data) intervention_sets.append(targets) domain_ids.append(1) + print('Targets are: ', targets) + # now for each S-node, apply a linear additive intervention - for s_node, targets in aug_graph.graph["S-nodes"].items(): - new_graph = pgraphs.functional.apply_soft_intervention( - graph.copy(), targets, random_state=seed - ) + # for s_node, domains in aug_graph.graph["S-nodes"].items(): + # s_node_targets = list(aug_graph.children(s_node)) + # print(list(s_node_targets)) + # print(domains) + # new_graph = pgraphs.functional.apply_linear_soft_intervention( + # graph.copy(), s_node_targets, random_state=seed + # ) - # generate dataset - data = linear.sample_from_graph(new_graph, n_samples=n_samples, random_state=seed) + # # generate dataset + # data = sample_from_graph(new_graph, n_samples=n_samples, random_state=seed) - datasets.append(data) - intervention_sets.append(targets) - domain_ids.append(2) + # datasets.append(data) + # intervention_sets.append(s_node_targets) + # domain_ids.append(domains) learner = LearnMultiDomainSkeleton(ci_estimator=FisherZCITest(), cd_estimator=KernelCDTest()) context = make_context(create_using=ContextBuilder).variables(data=datasets[0]).build() - learner.fit(data, context, domain_ids, intervention_sets) + learner.fit(datasets, context, domain_ids, intervention_sets) # first check the observational skeleton skel_graph = learner.adj_graph_ From 23ac97f23e67ceb36e345c30fe28a8028db72248 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 28 Jun 2023 13:15:47 -0400 Subject: [PATCH 56/61] WIP Signed-off-by: Adam Li --- dodiscover/cd/base.py | 13 ++- dodiscover/cd/bregman.py | 4 + dodiscover/cd/kernel_test.py | 6 +- dodiscover/cd/residual.py | 28 +++---- dodiscover/ci/kernel_utils.py | 2 + dodiscover/ci/oracle.py | 4 +- dodiscover/constraint/skeleton.py | 42 +++++++--- dodiscover/metrics.py | 2 - examples/plot_sfci_alg.py | 2 +- examples/plot_sfci_with_artificial_sachs.py | 16 ++-- tests/unit_tests/conditional/cd/test_cd.py | 30 +++---- .../skeleton/test_multidomain_skeleton.py | 83 +++++++++++-------- 12 files changed, 135 insertions(+), 97 deletions(-) diff --git a/dodiscover/cd/base.py b/dodiscover/cd/base.py index aea69c524..e0a3e5153 100644 --- a/dodiscover/cd/base.py +++ b/dodiscover/cd/base.py @@ -39,7 +39,9 @@ def _check_test_input( if x_vars is not None and any(col not in df.columns for col in x_vars): raise ValueError("The x variables are not all in the DataFrame.") if any(col not in df.columns for col in y_vars): - raise ValueError(f"The y variables, {y_vars} are not all in the DataFrame: {df.columns}") + raise ValueError( + f"The y variables, {y_vars} are not all in the DataFrame: {df.columns}" + ) if group_col_var not in df.columns: raise ValueError(f"The group column {group_col_var} is not in the DataFrame.") @@ -136,12 +138,17 @@ def _compute_propensity_scores(self, group_ind, **kwargs): return self.propensity_est_ @abstractmethod - def _statistic(self, Y: ArrayLike, group_ind: ArrayLike, X: ArrayLike=None) -> float: + def _statistic(self, Y: ArrayLike, group_ind: ArrayLike, X: ArrayLike = None) -> float: """Abstract method for computing the test statistic.""" pass def compute_null( - self, e_hat: ArrayLike,Y: ArrayLike, X: ArrayLike=None, null_reps: int = 1000, random_state=None + self, + e_hat: ArrayLike, + Y: ArrayLike, + X: ArrayLike = None, + null_reps: int = 1000, + random_state=None, ) -> ArrayLike: """Estimate null distribution using propensity weights. diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index 5ff4bf8d3..7779156b9 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -119,6 +119,10 @@ def _statistic(self, X: ArrayLike, Y: ArrayLike, group_ind: ArrayLike) -> float: Y1 = Y[first_group, :] Y2 = Y[second_group, :] + print('dodiscover') + print(X1.shape, X2.shape, Y1.shape, Y2.shape) + print(X1[:2], X2[:2], Y1[:2], Y2[:2]) + # first compute the centered correntropy matrices, C_xy^1 Cx1y1 = corrent_matrix(np.hstack((X1, Y1)), kwidth=self.kwidth) Cx2y2 = corrent_matrix(np.hstack((X2, Y2)), kwidth=self.kwidth) diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index 7877ea2aa..e4a5138a6 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -151,7 +151,7 @@ def test( pvalue = (1 + np.sum(null_dist >= stat)) / (1 + self.null_reps) return stat, pvalue - def _statistic(self, L: ArrayLike, group_ind: ArrayLike, K: ArrayLike=None) -> float: + def _statistic(self, L: ArrayLike, group_ind: ArrayLike, K: ArrayLike = None) -> float: n_samples = len(L) # compute L kernels @@ -170,7 +170,7 @@ def _statistic(self, L: ArrayLike, group_ind: ArrayLike, K: ArrayLike=None) -> f K1 = K[:, second_mask] KW0 = K0 @ W0 KW1 = K1 @ W1 - + # compute the three terms in Lemma 4.4 first_term = np.trace(KW0.T @ KW0 @ L0) second_term = np.trace(KW1.T @ KW0 @ L01) @@ -180,7 +180,7 @@ def _statistic(self, L: ArrayLike, group_ind: ArrayLike, K: ArrayLike=None) -> f first_term = np.trace(L0) second_term = np.trace(L01) third_term = np.trace(L1) - + # compute final statistic stat = (first_term - 2 * second_term + third_term) / n_samples return stat diff --git a/dodiscover/cd/residual.py b/dodiscover/cd/residual.py index 771d355d4..9dee51853 100644 --- a/dodiscover/cd/residual.py +++ b/dodiscover/cd/residual.py @@ -1,7 +1,8 @@ from typing import Set, Tuple + import numpy as np -from numpy.typing import ArrayLike import pandas as pd +from numpy.typing import ArrayLike from sklearn.metrics import r2_score from dodiscover.typing import Column @@ -58,9 +59,9 @@ def invariant_residual_test( predictor = RandomForestRegressor(max_features="sqrt", **method_kwargs) elif method == "gam": from sklearn.linear_model import LinearRegression - from sklearn.preprocessing import SplineTransformer - from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV + from sklearn.pipeline import Pipeline + from sklearn.preprocessing import SplineTransformer pipe = Pipeline( steps=[ @@ -72,8 +73,7 @@ def invariant_residual_test( "spline__n_knots": [3, 5, 7, 9], } predictor = GridSearchCV( - pipe, param_grid, n_jobs=-2, refit=True, - scoring="neg_mean_squared_error" + pipe, param_grid, n_jobs=-2, refit=True, scoring="neg_mean_squared_error" ) elif method == "linear": from sklearn.linear_model import LinearRegression @@ -88,8 +88,7 @@ def invariant_residual_test( r2 = r2_score(Y, Y_pred) if test == "whitney_levene": - from scipy.stats import mannwhitneyu - from scipy.stats import levene + from scipy.stats import levene, mannwhitneyu _, mean_pval = mannwhitneyu( residuals[np.asarray(z, dtype=bool)], @@ -118,19 +117,20 @@ def invariant_residual_test( return pval, r2, predictor else: return pval, r2 - -class ResidualCDTest(BaseConditionalDiscrepancyTest): - def __init__(self, method='gam', test_method='ks'): +class ResidualCDTest(BaseConditionalDiscrepancyTest): + def __init__(self, method="gam", test_method="ks"): super().__init__() self.method = method self.test_method = test_method - def _statistic(self, Y, group_ind, X = None) -> float: + def _statistic(self, Y, group_ind, X=None) -> float: return super()._statistic(Y, group_ind, X) - def test(self, df, group_col: Set[Column], y_vars: Set[Column], x_vars: Set[Column]) -> Tuple[float, float]: + def test( + self, df, group_col: Set[Column], y_vars: Set[Column], x_vars: Set[Column] + ) -> Tuple[float, float]: X = df[list(x_vars)].values Y = df[list(y_vars)].values z = df[list(group_col)].values @@ -138,7 +138,7 @@ def test(self, df, group_col: Set[Column], y_vars: Set[Column], x_vars: Set[Colu if x_vars == set(): from scipy.stats import kstest - stat, pval = kstest(Y[z==1], Y[z==0]) + stat, pval = kstest(Y[z == 1], Y[z == 0]) else: pval, r2 = invariant_residual_test( X, @@ -151,4 +151,4 @@ def test(self, df, group_col: Set[Column], y_vars: Set[Column], x_vars: Set[Colu combine_pvalues=True, ) stat = r2 - return stat, pval \ No newline at end of file + return stat, pval diff --git a/dodiscover/ci/kernel_utils.py b/dodiscover/ci/kernel_utils.py index 988b2081d..fa8e78670 100644 --- a/dodiscover/ci/kernel_utils.py +++ b/dodiscover/ci/kernel_utils.py @@ -200,10 +200,12 @@ def compute_kernel( med : float The estimated kernel width. """ + def check_2d(X): if X is not None and X.ndim == 1: X = X.reshape(-1, 1) return X + X = check_2d(X) # if the width of the kernel is not set, then use the median trick to set the diff --git a/dodiscover/ci/oracle.py b/dodiscover/ci/oracle.py index 7e1683873..bdccb54ff 100644 --- a/dodiscover/ci/oracle.py +++ b/dodiscover/ci/oracle.py @@ -23,7 +23,9 @@ class Oracle(BaseConditionalIndependenceTest): _allow_multivariate_input: bool = True - def __init__(self, graph: Graph, included_nodes: Optional[Set[Column]] = None, multivariate_x_vars=None) -> None: + def __init__( + self, graph: Graph, included_nodes: Optional[Set[Column]] = None, multivariate_x_vars=None + ) -> None: self.graph = graph self.included_nodes = included_nodes self.multivariate_xvars = multivariate_x_vars diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index a8a4152e4..09185ec07 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -38,7 +38,7 @@ def _test_xy_edges( data: pd.DataFrame, context: Context, cross_distribution_test: bool = False, - s_node =None, + s_node=None, ) -> Dict[str, Any]: """Private function used to test edge between X and Y in parallel for candidate separating sets. @@ -442,7 +442,7 @@ def _learn_skeleton( data, context, cross_distribution_test, - s_node=s_node + s_node=s_node, ) out.append(result) else: @@ -460,7 +460,7 @@ def _learn_skeleton( data, context, cross_distribution_test, - s_node=s_node + s_node=s_node, ) for x_var, y_var, possible_variables in self._generate_pairs_with_sepset( possible_x_nodes, @@ -660,7 +660,7 @@ def evaluate_edge( X: Column, Y: Column, Z: Optional[Set[Column]] = None, - **kwargs + **kwargs, ) -> Tuple[float, float]: """Test any specific edge for X || Y | Z. @@ -1099,7 +1099,15 @@ def _initialize_params(self, context) -> Context: return super()._initialize_params(context) - def _fit_single_distribution(self, data, context: Context, possible_x_nodes, skipped_y_nodes, skipped_z_nodes, cross_distribution_test): + def _fit_single_distribution( + self, + data, + context: Context, + possible_x_nodes, + skipped_y_nodes, + skipped_z_nodes, + cross_distribution_test, + ): # initially learn the skeleton without using PDS information # apply algorithm to learn skeleton self._learn_skeleton( @@ -1145,7 +1153,14 @@ def fit(self, data: pd.DataFrame, context: Context, check_input: bool = True): context = self._initialize_params(context) # fit the distribution - context = self._fit_single_distribution(data, context, possible_x_nodes=None, skipped_y_nodes=None, skipped_z_nodes=None, cross_distribution_test=False) + context = self._fit_single_distribution( + data, + context, + possible_x_nodes=None, + skipped_y_nodes=None, + skipped_z_nodes=None, + cross_distribution_test=False, + ) self.context_ = deepcopy(context.copy()) self.adj_graph_ = deepcopy(context.init_graph.copy()) @@ -1301,7 +1316,8 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr possible_x_nodes=list(context.get_non_augmented_nodes()), skipped_y_nodes=context.f_nodes, skipped_z_nodes=context.f_nodes, - cross_distribution_test=False) + cross_distribution_test=False, + ) context = self._prep_second_stage_skeleton(context) @@ -1624,7 +1640,7 @@ def fit( # pick a domain and distribution with the largest amount of data largest_data_idx = np.argmax([len(df) for df in data]) obs_data = data[largest_data_idx] - print('Using data from distribution ', largest_data_idx, ' for learning the skeleton.') + print("Using data from distribution ", largest_data_idx, " for learning the skeleton.") self.context_ = context.copy() # initialize learning parameters @@ -1643,7 +1659,7 @@ def fit( # initialize the augmented graph to be fully connected to observed casual variables causal_nodes = set(context.observed_variables) - + # XXX: contextbuilder creates an augmented graph, whereas we want to control that. for node in set(context.init_graph.nodes): if node not in causal_nodes: @@ -1683,7 +1699,7 @@ def fit( possible_x_nodes=causal_nodes, skipped_y_nodes=skip_nodes, skipped_z_nodes=skip_nodes, - cross_distribution_test=False + cross_distribution_test=False, ) # prepare the context object for the second stage of learning @@ -1704,7 +1720,9 @@ def fit( seen_domain_pairs = set() for idx, source in enumerate(range(1, n_domains + 1)): # analyze F-nodes only within the 'source' domain - source_fnodes = [node for node in augmented_nodes if set(node_domain_map[node]) == {source}] + source_fnodes = [ + node for node in augmented_nodes if set(node_domain_map[node]) == {source} + ] if debug: print(f"Trying to learn skeleton for {source} to remove F-nodes: {source_fnodes}") if source_fnodes: @@ -1738,7 +1756,7 @@ def fit( break if s_node is None: raise RuntimeError("wtf") - + # this is only possible if there is explicitly observational data between # the two domains # analyze S-nodes between source and target diff --git a/dodiscover/metrics.py b/dodiscover/metrics.py index d33f9d3e6..f47b636e3 100644 --- a/dodiscover/metrics.py +++ b/dodiscover/metrics.py @@ -146,5 +146,3 @@ def structure_hamming_dist( diff = diff + diff.T diff[diff > 1] = 1 # Ignoring the double edges. return np.sum(diff) / 2 - - diff --git a/examples/plot_sfci_alg.py b/examples/plot_sfci_alg.py index 72f185ee1..a5cd5d6fc 100644 --- a/examples/plot_sfci_alg.py +++ b/examples/plot_sfci_alg.py @@ -74,7 +74,7 @@ intervention_targets = [df.columns[idx] for idx in unique_ints] data_cols = [col for col in df.columns if col != "INT"] data = [] -domain_ids = np.array([1,1,1,1,1,1]) +domain_ids = np.array([1, 1, 1, 1, 1, 1]) for interv_idx in unique_ints: _data = df[df["INT"] == interv_idx][data_cols] data.append(_data) diff --git a/examples/plot_sfci_with_artificial_sachs.py b/examples/plot_sfci_with_artificial_sachs.py index 6abf0fdfb..982c6c5ec 100644 --- a/examples/plot_sfci_with_artificial_sachs.py +++ b/examples/plot_sfci_with_artificial_sachs.py @@ -25,6 +25,7 @@ from dodiscover import PsiFCI, SFCI, Context, make_context, InterventionalContextBuilder + def resample_dataset( G, df, @@ -62,12 +63,8 @@ def resample_dataset( child_idx = np.argwhere(df.columns == child).squeeze() # sample which index from 1, 2, or 3 it hit for children - child_sample_idx = rng.multinomial( - 1, pvals=child_prior, size=1 - ).squeeze() - child_sample = outcome_values[ - np.argwhere(child_sample_idx == 1).squeeze() - ] + child_sample_idx = rng.multinomial(1, pvals=child_prior, size=1).squeeze() + child_sample = outcome_values[np.argwhere(child_sample_idx == 1).squeeze()] new_df[idx, child_idx] = child_sample # print("New sample for ", child, child_sample) @@ -75,6 +72,7 @@ def resample_dataset( new_df.columns = df.columns return new_df + seed = 1234 n_jobs = -1 rng = np.random.default_rng(seed) @@ -100,9 +98,7 @@ def resample_dataset( G = draw(ground_truth_G, direction="TD", shape="circle") # generate now bernoulli probability exogenous per protein -prior_protein_exp = rng.dirichlet( - rng.standard_gamma(rng.integers(1, 4), size=3), 1 -).squeeze() +prior_protein_exp = rng.dirichlet(rng.standard_gamma(rng.integers(1, 4), size=3), 1).squeeze() outcome_values = np.array([1, 2, 3]) nodes_to_resample = np.array(["Erk", "PKC", "PIP2"]) @@ -204,4 +200,4 @@ def resample_dataset( # Visualize the graph without the F-nodes est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes()) dot_graph = draw(est_pag_no_fnodes, direction="LR") -dot_graph.render(outfile="psi_pag.png", view=True, cleanup=True) \ No newline at end of file +dot_graph.render(outfile="psi_pag.png", view=True, cleanup=True) diff --git a/tests/unit_tests/conditional/cd/test_cd.py b/tests/unit_tests/conditional/cd/test_cd.py index f4fe6689c..cf2474cdb 100644 --- a/tests/unit_tests/conditional/cd/test_cd.py +++ b/tests/unit_tests/conditional/cd/test_cd.py @@ -1,14 +1,13 @@ import numpy as np import pandas as pd import pytest +from pywhy_graphs import AugmentedGraph +from pywhy_graphs.functional import make_graph_linear_gaussian, sample_multidomain_lin_functions from sklearn.ensemble import RandomForestClassifier from dodiscover.cd import BregmanCDTest, KernelCDTest -from pywhy_graphs import AugmentedGraph - -from pywhy_graphs.functional import sample_multidomain_lin_functions, make_graph_linear_gaussian - from dodiscover.datasets import sample_from_graph + seed = 12345 # number of samples to use in generating test dataset; the lower the faster @@ -139,21 +138,20 @@ def test_cd_simulation(cd_func, df, env_type, cd_kwargs): assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" - - @pytest.mark.parametrize( ["cd_func", "cd_kwargs"], [ # [BregmanCDTest, dict()], [KernelCDTest, dict()] - ]) + ], +) def test_cd_with_selection_diagram(cd_func, cd_kwargs): alpha = 0.05 # create selection diagram S -> X -> Y G = AugmentedGraph() G.add_edge("x", "y", G.directed_edge_name) - G.add_s_node((1,2), {'x'}) + G.add_s_node((1, 2), {"x"}) # generate data from a selection diagram # G = make_graph_linear_gaussian( @@ -163,27 +161,29 @@ def test_cd_with_selection_diagram(cd_func, cd_kwargs): # random_state=seed # ) G = sample_multidomain_lin_functions( - G, + G, # node_mean_lims=[(3, 5), (100, 101)], node_std_lims=[(0.1, 0.3), (2.0, 3.0)], - random_state=seed + random_state=seed, ) data = [] for domain_id in G.domain_ids: - df = sample_from_graph(G, n_samples=50, sample_func='multidomain', random_state=seed, domain_id=domain_id) - df['domain_id'] = domain_id + df = sample_from_graph( + G, n_samples=50, sample_func="multidomain", random_state=seed, domain_id=domain_id + ) + df["domain_id"] = domain_id data.append(df) df = pd.concat(data, axis=0) # now test each conditional discrepancy test cd_estimator = cd_func(random_state=seed, null_reps=15, n_jobs=-1, **cd_kwargs) - group_col = 'domain_id' + group_col = "domain_id" # make domains all 0 or 1 df[group_col] = df[group_col] - 1 print(G.domains) print(df[group_col].unique()) - + # P(X) != P'(X) _, pvalue = cd_estimator.test(df, y_vars={"x"}, group_col={group_col}, x_vars=set()) assert pvalue > alpha, f"Fails with {pvalue} not less than {alpha}" @@ -198,4 +198,4 @@ def test_cd_with_selection_diagram(cd_func, cd_kwargs): # P(Y|X) = P'(Y|X) _, pvalue = cd_estimator.test(df, y_vars={"y"}, group_col={group_col}, x_vars={"x"}) - assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" \ No newline at end of file + assert pvalue < alpha, f"Fails with {pvalue} not less than {alpha}" diff --git a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py index d4222ff4f..e79766213 100644 --- a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py +++ b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py @@ -1,9 +1,10 @@ import math -import numpy as np + import networkx as nx +import numpy as np import pywhy_graphs as pgraphs -from dodiscover import ContextBuilder, make_context, InterventionalContextBuilder +from dodiscover import ContextBuilder, InterventionalContextBuilder, make_context from dodiscover.cd import KernelCDTest from dodiscover.ci import FisherZCITest, Oracle from dodiscover.constraint.skeleton import LearnMultiDomainSkeleton @@ -31,7 +32,6 @@ def basic_multidomain_augmented_graph(): return graph - def test_fnode_multidomain_skeleton_known_targets(): """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`.""" # first create the oracle @@ -68,13 +68,13 @@ def test_fnode_multidomain_skeleton_known_targets(): ) data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph)] context = ( - make_context(create_using=InterventionalContextBuilder) - .variables(data=data[0]) - .build() + make_context(create_using=InterventionalContextBuilder).variables(data=data[0]).build() ) domain_indices = [1, 1] - intervention_targets = [{}, {'x'}] - learner.fit(data, context, domain_indices=domain_indices, intervention_targets=intervention_targets) + intervention_targets = [{}, {"x"}] + learner.fit( + data, context, domain_indices=domain_indices, intervention_targets=intervention_targets + ) # first check the observational skeleton skel_graph = learner.adj_graph_ @@ -83,17 +83,17 @@ def test_fnode_multidomain_skeleton_known_targets(): ) for edge in skel_graph.edges(): if not expected_skeleton.has_edge(*edge): - print('extra edge: ', edge) + print("extra edge: ", edge) for edge in expected_skeleton.edges(): if not skel_graph.has_edge(*edge): - print('missing edge: ', edge) + print("missing edge: ", edge) assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) assert nx.is_isomorphic(expected_skeleton, skel_graph) def test_number_augmented_nodes_created(): """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`. - + However, this time, we have an S-node pointing to y. """ # first create the oracle @@ -115,8 +115,8 @@ def test_number_augmented_nodes_created(): edges = [ (("F", 0), "x"), (("F", 0), "y"), - (('S', 0), "y"), - (('S', 0), "x"), + (("S", 0), "y"), + (("S", 0), "x"), ("x", "w"), ("x", "z"), ("x", "y"), @@ -133,32 +133,39 @@ def test_number_augmented_nodes_created(): ) data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph), dummy_sample(non_f_graph)] context = ( - make_context(create_using=InterventionalContextBuilder) - .variables(data=data[0]) - .build() + make_context(create_using=InterventionalContextBuilder).variables(data=data[0]).build() ) domain_indices = [1, 2, 2] - intervention_targets = [{}, {}, {'x'}] + intervention_targets = [{}, {}, {"x"}] # test augmented nodes - augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map = learner._create_augmented_nodes(domain_indices, intervention_targets) + ( + augmented_nodes, + symmetric_diff_map, + sigma_map, + node_domain_map, + ) = learner._create_augmented_nodes(domain_indices, intervention_targets) assert len(augmented_nodes) == math.comb(len(domain_indices), 2) - assert symmetric_diff_map == {('F', 0): frozenset({'x'}), ('F', 1): frozenset({'x'})} - assert sigma_map == {('F', 0): [0, 2], ('F', 1): [1, 2], ('S', 0): [0, 1]} - assert node_domain_map == {('F', 0): [1, 2], ('F', 1): [2, 2], ('S', 0): [1, 2]} - + assert symmetric_diff_map == {("F", 0): frozenset({"x"}), ("F", 1): frozenset({"x"})} + assert sigma_map == {("F", 0): [0, 2], ("F", 1): [1, 2], ("S", 0): [0, 1]} + assert node_domain_map == {("F", 0): [1, 2], ("F", 1): [2, 2], ("S", 0): [1, 2]} - domain_indices = [1, 2, 2, 2, 2] + domain_indices = [1, 2, 2, 2, 2] intervention_targets = [{}, {}, {3}, {2}, {3}] # test augmented nodes - augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map = learner._create_augmented_nodes(domain_indices, intervention_targets) + ( + augmented_nodes, + symmetric_diff_map, + sigma_map, + node_domain_map, + ) = learner._create_augmented_nodes(domain_indices, intervention_targets) assert len(augmented_nodes) == math.comb(len(domain_indices), 2) - 1 def test_fnode_multidomain_skeleton_known_targets_with_snode(): """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`. - + However, this time, we have an S-node pointing to y. """ # first create the oracle @@ -183,7 +190,7 @@ def test_fnode_multidomain_skeleton_known_targets_with_snode(): (("F", 0), "y"), (("F", 1), "x"), (("F", 1), "y"), - (('S', 0), "y"), + (("S", 0), "y"), ("x", "w"), ("x", "z"), ("x", "y"), @@ -200,18 +207,23 @@ def test_fnode_multidomain_skeleton_known_targets_with_snode(): ) data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph), dummy_sample(non_f_graph)] context = ( - make_context(create_using=InterventionalContextBuilder) - .variables(data=data[0]) - .build() + make_context(create_using=InterventionalContextBuilder).variables(data=data[0]).build() ) domain_indices = [1, 2, 2] - intervention_targets = [{}, {}, {'x'}] + intervention_targets = [{}, {}, {"x"}] # test augmented nodes - augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map = learner._create_augmented_nodes(domain_indices, intervention_targets) + ( + augmented_nodes, + symmetric_diff_map, + sigma_map, + node_domain_map, + ) = learner._create_augmented_nodes(domain_indices, intervention_targets) assert len(augmented_nodes) == math.comb(len(domain_indices), 2) - learner.fit(data, context, domain_indices=domain_indices, intervention_targets=intervention_targets) + learner.fit( + data, context, domain_indices=domain_indices, intervention_targets=intervention_targets + ) # first check the observational skeleton skel_graph = learner.adj_graph_ @@ -220,15 +232,14 @@ def test_fnode_multidomain_skeleton_known_targets_with_snode(): ) for edge in skel_graph.edges(): if not expected_skeleton.has_edge(*edge): - print('extra edge: ', edge) + print("extra edge: ", edge) for edge in expected_skeleton.edges(): if not skel_graph.has_edge(*edge): - print('missing edge: ', edge) + print("missing edge: ", edge) assert nx.is_isomorphic(obs_expected_skeleton, obs_skel_graph, edge_match=None) assert nx.is_isomorphic(expected_skeleton, skel_graph) - def test_basic_multidomain_fsnode_skeleton(): """Test basic skeleton learning with a multidomain f-node and s-node.""" graph = basic_multidomain_augmented_graph() @@ -326,7 +337,7 @@ def test_basic_multidomain_fsnode_skeleton_with_lindata(): intervention_sets.append(targets) domain_ids.append(1) - print('Targets are: ', targets) + print("Targets are: ", targets) # now for each S-node, apply a linear additive intervention # for s_node, domains in aug_graph.graph["S-nodes"].items(): From bc236c8bf95fa00f0a69f5e1617223ee6b39e63a Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 28 Jun 2023 13:37:45 -0400 Subject: [PATCH 57/61] Finish merging Signed-off-by: Adam Li --- poetry.lock | 3769 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3769 insertions(+) create mode 100644 poetry.lock diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 000000000..2ff36fde4 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3769 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "alabaster" 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"01f55f382d992d1d0552c5e7a836162afdaca6524c63f52da30744ad0df33cb0" From 2e94cd88a5349eaca11caf68707c5cb4c05a3f3c Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 28 Jun 2023 14:09:58 -0400 Subject: [PATCH 58/61] Try to fix Signed-off-by: Adam Li --- .circleci/config.yml | 4 ++++ tests/unit_tests/constraint/test_psifcialg.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 92849e6b0..6c3b2afdf 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -65,6 +65,10 @@ jobs: name: Setup pandoc command: sudo apt update && sudo apt install -y pandoc optipng + - run: + name: Setup torch + command: sudo apt-get install nvidia-cuda-toolkit nvidia-cuda-toolkit-gcc + - run: name: Install Poetry command: | diff --git a/tests/unit_tests/constraint/test_psifcialg.py b/tests/unit_tests/constraint/test_psifcialg.py index 8884ec754..4966843a1 100644 --- a/tests/unit_tests/constraint/test_psifcialg.py +++ b/tests/unit_tests/constraint/test_psifcialg.py @@ -1,6 +1,5 @@ from itertools import permutations -import bnlearn import networkx as nx import numpy as np import pandas as pd @@ -258,6 +257,7 @@ def test_figure2_skeleton(self): @pytest.mark.skip() def test_psifci_withsachs(): + import bnlearn bnlearn.import_DAG() From 5afbd4bc5b57037e3a9881bc39df34e9144ec077 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 17 Jul 2023 14:20:06 -0400 Subject: [PATCH 59/61] Merging? Signed-off-by: Adam Li --- dodiscover/__init__.py | 1 - dodiscover/ci/categorical_test.py | 20 +- dodiscover/constraint/_classes.py | 3 + dodiscover/constraint/fcialg.py | 40 + dodiscover/constraint/intervention.py | 9 + dodiscover/constraint/pcalg.py | 17 + dodiscover/constraint/sfcialg.py | 22 +- dodiscover/constraint/skeleton.py | 90 +- dodiscover/context.py | 6 +- dodiscover/datasets/__init__.py | 1 - dodiscover/datasets/base.py | 92 - dodiscover/datasets/linear.py | 45 - dodiscover/datasets/multidomain.py | 54 - examples/{ => constraint}/plot_pc_alg.py | 0 examples/{ => constraint}/plot_psifci_alg.py | 1 + examples/{ => constraint}/plot_sfci_alg.py | 0 .../constraint/plot_simulation_sfci.ipynb | 2829 +++++++++++++++++ examples/{ => topological}/plot_score_alg.py | 0 .../{ => topological}/prior_know_score.py | 0 pyproject.toml | 4 +- 20 files changed, 2979 insertions(+), 255 deletions(-) delete mode 100644 dodiscover/datasets/__init__.py delete mode 100644 dodiscover/datasets/base.py delete mode 100644 dodiscover/datasets/linear.py delete mode 100644 dodiscover/datasets/multidomain.py rename examples/{ => constraint}/plot_pc_alg.py (100%) rename examples/{ => constraint}/plot_psifci_alg.py (99%) rename examples/{ => constraint}/plot_sfci_alg.py (100%) create mode 100644 examples/constraint/plot_simulation_sfci.ipynb rename examples/{ => topological}/plot_score_alg.py (100%) rename examples/{ => topological}/prior_know_score.py (100%) diff --git a/dodiscover/__init__.py b/dodiscover/__init__.py index da5a7b65c..c8e7d51e3 100644 --- a/dodiscover/__init__.py +++ b/dodiscover/__init__.py @@ -1,6 +1,5 @@ from . import cd # noqa: F401 from . import ci # noqa: F401 -from . import datasets # noqa: F401 from . import metrics # noqa: F401 from . import toporder from ._protocol import EquivalenceClass, Graph diff --git a/dodiscover/ci/categorical_test.py b/dodiscover/ci/categorical_test.py index 733f62c38..cf574085d 100644 --- a/dodiscover/ci/categorical_test.py +++ b/dodiscover/ci/categorical_test.py @@ -15,14 +15,15 @@ from dodiscover.typing import Column +# This is a modified function taken from pgmpy: License MIT def power_divergence( - X: ArrayLike, Y: ArrayLike, Z: ArrayLike, data: pd.DataFrame, lambda_: str = "cressie-read" + X, Y, Z, data: pd.DataFrame, lambda_: str = "cressie-read" ) -> Tuple[float, float, int]: - """ - Computes the Cressie-Read power divergence statistic [1]. The null hypothesis - for the test is X is independent of Y given Z. A lot of the frequency comparison - based statistics (eg. chi-square, G-test etc) belong to power divergence family, - and are special cases of this test. + """Computes the Cressie-Read power divergence statistic [1]. + + The null hypothesis for the test is X is independent of Y given Z. A lot of the + frequency comparison based statistics (eg. chi-square, G-test etc) belong to + power divergence family, and are special cases of this test. Parameters ---------- @@ -100,7 +101,7 @@ def power_divergence( else: chi = 0 dof = 0 - for z_state, df in data.groupby(Z): + for idx, (z_state, df) in enumerate(data.groupby(Z[0] if len(Z) == 1 else Z)): try: # Note: The fill value is set to 1e-7 to avoid the following error: # where there are not enough samples in the data, which results in a nan pvalue @@ -112,7 +113,7 @@ def power_divergence( # If one of the values is 0 in the 2x2 table. if isinstance(z_state, str): logging.info( - f"Skipping the test {X} \u27C2 {Y} | {Z[0]}={z_state}. Not enough samples" + f"Skipping the test {X} \u27C2 {Y} | {Z[idx]}={z_state}. Not enough samples" ) else: z_str = ", ".join([f"{var}={state}" for var, state in zip(Z, z_state)]) @@ -170,6 +171,9 @@ def test( ) -> Tuple[float, float]: x_vars = reduce(lambda x: x, x_vars) # type: ignore y_vars = reduce(lambda x: x, y_vars) # type: ignore + + if z_covariates is not None and len(z_covariates) > 0: + z_covariates = list(z_covariates) stat, pvalue, dof = power_divergence( x_vars, y_vars, z_covariates, data=df, lambda_=self.lambda_ ) diff --git a/dodiscover/constraint/_classes.py b/dodiscover/constraint/_classes.py index 091a17c8d..dcdcb97d0 100644 --- a/dodiscover/constraint/_classes.py +++ b/dodiscover/constraint/_classes.py @@ -100,6 +100,7 @@ def __init__( apply_orientations: bool = True, keep_sorted: bool = False, n_jobs: Optional[int] = None, + debug: bool = False, ): self.alpha = alpha self.ci_estimator = ci_estimator @@ -126,6 +127,8 @@ def __init__( # debugging mode self.n_ci_tests = 0 + self.debug = debug + self.debug_map = dict() def _initialize_sep_sets(self, init_graph: nx.Graph) -> SeparatingSet: # keep track of separating sets diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index d6083b0a6..1de654007 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -105,6 +105,7 @@ def __init__( selection_bias: bool = True, pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, n_jobs: Optional[int] = None, + debug: bool = False, ): super().__init__( ci_estimator, @@ -116,6 +117,7 @@ def __init__( keep_sorted=keep_sorted, apply_orientations=apply_orientations, n_jobs=n_jobs, + debug=debug, ) self.max_iter = max_iter self.max_path_length = max_path_length @@ -144,6 +146,7 @@ def orient_unshielded_triples(self, graph: EquivalenceClass, sep_set: Separating ): self._orient_collider(graph, v_i, u, v_j) + def _orient_collider( self, graph: EquivalenceClass, v_i: Column, u: Column, v_j: Column ) -> None: @@ -152,8 +155,12 @@ def _orient_collider( ) if graph.has_edge(v_i, u, graph.circle_edge_name): graph.orient_uncertain_edge(v_i, u) + if self.debug: + self.debug_map[(v_i, u)] = "collider" if graph.has_edge(v_j, u, graph.circle_edge_name): graph.orient_uncertain_edge(v_j, u) + if self.debug: + self.debug_map[(v_j, u)] = "collider" def _apply_rule1(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) -> bool: """Apply rule 1 of the FCI algorithm. @@ -197,6 +204,10 @@ def _apply_rule1(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) graph.remove_edge(c, u, graph.circle_edge_name) added_arrows = True + if added_arrows and self.debug: + self.debug_map[(u, c)] = f"rule 1: {a} *-> {u} o-* {c}" + self.debug_map[(c, u)] = f"rule 1: {a} *-> {u} o-* {c}" + return added_arrows def _apply_rule2(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) -> bool: @@ -257,6 +268,10 @@ def _apply_rule2(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) # orient a *-> c graph.orient_uncertain_edge(a, c) added_arrows = True + + if added_arrows and self.debug: + self.debug_map[(a, c)] = "rule2" + return added_arrows def _apply_rule3(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) -> bool: @@ -316,6 +331,9 @@ def _apply_rule3(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) logger.info(f"Rule 3: Orienting {v} -> {u}.") graph.orient_uncertain_edge(v, u) added_arrows = True + + if added_arrows and self.debug: + self.debug_map[(v, u)] = "rule3" return added_arrows def _apply_rule4( @@ -404,6 +422,12 @@ def _apply_rule4( logger.info(disc_path_str) added_arrows = True + if added_arrows and self.debug: + if last_node not in sep_set: + self.debug_map[(u, c)] = "rule4" + self.debug_map[(c, u)] = "rule4" + else: + self.debug_map[(u, c)] = "rule4" return added_arrows, explored_nodes def _apply_rule5(self, graph: EquivalenceClass, u: Column, a: Column) -> bool: @@ -451,6 +475,9 @@ def _apply_rule5(self, graph: EquivalenceClass, u: Column, a: Column) -> bool: graph.remove_edge(y, x, graph.circle_edge_name) graph.add_edge(x, y, graph.undirected_edge_name) + if added_tails and self.debug: + self.debug_map[(a, u)] = "rule5" + self.debug_map[(u, a)] = "rule5" return added_tails def _apply_rule6(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) -> bool: @@ -489,6 +516,8 @@ def _apply_rule6(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) ): graph.add_edge(c, u, graph.undirected_edge_name) + if added_tails and self.debug: + self.debug_map[(u, c)] = "rule6" return added_tails def _apply_rule7(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) -> bool: @@ -528,6 +557,8 @@ def _apply_rule7(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) ): graph.add_edge(c, u, graph.undirected_edge_name) + if added_tails and self.debug: + self.debug_map[(u, c)] = "rule7" return added_tails def _apply_rule8(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) -> bool: @@ -589,6 +620,10 @@ def _apply_rule8(self, graph: EquivalenceClass, u: Column, a: Column, c: Column) if graph.has_edge(c, a, graph.circle_edge_name): graph.remove_edge(c, a, graph.circle_edge_name) added_arrows = True + + if added_arrows and self.debug: + self.debug_map[(u, c)] = "rule8" + return added_arrows def _apply_rule9( @@ -640,6 +675,8 @@ def _apply_rule9( graph.remove_edge(c, a, graph.circle_edge_name) added_arrows = True + if added_arrows and self.debug: + self.debug_map[(u, c)] = "rule9" return added_arrows, uncov_path def _apply_rule10( @@ -753,6 +790,9 @@ def _apply_rule10( graph.remove_edge(c, a, graph.circle_edge_name) added_arrows = True + if added_arrows and self.debug: + self.debug_map[(u, c)] = "rule10" + return added_arrows, a_to_u_path, a_to_v_path def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingSet): diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index 59abb766f..7c8aec163 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -108,6 +108,7 @@ def __init__( pds_condsel_method: ConditioningSetSelection = ConditioningSetSelection.PDS, known_intervention_targets: bool = False, n_jobs: Optional[int] = None, + debug: bool = False, ): super().__init__( ci_estimator, @@ -123,6 +124,7 @@ def __init__( selection_bias=False, pds_condsel_method=pds_condsel_method, n_jobs=n_jobs, + debug=debug ) self.cd_estimator = cd_estimator self.known_intervention_targets = known_intervention_targets @@ -229,6 +231,10 @@ def _apply_rule11(self, graph: EquivalenceClass, context: Context) -> Tuple[bool graph.remove_edge(nbr, node) graph.add_edge(node, nbr, graph.directed_edge_name) oriented_edges.append((node, nbr)) + + if added_arrows and self.debug: + self.debug_map[(node, nbr)] = "Rule 11" + return added_arrows, oriented_edges def _apply_rule12( @@ -288,6 +294,9 @@ def _apply_rule12( graph.add_edge(a, c, graph.directed_edge_name) added_arrows = True + + if added_arrows and self.debug: + self.debug_map[(a, c)] = "Rule 12" return added_arrows def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingSet): diff --git a/dodiscover/constraint/pcalg.py b/dodiscover/constraint/pcalg.py index 74e51cb53..0e5d3d0da 100644 --- a/dodiscover/constraint/pcalg.py +++ b/dodiscover/constraint/pcalg.py @@ -100,6 +100,7 @@ def __init__( keep_sorted: bool = False, max_iter: int = 1000, n_jobs: Optional[int] = None, + debug: bool = False, ): super().__init__( ci_estimator, @@ -111,6 +112,7 @@ def __init__( apply_orientations=apply_orientations, keep_sorted=keep_sorted, n_jobs=n_jobs, + debug = debug, ) self.max_iter = max_iter @@ -263,10 +265,16 @@ def _orient_collider( f"orienting collider: {v_i} -> {u} and {v_j} -> {u} to make {v_i} -> {u} <- {v_j}." ) + # XXX: this should be a base method that is common to all constraint-based causal discovery + # We can integrate this with FCI probably if graph.has_edge(v_i, u, graph.undirected_edge_name): graph.orient_uncertain_edge(v_i, u) + if self.debug: + self.debug_map[(v_i, u)] = "collider" if graph.has_edge(v_j, u, graph.undirected_edge_name): graph.orient_uncertain_edge(v_j, u) + if self.debug: + self.debug_map[(v_j, u)] = "collider" def _apply_meek_rule1(self, graph: EquivalenceClass, i: Column, j: Column) -> bool: """Apply rule 1 of Meek's rules. @@ -294,6 +302,9 @@ def _apply_meek_rule1(self, graph: EquivalenceClass, i: Column, j: Column) -> bo added_arrows = True break + + if added_arrows and self.debug: + self.debug_map[(i, j)] = "rule1" return added_arrows def _apply_meek_rule2(self, graph: EquivalenceClass, i: Column, j: Column) -> bool: @@ -333,6 +344,9 @@ def _apply_meek_rule2(self, graph: EquivalenceClass, i: Column, j: Column) -> bo logger.info(f"R2: Removing edge {i}-{j} to form {i}->{j}.") graph.orient_uncertain_edge(i, j) added_arrows = True + + if added_arrows and self.debug: + self.debug_map[(i, j)] = "rule2" return added_arrows def _apply_meek_rule3(self, graph: EquivalenceClass, i: Column, j: Column) -> bool: @@ -375,4 +389,7 @@ def _apply_meek_rule3(self, graph: EquivalenceClass, i: Column, j: Column) -> bo graph.orient_uncertain_edge(i, j) added_arrows = True break + + if added_arrows and self.debug: + self.debug_map[(i, j)] = "rule3" return added_arrows diff --git a/dodiscover/constraint/sfcialg.py b/dodiscover/constraint/sfcialg.py index 23f16a3f5..54a347667 100644 --- a/dodiscover/constraint/sfcialg.py +++ b/dodiscover/constraint/sfcialg.py @@ -46,8 +46,8 @@ def __init__( max_path_length, pds_condsel_method, n_jobs=n_jobs, + debug=debug ) - self.debug = debug def learn_skeleton( self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None @@ -114,23 +114,6 @@ def fit(self, data: List[pd.DataFrame], context: Context, domain_indices, interv return super().fit(data, context) - def _apply_rule11(self, graph: EquivalenceClass, context: Context) -> Tuple[bool, List]: - augmented_nodes = context.f_nodes + context.s_nodes - - oriented_edges = [] - added_arrows = True - for node in augmented_nodes: - for nbr in graph.neighbors(node): - if nbr in augmented_nodes: - continue - - # remove all edges between node and nbr and orient this out - graph.remove_edge(node, nbr) - graph.remove_edge(nbr, node) - graph.add_edge(node, nbr, graph.directed_edge_name) - oriented_edges.append((node, nbr)) - return added_arrows, oriented_edges - def _apply_rule12( self, graph: EquivalenceClass, @@ -210,6 +193,9 @@ def _apply_rule12( graph.add_edge(a, c, graph.directed_edge_name) added_arrows = True + + if added_arrows and self.debug: + self.debug_map[(a, c)] = 'rule 12' return added_arrows def convert_skeleton_graph(self, graph: nx.Graph) -> EquivalenceClass: diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 09185ec07..4994306af 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1347,6 +1347,14 @@ def fit(self, data: List[pd.DataFrame], context: Context, check_input: bool = Tr for idx in range(len(sep_sets)): self.sep_set_[x_var][y_var][idx].update(context.get_augmented_nodes()) + # remove all edges between F-nodes + for x_var in context.get_augmented_nodes(): + for y_var in context.get_augmented_nodes(): + if x_var == y_var: + continue + if context.init_graph.has_edge(x_var, y_var): + context.init_graph.remove_edge(x_var, y_var) + # now, we'll fit the data using interventional data by looping over all # combinations of F-nodes and their neighbors # apply algorithm to learn skeleton @@ -1527,6 +1535,7 @@ def _create_augmented_nodes( # map augmented nodes to domains node_domain_map = dict() + reverse_domain_node_map = dict() symmetric_diff_map = dict() sigma_map = dict() s_nodes = [] @@ -1547,7 +1556,8 @@ def _create_augmented_nodes( # create S-nodes as N-domains choose 2 for idx, (source, target) in enumerate(combinations(unique_domains, 2)): s_node = ("S", idx) - node_domain_map[s_node] = [source, target] + node_domain_map[s_node] = frozenset([source, target]) + reverse_domain_node_map[frozenset([source, target])] = s_node s_nodes.append(s_node) # create F-nodes, which is now all combinations of distributions choose 2 @@ -1555,43 +1565,41 @@ def _create_augmented_nodes( seen_domain_pairs = dict() seen_distr_pairs = dict() - for idx, source in enumerate(domain_ids): - for jdx, target in enumerate(domain_ids): + # compare every pair of distributions to now add interventions if necessary + for dataset_idx, source in enumerate(domain_ids): + for dataset_jdx, target in enumerate(domain_ids): + # perform memoization to avoid duplicate augmented nodes domain_memo_key = frozenset([source, target]) - distr_memo_key = frozenset([idx, jdx]) - - if jdx <= idx: + distr_memo_key = frozenset([dataset_idx, dataset_jdx]) + if dataset_jdx <= dataset_idx: continue if domain_memo_key in seen_domain_pairs and distr_memo_key in seen_distr_pairs: continue - - seen_domain_pairs[distr_memo_key] = None - seen_distr_pairs[domain_memo_key] = None - - # check if we are dealing with two observational distributions, since those - # are assigned to S-nodes - if intervention_targets[idx] == set() and intervention_targets[jdx] == set(): + seen_domain_pairs[domain_memo_key] = None + seen_distr_pairs[distr_memo_key] = None + + # check if we are dealing with two observational distributions + # if so, compute the sigma mapping to map the S-node to the two distribution indices + if (intervention_targets[dataset_idx] == set()) and (intervention_targets[dataset_jdx] == set()): + s_node = reverse_domain_node_map[frozenset([source, target])] + sigma_map[s_node] = [dataset_idx, dataset_jdx] continue # map each augmented-node to a tuple of distribution indices, or to a set of nodes # representing the intervention targets - if intervention_targets[idx] is None or intervention_targets[jdx] is None: - targets = frozenset([idx, jdx]) + if intervention_targets[dataset_idx] is None or intervention_targets[dataset_jdx] is None: + targets = frozenset([dataset_idx, dataset_jdx]) else: - symm_diff = set(intervention_targets[idx]).symmetric_difference( - set(intervention_targets[jdx]) + symm_diff = set(intervention_targets[dataset_idx]).symmetric_difference( + set(intervention_targets[dataset_jdx]) ) targets = frozenset(symm_diff) - # get the S-node mapped to the obs data if there is observational data - if domain_obs[source] and domain_obs[target] and targets == frozenset(): - s_node = [ - key for key, val in node_domain_map.items() if set(val) == {source, target} - ][0] - sigma_map[s_node] = [idx, jdx] - continue - elif targets == frozenset(): - # there is not interventions to compare + if targets == frozenset() and source == target: + # the two interventional distributions are exactly the same + logger.warn( + f'Interventional distributions {dataset_idx} and {dataset_jdx} have the same interventions ' + f'within the same domain {source}.') continue # create the F-node @@ -1600,13 +1608,13 @@ def _create_augmented_nodes( # map each F-node to a set of domain(s) node_domain_map[f_node] = [source, target] - sigma_map[f_node] = [idx, jdx] + sigma_map[f_node] = [dataset_idx, dataset_jdx] symmetric_diff_map[f_node] = targets k += 1 augmented_nodes = set(s_nodes).union(set(f_nodes)) return augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map - + def fit( self, data: List[pd.DataFrame], @@ -1678,11 +1686,12 @@ def fit( f_nodes.append(node) # provide multi-domain context - n_domains = len(np.unique(domain_indices)) context.augmented_nodes = augmented_nodes + context.node_domain_map = node_domain_map + context.add_state_variable('node_domain_map', node_domain_map) + context.add_state_variable('augmented_nodes', augmented_nodes) context.symmetric_diff_map = symmetric_diff_map context.sigma_map = sigma_map - context.node_domain_map = node_domain_map context.s_nodes = s_nodes context.f_nodes = f_nodes @@ -1718,7 +1727,7 @@ def fit( # loop through each domain pair to learn the F-node skeleton seen_domain_pairs = set() - for idx, source in enumerate(range(1, n_domains + 1)): + for idx, source in enumerate(domain_indices): # analyze F-nodes only within the 'source' domain source_fnodes = [ node for node in augmented_nodes if set(node_domain_map[node]) == {source} @@ -1738,9 +1747,10 @@ def fit( cross_distribution_test=True, ) - for jdx, target in enumerate(range(1, n_domains + 1)): - # skip if source and target are the same domain - if idx == jdx: + for jdx, target in enumerate(domain_indices): + # skip if source and target are the same domain because we have + # already learned from these pairs of datasets + if source == target: continue # skip if we have already seen this domain pair if frozenset([source, target]) in seen_domain_pairs: @@ -1755,6 +1765,13 @@ def fit( s_node = node break if s_node is None: + print(s_nodes) + # print(context.state_variables) + print(node_domain_map) + print(source, target) + # print(context) + print('OKAY... \n\n') + print(source, target) raise RuntimeError("wtf") # this is only possible if there is explicitly observational data between @@ -1771,6 +1788,11 @@ def fit( print(this_s_nodes) print(symmetric_diff_map) print(sigma_map) + print('here... \n\n') + print(augmented_nodes) + print(source, target) + print(sigma_map) + print(symmetric_diff_map) if this_s_nodes: if debug: print( diff --git a/dodiscover/context.py b/dodiscover/context.py index 4a5187740..f491b41c4 100644 --- a/dodiscover/context.py +++ b/dodiscover/context.py @@ -45,7 +45,11 @@ class Context(BasePyWhy): intervention_targets : list of tuple List of intervention targets (known, or unknown), which correspond to the nodes in the graph (known), or indices of datasets that contain - interventions (unknown). + interventions (unknown). If the value of an element is `None`, then + it means the distribution corresponding to the element's index has + an unknown intervention target. If the value is an empty set, then it + implies this is an observational distribution. If the value is a non-empty + set, then it informs us of the intervention targets. Raises ------ diff --git a/dodiscover/datasets/__init__.py b/dodiscover/datasets/__init__.py deleted file mode 100644 index d2077077c..000000000 --- a/dodiscover/datasets/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .base import sample_from_graph diff --git a/dodiscover/datasets/base.py b/dodiscover/datasets/base.py deleted file mode 100644 index bb1062294..000000000 --- a/dodiscover/datasets/base.py +++ /dev/null @@ -1,92 +0,0 @@ -from typing import Optional - -import networkx as nx -import numpy as np -import pandas as pd -import pywhy_graphs as pgraphs -from joblib import Parallel, delayed - -from . import linear, multidomain - - -def sample_from_graph( - G: nx.DiGraph, - n_samples: int = 1000, - n_jobs: Optional[int] = None, - random_state=None, - sample_func="linear", - **sample_kwargs, -): - """Sample a dataset from a linear Gaussian graph. - - Assumes the graph only consists of directed edges. It is on the roadmap to - implement support for bidirected edges. - - Parameters - ---------- - G : Graph - A linear DAG from which to sample. Must have been set up with - :func:`pywhy_graphs.functional.make_graph_linear_gaussian`. - n_samples : int, optional - Number of samples to generate, by default 1000. - n_jobs : Optional[int], optional - Number of jobs to run in parallel, by default None. - random_state : int, optional - Random seed, by default None. - sample_func : str, optional - The sampling function to use. Can be one of 'linear' or 'multidomain'. - Defaults to 'linear'. - **sample_kwargs - Keyword arguments to pass to the sampling function. - - Returns - ------- - data : pd.DataFrame of shape (n_samples, n_nodes) - A pandas DataFrame with the iid samples. - """ - if hasattr(G, "get_graphs"): - directed_G = G.get_graphs("directed") - else: - directed_G = G - - if isinstance(G, nx.DiGraph): - G = pgraphs.AugmentedGraph(incoming_directed_edges=G) - - if not nx.is_directed_acyclic_graph(directed_G): - raise ValueError("The input graph must be a DAG.") - if not G.graph.get("linear_gaussian", True): - raise ValueError("The input graph must be a linear Gaussian graph.") - - rng = np.random.default_rng(random_state) - - # Create list of topologically sorted nodes - top_sort_idx = list(nx.topological_sort(directed_G)) - - if hasattr(G, "augmented_nodes"): - top_sort_idx = [node for node in top_sort_idx if node not in G.augmented_nodes] - ignored_nodes = G.augmented_nodes - else: - ignored_nodes = None - - if sample_func == "linear": - sample_func = linear._sample_from_graph - elif sample_func == "multidomain": - sample_func = multidomain._sample_from_graph - - # Sample from graph - if n_jobs == 1: - data = [] - for _ in range(n_samples): - node_samples = sample_func( - G, top_sort_idx, rng, ignored_nodes=ignored_nodes, **sample_kwargs - ) - data.append(node_samples) - data = pd.DataFrame.from_records(data) - else: - out = Parallel(n_jobs=n_jobs, verbose=0)( - delayed(sample_func)(G, top_sort_idx, rng, ignored_nodes=ignored_nodes, **sample_kwargs) - for _ in range(n_samples) - ) - data = pd.DataFrame.from_records(out) - - return data diff --git a/dodiscover/datasets/linear.py b/dodiscover/datasets/linear.py deleted file mode 100644 index a72499b79..000000000 --- a/dodiscover/datasets/linear.py +++ /dev/null @@ -1,45 +0,0 @@ -from typing import Dict - -import numpy as np - - -def _sample_from_graph( - G, - top_sort_idx, - rng: np.random.Generator, - ignored_nodes=None, -) -> Dict: - """Private function to sample a single iid sample from a graph for all nodes. - - Returns - ------- - nodes_sample : dict - The sample per node. - """ - nodes_sample = dict() - - for node_idx in top_sort_idx: - # get all parents - parents = G.parents(node_idx) - - # sample noise - mean = G.nodes[node_idx]["gaussian_noise_function"]["mean"] - std = G.nodes[node_idx]["gaussian_noise_function"]["std"] - node_noise = rng.normal(loc=mean, scale=std) - node_sample = 0 - - # sample weight and edge function for each parent - for parent in parents: - if parent in ignored_nodes or parent == node_idx: - continue - if len(G.nodes[node_idx]["parent_functions"]) == 0: - continue - - weight = G.nodes[node_idx]["parent_functions"][parent]["weight"] - func = G.nodes[node_idx]["parent_functions"][parent]["func"] - node_sample += weight * func(nodes_sample[parent]) - - # set the node attribute "functions" to hold the weight and function wrt each parent - node_sample += node_noise - nodes_sample[node_idx] = node_sample - return nodes_sample diff --git a/dodiscover/datasets/multidomain.py b/dodiscover/datasets/multidomain.py deleted file mode 100644 index dc8e9369a..000000000 --- a/dodiscover/datasets/multidomain.py +++ /dev/null @@ -1,54 +0,0 @@ -from typing import Dict - -import numpy as np - - -def _sample_from_graph( - G, - top_sort_idx, - rng: np.random.Generator, - domain_id: int, - ignored_nodes=None, -) -> Dict: - """Private function to sample a single iid sample from a graph for all nodes. - - Returns - ------- - nodes_sample : dict - The sample per node. - """ - nodes_sample = dict() - - for node_idx in top_sort_idx: - # get all parents - parents = G.parents(node_idx) - - # sample noise - if "domain_gaussian_noise_function" in G.nodes[node_idx]: - mean = G.nodes[node_idx]["domain_gaussian_noise_function"][domain_id]["mean"] - std = G.nodes[node_idx]["domain_gaussian_noise_function"][domain_id]["std"] - else: - mean = G.nodes[node_idx]["gaussian_noise_function"]["mean"] - std = G.nodes[node_idx]["gaussian_noise_function"]["std"] - node_noise = rng.normal(loc=mean, scale=std) - node_sample = 0 - - # sample weight and edge function for each parent - for parent in parents: - if parent in ignored_nodes or parent == node_idx: - continue - if len(G.nodes[node_idx]["parent_functions"]) == 0: - continue - - weight = G.nodes[node_idx]["parent_functions"][parent]["weight"] - func = G.nodes[node_idx]["parent_functions"][parent]["func"] - try: - node_sample += weight * func(nodes_sample[parent]) - except Exception as e: - print(node_idx, list(parents)) - raise e - - # set the node attribute "functions" to hold the weight and function wrt each parent - node_sample += node_noise - nodes_sample[node_idx] = node_sample - return nodes_sample diff --git a/examples/plot_pc_alg.py b/examples/constraint/plot_pc_alg.py similarity index 100% rename from examples/plot_pc_alg.py rename to examples/constraint/plot_pc_alg.py diff --git a/examples/plot_psifci_alg.py b/examples/constraint/plot_psifci_alg.py similarity index 99% rename from examples/plot_psifci_alg.py rename to examples/constraint/plot_psifci_alg.py index 875e96d6b..05158fd2e 100644 --- a/examples/plot_psifci_alg.py +++ b/examples/constraint/plot_psifci_alg.py @@ -146,6 +146,7 @@ # Looking at the supplemental figure 8b in :footcite:`Jaber2020causal`, we see that the # learned PAG matches quite well. +# %% # References # ---------- # .. footbibliography:: diff --git a/examples/plot_sfci_alg.py b/examples/constraint/plot_sfci_alg.py similarity index 100% rename from examples/plot_sfci_alg.py rename to examples/constraint/plot_sfci_alg.py diff --git a/examples/constraint/plot_simulation_sfci.ipynb b/examples/constraint/plot_simulation_sfci.ipynb new file mode 100644 index 000000000..5e0dbeee8 --- /dev/null +++ b/examples/constraint/plot_simulation_sfci.ipynb @@ -0,0 +1,2829 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3c6609d2-4b85-456c-8430-db2061788cda", + "metadata": {}, + "source": [ + "S-FCI Algorithm for Structure Learning on Simulated Data\n", + "========================================================\n", + "\n", + "The SFCI algorithm is shown to be a generalization of the FCI, I-FCI and $\\Psi$-FCI algorithms, which is\n", + "capable of structure learning over observational and/or interventional data collected across multiple\n", + "environments.\n", + "\n", + "In this example, we will leverage [pywhy_graphs](simulation_example) to simulate a discrete causal Bayesian\n", + "network, which acts as our causal selection diagram. We will also simulate different interventions and environments\n", + "to demonstrate how additional data that arises from different domains are useful." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d37c74db-b424-4297-9303-cd718cca0112", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "id": "290503ef-96a6-433a-b01c-e10ea04ba6f7", + "metadata": {}, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "\n", + "import bnlearn\n", + "import networkx as nx\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.stats\n", + "from pywhy_graphs.functional import sample_from_graph\n", + "from pywhy_graphs.functional.discrete import (\n", + " apply_discrete_soft_intervention,\n", + " make_random_discrete_graph,\n", + ")\n", + "from pywhy_graphs.viz import draw\n", + "\n", + "from dodiscover import (\n", + " FCI,\n", + " PC,\n", + " SFCI,\n", + " Context,\n", + " InterventionalContextBuilder,\n", + " PsiFCI,\n", + " make_context,\n", + ")\n", + "from dodiscover.ci import CategoricalCITest, GSquareCITest, Oracle" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3ec27151-eee2-49a3-bf74-0cc671ecac7c", + "metadata": {}, + "outputs": [], + "source": [ + "from pgmpy.factors.discrete import JointProbabilityDistribution\n", + "from pgmpy.factors.discrete.CPD import TabularCPD\n", + "\n", + "\n", + "def print_full(cpd):\n", + " backup = TabularCPD._truncate_strtable\n", + " TabularCPD._truncate_strtable = lambda self, x: x\n", + " print(cpd)\n", + " TabularCPD._truncate_strtable = backup" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7ebd007f-de4a-41d0-951a-057c7d4aa627", + "metadata": {}, + "outputs": [], + "source": [ + "n_jobs = -1" + ] + }, + { + "cell_type": "markdown", + "id": "7c1369c7-fb43-4ca8-8189-4f82dfe2e2ea", + "metadata": {}, + "source": [ + "Draw a graph\n", + "------------\n" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "8de9d34b-cc5f-4acb-8c49-a7a05fbb7b43", + "metadata": {}, + "outputs": [], + "source": [ + "edge_list = [\n", + " (\"A\", \"B\"),\n", + " (\"B\", \"C\"),\n", + " (\"C\", \"D\"),\n", + " (\"B\", \"D\"),\n", + " (\"X\", \"A\"),\n", + " (\"X\", \"C\"),\n", + " (\"C\", \"W\"),\n", + "]\n", + "G = nx.DiGraph()\n", + "\n", + "G.add_edges_from(edge_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "84b152fd-0a9b-410e-af5f-4bbe7b041744", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dot_graph = draw(G)\n", + "dot_graph.render(outfile=\"true_graph.png\", view=False, cleanup=True)\n", + "\n", + "dot_graph" + ] + }, + { + "cell_type": "markdown", + "id": "391415ef-d4ed-4f1b-a868-15b856561bf7", + "metadata": {}, + "source": [ + "Define the distributions for nodes and functions for edges\n", + "----------------------------------------------------------\n", + "\n", + "Now, we can parametrize the graph fully, so that sampling from it is possible." + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "id": "f1306805-a77f-4515-be77-de6ae630f8e9", + "metadata": {}, + "outputs": [], + "source": [ + "cardinality_lims = {node: [2, 4] for node in G.nodes}\n", + "weight_lims = {node: [1, 100] for node in G.nodes}\n", + "noise_ratio_lims = {node: [0.1, 0.1] for node in G.nodes}\n", + "seed = 1234" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "id": "8e116785-4421-4657-89f2-15cd163bfa99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DiGraph with 6 nodes and 7 edges\n" + ] + } + ], + "source": [ + "G = make_random_discrete_graph(\n", + " G,\n", + " cardinality_lims=cardinality_lims,\n", + " weight_lims=weight_lims,\n", + " noise_ratio_lims=noise_ratio_lims,\n", + " random_state=seed,\n", + " overwrite=True,\n", + ")\n", + "\n", + "obs_G = G.copy()\n", + "print(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "id": "60e9cb9a-d941-4cc0-bca0-decd7830756a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3 3 3]\n" + ] + } + ], + "source": [ + "# we can extract the conditional probability table for each node, which is a function of its parents\n", + "node_dict = G.nodes[\"C\"]\n", + "\n", + "print_full(node_dict[\"cpd\"].cardinality)" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "id": "30f2e2a4-667b-45e5-b9f3-c87d0d209854", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9ef5303132fb47f9b77424de0b0946d3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/6 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + "" + ], + "text/plain": [ + " A B C D W X\n", + "0 2 1 0 0 2 1\n", + "1 1 1 2 1 2 2\n", + "2 1 2 2 2 0 1\n", + "3 1 1 2 2 1 2\n", + "4 1 2 2 1 1 2" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(40000, 6)\n" + ] + } + ], + "source": [ + "display(df.head())\n", + "print(df.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "49daab35-2f8b-417f-9a2b-0710f42954f4", + "metadata": {}, + "source": [ + "Causal discovery: Observational data in a single domain\n", + "-------------------------------------------------------\n", + "\n", + "First, we demonstrate how SFCI is exactly the same as the FCI algorithm in the context of single-domain observational data." + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "9d50b505-2181-44d7-a62b-0d4890d169e6", + "metadata": {}, + "outputs": [], + "source": [ + "ci_estimator = CategoricalCITest(lambda_=\"cressie-read\")" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "be1b9ec1-59ce-4134-bfc8-c7429936698e", + "metadata": {}, + "outputs": [], + "source": [ + "context = make_context().variables(data=df).build()\n", + "\n", + "learner = SFCI(\n", + " ci_estimator=ci_estimator,\n", + " cd_estimator=ci_estimator,\n", + " n_jobs=n_jobs,\n", + " max_cond_set_size=2,\n", + " max_combinations=None,\n", + " # alpha=0.5,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "e7d3fccc-6b82-4b0d-b772-d360b3d83b9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using data from distribution 0 for learning the skeleton.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learner.fit([df], context, domain_indices=[1], intervention_targets=[None])" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "edefd463-f795-4ef4-a17a-dc762452b5aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's run FCI\n", + "fci_learner = FCI(\n", + " ci_estimator=ci_estimator,\n", + " n_jobs=n_jobs,\n", + " max_cond_set_size=2,\n", + " max_combinations=None,\n", + " # alpha=0.5,\n", + ")\n", + "fci_learner.fit(df, context)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "70754e13-0ce0-48db-a7e6-f26d3704ab68", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "SFCI graph\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "B->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "A->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph = learner.graph_\n", + "draw(graph, name=\"SFCI graph\")" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "4807951b-197d-4137-88bb-c5b49be48255", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "FCI graph\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "B->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "A->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph = fci_learner.graph_\n", + "draw(graph, name=\"FCI graph\")" + ] + }, + { + "cell_type": "markdown", + "id": "75c46ae3-e676-4e0f-b0e7-02320e46734d", + "metadata": {}, + "source": [ + "We see that both graphs are exactly the same, as we have shown theoretically, the SFCI algorithm is a generalization\n", + "of the FCI algorithm. Moreover, the S-PAG is a valid generalization of the PAG, and in this case, both graphs align perfectly." + ] + }, + { + "cell_type": "markdown", + "id": "38d8d65e-daed-44c5-8d76-946b6cff18e7", + "metadata": {}, + "source": [ + "Causal Discovery: Interventional data in a single domain\n", + "--------------------------------------------------------\n", + "\n", + "Now, when we add interventional distributions, we expect that there will be additional\n", + "edges that we can orient due to the extra information when comparing different distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "262ca3c7-2971-45c9-98e7-18c7ca18a715", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random.default_rng(seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "1b327575-69b0-42c8-a7d9-5dcd1bb77998", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3e3ebc16cf004cb0b9355d383636df98", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/6 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = int_learner.graph_.subgraph(ctx.get_non_augmented_nodes())\n", + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "draw(est_pag, direction=\"LR\")" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "0c9c5d55-534c-4aaf-b4ca-71132878bbbc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)\n", + "\n", + "('F', 2)\n", + "\n", + "\n", + "\n", + "('F', 2)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)\n", + "\n", + "('F', 3)\n", + "\n", + "\n", + "\n", + "('F', 3)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)\n", + "\n", + "('F', 1)\n", + "\n", + "\n", + "\n", + "('F', 1)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)\n", + "\n", + "('F', 5)\n", + "\n", + "\n", + "\n", + "('F', 5)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)\n", + "\n", + "('F', 4)\n", + "\n", + "\n", + "\n", + "('F', 4)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = int_learner.graph_\n", + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "draw(est_pag, direction=\"LR\")" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "51fcfcf0-46ec-41ec-9eba-ae1a1b1ee231", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[None, ['X'], ['C'], ['D']]\n" + ] + } + ], + "source": [ + "intervention_targets = targets.copy()\n", + "intervention_targets.insert(0, None)\n", + "print(intervention_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "a8bfb31a-dbfa-4106-8d3f-fc09af3e9f07", + "metadata": {}, + "outputs": [], + "source": [ + "learner = SFCI(\n", + " ci_estimator=ci_estimator,\n", + " cd_estimator=ci_estimator,\n", + " max_cond_set_size=2,\n", + " n_jobs=-1,\n", + ")\n", + "\n", + "learner.fit(\n", + " data,\n", + " ctx,\n", + " domain_indices=[1, 1, 1, 1],\n", + " intervention_targets=intervention_targets,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "fe520613-c5ce-4714-bce1-101077345ea9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = learner.graph_\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", + "draw(est_pag_no_fnodes, direction=\"LR\")" + ] + }, + { + "cell_type": "markdown", + "id": "0a3dc765-a4cd-46bf-aa7c-3847041419a7", + "metadata": {}, + "source": [ + "We see that additional edges can be oriented with the presence of interventional data. Moreover, the SFCI algorithm perfectly \n", + "replicates and builds on top of the interventional data." + ] + }, + { + "cell_type": "markdown", + "id": "1a15289e-32ee-45ab-a5ff-a6f3aeedf4b3", + "metadata": {}, + "source": [ + "Causal Discovery: Observational data across multiple domains\n", + "------------------------------------------------------------\n", + "\n", + "In the SFCI paper, it is shown that the $\\Psi$-FCI algorithm is equivalent to the SFCI algorithm when there is\n", + "observational data across multiple domains, where the F-nodes can be seen as equivalent to S-nodes.\n", + "\n", + "We can leverage the same distributions that were intervened on because the change in domain can be seen\n", + "as an unknown-target intervention that occurs over those variables. That is, nature changes the distribution (CPD)\n", + "of the intervened variables, but we do not know where nature intervened. Thus conceptually, we see observational\n", + "data across multiple domains is similar to the setting with unknown-target interventional data within\n", + "a single domain. \n", + "\n", + "We will see later that when we have interventional data across multiple domains, the story becomes more complex\n", + "(and as a result, more interesting)." + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "id": "f3478f02-7401-478c-b2bc-86058af4efc6", + "metadata": {}, + "outputs": [], + "source": [ + "# create context with information about the interventions\n", + "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", + "ctx: Context = (\n", + " ctx_builder.variables(data=data[0])\n", + " .num_distributions(len(data))\n", + " .obs_distribution(True)\n", + " .build()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "id": "deceeeac-3866-4ecd-9ca7-b1206a11b4cd", + "metadata": {}, + "outputs": [], + "source": [ + "int_learner = PsiFCI(\n", + " ci_estimator=ci_estimator,\n", + " cd_estimator=ci_estimator,\n", + " max_cond_set_size=2,\n", + " n_jobs=-1,\n", + " debug=True,\n", + ")\n", + "\n", + "int_learner = int_learner.fit(data, ctx)" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "id": "4116afe8-8dff-4487-930f-277bbd382301", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)\n", + "\n", + "('F', 2)\n", + "\n", + "\n", + "\n", + "('F', 2)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)\n", + "\n", + "('F', 3)\n", + "\n", + "\n", + "\n", + "('F', 3)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)\n", + "\n", + "('F', 1)\n", + "\n", + "\n", + "\n", + "('F', 1)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)\n", + "\n", + "('F', 5)\n", + "\n", + "\n", + "\n", + "('F', 5)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)\n", + "\n", + "('F', 4)\n", + "\n", + "\n", + "\n", + "('F', 4)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = int_learner.graph_\n", + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "draw(est_pag, direction=\"LR\")" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "id": "007ac3d5-c259-4442-9bdc-1bfe36901c7f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 242, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = int_learner.graph_.subgraph(ctx.get_non_augmented_nodes())\n", + "\n", + "# %%\n", + "# Visualize the full graph including the F-node\n", + "draw(est_pag, direction=\"LR\")" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "id": "f7c949df-f235-4bb7-a6fd-2738c7213d30", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[set(), set(), set(), set()]\n" + ] + } + ], + "source": [ + "intervention_targets = [set()] * len(data)\n", + "print(intervention_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "id": "2f52b02d-d0ce-408a-91ff-c976c46394c1", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using data from distribution 0 for learning the skeleton.\n", + "Trying to learn skeleton for 0 to remove F-nodes: []\n", + "[('S', 0)]\n", + "{}\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "here... \n", + "\n", + "\n", + "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", + "0 1\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "{}\n", + "Trying to learn skeleton for 0 and 1 to remove S-nodes: [('S', 0)]\n", + "Trying to learn skeleton for 0 and 1 to remove F-nodes: [] grouped with S-node: ('S', 0)\n", + "[('S', 1)]\n", + "{}\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "here... \n", + "\n", + "\n", + "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", + "0 2\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "{}\n", + "Trying to learn skeleton for 0 and 2 to remove S-nodes: [('S', 1)]\n", + "Trying to learn skeleton for 0 and 2 to remove F-nodes: [] grouped with S-node: ('S', 1)\n", + "[('S', 2)]\n", + "{}\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "here... \n", + "\n", + "\n", + "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", + "0 3\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "{}\n", + "Trying to learn skeleton for 0 and 3 to remove S-nodes: [('S', 2)]\n", + "Trying to learn skeleton for 0 and 3 to remove F-nodes: [] grouped with S-node: ('S', 2)\n", + "Trying to learn skeleton for 1 to remove F-nodes: []\n", + "[('S', 3)]\n", + "{}\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "here... \n", + "\n", + "\n", + "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", + "1 2\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "{}\n", + "Trying to learn skeleton for 1 and 2 to remove S-nodes: [('S', 3)]\n", + "Trying to learn skeleton for 1 and 2 to remove F-nodes: [] grouped with S-node: ('S', 3)\n", + "[('S', 4)]\n", + "{}\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "here... \n", + "\n", + "\n", + "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", + "1 3\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "{}\n", + "Trying to learn skeleton for 1 and 3 to remove S-nodes: [('S', 4)]\n", + "Trying to learn skeleton for 1 and 3 to remove F-nodes: [] grouped with S-node: ('S', 4)\n", + "Trying to learn skeleton for 2 to remove F-nodes: []\n", + "[('S', 5)]\n", + "{}\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "here... \n", + "\n", + "\n", + "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", + "2 3\n", + "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", + "{}\n", + "Trying to learn skeleton for 2 and 3 to remove S-nodes: [('S', 5)]\n", + "Trying to learn skeleton for 2 and 3 to remove F-nodes: [] grouped with S-node: ('S', 5)\n", + "Trying to learn skeleton for 3 to remove F-nodes: []\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 268, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learner = SFCI(\n", + " ci_estimator=ci_estimator,\n", + " cd_estimator=ci_estimator,\n", + " max_cond_set_size=2,\n", + " n_jobs=-1,\n", + " debug=True,\n", + ")\n", + "\n", + "learner.fit(\n", + " data,\n", + " ctx,\n", + " domain_indices=[0, 1, 2, 3],\n", + " intervention_targets=intervention_targets,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "id": "02ebb37d-f01a-4173-bc33-5dc5ef7071d7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "SFCI\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 272, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = learner.graph_\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", + "draw(est_pag_no_fnodes, direction=\"LR\", name=\"SFCI\")" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "id": "49034875-c60c-46ca-9b85-0e56b992f15d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "SFCI\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 3)\n", + "\n", + "('S', 3)\n", + "\n", + "\n", + "\n", + "('S', 3)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 3)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 1)\n", + "\n", + "('S', 1)\n", + "\n", + "\n", + "\n", + "('S', 1)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 5)\n", + "\n", + "('S', 5)\n", + "\n", + "\n", + "\n", + "('S', 5)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 5)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 2)\n", + "\n", + "('S', 2)\n", + "\n", + "\n", + "\n", + "('S', 2)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 4)\n", + "\n", + "('S', 4)\n", + "\n", + "\n", + "\n", + "('S', 4)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 4)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('S', 0)\n", + "\n", + "('S', 0)\n", + "\n", + "\n", + "\n", + "('S', 0)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 273, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = learner.graph_\n", + "est_pag_no_fnodes = est_pag\n", + "dot_graph = draw(\n", + " est_pag_no_fnodes,\n", + " direction=\"LR\",\n", + " # name=\"SFCI\"\n", + ")\n", + "\n", + "dot_graph.render(outfile=\"./sfci_multidomain_obs.png\", view=False, cleanup=True)\n", + "dot_graph" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "96f28add-3517-4947-a0d2-aa5bcdd7a25d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{('S', 0): frozenset({0, 1}), ('S', 1): frozenset({0, 2}), ('S', 2): frozenset({0, 3}), ('S', 3): frozenset({1, 2}), ('S', 4): frozenset({1, 3}), ('S', 5): frozenset({2, 3})}\n" + ] + } + ], + "source": [ + "print(learner.context_.state_variable(\"node_domain_map\"))" + ] + }, + { + "cell_type": "markdown", + "id": "8cbea675-a3ad-4796-95ad-6c7a58ce648b", + "metadata": {}, + "source": [ + "Now, we see that SFCI is equivalent to $\\Psi$-FCI in the presence of observational data that spans different domains as shown in the theoretical results of the SFCI paper.\n", + "The benefit of explicitly noting the fact that the observational data come from different domains is that we now have knowledge of the S-nodes that underlie the causal\n", + "selection diagram. Of course, the S-node edges are simply estimates and moreover it is only part of the Markov equivalence class, since inducing paths may cause extra edges.\n", + "However, the information of the S-nodes now provide the user how they should expect distributions and causal knowledge to change when going between different domains.\n", + "\n", + "For example, if we take a look at S-node (S, 2), representing domains 0 and 3, then this induces a potential change in the function, or exogenous variable distribution for variable D. However, because\n", + "of the colliders, we see that we could translate causal knowledge between domains 0 and 3 as long as we do not open up a path from (S, 2) node to the rest of the variables." + ] + }, + { + "cell_type": "markdown", + "id": "c002dcec-9fae-414d-9c81-ac94c337a995", + "metadata": {}, + "source": [ + "Causal Discovery: Observational data and interventional data across multiple domains\n", + "------------------------------------------------------------------------------------\n", + "\n", + "As we saw in the previous sections, SFCI is capable of exactly producing the same results as the FCI, and I/$\\Psi$-FCI algorithms given datasets in a single domain. In addition, when analyzing purely observational data coming from multiple domains, we see an equivalence in the results between SFCI and $\\Psi$FCI.\n", + "\n", + "Next, we will show how having both observational and interventional data from different domains can allow us to learn more." + ] + }, + { + "cell_type": "markdown", + "id": "105e9828-be6d-4c37-8b85-fc8d62954631", + "metadata": {}, + "source": [ + "First we will simulate data that comes from multipl domains using a similar procedure." + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "id": "8115bfcd-209e-4c71-b1bd-87639955d134", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[set(), ['X'], ['C'], ['D']]\n", + "[set(), 'W', 'X']\n" + ] + } + ], + "source": [ + "intervention_targets = targets.copy()\n", + "intervention_targets.insert(0, set())\n", + "print(intervention_targets)\n", + "\n", + "domain_one_targets = intervention_targets.copy()\n", + "domain_two_targets = [set(), \"W\", \"X\"]\n", + "\n", + "print(domain_two_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 371, + "id": "56b79f04-c6ac-4d42-a7a2-b165ff212abb", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2ab6f5b712c54b3cbb2528544f3f984b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/6 [00:00" + ] + }, + "execution_count": 382, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learner = SFCI(\n", + " ci_estimator=ci_estimator,\n", + " cd_estimator=ci_estimator,\n", + " max_cond_set_size=2,\n", + " n_jobs=-1,\n", + " debug=True,\n", + ")\n", + "\n", + "learner.fit(\n", + " data,\n", + " ctx,\n", + " domain_indices=domain_ids,\n", + " intervention_targets=intervention_targets,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 395, + "id": "09ba3ceb-5b6b-47c7-9eac-c4c73372a169", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['W', 'C', 'B', 'A', 'X', 'D']\n", + "['W', 'X', 'C', 'B', 'D', 'A', ('F', 2), ('F', 5), ('F', 11), ('F', 8), ('F', 14), ('F', 17), ('F', 0), ('F', 3), ('F', 9), ('F', 6), ('F', 12), ('F', 15), ('F', 18), ('F', 4), ('F', 1), ('F', 7), ('F', 10), ('F', 16), ('F', 13), ('F', 19), ('S', 0)]\n", + "{'C', 'B', 'D', 'W', 'X', 'A'}\n" + ] + } + ], + "source": [ + "print(context.init_graph.nodes)\n", + "print(est_pag.nodes)\n", + "print(ctx.get_non_augmented_nodes())" + ] + }, + { + "cell_type": "code", + "execution_count": 400, + "id": "968fb5ed-d8e9-4a6a-a471-baea43d635ed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "C->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 400, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = learner.graph_\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", + "\n", + "dot_graph = draw(\n", + " est_pag_no_fnodes,\n", + " direction=\"LR\",\n", + " # name=\"SFCI\"\n", + ")\n", + "\n", + "dot_graph.render(outfile=\"./sfci_multidomain_obsandint.png\", view=False, cleanup=True)\n", + "dot_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0f2d0db-a6f7-4d47-b999-21dc1d876f24", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pywhy-discover", + "language": "python", + "name": "pywhy-discover" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/plot_score_alg.py b/examples/topological/plot_score_alg.py similarity index 100% rename from examples/plot_score_alg.py rename to examples/topological/plot_score_alg.py diff --git a/examples/prior_know_score.py b/examples/topological/prior_know_score.py similarity index 100% rename from examples/prior_know_score.py rename to examples/topological/prior_know_score.py diff --git a/pyproject.toml b/pyproject.toml index 5e0e110da..93e2dc1d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,7 +48,7 @@ networkx = "^3.1" importlib-resources = { version = "*", python = "<3.10" } pywhy-graphs = { git = "https://github.com/py-why/pywhy-graphs.git", branch = 'main', optional = true } pygraphviz = { version = "^1.11", optional = true } -pygam = "^0.9.0" +pygam = { version = "^0.9.0", optional = true } [tool.poetry.group.style] optional = true @@ -79,6 +79,7 @@ joblib = { version = "^1.1.0" } # needed in dowhy's package tqdm = { version = "^4.64.0" } # needed in dowhy's package pre-commit = "^3.0.4" pooch = "^1.7.0" +pygam = "^0.9.0" [tool.poetry.group.docs] optional = true @@ -110,6 +111,7 @@ kiwisolver = "^1.4.4" [tool.poetry.extras] graph_func = ['pywhy-graphs'] +topological = ['pygam'] viz = ['pygraphviz'] [tool.portray] From fd965f09eaf1132b6c0d27576b7275ceb8a6cb6c Mon Sep 17 00:00:00 2001 From: Adam Li Date: Fri, 29 Sep 2023 16:34:59 -0400 Subject: [PATCH 60/61] Multidomain wip Signed-off-by: Adam Li --- dodiscover/ci/oracle.py | 3 +- dodiscover/constraint/fcialg.py | 68 + dodiscover/constraint/intervention.py | 4 +- dodiscover/constraint/skeleton.py | 238 +- .../constraint/plot_simulation_sfci.ipynb | 3215 +++++++++-------- .../skeleton/test_multidomain_skeleton.py | 104 +- 6 files changed, 1855 insertions(+), 1777 deletions(-) diff --git a/dodiscover/ci/oracle.py b/dodiscover/ci/oracle.py index d3a7e139d..be9b77b55 100644 --- a/dodiscover/ci/oracle.py +++ b/dodiscover/ci/oracle.py @@ -23,8 +23,7 @@ class Oracle(BaseConditionalIndependenceTest): _allow_multivariate_input: bool = True - def __init__( - self, graph: Graph, included_nodes: Optional[Set[Column]] = None) -> None: + def __init__(self, graph: Graph, included_nodes: Optional[Set[Column]] = None) -> None: self.graph = graph self.included_nodes = included_nodes diff --git a/dodiscover/constraint/fcialg.py b/dodiscover/constraint/fcialg.py index 6fef8d60f..bd6369492 100644 --- a/dodiscover/constraint/fcialg.py +++ b/dodiscover/constraint/fcialg.py @@ -859,6 +859,74 @@ def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingS break idx += 1 + def _apply_orientation_rules(self, graph: EquivalenceClass, sep_set: SeparatingSet): + idx = 0 + finished = False + while idx < self.max_iter and not finished: + change_flag = False + logger.info(f"Running R1-10 for iteration {idx}") + + # if self.stable: + # pass + # else: + for u in graph.nodes: + for (a, c) in permutations(graph.neighbors(u), 2): + logger.debug(f"Check {u} {a} {c}") + + # apply R1-3 to orient triples and arrowheads + r1_add = self._apply_rule1(graph, u, a, c) + r2_add = self._apply_rule2(graph, u, a, c) + r3_add = self._apply_rule3(graph, u, a, c) + + # apply R4, orienting discriminating paths + r4_add, _ = self._apply_rule4(graph, u, a, c, sep_set) + + # apply R5-7 to handle cases where selection bias is present + if self.selection_bias: + r5_add = self._apply_rule5(graph, u, a) + r6_add = self._apply_rule6(graph, u, a, c) + r7_add = self._apply_rule7(graph, u, a, c) + else: + r5_add = False + r6_add = False + r7_add = False + + # apply R8 to orient more tails + r8_add = self._apply_rule8(graph, u, a, c) + + # apply R9-10 to orient uncovered potentially directed paths + r9_add, _ = self._apply_rule9(graph, a, u, c) + + # a and c are neighbors of u, so u is the endpoint desired + r10_add, _, _ = self._apply_rule10(graph, a, c, u) + + # see if there was a change flag + all_flags = [ + r1_add, + r2_add, + r3_add, + r4_add, + r5_add, + r6_add, + r7_add, + r8_add, + r9_add, + r10_add, + ] + if any(all_flags) and not change_flag: + logger.info(f"{change_flag} with " f"{all_flags}") + change_flag = True + + # check if we should continue or not + if not change_flag: + finished = True + if not self.selection_bias: + logger.info(f"Finished applying R1-4, and R8-10 with {idx} iterations") + if self.selection_bias: + logger.info(f"Finished applying R1-10 with {idx} iterations") + break + idx += 1 + def learn_skeleton( self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None ) -> Tuple[nx.Graph, SeparatingSet]: diff --git a/dodiscover/constraint/intervention.py b/dodiscover/constraint/intervention.py index c3dbeb71b..cd10501a0 100644 --- a/dodiscover/constraint/intervention.py +++ b/dodiscover/constraint/intervention.py @@ -223,7 +223,7 @@ def _apply_rule11(self, graph: EquivalenceClass, context: Context) -> Tuple[bool augmented_nodes = context.get_augmented_nodes() oriented_edges = [] - added_arrows = True + added_arrows = False for node in augmented_nodes: for nbr in graph.neighbors(node): if nbr in augmented_nodes: @@ -235,6 +235,8 @@ def _apply_rule11(self, graph: EquivalenceClass, context: Context) -> Tuple[bool graph.add_edge(node, nbr, graph.directed_edge_name) oriented_edges.append((node, nbr)) + added_arrows = True + if added_arrows and self.debug: self.debug_map[(node, nbr)] = "Rule 11" diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index c27eac721..79c564637 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -7,6 +7,7 @@ import networkx as nx import numpy as np import pandas as pd +import pywhy_graphs as pg from joblib import Parallel, delayed from dodiscover.ci import BaseConditionalIndependenceTest, Oracle @@ -67,7 +68,7 @@ def _test_xy_edges( Whether to perform cross-distribution tests. If True, then the ``context`` object must contain a ``sigma_map`` attribute that maps each X-node to the corresponding distribution indices of interest. - + Returns ------- test_stat : float @@ -122,10 +123,15 @@ def _test_xy_edges( test_stat = np.inf pvalue = 0.0 else: + import traceback + + print("\n\ninside error message...") print(x_var, y_var, cond_set) print(this_data.columns) print(this_data.head()) print(this_data[x_var]) + print(context.init_graph.nodes) + traceback.print_exc() raise Exception(e) # if any "independence" is found through inability to reject @@ -1121,8 +1127,6 @@ def _fit_single_distribution( # if there is no second stage skeleton method to be run, then we # will stop with the skeleton here - print(self.second_stage_condsel_method) - print(context) if self.second_stage_condsel_method is None: self.context_ = deepcopy(context.copy()) self.adj_graph_ = deepcopy(context.init_graph.copy()) @@ -1269,12 +1273,12 @@ def _prep_second_stage_skeleton(self, context: Context) -> Context: # R9 allows us to leverage F-nodes being not in separating sets to # augment all separating sets that have non-empty sets with all # F-nodes to keep consistency with the algorithm - for x_var, y_vars in self.sep_set_.items(): - for y_var in y_vars: - sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore - if len(sep_sets) > 0: - for idx in range(len(sep_sets)): - self.sep_set_[x_var][y_var][idx].update(context.get_augmented_nodes()) + # for x_var, y_vars in self.sep_set_.items(): + # for y_var in y_vars: + # sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + # if len(sep_sets) > 0: + # for idx in range(len(sep_sets)): + # self.sep_set_[x_var][y_var][idx].update(context.get_augmented_nodes()) return super()._prep_second_stage_skeleton(context) @@ -1488,97 +1492,97 @@ def __init__( self.known_intervention_targets = known_intervention_targets - def _create_augmented_nodes( - self, domain_ids: List[int], intervention_targets: List[Optional[Set]] - ) -> Tuple[List, Dict, Dict, Dict]: - """Create augmented nodes, sigma map and optionally a symmetric difference map. - - Given a number of distributions attributed to interventions, one constructs - F-nodes to add to the causal graph by: - - - For all pairs of incoming distributions, form a new F-node for every - pair of distributions. Update ``node_domain_map`` to map the F-node to - a specific domain. - - If the pairs are from two known target-interventions (i.e. not `None` - value), then also add the symmetric difference mapping to - ``symmetric_diff_map``, which maps the F-node to the intervention target. - - where ``targets`` is a set of either nodes, or set of indices corresponding - to the input data distributions and ``domains`` is a set of domains, either - a single domain for F-nodes within domain, or a set of two domains for - F-nodes across domains. - - Parameters - ---------- - domain_ids : List[int] - A list of domain ids for each input distribution. - intervention_targets : List[Set] - A list of known intervention targets for each input distribution with ``None`` - representing unknown targets. If the distribution is observational, then - the empty set is used. - - Returns - ------- - augmented_nodes : List - Set of augmented nodes (i.e. F and S nodes). - symmetric_diff_map : Dict[Any, FrozenSet] - Mapping of augmented nodes to intervention targets, or distribution indices represented - by the node. - sigma_map : Dict[Any, FrozenSet] - Mapping of augmented nodes to distribution indices represented by the node. - node_domain_map : Dict[Any, FrozenSet] - Mapping of augmented nodes to domains. - """ - # map augmented nodes to domains - node_domain_map = dict() - symmetric_diff_map = dict() - sigma_map = dict() - f_nodes = [] - - # create F-nodes, which is now all combinations of distributions choose 2 - k = 0 - seen_domain_pairs = dict() - seen_distr_pairs = dict() - - # compare every pair of distributions to now add interventions if necessary - for dataset_idx, source in enumerate(domain_ids): - for dataset_jdx, target in enumerate(domain_ids): - # perform memoization to avoid duplicate augmented nodes - domain_memo_key = frozenset([source, target]) - distr_memo_key = frozenset([dataset_idx, dataset_jdx]) - if dataset_jdx <= dataset_idx: - continue - if domain_memo_key in seen_domain_pairs and distr_memo_key in seen_distr_pairs: - continue - seen_domain_pairs[domain_memo_key] = None - seen_distr_pairs[distr_memo_key] = None - - # map each augmented-node to a tuple of distribution indices, or to a set of nodes - # representing the intervention targets - if ( - intervention_targets[dataset_idx] is not None - and intervention_targets[dataset_jdx] is not None - and source == target - ): - symm_diff = set(intervention_targets[dataset_idx]).symmetric_difference( - set(intervention_targets[dataset_jdx]) - ) - targets = frozenset(symm_diff) - else: - targets = None - - # create the F-node - f_node = ("F", k) - f_nodes.append(f_node) - - # map each F-node to a set of domain(s) - node_domain_map[f_node] = [source, target] - sigma_map[f_node] = [dataset_idx, dataset_jdx] - symmetric_diff_map[f_node] = targets - - k += 1 - augmented_nodes = set(f_nodes) - return augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map + # def _create_augmented_nodes( + # self, domain_ids: List[int], intervention_targets: List[Optional[Set]] + # ) -> Tuple[List, Dict, Dict, Dict]: + # """Create augmented nodes, sigma map and optionally a symmetric difference map. + + # Given a number of distributions attributed to interventions, one constructs + # F-nodes to add to the causal graph by: + + # - For all pairs of incoming distributions, form a new F-node for every + # pair of distributions. Update ``node_domain_map`` to map the F-node to + # a specific domain. + # - If the pairs are from two known target-interventions (i.e. not `None` + # value), then also add the symmetric difference mapping to + # ``symmetric_diff_map``, which maps the F-node to the intervention target. + + # where ``targets`` is a set of either nodes, or set of indices corresponding + # to the input data distributions and ``domains`` is a set of domains, either + # a single domain for F-nodes within domain, or a set of two domains for + # F-nodes across domains. + + # Parameters + # ---------- + # domain_ids : List[int] + # A list of domain ids for each input distribution. + # intervention_targets : List[Set] + # A list of known intervention targets for each input distribution with ``None`` + # representing unknown targets. If the distribution is observational, then + # the empty set is used. + + # Returns + # ------- + # augmented_nodes : List + # Set of augmented nodes (i.e. F and S nodes). + # symmetric_diff_map : Dict[Any, FrozenSet] + # Mapping of augmented nodes to intervention targets, or distribution indices represented + # by the node. + # sigma_map : Dict[Any, FrozenSet] + # Mapping of augmented nodes to distribution indices represented by the node. + # node_domain_map : Dict[Any, FrozenSet] + # Mapping of augmented nodes to domains. + # """ + # # map augmented nodes to domains + # node_domain_map = dict() + # symmetric_diff_map = dict() + # sigma_map = dict() + # f_nodes = [] + + # # create F-nodes, which is now all combinations of distributions choose 2 + # k = 0 + # seen_domain_pairs = dict() + # seen_distr_pairs = dict() + + # # compare every pair of distributions to now add interventions if necessary + # for dataset_idx, source in enumerate(domain_ids): + # for dataset_jdx, target in enumerate(domain_ids): + # # perform memoization to avoid duplicate augmented nodes + # domain_memo_key = frozenset([source, target]) + # distr_memo_key = frozenset([dataset_idx, dataset_jdx]) + # if dataset_jdx <= dataset_idx: + # continue + # if domain_memo_key in seen_domain_pairs and distr_memo_key in seen_distr_pairs: + # continue + # seen_domain_pairs[domain_memo_key] = None + # seen_distr_pairs[distr_memo_key] = None + + # # map each augmented-node to a tuple of distribution indices, or to a set of nodes + # # representing the intervention targets + # if ( + # intervention_targets[dataset_idx] is not None + # and intervention_targets[dataset_jdx] is not None + # and source == target + # ): + # symm_diff = set(intervention_targets[dataset_idx]).symmetric_difference( + # set(intervention_targets[dataset_jdx]) + # ) + # targets = frozenset(symm_diff) + # else: + # targets = None + + # # create the F-node + # f_node = ("F", k) + # f_nodes.append(f_node) + + # # map each F-node to a set of domain(s) + # node_domain_map[f_node] = [source, target] + # sigma_map[f_node] = [dataset_idx, dataset_jdx] + # symmetric_diff_map[f_node] = targets + + # k += 1 + # augmented_nodes = set(f_nodes) + # return augmented_nodes, symmetric_diff_map, sigma_map, node_domain_map def learn_graph( self, @@ -1626,9 +1630,8 @@ def learn_graph( symmetric_diff_map, sigma_map, node_domain_map, - ) = self._create_augmented_nodes( - domain_ids=domain_indices, - intervention_targets=intervention_targets + ) = pg.classes.compute_augmented_nodes( + intervention_targets=intervention_targets, domain_ids=domain_indices ) # initialize the augmented graph to be fully connected to observed casual variables @@ -1644,7 +1647,7 @@ def learn_graph( # extract F and S-nodes f_nodes = augmented_nodes - + # skeleton discovery should not condition on augmented nodes skip_nodes = augmented_nodes @@ -1677,17 +1680,17 @@ def learn_graph( # R9 allows us to leverage F-nodes being not in separating sets to # augment all separating sets that have non-empty sets with all # F-nodes to keep consistency with the algorithm - for x_var, y_vars in self.sep_set_.items(): - for y_var in y_vars: - sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore - if len(sep_sets) > 0: - for idx in range(len(sep_sets)): - self.sep_set_[x_var][y_var][idx].update(context.f_nodes) - - # loop through each domain pair to learn the F-node skeleton + # for x_var, y_vars in self.sep_set_.items(): + # for y_var in y_vars: + # sep_sets: List = self.sep_set_.get(x_var).get(y_var) # type: ignore + # if len(sep_sets) > 0: + # for idx in range(len(sep_sets)): + # self.sep_set_[x_var][y_var][idx].update(context.f_nodes) + + # loop through each pair of datasets to learn the augmented F-node skeleton seen_domain_pairs = set() for idx, source in enumerate(domain_indices): - # analyze F-nodes only within the 'source' domain + # analyze F-nodes only within the 'source' single domain source_fnodes = [ node for node in augmented_nodes if set(node_domain_map[node]) == {source} ] @@ -1706,7 +1709,13 @@ def learn_graph( cross_distribution_test=True, ) + # now compute skeleton among all possible F-nodes representing domain pairs + for idx, source in enumerate(domain_indices): for jdx, target in enumerate(domain_indices): + # skip the same dataset + if idx == jdx: + continue + # skip if source and target are the same domain because we have # already learned from these pairs of datasets if source == target: @@ -1737,8 +1746,7 @@ def learn_graph( skipped_z_nodes=skip_nodes, cross_distribution_test=True, # debug=debug, - ) - + ) # prepare the context object for the second stage of learning # all separating sets are either: diff --git a/examples/constraint/plot_simulation_sfci.ipynb b/examples/constraint/plot_simulation_sfci.ipynb index 5e0dbeee8..aa91912a5 100644 --- a/examples/constraint/plot_simulation_sfci.ipynb +++ b/examples/constraint/plot_simulation_sfci.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 2, "id": "290503ef-96a6-433a-b01c-e10ea04ba6f7", "metadata": {}, "outputs": [], @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "3ec27151-eee2-49a3-bf74-0cc671ecac7c", "metadata": {}, "outputs": [], @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "7ebd007f-de4a-41d0-951a-057c7d4aa627", "metadata": {}, "outputs": [], @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 5, "id": "8de9d34b-cc5f-4acb-8c49-a7a05fbb7b43", "metadata": {}, "outputs": [], @@ -121,7 +121,28 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 32, + "id": "cba43643-7f55-46ff-9113-fd42c1b119b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "True\n" + ] + } + ], + "source": [ + "print(type(G))\n", + "# print(G.undirected_edges)\n", + "print(isinstance(G, type(nx.Graph())))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "id": "84b152fd-0a9b-410e-af5f-4bbe7b041744", "metadata": {}, "outputs": [ @@ -220,21 +241,87 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 164, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dot_graph = draw(G)\n", - "dot_graph.render(outfile=\"true_graph.png\", view=False, cleanup=True)\n", + "# get the layout position for the graph G using networkx\n", + "pos_G = nx.spring_layout(G, k=1)\n", + "\n", + "dot_graph = draw(G, pos=pos_G, prog=\"neato\")\n", + "dot_graph.render(outfile=\"true_graph.png\", view=False, cleanup=True, engine=\"neato\")\n", "\n", "dot_graph" ] }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9bde340d-d05a-430e-8663-23c824e25b6f", + "metadata": {}, + "outputs": [], + "source": [ + "node_order = list(nx.topological_sort(G))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1a781934-c086-487b-8fb9-b1bb63fdfa8a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.10793277 0.21842778]\n", + "digraph {\n", + "\tA [height=.5 pos=\"0.10793277386412264,0.21842778372323665\" shape=square width=.5]\n", + "\tX -> A [color=blue prog=neato]\n", + "\tB [height=.5 pos=\"-0.5083602744222117,-0.2879750418138882\" shape=square width=.5]\n", + "\tA -> B [color=blue prog=neato]\n", + "\tC [height=.5 pos=\"0.06927561966926396,-0.20876747677347904\" shape=square width=.5]\n", + "\tB -> C [color=blue prog=neato]\n", + "\tX -> C [color=blue prog=neato]\n", + "\tD [height=.5 pos=\"-0.9358469497119339,0.17196159773885009\" shape=square width=.5]\n", + "\tC -> D [color=blue prog=neato]\n", + "\tB -> D [color=blue prog=neato]\n", + "\tX [height=.5 pos=\"0.26699883060075913,0.5517523955094646\" shape=square width=.5]\n", + "\tW [height=.5 pos=\"1.0,-0.44539925838418415\" shape=square width=.5]\n", + "\tC -> W [color=blue prog=neato]\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "print(pos_G[\"A\"])\n", + "print(dot_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d3f8ac7f-8100-4981-87ae-c3ae1af470b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{}\n" + ] + } + ], + "source": [ + "print(dot_graph.node_attr)" + ] + }, { "cell_type": "markdown", "id": "391415ef-d4ed-4f1b-a868-15b856561bf7", @@ -248,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 340, + "execution_count": 16, "id": "f1306805-a77f-4515-be77-de6ae630f8e9", "metadata": {}, "outputs": [], @@ -261,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 341, + "execution_count": 17, "id": "8e116785-4421-4657-89f2-15cd163bfa99", "metadata": {}, "outputs": [ @@ -289,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 342, + "execution_count": 18, "id": "60e9cb9a-d941-4cc0-bca0-decd7830756a", "metadata": {}, "outputs": [ @@ -310,14 +397,27 @@ }, { "cell_type": "code", - "execution_count": 343, + "execution_count": 19, "id": "30f2e2a4-667b-45e5-b9f3-c87d0d209854", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inside: [('A', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}), ('B', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}), ('C', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x1768a8310>}), ('D', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x1768a8940>}), ('X', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x1051b5160>}), ('W', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>})]\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x1768a8310>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x1768a8940>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x1051b5160>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>}\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ef5303132fb47f9b77424de0b0946d3", + "model_id": "cbb992ae11824c748a2ff4d561458afa", "version_major": 2, "version_minor": 0 }, @@ -336,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 20, "id": "b0acf0c7-0ca9-4fde-8100-f91597d726d9", "metadata": {}, "outputs": [ @@ -365,8 +465,8 @@ " B\n", " C\n", " D\n", - " W\n", " X\n", + " W\n", " \n", " \n", " \n", @@ -376,8 +476,8 @@ " 1\n", " 0\n", " 0\n", - " 2\n", " 1\n", + " 2\n", " \n", " \n", " 1\n", @@ -394,8 +494,8 @@ " 2\n", " 2\n", " 2\n", - " 0\n", " 1\n", + " 0\n", " \n", " \n", " 3\n", @@ -403,8 +503,8 @@ " 1\n", " 2\n", " 2\n", - " 1\n", " 2\n", + " 1\n", " \n", " \n", " 4\n", @@ -412,20 +512,20 @@ " 2\n", " 2\n", " 1\n", - " 1\n", " 2\n", + " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " A B C D W X\n", - "0 2 1 0 0 2 1\n", + " A B C D X W\n", + "0 2 1 0 0 1 2\n", "1 1 1 2 1 2 2\n", - "2 1 2 2 2 0 1\n", - "3 1 1 2 2 1 2\n", - "4 1 2 2 1 1 2" + "2 1 2 2 2 1 0\n", + "3 1 1 2 2 2 1\n", + "4 1 2 2 1 2 1" ] }, "metadata": {}, @@ -457,74 +557,42 @@ }, { "cell_type": "code", - "execution_count": 123, - "id": "9d50b505-2181-44d7-a62b-0d4890d169e6", - "metadata": {}, - "outputs": [], - "source": [ - "ci_estimator = CategoricalCITest(lambda_=\"cressie-read\")" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "be1b9ec1-59ce-4134-bfc8-c7429936698e", + "execution_count": 250, + "id": "0f341994-ccb8-44c7-a1ac-d4af8513797f", "metadata": {}, "outputs": [], "source": [ - "context = make_context().variables(data=df).build()\n", - "\n", - "learner = SFCI(\n", - " ci_estimator=ci_estimator,\n", - " cd_estimator=ci_estimator,\n", - " n_jobs=n_jobs,\n", - " max_cond_set_size=2,\n", - " max_combinations=None,\n", - " # alpha=0.5,\n", + "ctx_builder = make_context()\n", + "ctx: Context = (\n", + " ctx_builder.variables(data=df)\n", + " # .obs_distribution(True)\n", + " .build()\n", ")" ] }, { "cell_type": "code", - "execution_count": 125, - "id": "e7d3fccc-6b82-4b0d-b772-d360b3d83b9a", + "execution_count": 251, + "id": "760c6d42-8bea-404b-987d-8a7c19699b37", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using data from distribution 0 for learning the skeleton.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "learner.fit([df], context, domain_indices=[1], intervention_targets=[None])" + "ci_estimator = CategoricalCITest()" ] }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 252, "id": "edefd463-f795-4ef4-a17a-dc762452b5aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 126, + "execution_count": 252, "metadata": {}, "output_type": "execute_result" } @@ -538,13 +606,13 @@ " max_combinations=None,\n", " # alpha=0.5,\n", ")\n", - "fci_learner.fit(df, context)" + "fci_learner.learn_graph(df, ctx)" ] }, { "cell_type": "code", - "execution_count": 127, - "id": "70754e13-0ce0-48db-a7e6-f26d3704ab68", + "execution_count": 258, + "id": "4807951b-197d-4137-88bb-c5b49be48255", "metadata": {}, "outputs": [ { @@ -556,227 +624,154 @@ "\n", "\n", - "\n", - "\n", - "\n", - "SFCI graph\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", + "\n", + "W\n", "\n", "\n", - "\n", + "\n", "B\n", - "\n", - "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", "\n", "\n", "\n", "B->C\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "A\n", - "\n", - "A\n", + "\n", + "A\n", "\n", "\n", - "\n", + "\n", "B->A\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "X\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "A->X\n", - "\n", - "\n", - "\n", + "\n", + "X\n", "\n", "\n", "\n", "X->C\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 127, + "execution_count": 258, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "graph = learner.graph_\n", - "draw(graph, name=\"SFCI graph\")" + "graph = fci_learner.graph_\n", + "# draw(graph, name=\"FCI graph\", pos=pos_G, prog=\"neato\")\n", + "dot_graph = draw(\n", + " graph,\n", + " # name=\"FCI graph\",\n", + " pos=pos_G, # prog=\"neato\" # , node_order=node_order\n", + ")\n", + "\n", + "dot_graph.render(\n", + " outfile=\"./fci_obs.png\",\n", + " view=False,\n", + " cleanup=True,\n", + " # engine=\"neato\"\n", + ")\n", + "dot_graph" ] }, { "cell_type": "code", - "execution_count": 128, - "id": "4807951b-197d-4137-88bb-c5b49be48255", + "execution_count": 254, + "id": "1739c748-c2e1-4025-9234-0d3d751ef57c", "metadata": {}, "outputs": [ { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "FCI graph\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", - "\n", - "\n", - "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B\n", - "\n", - "B\n", - "\n", - "\n", - "\n", - "B->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A\n", - "\n", - "A\n", - "\n", - "\n", - "\n", - "B->A\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "A->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X->C\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "digraph {\n", + "\tgraph [label=\"FCI graph\"]\n", + "\tX [height=.5 pos=\"0.7391505362251583,0.11699328808194805!\" shape=square width=.5]\n", + "\tA [height=.5 pos=\"1.0,-0.3140320449124699!\" shape=square width=.5]\n", + "\tB [height=.5 pos=\"-0.08608632483664021,0.2688828567162253!\" shape=square width=.5]\n", + "\tC [height=.5 pos=\"-0.22678494014014539,-0.37720753552784386!\" shape=square width=.5]\n", + "\tD [height=.5 pos=\"-0.8244997069657383,-0.42188428497794495!\" shape=square width=.5]\n", + "\tW [height=.5 pos=\"-0.6017795642826345,0.7272477206200854!\" shape=square width=.5]\n", + "\tB -> A [arrowhead=odot arrowtail=normal color=green dir=both]\n", + "\tC -> W [arrowhead=odot arrowtail=normal color=green dir=both]\n", + "\tX -> A [arrowhead=odot arrowtail=odot color=green dir=both]\n", + "\tB -> D [color=red dir=both]\n", + "\tB -> C [color=blue engine=neato]\n", + "\tX -> C [color=blue engine=neato]\n", + "\tC -> D [color=blue engine=neato]\n", + "}\n", + "\n" + ] } ], "source": [ - "graph = fci_learner.graph_\n", - "draw(graph, name=\"FCI graph\")" + "print(dot_graph)" ] }, { @@ -802,7 +797,17 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 66, + "id": "701726ae-1c84-4efc-8ce8-bd7eae39ce83", + "metadata": {}, + "outputs": [], + "source": [ + "n_sample_ints = 30_000" + ] + }, + { + "cell_type": "code", + "execution_count": 67, "id": "262ca3c7-2971-45c9-98e7-18c7ca18a715", "metadata": {}, "outputs": [], @@ -812,42 +817,27 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 68, "id": "1b327575-69b0-42c8-a7d9-5dcd1bb77998", "metadata": {}, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3e3ebc16cf004cb0b9355d383636df98", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/6 [00:00, 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}), ('B', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}), ('C', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x1768a8310>}), ('D', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x29011d820>}), ('X', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x1051b5160>}), ('W', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>})]\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x1768a8310>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x29011d820>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x1051b5160>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>}\n" + ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3635c37e07b542eaa613a6aa72b996e4", + "model_id": "e29e3fecb9544024877bee26611e512b", "version_major": 2, "version_minor": 0 }, @@ -862,8 +852,8 @@ "source": [ "# we will now simulate a set of interventions on the graph\n", "targets = [\n", - " [\"X\"],\n", - " [\"C\"],\n", + " # [\"X\"],\n", + " # [\"C\"],\n", " [\"D\"],\n", "]\n", "data = [df]\n", @@ -872,14 +862,16 @@ " int_G = apply_discrete_soft_intervention(G.copy(), target, random_state=seed)\n", "\n", " # now we sample from the graph the discrete dataset\n", - " int_df = sample_from_graph(int_G, n_samples=30000, n_jobs=1, random_state=seed)\n", + " int_df = sample_from_graph(\n", + " int_G, n_samples=n_sample_ints, n_jobs=1, random_state=seed\n", + " )\n", "\n", " data.append(int_df)" ] }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 69, "id": "b7fb1516-0c11-4c27-938e-d9b9c1e2113d", "metadata": {}, "outputs": [ @@ -887,7 +879,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "4\n" + "2\n" ] } ], @@ -897,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 70, "id": "8faa5572-0bf1-404b-b9bd-130196e5dcf5", "metadata": {}, "outputs": [], @@ -913,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 71, "id": "4e1cd006-b5f4-4cd1-b796-55587d226503", "metadata": {}, "outputs": [], @@ -923,14 +915,15 @@ " cd_estimator=ci_estimator,\n", " max_cond_set_size=2,\n", " n_jobs=-1,\n", + " debug=True,\n", ")\n", "\n", - "int_learner = int_learner.fit(data, ctx)" + "int_learner = int_learner.learn_graph(data, ctx)" ] }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 72, "id": "3b6f3e72-4c50-44b1-be16-c7b62f06b3ea", "metadata": {}, "outputs": [ @@ -943,100 +936,100 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "W\n", - "\n", - "W\n", + "\n", + "W\n", "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", "\n", "\n", "\n", "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X\n", - "\n", - "X\n", + "\n", + "D\n", "\n", - "\n", - "\n", - "C->X\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "A\n", - "\n", - "A\n", + "\n", + "A\n", "\n", - "\n", - "\n", - "X->A\n", - "\n", - "\n", + "\n", + "\n", + "B->A\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "B\n", - "\n", - "B\n", + "C\n", + "\n", + "C\n", "\n", "\n", - "\n", + "\n", "B->C\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", "\n", - "\n", - "\n", - "A->B\n", - "\n", - "\n", + "\n", + "\n", + "A->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 134, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -1046,12 +1039,15 @@ "\n", "# %%\n", "# Visualize the full graph including the F-node\n", - "draw(est_pag, direction=\"LR\")" + "dot_graph = draw(est_pag, pos=pos_G)\n", + "\n", + "dot_graph.render(outfile=\"./psifci_obsandint.png\", view=False, cleanup=True)\n", + "dot_graph" ] }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 74, "id": "0c9c5d55-534c-4aaf-b4ca-71132878bbbc", "metadata": {}, "outputs": [ @@ -1064,190 +1060,112 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "C->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A\n", - "\n", - "A\n", - "\n", - "\n", - "\n", - "X->A\n", - "\n", - "\n", + "\n", + "W\n", "\n", "\n", - "\n", + "\n", "B\n", - "\n", - "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", "\n", "\n", "\n", "B->C\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", "\n", "\n", "\n", "B->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 2)\n", - "\n", - "('F', 2)\n", - "\n", - "\n", - "\n", - "('F', 2)->D\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "('F', 3)\n", - "\n", - "('F', 3)\n", - "\n", - "\n", - "\n", - "('F', 3)->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 3)->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 1)\n", - "\n", - "('F', 1)\n", + "A\n", + "\n", + "A\n", "\n", - "\n", - "\n", - "('F', 1)->C\n", - "\n", - "\n", + "\n", + "\n", + "B->A\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 5)\n", - "\n", - "('F', 5)\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "('F', 5)->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 5)->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A->B\n", - "\n", - "\n", + "C->D\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 4)\n", - "\n", - "('F', 4)\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", "\n", - "\n", - "\n", - "('F', 4)->D\n", - "\n", - "\n", + "\n", + "\n", + "('F', 0)->D\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 4)->X\n", - "\n", - "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", "\n", - "\n", - "\n", - "('F', 0)\n", - "\n", - "('F', 0)\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 0)->X\n", - "\n", - "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 135, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -1257,204 +1175,70 @@ "\n", "# %%\n", "# Visualize the full graph including the F-node\n", - "draw(est_pag, direction=\"LR\")" + "draw(est_pag, pos=pos_G)" ] }, { "cell_type": "code", - "execution_count": 150, - "id": "51fcfcf0-46ec-41ec-9eba-ae1a1b1ee231", + "execution_count": 52, + "id": "58416873-6d8f-4a2e-880f-43c9d948db1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[None, ['X'], ['C'], ['D']]\n" + "{('A', 'B'): 'collider',\n", + " ('A', 'C'): 'rule9',\n", + " ('B', 'C'): 'rule 1: A *-> B o-* C',\n", + " ('B', 'D'): 'rule2',\n", + " ('C', 'B'): 'rule 1: A *-> B o-* C',\n", + " ('C', 'D'): 'rule 1: W *-> C o-* D',\n", + " ('D', 'B'): 'collider',\n", + " ('D', 'C'): 'rule 1: W *-> C o-* D',\n", + " ('W', 'C'): 'collider',\n", + " ('X', 'C'): 'collider'}\n" ] } ], "source": [ - "intervention_targets = targets.copy()\n", - "intervention_targets.insert(0, None)\n", - "print(intervention_targets)" + "pprint(int_learner.debug_map)" ] }, { - "cell_type": "code", - "execution_count": 141, - "id": "a8bfb31a-dbfa-4106-8d3f-fc09af3e9f07", + "cell_type": "markdown", + "id": "0a3dc765-a4cd-46bf-aa7c-3847041419a7", "metadata": {}, - "outputs": [], "source": [ - "learner = SFCI(\n", - " ci_estimator=ci_estimator,\n", - " cd_estimator=ci_estimator,\n", - " max_cond_set_size=2,\n", - " n_jobs=-1,\n", - ")\n", + "We see that additional edges can be oriented with the presence of interventional data. Moreover, the SFCI algorithm perfectly \n", + "replicates and builds on top of the interventional data." + ] + }, + { + "cell_type": "markdown", + "id": "1a15289e-32ee-45ab-a5ff-a6f3aeedf4b3", + "metadata": {}, + "source": [ + "Causal Discovery: Observational data across multiple domains\n", + "------------------------------------------------------------\n", "\n", - "learner.fit(\n", - " data,\n", - " ctx,\n", - " domain_indices=[1, 1, 1, 1],\n", - " intervention_targets=intervention_targets,\n", - ")" + "In the SFCI paper, it is shown that the $\\Psi$-FCI algorithm is equivalent to the SFCI algorithm when there is\n", + "observational data across multiple domains, where the F-nodes can be seen as equivalent to S-nodes.\n", + "\n", + "We can leverage the same distributions that were intervened on because the change in domain can be seen\n", + "as an unknown-target intervention that occurs over those variables. That is, nature changes the distribution (CPD)\n", + "of the intervened variables, but we do not know where nature intervened. Thus conceptually, we see observational\n", + "data across multiple domains is similar to the setting with unknown-target interventional data within\n", + "a single domain. \n", + "\n", + "We will see later that when we have interventional data across multiple domains, the story becomes more complex\n", + "(and as a result, more interesting)." ] }, { "cell_type": "code", - "execution_count": 160, - "id": "fe520613-c5ce-4714-bce1-101077345ea9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", - "\n", - "\n", - "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "C->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A\n", - "\n", - "A\n", - "\n", - "\n", - "\n", - "X->A\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B\n", - "\n", - "B\n", - "\n", - "\n", - "\n", - "B->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A->B\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est_pag = learner.graph_\n", - "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", - "draw(est_pag_no_fnodes, direction=\"LR\")" - ] - }, - { - "cell_type": "markdown", - "id": "0a3dc765-a4cd-46bf-aa7c-3847041419a7", - "metadata": {}, - "source": [ - "We see that additional edges can be oriented with the presence of interventional data. Moreover, the SFCI algorithm perfectly \n", - "replicates and builds on top of the interventional data." - ] - }, - { - "cell_type": "markdown", - "id": "1a15289e-32ee-45ab-a5ff-a6f3aeedf4b3", - "metadata": {}, - "source": [ - "Causal Discovery: Observational data across multiple domains\n", - "------------------------------------------------------------\n", - "\n", - "In the SFCI paper, it is shown that the $\\Psi$-FCI algorithm is equivalent to the SFCI algorithm when there is\n", - "observational data across multiple domains, where the F-nodes can be seen as equivalent to S-nodes.\n", - "\n", - "We can leverage the same distributions that were intervened on because the change in domain can be seen\n", - "as an unknown-target intervention that occurs over those variables. That is, nature changes the distribution (CPD)\n", - "of the intervened variables, but we do not know where nature intervened. Thus conceptually, we see observational\n", - "data across multiple domains is similar to the setting with unknown-target interventional data within\n", - "a single domain. \n", - "\n", - "We will see later that when we have interventional data across multiple domains, the story becomes more complex\n", - "(and as a result, more interesting)." - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "id": "f3478f02-7401-478c-b2bc-86058af4efc6", + "execution_count": 75, + "id": "f3478f02-7401-478c-b2bc-86058af4efc6", "metadata": {}, "outputs": [], "source": [ @@ -1470,7 +1254,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 76, "id": "deceeeac-3866-4ecd-9ca7-b1206a11b4cd", "metadata": {}, "outputs": [], @@ -1483,12 +1267,12 @@ " debug=True,\n", ")\n", "\n", - "int_learner = int_learner.fit(data, ctx)" + "int_learner = int_learner.learn_graph(data, ctx)" ] }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 245, "id": "4116afe8-8dff-4487-930f-277bbd382301", "metadata": {}, "outputs": [ @@ -1501,190 +1285,194 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "C\n", - "\n", - "C\n", - "\n", - "\n", - "\n", - "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", + "B\n", + "\n", + "B\n", "\n", "\n", - "\n", + "\n", "D\n", - "\n", - "D\n", + "\n", + "D\n", "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "X\n", - "\n", - "X\n", + "C\n", + "\n", + "C\n", "\n", - "\n", - "\n", - "C->X\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", "\n", - "\n", - "\n", - "A\n", - "\n", - "A\n", + "\n", + "\n", + "D->C\n", + "\n", + "\n", "\n", - "\n", - "\n", - "X->A\n", - "\n", - "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", "\n", - "\n", - "\n", - "B\n", - "\n", - "B\n", + "\n", + "\n", + "W->C\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "B->C\n", - "\n", - "\n", + "\n", + "\n", + "('F', 5)\n", + "\n", + "('F', 5)\n", "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "('F', 5)->W\n", + "\n", + "\n", "\n", "\n", "\n", "('F', 2)\n", - "\n", - "('F', 2)\n", + "\n", + "('F', 2)\n", "\n", - "\n", - "\n", - "('F', 2)->D\n", - "\n", - "\n", + "\n", + "\n", + "('F', 2)->W\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 3)\n", - "\n", - "('F', 3)\n", + "\n", + "\n", + "('F', 2)->C\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 3)->C\n", - "\n", - "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", "\n", - "\n", - "\n", - "('F', 3)->X\n", - "\n", - "\n", + "\n", + "\n", + "('F', 2)->X\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 1)\n", - "\n", - "('F', 1)\n", + "\n", + "('F', 1)\n", "\n", "\n", - "\n", + "\n", "('F', 1)->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 5)\n", - "\n", - "('F', 5)\n", - "\n", - "\n", - "\n", - "('F', 5)->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('F', 5)->D\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "A->B\n", - "\n", - "\n", + "('F', 1)->X\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 4)\n", - "\n", - "('F', 4)\n", + "\n", + "('F', 4)\n", "\n", - "\n", - "\n", - "('F', 4)->D\n", - "\n", - "\n", + "\n", + "\n", + "('F', 4)->W\n", + "\n", + "\n", "\n", - "\n", - "\n", - "('F', 4)->X\n", - "\n", - "\n", + "\n", + "\n", + "('F', 4)->C\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "('F', 0)\n", - "\n", - "('F', 0)\n", + "\n", + "('F', 0)\n", "\n", "\n", - "\n", + "\n", "('F', 0)->X\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)\n", + "\n", + "('F', 3)\n", + "\n", + "\n", + "\n", + "('F', 3)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 240, + "execution_count": 245, "metadata": {}, "output_type": "execute_result" } @@ -1699,7 +1487,7 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 48, "id": "007ac3d5-c259-4442-9bdc-1bfe36901c7f", "metadata": {}, "outputs": [ @@ -1712,100 +1500,100 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "C\n", - "\n", - "C\n", + "\n", + "C\n", "\n", "\n", "\n", "W\n", - "\n", - "W\n", + "\n", + "W\n", "\n", "\n", "\n", "C->W\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "D\n", - "\n", - "D\n", + "\n", + "D\n", "\n", "\n", - "\n", + "\n", "C->D\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "X\n", - "\n", - "X\n", + "\n", + "X\n", "\n", - "\n", + "\n", "\n", - "C->X\n", - "\n", - "\n", - "\n", + "X->C\n", + "\n", + "\n", + "\n", "\n", "\n", "\n", "A\n", - "\n", - "A\n", + "\n", + "A\n", "\n", "\n", "\n", "X->A\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "B\n", - "\n", - "B\n", - "\n", - "\n", + "\n", "\n", - "B->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A->B\n", - "\n", - "\n", + "A->B\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 242, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1815,7 +1603,10 @@ "\n", "# %%\n", "# Visualize the full graph including the F-node\n", - "draw(est_pag, direction=\"LR\")" + "dot_graph = draw(est_pag, direction=\"LR\")\n", + "\n", + "dot_graph.render(outfile=\"./sfci_obs.png\", view=False, cleanup=True)\n", + "dot_graph" ] }, { @@ -1837,465 +1628,6 @@ "print(intervention_targets)" ] }, - { - "cell_type": "code", - "execution_count": 268, - "id": "2f52b02d-d0ce-408a-91ff-c976c46394c1", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using data from distribution 0 for learning the skeleton.\n", - "Trying to learn skeleton for 0 to remove F-nodes: []\n", - "[('S', 0)]\n", - "{}\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "here... \n", - "\n", - "\n", - "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", - "0 1\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "{}\n", - "Trying to learn skeleton for 0 and 1 to remove S-nodes: [('S', 0)]\n", - "Trying to learn skeleton for 0 and 1 to remove F-nodes: [] grouped with S-node: ('S', 0)\n", - "[('S', 1)]\n", - "{}\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "here... \n", - "\n", - "\n", - "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", - "0 2\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "{}\n", - "Trying to learn skeleton for 0 and 2 to remove S-nodes: [('S', 1)]\n", - "Trying to learn skeleton for 0 and 2 to remove F-nodes: [] grouped with S-node: ('S', 1)\n", - "[('S', 2)]\n", - "{}\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "here... \n", - "\n", - "\n", - "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", - "0 3\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "{}\n", - "Trying to learn skeleton for 0 and 3 to remove S-nodes: [('S', 2)]\n", - "Trying to learn skeleton for 0 and 3 to remove F-nodes: [] grouped with S-node: ('S', 2)\n", - "Trying to learn skeleton for 1 to remove F-nodes: []\n", - "[('S', 3)]\n", - "{}\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "here... \n", - "\n", - "\n", - "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", - "1 2\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "{}\n", - "Trying to learn skeleton for 1 and 2 to remove S-nodes: [('S', 3)]\n", - "Trying to learn skeleton for 1 and 2 to remove F-nodes: [] grouped with S-node: ('S', 3)\n", - "[('S', 4)]\n", - "{}\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "here... \n", - "\n", - "\n", - "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", - "1 3\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "{}\n", - "Trying to learn skeleton for 1 and 3 to remove S-nodes: [('S', 4)]\n", - "Trying to learn skeleton for 1 and 3 to remove F-nodes: [] grouped with S-node: ('S', 4)\n", - "Trying to learn skeleton for 2 to remove F-nodes: []\n", - "[('S', 5)]\n", - "{}\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "here... \n", - "\n", - "\n", - "{('S', 2), ('S', 3), ('S', 1), ('S', 4), ('S', 5), ('S', 0)}\n", - "2 3\n", - "{('S', 0): [0, 1], ('S', 1): [0, 2], ('S', 2): [0, 3], ('S', 3): [1, 2], ('S', 4): [1, 3], ('S', 5): [2, 3]}\n", - "{}\n", - "Trying to learn skeleton for 2 and 3 to remove S-nodes: [('S', 5)]\n", - "Trying to learn skeleton for 2 and 3 to remove F-nodes: [] grouped with S-node: ('S', 5)\n", - "Trying to learn skeleton for 3 to remove F-nodes: []\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 268, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "learner = SFCI(\n", - " ci_estimator=ci_estimator,\n", - " cd_estimator=ci_estimator,\n", - " max_cond_set_size=2,\n", - " n_jobs=-1,\n", - " debug=True,\n", - ")\n", - "\n", - "learner.fit(\n", - " data,\n", - " ctx,\n", - " domain_indices=[0, 1, 2, 3],\n", - " intervention_targets=intervention_targets,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "id": "02ebb37d-f01a-4173-bc33-5dc5ef7071d7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SFCI\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", - "\n", - "\n", - "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "C->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A\n", - "\n", - "A\n", - "\n", - "\n", - "\n", - "X->A\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B\n", - "\n", - "B\n", - "\n", - "\n", - "\n", - "B->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A->B\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 272, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est_pag = learner.graph_\n", - "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", - "draw(est_pag_no_fnodes, direction=\"LR\", name=\"SFCI\")" - ] - }, - { - "cell_type": "code", - "execution_count": 273, - "id": "49034875-c60c-46ca-9b85-0e56b992f15d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SFCI\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", - "\n", - "\n", - "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "D\n", - "\n", - "D\n", - "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X\n", - "\n", - "X\n", - "\n", - "\n", - "\n", - "C->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A\n", - "\n", - "A\n", - "\n", - "\n", - "\n", - "X->A\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B\n", - "\n", - "B\n", - "\n", - "\n", - "\n", - "B->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 3)\n", - "\n", - "('S', 3)\n", - "\n", - "\n", - "\n", - "('S', 3)->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 3)->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 1)\n", - "\n", - "('S', 1)\n", - "\n", - "\n", - "\n", - "('S', 1)->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 5)\n", - "\n", - "('S', 5)\n", - "\n", - "\n", - "\n", - "('S', 5)->C\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 5)->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "A->B\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 2)\n", - "\n", - "('S', 2)\n", - "\n", - "\n", - "\n", - "('S', 2)->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 4)\n", - "\n", - "('S', 4)\n", - "\n", - "\n", - "\n", - "('S', 4)->D\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 4)->X\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "('S', 0)\n", - "\n", - "('S', 0)\n", - "\n", - "\n", - "\n", - "('S', 0)->X\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 273, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "est_pag = learner.graph_\n", - "est_pag_no_fnodes = est_pag\n", - "dot_graph = draw(\n", - " est_pag_no_fnodes,\n", - " direction=\"LR\",\n", - " # name=\"SFCI\"\n", - ")\n", - "\n", - "dot_graph.render(outfile=\"./sfci_multidomain_obs.png\", view=False, cleanup=True)\n", - "dot_graph" - ] - }, { "cell_type": "code", "execution_count": 274, @@ -2351,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 370, + "execution_count": 294, "id": "8115bfcd-209e-4c71-b1bd-87639955d134", "metadata": {}, "outputs": [ @@ -2359,46 +1691,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "[set(), ['X'], ['C'], ['D']]\n", - "[set(), 'W', 'X']\n" + "[frozenset(), ['D']]\n", + "[frozenset(), ['D']]\n", + "[frozenset(), ('D',)]\n" ] } ], "source": [ "intervention_targets = targets.copy()\n", - "intervention_targets.insert(0, set())\n", + "intervention_targets.insert(0, frozenset())\n", "print(intervention_targets)\n", "\n", "domain_one_targets = intervention_targets.copy()\n", - "domain_two_targets = [set(), \"W\", \"X\"]\n", + "domain_two_targets = [frozenset(), (\"D\",)]\n", "\n", + "print(domain_one_targets)\n", "print(domain_two_targets)" ] }, { "cell_type": "code", - "execution_count": 371, + "execution_count": 295, "id": "56b79f04-c6ac-4d42-a7a2-b165ff212abb", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inside: [('A', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}), ('B', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}), ('C', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x1768a8310>}), ('D', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x2929b2700>}), ('X', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x1051b5160>}), ('W', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>})]\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x1768a8310>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x2929b2700>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x1051b5160>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>}\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ab6f5b712c54b3cbb2528544f3f984b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/6 [00:00, 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}), ('B', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}), ('C', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x2907fae50>}), ('D', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x1768a8940>}), ('X', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x2907fa8b0>}), ('W', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>})]\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x2907fae50>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x1768a8940>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x2907fa8b0>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>}\n" + ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8abd31deed54bf09c4b0cd1d6a90c52", + "model_id": "cb3a0740dad84013b1d524fdeb4a3c85", "version_major": 2, "version_minor": 0 }, @@ -2426,26 +1799,40 @@ ], "source": [ "# we will now simulate a set of interventions on the graph\n", - "domain_one_data = [df]\n", - "for target in domain_one_targets[1:]:\n", - " int_G = apply_discrete_soft_intervention(G.copy(), target, random_state=seed)\n", + "domain_two_data = []\n", + "int_G = apply_discrete_soft_intervention(G.copy(), {\"X\", \"C\"}, random_state=seed)\n", "\n", - " # now we sample from the graph the discrete dataset\n", - " int_df = sample_from_graph(int_G, n_samples=30000, n_jobs=1, random_state=seed)\n", + "# now we sample from the graph the discrete dataset\n", + "int_df = sample_from_graph(int_G, n_samples=n_sample_ints, n_jobs=1, random_state=seed)\n", + "domain_two_data.append(int_df)\n", + "domain_two_G = int_G.copy()\n", "\n", - " domain_one_data.append(int_df)" + "domain_two_obs = int_df.copy()" ] }, { "cell_type": "code", - "execution_count": 372, - "id": "9ce8617a-e9f6-437b-afd3-222fff0e917c", + "execution_count": 297, + "id": "1f06bcc8-8e5b-479c-a3c3-8787458baf1d", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inside: [('A', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}), ('B', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}), ('C', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x2907fae50>}), ('D', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x2907e8310>}), ('X', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x2907fa8b0>}), ('W', {'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>})]\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x28e4f4670>, 'exogenous_distribution': . at 0x1768a8790>, 'parent_function': .parent_func at 0x1768a8430>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a8700>, 'exogenous_distribution': . at 0x1768a85e0>, 'parent_function': .parent_func at 0x1768a8550>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a83a0>, 'exogenous_distribution': . at 0x1768a84c0>, 'parent_function': .parent_func at 0x2907fae50>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.0, 'exogenous_function': . at 0x1768a8820>, 'exogenous_distribution': . at 0x1768a88b0>, 'parent_function': .parent_func at 0x2907e8310>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 1.0, 'exogenous_function': . at 0x104e5bee0>, 'exogenous_distribution': .parent_func at 0x2907fa8b0>}\n", + "{'cpd': , 'cardinality': 3, 'possible_values': [0, 1, 2], 'noise_ratio': 0.1, 'exogenous_function': . at 0x1768a89d0>, 'exogenous_distribution': . at 0x1768a8a60>, 'parent_function': .parent_func at 0x1768a8af0>}\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7613921b1bf64d4ebff8363cf417bca6", + "model_id": "fdb605b59a7447ad8bbaffb1bdb12d71", "version_major": 2, "version_minor": 0 }, @@ -2458,180 +1845,894 @@ } ], "source": [ - "# we will now simulate a set of interventions on the graph\n", - "domain_two_data = []\n", - "int_G = apply_discrete_soft_intervention(G.copy(), {\"X\", \"C\"}, random_state=seed)\n", + "# add an S-node to node 'X', starting after the observational \"set()\"\n", + "for target in domain_two_targets[1:]:\n", + " int_G = apply_discrete_soft_intervention(\n", + " domain_two_G.copy(), target, random_state=seed\n", + " )\n", "\n", - "# now we sample from the graph the discrete dataset\n", - "int_df = sample_from_graph(int_G, n_samples=30000, n_jobs=1, random_state=seed)\n", - "domain_two_data.append(int_df)\n", - "domain_two_G = int_G.copy()" + " # now we sample from the graph the discrete dataset\n", + " int_df = sample_from_graph(\n", + " int_G, n_samples=n_sample_ints, n_jobs=1, random_state=seed\n", + " )\n", + "\n", + " domain_two_data.append(int_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "id": "9cfb5d36-7e7f-4253-8d6d-d9dadfdc9d9e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + } + ], + "source": [ + "data = domain_one_data + domain_two_data\n", + "print(len(data))" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "id": "b1ae275e-9e59-450b-9c9c-f13b6b6e7f05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "[frozenset(), ['D'], frozenset(), ('D',)]\n" + ] + } + ], + "source": [ + "intervention_targets = domain_one_targets + domain_two_targets\n", + "print(len(intervention_targets))\n", + "print(intervention_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "id": "54dca048-f492-4b7c-a4b0-2cc5e920677c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + } + ], + "source": [ + "domain_ids = [0] * len(domain_one_targets) + [1] * len(domain_two_targets)\n", + "print(len(domain_ids))" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "id": "852441a3-6fca-46cb-a203-03a8fb7931ae", + "metadata": {}, + "outputs": [], + "source": [ + "# create context with information about the interventions\n", + "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", + "ctx: Context = ctx_builder.variables(data=data[0]).num_distributions(len(data)).build()" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "481e0736-4c0c-45db-ac16-fc9a55eaf13c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using data from distribution 0 for learning the skeleton.\n", + "Trying to learn skeleton for 0 to remove F-nodes: [('F', 0)]\n", + "Trying to learn skeleton for 0 to remove F-nodes: [('F', 0)]\n", + "Trying to learn skeleton for 1 to remove F-nodes: [('F', 5)]\n", + "Trying to learn skeleton for 1 to remove F-nodes: [('F', 5)]\n", + "Trying to learn skeleton for 0 and 1 to remove F-nodes: [('F', 2), ('F', 1), ('F', 4), ('F', 3)]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learner = SFCI(\n", + " ci_estimator=ci_estimator,\n", + " cd_estimator=ci_estimator,\n", + " max_cond_set_size=2,\n", + " n_jobs=-1,\n", + " debug=True,\n", + ")\n", + "\n", + "learner.learn_graph(\n", + " data,\n", + " ctx,\n", + " domain_indices=domain_ids,\n", + " intervention_targets=intervention_targets,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "09ba3ceb-5b6b-47c7-9eac-c4c73372a169", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('F', 2), 'W', 'B', ('F', 1), ('F', 4), 'D', 'A', ('F', 3), 'C', 'X', ('F', 0), ('F', 5)]\n", + "['W', 'B', 'C', ('F', 0), 'X', 'D', 'A']\n", + "{'W', 'B', 'D', 'A', 'C', 'X'}\n" + ] + } + ], + "source": [ + "print(ctx.init_graph.nodes)\n", + "print(est_pag.nodes)\n", + "print(ctx.get_non_augmented_nodes())" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "id": "75fa96b0-61b0-4632-a471-b38a8e3499a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[frozenset(), ['D'], frozenset(), ('D',)]\n" + ] + } + ], + "source": [ + "print(intervention_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "id": "d207660c-a336-4ef3-ac59-e57e37d9690a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{('F', 0): (0, 1), ('F', 1): (0, 2), ('F', 2): (0, 3), ('F', 3): (1, 2), ('F', 4): (1, 3), ('F', 5): (2, 3)}\n" + ] + } + ], + "source": [ + "print(ctx.sigma_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "id": "8afb55d8-313a-4f18-8da3-07ce89ab4b7d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)\n", + "\n", + "('F', 2)\n", + "\n", + "\n", + "\n", + "('F', 2)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 2)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)\n", + "\n", + "('F', 1)\n", + "\n", + "\n", + "\n", + "('F', 1)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 1)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)\n", + "\n", + "('F', 4)\n", + "\n", + "\n", + "\n", + "('F', 4)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 4)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)\n", + "\n", + "('F', 3)\n", + "\n", + "\n", + "\n", + "('F', 3)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 3)->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 0)\n", + "\n", + "('F', 0)\n", + "\n", + "\n", + "\n", + "('F', 0)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "('F', 5)\n", + "\n", + "('F', 5)\n", + "\n", + "\n", + "\n", + "('F', 5)->D\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 280, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = learner.graph_\n", + "\n", + "draw(\n", + " est_pag,\n", + " # direction=\"LR\",\n", + " pos=pos_G,\n", + " # name=\"SFCI\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "id": "968fb5ed-d8e9-4a6a-a471-baea43d635ed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "A->B\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 281, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "est_pag = learner.graph_\n", + "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", + "\n", + "dot_graph = draw(\n", + " est_pag_no_fnodes,\n", + " pos=pos_G,\n", + " # direction=\"LR\",\n", + " # name=\"SFCI\"\n", + ")\n", + "\n", + "dot_graph.render(outfile=\"./sfci_multidomain_obsandint.png\", view=False, cleanup=True)\n", + "dot_graph" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "90587357-0d97-4502-ae74-665ddb5358af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{(('F', 0), 'D'): 'collider',\n", + " (('F', 1), 'C'): 'collider',\n", + " (('F', 1), 'X'): 'collider',\n", + " (('F', 2), 'C'): 'collider',\n", + " (('F', 2), 'D'): 'collider',\n", + " (('F', 2), 'X'): 'collider',\n", + " (('F', 3), 'C'): 'collider',\n", + " (('F', 4), 'C'): 'collider',\n", + " (('F', 4), 'D'): 'collider',\n", + " (('F', 4), 'X'): 'collider',\n", + " ('A', 'C'): 'rule9',\n", + " ('A', 'X'): \"rule 1: ('F', 2) *-> X o-* A\",\n", + " ('B', 'A'): 'rule 1: X *-> A o-* B',\n", + " ('C', 'B'): 'rule 1: A *-> B o-* C',\n", + " (('F', 5), 'D'): 'Rule 11',\n", + " ('A', 'B'): 'rule 1: X *-> A o-* B',\n", + " ('B', 'C'): 'rule 1: A *-> B o-* C',\n", + " ('B', 'D'): 'collider',\n", + " ('C', 'D'): 'collider',\n", + " ('D', 'B'): 'collider',\n", + " ('D', 'C'): 'collider',\n", + " ('W', 'C'): 'collider',\n", + " ('X', 'C'): 'collider',\n", + " (('F', 3), 'D'): 'collider',\n", + " (('F', 3), 'X'): 'collider',\n", + " ('X', 'A'): \"rule 1: ('F', 2) *-> X o-* A\"}\n" + ] + } + ], + "source": [ + "pprint(learner.debug_map)" + ] + }, + { + "cell_type": "markdown", + "id": "3118d12f-3ee1-4577-9bf6-30446c7af10f", + "metadata": {}, + "source": [ + "# Stacking all the obs. data together" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "id": "c8b30f3d-e571-46c0-b384-91ea53549fd6", + "metadata": {}, + "outputs": [], + "source": [ + "# stack all the data together and then run through FCI\n", + "stacked_df = pd.concat((domain_one_obs, domain_two_obs), axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "id": "3869613c-8b5b-4bd1-977c-2abba38503a1", + "metadata": {}, + "outputs": [], + "source": [ + "ctx_builder = make_context()\n", + "ctx: Context = (\n", + " ctx_builder.variables(data=stacked_df)\n", + " # .obs_distribution(True)\n", + " .build()\n", + ")" ] }, { "cell_type": "code", - "execution_count": 373, - "id": "1f06bcc8-8e5b-479c-a3c3-8787458baf1d", + "execution_count": 285, + "id": "918546db-12ad-49b1-85bd-40ab1d6ea884", "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "911d766330b3443fb60a656f7813fa36", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ - " 0%| | 0/6 [00:00" ] }, + "execution_count": 285, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's run FCI\n", + "fci_learner = FCI(\n", + " ci_estimator=ci_estimator,\n", + " n_jobs=n_jobs,\n", + " max_cond_set_size=2,\n", + " max_combinations=None,\n", + " # alpha=0.5,\n", + ")\n", + "fci_learner.learn_graph(stacked_df, ctx)" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "7e13fcca-1b3d-4960-a75d-08b675f80e81", + "metadata": {}, + "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ebf83dba9fdb42f4975c7af480310a77", - "version_major": 2, - "version_minor": 0 - }, + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "W\n", + "\n", + "W\n", + "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", + "\n", + "\n", + "\n", + "C\n", + "\n", + "C\n", + "\n", + "\n", + "\n", + "B->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "D\n", + "\n", + "D\n", + "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "B->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], "text/plain": [ - " 0%| | 0/6 [00:00" ] }, + "execution_count": 286, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# add an S-node to node 'X', starting after the observational \"set()\"\n", - "for target in domain_two_targets[1:]:\n", - " int_G = apply_discrete_soft_intervention(\n", - " domain_two_G.copy(), target, random_state=seed\n", - " )\n", + "est_pag = fci_learner.graph_\n", "\n", - " # now we sample from the graph the discrete dataset\n", - " int_df = sample_from_graph(int_G, n_samples=30000, n_jobs=1, random_state=seed)\n", + "dot_graph = draw(\n", + " est_pag,\n", + " direction=\"LR\",\n", + ")\n", "\n", - " domain_two_data.append(int_df)" + "\n", + "dot_graph.render(outfile=\"./fci_multidomain_stacked_obs.png\", view=False, cleanup=True)\n", + "dot_graph" + ] + }, + { + "cell_type": "markdown", + "id": "7eaf60a1-7fb6-4acc-871b-49c3a58fbe36", + "metadata": {}, + "source": [ + "# Stacking all the obs. and interventional data together" ] }, { "cell_type": "code", - "execution_count": 374, - "id": "9cfb5d36-7e7f-4253-8d6d-d9dadfdc9d9e", + "execution_count": 287, + "id": "13003a43-4b0c-459c-9308-1b90708f048b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "7\n" + "2\n" ] } ], "source": [ - "data = domain_one_data + domain_two_data\n", - "print(len(data))" + "# stack all observational and interventional data together and run through Psi-FCI\n", + "stacked_data = [\n", + " stacked_df.copy(), # stacked obs\n", + " pd.concat((domain_one_data[1], domain_two_data[1]), axis=0),\n", + "]\n", + "\n", + "# stacked_data.extend(domain_one_data[1:])\n", + "# stacked_data.extend(domain_two_data[1:])\n", + "\n", + "print(len(stacked_data))" ] }, { "cell_type": "code", - "execution_count": 375, - "id": "b1ae275e-9e59-450b-9c9c-f13b6b6e7f05", + "execution_count": 288, + "id": "96a15459-ffcc-4dfe-8e5b-c3a55cc79c80", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "7\n", - "[set(), ['X'], ['C'], ['D'], set(), 'W', 'X']\n" + "[frozenset(), ['D']]\n", + "[frozenset(), ('D',)]\n" ] } ], "source": [ - "intervention_targets = domain_one_targets + domain_two_targets\n", - "print(len(intervention_targets))\n", - "print(intervention_targets)" + "print(domain_one_targets)\n", + "print(domain_two_targets)" ] }, { "cell_type": "code", - "execution_count": 376, - "id": "54dca048-f492-4b7c-a4b0-2cc5e920677c", + "execution_count": 289, + "id": "cb39bed5-10b5-4b00-97d8-70bd3538f406", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "7\n" + "[['D']]\n" ] } ], "source": [ - "domain_ids = [0] * len(domain_one_targets) + [1] * len(domain_two_targets)\n", - "print(len(domain_ids))" + "stacked_targets = domain_one_targets.copy()[1:]\n", + "# stacked_targets.extend(domain_two_targets[1:])\n", + "\n", + "print(stacked_targets)" ] }, { "cell_type": "code", - "execution_count": 378, - "id": "852441a3-6fca-46cb-a203-03a8fb7931ae", + "execution_count": 290, + "id": "0f6b3ba3-5718-4084-ae95-26db290687cb", "metadata": {}, "outputs": [], "source": [ "# create context with information about the interventions\n", "ctx_builder = make_context(create_using=InterventionalContextBuilder)\n", - "ctx: Context = ctx_builder.variables(data=data[0]).num_distributions(len(data)).build()" + "ctx: Context = (\n", + " ctx_builder.variables(data=stacked_data[0])\n", + " .num_distributions(len(stacked_data))\n", + " .intervention_targets(stacked_targets)\n", + " .obs_distribution(True)\n", + " .build()\n", + ")" ] }, { "cell_type": "code", - "execution_count": 382, - "id": "481e0736-4c0c-45db-ac16-fc9a55eaf13c", + "execution_count": 291, + "id": "356859c0-5c65-4147-8582-ed027a0e5d5f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using data from distribution 0 for learning the skeleton.\n", - "Trying to learn skeleton for 0 to remove F-nodes: [('F', 2), ('F', 5), ('F', 0), ('F', 6), ('F', 1), ('F', 10)]\n", - "[('S', 0)]\n", - "{('F', 0): frozenset({'X'}), ('F', 1): frozenset({'C'}), ('F', 2): frozenset({'D'}), ('F', 3): frozenset({'W'}), ('F', 4): frozenset({'X'}), ('F', 5): frozenset({'X', 'C'}), ('F', 6): frozenset({'X', 'D'}), ('F', 7): frozenset({'X'}), ('F', 8): frozenset({'W', 'X'}), ('F', 9): frozenset(), ('F', 10): frozenset({'D', 'C'}), ('F', 11): frozenset({'C'}), ('F', 12): frozenset({'W', 'C'}), ('F', 13): frozenset({'X', 'C'}), ('F', 14): frozenset({'D'}), ('F', 15): frozenset({'W', 'D'}), ('F', 16): frozenset({'X', 'D'}), ('F', 17): frozenset({'W'}), ('F', 18): frozenset({'X'}), ('F', 19): frozenset({'W', 'X'})}\n", - "{('F', 0): [0, 1], ('F', 1): [0, 2], ('F', 2): [0, 3], ('S', 0): [0, 4], ('F', 3): [0, 5], ('F', 4): [0, 6], ('F', 5): [1, 2], ('F', 6): [1, 3], ('F', 7): [1, 4], ('F', 8): [1, 5], ('F', 9): [1, 6], ('F', 10): [2, 3], ('F', 11): [2, 4], ('F', 12): [2, 5], ('F', 13): [2, 6], ('F', 14): [3, 4], ('F', 15): [3, 5], ('F', 16): [3, 6], ('F', 17): [4, 5], ('F', 18): [4, 6], ('F', 19): [5, 6]}\n", - "here... \n", - "\n", - "\n", - "{('F', 2), ('F', 5), ('F', 11), ('F', 8), ('F', 14), ('F', 17), ('F', 0), ('F', 3), ('F', 9), ('F', 6), ('F', 12), ('F', 15), ('F', 18), ('F', 4), ('F', 1), ('F', 7), ('F', 10), ('F', 16), ('F', 13), ('F', 19), ('S', 0)}\n", - "0 1\n", - "{('F', 0): [0, 1], ('F', 1): [0, 2], ('F', 2): [0, 3], ('S', 0): [0, 4], ('F', 3): [0, 5], ('F', 4): [0, 6], ('F', 5): [1, 2], ('F', 6): [1, 3], ('F', 7): [1, 4], ('F', 8): [1, 5], ('F', 9): [1, 6], ('F', 10): [2, 3], ('F', 11): [2, 4], ('F', 12): [2, 5], ('F', 13): [2, 6], ('F', 14): [3, 4], ('F', 15): [3, 5], ('F', 16): [3, 6], ('F', 17): [4, 5], ('F', 18): [4, 6], ('F', 19): [5, 6]}\n", - "{('F', 0): frozenset({'X'}), ('F', 1): frozenset({'C'}), ('F', 2): frozenset({'D'}), ('F', 3): frozenset({'W'}), ('F', 4): frozenset({'X'}), ('F', 5): frozenset({'X', 'C'}), ('F', 6): frozenset({'X', 'D'}), ('F', 7): frozenset({'X'}), ('F', 8): frozenset({'W', 'X'}), ('F', 9): frozenset(), ('F', 10): frozenset({'D', 'C'}), ('F', 11): frozenset({'C'}), ('F', 12): frozenset({'W', 'C'}), ('F', 13): frozenset({'X', 'C'}), ('F', 14): frozenset({'D'}), ('F', 15): frozenset({'W', 'D'}), ('F', 16): frozenset({'X', 'D'}), ('F', 17): frozenset({'W'}), ('F', 18): frozenset({'X'}), ('F', 19): frozenset({'W', 'X'})}\n", - "Trying to learn skeleton for 0 and 1 to remove S-nodes: [('S', 0)]\n", - "Trying to learn skeleton for 0 and 1 to remove F-nodes: [('F', 11), ('F', 8), ('F', 14), ('F', 3), ('F', 9), ('F', 12), ('F', 15), ('F', 4), ('F', 7), ('F', 16), ('F', 13)] grouped with S-node: ('S', 0)\n", - "Trying to learn skeleton for 0 to remove F-nodes: [('F', 2), ('F', 5), ('F', 0), ('F', 6), ('F', 1), ('F', 10)]\n", - "Trying to learn skeleton for 0 to remove F-nodes: [('F', 2), ('F', 5), ('F', 0), ('F', 6), ('F', 1), ('F', 10)]\n", - "Trying to learn skeleton for 0 to remove F-nodes: [('F', 2), ('F', 5), ('F', 0), ('F', 6), ('F', 1), ('F', 10)]\n", - "Trying to learn skeleton for 1 to remove F-nodes: [('F', 17), ('F', 18), ('F', 19)]\n", - "Trying to learn skeleton for 1 to remove F-nodes: [('F', 17), ('F', 18), ('F', 19)]\n", - "Trying to learn skeleton for 1 to remove F-nodes: [('F', 17), ('F', 18), ('F', 19)]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 382, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "learner = SFCI(\n", + "#\n", + "int_learner = PsiFCI(\n", " ci_estimator=ci_estimator,\n", " cd_estimator=ci_estimator,\n", " max_cond_set_size=2,\n", @@ -2639,40 +2740,13 @@ " debug=True,\n", ")\n", "\n", - "learner.fit(\n", - " data,\n", - " ctx,\n", - " domain_indices=domain_ids,\n", - " intervention_targets=intervention_targets,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 395, - "id": "09ba3ceb-5b6b-47c7-9eac-c4c73372a169", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['W', 'C', 'B', 'A', 'X', 'D']\n", - "['W', 'X', 'C', 'B', 'D', 'A', ('F', 2), ('F', 5), ('F', 11), ('F', 8), ('F', 14), ('F', 17), ('F', 0), ('F', 3), ('F', 9), ('F', 6), ('F', 12), ('F', 15), ('F', 18), ('F', 4), ('F', 1), ('F', 7), ('F', 10), ('F', 16), ('F', 13), ('F', 19), ('S', 0)]\n", - "{'C', 'B', 'D', 'W', 'X', 'A'}\n" - ] - } - ], - "source": [ - "print(context.init_graph.nodes)\n", - "print(est_pag.nodes)\n", - "print(ctx.get_non_augmented_nodes())" + "int_learner = int_learner.learn_graph(stacked_data, ctx)" ] }, { "cell_type": "code", - "execution_count": 400, - "id": "968fb5ed-d8e9-4a6a-a471-baea43d635ed", + "execution_count": 292, + "id": "812f0cd9-0f2e-4571-8e1a-7a365681b09b", "metadata": {}, "outputs": [ { @@ -2684,122 +2758,123 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "C\n", - "\n", - "C\n", - "\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "W\n", - "\n", - "W\n", - "\n", - "\n", - "\n", - "C->W\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "X\n", - "\n", - "X\n", + "\n", + "W\n", "\n", - "\n", - "\n", - "C->X\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "B\n", + "\n", + "B\n", "\n", "\n", - "\n", + "\n", "D\n", - "\n", - "D\n", + "\n", + "D\n", "\n", - "\n", - "\n", - "C->D\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "B->D\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "A\n", - "\n", - "A\n", + "\n", + "A\n", "\n", - "\n", - "\n", - "X->A\n", - "\n", - "\n", + "\n", + "\n", + "B->A\n", + "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "B\n", - "\n", - "B\n", + "C\n", + "\n", + "C\n", "\n", "\n", - "\n", + "\n", "B->C\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "B->D\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", "\n", - "\n", - "\n", - "A->B\n", - "\n", - "\n", + "\n", + "\n", + "A->X\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->W\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "C->D\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->C\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 400, + "execution_count": 292, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "est_pag = learner.graph_\n", + "est_pag = int_learner.graph_\n", "est_pag_no_fnodes = est_pag.subgraph(ctx.get_non_augmented_nodes())\n", "\n", "dot_graph = draw(\n", " est_pag_no_fnodes,\n", - " direction=\"LR\",\n", - " # name=\"SFCI\"\n", + " direction=\"TD\",\n", ")\n", "\n", - "dot_graph.render(outfile=\"./sfci_multidomain_obsandint.png\", view=False, cleanup=True)\n", + "dot_graph.render(\n", + " outfile=\"./psifci_multidomain_stacked_obsandint.png\", view=False, cleanup=True\n", + ")\n", "dot_graph" ] }, { "cell_type": "code", "execution_count": null, - "id": "b0f2d0db-a6f7-4d47-b999-21dc1d876f24", + "id": "8fd85143-ce80-4b8b-9da1-f592e9f59ede", "metadata": {}, "outputs": [], "source": [] diff --git a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py index 0031e0735..91ff49381 100644 --- a/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py +++ b/tests/unit_tests/constraint/skeleton/test_multidomain_skeleton.py @@ -72,7 +72,7 @@ def test_fnode_multidomain_skeleton_known_targets(): ) domain_indices = [1, 1] intervention_targets = [{}, {"x"}] - learner.fit( + learner.learn_graph( data, context, domain_indices=domain_indices, intervention_targets=intervention_targets ) @@ -91,78 +91,6 @@ def test_fnode_multidomain_skeleton_known_targets(): assert nx.is_isomorphic(expected_skeleton, skel_graph) -def test_number_augmented_nodes_created(): - """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`. - - However, this time, we have an S-node pointing to y. - """ - # first create the oracle - directed_edges = [ - ("x", "w"), - ("w", "y"), - ("z", "y"), - ] - bidirected_edges = [("x", "z"), ("z", "y")] - graph = pgraphs.AugmentedGraph( - incoming_directed_edges=directed_edges, incoming_bidirected_edges=bidirected_edges - ) - non_f_graph = graph.copy() - graph.add_f_node({"x"}, domain=1) - graph.add_s_node((1, 2), {"y"}) - oracle = Oracle(graph) - - # define the expected graph we will learn - edges = [ - (("F", 0), "x"), - (("F", 0), "y"), - (("S", 0), "y"), - (("S", 0), "x"), - ("x", "w"), - ("x", "z"), - ("x", "y"), - ("z", "y"), - ("w", "y"), - ] - expected_skeleton = nx.Graph(edges) - obs_expected_skeleton = expected_skeleton.copy() - obs_expected_skeleton.remove_node(("F", 0)) - - # define the learner and the context - learner = LearnMultiDomainSkeleton( - ci_estimator=oracle, cd_estimator=oracle, known_intervention_targets=True - ) - data = [dummy_sample(non_f_graph), dummy_sample(non_f_graph), dummy_sample(non_f_graph)] - context = ( - make_context(create_using=InterventionalContextBuilder).variables(data=data[0]).build() - ) - domain_indices = [1, 2, 2] - intervention_targets = [{}, {}, {"x"}] - - # test augmented nodes - ( - augmented_nodes, - symmetric_diff_map, - sigma_map, - node_domain_map, - ) = learner._create_augmented_nodes(domain_indices, intervention_targets) - assert len(augmented_nodes) == math.comb(len(domain_indices), 2) - assert symmetric_diff_map == {("F", 0): frozenset({"x"}), ("F", 1): frozenset({"x"})} - assert sigma_map == {("F", 0): [0, 2], ("F", 1): [1, 2], ("S", 0): [0, 1]} - assert node_domain_map == {("F", 0): [1, 2], ("F", 1): [2, 2], ("S", 0): [1, 2]} - - domain_indices = [1, 2, 2, 2, 2] - intervention_targets = [{}, {}, {3}, {2}, {3}] - - # test augmented nodes - ( - augmented_nodes, - symmetric_diff_map, - sigma_map, - node_domain_map, - ) = learner._create_augmented_nodes(domain_indices, intervention_targets) - assert len(augmented_nodes) == math.comb(len(domain_indices), 2) - 1 - - def test_fnode_multidomain_skeleton_known_targets_with_snode(): """Test learning the skeleton for Figure 3 in :footcite:`Kocaoglu2019characterization`. @@ -190,7 +118,7 @@ def test_fnode_multidomain_skeleton_known_targets_with_snode(): (("F", 0), "y"), (("F", 1), "x"), (("F", 1), "y"), - (("S", 0), "y"), + (("F", 2), "y"), ("x", "w"), ("x", "z"), ("x", "y"), @@ -212,16 +140,7 @@ def test_fnode_multidomain_skeleton_known_targets_with_snode(): domain_indices = [1, 2, 2] intervention_targets = [{}, {}, {"x"}] - # test augmented nodes - ( - augmented_nodes, - symmetric_diff_map, - sigma_map, - node_domain_map, - ) = learner._create_augmented_nodes(domain_indices, intervention_targets) - assert len(augmented_nodes) == math.comb(len(domain_indices), 2) - - learner.fit( + learner.learn_graph( data, context, domain_indices=domain_indices, intervention_targets=intervention_targets ) @@ -253,7 +172,7 @@ def test_basic_multidomain_fsnode_skeleton(): (("F", 0), "y"), (("F", 1), "x"), (("F", 1), "y"), - (("S", 0), "y"), + (("F", 2), "y"), # correspondence with the S-node ("x", "y"), ("y", "z"), ] @@ -272,7 +191,7 @@ def test_basic_multidomain_fsnode_skeleton(): # .intervention_targets([("x")]) .build() ) - learner.fit(data, context, domain_indices, intervention_targets) + learner.learn_graph(data, context, domain_indices, intervention_targets) # first check the observational skeleton skel_graph = learner.adj_graph_ @@ -283,7 +202,11 @@ def test_basic_multidomain_fsnode_skeleton(): sep_set = learner.sep_set_ # check the separating sets - assert sep_set["x"]["z"] == [{"y", ("F", 0), ("S", 0), ("F", 1)}] + # XXX: the edge is tested twice + assert sep_set["x"]["z"] == [ + {"y", ("F", 0), ("F", 2), ("F", 1)}, + {"y", ("F", 0), ("F", 2), ("F", 1)}, + ] # check the skeleton after obs data print(obs_expected_skeleton.edges()) @@ -296,6 +219,8 @@ def test_basic_multidomain_fsnode_skeleton(): assert nx.is_isomorphic(expected_skeleton, skel_graph) +# import pytest +# @pytest.mark.skip() def test_basic_multidomain_fsnode_skeleton_with_lindata(): seed = 1234 n_samples = 1000 @@ -316,7 +241,7 @@ def test_basic_multidomain_fsnode_skeleton_with_lindata(): obs_expected_skeleton = expected_skeleton.copy() # define functional relationships of the causal diagram - graph = pgraphs.functional.make_graph_linear_gaussian(graph, random_state=seed) + graph = pgraphs.functional.make_random_linear_gaussian_graph(graph, random_state=seed) datasets = [] domain_ids = [] @@ -330,6 +255,7 @@ def test_basic_multidomain_fsnode_skeleton_with_lindata(): graph.copy(), targets, random_state=seed ) + print(new_graph.nodes(data=True)) # generate dataset data = sample_from_graph(new_graph, n_samples=n_samples, random_state=seed) @@ -358,7 +284,7 @@ def test_basic_multidomain_fsnode_skeleton_with_lindata(): learner = LearnMultiDomainSkeleton(ci_estimator=FisherZCITest(), cd_estimator=KernelCDTest()) context = make_context(create_using=ContextBuilder).variables(data=datasets[0]).build() - learner.fit(datasets, context, domain_ids, intervention_sets) + learner.learn_graph(datasets, context, domain_ids, intervention_sets) # first check the observational skeleton skel_graph = learner.adj_graph_ From 6fc548bd7a330b89ae9be36c7e95e8fb3637cd20 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 18 Jul 2024 17:18:41 -0400 Subject: [PATCH 61/61] Mypy errors Signed-off-by: Adam Li --- dodiscover/cd/base.py | 6 ++++-- dodiscover/cd/bregman.py | 5 ++++- dodiscover/cd/kernel_test.py | 6 ++++-- dodiscover/constraint/sfcialg.py | 6 +++++- dodiscover/constraint/skeleton.py | 19 ++++++++++++++----- 5 files changed, 31 insertions(+), 11 deletions(-) diff --git a/dodiscover/cd/base.py b/dodiscover/cd/base.py index afc0bf4ad..d69f1d3dc 100644 --- a/dodiscover/cd/base.py +++ b/dodiscover/cd/base.py @@ -138,7 +138,9 @@ def _compute_propensity_scores(self, group_ind, **kwargs): return self.propensity_est_ @abstractmethod - def _statistic(self, Y: ArrayLike, group_ind: ArrayLike, X: ArrayLike = None) -> float: + def _statistic( + self, Y: ArrayLike, group_ind: ArrayLike, X: Optional[ArrayLike] = None + ) -> float: """Abstract method for computing the test statistic.""" pass @@ -146,7 +148,7 @@ def compute_null( self, e_hat: ArrayLike, Y: ArrayLike, - X: ArrayLike = None, + X: Optional[ArrayLike] = None, null_reps: int = 1000, random_state=None, ) -> ArrayLike: diff --git a/dodiscover/cd/bregman.py b/dodiscover/cd/bregman.py index acd68bbe9..9b91c9189 100644 --- a/dodiscover/cd/bregman.py +++ b/dodiscover/cd/bregman.py @@ -111,7 +111,10 @@ def test( pvalue = (1.0 + np.sum(null_dist >= conditional_div)) / (1 + self.null_reps) return conditional_div, pvalue - def _statistic(self, X: ArrayLike, Y: ArrayLike, group_ind: ArrayLike) -> float: + def _statistic(self, X: ArrayLike, Y: ArrayLike, group_ind: ArrayLike) -> float: # type: ignore + # def _statistic( + # self, Y: ArrayLike, group_ind: ArrayLike, X: Optional[ArrayLike] = None + # ) -> float: first_group = group_ind == 0 second_group = group_ind == 1 X1 = X[first_group, :] diff --git a/dodiscover/cd/kernel_test.py b/dodiscover/cd/kernel_test.py index e4a5138a6..5b11b4c31 100644 --- a/dodiscover/cd/kernel_test.py +++ b/dodiscover/cd/kernel_test.py @@ -1,4 +1,4 @@ -from typing import Set, Tuple +from typing import Optional, Set, Tuple import numpy as np import pandas as pd @@ -151,7 +151,9 @@ def test( pvalue = (1 + np.sum(null_dist >= stat)) / (1 + self.null_reps) return stat, pvalue - def _statistic(self, L: ArrayLike, group_ind: ArrayLike, K: ArrayLike = None) -> float: + def _statistic( + self, L: ArrayLike, group_ind: ArrayLike, K: Optional[ArrayLike] = None + ) -> float: n_samples = len(L) # compute L kernels diff --git a/dodiscover/constraint/sfcialg.py b/dodiscover/constraint/sfcialg.py index 2e28ce66c..20d0cfc92 100644 --- a/dodiscover/constraint/sfcialg.py +++ b/dodiscover/constraint/sfcialg.py @@ -49,7 +49,11 @@ def __init__( ) def learn_skeleton( - self, data: pd.DataFrame, context: Context, sep_set: Optional[SeparatingSet] = None, **params, + self, + data: pd.DataFrame, + context: Context, + sep_set: Optional[SeparatingSet] = None, + **params, ) -> Tuple[nx.Graph, SeparatingSet]: # now compute all possibly d-separating sets and learn a better skeleton self.skeleton_learner_ = LearnMultiDomainSkeleton( diff --git a/dodiscover/constraint/skeleton.py b/dodiscover/constraint/skeleton.py index 829c58659..aa189d75b 100644 --- a/dodiscover/constraint/skeleton.py +++ b/dodiscover/constraint/skeleton.py @@ -1612,11 +1612,12 @@ def __init__( def learn_graph( self, data: List[pd.DataFrame], - context: Context, - domain_indices: List[int], - intervention_targets: List[Optional[Set]], + context: Optional[Context] = None, + domain_indices: Optional[List[int]] = None, + intervention_targets: Optional[List[Optional[Set]]] = None, check_input: bool = True, debug: bool = False, + **params, ) -> None: """Fit data and context. @@ -1635,6 +1636,13 @@ def learn_graph( The intervention targets of each dataframe in ``data``. Is ``None`` if ``data`` is observational, or ``unknown`` if target is unknown. """ + if context is None: + # make a private Context object to store causal context used in this algorithm + # store the context + from dodiscover.context_builder import make_context + + context = make_context().build() + # ensure data is a list if isinstance(data, pd.DataFrame): data = [data] @@ -1677,8 +1685,9 @@ def learn_graph( skip_nodes = augmented_nodes # provide multi-domain context - context.augmented_nodes = augmented_nodes - context.node_domain_map = node_domain_map + # XXX: Do I need to do assignment? + # context.augmented_nodes = augmented_nodes + # context.node_domain_map = node_domain_map context.add_state_variable("node_domain_map", node_domain_map) context.add_state_variable("augmented_nodes", augmented_nodes) context.symmetric_diff_map = symmetric_diff_map