diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml index fdea2b3e..d7d21503 100644 --- a/.github/workflows/format.yml +++ b/.github/workflows/format.yml @@ -8,6 +8,11 @@ on: workflow_dispatch: +# Cancel superseded runs (e.g. a force-push) for the same workflow + ref. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: check: runs-on: ubuntu-latest diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index b0ea7b8f..79a83c39 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -8,6 +8,11 @@ on: workflow_dispatch: +# Cancel superseded runs (e.g. a force-push) for the same workflow + ref. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: check: runs-on: ubuntu-latest diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 15b7394a..3f0da630 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -8,6 +8,11 @@ on: workflow_dispatch: +# Cancel superseded runs (e.g. a force-push) for the same workflow + ref. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + jobs: check: runs-on: ${{ matrix.os }} diff --git a/.gitignore b/.gitignore index f3eb17ab..f2317242 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,19 @@ htmlcov/ venv/ +.venv/ env notebooks/scratch/ + +# Simulation run results / generated artifacts (large, never commit) +notebooks/RunExperiments/EXPERIMENT_RESULTS_*/ +notebooks/RunExperiments/RunDatasets/ +notebooks/RunExperiments/runners/plots/ +notebooks/RunExperiments/runners/tex/ +notebooks/RunExperiments/runners/causaltune + +# Accidentally-dropped credentials — never commit +*ray-gcp-key +*ray-gcp-key.pub .ropeproject/ .DS_Store .vscode @@ -108,4 +120,8 @@ data/* docs/_build/ docs/_any docs/_cache/ -docs/notebooks \ No newline at end of file +docs/notebooks +# Build artifacts +build/ +dist/ +*.egg-info/ diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..8aa0afa1 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,26 @@ +# CLAUDE.md — causaltune + +## Committing: DCO sign-off is REQUIRED + +This repo enforces the **Developer Certificate of Origin (DCO)** in CI. Every +commit MUST carry a `Signed-off-by:` trailer matching the commit author, or the +DCO check fails and the PR is blocked. + +ALWAYS commit with `-s` (sign-off): + +```bash +git commit -s -m "your message" +``` + +This appends `Signed-off-by: Egor Kraev ` (from +`git config user.name` / `user.email`). When amending or rebasing, keep the +trailer — use `git rebase --signoff` or `git commit -s --amend` as needed. + +Never create a commit here without the sign-off trailer. + +## Do NOT commit simulation run results + +`notebooks/RunExperiments/EXPERIMENT_RESULTS_*/`, `RunDatasets/`, and the +`runners/plots/` + `runners/tex/` output dirs are large generated artifacts +(tens of GB) and are gitignored. Only source code (`.py`, tests, docs, +`runners/*.py` scripts) gets committed. diff --git a/notebooks/Estimate assignment outcomes.ipynb b/notebooks/Estimate assignment outcomes.ipynb new file mode 100644 index 00000000..a19022e6 --- /dev/null +++ b/notebooks/Estimate assignment outcomes.ipynb @@ -0,0 +1,76 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "139511bd", + "metadata": {}, + "source": [ + "Use the results of a past A/B test to estimate outcomes of any other assignment\n", + "\n", + "(checkbox A)\n", + "* I have the results of a past A/B/N test with strictly random assignment (chosen by default)\n", + "* (advanced) I have the results of a past test using random assignment with variable probabilities\n", + "\n", + "(checkbox B)\n", + "* I have the variants I'd like to assign to each user in a new hypothetical assignment policy (chosen by default)\n", + "* (advanced) I have the assignment probabilities of each variant to each user, my new policy is probabilistic\n", + "\n", + "Then display the following text:\n", + "\n", + "Please upload a csv file with the following columns, spelled just like that:\n", + "- ActualAssignment: number of the variant actually assigned to that user in the test, sequential integers starting with zero, 0 being control (so for an A/B test 0 or 1)\n", + "- (if the first option is chosen in B) HypotheticalAssignment: number of the variant assigned to each user in the hypothetical assignment whose outcome you want to estimate\n", + "- (if the second option is chosen in B) HypotheticalAssignment0, HypotheticalAssignment1, ...: probabilities of the respective variant \n", + "- (show this only if the second option is chosen in A) PTT: The ex ante probability of choosing the variant that was chosen in the test (also known as propensity to treat)\n", + "\n", + "[Upload file]\n", + "\n", + "The estimated outcome for the new assignment is ...\n", + "The confidence interval for it is ..." + ] + }, + { + "cell_type": "markdown", + "id": "4181fc89", + "metadata": {}, + "source": [ + "Then display the following text:\n", + "\n", + "Please upload a csv file with the following columns, spelled just like that:\n", + "- ActualAssignment: number of the variant actually assigned to that user in the test, sequential integers starting with zero, 0 being control (so for an A/B test 0 or 1)\n", + "- (if the first option is chosen in B) HypotheticalAssignment: number of the variant assigned to each user in the hypothetical assignment whose outcome you want to estimate\n", + "- (if the second option is chosen in B) HypotheticalAssignment0, HypotheticalAssignment1, ...: probabilities of the respective variant \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1432d63", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:.conda-causaltune]", + "language": "python", + "name": "conda-env-.conda-causaltune-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/RunExperiments/cluster_config.yaml b/notebooks/RunExperiments/cluster_config.yaml index 80d602dd..c56cbb9c 100644 --- a/notebooks/RunExperiments/cluster_config.yaml +++ b/notebooks/RunExperiments/cluster_config.yaml @@ -6,7 +6,7 @@ cluster_name: default # The maximum number of workers nodes to launch in addition to the head # node. -max_workers: 8 +max_workers: 7 # The autoscaler will scale up the cluster faster with higher upscaling speed. # E.g., if the task requires adding more nodes then autoscaler will gradually @@ -93,7 +93,7 @@ available_node_types: min_workers: 1 # The maximum number of worker nodes of this type to launch. # This takes precedence over min_workers. - max_workers: 8 + max_workers: 7 # The resources provided by this node type. resources: {"CPU": 2} # Provider-specific config for the head node, e.g. instance type. By default diff --git a/notebooks/RunExperiments/runners/estimator_level_trials.py b/notebooks/RunExperiments/runners/estimator_level_trials.py new file mode 100644 index 00000000..5c2faec3 --- /dev/null +++ b/notebooks/RunExperiments/runners/estimator_level_trials.py @@ -0,0 +1,28 @@ +import os + +from experiment_runner import run_batch, get_estimator_list + +use_ray = True + +for kind in ["KC"]: + metrics = [ + "erupt", + "qini", + "auc", + "psw_energy_distance", + "codec", # NEW + "bite", # NEW + ] + estimators = get_estimator_list(kind) + for estimator in estimators: + short_name = estimator.split(".")[-1] + print(f"Running {kind} with {estimator}") + out_dir = run_batch( + identifier=f"Estimator_{short_name}", + kind=kind, + metrics=metrics, + estimators=[estimator], + dataset_path=os.path.realpath("../RunDatasets"), + num_trials=25, + use_ray=use_ray, + ) diff --git a/notebooks/RunExperiments/runners/experiment_plots.py b/notebooks/RunExperiments/runners/experiment_plots.py index 98617c5c..09bdcb93 100644 --- a/notebooks/RunExperiments/runners/experiment_plots.py +++ b/notebooks/RunExperiments/runners/experiment_plots.py @@ -1,7 +1,7 @@ import glob import os import pickle -from typing import Union, List +from typing import Union, List, Optional import matplotlib import numpy as np @@ -9,8 +9,50 @@ import pandas as pd from matplotlib import pyplot as plt + from causaltune.score.scoring import metrics_to_minimize, supported_metrics +metric_names = { + "psw_frobenius_norm": "PSW\nFrobenius\nNorm", + "frobenius_norm": "Frobenius\nNorm", + "erupt": "ERUPT", + "codec": "CODEC", + "auc": "AUUC", + "qini": "Qini", + "bite": "BITE", + "policy_risk": "Policy\nRisk", + "energy_distance": "Energy\nDistance", + "psw_energy_distance": "Energy\nDistance", + "norm_erupt": "Normalized\nERUPT", +} + + +def r2_score(y, yhat): + """ + Calculate the R-squared (coefficient of determination) for the given true vector y + and the estimated vector yhat. + + Parameters: + y (array-like): True values. + yhat (array-like): Predicted/estimated values. + + Returns: + float: R-squared value. + """ + # Convert to numpy arrays to ensure compatibility + y = np.array(y) + yhat = np.array(yhat) + + # Calculate the total sum of squares (TSS) + tss = np.sum((y - np.mean(y)) ** 2) + + # Calculate the residual sum of squares (RSS) + rss = np.sum((y - yhat) ** 2) + + # Compute R-squared + r2 = 1 - (rss / tss) + return r2 + def extract_metrics_datasets(out_dir: str): metrics = set() @@ -28,25 +70,30 @@ def make_filename(metric, dataset, i_run): return f"{metric}-run-{i_run}-{dataset.replace(' ', '_')}.pkl" -def get_all_test_scores(out_dir, dataset_name): - size, ds_type, case = dataset_name.split(" ") +def get_all_test_scores(out_dirs, lin_nonlin: str, kind: str): + if isinstance(out_dirs, str): + out_dirs = [out_dirs] all_scores = [] - for file in glob.glob(f"{out_dir}/*_{ds_type}_{case}.pkl"): - with open(file, "rb") as f: - results = pickle.load(f) - for x in results["all_scores"]: - all_scores.append( - {k: v for k, v in x["test"]["scores"].items() if k not in ["values"]} - ) + for out_dir in out_dirs: + for file in glob.glob(f"{out_dir}/*_{lin_nonlin}_{kind}.pkl"): + optimized_score = file.split("/")[-1].split("-")[0] + with open(file, "rb") as f: + results = pickle.load(f) + for x in results["all_scores"]: + tmp = {k: v for k, v in x["test"]["scores"].items() if k not in ["values"]} + tmp["optimized_score"] = optimized_score + all_scores.append(tmp) out = pd.DataFrame(all_scores) return out def generate_plots( out_dir: str, + metrics: list[str], log_scale: Union[List[str], None] = None, upper_bounds: Union[dict, None] = None, lower_bounds: Union[dict, None] = None, + prefix: str = "", font_size=0, ): if log_scale is None: @@ -56,37 +103,25 @@ def generate_plots( if lower_bounds is None: lower_bounds = {} # Use an empty dictionary if None - metrics, datasets = extract_metrics_datasets(out_dir) + kind = os.path.basename(out_dir) + save_dir = os.path.realpath(os.path.join(out_dir, "..")) + existing_metrics, datasets = extract_metrics_datasets(out_dir) # Remove 'ate' from metrics - metrics = [m for m in metrics if m.lower() not in ["ate", "norm_erupt"]] - - metric_names = { - "psw_frobenius_norm": "PSW\nFrobenius\nNorm", - "frobenius_norm": "Frobenius\nNorm", - "erupt": "ERUPT", - "codec": "CODEC", - "auc": "AUC", - "qini": "Qini", - "bite": "BITE", - "policy_risk": "Policy\nRisk", - "energy_distance": "Energy\nDistance", - "psw_energy_distance": "Energy\nDistance", - "norm_erupt": "Normalized\nERUPT", - } - - colors = ( - [matplotlib.colors.CSS4_COLORS["black"]] - + list(matplotlib.colors.TABLEAU_COLORS) - + [ - matplotlib.colors.CSS4_COLORS["lime"], - matplotlib.colors.CSS4_COLORS["yellow"], - matplotlib.colors.CSS4_COLORS["pink"], - ] - ) - markers = ["o", "s", "D", "^", "v", "<", ">", "P", "*", "h", "X", "|", "_", "8"] - - # Determine the problem type from the dataset name - problem = "iv" if any("IV" in dataset for dataset in datasets) else "backdoor" + metrics = [m for m in metrics if m in existing_metrics] + + # colors = ( + # [matplotlib.colors.CSS4_COLORS["black"]] + # + list(matplotlib.colors.TABLEAU_COLORS) + # + [ + # matplotlib.colors.CSS4_COLORS["lime"], + # matplotlib.colors.CSS4_COLORS["yellow"], + # matplotlib.colors.CSS4_COLORS["pink"], + # ] + # ) + # markers = ["o", "s", "D", "^", "v", "<", ">", "P", "*", "h", "X", "|", "_", "8"] + # + # # Determine the problem type from the dataset name + # problem = "iv" if any("IV" in dataset for dataset in datasets) else "backdoor" def plot_grid(title): # Use determined problem type instead of hardcoding "backdoor" @@ -116,6 +151,8 @@ def plot_grid(title): if dataset not in results_files: print(f"No results file found for dataset {dataset}") + out_metrics = [] + for j, dataset in enumerate(datasets): if dataset not in results_files: continue @@ -162,16 +199,22 @@ def plot_grid(title): ) # Calculate correlation coefficient - corr = np.corrcoef(CATE_gt, CATE_est)[0, 1] + from scipy.stats import kendalltau + + corr = kendalltau(CATE_gt, CATE_est).correlation + r2 = r2_score(CATE_gt, CATE_est) + out_metrics.append( + {"dataset": dataset, "metric": metric, "corr": corr, "r2": r2} + ) # Add correlation ax.text( 0.05, 0.95, - f"Corr: {corr:.2f}", + f"Rank corr: {corr:.2f}, R^2: {r2:.2f}", transform=ax.transAxes, verticalalignment="top", - fontsize=font_size + 12, + fontsize=font_size + 18, fontweight="bold", ) @@ -185,7 +228,7 @@ def plot_grid(title): transform=ax.transAxes, horizontalalignment="center", color="blue", - fontsize=font_size + 10, + fontsize=font_size + 24, ) except Exception as e: @@ -203,28 +246,97 @@ def plot_grid(title): # Create tight layout for ylabel ax.set_ylabel( metric_names.get(metric, metric), - fontsize=font_size + 12, + fontsize=font_size + 24, fontweight="bold", labelpad=5, # Reduce padding between label and plot ) if i == 0: - ax.set_title(dataset, fontsize=font_size + 14, fontweight="bold", pad=15) + ds_kind = dataset.replace(kind, "").replace("Large", "").strip() + ax.set_title(ds_kind, fontsize=font_size + 14, fontweight="bold", pad=15) ax.set_xticks([]) ax.set_yticks([]) plt.suptitle( - f"Estimated CATEs vs. True CATEs: {title}", + f"Estimated ITEs vs. True ITEs for {kind} datasets", fontsize=font_size + 18, fontweight="bold", ) # Adjust spacing between subplots plt.tight_layout(rect=[0.1, 0, 1, 0.96], h_pad=1.0, w_pad=0.5) - plt.savefig(os.path.join(out_dir, "CATE_grid.pdf"), format="pdf", bbox_inches="tight") - plt.savefig(os.path.join(out_dir, "CATE_grid.png"), format="png", bbox_inches="tight") + plt.savefig( + os.path.join(save_dir, f"{prefix}_{kind}_ITE_grid.pdf"), + format="pdf", + bbox_inches="tight", + ) + plt.savefig( + os.path.join(save_dir, f"{prefix}_{kind}_ITE_grid.png"), + format="png", + bbox_inches="tight", + ) plt.close() + return pd.DataFrame(out_metrics) + + # Generate plots + results = plot_grid("Experiment Results") + results.to_pickle(os.path.join(save_dir, f"{prefix}_{kind}_ITE_results.pkl")) + + +def generate_mse_plots( + out_dirs: Union[str, List[str]], + metrics: list[str], + save_dir: Optional[str] = None, + log_scale: Union[List[str], None] = None, + upper_bounds: Union[dict, None] = None, + lower_bounds: Union[dict, None] = None, + prefix: str = "", + font_size=0, +): + """ + + :param out_dirs: each dir must end with the kind of dataset (e.g. "RCT") + :param metrics: + :param save_dir: + :param log_scale: + :param upper_bounds: + :param lower_bounds: + :param prefix: + :param font_size: + :return: + """ + if log_scale is None: + log_scale = ["energy_distance", "psw_energy_distance", "frobenius_norm"] + if upper_bounds is None: + upper_bounds = {} # Use an empty dictionary if None + if lower_bounds is None: + lower_bounds = {} # Use an empty dictionary if None + + if isinstance(out_dirs, str): + out_dirs = [out_dirs] + out_dir = out_dirs[0] + + kind = os.path.basename(out_dir) + save_dir = save_dir or os.path.realpath(os.path.join(out_dir, "..")) + existing_metrics, datasets = extract_metrics_datasets(out_dir) + # Remove 'ate' from metrics + metrics = [m for m in metrics if m in existing_metrics] + + colors = ( + [matplotlib.colors.CSS4_COLORS["black"]] + + list(matplotlib.colors.TABLEAU_COLORS) + + [ + matplotlib.colors.CSS4_COLORS["lime"], + matplotlib.colors.CSS4_COLORS["yellow"], + matplotlib.colors.CSS4_COLORS["pink"], + ] + ) + markers = ["o", "s", "D", "^", "v", "<", ">", "P", "*", "h", "X", "|", "_", "8"] - def plot_mse_grid(title): - df = get_all_test_scores(out_dir, datasets[0]) + # Determine the problem type from the dataset name + problem = "iv" if any("IV" in dataset for dataset in datasets) else "backdoor" + + def plot_mse_grid(kind): + # _, lin_nonlin, kind = datasets[0].split(" ") + df = get_all_test_scores(out_dirs, "Linear", kind) est_names = sorted(df["estimator_name"].unique()) # Problem type already determined at top level @@ -238,6 +350,8 @@ def plot_mse_grid(title): if "psw_energy_distance" in all_metrics: all_metrics.remove("energy_distance") + all_metrics = [m for m in metrics if m in all_metrics] + fig, axs = plt.subplots( len(all_metrics), len(datasets), figsize=(20, 5 * len(all_metrics)), dpi=300 ) @@ -248,9 +362,10 @@ def plot_mse_grid(title): elif len(all_metrics) == 1 or len(datasets) == 1: axs = axs.reshape(-1, 1) if len(datasets) == 1 else axs.reshape(1, -1) + out_metrics = [] legend_elements = [] - for j, dataset in enumerate(datasets): - df = get_all_test_scores(out_dir, dataset) + for j, lin_nonlin in enumerate(["Linear", "NonLinear"]): + df = get_all_test_scores(out_dirs, lin_nonlin, kind) # Apply bounds filtering for m, value in upper_bounds.items(): if m in df.columns: @@ -265,6 +380,25 @@ def plot_mse_grid(title): this_df = this_df[~np.isinf(this_df[metric].values)] if len(this_df): + from scipy.stats import kendalltau + + corr = kendalltau(this_df["MSE"], this_df[metric]).correlation + if metric not in metrics_to_minimize(): + corr = -corr + out_metrics.append( + {"dataset": f"{kind} {lin_nonlin}", "metric": metric, "corr": corr} + ) + # Add correlation + ax.text( + 0.05, + 0.95, + f"Rank correlation: {corr:.2f}", + transform=ax.transAxes, + verticalalignment="top", + fontsize=font_size + 18, + fontweight="bold", + ) + for idx, est_name in enumerate(est_names): df_slice = this_df[this_df["estimator_name"] == est_name] if "Dummy" not in est_name and len(df_slice): @@ -277,8 +411,6 @@ def plot_mse_grid(title): marker=marker, linewidths=0.5, ) - if metric not in metrics_to_minimize(): - ax.invert_yaxis() trimmed_est_name = est_name.split(".")[-1] if i == 0 and j == 0: @@ -293,7 +425,8 @@ def plot_mse_grid(title): markersize=6, ) ) - + if metric not in metrics_to_minimize(): + ax.invert_yaxis() ax.set_xscale("log") if metric in log_scale: ax.set_yscale("log") @@ -317,10 +450,21 @@ def plot_mse_grid(title): labelpad=5, ) if i == 0: - ax.set_title(dataset, fontsize=font_size + 14, fontweight="bold", pad=15) + # ds_kind = dataset.replace(kind, "").replace("Large", "").strip() + ax.set_title( + lin_nonlin.replace("NonLinear", "Nonlinear"), + fontsize=font_size + 14, + fontweight="bold", + pad=15, + ) + kind_map = { + "RCT": "Randomized Controlled Trial", + "KCKP": "Known but variable PTT", + "KC": "Variable, estimated PTT", + } plt.suptitle( - f"MSE vs. Scores: {title}", + f"MSE vs. Scores for {kind_map[kind]} datasets ", fontsize=font_size + 18, fontweight="bold", ) @@ -331,19 +475,38 @@ def plot_mse_grid(title): # fig_legend, ax_legend = plt.subplots(figsize=(6, 6)) # ax_legend.legend(handles=legend_elements, loc="center", fontsize=10) # ax_legend.axis("off") - - plt.savefig(os.path.join(out_dir, "MSE_grid.pdf"), format="pdf", bbox_inches="tight") - plt.savefig(os.path.join(out_dir, "MSE_grid.png"), format="png", bbox_inches="tight") + fig.legend(handles=legend_elements, loc="lower center", fontsize=18, ncol=2) + os.makedirs(save_dir, exist_ok=True) + plt.savefig( + os.path.join(save_dir, f"{prefix}_{kind}_MSE_grid.pdf"), + format="pdf", + bbox_inches="tight", + ) + plt.savefig( + os.path.join(save_dir, f"{prefix}_{kind}_MSE_grid.png"), + format="png", + bbox_inches="tight", + ) plt.close() # # Create separate legend # fig_legend, ax_legend = plt.subplots(figsize=(6, 6)) # ax_legend.legend(handles=legend_elements, loc="center", fontsize=10) # ax_legend.axis("off") - # plt.savefig(os.path.join(out_dir, "MSE_legend.pdf"), format="pdf", bbox_inches="tight") - # plt.savefig(os.path.join(out_dir, "MSE_legend.png"), format="png", bbox_inches="tight") + # plt.savefig( + # os.path.join(save_dir, f"{prefix}_{kind}_MSE_legend.pdf"), + # format="pdf", + # bbox_inches="tight", + # ) + # plt.savefig( + # os.path.join(save_dir, f"{prefix}_{kind}_MSE_legend.png"), + # format="png", + # bbox_inches="tight", + # ) # plt.close() - # Generate plots - plot_grid("Experiment Results") - plot_mse_grid("Experiment Results") + return pd.DataFrame(out_metrics) + + results2 = plot_mse_grid(kind) + results2.to_pickle(os.path.join(save_dir, f"{prefix}_{kind}_MSE_results.pkl")) + return results2 diff --git a/notebooks/RunExperiments/runners/experiment_runner.py b/notebooks/RunExperiments/runners/experiment_runner.py index 4ebb081b..3ed090ed 100644 --- a/notebooks/RunExperiments/runners/experiment_runner.py +++ b/notebooks/RunExperiments/runners/experiment_runner.py @@ -115,7 +115,9 @@ def run_experiment( estimators: List[str], dataset_path: str, use_ray: bool, + identifier: str, propensity_automl_estimators: Optional[List[str]] = None, + components_time_budget: int = 120, ): # Process datasets data_sets = {} @@ -141,7 +143,7 @@ def run_experiment( already_running = False if use_ray: try: - runner = ray.get_actor(f"TaskRunner {run_kind}") + runner = ray.get_actor(f"TaskRunner {identifier} {run_kind}") print("\n" * 4) print( "!!! Found an existing detached TaskRunner. Will assume the tasks have already been submitted." @@ -156,7 +158,9 @@ def run_experiment( print("Ray: no detached TaskRunner found, creating...") # This thing will be alive even if the host program exits # Must be killed explicitly: ray.kill(ray.get_actor(f"TaskRunner {run_kind}")) - runner = TaskRunner.options(name=f"TaskRunner {run_kind}", lifetime="detached").remote() + runner = TaskRunner.options( + name=f"TaskRunner {identifier} {run_kind}", lifetime="detached" + ).remote() out = [] if not already_running: @@ -190,7 +194,7 @@ def run_experiment( metric, args.test_size, args.num_samples, - args.components_time_budget, + components_time_budget, out_fn, estimators, propensity_automl_estimators, @@ -203,7 +207,7 @@ def run_experiment( metric, args.test_size, args.num_samples, - args.components_time_budget, + components_time_budget, out_fn, estimators, propensity_automl_estimators, @@ -227,11 +231,8 @@ def run_experiment( pickle.dump(results, f) if use_ray: - destroy = input("Ray: seems like the results fetched OK. Destroy TaskRunner? ") - if destroy.lower().startswith("y"): - print("Destroying TaskRunner... ", end="") - ray.kill(runner) - print("success!") + ray.kill(runner) + print("success!") return out_dir @@ -243,37 +244,41 @@ def run_batch( estimators: List[str], dataset_path: str, use_ray: bool = False, + num_trials: int = 100, propensity_automl_estimators: Optional[List[str]] = None, + components_time_budget: int = 120, ): args = parse_arguments() args.identifier = identifier args.metrics = metrics # run_experiment assumes we don't mix large and small datasets in the same call args.datasets = [f"Large Linear_{kind}", f"Large NonLinear_{kind}"] - args.num_samples = 100 + args.num_samples = num_trials args.timestamp_in_dirname = False args.outcome_model = "auto" # or use "nested" for the old-style nested model - args.components_time_budget = 120 if use_ray: import ray # Assuming we port-mapped already by running ray dashboard - ray.init( - "ray://localhost:10001", - runtime_env={ - "working_dir": ".", - "pip": ["causaltune", "catboost", "ray[tune]", "flaml[blendsearch]"], - }, - namespace=RAY_NAMESPACE, - ) + if not ray.is_initialized(): + ray.init( + "ray://localhost:10001", + runtime_env={ + "working_dir": ".", + "pip": ["causaltune", "catboost", "ray[tune]", "flaml[blendsearch]"], + }, + namespace=RAY_NAMESPACE, + ) out_dir = run_experiment( args, estimators=estimators, + identifier=identifier, dataset_path=dataset_path, use_ray=use_ray, propensity_automl_estimators=propensity_automl_estimators, + components_time_budget=components_time_budget, ) return out_dir diff --git a/notebooks/RunExperiments/runners/good_kc.py b/notebooks/RunExperiments/runners/good_kc.py new file mode 100644 index 00000000..fa094c7e --- /dev/null +++ b/notebooks/RunExperiments/runners/good_kc.py @@ -0,0 +1,44 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +prefix = "good" +kind = "KC" +metrics = [ + "erupt", + "psw_energy_distance", + "bite", # NEW + "qini", + "auc", + "codec", # NEW +] + +estimators = get_estimator_list( + kind, + include_patterns=[ + "LinearDML", + "SparseLinearDML", + "LinearDRLearner", + "SparseLinearDRLearner", + "TLearner", + "XLearner", + ], +) + +use_ray = True +run_batch( + identifier=f"Egor_test_{prefix}", + kind=kind, + metrics=metrics, + estimators=estimators, + dataset_path=os.path.realpath("../RunDatasets"), + use_ray=use_ray, +) + +out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_Egor_test_{prefix}/Large")) +# plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +generate_plots(os.path.join(out_dir, kind), metrics, prefix=prefix) # , upper_bounds, lower_bounds) +print("yay!") diff --git a/notebooks/RunExperiments/runners/good_kckp.py b/notebooks/RunExperiments/runners/good_kckp.py new file mode 100644 index 00000000..693226ed --- /dev/null +++ b/notebooks/RunExperiments/runners/good_kckp.py @@ -0,0 +1,44 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +prefix = "good" +kind = "KCKP" +metrics = [ + "erupt", + "psw_energy_distance", + "bite", # NEW + "qini", + "auc", + "codec", # NEW +] + +estimators = get_estimator_list( + kind, + include_patterns=[ + "LinearDML", + "SparseLinearDML", + "LinearDRLearner", + "SparseLinearDRLearner", + "TLearner", + "XLearner", + ], +) + +use_ray = True +run_batch( + identifier=f"Egor_test_{prefix}", + kind=kind, + metrics=metrics, + estimators=estimators, + dataset_path=os.path.realpath("../RunDatasets"), + use_ray=use_ray, +) + +out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_Egor_test_{prefix}/Large")) +# plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +generate_plots(os.path.join(out_dir, kind), metrics, prefix=prefix) # , upper_bounds, lower_bounds) +print("yay!") diff --git a/notebooks/RunExperiments/runners/good_rct.py b/notebooks/RunExperiments/runners/good_rct.py new file mode 100644 index 00000000..1ee90d79 --- /dev/null +++ b/notebooks/RunExperiments/runners/good_rct.py @@ -0,0 +1,44 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +prefix = "good" +kind = "RCT" +metrics = [ + "erupt", + "psw_energy_distance", + "bite", # NEW + "qini", + "auc", + "codec", # NEW +] + +estimators = get_estimator_list( + kind, + include_patterns=[ + "LinearDML", + "SparseLinearDML", + "LinearDRLearner", + "SparseLinearDRLearner", + "TLearner", + "XLearner", + ], +) + +use_ray = True +run_batch( + identifier=f"Egor_test_{prefix}", + kind=kind, + metrics=metrics, + estimators=estimators, + dataset_path=os.path.realpath("../RunDatasets"), + use_ray=use_ray, +) + +out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_Egor_test_{prefix}/Large")) +# plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +generate_plots(os.path.join(out_dir, kind), metrics, prefix=prefix) # , upper_bounds, lower_bounds) +print("yay!") diff --git a/notebooks/RunExperiments/runners/kc_no_meta.py b/notebooks/RunExperiments/runners/kc_no_meta.py index 7be94660..f3c756cb 100644 --- a/notebooks/RunExperiments/runners/kc_no_meta.py +++ b/notebooks/RunExperiments/runners/kc_no_meta.py @@ -3,37 +3,40 @@ from experiment_runner import run_batch, get_estimator_list from experiment_plots import generate_plots -identifier = "Egor_test" +identifier = "Egor_test_no_meta" kind = "KC" metrics = [ "erupt", # "greedy_erupt", # regular erupt was made probabilistic, - "policy_risk", # NEW + # "policy_risk", # NEW + "psw_energy_distance", + "bite", # NEW + # "frobenius_norm", # NEW "qini", "auc", - "psw_energy_distance", - "frobenius_norm", # NEW "codec", # NEW - "bite", # NEW ] + estimators = get_estimator_list(kind, exclude_patterns=["SLearner", "TLearner", "XLearner"]) -ptt_estimators = [ - "lgbm", - "lrl2", -] +ptt_estimators = ["lgbm", "lrl2", "kneighbor", "rf"] # "histgb", "catboost", -use_ray = True -out_dir = run_batch( - identifier, - kind, - metrics, - estimators=estimators, - propensity_automl_estimators=ptt_estimators, - dataset_path=os.path.realpath("../RunDatasets"), - use_ray=use_ray, -) +# use_ray = True +# out_dir = run_batch( +# identifier, +# kind, +# metrics, +# estimators=estimators, +# propensity_automl_estimators=ptt_estimators, +# components_time_budget=120, +# dataset_path=os.path.realpath("../RunDatasets"), +# use_ray=use_ray, +# ) +# # plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_{identifier}/Large")) # plot results # upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} # lower_bounds = {"erupt": 0.06, "bite": 0.75} -generate_plots(os.path.join(out_dir, kind)) # , upper_bounds, lower_bounds) +generate_plots(os.path.join(out_dir, kind), metrics) # , upper_bounds, lower_bounds) print("yay!") diff --git a/notebooks/RunExperiments/runners/kckp_no_meta.py b/notebooks/RunExperiments/runners/kckp_no_meta.py index 13f2bd7f..18559ac7 100644 --- a/notebooks/RunExperiments/runners/kckp_no_meta.py +++ b/notebooks/RunExperiments/runners/kckp_no_meta.py @@ -8,28 +8,31 @@ metrics = [ "erupt", # "greedy_erupt", # regular erupt was made probabilistic, - "policy_risk", # NEW + # "policy_risk", # NEW + "psw_energy_distance", + "bite", # NEW + # "frobenius_norm", # NEW "qini", "auc", - "psw_energy_distance", - "frobenius_norm", # NEW "codec", # NEW - "bite", # NEW ] estimators = get_estimator_list(kind, exclude_patterns=["SLearner", "TLearner", "XLearner"]) -use_ray = True -out_dir = run_batch( - identifier, - kind, - metrics, - estimators=estimators, - dataset_path=os.path.realpath("../RunDatasets"), - use_ray=use_ray, -) -# plot results -# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} -# lower_bounds = {"erupt": 0.06, "bite": 0.75} -generate_plots(os.path.join(out_dir, kind)) # , upper_bounds, lower_bounds) -print("yay!") +if __name__ == "__main__": + + # use_ray = True + # out_dir = run_batch( + # identifier, + # kind, + # metrics, + # estimators=estimators, + # dataset_path=os.path.realpath("../RunDatasets"), + # use_ray=use_ray, + # ) + out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_{identifier}/Large")) + # plot results + # upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} + # lower_bounds = {"erupt": 0.06, "bite": 0.75} + generate_plots(os.path.join(out_dir, kind), metrics) # , upper_bounds, lower_bounds) + print("yay!") diff --git a/notebooks/RunExperiments/runners/latex_utils.py b/notebooks/RunExperiments/runners/latex_utils.py new file mode 100644 index 00000000..12048351 --- /dev/null +++ b/notebooks/RunExperiments/runners/latex_utils.py @@ -0,0 +1,118 @@ +from experiment_plots import metric_names + + +def df_to_latex(df, filename): + latex = df.to_latex(escape=False, index=False) + latex = prettify_latex(latex) + with open(filename, "w") as file: + file.write(latex) + + +def prettify_latex(latex: str): + latex = "\\renewcommand{\\arraystretch}{1.1}\n" + latex.replace("\\toprule", "\\hline").replace( + "\\midrule", "\\hline" + ).replace("\\bottomrule", "\\hline") + return latex + + +def rename(x): + if x in metric_names: + return metric_names[x].replace("\n", " ") + return x + + +def create_latex_table( + data, + dataset_col, + metric_col, + corr_col, + row_order, + column_order=None, + filename=None, + flag_max=True, + first_col_precision: bool = False, +): + """ + Create a LaTeX table from a long-format dataset with customizable column names, + row and column order, and formatting options. + + Parameters: + data (pd.DataFrame): A DataFrame containing the data. + dataset_col (str): Column name for datasets (columns in the output table). + metric_col (str): Column name for metrics (rows in the output table). + corr_col (str): Column name for correlation values (cells in the table). + row_order (list): Ordered list of metric values to arrange the rows in the table. + column_order (list, optional): Ordered list of dataset values to arrange the columns in the table. + filename (str, optional): File path to save the LaTeX table. If None, the table is not saved. + + Returns: + str: A LaTeX table as a string. + """ + if column_order is None: + + cols = [] + for kind in [ + "RCT", + "KCKP", + "KC", + ]: + cols += [f"Linear {kind}", f"NonLinear {kind}"] + column_order = cols + + # Pivot the data into a wide format + table = data.pivot(index=metric_col, columns=dataset_col, values=corr_col) + + table = table.loc[row_order] + if column_order: + table = table[column_order] + + # Reorder rows and columns based on the provided order + table.columns = [ + c.replace("NonLinear", "Nonlin.") + .replace("KCKP", "KPTT") + .replace("KC", "FPTT") + .replace("Linear", "Lin.") + for c in table.columns + ] + + # Function to bold the maximum value in each column + def bold_max(series): + if flag_max: + max_value = series.max() + else: + max_value = series.min() + if series.name == "Linear RCT" and first_col_precision: + return [f"\\textbf{{{v:.3f}}}" if v == max_value else f"{v:.3f}" for v in series] + else: + return [f"\\textbf{{{v:.2f}}}" if v == max_value else f"{v:.2f}" for v in series] + + # Apply bold_max to each column + formatted_table = table.apply(bold_max, axis=0) + + # Generate the LaTeX table with a vertical line after row names + latex_table = formatted_table.to_latex( + escape=False, # Allow LaTeX formatting + column_format="|l|" + "r" * len(table.columns) + "|", # Vertical line after row names + header=True, # Include column headers (dataset names) + ) + + # Remove the dataset column name from the LaTeX table + lines = latex_table.splitlines() + lines[0] = lines[0].replace(f"{dataset_col} ", "") # Remove the dataset column header + lines[1] = lines[1].replace(f"{'-' * len(dataset_col)} ", "") # Adjust the alignment line + filtered_lines = [line for line in lines if not line.startswith(metric_col)] + latex_table = ( + "\n".join(filtered_lines) + .replace("\\toprule", "\\hline") + .replace("\\midrule", "\\hline") + .replace("\\bottomrule", "\\hline") + ) + + latex_table = "\\renewcommand{\\arraystretch}{1.1}\n" + latex_table + + # Save the table to a file if a filename is provided + if filename: + with open(filename, "w") as file: + file.write(latex_table) + + return latex_table diff --git a/notebooks/RunExperiments/runners/make_summary_tables.py b/notebooks/RunExperiments/runners/make_summary_tables.py new file mode 100644 index 00000000..ff8a7210 --- /dev/null +++ b/notebooks/RunExperiments/runners/make_summary_tables.py @@ -0,0 +1,64 @@ +import os + +from latex_utils import create_latex_table, rename + +from no_meta_rct import metrics + +import pandas as pd + +if __name__ == "__main__": + + identifier = "Egor_test_no_meta" + data_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_{identifier}/Large")) + + results_ite = [] + results_mse = [] + cols = [] + for kind in [ + "RCT", + "KCKP", + "KC", + ]: + results_ite.append(pd.read_pickle(os.path.join(data_dir, f"{kind}_ITE_results.pkl"))) + results_mse.append(pd.read_pickle(os.path.join(data_dir, f"{kind}_MSE_results.pkl"))) + cols += [f"Linear {kind}", f"Nonlinear {kind}"] + + results_ite = pd.concat(results_ite) + results_mse = pd.concat(results_mse) + + results_ite["dataset"] = results_ite["dataset"].str.replace("Large ", "") + results_mse["dataset"] = results_mse["dataset"].str.replace("Large ", "") + + results_ite["metric"] = results_ite["metric"].apply(rename) + results_mse["metric"] = results_mse["metric"].apply(rename) + + create_latex_table( + results_ite, + dataset_col="dataset", + metric_col="metric", + corr_col="corr", + row_order=[rename(m) for m in metrics], + column_order=cols, + filename=os.path.join(data_dir, "table_ite_corr.tex"), + ) + + create_latex_table( + results_ite, + dataset_col="dataset", + metric_col="metric", + corr_col="r2", + row_order=[rename(m) for m in metrics], + column_order=cols, + filename=os.path.join(data_dir, "table_ite_r2.tex"), + ) + + create_latex_table( + results_mse, + dataset_col="dataset", + metric_col="metric", + corr_col="corr", + row_order=[rename(m) for m in metrics], + column_order=cols, + filename=os.path.join("tex", "table_mse_corr.tex"), + ) + print("yay!") diff --git a/notebooks/RunExperiments/runners/meta_kc.py b/notebooks/RunExperiments/runners/meta_kc.py new file mode 100644 index 00000000..dfbe6ecc --- /dev/null +++ b/notebooks/RunExperiments/runners/meta_kc.py @@ -0,0 +1,40 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +prefix = "meta" + +kind = "KC" +metrics = [ + "erupt", + # "greedy_erupt", # regular erupt was made probabilistic, + # "policy_risk", # NEW + "psw_energy_distance", + "bite", # NEW + # "frobenius_norm", # NEW + "qini", + "auc", + "codec", # NEW +] + +estimators = get_estimator_list(kind, include_patterns=["SLearner", "TLearner", "XLearner"]) +ptt_estimators = ["lgbm", "lrl2", "kneighbor", "rf"] # "histgb", "catboost", +# +# use_ray = True +# run_batch( +# identifier=f"Egor_test_{prefix}", +# kind, +# metrics, +# estimators=estimators, +# propensity_automl_estimators=ptt_estimators, +# components_time_budget=120, +# dataset_path=os.path.realpath("../RunDatasets"), +# use_ray=use_ray, +# ) +out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_Egor_test_{prefix}/Large")) +# plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +generate_plots(os.path.join(out_dir, kind), metrics, prefix=prefix) # , upper_bounds, lower_bounds) +print("yay!") diff --git a/notebooks/RunExperiments/runners/meta_kckp.py b/notebooks/RunExperiments/runners/meta_kckp.py new file mode 100644 index 00000000..8ee5ee1e --- /dev/null +++ b/notebooks/RunExperiments/runners/meta_kckp.py @@ -0,0 +1,39 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +prefix = "meta" +kind = "KCKP" +metrics = [ + "erupt", + # "greedy_erupt", # regular erupt was made probabilistic, + # "policy_risk", # NEW + "qini", + "auc", + "psw_energy_distance", + # "frobenius_norm", # NEW + "codec", # NEW + "bite", # NEW +] + +estimators = get_estimator_list(kind, include_patterns=["SLearner", "TLearner", "XLearner"]) + +use_ray = True +# run_batch( +# identifier=f"Egor_test_{prefix}", +# kind=kind, +# metrics=metrics, +# estimators=estimators, +# dataset_path=os.path.realpath("../RunDatasets"), +# use_ray=use_ray, +# ) +# plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +out_dir = os.path.realpath( + os.path.join(f"../EXPERIMENT_RESULTS_Egor_test_{prefix}/Large") +) # , upper_bounds, lower_bounds) +generate_plots(os.path.join(out_dir, kind), metrics, prefix=prefix) # , upper_bounds, lower_bounds) + +print("yay!") diff --git a/notebooks/RunExperiments/runners/meta_rct.py b/notebooks/RunExperiments/runners/meta_rct.py new file mode 100644 index 00000000..60ba2e55 --- /dev/null +++ b/notebooks/RunExperiments/runners/meta_rct.py @@ -0,0 +1,37 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +prefix = "meta" +kind = "RCT" +metrics = [ + "erupt", + # "greedy_erupt", # regular erupt was made probabilistic, + # "policy_risk", # NEW + "psw_energy_distance", + "bite", # NEW + # "frobenius_norm", # NEW + "qini", + "auc", + "codec", # NEW +] + +estimators = get_estimator_list(kind, include_patterns=["SLearner", "TLearner", "XLearner"]) + +# use_ray = True +# run_batch( +# identifier, +# kind, +# metrics, +# estimators=estimators, +# dataset_path=os.path.realpath("../RunDatasets"), +# use_ray=use_ray, +# ) + +out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_Egor_test_{prefix}/Large")) +# plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +generate_plots(os.path.join(out_dir, kind), metrics, prefix=prefix) # , upper_bounds, lower_bounds) +print("yay!") diff --git a/notebooks/RunExperiments/runners/no_meta_rct.py b/notebooks/RunExperiments/runners/no_meta_rct.py new file mode 100644 index 00000000..e0629650 --- /dev/null +++ b/notebooks/RunExperiments/runners/no_meta_rct.py @@ -0,0 +1,39 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots, generate_mse_plots + +identifier = "no_meta" +kind = "RCT" +metrics = [ + "erupt", + # "greedy_erupt", # regular erupt was made probabilistic, + # "policy_risk", # NEW + "psw_energy_distance", + "bite", # NEW + # "frobenius_norm", # NEW + "qini", + "auc", + "codec", # NEW +] + +estimators = get_estimator_list(kind, exclude_patterns=["SLearner", "TLearner", "XLearner"]) + +if __name__ == "__main__": + # use_ray = True + # out_dir = run_batch( + # identifier, + # kind, + # metrics, + # estimators=estimators, + # dataset_path=os.path.realpath("../RunDatasets"), + # use_ray=use_ray, + # ) + + out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_{identifier}/Large")) + # plot results + # upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} + # lower_bounds = {"erupt": 0.06, "bite": 0.75} + # generate_plots(os.path.join(out_dir, kind), metrics) # , upper_bounds, lower_bounds) + generate_mse_plots(os.path.join(out_dir, kind), metrics) + print("yay!") diff --git a/notebooks/RunExperiments/runners/no_meta_to_kc.py b/notebooks/RunExperiments/runners/no_meta_to_kc.py new file mode 100644 index 00000000..f998f2f8 --- /dev/null +++ b/notebooks/RunExperiments/runners/no_meta_to_kc.py @@ -0,0 +1,44 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +identifier = "Egor_test_no_meta_to" +kind = "KC" +metrics = [ + # "erupt", + # # "greedy_erupt", # regular erupt was made probabilistic, + # # "policy_risk", # NEW + # "psw_energy_distance", + # "bite", # NEW + # # "frobenius_norm", # NEW + "qini", + "auc", + # "codec", # NEW +] + +estimators = get_estimator_list( + kind, exclude_patterns=["SLearner", "TLearner", "XLearner", "TransformedOutcome"] +) +ptt_estimators = ["lgbm", "lrl2", "kneighbor", "rf"] # "histgb", "catboost", + +use_ray = True +run_batch( + identifier, + kind, + metrics, + estimators=estimators, + propensity_automl_estimators=ptt_estimators, + components_time_budget=120, + dataset_path=os.path.realpath("../RunDatasets"), + use_ray=use_ray, +) +# # plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_{identifier}/Large")) +# plot results +# upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} +# lower_bounds = {"erupt": 0.06, "bite": 0.75} +generate_plots(os.path.join(out_dir, kind), metrics) # , upper_bounds, lower_bounds) +print("yay!") diff --git a/notebooks/RunExperiments/runners/no_meta_to_kckp.py b/notebooks/RunExperiments/runners/no_meta_to_kckp.py new file mode 100644 index 00000000..ec79f753 --- /dev/null +++ b/notebooks/RunExperiments/runners/no_meta_to_kckp.py @@ -0,0 +1,40 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +identifier = "Egor_test_no_meta_to" +kind = "KCKP" +metrics = [ + # "erupt", + # # "greedy_erupt", # regular erupt was made probabilistic, + # # "policy_risk", # NEW + # "psw_energy_distance", + # "bite", # NEW + # # "frobenius_norm", # NEW + "qini", + # "auc", + # "codec", # NEW +] + +estimators = get_estimator_list( + kind, exclude_patterns=["SLearner", "TLearner", "XLearner", "TransformedOutcome"] +) + +if __name__ == "__main__": + + use_ray = True + run_batch( + identifier, + kind, + metrics, + estimators=estimators, + dataset_path=os.path.realpath("../RunDatasets"), + use_ray=use_ray, + ) + out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_{identifier}/Large")) + # plot results + # upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} + # lower_bounds = {"erupt": 0.06, "bite": 0.75} + generate_plots(os.path.join(out_dir, kind), metrics) # , upper_bounds, lower_bounds) + print("yay!") diff --git a/notebooks/RunExperiments/runners/no_meta_to_rct.py b/notebooks/RunExperiments/runners/no_meta_to_rct.py new file mode 100644 index 00000000..46c9a110 --- /dev/null +++ b/notebooks/RunExperiments/runners/no_meta_to_rct.py @@ -0,0 +1,41 @@ +import os + +from experiment_runner import run_batch, get_estimator_list +from experiment_plots import generate_plots + +identifier = "Egor_test_no_meta_to" +kind = "RCT" +metrics = [ + # "erupt", + # # "greedy_erupt", # regular erupt was made probabilistic, + # # "policy_risk", # NEW + # "psw_energy_distance", + # "bite", # NEW + # # "frobenius_norm", # NEW + # "qini", + # "auc", + "codec", # NEW +] + +estimators = get_estimator_list( + kind, + exclude_patterns=["SLearner", "TLearner", "XLearner", "TransformedOutcome"], +) + +if __name__ == "__main__": + use_ray = True + run_batch( + identifier, + kind, + metrics, + estimators=estimators, + dataset_path=os.path.realpath("../RunDatasets"), + use_ray=use_ray, + ) + + out_dir = os.path.realpath(os.path.join(f"../EXPERIMENT_RESULTS_{identifier}/Large")) + # plot results + # upper_bounds = {"MSE": 1e2, "policy_risk": 0.2} + # lower_bounds = {"erupt": 0.06, "bite": 0.75} + generate_plots(os.path.join(out_dir, kind), metrics) # , upper_bounds, lower_bounds) + print("yay!") diff --git a/notebooks/RunExperiments/runners/scores_by_estimator.py b/notebooks/RunExperiments/runners/scores_by_estimator.py new file mode 100644 index 00000000..1afcc02a --- /dev/null +++ b/notebooks/RunExperiments/runners/scores_by_estimator.py @@ -0,0 +1,236 @@ +import os.path +from pathlib import Path +from collections import defaultdict + +import numpy as np +import pandas as pd + +from causaltune.score.scoring import metrics_to_minimize +from experiment_plots import generate_mse_plots +from experiment_plots import get_all_test_scores +from experiment_runner import get_estimator_list +from latex_utils import df_to_latex, create_latex_table, rename + +metrics = [ + "erupt", + "psw_energy_distance", + "bite", # NEW + "qini", + "auc", + "codec", # NEW +] + + +def average_clipped_array(arr, prctile: int = 50): + # Flatten the array in case it's multi-dimensional + flat_arr = arr.flatten() + + # Remove NaNs and Infs + valid_arr = flat_arr[np.isfinite(flat_arr)] # Keeps only finite values + + if valid_arr.size == 0: # Handle edge case where all values were NaN/Inf + return np.nan # Return NaN to indicate no valid values + + # Calculate the absolute values + abs_arr = np.abs(valid_arr) + + # Determine the 99th percentile value + threshold = np.percentile(abs_arr, 99) + + # Clip the values above the threshold + clipped_arr = valid_arr[abs_arr <= threshold] + + if clipped_arr.size == 0: # Handle edge case where all valid values got clipped + return np.nan # Return NaN to indicate no values left + + return ( + np.mean(clipped_arr), + np.std(clipped_arr), + np.percentile(clipped_arr, 50), + np.percentile(clipped_arr, 10), + ) + + +# Load data files from all the experiments, do a tab +old = False +if old: + identifiers = [f"Egor_test _{i}" for i in ["meta", "no_meta"]] +else: + estimators = get_estimator_list("RCT") + identifiers = [f"Estimator_{estimator.split('.')[-1]}" for estimator in estimators] + +kinds = ["RCT", "KCKP", "KC"] + +missing_runs = [] +dfs = [] +out_dirs = [] +for identifier in identifiers: + for kind in kinds: + out_dir = Path(f"../EXPERIMENT_RESULTS_{identifier}/Large/{kind}") + if os.path.isdir(out_dir) and len(os.listdir(out_dir)): + out_dirs.append(out_dir) + else: + missing_runs.append(identifier.split("_")[-1]) + + for lin in ["Linear", "NonLinear"]: + dataset = " ".join([lin, kind]) + tmp = get_all_test_scores(str(out_dir), lin, kind) + tmp["dataset"] = dataset + dfs.append(tmp) + + +def contains_any(s: str, substrings: list) -> bool: + for substring in substrings: + if substring in s: + return True + return False + + +# patch the holes with old runs +for lin in ["Linear", "NonLinear"]: + dirs = [f"../EXPERIMENT_RESULTS_Egor_test_{i}/Large/KC" for i in ["meta", "no_meta"]] + patch_data = get_all_test_scores(dirs, lin, "KC") + patch_data["dataset"] = f"{lin} KC" + filter = patch_data["estimator_name"].apply(lambda x: contains_any(x, missing_runs)) + patch_data = patch_data[filter] + dfs.append(patch_data) + + +master_df = pd.concat(dfs) + +out = [] + +prctile = 25 +for (est_name, ds), df in master_df.groupby(["estimator_name", "dataset"]): + mse_mean, mse_std, mse_median, mse_quart = average_clipped_array(df["MSE"].values) + out.append( + { + "Estimator": est_name.split(".")[-1], + "dataset": ds, + "Median": mse_median, + "25\\% Quantile": mse_quart, + "MSE_mean": mse_mean, + "MSE_std": mse_std, + } + ) + + +out_df = pd.DataFrame(out) +for c in [ + "Median", + "25\\% Quantile", +]: + + # Step 1: Group by dataset and rescale "Median" by the mean of each group + out_df["Rescaled"] = out_df[c] / out_df.groupby("dataset")[c].transform("mean") + + # Step 2: Compute the average of the rescaled values grouped by "Estimator" + estimator_scores = out_df.groupby("Estimator")["Rescaled"].mean() + + # Step 3: Get the ordering of "Estimator" values by the computed average (descending order) + ordering = estimator_scores.sort_values(ascending=True).index.tolist() + # sort rows by pveral MSE + out_by_estimator = ( + out_df.groupby("Estimator", as_index=False).mean().reset_index().sort_values(c) + ) + + create_latex_table( + out_df, + dataset_col="dataset", + metric_col="Estimator", + corr_col=c, + row_order=ordering, + filename="tex/mse_by_estimator.tex" if c == "Median" else "tex/mse_by_estimator_25.tex", + flag_max=False, + first_col_precision=True, + ) +out2 = [] +for (score, ds), df in master_df.groupby(["optimized_score", "dataset"]): + if score in metrics_to_minimize(): + best_score = df[score].min() + else: + best_score = df[score].max() + best_MSE = df[df[score] == best_score]["MSE"].median() + out2.append( + { + "optimized_score": score, + "dataset": ds, + "MSE": best_MSE, + } + ) + +best_MSE = pd.DataFrame(out2) +best_MSE["optimized_score"] = best_MSE["optimized_score"].apply(rename) + +create_latex_table( + best_MSE, + dataset_col="dataset", + metric_col="optimized_score", + corr_col="MSE", + row_order=[rename(m) for m in metrics], + filename="tex/best_mse_by_score.tex", + flag_max=False, + first_col_precision=False, +) +# for c in ["Median", "25\\% Quantile", "MSE_mean", "MSE_std"]: +# out_df[c] = out_df[c].apply(lambda x: f"{x:.3f}") +# +# df_to_latex(out_df[["Estimator", "25\\% Quantile", "Median"]], "tex/estimator_mse.tex") + +print("yay!") + +# now do the same for the ITE scores + +save_dir = os.path.realpath("./plots") +results = defaultdict(list) + + +for pick_nice in [True, False]: + for kind in kinds: + out_dirs = [] + if pick_nice: + nice_estimators = [ + "LinearDML", + "SparseLinearDML", + "LinearDRLearner", + "XLearner", + "DomainAdaptationLearner", + "TLearner", + "SparseLinearDRLearner", + ] + + used_identifiers = [c for c in identifiers if any([e in c for e in nice_estimators])] + else: + used_identifiers = identifiers + + for identifier in used_identifiers: + out_dir = Path(f"../EXPERIMENT_RESULTS_{identifier}/Large/{kind}") + out_dirs.append(out_dir) + upper_bounds = {"MSE": 1e2} + lower_bounds = {"bite": 0.0} + tmp_results = generate_mse_plots( + [str(d) for d in out_dirs], + metrics, + save_dir=save_dir, + prefix="nice" if pick_nice else "est_level", + upper_bounds=upper_bounds, + lower_bounds=lower_bounds, + ) + results[pick_nice].append(tmp_results) + print("aya") + + combined = pd.concat(results[pick_nice]) + combined["metric"] = combined["metric"].apply(rename) + combined["dataset"] = combined["dataset"].apply(lambda x: " ".join(x.split(" ")[1::-1])) + create_latex_table( + combined, + 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long_description_content_type="text/markdown", - author="Wise", - url="https://github.com/py-why/causaltune", - classifiers=[ - "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - ], - python_requires=">=3.10,<3.13", - install_requires=[ - # Tightly-coupled / version-sensitive: pinned exactly. - "dowhy==0.14", - "econml==0.16.0", - "FLAML==2.6.0", - "xgboost==2.1.4", - "numpy==2.2.6", - # Loosely-coupled: floors (with caps where an upstream ceiling is real). - "pandas>=2,<3", - "scikit_learn>=1.4,<1.7", - "category_encoders>=2.6.3", - "pytest", - "matplotlib", - "dcor", - "holidays", - "wise-pizza", - "seaborn", - ], - extras_require={ - "test": [ - "autoflake", - "black==23.3.0", - "flake8", - "isort", - "pytest", - "pytest-cov", - "nbmake", - ], - "ray": ["ray[tune]>=2.9"], - }, - packages=find_packages( - include=["causaltune", "causaltune.*"], - exclude=["tests*"], - ), - include_package_data=True, - keywords="causaltune", -)