diff --git a/.coveragerc b/.coveragerc deleted file mode 100644 index 08bcdef..0000000 --- a/.coveragerc +++ /dev/null @@ -1,4 +0,0 @@ -[run] -branch = True -source = skrebate -include = */skrebate/* diff --git a/.github/workflows/publish_package.yml b/.github/workflows/publish_package.yml new file mode 100644 index 0000000..fc54635 --- /dev/null +++ b/.github/workflows/publish_package.yml @@ -0,0 +1,38 @@ +name: Publish Python Package + +on: + release: + types: [published] + tags: + - 'v*' + workflow_dispatch: + +jobs: + publish: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" # Python version for building/uploading + + - name: Install build tools + run: | + python -m pip install --upgrade pip + pip install build twine + + # Optional: Generate version file from GitHub tag + # - name: Generate version file + # run: echo "__version__ = '${GITHUB_REF##refs/tags/}'" > your_package/_version.py + + - name: Build package + run: python -m build + + - name: Publish to PyPI + env: + TWINE_USERNAME: __token__ + TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} + run: twine upload dist/* \ No newline at end of file diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..304c00d --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,38 @@ +name: Python Package CI + +on: + #push: + # branches: [master] + # push: + # branches: [dev-kia-pypi] + pull_request: + branches: [master] + +jobs: + test: + runs-on: ubuntu-latest + + strategy: + matrix: + python-version: ["3.9", "3.10", "3.11", "3.12"] + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Upgrade pip, install build tools + run: | + python -m pip install --upgrade pip setuptools wheel build + + - name: Install your package with test dependencies + run: | + pip install .[test] # assumes you've defined [project.optional-dependencies] in pyproject.toml + + - name: Run pytest + run: | + pytest --disable-warnings --maxfail=1 --tb=short \ No newline at end of file diff --git a/.gitignore b/.gitignore index f1fb64e..402bb38 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ run_test.py __pycache__/ *.py[cod] *$py.class +.pytest_cache # C extensions *.so @@ -61,17 +62,27 @@ docs/_build/ # PyBuilder target/ -#Ipython Notebook +# Others .ipynb_checkpoints - -testing.ipynb - *.lprof - *.prof -/demo_scikitrebate.ipynb - *.DS_Store -.idea/ - -analysis_pipeline/skrebatewip +.idea/ + +# Files +demo/data +BenchmarkData.zip +analysis_pipeline/ +BenchmarkData/ +BenchmarkData2/ +BenchmarkData3/ +BenchmarkData4/ +skrebate_old +analysis/BasicXOR/ +analysis/BasicXOR2/ +analysis/BasicXOR2_2/ +analysis/BasicXOR3/ +analysis/jobs/ +analysis/logs/ +analysis/core2wayEpistasis +*.zip diff --git a/.landscape.yaml b/.landscape.yaml deleted file mode 100644 index 2b3342a..0000000 --- a/.landscape.yaml +++ /dev/null @@ -1,5 +0,0 @@ -doc-warnings: yes - -ignore-patterns: - - __init__.py - diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index f1fcf14..0000000 --- a/.travis.yml +++ /dev/null @@ -1,18 +0,0 @@ -language: python -virtualenv: - system_site_packages: true -env: - matrix: - # let's start simple: - - PYTHON_VERSION="2.7" LATEST="true" - - PYTHON_VERSION="3.6" COVERAGE="true" LATEST="true" - - PYTHON_VERSION="3.6" LATEST="true" -install: source ./ci/.travis_install.sh -script: bash ./ci/.travis_test.sh -after_success: - # Ignore coveralls failures as the coveralls server is not very reliable - # but we don't want travis to report a failure in the github UI just - # because the coverage report failed to be published. - - if [[ "$COVERAGE" == "true" ]]; then coveralls || echo "failed"; fi -cache: apt -sudo: false diff --git a/README.md b/README.md index 7cee961..68c946e 100644 --- a/README.md +++ b/README.md @@ -12,30 +12,33 @@ Package information: ![Python 2.7](https://img.shields.io/badge/python-2.7-blue. [![PyPI version](https://badge.fury.io/py/skrebate.svg)](https://badge.fury.io/py/skrebate) # scikit-rebate -This package includes a scikit-learn-compatible Python implementation of ReBATE, a suite of [Relief-based feature selection algorithms](https://en.wikipedia.org/wiki/Relief_(feature_selection)) for Machine Learning. These Relief-Based algorithms (RBAs) are designed for feature weighting/selection as part of a machine learning pipeline (supervised learning). Presently this includes the following core RBAs: ReliefF, SURF, SURF\*, MultiSURF\*, and MultiSURF. Additionally, an implementation of the iterative TuRF mechanism and VLSRelief is included. **It is still under active development** and we encourage you to check back on this repository regularly for updates. -These algorithms offer a computationally efficient way to perform feature selection that is sensitive to feature interactions as well as simple univariate associations, unlike most currently available filter-based feature selection methods. The main benefit of Relief algorithms is that they identify feature interactions without having to exhaustively check every pairwise interaction, thus taking significantly less time than exhaustive pairwise search. +This package includes a scikit-learn-compatible Python implementation of ReBATE, a suite of [Relief-based feature selection algorithms]() for Machine Learning. These Relief-based algorithms (RBAs) are designed for feature weighting/selection as part of a machine learning pipeline (supervised learning). Presently this includes the following core RBAs: ReliefF, SURF, SURF\*, MultiSURF\*, MultiSURF, SWRF\*, SWRF, MultiSWRF\*, MultiSWRF, MultiSWRFDB\*, MultiSWRFDB, and μ-Relief. Additionally, an implementation of the iterative TuRF mechanism and VLSRelief is included. **It is still under active development** and we encourage you to check back on this repository regularly for updates. -Certain algorithms require user specified run parameters (e.g. ReliefF requires the user to specify some 'k' number of nearest neighbors). +These algorithms offer a computationally efficient way to perform feature selection that is sensitive to feature interactions as well as simple univariate associations, unlike most currently available filter-based feature selection methods. The main benefit of Relief-based algorithms is that they identify feature interactions without having to exhaustively check every pairwise interaction, thus taking significantly less time than exhaustive pairwise search. -Relief algorithms are commonly applied to genetic analyses, where epistasis (i.e., feature interactions) is common. However, the algorithms implemented in this package can be applied to almost any supervised classification data set and supports: + -* Feature sets that are discrete/categorical, continuous-valued or a mix of both +Certain algorithms have run parameters that the user can specify, or if not specified, default to preset values (e.g. ReliefF’s parameter for ‘k’ number of nearest neighbors). -* Data with missing values +Relief-based algorithms are commonly applied to genetic analyses, where epistasis (i.e., feature interactions) is common. However, the algorithms implemented in this package can be applied to almost any supervised, structured data set and support: -* Binary endpoints (i.e., classification) +- Feature sets that are discrete/categorical, continuous-valued or a mix of both -* Multi-class endpoints (i.e., classification) +- Data with missing values -* Continuous endpoints (i.e., regression) +- Binary endpoints (i.e., classification) -Built into this code, is a strategy to 'automatically' detect from the loaded data, these relevant characteristics. +- Multi-class endpoints (i.e., classification) -Of our two initial ReBATE software releases, this scikit-learn compatible version primarily focuses on ease of incorporation into a scikit learn analysis pipeline. +- Continuous endpoints (i.e., regression) + +Built into this code is a strategy to 'automatically' detect these relevant characteristics from the loaded data. + +Of our two initial ReBATE software releases, this scikit-learn compatible version primarily focuses on ease of incorporation into a scikit-learn analysis pipeline. This code is most appropriate for scikit-learn users, Windows operating system users, beginners, or those looking for the most recent ReBATE developments. -An alternative 'stand-alone' version of [ReBATE](https://github.com/EpistasisLab/ReBATE) is also available that focuses on improving run-time with the use of Cython for optimization. This implementation also outputs feature names and associated feature scores as a text file by default. +An alternative 'stand-alone' version of [ReBATE](https://github.com/EpistasisLab/ReBATE) is also available that focuses on improving run-time with the use of Cython for optimization. This implementation also outputs feature names and associated feature scores as a text file by default. ## License @@ -47,11 +50,11 @@ Generally, we have licensed scikit-rebate to make it as widely usable as possibl scikit-rebate is built on top of the following existing Python packages: -* NumPy +- NumPy -* SciPy +- SciPy -* scikit-learn +- scikit-learn All of the necessary Python packages can be installed via the [Anaconda Python distribution](https://www.continuum.io/downloads), which we strongly recommend that you use. We also strongly recommend that you use Python 3 over Python 2 if you're given the choice. @@ -71,30 +74,97 @@ Please [file a new issue](https://github.com/EpistasisLab/scikit-rebate/issues/n ## Usage -We have designed the Relief algorithms to be integrated directly into scikit-learn machine learning workflows. For example, the ReliefF algorithm can be used as a feature selection step in a scikit-learn pipeline as follows. +### Basic Usage + +To use an algorithm from ReBATE as a feature selection method: -```python +```Python +# Import necessary packages +import pandas as pd +from skrebate import ReliefF + +# Load the example dataset +genetic_data = pd.read_csv( + './data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv') + +# Separate the features and labels from the dataset +features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values + +# Apply the ReliefF algorithm for feature selection +fs = ReliefF() +fs.fit(features, labels) + +# Print out the results +feature_name = genetic_data.drop('class', axis=1).columns +fs.summary(feature_name=feature_name) + +>>> Feature name Feature importances Feature rank +>>> P2 0.12330000 1 +>>> P1 0.11892500 2 +>>> N0 -0.00018125 3 +>>> N10 -0.00075625 4 +>>> N13 -0.00320625 5 +>>> N14 -0.00402500 6 +>>> N4 -0.00582500 7 +>>> N1 -0.00595000 8 +>>> N8 -0.00653750 9 +>>> N12 -0.00696250 10 +>>> N16 -0.00705000 11 +>>> N17 -0.00740625 12 +>>> N5 -0.00788750 13 +>>> N11 -0.00822500 14 +>>> N9 -0.00826250 15 +>>> N2 -0.00871875 16 +>>> N3 -0.00872500 17 +>>> N7 -0.00991875 18 +>>> N6 -0.01038750 19 +>>> N15 -0.01044375 20 +``` + +### Using as End-to-end Pipeline + +We have designed the Relief-based algorithms to be integrated directly into scikit-learn machine learning workflows. For example, the ReliefF algorithm can be used as a feature selection step in a scikit-learn pipeline as follows: + +```Python +# Import necessary packages import pandas as pd -import numpy as np from sklearn.pipeline import make_pipeline from skrebate import ReliefF from sklearn.ensemble import RandomForestClassifier -from sklearn.model_selection import cross_val_score +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score -genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/' - 'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz', - sep='\t', compression='gzip') +# Load the example dataset +genetic_data = pd.read_csv( + './data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv') +# Separate the features and labels from the dataset features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values -clf = make_pipeline(ReliefF(n_features_to_select=2, n_neighbors=100), - RandomForestClassifier(n_estimators=100)) +# Split the data to training and testing +X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3, random_state=42) -print(np.mean(cross_val_score(clf, features, labels))) ->>> 0.795 +# Make pipeline +clf = make_pipeline( + ReliefF(n_features_to_select=2), + RandomForestClassifier(n_estimators=100) +) + +# Train the model +clf.fit(X_train, y_train) + +# Evaluate the model on testing set +y_pred = clf.predict(X_test) +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy:.3f}") +>>> Accuracy: 0.781 ``` -For more information on the Relief algorithms available in this package and how to use them, please refer to our [usage documentation](https://EpistasisLab.github.io/scikit-rebate/using/). + + +For more information on the Relief-based algorithms available in this package and how to use them, please refer to our [usage documentation](https://urbslab.github.io/scikit-rebate/using/). ## Contributing to scikit-rebate @@ -106,15 +176,19 @@ Please refer to our [contribution guidelines](https://EpistasisLab.github.io/sci If you use scikit-rebate in a scientific publication, please consider citing the following paper: -Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore (2017). [Benchmarking Relief-Based Feature Selection Methods](https://arxiv.org/abs/1711.08477). *arXiv preprint*, under review. +Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore (2018). Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining. _Journal of Biomedical Informatics_, 85, 168-188. DOI: [10.1016/j.jbi.2018.07.015](https://doi.org/10.1016/j.jbi.2018.07.015) -BibTeX entry: +### BibTeX entry: ```bibtex -@misc{Urbanowicz2017Benchmarking, - author = {Urbanowicz, Ryan J. and Olson, Randal S. and Schmitt, Peter and Meeker, Melissa and Moore, Jason H.}, - title = {Benchmarking Relief-Based Feature Selection Methods}, - year = {2017}, - howpublished = {arXiv e-print. https://arxiv.org/abs/1711.08477}, +@article{Urbanowicz2018Benchmarking, + author = {Urbanowicz, Ryan J. and Olson, Randal S. and Schmitt, Peter and Meeker, Melissa and Moore, Jason H.}, + title = {Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining}, + journal = {Journal of Biomedical Informatics}, + volume = {85}, + pages = {168--188}, + year = {2018}, + doi = {10.1016/j.jbi.2018.07.015}, + url = {https://doi.org/10.1016/j.jbi.2018.07.015} } ``` diff --git a/analysis/check_run_validity.py b/analysis/check_run_validity.py new file mode 100644 index 0000000..24f93a6 --- /dev/null +++ b/analysis/check_run_validity.py @@ -0,0 +1,30 @@ +import os + +def find_error_runs(log_dir, error_keywords=None): + if error_keywords is None: + # Add more as needed + error_keywords = ["error", "failed", "segmentation fault", "traceback", "exception", "abort", "core dumped"] + + error_runs = [] + + for root, _, files in os.walk(log_dir): + for file in files: + if file.endswith((".err", ".log", ".out")): + filepath = os.path.join(root, file) + try: + with open(filepath, "r", encoding="utf-8", errors="ignore") as f: + content = f.read().lower() + if any(keyword in content for keyword in error_keywords): + error_runs.append(filepath) + except Exception as e: + print(f"Error reading {filepath}: {e}") + + return error_runs + +# Example usage: +log_directory = "./logs" # Change this to your logs directory +errors = find_error_runs(log_directory) + +print("Jobs with errors:") +for err_file in errors: + print(err_file) diff --git a/analysis/higher_order_interactions/Heatmaps.ipynb b/analysis/higher_order_interactions/Heatmaps.ipynb deleted file mode 100644 index 12d8313..0000000 --- a/analysis/higher_order_interactions/Heatmaps.ipynb +++ /dev/null @@ -1,247 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Below is the parsing code that goes through all result files and calculates the ranks for each feature for all replicates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the path to the 'Results' directory\n", - "results_dir = 'my_dir' # Change this to the path of your 'Results' directory\n", - "\n", - "# Initialize a DataFrame to hold all rankings\n", - "all_rankings_df = pd.DataFrame()\n", - "\n", - "# Initialize a dictionary to hold the total number of features (N) for each RBA method\n", - "total_features_per_rba = {}\n", - "\n", - "# Iterate over each subfolder in the Results directory, each named for an RBA\n", - "for rba in os.listdir(results_dir):\n", - " rba_path = os.path.join(results_dir, rba)\n", - " if os.path.isdir(rba_path): # Ensure it's a directory\n", - " method_feature_counts = [] # To store feature counts for each dataset within this method\n", - " for file in os.listdir(rba_path):\n", - " if file.endswith('.txt'): # Ensure the file is a .txt file\n", - " file_path = os.path.join(rba_path, file)\n", - " \n", - " # Extract the dataset identifier from the file name\n", - " parts = file.split('_')\n", - " dataset_id = '_'.join(parts[-2].split('_')[:2])\n", - " \n", - " # Determine Ranks\n", - " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature'])\n", - " if rba == \"RandomShuffle\":\n", - " df['Rank'] = df.index + 1\n", - " else:\n", - " column_to_use = 'ABS_Feature_Importance' if \"ABS\" in rba else 'Feature_Importance'\n", - " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature', column_to_use])\n", - " df.sort_values(by=column_to_use, ascending=False, inplace=True)\n", - " df.reset_index(drop=True, inplace=True)\n", - " df['Rank'] = df.index + 1\n", - " \n", - " # Store in a dataframe\n", - " predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Rank']]\n", - " predictive_df['RBA'] = rba\n", - " predictive_df['Dataset'] = dataset_id\n", - " \n", - " all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True)\n", - " \n", - " # Add the feature count of this dataset to the list for this method\n", - " method_feature_counts.append(df['Feature'].nunique())\n", - " \n", - " # Store the maximum feature count encountered for this method as N\n", - " total_features_per_rba[rba] = max(method_feature_counts)\n", - "\n", - "# Now, you have both the rankings and the total number of features (N) for each RBA method\n", - "# You can use total_features_per_rba to access N for each method as needed\n", - "\n", - "# Specify the path to save the consolidated rankings file\n", - "save_path = os.path.join(results_dir, 'consolidated_rankings.csv')\n", - "all_rankings_df.to_csv(save_path, index=False)\n", - "\n", - "print(f\"Consolidated rankings saved to: {save_path}\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### This code generates the ranking percentages that are needed to create the heatmaps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Assuming all_rankings_df is correctly prepared and contains 'RBA', 'Feature', 'Rank', 'Dataset'\n", - "\n", - "# Since all datasets have the same N, we can pick the N from any RBA method from total_features_per_rba\n", - "N = next(iter(total_features_per_rba.values()))\n", - "\n", - "# Step 1: Identify the lowest-ranked predictive feature for each dataset for each RBA\n", - "lowest_ranks = all_rankings_df.groupby(['RBA', 'Dataset'])['Rank'].max().reset_index()\n", - "\n", - "# Initialize a structure to hold the calculated percentages for each RBA method\n", - "percentages = {rba: [0] * N for rba in lowest_ranks['RBA'].unique()}\n", - "\n", - "# Step 2: Calculate percentages for each position for each RBA\n", - "for rba in percentages.keys():\n", - " rba_data = lowest_ranks[lowest_ranks['RBA'] == rba]\n", - " for position in range(1, N + 1):\n", - " # Count how many of the lowest ranks are better (lower number) than the current position\n", - " count_higher = rba_data[rba_data['Rank'] <= (position)].shape[0]\n", - " total_datasets = rba_data.shape[0] # Should be 30 per RBA if there are 30 datasets\n", - " percentages[rba][position - 1] = (count_higher / total_datasets) * 100\n", - "\n", - "# Convert the percentages to a DataFrame for visualization\n", - "percentages_df = pd.DataFrame(percentages, index=range(1, N + 1))\n", - "\n", - "# Save percentages_df to a CSV file\n", - "save_path_percentages = os.path.join(results_dir, 'percentages_df.csv')\n", - "percentages_df.to_csv(save_path_percentages, index_label='Ranking Position')\n", - "\n", - "print(\"Percentages saved to percentages_df.csv.\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting code for heatmaps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create custom color scheme\n", - "custom_cmap = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:]\n", - "\n", - "# Define your preferred order of the RBAs as a list\n", - "rba_order = [\n", - " 'RandomShuffle',\n", - " 'MutualInformation',\n", - " 'ReliefF10',\n", - " 'ReliefF',\n", - " 'MultiSURF',\n", - " 'MultiSURFstar',\n", - " 'ABS_ReliefF10',\n", - " 'ABS_ReliefF',\n", - " 'ABS_MultiSURF',\n", - " 'ABS_MultiSURFstar'\n", - "]\n", - "\n", - "# Define a mapping from your RBA order to new descriptive names\n", - "rba_descriptive_names = {\n", - " 'RandomShuffle': 'Random Shuffle',\n", - " 'MutualInformation': 'Mutual Information',\n", - " 'ReliefF10': 'ReliefF 10NN',\n", - " 'ReliefF': 'ReliefF 100NN',\n", - " 'MultiSURF': 'MultiSURF',\n", - " 'MultiSURFstar': 'MultiSURF*',\n", - " 'ABS_ReliefF10': 'ReliefF 10NN ABS',\n", - " 'ABS_ReliefF': 'ReliefF 100NN ABS',\n", - " 'ABS_MultiSURF': 'MultiSURF ABS',\n", - " 'ABS_MultiSURFstar': 'MultiSURF* ABS'\n", - "}\n", - "\n", - "percentages_df = percentages_df.iloc[1:] # Drop the first row as this will always be 0\n", - "percentages_df_transposed = percentages_df.T # Transpose percentages_df to switch rows and columns for horizontal orientation\n", - "\n", - "# Reorder the DataFrame according to your defined RBA order\n", - "percentages_df_ordered = percentages_df_transposed.loc[rba_order] # The .loc indexer reindexes the DataFrame to the specified order; any missing labels will result in NaN rows\n", - "\n", - "# Define the tick labels as percentages of optimality\n", - "xtick_labels = ['Optimal', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']\n", - "# Generate a list of positions at which to place the x-tick labels, assuming they should be placed at even intervals\n", - "xtick_positions = np.linspace(start=0, stop=percentages_df_transposed.shape[1] - 0.13, num=len(xtick_labels))\n", - "\n", - "# Create the heatmap with the reordered DataFrame\n", - "plt.figure(figsize=(12, 7)) # Adjust the size as necessary\n", - "heatmap = sns.heatmap(percentages_df_ordered, annot=False, fmt=\".1f\", cmap=custom_cmap, cbar_kws={'label': 'Power (Frequency of Success)'})\n", - "\n", - "# Add horizontal lines manually between Methods\n", - "for i in range(percentages_df_ordered.shape[0] - 1):\n", - " heatmap.axhline(i + 1, color='black', linewidth=1.5)\n", - "\n", - "# Adding black border around the heatmap\n", - "for _, spine in heatmap.spines.items():\n", - " spine.set_visible(True)\n", - " spine.set_linewidth(1.5)\n", - " spine.set_edgecolor(\"black\")\n", - "\n", - "# Adding black border around the color bar in legend\n", - "cbar = heatmap.collections[0].colorbar\n", - "cbar.outline.set_linewidth(1.5)\n", - "cbar.outline.set_edgecolor(\"black\")\n", - "\n", - "# Set the title and axis labels appropriately\n", - "heatmap.set_title('XOR2_a20_ABS', fontsize = 16)\n", - "heatmap.set_xlabel('Predictive features in top % of ranked features', fontsize = 14)\n", - "heatmap.set_ylabel('Method', fontsize = 14)\n", - "\n", - "# Set the custom x-tick labels, positions, and fontsize\n", - "heatmap.set_xticks(xtick_positions)\n", - "heatmap.set_xticklabels(xtick_labels, rotation=0, fontsize=11)\n", - "\n", - "# Set the custom y-tick labels fontsize\n", - "new_ytick_labels = [rba_descriptive_names[rba] for rba in rba_order]\n", - "heatmap.set_yticklabels(new_ytick_labels, fontsize=11)\n", - "\n", - "# Save and show the Plot\n", - "plt.rcParams['font.sans-serif'] = 'Helvetica'\n", - "plt.rcParams['font.family'] = 'sans-serif'\n", - "plt.rcParams['pdf.fonttype'] = 42\n", - "plt.tight_layout() # Adjust the layout\n", - "save_path = os.path.join(results_dir, 'XOR2_a20_ABS.pdf') # Save\n", - "plt.savefig(save_path, format='pdf', bbox_inches='tight') # Save\n", - "plt.show() # Display the heatmap" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/analysis/higher_order_interactions/MScoring.ipynb b/analysis/higher_order_interactions/MScoring.ipynb deleted file mode 100644 index eea5b4f..0000000 --- a/analysis/higher_order_interactions/MScoring.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Function to process results directory and return a pivot table with feature importance\n", - "def process_results_directory(root_dir, meaningful_parts):\n", - " all_data = [] # List to store all data\n", - " \n", - " # Walk through the directory tree\n", - " for subdir, dirs, files in os.walk(root_dir):\n", - " # Check if subdir contains 'ABS' or 'MutualInformation'\n", - " if 'ABS' in subdir or 'MutualInformation' in subdir:\n", - " if subdir != root_dir: # Ensure not processing the root directory\n", - " method = subdir.split(os.sep)[-1].replace('ABS_', '') # Extract method name from subdir\n", - " subdir_data = [] # List to store data for current subdir\n", - " for file in files:\n", - " if file.endswith('.txt'): # Process only .txt files\n", - " file_path = os.path.join(subdir, file) # Construct file path\n", - " try:\n", - " data = pd.read_csv(file_path, delimiter='\\t') # Read data from file\n", - " m_data = data[data['Feature'].str.startswith('M')] # Filter data for features starting with 'M'\n", - " importance_cols = [col for col in data.columns if \"Feature_Importance\" in col] # Find importance columns\n", - "\n", - " # Iterate over filtered data and importance columns\n", - " for index, row in m_data.iterrows():\n", - " for col in importance_cols:\n", - " importance_type = col.replace('Feature_Importance', '').strip() # Extract importance type\n", - " method_column_name = f\"{method}_{importance_type}\".rstrip('_') # Create method column name\n", - " entry = {\n", - " 'Feature': row['Feature'], # Feature name\n", - " 'Method': method_column_name, # Method and importance type\n", - " 'Importance': row[col], # Importance value\n", - " **meaningful_parts # Add meaningful parts\n", - " }\n", - " subdir_data.append(entry) # Add entry to subdir_data\n", - " except Exception as e:\n", - " print(f\"Error reading {file_path}: {e}\") # Print error if reading fails\n", - "\n", - " if subdir_data:\n", - " all_data.extend(subdir_data) # Add subdir data to all_data\n", - "\n", - " if all_data:\n", - " results_df = pd.DataFrame(all_data) # Create DataFrame from all_data\n", - " pivot_df = results_df.pivot_table(index=['Feature', *meaningful_parts.keys()], columns='Method', values='Importance', aggfunc='mean').reset_index()\n", - " # Get the current non-Method columns\n", - " non_method_cols = ['Feature', *meaningful_parts.keys()]\n", - " # Get the current Method columns and sort them as required\n", - " method_cols = [col for col in pivot_df.columns if col not in non_method_cols]\n", - " sorted_method_cols = ['ReliefF10', 'ReliefF10_ABS', 'ReliefF', 'ReliefF_ABS', 'MultiSURF', 'MultiSURF_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'MutualInformation']\n", - " # Combine the non-Method columns with the sorted Method columns\n", - " pivot_df = pivot_df[non_method_cols + sorted_method_cols]\n", - " return pivot_df # Return the pivot table\n", - " else:\n", - " return pd.DataFrame() # Return an empty DataFrame if no data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def extract_numeric_parts(s):\n", - " \"\"\"Extract numeric parts from a string and return as a tuple of integers.\"\"\"\n", - " # Use regular expression to find all sequences of digits in the string\n", - " # Convert each sequence of digits to an integer and return them as a tuple\n", - " return tuple(map(int, re.findall(r'\\d+', s)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def find_and_process_results(start_dir):\n", - " master_df = pd.DataFrame() # Initialize an empty DataFrame to store the master results\n", - " for root, dirs, files in os.walk(start_dir): # Walk through the directory tree\n", - " if 'Results' in dirs: # Check if 'Results' directory exists in the current directory\n", - " path_parts = os.path.relpath(root, start_dir).split(os.sep) # Get the relative path parts\n", - " meaningful_parts = {} # Dictionary to store meaningful parts\n", - " for part in path_parts: # Iterate over path parts - will need to update this for new descriptors if needed\n", - " if 'xor_' in part:\n", - " meaningful_parts['X1'] = part # Order of epistasis with additional descriptors\n", - " elif 'a_' in part:\n", - " meaningful_parts['X2'] = part # Feature count\n", - "\n", - " results_dir = os.path.join(root, 'Results') # Construct the path to the 'Results' directory\n", - " print(f\"Processing 'Results' folder at: {results_dir}\")\n", - " results_df = process_results_directory(results_dir, meaningful_parts) # Process the results directory\n", - " if not results_df.empty: # If the results DataFrame is not empty\n", - " csv_path = os.path.join(results_dir, 'M_average_feature_importance.csv') # Path to save the results CSV\n", - " results_df.to_csv(csv_path, index=False) # Save the results DataFrame to a CSV file\n", - " print(f\"Average feature importance results saved to {csv_path}\")\n", - "\n", - " # Create a numeric tuple for sorting X1\n", - " results_df['X1_numeric'] = results_df['X1'].apply(extract_numeric_parts)\n", - " \n", - " # Append to the master DataFrame\n", - " master_df = pd.concat([master_df, results_df], ignore_index=True)\n", - "\n", - " # Sort the master DataFrame\n", - " if not master_df.empty: # If the master DataFrame is not empty\n", - " master_df = master_df.sort_values(by=['X1_numeric', 'Feature']) # Sort by numeric parts of X1 and Feature\n", - " master_df.drop('X1_numeric', axis=1, inplace=True) # Remove the auxiliary column after sorting\n", - " master_csv_path = os.path.join(start_dir, 'M_master_feature_importance.csv') # Path to save the master CSV\n", - " master_df.to_csv(master_csv_path, index=False) # Save the master DataFrame to a CSV file\n", - " print(f\"Master feature importance results saved to {master_csv_path}\") # Print confirmation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Start the process from the current working directory\n", - "find_and_process_results('.')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/analysis/higher_order_interactions/MasterRunScript.ipynb b/analysis/higher_order_interactions/MasterRunScript.ipynb deleted file mode 100644 index d6a8233..0000000 --- a/analysis/higher_order_interactions/MasterRunScript.ipynb +++ /dev/null @@ -1,557 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import necessary packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import fnmatch\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.pipeline import make_pipeline\n", - "from skrebate import ReliefF # for reproducibility, need to install skrebate version 0.62\n", - "from skrebate import SURF\n", - "from skrebate import SURFstar\n", - "from skrebate import MultiSURF\n", - "from skrebate import MultiSURFstar\n", - "from skrebate import TuRF\n", - "from sklearn.feature_selection import RFE\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.model_selection import cross_val_score\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.feature_selection import mutual_info_classif" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define function for directory maintenance" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def ensure_dir(directory): # Check if directories exist. If they do not, they will be created in the next function\n", - " if not os.path.exists(directory):\n", - " os.makedirs(directory)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define functions for random shuffle" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def random_shuffle(file_path):\n", - " # Define the directory to store results\n", - " results_dir = os.path.join(os.path.dirname(file_path), \"Results\", \"RandomShuffle\")\n", - " ensure_dir(results_dir) # Ensure the Results/RandomShuffle directory exists\n", - "\n", - " # Read the .txt file into a DataFrame\n", - " try:\n", - " df = pd.read_csv(file_path, sep='\\t') # Assuming tab-separated values; adjust the separator as needed\n", - " except Exception as e:\n", - " print(f\"Error reading {file_path}: {e}\")\n", - " return\n", - "\n", - " # Ensure 'Class' column is not included in the shuffle\n", - " if 'Class' in df.columns:\n", - " columns_to_shuffle = df.drop('Class', axis=1).columns.tolist()\n", - " else:\n", - " columns_to_shuffle = df.columns.tolist()\n", - "\n", - " # Shuffle the column names\n", - " shuffled_columns = np.random.permutation(columns_to_shuffle)\n", - "\n", - " # Create a new DataFrame with the shuffled column names\n", - " new_df = pd.DataFrame(shuffled_columns, columns=['Feature'])\n", - "\n", - " # Construct the output file name based on the original file's name\n", - " base_name = os.path.basename(file_path)\n", - " new_file_name = f\"{os.path.splitext(base_name)[0]}_RandShuffle.txt\"\n", - " output_path = os.path.join(results_dir, new_file_name)\n", - "\n", - " # Write the new DataFrame to disk\n", - " new_df.to_csv(output_path, index=False, sep='\\t')\n", - "\n", - "def find_and_random_shuffle(root_dir):\n", - " for dirpath, dirnames, filenames in os.walk(root_dir):\n", - " # Check if 'EDM' is in the directory name directly above the files\n", - " if \"EDM\" in os.path.basename(dirpath):\n", - " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", - " for filename in filenames:\n", - " if filename.endswith('.txt'): # Ensure it's a .txt file\n", - " file_path = os.path.join(dirpath, filename)\n", - " #print(f\"Processing file: {file_path}\") # Debugging line\n", - " random_shuffle(file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define current working directory (GAMETES_2.2_dev_peter_XOR)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "root_dir = 'my_dir' # Change this to your parent directory path" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run random shuffle process on directory" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Run through the directory and produce random assortments of the features. This is the Random Shuffle that will be presented in the paper\n", - "find_and_random_shuffle(root_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define functions for Mutual Information" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def process_mutual_info(file_path):\n", - " # Load the genetic data\n", - " genetic_data = pd.read_csv(file_path, sep='\\t') # Ensure correct delimiter is used\n", - " \n", - " # Split the data\n", - " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", - " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", - " \n", - " # Apply Mutual Information\n", - " mi_scores = mutual_info_classif(X_train, y_train)\n", - " \n", - " # Pair feature names with MI scores\n", - " temp_list = []\n", - " for feature_name, mi_score in zip(genetic_data.drop('Class', axis=1).columns, mi_scores):\n", - " temp_list.append([feature_name, mi_score])\n", - " \n", - " # Process results\n", - " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", - " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", - " \n", - " # Define directories\n", - " base_dir = os.path.dirname(file_path)\n", - " results_dir = os.path.join(base_dir, \"Results\")\n", - " mi_dir = os.path.join(results_dir, \"MutualInformation\")\n", - " ensure_dir(mi_dir)\n", - " \n", - " # Extract base filename without extension\n", - " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", - " \n", - " # Save the files\n", - " Results.to_csv(os.path.join(mi_dir, f\"{base_name}_MIResults.txt\"), index=False, sep='\\t')\n", - "\n", - "def find_and_mutual_info(root_dir):\n", - " for dirpath, dirnames, filenames in os.walk(root_dir):\n", - " # Check if 'EDM' is in the directory name directly above the files\n", - " if \"EDM\" in os.path.basename(dirpath):\n", - " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", - " for filename in filenames:\n", - " if filename.endswith('.txt'): # Ensure it's a .txt file\n", - " file_path = os.path.join(dirpath, filename)\n", - " #print(f\"Processing file: {file_path}\") # Debugging line\n", - " process_mutual_info(file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run Mutual Information on directory" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Run through the directory and perform Mutual Information.\n", - "find_and_mutual_info(root_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define functions for ReliefF with 10 NN" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def process_relieff10(file_path):\n", - " # Load the genetic data\n", - " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", - " \n", - " # Split the data\n", - " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", - " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", - " \n", - " # Apply ReliefF\n", - " fs = ReliefF(n_features_to_select=2, n_neighbors=10)\n", - " fs.fit(X_train, y_train)\n", - " \n", - " temp_list = []\n", - " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", - " temp_list.append([feature_name, feature_score])\n", - " \n", - " # Process results\n", - " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", - " ABSResults = Results.copy()\n", - " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", - " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", - " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", - " \n", - " # Define directories\n", - " base_dir = os.path.dirname(file_path)\n", - " results_dir = os.path.join(base_dir, \"Results\")\n", - " relief_dir = os.path.join(results_dir, \"ReliefF10\")\n", - " abs_relief_dir = os.path.join(results_dir, \"ABS_ReliefF10\")\n", - " ensure_dir(relief_dir)\n", - " ensure_dir(abs_relief_dir)\n", - " \n", - " # Extract base filename without extension\n", - " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", - " \n", - " # Save the files\n", - " Results.to_csv(os.path.join(relief_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", - " ABSResults.to_csv(os.path.join(abs_relief_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", - "\n", - "def find_and_relieff10(root_dir):\n", - " for dirpath, dirnames, filenames in os.walk(root_dir):\n", - " # Check if 'EDM' is in the directory name directly above the files\n", - " if \"EDM\" in os.path.basename(dirpath):\n", - " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", - " for filename in filenames:\n", - " if filename.endswith('.txt'): # Ensure it's a .txt file\n", - " file_path = os.path.join(dirpath, filename)\n", - " #print(f\"Processing file: {file_path}\") # Debugging line\n", - " process_relieff10(file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run ReliefF (10 NN) on directory" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "find_and_relieff10(root_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define functions for ReliefF with 100 NN" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def process_relieff(file_path):\n", - " # Load the genetic data\n", - " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", - " \n", - " # Split the data\n", - " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", - " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", - " \n", - " # Apply ReliefF\n", - " fs = ReliefF(n_features_to_select=2, n_neighbors=100)\n", - " fs.fit(X_train, y_train)\n", - " \n", - " temp_list = []\n", - " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", - " temp_list.append([feature_name, feature_score])\n", - " \n", - " # Process results\n", - " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", - " ABSResults = Results.copy()\n", - " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", - " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", - " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", - " \n", - " # Define directories\n", - " base_dir = os.path.dirname(file_path)\n", - " results_dir = os.path.join(base_dir, \"Results\")\n", - " relief_dir = os.path.join(results_dir, \"ReliefF\")\n", - " abs_relief_dir = os.path.join(results_dir, \"ABS_ReliefF\")\n", - " ensure_dir(relief_dir)\n", - " ensure_dir(abs_relief_dir)\n", - " \n", - " # Extract base filename without extension\n", - " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", - " \n", - " # Save the files\n", - " Results.to_csv(os.path.join(relief_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", - " ABSResults.to_csv(os.path.join(abs_relief_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", - "\n", - "def find_and_relieff(root_dir):\n", - " for dirpath, dirnames, filenames in os.walk(root_dir):\n", - " # Check if 'EDM' is in the directory name directly above the files\n", - " if \"EDM\" in os.path.basename(dirpath):\n", - " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", - " for filename in filenames:\n", - " if filename.endswith('.txt'): # Ensure it's a .txt file\n", - " file_path = os.path.join(dirpath, filename)\n", - " #print(f\"Processing file: {file_path}\") # Debugging line\n", - " process_relieff(file_path)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run ReliefF (100 NN) on directory" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Run through the directory and perform ReliefF 100 NN.\n", - "find_and_relieff(root_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define functions for MultiSURF" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def process_MultiSurf(file_path):\n", - " # Load the genetic data\n", - " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", - " \n", - " # Split the data\n", - " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", - " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", - " \n", - " # Apply MultiSurf\n", - " fs = MultiSURF()\n", - " fs.fit(X_train, y_train)\n", - " \n", - " temp_list = []\n", - " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", - " temp_list.append([feature_name, feature_score])\n", - " \n", - " # Process results\n", - " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", - " ABSResults = Results.copy()\n", - " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", - " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", - " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", - " \n", - " # Define directories\n", - " base_dir = os.path.dirname(file_path)\n", - " results_dir = os.path.join(base_dir, \"Results\")\n", - " multisurf_dir = os.path.join(results_dir, \"MultiSURF\")\n", - " abs_multisurf_dir = os.path.join(results_dir, \"ABS_MultiSURF\")\n", - " ensure_dir(multisurf_dir)\n", - " ensure_dir(abs_multisurf_dir)\n", - " \n", - " # Extract base filename without extension\n", - " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", - " \n", - " # Save the files\n", - " Results.to_csv(os.path.join(multisurf_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", - " ABSResults.to_csv(os.path.join(abs_multisurf_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", - "\n", - "def find_and_MultiSurf(root_dir):\n", - " for dirpath, dirnames, filenames in os.walk(root_dir):\n", - " # Check if 'EDM' is in the directory name directly above the files\n", - " if \"EDM\" in os.path.basename(dirpath):\n", - " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", - " for filename in filenames:\n", - " if filename.endswith('.txt'): # Ensure it's a .txt file\n", - " file_path = os.path.join(dirpath, filename)\n", - " #print(f\"Processing file: {file_path}\") # Debugging line\n", - " process_MultiSurf(file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run MultiSURF on directory" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Run through the directory and perform MultiSURF.\n", - "find_and_MultiSurf(root_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define functions for MultiSURFstar" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def process_MultiSurfstar(file_path):\n", - " # Load the genetic data\n", - " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", - " \n", - " # Split the data\n", - " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", - " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", - " \n", - " # Apply MultiSurf\n", - " fs = MultiSURFstar()\n", - " fs.fit(X_train, y_train)\n", - " \n", - " temp_list = []\n", - " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", - " temp_list.append([feature_name, feature_score])\n", - " \n", - " # Process results\n", - " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", - " ABSResults = Results.copy()\n", - " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", - " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", - " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", - " \n", - " # Define directories\n", - " base_dir = os.path.dirname(file_path)\n", - " results_dir = os.path.join(base_dir, \"Results\")\n", - " multisurfstar_dir = os.path.join(results_dir, \"MultiSURFstar\")\n", - " abs_multisurfstar_dir = os.path.join(results_dir, \"ABS_MultiSURFstar\")\n", - " ensure_dir(multisurfstar_dir)\n", - " ensure_dir(abs_multisurfstar_dir)\n", - " \n", - " # Extract base filename without extension\n", - " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", - " \n", - " # Save the files\n", - " Results.to_csv(os.path.join(multisurfstar_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", - " ABSResults.to_csv(os.path.join(abs_multisurfstar_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", - "\n", - "def find_and_MultiSurfstar(root_dir):\n", - " for dirpath, dirnames, filenames in os.walk(root_dir):\n", - " # Check if 'EDM' is in the directory name directly above the files\n", - " if \"EDM\" in os.path.basename(dirpath):\n", - " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", - " for filename in filenames:\n", - " if filename.endswith('.txt'): # Ensure it's a .txt file\n", - " file_path = os.path.join(dirpath, filename)\n", - " #print(f\"Processing file: {file_path}\") # Debugging line\n", - " process_MultiSurfstar(file_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run MultiSURFstar on directory" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Run through the first directory and perform MultiSURFstar.\n", - "find_and_MultiSurfstar(root_dir)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/analysis/higher_order_interactions/Nscoring.ipynb b/analysis/higher_order_interactions/Nscoring.ipynb deleted file mode 100644 index fac3744..0000000 --- a/analysis/higher_order_interactions/Nscoring.ipynb +++ /dev/null @@ -1,153 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import re\n", - "\n", - "# Function to process results directory and return a pivot table with feature importance\n", - "def process_results_directory(root_dir, meaningful_parts):\n", - " all_data = [] # List to store all data\n", - " \n", - " # Walk through the directory tree\n", - " for subdir, dirs, files in os.walk(root_dir):\n", - " # Check if subdir contains 'ABS' or 'MutualInformation'\n", - " if 'ABS' in subdir or 'MutualInformation' in subdir:\n", - " if subdir != root_dir: # Ensure not processing the root directory\n", - " method = subdir.split(os.sep)[-1].replace('ABS_', '') # Extract method name from subdir\n", - " file_averages = [] # List to store average importances for each file\n", - " for file in files:\n", - " if file.endswith('.txt'): # Process only .txt files\n", - " file_path = os.path.join(subdir, file) # Construct file path\n", - " try:\n", - " data = pd.read_csv(file_path, delimiter='\\t') # Read data from file\n", - " n_data = data[data['Feature'].str.startswith('N')] # Filter data for features starting with 'N'\n", - " importance_cols = [col for col in data.columns if \"Feature_Importance\" in col] # Find importance columns\n", - "\n", - " # Check if there are 'N' features and importance columns\n", - " if not n_data.empty and importance_cols:\n", - " # Calculate average importance for 'N' features within the current file\n", - " for col in importance_cols:\n", - " avg_importance = n_data[col].mean() # Calculate average importance\n", - " importance_type = col.replace('Feature_Importance', '').strip() # Extract importance type\n", - " method_column_name = f\"{method}_{importance_type}\".rstrip('_') # Create method column name\n", - " \n", - " entry = {\n", - " 'Feature': 'average_N', # Use 'average_N' as the feature name\n", - " 'Method': method_column_name, # Method and importance type\n", - " 'Importance': avg_importance, # Average importance value within the file\n", - " **meaningful_parts # Add meaningful parts\n", - " }\n", - " file_averages.append(entry) # Add entry to file_averages\n", - " else:\n", - " print(f\"No 'N' features or importance columns in {file_path}\")\n", - "\n", - " except Exception as e:\n", - " print(f\"Error reading {file_path}: {e}\") # Print error if reading fails\n", - "\n", - " if file_averages:\n", - " # Aggregate the file-level averages for the current method\n", - " df_file_averages = pd.DataFrame(file_averages)\n", - " method_average = df_file_averages.groupby(['Feature', 'Method', *meaningful_parts.keys()]).mean().reset_index()\n", - " all_data.extend(method_average.to_dict('records')) # Add aggregated data to all_data\n", - " else:\n", - " print(f\"No valid data found in {subdir}\")\n", - "\n", - " if all_data:\n", - " results_df = pd.DataFrame(all_data) # Create DataFrame from all_data\n", - " pivot_df = results_df.pivot_table(index=['Feature', *meaningful_parts.keys()], columns='Method', values='Importance', aggfunc='mean').reset_index()\n", - " # Get the current non-Method columns\n", - " non_method_cols = ['Feature', *meaningful_parts.keys()]\n", - " # Get the current Method columns and sort them as required\n", - " method_cols = [col for col in pivot_df.columns if col not in non_method_cols]\n", - " sorted_method_cols = ['ReliefF10', 'ReliefF10_ABS', 'ReliefF', 'ReliefF_ABS', 'MultiSURF', 'MultiSURF_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'MutualInformation']\n", - " # Combine the non-Method columns with the sorted Method columns\n", - " pivot_df = pivot_df[non_method_cols + sorted_method_cols]\n", - " return pivot_df # Return the pivot table\n", - " else:\n", - " print(\"No data collected from any files\")\n", - " return pd.DataFrame() # Return an empty DataFrame if no data\n", - "\n", - "def extract_numeric_parts(s):\n", - " \"\"\"Extract numeric parts from a string and return as a tuple of integers.\"\"\"\n", - " return tuple(map(int, re.findall(r'\\d+', s)))\n", - "\n", - "def find_and_process_results(start_dir):\n", - " master_df = pd.DataFrame() # Initialize an empty DataFrame to store the master results\n", - " for root, dirs, files in os.walk(start_dir): # Walk through the directory tree\n", - " if 'Results' in dirs: # Check if 'Results' directory exists in the current directory\n", - " path_parts = os.path.relpath(root, start_dir).split(os.sep) # Get the relative path parts\n", - " meaningful_parts = {} # Dictionary to store meaningful parts\n", - " for part in path_parts: # Iterate over path parts - will need to update this for new descriptors if needed\n", - " if 'xor_' in part:\n", - " meaningful_parts['X1'] = part # Order of epistasis with additional descriptors\n", - " elif 'a_' in part:\n", - " meaningful_parts['X2'] = part # Feature count\n", - "\n", - " results_dir = os.path.join(root, 'Results') # Construct the path to the 'Results' directory\n", - " print(f\"Processing 'Results' folder at: {results_dir}\")\n", - " results_df = process_results_directory(results_dir, meaningful_parts) # Process the results directory\n", - " if not results_df.empty: # If the results DataFrame is not empty\n", - " csv_path = os.path.join(results_dir, 'N_average_feature_importance.csv') # Path to save the results CSV\n", - " results_df.to_csv(csv_path, index=False) # Save the results DataFrame to a CSV file\n", - " print(f\"Average feature importance results saved to {csv_path}\")\n", - "\n", - " # Create a numeric tuple for sorting X1\n", - " results_df['X1_numeric'] = results_df['X1'].apply(extract_numeric_parts)\n", - " \n", - " # Append to the master DataFrame\n", - " master_df = pd.concat([master_df, results_df], ignore_index=True)\n", - " else:\n", - " print(f\"No data found in 'Results' folder at: {results_dir}\")\n", - "\n", - " # Sort the master DataFrame\n", - " if not master_df.empty: # If the master DataFrame is not empty\n", - " master_df = master_df.sort_values(by=['X1_numeric', 'Feature']) # Sort by numeric parts of X1 and Feature\n", - " master_df.drop('X1_numeric', axis=1, inplace=True) # Remove the auxiliary column after sorting\n", - "\n", - " # Calculate grand average for all numeric columns and append it as the last row\n", - " numeric_cols = master_df.select_dtypes(include='number').columns\n", - " grand_average = master_df[numeric_cols].mean().to_frame().T\n", - " for col in master_df.columns:\n", - " if col not in numeric_cols:\n", - " grand_average[col] = 'Grand_Average'\n", - " master_df = pd.concat([master_df, grand_average], ignore_index=True)\n", - "\n", - " master_csv_path = os.path.join(start_dir, 'N_master_feature_importance.csv') # Path to save the master CSV\n", - " master_df.to_csv(master_csv_path, index=False) # Save the master DataFrame to a CSV file\n", - " print(f\"Master feature importance results saved to {master_csv_path}\") # Print confirmation\n", - " else:\n", - " print(\"No master data to save\")\n", - "\n", - "# Start the process from the current working directory\n", - "find_and_process_results('.')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "base", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/analysis/higher_order_interactions/README.txt b/analysis/higher_order_interactions/README.txt deleted file mode 100644 index 7de7760..0000000 --- a/analysis/higher_order_interactions/README.txt +++ /dev/null @@ -1,21 +0,0 @@ -Assessing the Limitations of Relief-Based Algorithms in Detecting Higher-Order Interactions -Freda et al. 2024 - -README: Script descriptions - -MasterRunScript.ipynb: This notebook will run Random Shuffle, Mutual Information, and all RBAs in a designated user directory. -It stores all results, including both standard and absolute rankings for RBAs, in a new directory called 'Results'. -Results will be stored within subdirectories entitled using the names of each method. -This can be run globally but we suggest to run this script within each dataset group (patten of association - See Table 1 in the paper for further information). - -Heatmaps.ipynb: This notebook consolidates all rankings, calculates the percentages of best rankings, and constructs heatmaps for visualization. -This script will need to be run for each dataset configuration within each dataset group (patten of association). - -MScoring.ipynb: This notebook calculates the average feature importances, derived from RBA methods and Mutual Information, for each predictive feature per experiment. -It also produces a master sheet that consolidates all average feature importances for predictive features across all configurations within a dataset group (pattern of association) (e.g., XOR datasets). - -NScoring.ipynb: This notebook does the same as above but for non-predictive features. -Instead of outputting the average scores per predictive feature, this script averages across all non-predictive features within and across experiments. -As with Mscoring.ipynb, this will need to be run for each dataset group (pattern of association) - -Data_feature_set_extender.ipynb: This notebook adds non-predictive features to existing datasets (i.e. from 20 to 100 features) for this study using datasets taken from the original Relief benchmarking paper (Urbanowicz 2018). diff --git a/analysis/job_global_rba_rankings.py b/analysis/job_global_rba_rankings.py new file mode 100644 index 0000000..900c1a8 --- /dev/null +++ b/analysis/job_global_rba_rankings.py @@ -0,0 +1,281 @@ +# job_global_rba_rankings.py +import os +import argparse +import pandas as pd + +def collect_rankings(root_dir, include_subdirs=None): + """ + Collects all rankings_list.csv files from subdirectories under root_dir. + Optionally filter which subdirectories to include. + """ + all_dfs = [] + + for sub in os.listdir(root_dir): + sub_path = os.path.join(root_dir, sub) + if not os.path.isdir(sub_path): + continue + + # If --include is specified, skip subdirs not in the list + if include_subdirs and sub not in include_subdirs: + continue + + if sub == "GAMETES_2.2_dev_peter_XOR": # for XOR, will only use the xor-2 and xor-3 configurations (excluding 4 and 5-way) + # get path to xor-2 and xor-3 rankings_list.csv only (rankings_path1 & rankings_path2), turn it into df1 and df2, then combine them into df + rankings_path_xor2 = os.path.join(sub_path, "xor_2", "a_100", "s_1600", "xor_2_a_100s_1600_EDM-1", "Results", "rankings_list.csv") + rankings_path_xor3 = os.path.join(sub_path, "xor_3", "a_100", "s_1600", "xor_3_a_100s_1600_EDM-1", "Results", "rankings_list.csv") + + if not os.path.exists(rankings_path_xor2) or not os.path.exists(rankings_path_xor3): + print(f"[WARN] rankings_list.csv not found in {sub_path} for xor-2 or xor-3, skipping.") + continue + + try: + df_xor2 = pd.read_csv(rankings_path_xor2, comment='#') # ignore comment line with title + df_xor2['subdir'] = "xor_2" + + df_xor3 = pd.read_csv(rankings_path_xor3, comment='#') # ignore comment line with title + df_xor3['subdir'] = "xor_3" + + df = pd.concat([df_xor2, df_xor3], ignore_index=True) # combining xor-2 and xor-3 into one df + except Exception as e: + print(f"[ERROR] Could not read {rankings_path_xor2} or {rankings_path_xor3}: {e}") + continue + else: + rankings_path = os.path.join(sub_path, 'rankings_list.csv') + + if not os.path.exists(rankings_path): + print(f"[WARN] rankings_list.csv not found in {sub_path}, skipping.") + continue + + try: + df = pd.read_csv(rankings_path, comment='#') # ignore comment line with title + df['subdir'] = sub + except Exception as e: + print(f"[ERROR] Could not read {rankings_path}: {e}") + continue + + if "a_1000/" in rankings_path or "1-feature_1000_" in rankings_path: + df['Rank'] = 1 + (df['Rank'] - 1) * 99 / 999 + elif "a_10000/" in rankings_path or "1-feature_10000_" in rankings_path: + df['Rank'] = 1 + (df['Rank'] - 1) * 99 / 9999 + elif "a_20000/" in rankings_path: + df['Rank'] = 1 + (df['Rank'] - 1) * 99 / 19999 + elif "a_50000/" in rankings_path: + df['Rank'] = 1 + (df['Rank'] - 1) * 99 / 49999 + elif "a_100000/" in rankings_path or "1-feature_100000_" in rankings_path: + df['Rank'] = 1 + (df['Rank'] - 1) * 99 / 99999 + + all_dfs.append(df) + print(f"[INFO] Loaded rankings_list.csv from {sub}") + + if not all_dfs: + print("[ERROR] No valid rankings_list.csv files found.") + return None + + combined_df = pd.concat(all_dfs, ignore_index=True) + return combined_df + +def compute_global_summary(all_rankings_df): + """ + Computes global mean and median rank per RBA. + """ + summary = ( + all_rankings_df.groupby('RBA')['Rank'] + .agg(['mean', 'median']) + .reset_index() + .rename(columns={'mean': 'Mean', 'median': 'Median'}) + ) + summary.sort_values(by=['Mean', 'Median'], inplace=True) + return summary + + +def main(): + parser = argparse.ArgumentParser(description="Generate global rankings from multiple subdirectories.") + parser.add_argument("root_dir", help="Top-level directory containing dataset subdirectories.") + parser.add_argument("--include", nargs="+", default=None, + help="Optional list of subdirectories to include (default: all).") + + args = parser.parse_args() + root_dir = args.root_dir + + # short names -> actual subdirectory names + SHORT_TO_FULL_SUBDIR = { + "maineff": "GAMETES_2.2_dev_peter_mainEff_Datasets_Loc_1_Qnt_2_Pop_100000", + "core2wayepistasis": "GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000", + "xor": "GAMETES_2.2_dev_peter_XOR", + "maineff_2": "GAMETES_2.2_dev_peter_mainEff_additive_2_Datasets_2Het_Loc_1_Qnt_2_Pop_100000", + "maineff_4": "GAMETES_2.2_dev_peter_mainEff_additive_4_Datasets_2Het_Loc_1_Qnt_2_Pop_100000", + "2wayepi": "GAMETES_2.2_dev_peter_2wayEpiHeterogeneity_Datasets_2Het_Loc_2_Qnt_2_Pop_100000", + "epi_order": "GAMETES_2.2_dev_peter_epi_order_Datasets_Loc_3_Qnt_2_Pop_100000", + } + + user_include = args.include + + if user_include: + # map the user-specified short names to actual subdirectory names + include_subdirs = [] + for short_name in user_include: + # full_name = SHORT_TO_FULL_SUBDIR.get(short_name) + full_name = SHORT_TO_FULL_SUBDIR.get(short_name.lower()) + if full_name: + include_subdirs.append(full_name) + else: + print(f"[WARN] No mapping found for '{short_name}', skipping.") + else: + include_subdirs = None # default: include all subdirectories + + print(f"[INFO] Subdirectories to include: {include_subdirs}") + + # NEW: going back up to data directory from AbsVal_Benchmark_Data to then enter Sanity_Check_Data + parent_dir = os.path.dirname(root_dir) + largerfeature_2way_dir = os.path.join( + parent_dir, + "Sanity_Check_Data", + "benchmark-data", + "Simulated_Benchmark_Archive", + "GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000" + ) + include_subdirs_largerfeature_2way = ["a_1000", "a_10000", "a_20000", "a_50000", "a_100000"] + + # now adding larger feature set (main effect) directory + largerfeature_mainEff_dir = os.path.join( + parent_dir, + "mainEff_largerfeatures_data" + ) + + combined_df = collect_rankings(root_dir, include_subdirs) + if combined_df is None: + return + + # getting rankings from a_1000, a_10000, a_20000, a_50000, a_100000 (subdirs of largerfeature_2way_dir) + largerfeature_2way_df = collect_rankings(largerfeature_2way_dir, include_subdirs_largerfeature_2way) + + largerfeature_mainEff_df = collect_rankings(largerfeature_mainEff_dir) # rankings for larger feature mainEff data + + # all rankings from all tested datasets with >= 100 features + final_combined_df = pd.concat([combined_df, largerfeature_2way_df, largerfeature_mainEff_df], ignore_index=True) + + # all univariate effect data + univariate_df = final_combined_df[ + final_combined_df['subdir'].isin(["GAMETES_2.2_dev_peter_mainEff_Datasets_Loc_1_Qnt_2_Pop_100000", # 1-Feature Main Effect + "GAMETES_2.2_dev_peter_mainEff_additive_2_Datasets_2Het_Loc_1_Qnt_2_Pop_100000", # 2-Feature Additive Effect + "GAMETES_2.2_dev_peter_mainEff_additive_4_Datasets_2Het_Loc_1_Qnt_2_Pop_100000", # 4-Feature Additive Effect + "1-feature_1000_Feature_H_0.4_MAF_0.2_EDM-2", # 1,000 total features, main effect + "1-feature_10000_Feature_H_0.4_MAF_0.2_EDM-2", # 10,000 total features, main effect + "1-feature_100000_Feature_H_0.4_MAF_0.2_EDM-2" # 100,000 total features, main effect + ]) + ].reset_index(drop=True) + + # all 2-way interaction data + twoway_df = final_combined_df[ + final_combined_df['subdir'].isin(["GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000", # 2-way Pure Epistasis + "xor_2", # 2-way XOR + "GAMETES_2.2_dev_peter_2wayEpiHeterogeneity_Datasets_2Het_Loc_2_Qnt_2_Pop_100000", # 2-way Epi Heterogeneity + "a_1000", # 1,000 total features, 2-way interaction effect + "a_10000", # 10,000 total features, 2-way interaction effect + "a_20000", # 20,000 total features, 2-way interaction effect + "a_50000", # 50,000 total features, 2-way interaction effect + "a_100000" # 100,000 total features, 2-way interaction effect + ]) + ].reset_index(drop=True) + + # all 3-way interaction data + threeway_df = final_combined_df[ + final_combined_df['subdir'].isin(["GAMETES_2.2_dev_peter_epi_order_Datasets_Loc_3_Qnt_2_Pop_100000", # 3-way Pure Epistasis + "xor_3" # 3-way XOR + ]) + ].reset_index(drop=True) + + # mean and median metrics for each algorithm within each effect type (univariate, 2-way, 3-way) + univariate_summary_df = compute_global_summary(univariate_df) + twoway_summary_df = compute_global_summary(twoway_df) + threeway_summary_df = compute_global_summary(threeway_df) + + # *** Creating global metrics: 2-way only, univariate + 2-way, univariate + 2-way + 3-way + + # creating a combined dataframe with univariate and 2-way metrics + merged_uni_twoway_df = univariate_summary_df.merge( + twoway_summary_df, + on='RBA', + suffixes=('_uni', '_two') + ) + + # creating the final weighted (univariate, 2-way) global metrics + univariate_twoway_final_df = pd.DataFrame({ + 'RBA': merged_uni_twoway_df['RBA'], + 'Avg_Mean': ( + (merged_uni_twoway_df['Mean_uni'] + merged_uni_twoway_df['Mean_two']) / 2 + ), + 'Avg_Median': ( + (merged_uni_twoway_df['Median_uni'] + merged_uni_twoway_df['Median_two']) / 2 + ) + }) + + # sorting this dataframe + univariate_twoway_final_df = ( + univariate_twoway_final_df + .sort_values(by=['Avg_Mean', 'Avg_Median'], ascending=[True, True]) + .reset_index(drop=True) + ) + + # creating a combined dataframe with univariate, 2-way, and 3-way metrics + merged_uni_twoway_threeway_df = merged_uni_twoway_df.merge( + threeway_summary_df, + on='RBA' + ) + # for naming clarity + merged_uni_twoway_threeway_df = merged_uni_twoway_threeway_df.rename(columns={ + 'Mean': 'Mean_three', + 'Median': 'Median_three' + }) + + # creating the final weighted (univariate, 2-way, 3-way) global metrics + univariate_twoway_threeway_final_df = pd.DataFrame({ + 'RBA': merged_uni_twoway_threeway_df['RBA'], + 'Avg_Mean': ( + (merged_uni_twoway_threeway_df['Mean_uni'] + + merged_uni_twoway_threeway_df['Mean_two'] + + merged_uni_twoway_threeway_df['Mean_three']) / 3 + ), + 'Avg_Median': ( + (merged_uni_twoway_threeway_df['Median_uni'] + + merged_uni_twoway_threeway_df['Median_two'] + + merged_uni_twoway_threeway_df['Median_three']) / 3 + ) + }) + # univariate_twoway_threeway_final_df = pd.DataFrame({ + # 'RBA': merged_uni_twoway_threeway_df['RBA'], + # 'Weighted_Mean': ( + # merged_uni_twoway_threeway_df['Mean_uni'] * 0.4 + + # merged_uni_twoway_threeway_df['Mean_two'] * 0.4 + + # merged_uni_twoway_threeway_df['Mean_three'] * 0.2 + # ), + # 'Weighted_Median': ( + # merged_uni_twoway_threeway_df['Median_uni'] * 0.4 + + # merged_uni_twoway_threeway_df['Median_two'] * 0.4 + + # merged_uni_twoway_threeway_df['Median_three'] * 0.2 + # ) + # }) + # sorting this dataframe + univariate_twoway_threeway_final_df = ( + univariate_twoway_threeway_final_df + .sort_values(by=['Avg_Mean', 'Avg_Median'], ascending=[True, True]) + .reset_index(drop=True) + ) + + # --- Save final .csv's with metrics for (2-way only), (univariate + 2-way), (univariate + 2-way + 3-way) --- + twoway_path = os.path.join(parent_dir, 'global_rba_rankings_2wayonly.csv') + univariate_twoway_path = os.path.join(parent_dir, 'global_rba_rankings_uni2way.csv') + univariate_twoway_threeway_path = os.path.join(parent_dir, 'global_rba_rankings_uni2way3way.csv') + + twoway_summary_df.to_csv(twoway_path, index=False) + univariate_twoway_final_df.to_csv(univariate_twoway_path, index=False) + univariate_twoway_threeway_final_df.to_csv(univariate_twoway_threeway_path, index=False) + + print(f"[INFO] Saved (2-way only) rankings to {twoway_path}") + print(f"[INFO] Saved (univariate + 2-way) rankings to {univariate_twoway_path}") + print(f"[INFO] Saved (univariate + 2-way + 3-way) rankings to {univariate_twoway_threeway_path}") + + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/job_process_heatmap.py b/analysis/job_process_heatmap.py new file mode 100644 index 0000000..2d8df66 --- /dev/null +++ b/analysis/job_process_heatmap.py @@ -0,0 +1,179 @@ +import os +import sys +import argparse +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +from matplotlib.colors import ListedColormap +import numpy as np + +def process_results_dir(results_dir, prefix=""): + print(f"Processing: {results_dir}") + + all_rankings_df = pd.DataFrame() + total_features_per_rba = {} + + for rba in os.listdir(results_dir): + rba_path = os.path.join(results_dir, rba) + if os.path.isdir(rba_path): + method_feature_counts = [] + for file in os.listdir(rba_path): + if file.endswith('.txt'): + file_path = os.path.join(rba_path, file) + parts = file.split('_') + dataset_id = '_'.join(parts[-2].split('_')[:2]) + + if rba == "RandomShuffle": + df = pd.read_csv(file_path, sep='\t', usecols=['Feature']) + df['Rank'] = df.index + 1 + else: + column_to_use = 'ABS_Feature_Importance' if "ABS" in rba else 'Feature_Importance' + df = pd.read_csv(file_path, sep='\t', usecols=['Feature', column_to_use]) + df.sort_values(by=column_to_use, ascending=False, inplace=True) + df.reset_index(drop=True, inplace=True) + df['Rank'] = df.index + 1 + + predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Rank']] + predictive_df['RBA'] = rba + predictive_df['Dataset'] = dataset_id + + all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True) + method_feature_counts.append(df['Feature'].nunique()) + + total_features_per_rba[rba] = max(method_feature_counts) + + rankings_path = os.path.join(results_dir, 'consolidated_rankings.csv') + all_rankings_df.to_csv(rankings_path, index=False) + + N = next(iter(total_features_per_rba.values())) + lowest_ranks = all_rankings_df.groupby(['RBA', 'Dataset'])['Rank'].max().reset_index() + percentages = {rba: [0] * N for rba in lowest_ranks['RBA'].unique()} + for rba in percentages: + rba_data = lowest_ranks[lowest_ranks['RBA'] == rba] + for pos in range(1, N + 1): + count_higher = rba_data[rba_data['Rank'] <= pos].shape[0] + percentages[rba][pos - 1] = (count_higher / rba_data.shape[0]) * 100 + + percentages_df = pd.DataFrame(percentages, index=range(1, N + 1)) + perc_path = os.path.join(results_dir, 'percentages_df.csv') + percentages_df.to_csv(perc_path, index_label='Ranking Position') + + # Plot heatmap + # custom_cmap = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:] + # ** Heatmap tweaked so that 100% is a distinct purple shade and 0% is a distinct white + colors = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:] + colors = np.array(colors) # converting to mutable array + colors[0] = [1.0, 1.0, 1.0] # replacing the lowest value color with white (for 0% on the heatmap) + colors[-1] = [0.5, 0.0, 0.5] # replacing the highest value color with purple (for 100% on the heatmap) + custom_cmap = ListedColormap(colors) + + # Define your preferred order of the RBAs as a list + rba_order = [ + 'RandomShuffle', + 'MutualInfo', + 'ReliefF10', + 'ReliefF100', + 'MuRelief10', + 'MuRelief100', + 'SURF', + 'MultiSURF', + 'SWRF', + 'MultiSWRF', + 'MultiSWRFDB', + 'SURFstar', + 'MultiSURFstar', + 'SWRFstar', + 'MultiSWRFstar', + 'MultiSWRFDBstar', + ] + + # Define a mapping from your RBA order to new descriptive names + rba_descriptive_names = { + 'RandomShuffle': 'Random Shuffle', + 'MutualInfo': 'Mutual Info', + 'ReliefF10': 'ReliefF 10NN', + 'ReliefF100': 'ReliefF 100NN', + 'MuRelief10': 'Mu-Relief 10N', + 'MuRelief100': 'Mu-Relief 100N', + 'SURF': 'SURF', + 'MultiSURF': 'MultiSURF', + 'SWRF': 'SWRF', + 'MultiSWRF': 'MultiSWRF', + 'MultiSWRFDB': 'MultiSWRFDB', + 'SURFstar': 'SURF*', + 'MultiSURFstar': 'MultiSURF*', + 'SWRFstar': 'SWRF*', + 'MultiSWRFstar': 'MultiSWRF*', + 'MultiSWRFDBstar': 'MultiSWRFDB*', + } + + n_pred = all_rankings_df['Feature'].nunique() + percentages_df = percentages_df.iloc[n_pred-1:] + percentages_df_transposed = percentages_df.T + percentages_df_ordered = percentages_df_transposed.loc[[r for r in rba_order if r in percentages_df_transposed.index]] + xtick_labels = ['Optimal', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'] + xtick_positions = np.linspace(0, percentages_df_ordered.shape[1] - 0.13, num=len(xtick_labels)) + + plt.figure(figsize=(12, 7)) + print(percentages_df_ordered) + heatmap = sns.heatmap(percentages_df_ordered, annot=False, fmt=".1f", cmap=custom_cmap, vmin=0, vmax=100, cbar_kws={'label': 'Power (Frequency of Success)'}) # explicit 0% and 100% for min and max values that heatmap colorscheme corresponds to + # heatmap = sns.heatmap(percentages_df_ordered, annot=False, fmt=".1f", cmap=custom_cmap, vmin=0, vmax=100, cbar=False) # if no color bar legend is desired + for i in range(percentages_df_ordered.shape[0] - 1): + heatmap.axhline(i + 1, color='black', linewidth=1.5) + + for _, spine in heatmap.spines.items(): + spine.set_visible(True) + spine.set_linewidth(1.5) + spine.set_edgecolor("black") + + cbar = heatmap.collections[0].colorbar + cbar.outline.set_linewidth(1.5) + cbar.outline.set_edgecolor("black") + # Increase label font size + cbar.set_label('Power (Frequency of Success)', fontsize=16) + cbar.ax.yaxis.set_label_coords(3.0, 0.5) # explicitly placing the cbar label closer to the cbar + # Increase tick label size + cbar.ax.tick_params(labelsize=14) + # adding clear labeling of 0% power as white and 100% power as purple + cbar.set_ticks([0, 20, 40, 60, 80, 100]) + cbar.set_ticklabels([ + '0 (white)', + '20', + '40', + '60', + '80', + '100 (purple)' + ]) + + dataset_id = os.path.basename(os.path.dirname(results_dir)) + # heatmap.set_title(prefix + dataset_id, fontsize=16) + heatmap.set_xlabel('Predictive features in top % of ranked features', fontsize=16) + # heatmap.set_ylabel('Method', fontsize=16) + heatmap.set_xticks(xtick_positions) + heatmap.set_xticklabels(xtick_labels, rotation=0, fontsize=16) + new_ytick_labels = [rba_descriptive_names[rba] for rba in percentages_df_ordered.index] + heatmap.set_yticklabels(new_ytick_labels, fontsize=16) + # to remove xticks altogether: + # heatmap.set_xticks([]) + + plt.rcParams['font.sans-serif'] = 'Helvetica' + plt.rcParams['font.family'] = 'sans-serif' + plt.rcParams['pdf.fonttype'] = 42 + plt.tight_layout() + + heatmap.collections[0].set_rasterized(True) # rasterizing the heatmap to improve rendering of PDF with high feature counts + + save_path = os.path.join(results_dir, prefix + dataset_id + '.pdf') + plt.savefig(save_path, format='pdf', dpi=600, bbox_inches='tight') # high dpi for higher resolution/quality rasterized heatmap + plt.close() + + print(f"Completed processing for: {results_dir}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("results_dir", help="Path to the Results folder.") + parser.add_argument("--prefix", default="", help="Prefix to add to output heatmap filenames.") + args = parser.parse_args() + + process_results_dir(args.results_dir, prefix=args.prefix) diff --git a/analysis/job_process_heatmap_unified.py b/analysis/job_process_heatmap_unified.py new file mode 100644 index 0000000..25377a3 --- /dev/null +++ b/analysis/job_process_heatmap_unified.py @@ -0,0 +1,358 @@ +import os +import sys +import argparse +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import numpy as np +import re +import matplotlib.lines as mlines +import matplotlib.gridspec as gridspec +from matplotlib.colors import ListedColormap +import time + +# Helper to create the percentages_df (copied from old job_process_heatmap) +def compute_percentages(results_dir): + all_rankings_df = pd.DataFrame() + total_features_per_rba = {} + + for rba in os.listdir(results_dir): + rba_path = os.path.join(results_dir, rba) + if os.path.isdir(rba_path): + method_feature_counts = [] + for file in os.listdir(rba_path): + if file.endswith('.txt'): + file_path = os.path.join(rba_path, file) + parts = file.split('_') + dataset_id = '_'.join(parts[-2].split('_')[:2]) + + if rba == "RandomShuffle": + df = pd.read_csv(file_path, sep='\t', usecols=['Feature']) + df['Rank'] = df.index + 1 + else: + column_to_use = 'ABS_Feature_Importance' if "ABS" in rba else 'Feature_Importance' + df = pd.read_csv(file_path, sep='\t', usecols=['Feature', column_to_use]) + df.sort_values(by=column_to_use, ascending=False, inplace=True) + df.reset_index(drop=True, inplace=True) + df['Rank'] = df.index + 1 + + predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Rank']] + predictive_df['RBA'] = rba + predictive_df['Dataset'] = dataset_id + + all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True) + method_feature_counts.append(df['Feature'].nunique()) + + total_features_per_rba[rba] = max(method_feature_counts) if method_feature_counts else 0 + + N = next(iter(total_features_per_rba.values())) + lowest_ranks = all_rankings_df.groupby(['RBA', 'Dataset'])['Rank'].max().reset_index() + percentages = {rba: [0] * N for rba in lowest_ranks['RBA'].unique()} + for rba in percentages: + rba_data = lowest_ranks[lowest_ranks['RBA'] == rba] + for pos in range(1, N + 1): + count_higher = rba_data[rba_data['Rank'] <= pos].shape[0] + percentages[rba][pos - 1] = (count_higher / rba_data.shape[0]) * 100 if rba_data.shape[0] > 0 else 0 + + percentages_df = pd.DataFrame(percentages, index=range(1, N + 1)) + n_pred = all_rankings_df['Feature'].nunique() + # print("N_pred:", n_pred, "\n") + # pd.set_option('display.max_rows', None) + # print(percentages_df["RandomShuffle"]) + # pd.reset_option('display.max_rows') # optional: restore default + return percentages_df.iloc[n_pred-1:] # same trimming + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("basedir", help="Directory containing multiple Results folders.") + parser.add_argument("--prefix", default="", help="Prefix for unified PDF filename.") + args = parser.parse_args() + + # custom colormap & ordering + # custom_cmap = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:] + # ** Heatmap tweaked so that 100% is a distinct purple shade and 0% is a distinct white + colors = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:] + colors = np.array(colors) # converting to mutable array + colors[0] = [1.0, 1.0, 1.0] # replacing the lowest value color with white (for 0% on the heatmap) + colors[-1] = [0.5, 0.0, 0.5] # replacing the highest value color with purple (for 100% on the heatmap) + custom_cmap = ListedColormap(colors) + + # MAIN: + rba_order = [ + 'RandomShuffle','MutualInfo','ReliefF10','ReliefF100','MuRelief10','MuRelief100','SURF', + 'MultiSURF','SWRF','MultiSWRF','MultiSWRFDB','SURFstar','MultiSURFstar','SWRFstar','MultiSWRFstar','MultiSWRFDBstar' + ] + + # Grab all Results folders (for 100 feature datasets) + # * can later potentially add the dataset feature lengths you want for the heatmap as a parameter (ex. 100 for a_100 datasets) + results_dirs = [] + for root, dirs, _ in os.walk(args.basedir): + for d in dirs: + if d == 'Results' and 'a_100' in root: + results_dirs.append(os.path.join(root, d)) + + is_core2wayEpistasis = ('core2wayEpistasis' in args.basedir) + is_mainEff = ('mainEff_Datasets' in args.basedir) + + # Build a mapping of (n_instances, heritability, EDMtype) -> percentages_df + pattern_n = re.compile(r"/s_(\d+)") + pattern_h = re.compile(r"her_(\d+\.\d+)__maf") + pattern_edm = re.compile(r"EDM-(\d+)") + + data_dict = {} + for rd in results_dirs: + print("Results directory:", rd) + n_match = pattern_n.search(rd) + h_match = pattern_h.search(rd) + edm_match = pattern_edm.search(rd) + if n_match and h_match and edm_match: + n = int(n_match.group(1)) + h = float(h_match.group(1)) + edm = edm_match.group(1) # '1' or '2' + perc_df = compute_percentages(rd) + perc_df_T = perc_df.T + perc_df_ordered = perc_df_T.loc[[r for r in rba_order if r in perc_df_T.index]] + data_dict[(n, h, edm)] = perc_df_ordered + + n_values = sorted({k[0] for k in data_dict.keys()}) + h_values = sorted({k[1] for k in data_dict.keys()}) + edm_values = sorted({k[2] for k in data_dict.keys()}) + print("Data dictionary:", data_dict, "\n") + print("H values:", h_values, "\n") + print("N values:", n_values, "\n") + + # --- INCREASING SPACING + # only mainEff and core2wayEpistasis have both EDM-1 and EDM-2 + if is_core2wayEpistasis: + total_rows = len(h_values) * 2 + else: + total_rows = len(edm_values) # 2 rows for mainEff + + if is_mainEff: + total_cols = len(h_values) # = 4 + else: + total_cols = len(n_values) + + # Create custom height and width ratios + # --> insert an extra gap between certain rows by slightly enlarging the space below + height_ratios = [] + for i in range(total_rows): + height_ratios.append(1) + if is_core2wayEpistasis: + if i % 2 == 1 and i != total_rows - 1: # after every 2nd row (except last) + # height_ratios.append(0.15) # this adds vertical gap spacing + height_ratios.append(0.05) + else: + if i != total_rows - 1: + if is_mainEff: + height_ratios.append(0.10) + + # For columns, add an extra gap after every column + width_ratios = [] + for j in range(total_cols): + width_ratios.append(1) + if j != total_cols - 1: # after each column except last + # width_ratios.append(0.10) # horizontal gap spacing + if is_core2wayEpistasis or is_mainEff: + width_ratios.append(0.05) + + # Define figure and gridspec with custom spacing + if is_core2wayEpistasis: + fig = plt.figure(figsize=(4*len(n_values) * 1.15, 3*len(h_values)*2 * 1.1)) + elif is_mainEff: + fig = plt.figure(figsize=(4*len(h_values) * 1.15, 4*len(edm_values) * 1.15)) + + if is_core2wayEpistasis: + spacing_value = 0.10 + elif is_mainEff: + spacing_value = 0.05 + gs = gridspec.GridSpec( + nrows=len(height_ratios), + ncols=len(width_ratios), + figure=fig, + height_ratios=height_ratios, + width_ratios=width_ratios, + hspace=spacing_value, # fine-tune base spacing + wspace=spacing_value + ) + + # Build axes array only in the actual plot cells (skip gap cells) + axes = np.empty((total_rows, total_cols), dtype=object) + row_ptr, col_ptr = 0, 0 + for i in range(len(height_ratios)): + if is_core2wayEpistasis: + if i % 3 == 2: # skip gap row + continue + else: + if i % 2 == 1: # skip gap row + continue + col_ptr = 0 + for j in range(len(width_ratios)): + if j % 2 == 1: # skip gap column + continue + axes[row_ptr, col_ptr] = fig.add_subplot(gs[i, j]) + col_ptr += 1 + row_ptr += 1 + + if len(h_values)*2 == 1 and len(n_values) == 1: + axes = np.array([[axes]]) # ensure 2D + + # where to draw black lines separating RBA groups in each heatmap + separators = [2, 6, 11] + if is_core2wayEpistasis: + # flipped so that y-axis is shown in ascending order from bottom to top + for i, h in enumerate(sorted(h_values, reverse=True)): + for j, n in enumerate(n_values): + # top EDM-2 + ax_top = axes[i*2, j] if len(h_values)*2 > 1 else axes[0, j] + ax_bot = axes[i*2+1, j] if len(h_values)*2 > 1 else axes[1, j] + + df2 = data_dict.get((n,h,'2')) + df1 = data_dict.get((n,h,'1')) + + if df2 is not None: + sns.heatmap(df2, ax=ax_top, annot=False, cmap=custom_cmap, cbar=False, xticklabels=False, yticklabels=False, vmin=0, vmax=100) + + for y in separators: + ax_top.axhline(y, color='black', linewidth=1.2) + # outline around each individual heatmap: + for spine in ax_top.spines.values(): + spine.set_visible(True) + spine.set_linewidth(0.1) + spine.set_edgecolor("gray") + else: + ax_top.axis('off') + if df1 is not None: + sns.heatmap(df1, ax=ax_bot, annot=False, cmap=custom_cmap, cbar=False, xticklabels=False, yticklabels=False, vmin=0, vmax=100) + for y in separators: + ax_bot.axhline(y, color='black', linewidth=1.2) + # outline around each individual heatmap: + for spine in ax_bot.spines.values(): + spine.set_visible(True) + spine.set_linewidth(0.1) + spine.set_edgecolor("gray") + else: + ax_bot.axis('off') + + # Put "E" and "H" labels on the right side of the heatmaps in the last column + if j == len(n_values) - 1: + # maybe add if clauses in the event that a dataset does not have EDM-1 or EDM-2 + ax_top.set_ylabel("E", rotation=0, labelpad=20, fontsize=26) + ax_top.yaxis.set_label_position("right") + + ax_bot.set_ylabel("H", rotation=0, labelpad=20, fontsize=26) + ax_bot.yaxis.set_label_position("right") + elif is_mainEff: + for j, edm in enumerate(sorted(edm_values, reverse=True)): + for i, h in enumerate(sorted(h_values)): + ax = axes[j, i] + + # Find the value in the dict corresponding to this n and h (could be either EDM-1 or EDM-2 depending on dataset, but only one of them) + df = next( + data_dict[key] + for key in data_dict + if key[2] == edm and key[1] == h + ) + + if df is not None: + sns.heatmap(df, ax=ax, annot=False, cmap=custom_cmap, cbar=False, xticklabels=False, yticklabels=False, vmin=0, vmax=100) + for y in separators: + ax.axhline(y, color='black', linewidth=1.2) + # outline around each individual heatmap: + for spine in ax.spines.values(): + spine.set_visible(True) + spine.set_linewidth(0.1) + spine.set_edgecolor("gray") + else: + ax.axis('off') + + if i == 0 and edm == '2': # if first column and the EDM = 2 (E) row + ax.set_ylabel("E", rotation=0, labelpad=20, fontsize=30) + ax.yaxis.set_label_position("left") + ax.yaxis.set_label_coords(-0.1, 0.4) + elif i == 0 and edm == '1': # if first column and the EDM = 1 (H) row + ax.set_ylabel("H", rotation=0, labelpad=20, fontsize=30) + ax.yaxis.set_label_position("left") + ax.yaxis.set_label_coords(-0.1, 0.4) + + if is_core2wayEpistasis: + # Set x-axis labels for Number of Training Instances + for j, n in enumerate(n_values): + # Place label centered below the corresponding column (under the last row for that column) + mid_axs = axes[-1, j] if len(h_values)*2 > 1 else axes[1, j] + mid_axs.set_xlabel(str(n), fontsize=26) + mid_axs.xaxis.set_label_coords(0.5, -0.2) # adjust vertical padding + elif is_mainEff: + # Set x-axis labels for Heritability of Model + for i, h in enumerate(h_values): + # Place label centered below the corresponding column (under the last row for that column) + mid_axs = axes[-1, i] if len(edm_values)*2 > 1 else axes[1, i] + mid_axs.set_xlabel(str(h), fontsize=30) + mid_axs.xaxis.set_label_coords(0.5, -0.1) # adjust vertical padding + + if is_core2wayEpistasis: + # Set y-axis labels for Heritability of Model (once per heritability row) + for i, h in enumerate(sorted(h_values, reverse=True)): + # First column only + ax_top = axes[i*2, 0] + ax_bot = axes[i*2 + 1, 0] + + # Position the label in the middle of top and bottom heatmaps + mid_y = 0 # normalized vertical coordinate (0 = bottom of ax, 1 = top of ax) + + # if there is only one column, can't use set_ylabel twice on the same axis (will overwrite the first one, "E"); so use .text instead + if len(n_values) == 1: + ax_top.text(-0.2, mid_y - 0.1, str(h), rotation=0, fontsize=26, va='center', ha='center', transform=ax_top.transAxes) + else: + # Use the top subplot to place the label vertically centered + ax_top.set_ylabel(str(h), rotation=0, fontsize=26) + ax_top.yaxis.set_label_coords(-0.2, mid_y - 0.1) + + # Tight layout with extra spacing + plt.tight_layout(rect=[0.05, 0.05, 0.95, 0.95]) + + # ** LINES BETWEEN INSTANCE/HERITABILITY COMBINATIONS: + # --- DRAW DIVIDER LINES THROUGH GAP COLUMNS AND ROWS --- + # Use figure coordinates (0–1 range) + if is_core2wayEpistasis: + # Vertical dividers after every column + for j in range(total_cols - 1): + # Compute midpoint between the right edge of column j and left edge of next column + left_bbox = axes[0, j].get_position() + right_bbox = axes[0, j+1].get_position() + x_mid = (left_bbox.x1 + right_bbox.x0) / 2 + y_bottom = axes[-1,0].get_position().y0 + y_top = axes[0,0].get_position().y1 + + # Vertical line (spanning entire figure) + line = mlines.Line2D([x_mid, x_mid], [y_bottom, y_top], + transform=fig.transFigure, color='black', linewidth=1.5, alpha=0.3) + + fig.add_artist(line) + + # Horizontal dividers after every heritability block (every 2 rows) + for i in range(1, len(h_values)): + # Get bounding boxes for last heatmap in block i-1 and first in block i + prev_bottom = axes[(i-1)*2 + 1, 0].get_position().y0 # bottom of bottom subplot of previous block + next_top = axes[i*2, 0].get_position().y1 # top of top subplot of next block + y_mid = (prev_bottom + next_top) / 2 + x_left = axes[0,0].get_position().x0 + x_right = axes[0,-1].get_position().x1 + + # Horizontal line (spanning entire figure) + line = mlines.Line2D([x_left, x_right], [y_mid, y_mid], + transform=fig.transFigure, color='black', linewidth=1.5, alpha=0.3) + + fig.add_artist(line) + + + outdir = os.path.basename(os.path.normpath(args.basedir)) + parentdir = os.path.dirname(os.path.normpath(args.basedir)) + # save_path = os.path.join(parentdir, outdir, args.prefix + "unified_heatmaps.pdf") + save_path = os.path.join(parentdir, outdir, args.prefix + "unified_heatmaps_withMuRelief_biggerfont.pdf") + plt.savefig(save_path, format='pdf', bbox_inches='tight') + plt.close() + print(f"Unified heatmap saved to {save_path}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/job_process_mannwhitney.py b/analysis/job_process_mannwhitney.py new file mode 100644 index 0000000..f29e504 --- /dev/null +++ b/analysis/job_process_mannwhitney.py @@ -0,0 +1,85 @@ +# job_process_mannwhitney.py +import os +import argparse +import pandas as pd +import numpy as np +from itertools import combinations +from scipy.stats import mannwhitneyu +from statsmodels.stats.multitest import multipletests + +def permutation_test(x, y, U_obs, n_permutations=10000, seed=42): + np.random.seed(seed) + combined = np.concatenate([x, y]) + count = 0 + n_x = len(x) + expected_U = len(x) * len(y) / 2 + for _ in range(n_permutations): + np.random.shuffle(combined) + new_x = combined[:n_x] + new_y = combined[n_x:] + # U_perm = U score + U_perm = mannwhitneyu(new_x, new_y, alternative="two-sided", method="asymptotic").statistic + + if abs(U_perm - expected_U) >= abs(U_obs - expected_U): + count += 1 + return count / n_permutations + +def process_dir(dir_path, column='rank', exclude_patterns=None): + exclude_patterns = exclude_patterns or [] + df = pd.read_csv(os.path.join(dir_path, 'rankings_list.csv'), comment='#') + + # Normalize column names + col_to_use = 'Rank' if column == 'rank' else 'Normalized_Feature_Importance' + + # Group by RBA + rba_groups = {rba: g[col_to_use].values for rba, g in df.groupby('RBA') + if not any(p.lower() in rba.lower() for p in exclude_patterns)} + + results = [] + + for rba1, rba2 in combinations(rba_groups.keys(), 2): + x = rba_groups[rba1] + y = rba_groups[rba2] + + mannwhitney_res = mannwhitneyu(x, y, alternative="two-sided", method="asymptotic") + row = { + 'RBA1': rba1, + 'RBA2': rba2, + 'mannwhitney_statistic': mannwhitney_res.statistic, + 'mannwhitney_pvalue': mannwhitney_res.pvalue + } + + # Permutation test only for rank + if column == 'rank': + perm_p = permutation_test(x, y, mannwhitney_res.statistic) + row['permutation_pvalue'] = perm_p + + results.append(row) + + results_df = pd.DataFrame(results) + + # Sorting + if column == 'rank': + # Benjamini-Hochberg + results_df['mannwhitney_p_adj'] = multipletests(results_df['mannwhitney_pvalue'], method='fdr_bh')[1] + results_df['permutation_p_adj'] = multipletests(results_df['permutation_pvalue'], method='fdr_bh')[1] + results_df.sort_values(by=['mannwhitney_p_adj', 'permutation_p_adj'], ascending=True, inplace=True) + output_file = os.path.join(dir_path, 'mannwhitney_ranks.csv') + else: + # Benjamini-Hochberg + results_df['mannwhitney_p_adj'] = multipletests(results_df['mannwhitney_pvalue'], method='fdr_bh')[1] + results_df.sort_values(by=['mannwhitney_p_adj'], ascending=True, inplace=True) + # output_file = os.path.join(dir_path, 'mannwhitney_feature_importances.csv') + output_file = os.path.join(dir_path, 'mannwhitney_normalized_feature_importances.csv') + + results_df.to_csv(output_file, index=False) + print(f"Results saved to {output_file}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("dir_path", help="Directory containing rankings_list.csv") + parser.add_argument("--column", choices=['rank', 'feature_importance'], default='rank', help="Column to test") + parser.add_argument("--exclude", nargs='*', default=[], help="RBA name patterns to exclude (case insensitive)") + args = parser.parse_args() + + process_dir(args.dir_path, column=args.column, exclude_patterns=args.exclude) \ No newline at end of file diff --git a/analysis/job_process_rba_rankings.py b/analysis/job_process_rba_rankings.py new file mode 100644 index 0000000..7fa477c --- /dev/null +++ b/analysis/job_process_rba_rankings.py @@ -0,0 +1,135 @@ +# job_process_rba_rankings.py +import os +import sys +import argparse +import pandas as pd +import numpy as np + +def collect_rba_rankings(root_dir): + print(f"Searching for Results folders under: {root_dir}") + all_rankings_df = pd.DataFrame() + + # dict to create cleaner RBA names in mean/median rank tables + rba_descriptive_names = { + 'RandomShuffle': 'Random Shuffle', + 'MutualInfo': 'Mutual Info', + 'ReliefF10': 'ReliefF 10NN', + 'ReliefF100': 'ReliefF 100NN', + 'SURF': 'SURF', + 'SURFstar': 'SURF*', + 'MultiSURF': 'MultiSURF', + 'MultiSURFstar': 'MultiSURF*', + 'SWRF': 'SWRF', + 'SWRFstar': 'SWRF*', + 'MultiSWRF': 'MultiSWRF', + 'MultiSWRFstar': 'MultiSWRF*', + 'MultiSWRFDB': 'MultiSWRFDB', + 'MultiSWRFDBstar': 'MultiSWRFDB*', + 'MuRelief10': 'Mu-Relief 10N', + 'MuRelief100': 'Mu-Relief 100N', + } + + for subdir, dirs, _ in os.walk(root_dir): + subgroup_rankings_df = pd.DataFrame() + # Only process Results folders that are within these directories + if os.path.basename(subdir) == "Results" and any(x in subdir for x in ["a_100", "a_1000", "a_10000", "a_100000", "mainEff_largerfeatures_data", "a_50000", "a_20000"]): + for rba in os.listdir(subdir): + if rba not in rba_descriptive_names: + continue + rba_path = os.path.join(subdir, rba) + if not os.path.isdir(rba_path): + continue + + for file in os.listdir(rba_path): + if not file.endswith('.txt'): + continue + file_path = os.path.join(rba_path, file) + + if "Shuffle" in rba: + try: + df = pd.read_csv(file_path, sep='\t', usecols=['Feature']) + except Exception as e: + print(f"Skipping {file_path} (error: {e})") + continue + + df['Rank'] = df.index + 1 + df['Feature_Importance'] = np.nan + df['Normalized_Feature_Importance'] = np.nan + else: + try: + df = pd.read_csv(file_path, sep='\t', usecols=['Feature', 'Feature_Importance']) + except Exception as e: + print(f"Skipping {file_path} (error: {e})") + continue + + # Sort by descending feature importance and assign ranks + df.sort_values(by='Feature_Importance', ascending=False, inplace=True) + df.reset_index(drop=True, inplace=True) + df['Rank'] = df.index + 1 + # Normalize feature importance between 0 and 1 + df['Normalized_Feature_Importance'] = (df['Feature_Importance'] - df['Feature_Importance'].min()) / \ + (df['Feature_Importance'].max() - df['Feature_Importance'].min()) + + # Only keep true predictive features + predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Feature_Importance', 'Normalized_Feature_Importance', 'Rank']] + predictive_df['RBA'] = rba_descriptive_names[rba] + + # for subgroup ranking (ex. mainEff, her=0.2, EDM-1); i.e. group of 30 replicate dataset files + subgroup_rankings_df = pd.concat([subgroup_rankings_df, predictive_df], ignore_index=True) + # for whole dataset group ranking (ex. mainEff) + all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True) + + compute_summary_stats(subgroup_rankings_df, subdir) + + compute_summary_stats(all_rankings_df, root_dir) + + +def compute_summary_stats(rankings_df, save_dir): + if rankings_df.empty: + print("Valid ranking data not found.") + sys.exit(0) + + summary = ( + rankings_df.groupby('RBA')['Rank'] + .agg(['mean', 'median']) + .reset_index() + .rename(columns={'mean': 'Mean', 'median': 'Median'}) + ) + + summary.sort_values(by=['Mean', 'Median'], inplace=True) + + # Get title from basename of save_dir + if os.path.basename(os.path.normpath(save_dir)) == "Results": + title = os.path.basename(os.path.dirname(save_dir)) + else: + title = os.path.basename(os.path.normpath(save_dir)) + + summary_path = os.path.join(save_dir, 'rba_rankings.csv') + + rankings_list_path = os.path.join(save_dir, 'rankings_list.csv') + + # Write summary CSV with title + with open(summary_path, 'w') as f: + f.write(f"# {title}\n") + summary.to_csv(f, index=False) + print(f"Saved summary CSV: {summary_path}") + + # Write detailed rankings CSV with title + with open(rankings_list_path, 'w') as f: + f.write(f"# {title}\n") + rankings_df[['Feature', 'Feature_Importance', 'Normalized_Feature_Importance', 'Rank', 'RBA']].to_csv(f, index=False) + print(f"Saved detailed rankings list: {rankings_list_path}") + + +def main(): + parser = argparse.ArgumentParser(description="Compute global mean and median ranks for RBAs.") + parser.add_argument("root_dir", help="Path to the dataset group directory (e.g., path/to/mainEff).") + args = parser.parse_args() + + collect_rba_rankings(args.root_dir) + + print("Completed processing for:", args.root_dir) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/job_rebate_analysis.py b/analysis/job_rebate_analysis.py new file mode 100644 index 0000000..d42f69b --- /dev/null +++ b/analysis/job_rebate_analysis.py @@ -0,0 +1,207 @@ +import os +import time +import sys +import argparse +import pandas as pd +import numpy as np +from numpy.random import default_rng +from sklearn.model_selection import train_test_split +from sklearn.feature_selection import mutual_info_classif, mutual_info_regression +import hashlib +from functools import partial + +package_path = os.path.abspath(os.path.join("", "..")) +sys.path.insert(0, package_path) +from skrebate import ReliefF, SURF, SURFstar, MultiSURF, MultiSURFstar, SWRFstar, SWRF, MultiSWRFstar, MultiSWRF, MultiSWRFDBstar, MultiSWRFDB, MuRelief + +# NEW: added exist_ok=True +def ensure_dir(directory): + if not os.path.exists(directory): + os.makedirs(directory, exist_ok=True) + +def process_and_save_results(file_path, fs, method_name): + df = pd.read_csv(file_path, sep='\t') + + X, y = df.drop('Class', axis=1).values, df['Class'].values + + # to keep track of runtime for large feature datasets + start_time = time.time() + try: + fs.fit(X, y) + except AttributeError: + scores = fs(X, y) + fs = type('MI', (), {'feature_importances_': scores})() + end_time = time.time() + + temp_list = [] + for feature_name, feature_score in zip(df.drop('Class', axis=1).columns, fs.feature_importances_): + temp_list.append([feature_name, feature_score]) + + Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance']) + ABSResults = Results.copy() + ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs() + Results.sort_values(by='Feature_Importance', ascending=False, inplace=True) + ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True) + + base_dir = os.path.dirname(file_path) + results_dir = os.path.join(base_dir, "Results") + method_dir = os.path.join(results_dir, method_name) + abs_method_dir = os.path.join(results_dir, f"ABS_{method_name}") + ensure_dir(method_dir) + ensure_dir(abs_method_dir) + + base_name = os.path.splitext(os.path.basename(file_path))[0] + Results.to_csv(os.path.join(method_dir, f"{base_name}_Results.txt"), index=False, sep='\t') + ABSResults.to_csv(os.path.join(abs_method_dir, f"{base_name}_ABSResults.txt"), index=False, sep='\t') + # Save runtime CSV + runtime = end_time - start_time + runtime_df = pd.DataFrame([{ + "Dataset": base_name, + "Algorithm": method_name, + "Runtime (sec)": round(runtime, 4), + "Runtime (min)": round(runtime / 60, 4) + }]) + runtime_df.to_csv(os.path.join(method_dir, f"{base_name}_runtime_postnanhandling.csv"), index=False) + + # ******** If I uncomment this function, I need to go back and uncomment logging lines within other files + # if method_name in ["SWRFstar", "SWRF", "MultiSWRF", "MultiSWRFstar", "MultiSWRFDB", "MultiSWRFDBstar", "MultiSWRFDBlinear", "MultiSWRFDBlinearstar", "MultiSWRFDBexponential", "MultiSWRFDBexponentialstar", "MultiSWRFDBlinear3SD", "MultiSWRFDBlinear3SDstar", "MultiSWRFDBexponential3SD", "MultiSWRFDBexponential3SDstar", "SURF", "SURFstar", "MultiSURF", "MultiSURFstar", "MuRelief10", "MuRelief100"]: + # fs.plot_distance_weight_map(save_fig=os.path.join(method_dir, f"{base_name}_WeightPlot.png"), show_expected=True) + # # fs.plot_distance_weight_map(save_fig=os.path.join(method_dir, f"{base_name}_WeightPlot.png"), save_file=os.path.join(method_dir, f"{base_name}_stdweightlog.txt"), show_expected=True) + + print(f"Processed {file_path} with {method_name}. Results saved to {method_dir} and {abs_method_dir}.") + +def process_random_shuffle(file_path): + # Define the directory to store results + results_dir = os.path.join(os.path.dirname(file_path), "Results", "RandomShuffle") + ensure_dir(results_dir) + + # Read the file + try: + df = pd.read_csv(file_path, sep='\t') + except Exception as e: + print(f"Error reading {file_path}: {e}") + return + + # Shuffle feature column names (excluding 'Class' if present) + if 'Class' in df.columns: + columns_to_shuffle = sorted(df.drop('Class', axis=1).columns.tolist()) + else: + columns_to_shuffle = sorted(df.columns.tolist()) + + base_name = os.path.splitext(os.path.basename(file_path))[0] + # Take the last 2 characters of base_name (i.e. the file number) + seed_str = base_name[-2:] # example: "01" + # Convert to deterministic integer seed + file_seed = int(hashlib.sha256(seed_str.encode()).hexdigest(), 16) % (2**32) + + for i in range(40): # 40 random shuffles per replicate file (40 X 30 = 1200 random shuffles per configuration) + seed = file_seed + i + # creating a local RNG seeded from the file name + rng = default_rng(seed) + # shuffle columns deterministically for this file + shuffled_columns = rng.permutation(columns_to_shuffle) + + shuffled_df = pd.DataFrame(shuffled_columns, columns=['Feature']) + + output_path = os.path.join(results_dir, f"{base_name}{i}_RandShuffle.txt") + shuffled_df.to_csv(output_path, index=False, sep='\t') + +def process_mutual_info(file_path): + df = pd.read_csv(file_path, sep='\t') + # counting the number of labels in the 'Class' column to determine whether this is a classification or regression problem: + num_labels = df['Class'].nunique() + if num_labels <= 10: + fs = partial(mutual_info_classif, random_state=42) # setting random_state for mutual_info + else: + fs = partial(mutual_info_regression, random_state=42) + process_and_save_results(file_path, fs, "MutualInfo") + +def process_relieff10(file_path): + fs = ReliefF(n_features_to_select=2,n_neighbors=10,n_jobs=1) + process_and_save_results(file_path, fs, "ReliefF10") + +def process_relieff100(file_path): + fs = ReliefF(n_features_to_select=2,n_neighbors=100,n_jobs=16) + process_and_save_results(file_path, fs, "ReliefF100") + +def process_surf(file_path): + fs = SURF(n_jobs=1) + process_and_save_results(file_path, fs, "SURF") + +def process_surfstar(file_path): + fs = SURFstar(n_jobs=16) + process_and_save_results(file_path, fs, "SURFstar") + +def process_multisurf(file_path): + fs = MultiSURF(n_jobs=16) + process_and_save_results(file_path, fs, "MultiSURF") + +def process_multisurfstar(file_path): + fs = MultiSURFstar(n_jobs=16) + process_and_save_results(file_path, fs, "MultiSURFstar") + +def process_swrfstar(file_path): + fs = SWRFstar(n_jobs=1) + process_and_save_results(file_path, fs, "SWRFstar") + +def process_swrf(file_path): + fs = SWRF(n_jobs=16) + process_and_save_results(file_path, fs, "SWRF") + +def process_multiswrfstar(file_path): + fs = MultiSWRFstar(n_jobs=16) + process_and_save_results(file_path, fs, "MultiSWRFstar") + +def process_multiswrf(file_path): + fs = MultiSWRF(n_jobs=16) + process_and_save_results(file_path, fs, "MultiSWRF") + +def process_multiswrfdbstar(file_path): + fs = MultiSWRFDBstar(n_jobs=16) + process_and_save_results(file_path, fs, "MultiSWRFDBstar") + +def process_multiswrfdb(file_path): + fs = MultiSWRFDB(n_jobs=16) + process_and_save_results(file_path, fs, "MultiSWRFDB") + +def process_murelief10(file_path): + fs = MuRelief(n_features_to_select=2,n_neighbors=10,n_jobs=1) + process_and_save_results(file_path, fs, "MuRelief10") + +def process_murelief100(file_path): + fs = MuRelief(n_features_to_select=2,n_neighbors=100,n_jobs=1) + process_and_save_results(file_path, fs, "MuRelief100") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--algorithm', required=True, help='Algorithm to use') + parser.add_argument('--input_file', required=True, help='Path to the input .txt file') + args = parser.parse_args() + + alg_map = { + 'random': process_random_shuffle, + 'mutual_info': process_mutual_info, + 'relieff10': process_relieff10, + 'relieff100': process_relieff100, + 'surf': process_surf, + 'surfstar': process_surfstar, + 'multisurf': process_multisurf, + 'multisurfstar': process_multisurfstar, + 'swrfstar': process_swrfstar, + 'swrf': process_swrf, + 'multiswrfstar': process_multiswrfstar, + 'multiswrf': process_multiswrf, + 'multiswrfdbstar': process_multiswrfdbstar, + 'multiswrfdb': process_multiswrfdb, + 'murelief10': process_murelief10, + 'murelief100': process_murelief100, + } + + if args.algorithm not in alg_map: + raise ValueError(f"Unsupported algorithm: {args.algorithm}") + + alg_map[args.algorithm](args.input_file) + +if __name__ == "__main__": + main() diff --git a/analysis/higher_order_interactions/Data_feature_set_extender.ipynb b/analysis/oldScripts/Data_feature_set_extender.ipynb similarity index 99% rename from analysis/higher_order_interactions/Data_feature_set_extender.ipynb rename to analysis/oldScripts/Data_feature_set_extender.ipynb index b2dd5df..329df40 100644 --- a/analysis/higher_order_interactions/Data_feature_set_extender.ipynb +++ b/analysis/oldScripts/Data_feature_set_extender.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "e58387ad", "metadata": {}, "outputs": [], @@ -52,7 +52,7 @@ "completed = [20,100]\n", "min_MAF = 0.01\n", "max_MAF = 0.5\n", - "folder_path = \"AbsVal Benchmark Data\"\n" + "folder_path = \"../Benchmark Data\"\n" ] }, { diff --git a/analysis/oldScripts/Data_feature_set_extender.py b/analysis/oldScripts/Data_feature_set_extender.py new file mode 100644 index 0000000..068abe3 --- /dev/null +++ b/analysis/oldScripts/Data_feature_set_extender.py @@ -0,0 +1,72 @@ +# %% +import os +import pandas as pd +import numpy as np + +# %% +def hardy_weinberg_equilibrium(freq): + p = np.sqrt(freq) + q = 1 - p + return np.random.choice([0, 1, 2], p=[q**2, 2*p*q, p**2]) + +# %% +def get_max_random_feature(column_names): + column_names = [x for x in column_names if x != 'Class' and x.startswith('N')] + column_names = [x[1:] if x.startswith('N') else x for x in column_names] + max_value = max(int(x) for x in column_names) + return max_value + + +# %% +#total_features = 40 #20, 40, 60, 80, 100 +feature_list = [40,60,80] +completed = [20,100] +min_MAF = 0.01 +max_MAF = 0.5 +folder_path = "../Benchmark Data" + + +# %% +#go through features to generate list one at a time + +def process_files_in_folder(folder_path): + for root, dirs, files in os.walk(folder_path): + print(root) + if 'a_20' in root: + print(root) + for total_features in feature_list: + print(total_features) + + for file in files: + if file.endswith(".txt"): + file_path = os.path.join(root, file) + + # Load the dataset + df = pd.read_csv(file_path, sep='\t') + column_names = df.columns.tolist() + max_value = get_max_random_feature(column_names) + num_columns = df.shape[1] + n = total_features +1 - num_columns + + # Generate a random minor allele frequency between 0.05 and 0.5 + freq = np.random.uniform(min_MAF, max_MAF) + + # Add n new columns with values 0, 1, or 2 based on Hardy-Weinberg equilibrium + for i in range(n): + col_name = 'N'+str(i+max_value+1) + df[col_name] = df.apply(lambda _: hardy_weinberg_equilibrium(freq), axis=1) + + new_root = root.replace('a_20', 'a_'+str(total_features)) + + if not os.path.exists(new_root): + os.makedirs(new_root) + + new_file_path = file_path.replace('a_20', 'a_'+str(total_features)) + + # Save the modified dataset + df.to_csv(new_file_path, sep='\t', index=False) + +# %% +process_files_in_folder(folder_path) + + diff --git a/analysis/oldScripts/Heatmaps.ipynb b/analysis/oldScripts/Heatmaps.ipynb new file mode 100644 index 0000000..4adf85d --- /dev/null +++ b/analysis/oldScripts/Heatmaps.ipynb @@ -0,0 +1,622 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Below is the parsing code that goes through all result files and calculates the ranks for each feature for all replicates" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consolidated rankings saved to: ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-1/Results/consolidated_rankings.csv\n" + ] + } + ], + "source": [ + "# Define the path to the 'Results' directory\n", + "results_dir = '../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-1/Results' # Change this to the path of your 'Results' directory\n", + "\n", + "# Initialize a DataFrame to hold all rankings\n", + "all_rankings_df = pd.DataFrame()\n", + "\n", + "# Initialize a dictionary to hold the total number of features (N) for each RBA method\n", + "total_features_per_rba = {}\n", + "\n", + "# Iterate over each subfolder in the Results directory, each named for an RBA\n", + "for rba in os.listdir(results_dir):\n", + " rba_path = os.path.join(results_dir, rba)\n", + " if os.path.isdir(rba_path): # Ensure it's a directory\n", + " method_feature_counts = [] # To store feature counts for each dataset within this method\n", + " for file in os.listdir(rba_path):\n", + " if file.endswith('.txt'): # Ensure the file is a .txt file\n", + " file_path = os.path.join(rba_path, file)\n", + " \n", + " # Extract the dataset identifier from the file name\n", + " parts = file.split('_')\n", + " dataset_id = '_'.join(parts[-2].split('_')[:2])\n", + " \n", + " # Determine Ranks\n", + " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature'])\n", + " if rba == \"RandomShuffle\":\n", + " df['Rank'] = df.index + 1\n", + " else:\n", + " column_to_use = 'ABS_Feature_Importance' if \"ABS\" in rba else 'Feature_Importance'\n", + " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature', column_to_use])\n", + " df.sort_values(by=column_to_use, ascending=False, inplace=True)\n", + " df.reset_index(drop=True, inplace=True)\n", + " df['Rank'] = df.index + 1\n", + " \n", + " # Store in a dataframe\n", + " predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Rank']]\n", + " predictive_df['RBA'] = rba\n", + " predictive_df['Dataset'] = dataset_id\n", + " \n", + " all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True)\n", + " \n", + " # Add the feature count of this dataset to the list for this method\n", + " method_feature_counts.append(df['Feature'].nunique())\n", + " \n", + " # Store the maximum feature count encountered for this method as N\n", + " total_features_per_rba[rba] = max(method_feature_counts)\n", + "\n", + "# Now, you have both the rankings and the total number of features (N) for each RBA method\n", + "# You can use total_features_per_rba to access N for each method as needed\n", + "\n", + "# Specify the path to save the consolidated rankings file\n", + "save_path = os.path.join(results_dir, 'consolidated_rankings.csv')\n", + "all_rankings_df.to_csv(save_path, index=False)\n", + "\n", + "print(f\"Consolidated rankings saved to: {save_path}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### This code generates the ranking percentages that are needed to create the heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentages saved to percentages_df.csv.\n" + ] + } + ], + "source": [ + "# Assuming all_rankings_df is correctly prepared and contains 'RBA', 'Feature', 'Rank', 'Dataset'\n", + "\n", + "# Since all datasets have the same N, we can pick the N from any RBA method from total_features_per_rba\n", + "N = next(iter(total_features_per_rba.values()))\n", + "\n", + "# Step 1: Identify the lowest-ranked predictive feature for each dataset for each RBA\n", + "lowest_ranks = all_rankings_df.groupby(['RBA', 'Dataset'])['Rank'].max().reset_index()\n", + "\n", + "# Initialize a structure to hold the calculated percentages for each RBA method\n", + "percentages = {rba: [0] * N for rba in lowest_ranks['RBA'].unique()}\n", + "\n", + "# Step 2: Calculate percentages for each position for each RBA\n", + "for rba in percentages.keys():\n", + " rba_data = lowest_ranks[lowest_ranks['RBA'] == rba]\n", + " for position in range(1, N + 1):\n", + " # Count how many of the lowest ranks are better (lower number) than the current position\n", + " count_higher = rba_data[rba_data['Rank'] <= (position)].shape[0]\n", + " total_datasets = rba_data.shape[0] # Should be 30 per RBA if there are 30 datasets\n", + " percentages[rba][position - 1] = (count_higher / total_datasets) * 100\n", + "\n", + "# Convert the percentages to a DataFrame for visualization\n", + "percentages_df = pd.DataFrame(percentages, index=range(1, N + 1))\n", + "\n", + "# Save percentages_df to a CSV file\n", + "save_path_percentages = os.path.join(results_dir, 'percentages_df.csv')\n", + "percentages_df.to_csv(save_path_percentages, index_label='Ranking Position')\n", + "\n", + "print(\"Percentages saved to percentages_df.csv.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting code for heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Helvetica\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Helvetica\n", + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create custom color scheme\n", + "custom_cmap = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:]\n", + "\n", + "# Define your preferred order of the RBAs as a list\n", + "rba_order = [\n", + " 'RandomShuffle',\n", + " 'MutualInfo',\n", + " 'SURFstar',\n", + " 'MultiSURFstar',\n", + " 'SWRFstar',\n", + " 'ABS_SURFstar',\n", + " 'ABS_MultiSURFstar',\n", + " 'ABS_SWRFstar',\n", + "]\n", + "\n", + "# Define a mapping from your RBA order to new descriptive names\n", + "rba_descriptive_names = {\n", + " 'RandomShuffle': 'Random Shuffle',\n", + " 'MutualInfo': 'Mutual Information',\n", + " 'SURFstar': 'SURF*',\n", + " 'SWRFstar': 'SWRF*',\n", + " 'MultiSURFstar': 'MultiSURF*',\n", + " 'ABS_SURFstar': 'SURF* ABS',\n", + " 'ABS_MultiSURFstar': 'MultiSURF* ABS',\n", + " 'ABS_SWRFstar': 'SWRF* ABS',\n", + "}\n", + "\n", + "percentages_df = percentages_df.iloc[1:] # Drop the first row as this will always be 0\n", + "percentages_df_transposed = percentages_df.T # Transpose percentages_df to switch rows and columns for horizontal orientation\n", + "\n", + "# Reorder the DataFrame according to your defined RBA order\n", + "percentages_df_ordered = percentages_df_transposed.loc[rba_order] # The .loc indexer reindexes the DataFrame to the specified order; any missing labels will result in NaN rows\n", + "\n", + "# Define the tick labels as percentages of optimality\n", + "xtick_labels = ['Optimal', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']\n", + "# Generate a list of positions at which to place the x-tick labels, assuming they should be placed at even intervals\n", + "xtick_positions = np.linspace(start=0, stop=percentages_df_transposed.shape[1] - 0.13, num=len(xtick_labels))\n", + "\n", + "# Create the heatmap with the reordered DataFrame\n", + "plt.figure(figsize=(12, 7)) # Adjust the size as necessary\n", + "heatmap = sns.heatmap(percentages_df_ordered, annot=False, fmt=\".1f\", cmap=custom_cmap, cbar_kws={'label': 'Power (Frequency of Success)'})\n", + "\n", + "# Add horizontal lines manually between Methods\n", + "for i in range(percentages_df_ordered.shape[0] - 1):\n", + " heatmap.axhline(i + 1, color='black', linewidth=1.5)\n", + "\n", + "# Adding black border around the heatmap\n", + "for _, spine in heatmap.spines.items():\n", + " spine.set_visible(True)\n", + " spine.set_linewidth(1.5)\n", + " spine.set_edgecolor(\"black\")\n", + "\n", + "# Adding black border around the color bar in legend\n", + "cbar = heatmap.collections[0].colorbar\n", + "cbar.outline.set_linewidth(1.5)\n", + "cbar.outline.set_edgecolor(\"black\")\n", + "\n", + "dataset_id = results_dir.split('/')[-2] # Extract the dataset identifier from the results directory path\n", + "\n", + "# Set the title and axis labels appropriately\n", + "heatmap.set_title('core2wayEpi_ABS_' + dataset_id, fontsize = 16)\n", + "heatmap.set_xlabel('Predictive features in top % of ranked features', fontsize = 14)\n", + "heatmap.set_ylabel('Method', fontsize = 14)\n", + "\n", + "# Set the custom x-tick labels, positions, and fontsize\n", + "heatmap.set_xticks(xtick_positions)\n", + "heatmap.set_xticklabels(xtick_labels, rotation=0, fontsize=11)\n", + "\n", + "# Set the custom y-tick labels fontsize\n", + "new_ytick_labels = [rba_descriptive_names[rba] for rba in rba_order]\n", + "heatmap.set_yticklabels(new_ytick_labels, fontsize=11)\n", + "\n", + "# Save and show the Plot\n", + "plt.rcParams['font.sans-serif'] = 'Helvetica'\n", + "plt.rcParams['font.family'] = 'sans-serif'\n", + "plt.rcParams['pdf.fonttype'] = 42\n", + "plt.tight_layout() # Adjust the layout\n", + "save_path = os.path.join(results_dir, 'core2wayEpi_ABS_' + dataset_id + '.pdf') # Save\n", + "plt.savefig(save_path, format='pdf', bbox_inches='tight') # Save\n", + "plt.show() # Display the heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "slcs", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/oldScripts/Heatmaps.py b/analysis/oldScripts/Heatmaps.py new file mode 100644 index 0000000..2e18b1f --- /dev/null +++ b/analysis/oldScripts/Heatmaps.py @@ -0,0 +1,190 @@ +# %% +import os +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import numpy as np + +# %% [markdown] +# ### Below is the parsing code that goes through all result files and calculates the ranks for each feature for all replicates + +# %% +# Define the path to the 'Results' directory +results_dir = '../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-1/Results' # Change this to the path of your 'Results' directory + +# Initialize a DataFrame to hold all rankings +all_rankings_df = pd.DataFrame() + +# Initialize a dictionary to hold the total number of features (N) for each RBA method +total_features_per_rba = {} + +# Iterate over each subfolder in the Results directory, each named for an RBA +for rba in os.listdir(results_dir): + rba_path = os.path.join(results_dir, rba) + if os.path.isdir(rba_path): # Ensure it's a directory + method_feature_counts = [] # To store feature counts for each dataset within this method + for file in os.listdir(rba_path): + if file.endswith('.txt'): # Ensure the file is a .txt file + file_path = os.path.join(rba_path, file) + + # Extract the dataset identifier from the file name + parts = file.split('_') + dataset_id = '_'.join(parts[-2].split('_')[:2]) + + # Determine Ranks + df = pd.read_csv(file_path, sep='\t', usecols=['Feature']) + if rba == "RandomShuffle": + df['Rank'] = df.index + 1 + else: + column_to_use = 'ABS_Feature_Importance' if "ABS" in rba else 'Feature_Importance' + df = pd.read_csv(file_path, sep='\t', usecols=['Feature', column_to_use]) + df.sort_values(by=column_to_use, ascending=False, inplace=True) + df.reset_index(drop=True, inplace=True) + df['Rank'] = df.index + 1 + + # Store in a dataframe + predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Rank']] + predictive_df['RBA'] = rba + predictive_df['Dataset'] = dataset_id + + all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True) + + # Add the feature count of this dataset to the list for this method + method_feature_counts.append(df['Feature'].nunique()) + + # Store the maximum feature count encountered for this method as N + total_features_per_rba[rba] = max(method_feature_counts) + +# Now, you have both the rankings and the total number of features (N) for each RBA method +# You can use total_features_per_rba to access N for each method as needed + +# Specify the path to save the consolidated rankings file +save_path = os.path.join(results_dir, 'consolidated_rankings.csv') +all_rankings_df.to_csv(save_path, index=False) + +print(f"Consolidated rankings saved to: {save_path}") + + +# %% [markdown] +# ### This code generates the ranking percentages that are needed to create the heatmaps + +# %% +# Assuming all_rankings_df is correctly prepared and contains 'RBA', 'Feature', 'Rank', 'Dataset' + +# Since all datasets have the same N, we can pick the N from any RBA method from total_features_per_rba +N = next(iter(total_features_per_rba.values())) + +# Step 1: Identify the lowest-ranked predictive feature for each dataset for each RBA +lowest_ranks = all_rankings_df.groupby(['RBA', 'Dataset'])['Rank'].max().reset_index() + +# Initialize a structure to hold the calculated percentages for each RBA method +percentages = {rba: [0] * N for rba in lowest_ranks['RBA'].unique()} + +# Step 2: Calculate percentages for each position for each RBA +for rba in percentages.keys(): + rba_data = lowest_ranks[lowest_ranks['RBA'] == rba] + for position in range(1, N + 1): + # Count how many of the lowest ranks are better (lower number) than the current position + count_higher = rba_data[rba_data['Rank'] <= (position)].shape[0] + total_datasets = rba_data.shape[0] # Should be 30 per RBA if there are 30 datasets + percentages[rba][position - 1] = (count_higher / total_datasets) * 100 + +# Convert the percentages to a DataFrame for visualization +percentages_df = pd.DataFrame(percentages, index=range(1, N + 1)) + +# Save percentages_df to a CSV file +save_path_percentages = os.path.join(results_dir, 'percentages_df.csv') +percentages_df.to_csv(save_path_percentages, index_label='Ranking Position') + +print("Percentages saved to percentages_df.csv.") + + +# %% [markdown] +# ### Plotting code for heatmaps + +# %% +# Create custom color scheme +custom_cmap = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:] + +# Define your preferred order of the RBAs as a list +rba_order = [ + 'RandomShuffle', + 'MutualInfo', + 'SURFstar', + 'MultiSURFstar', + 'SWRFstar', + 'ABS_SURFstar', + 'ABS_MultiSURFstar', + 'ABS_SWRFstar', +] + +# Define a mapping from your RBA order to new descriptive names +rba_descriptive_names = { + 'RandomShuffle': 'Random Shuffle', + 'MutualInfo': 'Mutual Information', + 'SURFstar': 'SURF*', + 'SWRFstar': 'SWRF*', + 'MultiSURFstar': 'MultiSURF*', + 'ABS_SURFstar': 'SURF* ABS', + 'ABS_MultiSURFstar': 'MultiSURF* ABS', + 'ABS_SWRFstar': 'SWRF* ABS', +} + +percentages_df = percentages_df.iloc[1:] # Drop the first row as this will always be 0 +percentages_df_transposed = percentages_df.T # Transpose percentages_df to switch rows and columns for horizontal orientation + +# Reorder the DataFrame according to your defined RBA order +percentages_df_ordered = percentages_df_transposed.loc[rba_order] # The .loc indexer reindexes the DataFrame to the specified order; any missing labels will result in NaN rows + +# Define the tick labels as percentages of optimality +xtick_labels = ['Optimal', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'] +# Generate a list of positions at which to place the x-tick labels, assuming they should be placed at even intervals +xtick_positions = np.linspace(start=0, stop=percentages_df_transposed.shape[1] - 0.13, num=len(xtick_labels)) + +# Create the heatmap with the reordered DataFrame +plt.figure(figsize=(12, 7)) # Adjust the size as necessary +heatmap = sns.heatmap(percentages_df_ordered, annot=False, fmt=".1f", cmap=custom_cmap, cbar_kws={'label': 'Power (Frequency of Success)'}) + +# Add horizontal lines manually between Methods +for i in range(percentages_df_ordered.shape[0] - 1): + heatmap.axhline(i + 1, color='black', linewidth=1.5) + +# Adding black border around the heatmap +for _, spine in heatmap.spines.items(): + spine.set_visible(True) + spine.set_linewidth(1.5) + spine.set_edgecolor("black") + +# Adding black border around the color bar in legend +cbar = heatmap.collections[0].colorbar +cbar.outline.set_linewidth(1.5) +cbar.outline.set_edgecolor("black") + +dataset_id = results_dir.split('/')[-2] # Extract the dataset identifier from the results directory path + +# Set the title and axis labels appropriately +heatmap.set_title('core2wayEpi_ABS_' + dataset_id, fontsize = 16) +heatmap.set_xlabel('Predictive features in top % of ranked features', fontsize = 14) +heatmap.set_ylabel('Method', fontsize = 14) + +# Set the custom x-tick labels, positions, and fontsize +heatmap.set_xticks(xtick_positions) +heatmap.set_xticklabels(xtick_labels, rotation=0, fontsize=11) + +# Set the custom y-tick labels fontsize +new_ytick_labels = [rba_descriptive_names[rba] for rba in rba_order] +heatmap.set_yticklabels(new_ytick_labels, fontsize=11) + +# Save and show the Plot +plt.rcParams['font.sans-serif'] = 'Helvetica' +plt.rcParams['font.family'] = 'sans-serif' +plt.rcParams['pdf.fonttype'] = 42 +plt.tight_layout() # Adjust the layout +save_path = os.path.join(results_dir, 'core2wayEpi_ABS_' + dataset_id + '.pdf') # Save +plt.savefig(save_path, format='pdf', bbox_inches='tight') # Save +plt.show() # Display the heatmap + +# %% + + + diff --git a/analysis/oldScripts/MScoring.ipynb b/analysis/oldScripts/MScoring.ipynb new file mode 100644 index 0000000..f4cd23e --- /dev/null +++ b/analysis/oldScripts/MScoring.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to process results directory and return a pivot table with feature importance\n", + "def process_results_directory(root_dir, meaningful_parts):\n", + " all_data = [] # List to store all data\n", + " \n", + " # Walk through the directory tree\n", + " for subdir, dirs, files in os.walk(root_dir):\n", + " # Check if subdir contains 'ABS' or 'MutualInfo'\n", + " if 'ABS' in subdir or 'MutualInfo' in subdir:\n", + " if subdir != root_dir: # Ensure not processing the root directory\n", + " method = subdir.split(os.sep)[-1].replace('ABS_', '') # Extract method name from subdir\n", + " subdir_data = [] # List to store data for current subdir\n", + " for file in files:\n", + " if file.endswith('.txt'): # Process only .txt files\n", + " file_path = os.path.join(subdir, file) # Construct file path\n", + " try:\n", + " data = pd.read_csv(file_path, delimiter='\\t') # Read data from file\n", + " m_data = data[data['Feature'].str.startswith('M')] # Filter data for features starting with 'M'\n", + " importance_cols = [col for col in data.columns if \"Feature_Importance\" in col] # Find importance columns\n", + "\n", + " # Iterate over filtered data and importance columns\n", + " for index, row in m_data.iterrows():\n", + " for col in importance_cols:\n", + " importance_type = col.replace('Feature_Importance', '').strip() # Extract importance type\n", + " method_column_name = f\"{method}_{importance_type}\".rstrip('_') # Create method column name\n", + " entry = {\n", + " 'Feature': row['Feature'], # Feature name\n", + " 'Method': method_column_name, # Method and importance type\n", + " 'Importance': row[col], # Importance value\n", + " **meaningful_parts # Add meaningful parts\n", + " }\n", + " subdir_data.append(entry) # Add entry to subdir_data\n", + " except Exception as e:\n", + " print(f\"Error reading {file_path}: {e}\") # Print error if reading fails\n", + "\n", + " if subdir_data:\n", + " all_data.extend(subdir_data) # Add subdir data to all_data\n", + "\n", + " if all_data:\n", + " results_df = pd.DataFrame(all_data) # Create DataFrame from all_data\n", + " pivot_df = results_df.pivot_table(index=['Feature', *meaningful_parts.keys()], columns='Method', values='Importance', aggfunc='mean').reset_index()\n", + " # Get the current non-Method columns\n", + " non_method_cols = ['Feature', *meaningful_parts.keys()]\n", + " # Get the current Method columns and sort them as required\n", + " method_cols = [col for col in pivot_df.columns if col not in non_method_cols]\n", + " # sorted_method_cols = ['ReliefF10', 'ReliefF10_ABS', 'ReliefF', 'ReliefF_ABS', 'MultiSURF', 'MultiSURF_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'MutualInformation']\n", + " sorted_method_cols = ['SURFstar', 'SURFstar_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'SWRFstar','SWRFstar_ABS','MutualInfo'\n", + " '']\n", + " # Combine the non-Method columns with the sorted Method columns\n", + " pivot_df = pivot_df[non_method_cols + sorted_method_cols]\n", + " return pivot_df # Return the pivot table\n", + " else:\n", + " return pd.DataFrame() # Return an empty DataFrame if no data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_numeric_parts(s):\n", + " \"\"\"Extract numeric parts from a string and return as a tuple of integers.\"\"\"\n", + " # Use regular expression to find all sequences of digits in the string\n", + " # Convert each sequence of digits to an integer and return them as a tuple\n", + " return tuple(map(int, re.findall(r'\\d+', s)))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def find_and_process_results(start_dir):\n", + " master_df = pd.DataFrame() # Initialize an empty DataFrame to store the master results\n", + " for root, dirs, files in os.walk(start_dir): # Walk through the directory tree\n", + " if 'Results' in dirs: # Check if 'Results' directory exists in the current directory\n", + " path_parts = os.path.relpath(root, start_dir).split(os.sep) # Get the relative path parts\n", + " meaningful_parts = {} # Dictionary to store meaningful parts\n", + " for part in path_parts: # Iterate over path parts - will need to update this for new descriptors if needed\n", + " if 'xor_' in part:\n", + " meaningful_parts['X1'] = part # Order of epistasis with additional descriptors\n", + " elif 'a_' in part:\n", + " meaningful_parts['X2'] = part # Feature count\n", + "\n", + " results_dir = os.path.join(root, 'Results') # Construct the path to the 'Results' directory\n", + " print(f\"Processing 'Results' folder at: {results_dir}\")\n", + " results_df = process_results_directory(results_dir, meaningful_parts) # Process the results directory\n", + " if not results_df.empty: # If the results DataFrame is not empty\n", + " csv_path = os.path.join(results_dir, 'M_average_feature_importance.csv') # Path to save the results CSV\n", + " results_df.to_csv(csv_path, index=False) # Save the results DataFrame to a CSV file\n", + " print(f\"Average feature importance results saved to {csv_path}\")\n", + "\n", + " # Create a numeric tuple for sorting X1\n", + " try:\n", + " results_df['Value'] = results_df['X1'].apply(extract_numeric_parts) # Extract numeric parts from X1\n", + " except Exception as e:\n", + " results_df['Value'] = results_df['X2'].apply(extract_numeric_parts)\n", + " # results_df['X1_numeric'] = results_df['X2_numeric'] # Fallback to X2 if X1 extraction fails\n", + " \n", + " # Append to the master DataFrame\n", + " master_df = pd.concat([master_df, results_df], ignore_index=True)\n", + "\n", + " # Sort the master DataFrame\n", + " if not master_df.empty: # If the master DataFrame is not empty\n", + " master_df = master_df.sort_values(by=['Value', 'Feature']) # Sort by numeric parts of X1 and Feature\n", + " master_df.drop('Value', axis=1, inplace=True) # Remove the auxiliary column after sorting\n", + " master_csv_path = os.path.join(start_dir, 'M_master_feature_importance.csv') # Path to save the master CSV\n", + " master_df.to_csv(master_csv_path, index=False) # Save the master DataFrame to a CSV file\n", + " print(f\"Master feature importance results saved to {master_csv_path}\") # Print confirmation" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-2/Results/M_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-1/Results/M_average_feature_importance.csv\n", + "Master feature importance results saved to ../core2wayEpistasis/M_master_feature_importance.csv\n" + ] + } + ], + "source": [ + "# Start the process from the current working directory\n", + "find_and_process_results('../core2wayEpistasis/')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "slcs", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/oldScripts/MScoring.py b/analysis/oldScripts/MScoring.py new file mode 100644 index 0000000..32c1306 --- /dev/null +++ b/analysis/oldScripts/MScoring.py @@ -0,0 +1,110 @@ +# %% +import os +import pandas as pd +import re + +# %% +# Function to process results directory and return a pivot table with feature importance +def process_results_directory(root_dir, meaningful_parts): + all_data = [] # List to store all data + + # Walk through the directory tree + for subdir, dirs, files in os.walk(root_dir): + # Check if subdir contains 'ABS' or 'MutualInfo' + if 'ABS' in subdir or 'MutualInfo' in subdir: + if subdir != root_dir: # Ensure not processing the root directory + method = subdir.split(os.sep)[-1].replace('ABS_', '') # Extract method name from subdir + subdir_data = [] # List to store data for current subdir + for file in files: + if file.endswith('.txt'): # Process only .txt files + file_path = os.path.join(subdir, file) # Construct file path + try: + data = pd.read_csv(file_path, delimiter='\t') # Read data from file + m_data = data[data['Feature'].str.startswith('M')] # Filter data for features starting with 'M' + importance_cols = [col for col in data.columns if "Feature_Importance" in col] # Find importance columns + + # Iterate over filtered data and importance columns + for index, row in m_data.iterrows(): + for col in importance_cols: + importance_type = col.replace('Feature_Importance', '').strip() # Extract importance type + method_column_name = f"{method}_{importance_type}".rstrip('_') # Create method column name + entry = { + 'Feature': row['Feature'], # Feature name + 'Method': method_column_name, # Method and importance type + 'Importance': row[col], # Importance value + **meaningful_parts # Add meaningful parts + } + subdir_data.append(entry) # Add entry to subdir_data + except Exception as e: + print(f"Error reading {file_path}: {e}") # Print error if reading fails + + if subdir_data: + all_data.extend(subdir_data) # Add subdir data to all_data + + if all_data: + results_df = pd.DataFrame(all_data) # Create DataFrame from all_data + pivot_df = results_df.pivot_table(index=['Feature', *meaningful_parts.keys()], columns='Method', values='Importance', aggfunc='mean').reset_index() + # Get the current non-Method columns + non_method_cols = ['Feature', *meaningful_parts.keys()] + # Get the current Method columns and sort them as required + method_cols = [col for col in pivot_df.columns if col not in non_method_cols] + # sorted_method_cols = ['ReliefF10', 'ReliefF10_ABS', 'ReliefF', 'ReliefF_ABS', 'MultiSURF', 'MultiSURF_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'MutualInformation'] + sorted_method_cols = ['SURFstar', 'SURFstar_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'SWRFstar','SWRFstar_ABS','MutualInfo' + ''] + # Combine the non-Method columns with the sorted Method columns + pivot_df = pivot_df[non_method_cols + sorted_method_cols] + return pivot_df # Return the pivot table + else: + return pd.DataFrame() # Return an empty DataFrame if no data + +# %% +def extract_numeric_parts(s): + """Extract numeric parts from a string and return as a tuple of integers.""" + # Use regular expression to find all sequences of digits in the string + # Convert each sequence of digits to an integer and return them as a tuple + return tuple(map(int, re.findall(r'\d+', s))) + +# %% +def find_and_process_results(start_dir): + master_df = pd.DataFrame() # Initialize an empty DataFrame to store the master results + for root, dirs, files in os.walk(start_dir): # Walk through the directory tree + if 'Results' in dirs: # Check if 'Results' directory exists in the current directory + path_parts = os.path.relpath(root, start_dir).split(os.sep) # Get the relative path parts + meaningful_parts = {} # Dictionary to store meaningful parts + for part in path_parts: # Iterate over path parts - will need to update this for new descriptors if needed + if 'xor_' in part: + meaningful_parts['X1'] = part # Order of epistasis with additional descriptors + elif 'a_' in part: + meaningful_parts['X2'] = part # Feature count + + results_dir = os.path.join(root, 'Results') # Construct the path to the 'Results' directory + print(f"Processing 'Results' folder at: {results_dir}") + results_df = process_results_directory(results_dir, meaningful_parts) # Process the results directory + if not results_df.empty: # If the results DataFrame is not empty + csv_path = os.path.join(results_dir, 'M_average_feature_importance.csv') # Path to save the results CSV + results_df.to_csv(csv_path, index=False) # Save the results DataFrame to a CSV file + print(f"Average feature importance results saved to {csv_path}") + + # Create a numeric tuple for sorting X1 + try: + results_df['Value'] = results_df['X1'].apply(extract_numeric_parts) # Extract numeric parts from X1 + except Exception as e: + results_df['Value'] = results_df['X2'].apply(extract_numeric_parts) + # results_df['X1_numeric'] = results_df['X2_numeric'] # Fallback to X2 if X1 extraction fails + + # Append to the master DataFrame + master_df = pd.concat([master_df, results_df], ignore_index=True) + + # Sort the master DataFrame + if not master_df.empty: # If the master DataFrame is not empty + master_df = master_df.sort_values(by=['Value', 'Feature']) # Sort by numeric parts of X1 and Feature + master_df.drop('Value', axis=1, inplace=True) # Remove the auxiliary column after sorting + master_csv_path = os.path.join(start_dir, 'M_master_feature_importance.csv') # Path to save the master CSV + master_df.to_csv(master_csv_path, index=False) # Save the master DataFrame to a CSV file + print(f"Master feature importance results saved to {master_csv_path}") # Print confirmation + +# %% +# Start the process from the current working directory +find_and_process_results('../core2wayEpistasis/') + + diff --git a/analysis/oldScripts/MasterRunScript_Old.ipynb b/analysis/oldScripts/MasterRunScript_Old.ipynb new file mode 100644 index 0000000..ff4e763 --- /dev/null +++ b/analysis/oldScripts/MasterRunScript_Old.ipynb @@ -0,0 +1,2707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os \n", + "import sys\n", + "# Get the absolute path to the directory containing the package\n", + "package_path = os.path.abspath(os.path.join(\"\", \"..\"))\n", + "\n", + "# Add the package path to sys.path\n", + "sys.path.insert(0, package_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import fnmatch\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.pipeline import make_pipeline\n", + "from skrebate import ReliefF # for reproducibility, need to install skrebate version 0.62\n", + "from skrebate import SURF\n", + "from skrebate import SURFstar\n", + "from skrebate import MultiSURF\n", + "from skrebate import MultiSURFstar\n", + "from skrebate import TURF\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.feature_selection import mutual_info_classif" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define function for directory maintenance" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def ensure_dir(directory): # Check if directories exist. If they do not, they will be created in the next function\n", + " if not os.path.exists(directory):\n", + " os.makedirs(directory)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define functions for random shuffle" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def random_shuffle(file_path):\n", + " # Define the directory to store results\n", + " results_dir = os.path.join(os.path.dirname(file_path), \"Results\", \"RandomShuffle\")\n", + " ensure_dir(results_dir) # Ensure the Results/RandomShuffle directory exists\n", + "\n", + " # Read the .txt file into a DataFrame\n", + " try:\n", + " df = pd.read_csv(file_path, sep='\\t') # Assuming tab-separated values; adjust the separator as needed\n", + " except Exception as e:\n", + " print(f\"Error reading {file_path}: {e}\")\n", + " return\n", + "\n", + " # Ensure 'Class' column is not included in the shuffle\n", + " if 'Class' in df.columns:\n", + " columns_to_shuffle = df.drop('Class', axis=1).columns.tolist()\n", + " else:\n", + " columns_to_shuffle = df.columns.tolist()\n", + "\n", + " # Shuffle the column names\n", + " shuffled_columns = np.random.permutation(columns_to_shuffle)\n", + "\n", + " # Create a new DataFrame with the shuffled column names\n", + " new_df = pd.DataFrame(shuffled_columns, columns=['Feature'])\n", + "\n", + " # Construct the output file name based on the original file's name\n", + " base_name = os.path.basename(file_path)\n", + " new_file_name = f\"{os.path.splitext(base_name)[0]}_RandShuffle.txt\"\n", + " output_path = os.path.join(results_dir, new_file_name)\n", + "\n", + " # Write the new DataFrame to disk\n", + " new_df.to_csv(output_path, index=False, sep='\\t')\n", + "\n", + "def find_and_random_shuffle(root_dir):\n", + " print(list(os.walk(root_dir)))\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " # Check if 'EDM' is in the directory name directly above the files\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'): # Ensure it's a .txt file\n", + " file_path = os.path.join(dirpath, filename)\n", + " print(f\"Processing file: {file_path}\") # Debugging line\n", + " random_shuffle(file_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define current working directory (GAMETES_2.2_dev_peter_XOR)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "root_dir = '../BenchmarkData/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/' " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run random shuffle process on directory" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": 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+ "Processing file: ../BenchmarkData/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/a_20/s_800/her_0.1__maf_0.2/a_20s_800her_0.1__maf_0.2_EDM-2/a_20s_800her_0.1__maf_0.2_EDM-2_28.txt\n", + "Processing file: ../BenchmarkData/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/a_20/s_800/her_0.1__maf_0.2/a_20s_800her_0.1__maf_0.2_EDM-2/a_20s_800her_0.1__maf_0.2_EDM-2_16.txt\n", + "Processing file: ../BenchmarkData/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/a_20/s_800/her_0.1__maf_0.2/a_20s_800her_0.1__maf_0.2_EDM-2/a_20s_800her_0.1__maf_0.2_EDM-2_02.txt\n", + "Processing file: ../BenchmarkData/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/a_20/s_800/her_0.1__maf_0.2/a_20s_800her_0.1__maf_0.2_EDM-2/a_20s_800her_0.1__maf_0.2_EDM-2_03.txt\n", + "Processing file: 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../BenchmarkData/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/a_100/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-1/a_100s_800her_0.2__maf_0.2_EDM-1_18.txt\n" + ] + } + ], + "source": [ + "# Run through the directory and produce random assortments of the features. This is the Random Shuffle that will be presented in the paper\n", + "find_and_random_shuffle(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define functions for Mutual Information" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def process_mutual_info(file_path):\n", + " # Load the genetic data\n", + " genetic_data = pd.read_csv(file_path, sep='\\t') # Ensure correct delimiter is used\n", + " \n", + " # Split the data\n", + " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", + " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + " \n", + " # Apply Mutual Information\n", + " mi_scores = mutual_info_classif(X_train, y_train)\n", + " \n", + " # Pair feature names with MI scores\n", + " temp_list = []\n", + " for feature_name, mi_score in zip(genetic_data.drop('Class', axis=1).columns, mi_scores):\n", + " temp_list.append([feature_name, mi_score])\n", + " \n", + " # Process results\n", + " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", + " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", + " \n", + " # Define directories\n", + " base_dir = os.path.dirname(file_path)\n", + " results_dir = os.path.join(base_dir, \"Results\")\n", + " mi_dir = os.path.join(results_dir, \"MutualInformation\")\n", + " ensure_dir(mi_dir)\n", + " \n", + " # Extract base filename without extension\n", + " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", + " \n", + " # Save the files\n", + " Results.to_csv(os.path.join(mi_dir, f\"{base_name}_MIResults.txt\"), index=False, sep='\\t')\n", + "\n", + "def find_and_mutual_info(root_dir):\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " # Check if 'EDM' is in the directory name directly above the files\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'): # Ensure it's a .txt file\n", + " file_path = os.path.join(dirpath, filename)\n", + " #print(f\"Processing file: {file_path}\") # Debugging line\n", + " process_mutual_info(file_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Mutual Information on directory" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Run through the directory and perform Mutual Information.\n", + "find_and_mutual_info(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define functions for ReliefF with 10 NN" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def process_relieff10(file_path):\n", + " # Load the genetic data\n", + " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", + " \n", + " # Split the data\n", + " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", + " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + " \n", + " # Apply ReliefF\n", + " fs = ReliefF(n_features_to_select=2, n_neighbors=10)\n", + " fs.fit(X_train, y_train)\n", + " \n", + " temp_list = []\n", + " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", + " temp_list.append([feature_name, feature_score])\n", + " \n", + " # Process results\n", + " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", + " ABSResults = Results.copy()\n", + " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", + " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", + " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", + " \n", + " # Define directories\n", + " base_dir = os.path.dirname(file_path)\n", + " results_dir = os.path.join(base_dir, \"Results\")\n", + " relief_dir = os.path.join(results_dir, \"ReliefF10\")\n", + " abs_relief_dir = os.path.join(results_dir, \"ABS_ReliefF10\")\n", + " ensure_dir(relief_dir)\n", + " ensure_dir(abs_relief_dir)\n", + " \n", + " # Extract base filename without extension\n", + " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", + " \n", + " # Save the files\n", + " Results.to_csv(os.path.join(relief_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", + " ABSResults.to_csv(os.path.join(abs_relief_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", + "\n", + "def find_and_relieff10(root_dir):\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " # Check if 'EDM' is in the directory name directly above the files\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'): # Ensure it's a .txt file\n", + " file_path = os.path.join(dirpath, filename)\n", + " #print(f\"Processing file: {file_path}\") # Debugging line\n", + " process_relieff10(file_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run ReliefF (10 NN) on directory" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "find_and_relieff10(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define functions for ReliefF with 100 NN" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def process_relieff(file_path):\n", + " # Load the genetic data\n", + " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", + " \n", + " # Split the data\n", + " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", + " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + " \n", + " # Apply ReliefF\n", + " fs = ReliefF(n_features_to_select=2, n_neighbors=100)\n", + " fs.fit(X_train, y_train)\n", + " \n", + " temp_list = []\n", + " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", + " temp_list.append([feature_name, feature_score])\n", + " \n", + " # Process results\n", + " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", + " ABSResults = Results.copy()\n", + " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", + " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", + " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", + " \n", + " # Define directories\n", + " base_dir = os.path.dirname(file_path)\n", + " results_dir = os.path.join(base_dir, \"Results\")\n", + " relief_dir = os.path.join(results_dir, \"ReliefF\")\n", + " abs_relief_dir = os.path.join(results_dir, \"ABS_ReliefF\")\n", + " ensure_dir(relief_dir)\n", + " ensure_dir(abs_relief_dir)\n", + " \n", + " # Extract base filename without extension\n", + " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", + " \n", + " # Save the files\n", + " Results.to_csv(os.path.join(relief_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", + " ABSResults.to_csv(os.path.join(abs_relief_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", + "\n", + "def find_and_relieff(root_dir):\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " # Check if 'EDM' is in the directory name directly above the files\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'): # Ensure it's a .txt file\n", + " file_path = os.path.join(dirpath, filename)\n", + " #print(f\"Processing file: {file_path}\") # Debugging line\n", + " process_relieff(file_path)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run ReliefF (100 NN) on directory" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Run through the directory and perform ReliefF 100 NN.\n", + "find_and_relieff(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define functions for MultiSURF" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def process_MultiSurf(file_path):\n", + " # Load the genetic data\n", + " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", + " \n", + " # Split the data\n", + " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", + " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + " \n", + " # Apply MultiSurf\n", + " fs = MultiSURF()\n", + " fs.fit(X_train, y_train)\n", + " \n", + " temp_list = []\n", + " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", + " temp_list.append([feature_name, feature_score])\n", + " \n", + " # Process results\n", + " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", + " ABSResults = Results.copy()\n", + " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", + " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", + " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", + " \n", + " # Define directories\n", + " base_dir = os.path.dirname(file_path)\n", + " results_dir = os.path.join(base_dir, \"Results\")\n", + " multisurf_dir = os.path.join(results_dir, \"MultiSURF\")\n", + " abs_multisurf_dir = os.path.join(results_dir, \"ABS_MultiSURF\")\n", + " ensure_dir(multisurf_dir)\n", + " ensure_dir(abs_multisurf_dir)\n", + " \n", + " # Extract base filename without extension\n", + " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", + " \n", + " # Save the files\n", + " Results.to_csv(os.path.join(multisurf_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", + " ABSResults.to_csv(os.path.join(abs_multisurf_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", + "\n", + "def find_and_MultiSurf(root_dir):\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " # Check if 'EDM' is in the directory name directly above the files\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'): # Ensure it's a .txt file\n", + " file_path = os.path.join(dirpath, filename)\n", + " #print(f\"Processing file: {file_path}\") # Debugging line\n", + " process_MultiSurf(file_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run MultiSURF on directory" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Run through the directory and perform MultiSURF.\n", + "find_and_MultiSurf(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define functions for MultiSURFstar" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def process_MultiSurfstar(file_path):\n", + " # Load the genetic data\n", + " genetic_data = pd.read_csv(file_path, sep = '\\t')\n", + " \n", + " # Split the data\n", + " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", + " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + " \n", + " # Apply MultiSurf\n", + " fs = MultiSURFstar()\n", + " fs.fit(X_train, y_train)\n", + " \n", + " temp_list = []\n", + " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", + " temp_list.append([feature_name, feature_score])\n", + " \n", + " # Process results\n", + " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", + " ABSResults = Results.copy()\n", + " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", + " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", + " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", + " \n", + " # Define directories\n", + " base_dir = os.path.dirname(file_path)\n", + " results_dir = os.path.join(base_dir, \"Results\")\n", + " multisurfstar_dir = os.path.join(results_dir, \"MultiSURFstar\")\n", + " abs_multisurfstar_dir = os.path.join(results_dir, \"ABS_MultiSURFstar\")\n", + " ensure_dir(multisurfstar_dir)\n", + " ensure_dir(abs_multisurfstar_dir)\n", + " \n", + " # Extract base filename without extension\n", + " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", + " \n", + " # Save the files\n", + " Results.to_csv(os.path.join(multisurfstar_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", + " ABSResults.to_csv(os.path.join(abs_multisurfstar_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", + "\n", + "def find_and_MultiSurfstar(root_dir):\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " # Check if 'EDM' is in the directory name directly above the files\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " #print(f\"Processing in EDM directory: {dirpath}\") # Debugging line\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'): # Ensure it's a .txt file\n", + " file_path = os.path.join(dirpath, filename)\n", + " #print(f\"Processing file: {file_path}\") # Debugging line\n", + " process_MultiSurfstar(file_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run MultiSURFstar on directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run through the first directory and perform MultiSURFstar.\n", + "find_and_MultiSurfstar(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run SURFstar on directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from skrebate import SURFstar # make sure this import is active\n", + "\n", + "def process_SURFstar(file_path):\n", + " # Load the genetic data\n", + " genetic_data = pd.read_csv(file_path, sep='\\t')\n", + "\n", + " # Split the data\n", + " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", + " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + "\n", + " # Apply SURFstar\n", + " fs = SURFstar()\n", + " fs.fit(X_train, y_train)\n", + "\n", + " temp_list = []\n", + " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", + " temp_list.append([feature_name, feature_score])\n", + "\n", + " # Process results\n", + " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", + " ABSResults = Results.copy()\n", + " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", + " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", + " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", + "\n", + " # Define directories\n", + " base_dir = os.path.dirname(file_path)\n", + " results_dir = os.path.join(base_dir, \"Results\")\n", + " surfstar_dir = os.path.join(results_dir, \"SURFstar\")\n", + " abs_surfstar_dir = os.path.join(results_dir, \"ABS_SURFstar\")\n", + " ensure_dir(surfstar_dir)\n", + " ensure_dir(abs_surfstar_dir)\n", + "\n", + " # Extract base filename without extension\n", + " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", + "\n", + " # Save the files\n", + " Results.to_csv(os.path.join(surfstar_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", + " ABSResults.to_csv(os.path.join(abs_surfstar_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", + "\n", + "def find_and_SURFstar(root_dir):\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " # Check if 'EDM' is in the directory name directly above the files\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'): # Ensure it's a .txt file\n", + " file_path = os.path.join(dirpath, filename)\n", + " process_SURFstar(file_path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "find_and_SURFstar(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run SWRFstar on directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from skrebate import SWRFstar # ensure your implementation is correctly accessible\n", + "\n", + "def process_SWRFstar(file_path):\n", + " # Load the genetic data\n", + " genetic_data = pd.read_csv(file_path, sep='\\t')\n", + "\n", + " # Split the data\n", + " features, labels = genetic_data.drop('Class', axis=1).values, genetic_data['Class'].values\n", + " X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + "\n", + " # Apply SWRFstar\n", + " fs = SWRFstar()\n", + " fs.fit(X_train, y_train)\n", + "\n", + " temp_list = []\n", + " for feature_name, feature_score in zip(genetic_data.drop('Class', axis=1).columns, fs.feature_importances_):\n", + " temp_list.append([feature_name, feature_score])\n", + "\n", + " # Process results\n", + " Results = pd.DataFrame(temp_list, columns=['Feature', 'Feature_Importance'])\n", + " ABSResults = Results.copy()\n", + " ABSResults['ABS_Feature_Importance'] = ABSResults['Feature_Importance'].abs()\n", + " Results.sort_values(by='Feature_Importance', ascending=False, inplace=True)\n", + " ABSResults.sort_values(by='ABS_Feature_Importance', ascending=False, inplace=True)\n", + "\n", + " # Define directories\n", + " base_dir = os.path.dirname(file_path)\n", + " results_dir = os.path.join(base_dir, \"Results\")\n", + " swrfstar_dir = os.path.join(results_dir, \"SWRFstar\")\n", + " abs_swrfstar_dir = os.path.join(results_dir, \"ABS_SWRFstar\")\n", + " ensure_dir(swrfstar_dir)\n", + " ensure_dir(abs_swrfstar_dir)\n", + "\n", + " # Extract base filename without extension\n", + " base_name = os.path.splitext(os.path.basename(file_path))[0]\n", + "\n", + " # Save the files\n", + " Results.to_csv(os.path.join(swrfstar_dir, f\"{base_name}_Results.txt\"), index=False, sep='\\t')\n", + " ABSResults.to_csv(os.path.join(abs_swrfstar_dir, f\"{base_name}_ABSResults.txt\"), index=False, sep='\\t')\n", + "\n", + "def find_and_SWRFstar(root_dir):\n", + " for dirpath, dirnames, filenames in os.walk(root_dir):\n", + " if \"EDM\" in os.path.basename(dirpath):\n", + " for filename in filenames:\n", + " if filename.endswith('.txt'):\n", + " file_path = os.path.join(dirpath, filename)\n", + " process_SWRFstar(file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "find_and_SWRFstar(root_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "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": 2 +} diff --git a/analysis/oldScripts/Nscoring.ipynb b/analysis/oldScripts/Nscoring.ipynb new file mode 100644 index 0000000..bb6a363 --- /dev/null +++ b/analysis/oldScripts/Nscoring.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.2__maf_0.2/a_100s_400her_0.2__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.4__maf_0.2/a_100s_400her_0.4__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.05__maf_0.2/a_100s_400her_0.05__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_400/her_0.1__maf_0.2/a_100s_400her_0.1__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.2__maf_0.2/a_100s_200her_0.2__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.4__maf_0.2/a_100s_200her_0.4__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.05__maf_0.2/a_100s_200her_0.05__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_200/her_0.1__maf_0.2/a_100s_200her_0.1__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.2__maf_0.2/a_100s_1600her_0.2__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.4__maf_0.2/a_100s_1600her_0.4__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.05__maf_0.2/a_100s_1600her_0.05__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_1600/her_0.1__maf_0.2/a_100s_1600her_0.1__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.2__maf_0.2/a_100s_800her_0.2__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.4__maf_0.2/a_100s_800her_0.4__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.05__maf_0.2/a_100s_800her_0.05__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-2/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-2/Results/N_average_feature_importance.csv\n", + "Processing 'Results' folder at: ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-1/Results\n", + "Average feature importance results saved to ../core2wayEpistasis/s_800/her_0.1__maf_0.2/a_100s_800her_0.1__maf_0.2_EDM-1/Results/N_average_feature_importance.csv\n", + "Master feature importance results saved to ../core2wayEpistasis/N_master_feature_importance.csv\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import re\n", + "\n", + "# Function to process results directory and return a pivot table with feature importance\n", + "def process_results_directory(root_dir, meaningful_parts):\n", + " all_data = [] # List to store all data\n", + " \n", + " # Walk through the directory tree\n", + " for subdir, dirs, files in os.walk(root_dir):\n", + " # Check if subdir contains 'ABS' or 'MutualInfo'\n", + " if 'ABS' in subdir or 'MutualInfo' in subdir:\n", + " if subdir != root_dir: # Ensure not processing the root directory\n", + " method = subdir.split(os.sep)[-1].replace('ABS_', '') # Extract method name from subdir\n", + " file_averages = [] # List to store average importances for each file\n", + " for file in files:\n", + " if file.endswith('.txt'): # Process only .txt files\n", + " file_path = os.path.join(subdir, file) # Construct file path\n", + " try:\n", + " data = pd.read_csv(file_path, delimiter='\\t') # Read data from file\n", + " n_data = data[data['Feature'].str.startswith('N')] # Filter data for features starting with 'N'\n", + " importance_cols = [col for col in data.columns if \"Feature_Importance\" in col] # Find importance columns\n", + "\n", + " # Check if there are 'N' features and importance columns\n", + " if not n_data.empty and importance_cols:\n", + " # Calculate average importance for 'N' features within the current file\n", + " for col in importance_cols:\n", + " avg_importance = n_data[col].mean() # Calculate average importance\n", + " importance_type = col.replace('Feature_Importance', '').strip() # Extract importance type\n", + " method_column_name = f\"{method}_{importance_type}\".rstrip('_') # Create method column name\n", + " \n", + " entry = {\n", + " 'Feature': 'average_N', # Use 'average_N' as the feature name\n", + " 'Method': method_column_name, # Method and importance type\n", + " 'Importance': avg_importance, # Average importance value within the file\n", + " **meaningful_parts # Add meaningful parts\n", + " }\n", + " file_averages.append(entry) # Add entry to file_averages\n", + " else:\n", + " print(f\"No 'N' features or importance columns in {file_path}\")\n", + "\n", + " except Exception as e:\n", + " print(f\"Error reading {file_path}: {e}\") # Print error if reading fails\n", + "\n", + " if file_averages:\n", + " # Aggregate the file-level averages for the current method\n", + " df_file_averages = pd.DataFrame(file_averages)\n", + " method_average = df_file_averages.groupby(['Feature', 'Method', *meaningful_parts.keys()]).mean().reset_index()\n", + " all_data.extend(method_average.to_dict('records')) # Add aggregated data to all_data\n", + " else:\n", + " print(f\"No valid data found in {subdir}\")\n", + "\n", + " if all_data:\n", + " results_df = pd.DataFrame(all_data) # Create DataFrame from all_data\n", + " pivot_df = results_df.pivot_table(index=['Feature', *meaningful_parts.keys()], columns='Method', values='Importance', aggfunc='mean').reset_index()\n", + " # Get the current non-Method columns\n", + " non_method_cols = ['Feature', *meaningful_parts.keys()]\n", + " # Get the current Method columns and sort them as required\n", + " method_cols = [col for col in pivot_df.columns if col not in non_method_cols]\n", + " # sorted_method_cols = ['ReliefF10', 'ReliefF10_ABS', 'ReliefF', 'ReliefF_ABS', 'MultiSURF', 'MultiSURF_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'MutualInformation']\n", + " sorted_method_cols = ['SURFstar', 'SURFstar_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'SWRFstar','SWRFstar_ABS','MutualInfo']\n", + " \n", + " # Combine the non-Method columns with the sorted Method columns\n", + " pivot_df = pivot_df[non_method_cols + sorted_method_cols]\n", + " return pivot_df # Return the pivot table\n", + " else:\n", + " print(\"No data collected from any files\")\n", + " return pd.DataFrame() # Return an empty DataFrame if no data\n", + "\n", + "def extract_numeric_parts(s):\n", + " \"\"\"Extract numeric parts from a string and return as a tuple of integers.\"\"\"\n", + " return tuple(map(int, re.findall(r'\\d+', s)))\n", + "\n", + "def find_and_process_results(start_dir):\n", + " master_df = pd.DataFrame() # Initialize an empty DataFrame to store the master results\n", + " for root, dirs, files in os.walk(start_dir): # Walk through the directory tree\n", + " if 'Results' in dirs: # Check if 'Results' directory exists in the current directory\n", + " path_parts = os.path.relpath(root, start_dir).split(os.sep) # Get the relative path parts\n", + " meaningful_parts = {} # Dictionary to store meaningful parts\n", + " for part in path_parts: # Iterate over path parts - will need to update this for new descriptors if needed\n", + " if 'xor_' in part:\n", + " meaningful_parts['X1'] = part # Order of epistasis with additional descriptors\n", + " elif 'a_' in part:\n", + " meaningful_parts['X2'] = part # Feature count\n", + "\n", + " results_dir = os.path.join(root, 'Results') # Construct the path to the 'Results' directory\n", + " print(f\"Processing 'Results' folder at: {results_dir}\")\n", + " results_df = process_results_directory(results_dir, meaningful_parts) # Process the results directory\n", + " if not results_df.empty: # If the results DataFrame is not empty\n", + " csv_path = os.path.join(results_dir, 'N_average_feature_importance.csv') # Path to save the results CSV\n", + " results_df.to_csv(csv_path, index=False) # Save the results DataFrame to a CSV file\n", + " print(f\"Average feature importance results saved to {csv_path}\")\n", + "\n", + " # Create a numeric tuple for sorting X1\n", + " try:\n", + " results_df['Value'] = results_df['X1'].apply(extract_numeric_parts) # Extract numeric parts from X1\n", + " except Exception as e:\n", + " results_df['Value'] = results_df['X2'].apply(extract_numeric_parts)\n", + " # results_df['X1_numeric'] = results_df['X2_numeric'] # Fallback to X2 if X1 extraction fails\n", + " \n", + " # Append to the master DataFrame\n", + " master_df = pd.concat([master_df, results_df], ignore_index=True)\n", + " else:\n", + " print(f\"No data found in 'Results' folder at: {results_dir}\")\n", + "\n", + " # Sort the master DataFrame\n", + " if not master_df.empty: # If the master DataFrame is not empty\n", + " master_df = master_df.sort_values(by=['Value', 'Feature']) # Sort by numeric parts of X1 and Feature\n", + " master_df.drop('Value', axis=1, inplace=True) # Remove the auxiliary column after sorting\n", + "\n", + " # Calculate grand average for all numeric columns and append it as the last row\n", + " numeric_cols = master_df.select_dtypes(include='number').columns\n", + " grand_average = master_df[numeric_cols].mean().to_frame().T\n", + " for col in master_df.columns:\n", + " if col not in numeric_cols:\n", + " grand_average[col] = 'Grand_Average'\n", + " master_df = pd.concat([master_df, grand_average], ignore_index=True)\n", + "\n", + " master_csv_path = os.path.join(start_dir, 'N_master_feature_importance.csv') # Path to save the master CSV\n", + " master_df.to_csv(master_csv_path, index=False) # Save the master DataFrame to a CSV file\n", + " print(f\"Master feature importance results saved to {master_csv_path}\") # Print confirmation\n", + " else:\n", + " print(\"No master data to save\")\n", + "\n", + "# Start the process from the current working directory\n", + "find_and_process_results('../core2wayEpistasis')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "slcs", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/oldScripts/Nscoring.py b/analysis/oldScripts/Nscoring.py new file mode 100644 index 0000000..c9cc584 --- /dev/null +++ b/analysis/oldScripts/Nscoring.py @@ -0,0 +1,129 @@ +# %% +import os +import pandas as pd +import re + +# Function to process results directory and return a pivot table with feature importance +def process_results_directory(root_dir, meaningful_parts): + all_data = [] # List to store all data + + # Walk through the directory tree + for subdir, dirs, files in os.walk(root_dir): + # Check if subdir contains 'ABS' or 'MutualInfo' + if 'ABS' in subdir or 'MutualInfo' in subdir: + if subdir != root_dir: # Ensure not processing the root directory + method = subdir.split(os.sep)[-1].replace('ABS_', '') # Extract method name from subdir + file_averages = [] # List to store average importances for each file + for file in files: + if file.endswith('.txt'): # Process only .txt files + file_path = os.path.join(subdir, file) # Construct file path + try: + data = pd.read_csv(file_path, delimiter='\t') # Read data from file + n_data = data[data['Feature'].str.startswith('N')] # Filter data for features starting with 'N' + importance_cols = [col for col in data.columns if "Feature_Importance" in col] # Find importance columns + + # Check if there are 'N' features and importance columns + if not n_data.empty and importance_cols: + # Calculate average importance for 'N' features within the current file + for col in importance_cols: + avg_importance = n_data[col].mean() # Calculate average importance + importance_type = col.replace('Feature_Importance', '').strip() # Extract importance type + method_column_name = f"{method}_{importance_type}".rstrip('_') # Create method column name + + entry = { + 'Feature': 'average_N', # Use 'average_N' as the feature name + 'Method': method_column_name, # Method and importance type + 'Importance': avg_importance, # Average importance value within the file + **meaningful_parts # Add meaningful parts + } + file_averages.append(entry) # Add entry to file_averages + else: + print(f"No 'N' features or importance columns in {file_path}") + + except Exception as e: + print(f"Error reading {file_path}: {e}") # Print error if reading fails + + if file_averages: + # Aggregate the file-level averages for the current method + df_file_averages = pd.DataFrame(file_averages) + method_average = df_file_averages.groupby(['Feature', 'Method', *meaningful_parts.keys()]).mean().reset_index() + all_data.extend(method_average.to_dict('records')) # Add aggregated data to all_data + else: + print(f"No valid data found in {subdir}") + + if all_data: + results_df = pd.DataFrame(all_data) # Create DataFrame from all_data + pivot_df = results_df.pivot_table(index=['Feature', *meaningful_parts.keys()], columns='Method', values='Importance', aggfunc='mean').reset_index() + # Get the current non-Method columns + non_method_cols = ['Feature', *meaningful_parts.keys()] + # Get the current Method columns and sort them as required + method_cols = [col for col in pivot_df.columns if col not in non_method_cols] + # sorted_method_cols = ['ReliefF10', 'ReliefF10_ABS', 'ReliefF', 'ReliefF_ABS', 'MultiSURF', 'MultiSURF_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'MutualInformation'] + sorted_method_cols = ['SURFstar', 'SURFstar_ABS', 'MultiSURFstar', 'MultiSURFstar_ABS', 'SWRFstar','SWRFstar_ABS','MutualInfo'] + + # Combine the non-Method columns with the sorted Method columns + pivot_df = pivot_df[non_method_cols + sorted_method_cols] + return pivot_df # Return the pivot table + else: + print("No data collected from any files") + return pd.DataFrame() # Return an empty DataFrame if no data + +def extract_numeric_parts(s): + """Extract numeric parts from a string and return as a tuple of integers.""" + return tuple(map(int, re.findall(r'\d+', s))) + +def find_and_process_results(start_dir): + master_df = pd.DataFrame() # Initialize an empty DataFrame to store the master results + for root, dirs, files in os.walk(start_dir): # Walk through the directory tree + if 'Results' in dirs: # Check if 'Results' directory exists in the current directory + path_parts = os.path.relpath(root, start_dir).split(os.sep) # Get the relative path parts + meaningful_parts = {} # Dictionary to store meaningful parts + for part in path_parts: # Iterate over path parts - will need to update this for new descriptors if needed + if 'xor_' in part: + meaningful_parts['X1'] = part # Order of epistasis with additional descriptors + elif 'a_' in part: + meaningful_parts['X2'] = part # Feature count + + results_dir = os.path.join(root, 'Results') # Construct the path to the 'Results' directory + print(f"Processing 'Results' folder at: {results_dir}") + results_df = process_results_directory(results_dir, meaningful_parts) # Process the results directory + if not results_df.empty: # If the results DataFrame is not empty + csv_path = os.path.join(results_dir, 'N_average_feature_importance.csv') # Path to save the results CSV + results_df.to_csv(csv_path, index=False) # Save the results DataFrame to a CSV file + print(f"Average feature importance results saved to {csv_path}") + + # Create a numeric tuple for sorting X1 + try: + results_df['Value'] = results_df['X1'].apply(extract_numeric_parts) # Extract numeric parts from X1 + except Exception as e: + results_df['Value'] = results_df['X2'].apply(extract_numeric_parts) + # results_df['X1_numeric'] = results_df['X2_numeric'] # Fallback to X2 if X1 extraction fails + + # Append to the master DataFrame + master_df = pd.concat([master_df, results_df], ignore_index=True) + else: + print(f"No data found in 'Results' folder at: {results_dir}") + + # Sort the master DataFrame + if not master_df.empty: # If the master DataFrame is not empty + master_df = master_df.sort_values(by=['Value', 'Feature']) # Sort by numeric parts of X1 and Feature + master_df.drop('Value', axis=1, inplace=True) # Remove the auxiliary column after sorting + + # Calculate grand average for all numeric columns and append it as the last row + numeric_cols = master_df.select_dtypes(include='number').columns + grand_average = master_df[numeric_cols].mean().to_frame().T + for col in master_df.columns: + if col not in numeric_cols: + grand_average[col] = 'Grand_Average' + master_df = pd.concat([master_df, grand_average], ignore_index=True) + + master_csv_path = os.path.join(start_dir, 'N_master_feature_importance.csv') # Path to save the master CSV + master_df.to_csv(master_csv_path, index=False) # Save the master DataFrame to a CSV file + print(f"Master feature importance results saved to {master_csv_path}") # Print confirmation + else: + print("No master data to save") + +# Start the process from the current working directory +find_and_process_results('../core2wayEpistasis') + + diff --git a/analysis/oldScripts/README.txt b/analysis/oldScripts/README.txt new file mode 100644 index 0000000..9cb3642 --- /dev/null +++ b/analysis/oldScripts/README.txt @@ -0,0 +1 @@ +Based on Assessing the Limitations of Relief-Based Algorithms in Detecting Higher-Order Interactions, Freda et al. 2024 \ No newline at end of file diff --git a/analysis/run_commands.txt b/analysis/run_commands.txt new file mode 100644 index 0000000..3e87998 --- /dev/null +++ b/analysis/run_commands.txt @@ -0,0 +1,459 @@ +Main Benchmarking +  +Heatmaps +  +# 1) running run_rebate_analysis for all mainEff data with 100 features (a_100); no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_Datasets_Loc_1_Qnt_2_Pop_100000/a_100 +  +# command to get unified heatmap visualization for mainEff (100 features datasets) +  +python run_process_heatmap_unified.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_Datasets_Loc_1_Qnt_2_Pop_100000/a_100 --prefix=mainEff_a100_rerun_ +  +# 2) running run_rebate_analysis for all core2wayEpistasis data with 100 features (a_100); no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/a_100 +  +# command to get unified heatmap visualization for core2wayEpistasis (100 features datasets) +  +python run_process_heatmap_unified.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000/a_100 --prefix=core2wayEpistasis_a100_rerun_ +  +# 3) running run_rebate_analysis for all XOR data (path structure is a bit different to core2wayEpistasis and mainEff, so can’t directly access 100 features datasets; do that in job_process_heatmap_unified) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_XOR +  +# command to get individual heatmaps for XOR (100 features datasets) +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_XOR --prefix=XOR_a100_rerun_  + +# zipping up heatmaps for mainEff, core2wayEpistasis, XOR + +zip -r heatmaps_mainbenchmarking_rerun.zip AbsVal_Benchmark_Data -i '*rerun*.pdf' + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/unified_heatmaps_mainbenchmarking_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\" +------------------------------------------------------------------  +Mean/median rank tables +  +# computing mean/median rankings given by RBAs to predictive features (amongst mainEff only) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_Datasets_Loc_1_Qnt_2_Pop_100000 +  +# computing mean/median rankings given by RBAs to predictive features (amongst core2wayEpistasis only) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000 +  +# computing mean/median rankings given by RBAs to predictive features (amongst XOR only) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_XOR + +# zip up RBA rankings results + +zip -r RBArankings_mainbenchmarking_rerun.zip AbsVal_Benchmark_Data -i "*mainEff_Datasets*rankings_list.csv" "*mainEff_Datasets*rba_rankings.csv" "*core2wayEpistasis*rankings_list.csv" "*core2wayEpistasis*rba_rankings.csv" "*XOR*rankings_list.csv" "*XOR*rba_rankings.csv" + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/RBArankings_mainbenchmarking_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\" +----------------------------------------------------------------------------  +Statistical Significance Tests +  +# computing Mann-Whitney U and Permutation Test for mainEff (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_Datasets_Loc_1_Qnt_2_Pop_100000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for mainEff (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_Datasets_Loc_1_Qnt_2_Pop_100000 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for core2wayEpistasis (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for core2wayEpistasis (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_core2wayEpistasis_Datasets_Loc_2_Qnt_2_Pop_100000 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for XOR 3-way (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_XOR/xor_3/a_100/s_1600/xor_3_a_100s_1600_EDM-1/Results --column rank --exclude shuffle +  +# computing only Mann-Whitney U for XOR 3-way (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_XOR/xor_3/a_100/s_1600/xor_3_a_100s_1600_EDM-1/Results --column feature_importance --exclude shuffle + +# save mannwhitney_ranks.csv and mannwhitney_normalized_feature_importances.csv files (for mainEff, core2wayEpistasis, and XOR 3-way) + +zip -r mannwhitney_results_rerun.zip AbsVal_Benchmark_Data -i '*mannwhitney_ranks*' '*mannwhitney_normalized_feature_importances*' + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/mannwhitney_results_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  +-----------------------------------------------------------------------  +Larger Feature Datasets (2wayEpiFeatures) +  +Heatmaps +  +# running run_rebate_analysis for 100 feature dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100 +  +# running run_rebate_analysis for 1000 feature dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_1000 +  +# running run_rebate_analysis for 10000 feature dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_10000 +  +# running run_rebate_analysis for 100000 feature dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100000  +----------------  +# command to get heatmap visualization for 100 features dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100 --prefix=100features_main_rerun_ +  +# command to get heatmap visualization for 1000 features dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_1000 --prefix=1000features_main_rerun_ +  +# command to get heatmap visualization for 10000 features dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_10000 --prefix=10000features_main_rerun_ +  +# command to get heatmap visualization for 100000 features dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100000 --prefix=100000features_main_rerun_ + +# zipping up heatmaps and runtimes for 2wayEpiFeatures datasets + +zip -r heatmaps_andruntimes_2wayEpiFeatures_rerun.zip GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000 -i '*rerun*.pdf' '*runtime.csv' + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/heatmaps_andruntimes_2wayEpiFeatures_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  +--------------------------------------  +Mean/median rank tables +  +# computing mean/median rankings given by RBAs to predictive features (amongst 100 feature dataset) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100 +  +# computing mean/median rankings given by RBAs to predictive features (amongst 1000 feature dataset) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_1000 +  +# computing mean/median rankings given by RBAs to predictive features (amongst 10000 feature dataset) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_10000 +  +# computing mean/median rankings given by RBAs to predictive features (amongst 100000 feature dataset) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100000 + +# zip up RBA rankings results + +zip -r RBArankings_largefeaturedatasets_rerun.zip GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000 -i "*rankings_list.csv" "*rba_rankings.csv" + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/RBArankings_largefeaturedatasets_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  +----------------------------------------------------------------------------  +Statistical Significance Tests +  +# computing Mann-Whitney U and Permutation Test for 100 features (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for 100 features (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for 1000 features (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_1000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for 1000 features (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_1000 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for 10,000 features (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_10000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for 10,000 features (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_10000 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for 100,000 features (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for 100,000 features (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000/a_100000 --column feature_importance --exclude shuffle + +# save mannwhitney_ranks.csv and mannwhitney_normalized_feature_importances.csv files (for 2wayEpiFeatures) + +zip -r mannwhitney_results_largefeaturedatasets_rerun.zip GAMETES_2.2_dev_peter_2wayEpiFeatures_Datasets_Loc_2_Qnt_2_Pop_100000 -i '*mannwhitney_ranks*' '*mannwhitney_normalized_feature_importances*' + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/mannwhitney_results_largefeaturedatasets_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  +------------------------------------------------------------  +Larger Feature Datasets (1-feature mainEff) +  +Heatmaps +  +# running run_rebate_analysis for 1000 feature dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_1000_Feature_H_0.4_MAF_0.2_EDM-2 +  +# running run_rebate_analysis for 10000 feature dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_10000_Feature_H_0.4_MAF_0.2_EDM-2 +  +# running run_rebate_analysis for 100000 feature dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_100000_Feature_H_0.4_MAF_0.2_EDM-2  +----------------  +# command to get heatmap visualization for 1000 features dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_1000_Feature_H_0.4_MAF_0.2_EDM-2 --prefix=1000features_main_rerun_ +  +# command to get heatmap visualization for 10000 features dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_10000_Feature_H_0.4_MAF_0.2_EDM-2 --prefix=10000features_main_rerun_ +  +# command to get heatmap visualization for 100000 features dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_100000_Feature_H_0.4_MAF_0.2_EDM-2 --prefix=100000features_main_rerun_ + +# zipping up heatmaps and runtimes for mainEff_largerfeatures datasets + +zip -r heatmaps_andruntimes_mainEfflargerfeatures_rerun.zip mainEff_largerfeatures_data -i '*rerun*.pdf' '*runtime*.csv' + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/heatmaps_andruntimes_mainEfflargerfeatures_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  +--------------------------------------  +Mean/median rank tables +  +# computing mean/median rankings given by RBAs to predictive features (amongst 1000 feature dataset) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_1000_Feature_H_0.4_MAF_0.2_EDM-2 +  +# computing mean/median rankings given by RBAs to predictive features (amongst 10000 feature dataset) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_10000_Feature_H_0.4_MAF_0.2_EDM-2 +  +# computing mean/median rankings given by RBAs to predictive features (amongst 100000 feature dataset) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/mainEff_largerfeatures_data/1-feature_100000_Feature_H_0.4_MAF_0.2_EDM-2 + +# zip up RBA rankings results + +zip -r RBArankings_mainEfflargefeaturedatasets_rerun.zip mainEff_largerfeatures_data -i "*rankings_list.csv" "*rba_rankings.csv" + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/RBArankings_mainEfflargefeaturedatasets_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  +----------------------------------------------------------------------------  +Additional Dataset Groups (from Phil paper) +  +Heatmaps +  +# 2wayEpiHeterogeneity (100 feature datasets) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_2wayEpiHeterogeneity_Datasets_2Het_Loc_2_Qnt_2_Pop_100000/a_100 +   +# epi_order (100 feature datasets) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_epi_order_Datasets_Loc_3_Qnt_2_Pop_100000/a_100 + +# mainEff_additive_2 (100 feature datasets) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_2_Datasets_2Het_Loc_1_Qnt_2_Pop_100000/a_100 +   +# mainEff_additive_4 (100 feature datasets) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_4_Datasets_2Het_Loc_1_Qnt_2_Pop_100000/a_100   +----------------------------------------------------  +# ** now getting individual configuration heatmaps for each dataset group +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_2wayEpiHeterogeneity_Datasets_2Het_Loc_2_Qnt_2_Pop_100000/a_100 --prefix=2wayEpiHeterogeneity_main_rerun_ +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_epi_order_Datasets_Loc_3_Qnt_2_Pop_100000/a_100 --prefix=epiorder_main_rerun_ +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_2_Datasets_2Het_Loc_1_Qnt_2_Pop_100000/a_100 --prefix=mainEffadditive2_main_rerun_ +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_4_Datasets_2Het_Loc_1_Qnt_2_Pop_100000/a_100 --prefix=mainEffadditive4_main_rerun_ + +# zipping up heatmaps from new dataset groups + +zip -r heatmaps_new_datasetgroups_rerun.zip AbsVal_Benchmark_Data -i "*rerun*.pdf" -x "*mainEff_Datasets*" "*core2wayEpistasis*" "*XOR*" + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/heatmaps_new_datasetgroups_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"   +---------------------------------------------------------------------------  +Mean/median rank tables +  +# computing mean/median rankings given by RBAs to predictive features (amongst 2wayEpiHeterogeneity only) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_2wayEpiHeterogeneity_Datasets_2Het_Loc_2_Qnt_2_Pop_100000 +  +# computing mean/median rankings given by RBAs to predictive features (amongst epi_order only) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_epi_order_Datasets_Loc_3_Qnt_2_Pop_100000 +  +# computing mean/median rankings given by RBAs to predictive features (amongst mainEff_additive_2 only) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_2_Datasets_2Het_Loc_1_Qnt_2_Pop_100000 +  +# computing mean/median rankings given by RBAs to predictive features (amongst mainEff_additive_4 only) +  +python run_process_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_4_Datasets_2Het_Loc_1_Qnt_2_Pop_100000 + +# zip up RBA rankings results + +zip -r RBArankings_newdatasetgroupsPhilpaper_rerun.zip AbsVal_Benchmark_Data -i "*mainEff_additive*rankings_list.csv" "*mainEff_additive*rba_rankings.csv" "*2wayEpiHeterogeneity*rankings_list.csv" "*2wayEpiHeterogeneity*rba_rankings.csv" "*epi_order*rankings_list.csv" "*epi_order*rba_rankings.csv" + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/RBArankings_newdatasetgroupsPhilpaper_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"   +----------------------------------------------------------------------------------------  +Statistical Significance Tests +  +# computing Mann-Whitney U and Permutation Test for 2wayEpiHeterogeneity (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_2wayEpiHeterogeneity_Datasets_2Het_Loc_2_Qnt_2_Pop_100000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for 2wayEpiHeterogeneity (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_2wayEpiHeterogeneity_Datasets_2Het_Loc_2_Qnt_2_Pop_100000 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for epi_order (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_epi_order_Datasets_Loc_3_Qnt_2_Pop_100000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for epi_order (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_epi_order_Datasets_Loc_3_Qnt_2_Pop_100000 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for mainEff_additive_2 (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_2_Datasets_2Het_Loc_1_Qnt_2_Pop_100000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for mainEff_additive_2 (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_2_Datasets_2Het_Loc_1_Qnt_2_Pop_100000 --column feature_importance --exclude shuffle +  +# computing Mann-Whitney U and Permutation Test for mainEff_additive_4 (on ranks) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_4_Datasets_2Het_Loc_1_Qnt_2_Pop_100000 --column rank --exclude shuffle +  +# computing only Mann-Whitney U for mainEff_additive_4 (on feature_importances) +  +python run_process_mannwhitney.py --basedir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data/GAMETES_2.2_dev_peter_mainEff_additive_4_Datasets_2Het_Loc_1_Qnt_2_Pop_100000 --column feature_importance --exclude shuffle + +# save mannwhitney_ranks.csv and mannwhitney_normalized_feature_importances.csv files (for new dataset groups from Phil paper) + +zip -r mannwhitney_results_newdatasetgroupsPhilpaper_rerun.zip AbsVal_Benchmark_Data -i '*mannwhitney_ranks*' '*mannwhitney_normalized_feature_importances*' -x "*mainEff_Datasets*" "*core2wayEpistasis*" "*XOR*" + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/mannwhitney_results_newdatasetgroupsPhilpaper_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  +--------------------------------------------------------------------------------------------  +Sanity Check Datasets +  +Heatmaps +  +# running run_rebate_analysis for CA (continuous attribute) dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/CA_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 +  +# running run_rebate_analysis for MA (mixed attribute) dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/MA_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 +  +# running run_rebate_analysis for CE_Threshold (continuous endpoint, threshold) dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/CE_Threshold_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 +  +# running run_rebate_analysis for contEndpoint (continuous endpoint, SD version) dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpi_contEndpoint_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 +  +# running run_rebate_analysis for NA (null values) dataset; can’t use mutual_info with missing data, so explicitly naming algorithms to use (instead of just using default) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpi_NA_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 --algorithms random relieff10 relieff100 surf surfstar multisurf multisurfstar swrfstar swrf multiswrfstar multiswrf multiswrfdbstar multiswrfdb murelief10 murelief100 +   +# running run_rebate_analysis for ImbalancedData dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpi_ImbalancedData_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 +   +# running run_rebate_analysis for 3Class (multi-class endpoint, 3 classes) dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/3Class_Datasets_Loc_2 +   +# running run_rebate_analysis for 9Class (multi-class endpoint, 9 classes) dataset; no --algorithms specified means use the script’s default algorithms (all 16) +  +python run_rebate_analysis.py --datadir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/9Class_Datasets_Loc_2   +------------------------  +# command to get heatmap visualization for CA dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/CA_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 --prefix=CA_main_rerun_ +  +# command to get heatmap visualization for MA dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/MA_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 --prefix=MA_main_rerun_ +  +# command to get heatmap visualization for CE_Threshold dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/CE_Threshold_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 --prefix=CEThreshold_main_rerun_ +  +# command to get heatmap visualization for contEndpoint dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpi_contEndpoint_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 --prefix=contEndpoint_main_rerun_ +  +# command to get heatmap visualization for NA dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpi_NA_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 --prefix=NA_main_rerun_ +  +# command to get heatmap visualization for ImbalancedData dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/GAMETES_2.2_dev_peter_2wayEpi_ImbalancedData_Datasets_Loc_2_Qnt_2_Pop_100000/a_20 --prefix=ImbalancedData_main_rerun_ +  +# command to get heatmap visualization for 3Class dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/3Class_Datasets_Loc_2 --prefix=3Class_main_rerun_ +  +# command to get heatmap visualization for 9Class dataset +  +python run_process_heatmap.py --basedir=/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/Simulated_Benchmark_Archive/9Class_Datasets_Loc_2 --prefix=9Class_main_rerun_ + +# zipping up heatmaps for Sanity Check datasets + +zip -r heatmaps_sanitycheckdatasets_rerun.zip Simulated_Benchmark_Archive -i '*rerun*.pdf' -x '*2wayEpiFeatures*' + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/data/Sanity_Check_Data/benchmark-data/heatmaps_sanitycheckdatasets_rerun.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"   +----------------------------------------------------------------  +Global Metrics + +Produces unique output file/metrics for: 2-way epistasis datasets, univariate + 2-way datasets, and univariate + 2-way + 3-way datasets +  +# computing global mean/median ranks given by RBAs to predictive features (all dataset configurations with >= 100 features, excluding xor-4 and xor-5) +  +python run_global_rba_rankings.py --rootdir=/home/kazemik/Repos/ReBATE/data/AbsVal_Benchmark_Data +  +# zipping up different use case scenario ranking files + +zip -r global_rankings.zip data -i '*global_rba_rankings_2wayonly.csv' '*global_rba_rankings_uni2way.csv' '*global_rba_rankings_uni2way3way.csv' + +# creating a copy of the zip file locally (HPC to local desktop) + +pscp kazemik@esplhpccompbio-lv01:/home/kazemik/Repos/ReBATE/global_rankings.zip "C:\Users\KazemiK\OneDrive - Cedars-Sinai Health System\Desktop\"  diff --git a/analysis/run_global_rba_rankings.py b/analysis/run_global_rba_rankings.py new file mode 100644 index 0000000..ef8f8f2 --- /dev/null +++ b/analysis/run_global_rba_rankings.py @@ -0,0 +1,69 @@ +# run_global_rba_rankings.py +import os +import argparse +import time + +def generate_job_script(scheduler, python_script, root_dir, job_name, job_dir, log_dir, include_subdirs=None): + job_script_path = os.path.join(job_dir, f"{job_name}.sh") + include_arg = '' + if include_subdirs: + include_arg = ' '.join(include_subdirs) + include_arg = f"--include {include_arg}" + + if scheduler == 'lsf': + script = f"""#!/bin/bash +#BSUB -J {job_name} +#BSUB -o {log_dir}/{job_name}.out +#BSUB -e {log_dir}/{job_name}.err +#BSUB -n 1 +#BSUB -R "rusage[mem=4096]" +#BSUB -W 02:00 +#BSUB -q i2c2_normal + +python {python_script} "{root_dir}" {include_arg} +""" + submit_cmd = f"bsub < {job_script_path}" + + elif scheduler == 'slurm': + script = f"""#!/bin/bash +#SBATCH --job-name={job_name} +#SBATCH --output={log_dir}/{job_name}.out +#SBATCH --error={log_dir}/{job_name}.err +#SBATCH --ntasks=1 +#SBATCH --mem=4G +#SBATCH --time=02:00:00 +#SBATCH --partition=defq + +python {python_script} "{root_dir}" {include_arg} +""" + submit_cmd = f"sbatch {job_script_path}" + + else: + raise ValueError("Unsupported scheduler type. Use 'lsf' or 'slurm'.") + + with open(job_script_path, 'w') as f: + f.write(script) + + os.system(submit_cmd) + print(f"[INFO] Submitted job: {job_name}") + + +def main(): + parser = argparse.ArgumentParser(description="Submit global RBA rankings job(s).") + parser.add_argument('--rootdir', required=True, help='Root directory containing dataset subdirectories.') + parser.add_argument('--hpctype', choices=['lsf', 'slurm'], default='slurm', help='Scheduler type.') + parser.add_argument('--script', default='job_global_rba_rankings.py', help='Python script to run.') + parser.add_argument('--jobdir', default='jobs', help='Directory to store job scripts.') + parser.add_argument('--logdir', default='logs', help='Directory to store logs.') + parser.add_argument('--include', nargs='+', default=None, help='Optional list of subdirectories to include (short names).') + args = parser.parse_args() + + os.makedirs(args.jobdir, exist_ok=True) + os.makedirs(args.logdir, exist_ok=True) + + job_name = f"global_rba_{int(time.time())}" + generate_job_script(args.hpctype, args.script, args.rootdir, job_name, args.jobdir, args.logdir, args.include) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/run_process_heatmap.py b/analysis/run_process_heatmap.py new file mode 100644 index 0000000..dc81a58 --- /dev/null +++ b/analysis/run_process_heatmap.py @@ -0,0 +1,70 @@ +# make_jobs.py +import os +import time +import argparse + +def generate_job_script(scheduler, python_script, results_path, job_name, job_dir, log_dir, prefix): + job_script_path = os.path.join(job_dir, f"{job_name}.sh") + prefix_flag = f"--prefix {prefix}" if prefix else "" + + if scheduler == 'lsf': + script = f"""#!/bin/bash +#BSUB -J {job_name} +#BSUB -o {log_dir}/{job_name}.out +#BSUB -e {log_dir}/{job_name}.err +#BSUB -n 1 +#BSUB -R "rusage[mem=4096]" +#BSUB -W 01:00 +#BSUB -q i2c2_normal + +python {python_script} "{results_path}" {prefix_flag} +""" + submit_cmd = f"bsub < {job_script_path}" + + elif scheduler == 'slurm': + script = f"""#!/bin/bash +#SBATCH --job-name={job_name} +#SBATCH --output={log_dir}/{job_name}.out +#SBATCH --error={log_dir}/{job_name}.err +#SBATCH --ntasks=1 +#SBATCH --mem=4G +#SBATCH --time=01:00:00 +#SBATCH --partition=defq + +python {python_script} "{results_path}" {prefix_flag} +""" + submit_cmd = f"sbatch {job_script_path}" + + else: + raise ValueError("Unsupported scheduler type. Use 'lsf' or 'slurm'.") + + with open(job_script_path, 'w') as f: + f.write(script) + + os.system(submit_cmd) + +def main(): + parser = argparse.ArgumentParser(description="Generate and submit jobs for processing Results folders.") + parser.add_argument('--basedir', required=True, help='Base experiment directory containing Results subfolders.') + parser.add_argument('--hpctype', choices=['lsf', 'slurm'], default='slurm', help='Scheduler type: lsf or slurm.') + parser.add_argument('--script', default='job_process_heatmap.py', help='Python script to run.') + parser.add_argument('--jobdir', default='jobs', help='Directory to store job scripts.') + parser.add_argument('--logdir', default='logs', help='Directory to store output and error logs.') + parser.add_argument('--prefix', default='', help='Prefix for all output files (e.g. for heatmap PDF).') + + args = parser.parse_args() + + os.makedirs(args.jobdir, exist_ok=True) + os.makedirs(args.logdir, exist_ok=True) + + job_count = 0 + for root, dirs, _ in os.walk(args.basedir): + for d in dirs: + if d == 'Results': + results_path = os.path.join(root, d) + job_name = f"job_{job_count}_{int(time.time())}" + generate_job_script(args.hpctype, args.script, results_path, job_name, args.jobdir, args.logdir, args.prefix) + job_count += 1 + +if __name__ == "__main__": + main() diff --git a/analysis/run_process_heatmap_unified.py b/analysis/run_process_heatmap_unified.py new file mode 100644 index 0000000..277795d --- /dev/null +++ b/analysis/run_process_heatmap_unified.py @@ -0,0 +1,58 @@ +import os +import argparse +import time + +def main(): + parser = argparse.ArgumentParser(description="Generate and submit one job for unified heatmap.") + parser.add_argument('--basedir', required=True, help='Directory containing multiple Results folders.') + parser.add_argument('--hpctype', choices=['lsf', 'slurm'], default='slurm', help='Scheduler type.') + parser.add_argument('--script', default='job_process_heatmap_unified.py', help='Python script to run.') + parser.add_argument('--jobdir', default='jobs', help='Directory to store job scripts.') + parser.add_argument('--logdir', default='logs', help='Directory to store output and error logs.') + parser.add_argument('--prefix', default='', help='Prefix for unified PDF filename.') + args = parser.parse_args() + + os.makedirs(args.jobdir, exist_ok=True) + os.makedirs(args.logdir, exist_ok=True) + + # We’ll pass the whole basedir once to the script + job_name = f"unified_job_{int(time.time())}" + job_script_path = os.path.join(args.jobdir, f"{job_name}.sh") + prefix_flag = f"--prefix {args.prefix}" if args.prefix else "" + + if args.hpctype == 'lsf': + script = f"""#!/bin/bash +#BSUB -J {job_name} +#BSUB -o {args.logdir}/{job_name}.out +#BSUB -e {args.logdir}/{job_name}.err +#BSUB -n 1 +#BSUB -R "rusage[mem=4096]" +#BSUB -W 02:00 +#BSUB -q i2c2_normal + +python {args.script} "{args.basedir}" {prefix_flag} +""" + submit_cmd = f"bsub < {job_script_path}" + elif args.hpctype == 'slurm': + script = f"""#!/bin/bash +#SBATCH --job-name={job_name} +#SBATCH --output={args.logdir}/{job_name}.out +#SBATCH --error={args.logdir}/{job_name}.err +#SBATCH --ntasks=1 +#SBATCH --mem=4G +#SBATCH --time=02:00:00 +#SBATCH --partition=defq + +python {args.script} "{args.basedir}" {prefix_flag} +""" + submit_cmd = f"sbatch {job_script_path}" + else: + raise ValueError("Unsupported scheduler type. Use 'lsf' or 'slurm'.") + + with open(job_script_path, 'w') as f: + f.write(script) + + os.system(submit_cmd) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/run_process_mannwhitney.py b/analysis/run_process_mannwhitney.py new file mode 100644 index 0000000..173684a --- /dev/null +++ b/analysis/run_process_mannwhitney.py @@ -0,0 +1,69 @@ +# run_process_mannwhitney.py +import os +import time +import argparse + +def generate_job_script(scheduler, python_script, dir_path, job_name, job_dir, log_dir, column, exclude): + job_script_path = os.path.join(job_dir, f"{job_name}.sh") + column_flag = f"--column {column}" if column else "" + exclude_flag = f"--exclude {' '.join(exclude)}" if exclude else "" + + if scheduler == 'lsf': + script = f"""#!/bin/bash +#BSUB -J {job_name} +#BSUB -o {log_dir}/{job_name}.out +#BSUB -e {log_dir}/{job_name}.err +#BSUB -n 1 +#BSUB -R "rusage[mem=4096]" +#BSUB -W 03:00 +#BSUB -q i2c2_normal + +python {python_script} "{dir_path}" {column_flag} {exclude_flag} +""" + submit_cmd = f"bsub < {job_script_path}" + + elif scheduler == 'slurm': + script = f"""#!/bin/bash +#SBATCH --job-name={job_name} +#SBATCH --output={log_dir}/{job_name}.out +#SBATCH --error={log_dir}/{job_name}.err +#SBATCH --ntasks=1 +#SBATCH --mem=4G +#SBATCH --time=03:00:00 +#SBATCH --partition=defq + +python {python_script} "{dir_path}" {column_flag} {exclude_flag} +""" + submit_cmd = f"sbatch {job_script_path}" + else: + raise ValueError("Unsupported scheduler type. Use 'lsf' or 'slurm'.") + + with open(job_script_path, 'w') as f: + f.write(script) + + os.system(submit_cmd) + +def main(): + parser = argparse.ArgumentParser(description="Generate and submit Mann-Whitney U test jobs.") + parser.add_argument('--basedir', required=True, help='Base directory to search for rankings_list.csv.') + parser.add_argument('--hpctype', choices=['lsf', 'slurm'], default='slurm', help='Scheduler type: lsf or slurm.') + parser.add_argument('--script', default='job_process_mannwhitney.py', help='Python script to run.') + parser.add_argument('--jobdir', default='jobs', help='Directory to store job scripts.') + parser.add_argument('--logdir', default='logs', help='Directory to store output and error logs.') + parser.add_argument('--column', choices=['rank', 'feature_importance'], default='rank', help='Column to use for tests.') + parser.add_argument('--exclude', nargs='*', default=[], help='List of patterns to exclude RBAs.') + + args = parser.parse_args() + + os.makedirs(args.jobdir, exist_ok=True) + os.makedirs(args.logdir, exist_ok=True) + + job_count = 0 + dir_path = args.basedir # only look directly inside basedir + if 'rankings_list.csv' in os.listdir(dir_path): + job_name = f"mannwhitney_job_{job_count}_{int(time.time())}" + generate_job_script(args.hpctype, args.script, dir_path, job_name, args.jobdir, args.logdir, args.column, args.exclude) + job_count += 1 + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/run_process_rba_rankings.py b/analysis/run_process_rba_rankings.py new file mode 100644 index 0000000..baea250 --- /dev/null +++ b/analysis/run_process_rba_rankings.py @@ -0,0 +1,67 @@ +# run_process_rba_rankings.py +import os +import time +import argparse + +def generate_job_script(scheduler, python_script, root_dir, job_name, job_dir, log_dir): + job_script_path = os.path.join(job_dir, f"{job_name}.sh") + + if scheduler == 'lsf': + script = f"""#!/bin/bash +#BSUB -J {job_name} +#BSUB -o {log_dir}/{job_name}.out +#BSUB -e {log_dir}/{job_name}.err +#BSUB -n 1 +#BSUB -R "rusage[mem=4096]" +#BSUB -W 02:00 +#BSUB -q i2c2_normal + +python {python_script} "{root_dir}" +""" + submit_cmd = f"bsub < {job_script_path}" + + elif scheduler == 'slurm': + script = f"""#!/bin/bash +#SBATCH --job-name={job_name} +#SBATCH --output={log_dir}/{job_name}.out +#SBATCH --error={log_dir}/{job_name}.err +#SBATCH --ntasks=1 +#SBATCH --mem=4G +#SBATCH --time=02:00:00 +#SBATCH --partition=defq + +python {python_script} "{root_dir}" +""" + submit_cmd = f"sbatch {job_script_path}" + + else: + raise ValueError("Unsupported scheduler type. Use 'lsf' or 'slurm'.") + + with open(job_script_path, 'w') as f: + f.write(script) + + os.system(submit_cmd) + + +def main(): + parser = argparse.ArgumentParser(description="Submit a job to compute global RBA rankings.") + parser.add_argument('--rootdir', required=True, help='Root directory containing results folders.') + parser.add_argument('--hpctype', choices=['lsf', 'slurm'], default='slurm', help='Scheduler type: lsf or slurm.') + parser.add_argument('--script', default='job_process_rba_rankings.py', help='Python script to run.') + parser.add_argument('--jobdir', default='jobs', help='Directory to store job scripts.') + parser.add_argument('--logdir', default='logs', help='Directory to store output and error logs.') + + args = parser.parse_args() + + os.makedirs(args.jobdir, exist_ok=True) + os.makedirs(args.logdir, exist_ok=True) + + root_basename = os.path.basename(os.path.normpath(args.rootdir)) + + job_name = f"rba_rankings_{root_basename}_{int(time.time())}" + generate_job_script(args.hpctype, args.script, args.rootdir, job_name, args.jobdir, args.logdir) + print(f"Submitted single job for: {args.rootdir}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/analysis/run_rebate_analysis.py b/analysis/run_rebate_analysis.py new file mode 100644 index 0000000..f316dc8 --- /dev/null +++ b/analysis/run_rebate_analysis.py @@ -0,0 +1,118 @@ +import os +import time + +ALGORITHMS = [ + "mutual_info", + "relieff10", + "relieff100", + "surf", + "surfstar", + "multisurf", + "multisurfstar", + "random", + "swrfstar", + "swrf", + "multiswrfstar", + "multiswrf", + "multiswrfdbstar", + "multiswrfdb", + "murelief10", + "murelief100" +] + +LSF_TEMPLATE = """#!/bin/bash +#BSUB -J {job_name} +#BSUB -o logs/{job_name}.out +#BSUB -e logs/{job_name}.err +#BSUB -n 1 +#BSUB -R "rusage[mem=4096]" +#BSUB -W 01:00 +#BSUB -q i2c2_normal + +python job_rebate_analysis.py --algorithm {algorithm} --input_file "{input_file}" +""" + +SLURM_TEMPLATE = """#!/bin/bash +#SBATCH --job-name={job_name} +#SBATCH --output=logs/{job_name}.out +#SBATCH --error=logs/{job_name}.err +#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=1 +#SBATCH --mem=128G +#SBATCH --time=7-00:00:00 +#SBATCH --partition=defq + +python job_rebate_analysis.py --algorithm {algorithm} --input_file "{input_file}" +""" + +def ensure_dir(directory): + if not os.path.exists(directory): + os.makedirs(directory) + +def find_dataset_files(root_dir): + dataset_files = [] + for dirpath, _, filenames in os.walk(root_dir): + if "EDM" in os.path.basename(dirpath) or os.path.basename(dirpath) == "3Class_Datasets_Loc_2" or os.path.basename(dirpath) == "9Class_Datasets_Loc_2": + for filename in filenames: + if filename.endswith(".txt"): + dataset_files.append(os.path.join(dirpath, filename)) + return dataset_files + +def generate_hpc_jobs(data_dir, dataset_files, algorithms, job_dir, suffix, hpctype="slurm"): + ensure_dir(job_dir) + if hpctype == "lsf": + TEMPLATE = LSF_TEMPLATE + elif hpctype == "slurm": + TEMPLATE = SLURM_TEMPLATE + else: + raise Exception(f"Unsupported HPC type: {hpctype}") + job_paths = [] + for dataset in dataset_files: + rel_path = os.path.relpath(dataset, data_dir) + parts = rel_path.split(os.sep) # Split into parts + last_dirs_and_file = parts[-3:] # [-3:] = last 2 dirs + file + safe_path = "_".join(last_dirs_and_file) # Join with underscore + safe_path = safe_path.replace(".", "") + base_name = os.path.splitext(safe_path)[0] + for algo in algorithms: + job_name = f"{base_name}_{algo}_{suffix}" + job_path = os.path.join(job_dir, f"{job_name}.sh") + with open(job_path, "w") as f: + f.write(TEMPLATE.format( + job_name=job_name, + algorithm=algo, + input_file=dataset, + )) + job_paths.append(job_path) + return job_paths + +def main(): + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--datadir", required=True, help="Root directory to search for dataset files") + parser.add_argument("--jobdir", default="jobs", help="Directory to save LSF job scripts") + parser.add_argument("--algorithms", nargs="+", default=ALGORITHMS, help="Algorithms to run") + parser.add_argument("--hpctype", choices=["lsf", "slurm"], default="slurm", help="HPC type (lsf or slurm)") + args = parser.parse_args() + + suffix = time.strftime("%Y%m%d_%H%M%S") + dataset_files = find_dataset_files(args.datadir) + job_files = generate_hpc_jobs(args.datadir, dataset_files, args.algorithms, args.jobdir, suffix, hpctype=args.hpctype) + ensure_dir(os.path.join("", "logs")) + + print(f"Generated {len(job_files)} job scripts in: {args.jobdir}") + + # Automatically submit jobs + for job_file in job_files: + if args.hpctype == "lsf": + os.system(f"bsub < {job_file}") + elif args.hpctype == "slurm": + os.system(f"sbatch {job_file}") + else: + raise Exception(f"Unsupported HPC type: {args.hpctype}") + print(f"Submitted job: {job_file}") + + print("All jobs submitted successfully.") + +if __name__ == "__main__": + main() diff --git a/ci/.travis_install.sh b/ci/.travis_install.sh deleted file mode 100644 index a7f9fed..0000000 --- a/ci/.travis_install.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash - -# modified from https://github.com/trevorstephens/gplearn - -# This script is meant to be called by the "install" step defined in -# .travis.yml. See http://docs.travis-ci.com/ for more details. -# The behavior of the script is controlled by environment variabled defined -# in the .travis.yml in the top level folder of the project. - - -# License: GNU/GPLv3 - -set -e - -# Fix the compilers to workaround avoid having the Python 3.4 build -# lookup for g++44 unexpectedly. -export CC=gcc -export CXX=g++ - -# Deactivate the travis-provided virtual environment and setup a -# conda-based environment instead -deactivate - -# Use the miniconda installer for faster download / install of conda -# itself -wget http://repo.continuum.io/miniconda/Miniconda-3.9.1-Linux-x86_64.sh \ - -O miniconda.sh -chmod +x miniconda.sh && ./miniconda.sh -b -export PATH=/home/travis/miniconda/bin:$PATH -conda update --yes conda - -# Configure the conda environment and put it in the path using the -# provided versions -conda create -n testenv --yes python=$PYTHON_VERSION pip nose \ - numpy scipy scikit-learn cython pandas - -source activate testenv - -if [[ "$COVERAGE" == "true" ]]; then - pip install coverage coveralls -fi - -# build output in the travis output when it succeeds. -python --version -python -c "import numpy; print('numpy %s' % numpy.__version__)" -python -c "import scipy; print('scipy %s' % scipy.__version__)" -python -c "import sklearn; print('sklearn %s' % sklearn.__version__)" -python -c "import pandas; print('pandas %s' % pandas.__version__)" -python setup.py build_ext --inplace diff --git a/ci/.travis_test.sh b/ci/.travis_test.sh deleted file mode 100644 index f04fd41..0000000 --- a/ci/.travis_test.sh +++ /dev/null @@ -1,22 +0,0 @@ -# modified from https://github.com/trevorstephens/gplearn - -# This script is meant to be called by the "install" step defined in -# .travis.yml. See http://docs.travis-ci.com/ for more details. -# The behavior of the script is controlled by environment variabled defined -# in the .travis.yml in the top level folder of the project. - -# License: GNU/GPLv3 - -set -e - -python --version -python -c "import numpy; print('numpy %s' % numpy.__version__)" -python -c "import scipy; print('scipy %s' % scipy.__version__)" -python -c "import sklearn; print('sklearn %s' % sklearn.__version__)" -python -c "import pandas; print('pandas %s' % pandas.__version__)" - -if [[ "$COVERAGE" == "true" ]]; then - nosetests -s -v --with-coverage -else - nosetests -s -v -fi diff --git a/data/3Class_Datasets_Loc_2_01.csv b/data/3Class_Datasets_Loc_2_01.csv new file mode 100644 index 0000000..4b55f77 --- /dev/null +++ b/data/3Class_Datasets_Loc_2_01.csv @@ -0,0 +1,1601 @@ +N0,N1,N2,N3,N4,N5,N6,N7,N8,N9,N10,N11,N12,N13,N14,N15,N16,N17,M0P0,M0P1,class +0,0,0,0,0,0,0,2,0,0,1,2,1,2,1,0,1,0,2,1,0 +1,0,0,0,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,2 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+1,0,0,1,98.43361683933568,32.79461042354803,41.60709981781848,0,18.19876036434009,0,84.93723320376688,1,66.19994752923276,2,1,31.12702767580972,2,0,25.093576721042187,27.27701705351405,0 +0,0,0,1,56.85253219378784,5.167184434489097,8.973328939429603,0,49.092261802006135,0,48.22216220595666,0,16.07848157174203,1,2,97.00935578796415,0,1,54.73258442426688,21.847672162752303,0 +0,1,0,1,10.408731517892722,35.703438738979536,75.63774268315842,2,23.578170476209745,0,88.31890423768262,2,86.17762909722099,1,1,87.52487592019716,1,1,58.34710059950671,6.700774296068329,0 +0,1,0,2,74.63831418712289,28.56163355133885,97.6660679677837,1,29.292272073931876,1,39.0085436795935,1,17.184334419697105,1,0,2.1415453078625744,1,1,18.71019984234377,55.95963422903199,0 +0,0,0,2,96.08550487281512,19.20227279687652,33.08468486801294,0,10.029842339070688,2,143.78545259886354,0,122.93766779034736,0,0,43.45790296385215,1,2,45.77004723294464,54.90546701691953,0 +0,1,1,1,69.35124272431264,34.7922999161402,22.543842942880804,1,7.668767764948408,1,33.242084077687814,2,75.06431087256797,0,2,61.061279669958935,1,0,16.850914118535666,24.07690080163932,0 +0,0,0,2,48.72696827795417,31.22131078126756,16.01818817965582,1,20.79812510883813,1,50.59099490275024,1,134.0456613526137,0,0,18.34052394950854,1,0,49.54902051619327,57.39224346376853,0 +0,1,0,1,81.84826389425898,46.88148889485788,46.3510390319232,1,32.87599854698273,0,36.450602038671455,1,40.34839261882076,1,0,76.35088398011032,0,0,1.468001768914412,92.26682743539268,0 diff --git a/data/GAMETES_Epistasis_2-Way_mixed_attribute_a_20s_1600her_0.4__maf_0.2_EDM-2_01.tsv.gz b/data/GAMETES_Epistasis_2-Way_mixed_attribute_a_20s_1600her_0.4__maf_0.2_EDM-2_01.tsv.gz deleted file mode 100644 index c568d26..0000000 Binary files a/data/GAMETES_Epistasis_2-Way_mixed_attribute_a_20s_1600her_0.4__maf_0.2_EDM-2_01.tsv.gz and /dev/null differ diff --git a/data/README.md b/data/README.md new file mode 100644 index 0000000..7c8c150 --- /dev/null +++ b/data/README.md @@ -0,0 +1,26 @@ +# Example Data + +The example datasets are generated by [GAMETES](https://github.com/URBSLab/GAMETES), a tool designed to simulate genetic interactions (epistasis). These datasets comprise 1600 samples each, with: +* 18 non-informative (low importance) features labeled as `N0` through `N17`. +* 2 informative (high importance) features labeled as `P1` and `P2` or `M0P0` and `M0P1`. +* A final column labeled `class`, indicates the outcome. + +Below is the summary of the key differences between each example files. + +| File Name | Feature Type (Number of Categories per Feature) | Label Type (Number of Labels) | Missing Data | +| ----- | ----- | ----- | ----- | +| `GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv` | Discrete (3) | Binary | No | +| `GAMETES_Epistasis_2-Way_continuous_endpoint_a_20s_1600her_0.4__maf_0.2_EDM-2_01.csv` | Discrete (3) | **Continuous** | No | +| `GAMETES_Epistasis_2-Way_mixed_attribute_a_20s_1600her_0.4__maf_0.2_EDM-2_01.csv` | **Mix discrete (3) and continuous** | Binary | No | +| `GAMETES_Epistasis_2-Way_missing_values_0.1_a_20s_1600her_0.4__maf_0.2_EDM-2_01.csv` | Discrete (3) | Binary | **Yes** | +| `3Class_Datasets_Loc_2_01.csv` | Discrete (3) | **Multiclass (3)** | No | + +## Reference + +For more details, please refer to the following paper: + +Urbanowicz, Ryan J., Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Tamra Heberling, Jonathan M. Fisher, and Jason H. Moore. 2012. “GAMETES: A Fast, Direct Algorithm for Generating Pure, Strict, Epistatic Models with Random Architectures.” BioData Mining 5 (1): 16. https://doi.org/10.1186/1756-0381-5-16. + + + + diff --git a/demo/examples_basic_usage.ipynb b/demo/examples_basic_usage.ipynb new file mode 100644 index 0000000..7b97848 --- /dev/null +++ b/demo/examples_basic_usage.ipynb @@ -0,0 +1,524 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "23a20c20-5c12-47e9-b61e-0b63bbb91d02", + "metadata": {}, + "outputs": [], + "source": [ + "import os \n", + "import sys\n", + "# Get the absolute path to the directory containing the package\n", + "package_path = os.path.abspath(os.path.join(\"\", \"..\"))\n", + "\n", + "# Add the package path to sys.path\n", + "sys.path.insert(0, package_path)" + ] + }, + { + "cell_type": "markdown", + "id": "50df7de4-2f60-4c86-ae1f-9ca7772ef54d", + "metadata": {}, + "source": [ + "## Using the Core Algorithms\n", + "### ReliefF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3709ec0c-6f04-4a93-be55-2dc1f08e48f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank \n", + "M0P1 0.12228125 1 \n", + "M0P0 0.11928750 2 \n", + "N0 -0.00030625 3 \n", + "N10 -0.00059375 4 \n", + "N13 -0.00316250 5 \n", + "N14 -0.00317500 6 \n", + "N1 -0.00495625 7 \n", + "N8 -0.00607500 8 \n", + "N12 -0.00691875 9 \n", + "N16 -0.00705000 10 \n", + "N4 -0.00768125 11 \n", + "N17 -0.00790000 12 \n", + "N9 -0.00796875 13 \n", + "N3 -0.00800000 14 \n", + "N11 -0.00832500 15 \n", + "N5 -0.00835625 16 \n", + "N2 -0.00941250 17 \n", + "N7 -0.00959375 18 \n", + "N6 -0.01013125 19 \n", + "N15 -0.01020625 20 \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import ReliefF\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the ReliefF algorithm for feature selection\n", + "fs = ReliefF()\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(feature_name=feature_name)" + ] + }, + { + "cell_type": "markdown", + "id": "40dabab1-8572-43cf-a86c-f76026020804", + "metadata": {}, + "source": [ + "### SURF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcb66091-bbce-40d5-a5f3-d696a8ec3a2f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank \n", + "M0P0 0.06157283 1 \n", + "M0P1 0.06155927 2 \n", + "N10 -0.00049277 3 \n", + "N0 -0.00059267 4 \n", + "N13 -0.00132625 5 \n", + "N5 -0.00222361 6 \n", + "N16 -0.00256536 7 \n", + "N1 -0.00269856 8 \n", + "N8 -0.00300784 9 \n", + "N14 -0.00301537 10 \n", + "N9 -0.00302025 11 \n", + "N11 -0.00361912 12 \n", + "N12 -0.00419101 13 \n", + "N7 -0.00423327 14 \n", + "N2 -0.00453031 15 \n", + "N4 -0.00455866 16 \n", + "N15 -0.00468981 17 \n", + "N17 -0.00504565 18 \n", + "N6 -0.00590625 19 \n", + "N3 -0.00634880 20 \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import SURF\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the algorithm for feature selection\n", + "fs = SURF()\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(feature_name=feature_name)" + ] + }, + { + "cell_type": "markdown", + "id": "ea7d936c-1ec4-4125-9031-c52d98e9005d", + "metadata": {}, + "source": [ + "### SURF*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef70c9cf-c311-450a-a255-d1103d9bb31a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank \n", + "M0P1 0.12864101 1 \n", + "M0P0 0.12792470 2 \n", + "N10 -0.00106372 3 \n", + "N0 -0.00136069 4 \n", + "N13 -0.00307977 5 \n", + "N5 -0.00538099 6 \n", + "N1 -0.00616617 7 \n", + "N16 -0.00650725 8 \n", + "N14 -0.00663881 9 \n", + "N8 -0.00690113 10 \n", + "N7 -0.00765252 11 \n", + "N12 -0.00810566 12 \n", + "N9 -0.00878649 13 \n", + "N4 -0.00882028 14 \n", + "N11 -0.00897106 15 \n", + "N2 -0.00911812 16 \n", + "N15 -0.01001887 17 \n", + "N17 -0.01019251 18 \n", + "N6 -0.01174531 19 \n", + "N3 -0.01225238 20 \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import SURFstar\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the algorithm for feature selection\n", + "fs = SURFstar()\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(feature_name=feature_name)" + ] + }, + { + "cell_type": "markdown", + "id": "570853d3-0c28-4bf1-9ed5-6300af967c0d", + "metadata": {}, + "source": [ + "### MultiSURF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "384d9a87-4e17-47ff-8460-d3b6ed145847", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank \n", + "M0P1 0.08845469 1 \n", + "M0P0 0.08807613 2 \n", + "N0 -0.00049561 3 \n", + "N10 -0.00056682 4 \n", + "N13 -0.00212670 5 \n", + "N14 -0.00467041 6 \n", + "N8 -0.00613933 7 \n", + "N9 -0.00624673 8 \n", + "N1 -0.00625501 9 \n", + "N12 -0.00658989 10 \n", + "N16 -0.00666320 11 \n", + "N11 -0.00764327 12 \n", + "N5 -0.00822562 13 \n", + "N4 -0.00838633 14 \n", + "N15 -0.00857736 15 \n", + "N2 -0.00903556 16 \n", + "N6 -0.00912725 17 \n", + "N7 -0.00916942 18 \n", + "N3 -0.00931820 19 \n", + "N17 -0.00978209 20 \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import MultiSURF\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the algorithm for feature selection\n", + "fs = MultiSURF()\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(feature_name=feature_name)" + ] + }, + { + "cell_type": "markdown", + "id": "5ef6380f-5194-4fdc-8042-0942b003576d", + "metadata": {}, + "source": [ + "### MultiSURF*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2d5078a-4dc7-41ce-ae90-d694966b30fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank \n", + "M0P1 0.17498272 1 \n", + "M0P0 0.17344628 2 \n", + "N10 -0.00179863 3 \n", + "N0 -0.00181761 4 \n", + "N13 -0.00553229 5 \n", + "N14 -0.01039264 6 \n", + "N8 -0.01294620 7 \n", + "N12 -0.01297249 8 \n", + "N5 -0.01327019 9 \n", + "N1 -0.01362589 10 \n", + "N16 -0.01397024 11 \n", + "N9 -0.01406904 12 \n", + "N11 -0.01442700 13 \n", + "N7 -0.01500246 14 \n", + "N4 -0.01541660 15 \n", + "N15 -0.01603204 16 \n", + "N2 -0.01606567 17 \n", + "N3 -0.01765580 18 \n", + "N17 -0.01826211 19 \n", + "N6 -0.01848672 20 \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import MultiSURFstar\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the algorithm for feature selection\n", + "fs = MultiSURFstar()\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(feature_name=feature_name)" + ] + }, + { + "cell_type": "markdown", + "id": "6e0ff9ee", + "metadata": {}, + "source": [ + "### SWRF*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2539b79e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank \n", + "M0P1 0.35784528 1 \n", + "M0P0 0.35567375 2 \n", + "N10 -0.00280410 3 \n", + "N0 -0.00443871 4 \n", + "N13 -0.01114346 5 \n", + "N16 -0.01239890 6 \n", + "N5 -0.01295049 7 \n", + "N12 -0.01493585 8 \n", + "N7 -0.01494721 9 \n", + "N8 -0.01545232 10 \n", + "N11 -0.01629748 11 \n", + "N9 -0.01708514 12 \n", + "N14 -0.01774524 13 \n", + "N2 -0.01867276 14 \n", + "N1 -0.01870363 15 \n", + "N15 -0.01902452 16 \n", + "N4 -0.01911098 17 \n", + "N17 -0.02216275 18 \n", + "N3 -0.02343095 19 \n", + "N6 -0.02517648 20 \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import SWRFstar\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the algorithm for feature selection\n", + "fs = SWRFstar()\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(feature_name=feature_name)" + ] + }, + { + "cell_type": "markdown", + "id": "15266abe-3190-460b-8be5-1054b0fb5a21", + "metadata": {}, + "source": [ + "## Using as End-to-end Pipeline\n", + "### Single train/test split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17e11f7d-682c-460b-a49a-f098805c5d88", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.781\n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from sklearn.pipeline import make_pipeline\n", + "from skrebate import ReliefF\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Split the data to training and testing\n", + "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3, random_state=42)\n", + "\n", + "# Make pipeline\n", + "clf = make_pipeline(\n", + " ReliefF(n_features_to_select=2),\n", + " RandomForestClassifier(n_estimators=100)\n", + ")\n", + "\n", + "# Train the model\n", + "clf.fit(X_train, y_train)\n", + "\n", + "# Evaluate the model on testing set\n", + "y_pred = clf.predict(X_test)\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuracy: {accuracy:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "a9e65954-4d6d-4068-8af9-4867505c9c1f", + "metadata": {}, + "source": [ + "### Cross-validation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50f8f279-ed80-4dd8-8467-78be501eed2a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean accuracy: 0.793\n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.pipeline import make_pipeline\n", + "from skrebate import ReliefF\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Make pipeline\n", + "clf = make_pipeline(\n", + " ReliefF(n_features_to_select=2),\n", + " RandomForestClassifier(n_estimators=100)\n", + ")\n", + "\n", + "print(f\"Mean accuracy: {np.mean(cross_val_score(clf, features, labels)):.3f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "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/demo/feature_type_test.ipynb b/demo/feature_type_test.ipynb new file mode 100644 index 0000000..dec8e6e --- /dev/null +++ b/demo/feature_type_test.ipynb @@ -0,0 +1,437 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8646c1e9-2414-4a4f-8916-23a748bf3924", + "metadata": {}, + "outputs": [], + "source": [ + "import os \n", + "import sys\n", + "# Get the absolute path to the directory containing the package\n", + "package_path = os.path.abspath(os.path.join(\"\", \"..\"))\n", + "\n", + "# Add the package path to sys.path\n", + "sys.path.insert(0, package_path)" + ] + }, + { + "cell_type": "markdown", + "id": "02ec022a-e78e-414c-b965-637ec25a5d49", + "metadata": {}, + "source": [ + "## Examples on documentation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6805819-faec-475d-b361-95377a665e43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank Feature type \n", + "N0 0.00175000 6 discrete \n", + "N1 0.00151250 7 discrete \n", + "N2 0.00364375 3 discrete \n", + "N3 -0.00049375 10 discrete \n", + "N4 -0.00167332 14 continuous \n", + "N5 -0.00006580 9 continuous \n", + "N6 -0.00186120 15 continuous \n", + "N7 -0.00221250 18 discrete \n", + "N8 -0.00221196 17 continuous \n", + "N9 0.00262500 4 discrete \n", + "N10 -0.00113065 12 continuous \n", + "N11 0.00223750 5 discrete \n", + "N12 -0.00166648 13 continuous \n", + "N13 -0.00423750 20 discrete \n", + "N14 -0.00414375 19 discrete \n", + "N15 -0.00198510 16 continuous \n", + "N16 0.00032500 8 discrete \n", + "N17 -0.00103750 11 discrete \n", + "M0P0 0.01765217 1 continuous \n", + "M0P1 0.01097106 2 continuous \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import ReliefF\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_mixed_attribute_a_20s_1600her_0.4__maf_0.2_EDM-2_01.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the ReliefF algorithm for feature selection\n", + "fs = ReliefF()\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(sort=False, feature_name=feature_name, show_feature_type=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c175f223-b73e-4bad-af99-32a353079107", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature name Feature importances Feature rank Feature type \n", + "N0 0.00175000 6 discrete \n", + "N1 0.00151250 7 discrete \n", + "N2 0.00364375 3 discrete \n", + "N3 -0.00049375 10 discrete \n", + "N4 -0.00167332 14 continuous \n", + "N5 -0.00006580 9 continuous \n", + "N6 -0.00186120 15 continuous \n", + "N7 -0.00221250 18 discrete \n", + "N8 -0.00221196 17 continuous \n", + "N9 0.00262500 4 discrete \n", + "N10 -0.00113065 12 continuous \n", + "N11 0.00223750 5 discrete \n", + "N12 -0.00166648 13 continuous \n", + "N13 -0.00423750 20 discrete \n", + "N14 -0.00414375 19 discrete \n", + "N15 -0.00198510 16 continuous \n", + "N16 0.00032500 8 discrete \n", + "N17 -0.00103750 11 discrete \n", + "M0P0 0.01765217 1 continuous \n", + "M0P1 0.01097106 2 continuous \n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import pandas as pd\n", + "from skrebate import ReliefF\n", + "\n", + "# Load the example dataset\n", + "genetic_data = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_mixed_attribute_a_20s_1600her_0.4__maf_0.2_EDM-2_01.csv')\n", + "\n", + "# Separate the features and labels from the dataset\n", + "features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values\n", + "\n", + "# Apply the ReliefF algorithm for feature selection\n", + "fs = ReliefF(categorical_features=[0, 1, 2, 3, 7, 9, 11, 13, 14, 16, 17])\n", + "fs.fit(features, labels)\n", + "\n", + "# Print out the results\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(sort=False, feature_name=feature_name, show_feature_type=True)" + ] + }, + { + "cell_type": "markdown", + "id": "199dac97-7c62-499a-96ed-c8a96fc6a7cf", + "metadata": {}, + "source": [ + "## Other Tests" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "756975c0-fa40-49f9-a836-8c42fd32f360", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "907495ad-9ccf-47a8-a6ea-d080f7a1b437", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data: 1200; Testing data: 400\n" + ] + } + ], + "source": [ + "# Prepare the data\n", + "genetic_data_mixed_attributes = pd.read_csv(\n", + " '../data/GAMETES_Epistasis_2-Way_mixed_attribute_a_20s_1600her_0.4__maf_0.2_EDM-2_01.csv')\n", + "\n", + "features, labels = genetic_data_mixed_attributes.drop('class', axis=1).values, genetic_data_mixed_attributes['class'].values\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(features, labels)\n", + "print(f'Training data: {len(X_train)}; Testing data: {len(X_test)}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2872527-17d6-4c40-9afc-7f0d55944fac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically identify label type as binary\n", + "Created distance array in 0.010340213775634766 seconds.\n", + "Feature scoring under way ...\n", + "Completed scoring in 7.872896671295166 seconds.\n", + "Feature name Feature importances Feature rank Feature type \n", + "N0 0.00002500 6 continuous \n", + "N1 -0.00078333 12 continuous \n", + "N2 -0.00082083 13 continuous \n", + "N3 -0.00138750 15 continuous \n", + "N4 -0.00167789 18 continuous \n", + "N5 -0.00071363 11 continuous \n", + "N6 0.00034540 4 continuous \n", + "N7 -0.00141667 16 continuous \n", + "N8 -0.00059514 9 continuous \n", + "N9 -0.00138333 14 continuous \n", + "N10 -0.00167647 17 continuous \n", + "N11 0.00004583 5 continuous \n", + "N12 -0.00046142 8 continuous \n", + "N13 -0.00285417 20 continuous \n", + "N14 -0.00186250 19 continuous \n", + "N15 -0.00013726 7 continuous \n", + "N16 0.00074167 3 continuous \n", + "N17 -0.00069583 10 continuous \n", + "M0P0 0.01436958 1 continuous \n", + "M0P1 0.01428217 2 continuous \n" + ] + } + ], + "source": [ + "# Test 1 - default value: categorical threshold = 2\n", + "fs = ReliefF(verbose=True)\n", + "fs.fit(X_train, y_train)\n", + "\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(sort=False, feature_name=feature_name, show_feature_type=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31abcefb-e4bc-47ff-9b3a-051f0eeb1299", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically identify label type as binary\n", + "Created distance array in 0.016304731369018555 seconds.\n", + "Feature scoring under way ...\n", + "Completed scoring in 6.505752086639404 seconds.\n", + "Feature name Feature importances Feature rank Feature type \n", + "N0 0.00152500 6 discrete \n", + "N1 0.00121667 7 discrete \n", + "N2 0.00073333 10 discrete \n", + "N3 0.00230000 5 discrete \n", + "N4 -0.00303759 17 continuous \n", + "N5 -0.00088524 12 continuous \n", + "N6 0.00403174 4 continuous \n", + "N7 -0.00135833 15 discrete \n", + "N8 -0.00066926 11 continuous \n", + "N9 0.00114167 8 discrete \n", + "N10 -0.00476300 19 continuous \n", + "N11 0.00430833 3 discrete \n", + "N12 -0.00112665 13 continuous \n", + "N13 -0.00591667 20 discrete \n", + "N14 -0.00125833 14 discrete \n", + "N15 -0.00370901 18 continuous \n", + "N16 0.00112500 9 discrete \n", + "N17 -0.00245000 16 discrete \n", + "M0P0 0.01583936 1 continuous \n", + "M0P1 0.01070135 2 continuous \n" + ] + } + ], + "source": [ + "# Test 2 - set up categorical threshold = 10\n", + "fs = ReliefF(categorical_threshold=10, verbose=True)\n", + "fs.fit(X_train, y_train)\n", + "\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(sort=False, feature_name=feature_name, show_feature_type=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "63e7885b-b314-43af-9a75-9e0f867392e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically identify label type as binary\n", + "Created distance array in 0.009785175323486328 seconds.\n", + "Feature scoring under way ...\n", + "Completed scoring in 7.633818864822388 seconds.\n", + "Feature name Feature importances Feature rank Feature type \n", + "N0 0.00002500 6 continuous \n", + "N1 -0.00078333 12 continuous \n", + "N2 -0.00082083 13 continuous \n", + "N3 -0.00138750 15 continuous \n", + "N4 -0.00167789 18 continuous \n", + "N5 -0.00071363 11 continuous \n", + "N6 0.00034540 4 continuous \n", + "N7 -0.00141667 16 continuous \n", + "N8 -0.00059514 9 continuous \n", + "N9 -0.00138333 14 continuous \n", + "N10 -0.00167647 17 continuous \n", + "N11 0.00004583 5 continuous \n", + "N12 -0.00046142 8 continuous \n", + "N13 -0.00285417 20 continuous \n", + "N14 -0.00186250 19 continuous \n", + "N15 -0.00013726 7 continuous \n", + "N16 0.00074167 3 continuous \n", + "N17 -0.00069583 10 continuous \n", + "M0P0 0.01436958 1 continuous \n", + "M0P1 0.01428217 2 continuous \n" + ] + } + ], + "source": [ + "# Test 3 - manaully set up feature type: empty list\n", + "fs = ReliefF(categorical_features=[], verbose=True)\n", + "fs.fit(X_train, y_train)\n", + "\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(sort=False, feature_name=feature_name, show_feature_type=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3e7736ca-886c-482b-a843-c4cb72060029", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically identify label type as binary\n", + "Created distance array in 0.014851093292236328 seconds.\n", + "Feature scoring under way ...\n", + "Completed scoring in 6.979879856109619 seconds.\n", + "Feature name Feature importances Feature rank Feature type \n", + "N0 0.00152500 6 discrete \n", + "N1 0.00121667 7 discrete \n", + "N2 0.00073333 10 discrete \n", + "N3 0.00230000 5 discrete \n", + "N4 -0.00303759 17 continuous \n", + "N5 -0.00088524 12 continuous \n", + "N6 0.00403174 4 continuous \n", + "N7 -0.00135833 15 discrete \n", + "N8 -0.00066926 11 continuous \n", + "N9 0.00114167 8 discrete \n", + "N10 -0.00476300 19 continuous \n", + "N11 0.00430833 3 discrete \n", + "N12 -0.00112665 13 continuous \n", + "N13 -0.00591667 20 discrete \n", + "N14 -0.00125833 14 discrete \n", + "N15 -0.00370901 18 continuous \n", + "N16 0.00112500 9 discrete \n", + "N17 -0.00245000 16 discrete \n", + "M0P0 0.01583936 1 continuous \n", + "M0P1 0.01070135 2 continuous \n" + ] + } + ], + "source": [ + "# Test 4 - manaully set up feature type: customized list\n", + "fs = ReliefF(categorical_features=[0, 1, 2, 3, 7, 9, 11, 13, 14, 16, 17], verbose=True)\n", + "fs.fit(X_train, y_train)\n", + "\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(sort=False, feature_name=feature_name, show_feature_type=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a7decb0-6a3e-49c0-957a-82627ba2593c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically identify label type as binary\n", + "Created distance array in 0.010467052459716797 seconds.\n", + "Feature scoring under way ...\n", + "Completed scoring in 7.745252847671509 seconds.\n", + "Feature name Feature importances Feature rank Feature type \n", + "N0 0.00002500 6 continuous \n", + "N1 -0.00078333 12 continuous \n", + "N2 -0.00082083 13 continuous \n", + "N3 -0.00138750 15 continuous \n", + "N4 -0.00167789 18 continuous \n", + "N5 -0.00071363 11 continuous \n", + "N6 0.00034540 4 continuous \n", + "N7 -0.00141667 16 continuous \n", + "N8 -0.00059514 9 continuous \n", + "N9 -0.00138333 14 continuous \n", + "N10 -0.00167647 17 continuous \n", + "N11 0.00004583 5 continuous \n", + "N12 -0.00046142 8 continuous \n", + "N13 -0.00285417 20 continuous \n", + "N14 -0.00186250 19 continuous \n", + "N15 -0.00013726 7 continuous \n", + "N16 0.00074167 3 continuous \n", + "N17 -0.00069583 10 continuous \n", + "M0P0 0.01436958 1 continuous \n", + "M0P1 0.01428217 2 continuous \n" + ] + } + ], + "source": [ + "# Test 5 - enter both, should only using the cate_gorical_features\n", + "fs = ReliefF(categorical_threshold=10, categorical_features=[], verbose=True)\n", + "fs.fit(X_train, y_train)\n", + "\n", + "feature_name = genetic_data.drop('class', axis=1).columns\n", + "fs.summary(sort=False, feature_name=feature_name, show_feature_type=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "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/demo/heatmaps.ipynb b/demo/heatmaps.ipynb new file mode 100644 index 0000000..6e2d766 --- /dev/null +++ b/demo/heatmaps.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the path to the 'Results' directory\n", + "results_dir = './data'" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize a DataFrame to hold all rankings\n", + "all_rankings_df = pd.DataFrame()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize a dictionary to hold the total number of features (N) for each RBA method\n", + "total_features_per_rba = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# Iterate over each subfolder in the Results directory, each named for an RBA\n", + "for rba in os.listdir(results_dir):\n", + " rba_path = os.path.join(results_dir, rba)\n", + " if os.path.isdir(rba_path): # Ensure it's a directory\n", + " method_feature_counts = [] # To store feature counts for each dataset within this method\n", + " for file in os.listdir(rba_path):\n", + " if file.endswith('.txt'): # Ensure the file is a .txt file\n", + " file_path = os.path.join(rba_path, file)\n", + " \n", + " # Extract the dataset identifier from the file name\n", + " parts = file.split('_')\n", + " dataset_id = '_'.join(parts[-2].split('_')[:2])\n", + " \n", + " # Determine Ranks\n", + " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature'])\n", + " column_to_use = 'ABS_Feature_Importance' if \"ABS\" in rba else 'Feature_Importance'\n", + " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature', column_to_use])\n", + " df.sort_values(by=column_to_use, ascending=False, inplace=True)\n", + " df.reset_index(drop=True, inplace=True)\n", + " df['Rank'] = df.index + 1\n", + " \n", + " # Store in a dataframe\n", + " predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Rank']]\n", + " predictive_df['RBA'] = rba\n", + " predictive_df['Dataset'] = dataset_id\n", + " \n", + " all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True)\n", + " \n", + " # Add the feature count of this dataset to the list for this method\n", + " method_feature_counts.append(df['Feature'].nunique())\n", + " \n", + " # Store the maximum feature count encountered for this method as N\n", + " total_features_per_rba[rba] = max(method_feature_counts)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming all_rankings_df is correctly prepared and contains 'RBA', 'Feature', 'Rank', 'Dataset'\n", + "\n", + "# Since all datasets have the same N, we can pick the N from any RBA method from total_features_per_rba\n", + "N = next(iter(total_features_per_rba.values()))\n", + "\n", + "# Step 1: Identify the lowest-ranked predictive feature for each dataset for each RBA\n", + "lowest_ranks = all_rankings_df.groupby(['RBA', 'Dataset'])['Rank'].max().reset_index()\n", + "\n", + "# Initialize a structure to hold the calculated percentages for each RBA method\n", + "percentages = {rba: [0] * N for rba in lowest_ranks['RBA'].unique()}\n", + "\n", + "# Step 2: Calculate percentages for each position for each RBA\n", + "for rba in percentages.keys():\n", + " rba_data = lowest_ranks[lowest_ranks['RBA'] == rba]\n", + " for position in range(1, N + 1):\n", + " # Count how many of the lowest ranks are better (lower number) than the current position\n", + " count_higher = rba_data[rba_data['Rank'] <= (position)].shape[0]\n", + " total_datasets = rba_data.shape[0] # Should be 30 per RBA if there are 30 datasets\n", + " percentages[rba][position - 1] = (count_higher / total_datasets) * 100\n", + "\n", + "# Convert the percentages to a DataFrame for visualization\n", + "percentages_df = pd.DataFrame(percentages, index=range(1, N + 1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create custom color scheme\n", + "custom_cmap = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:]\n", + "\n", + "# Define your preferred order of the RBAs as a list\n", + "rba_order = [\n", + " 'RandomShuffle',\n", + " 'MutualInformation',\n", + " 'SURFstar',\n", + " 'MultiSURFstar',\n", + " 'SWRFstar',\n", + "]\n", + "\n", + "# Define a mapping from your RBA order to new descriptive names\n", + "rba_descriptive_names = {\n", + " 'RandomShuffle': 'Random Shuffle',\n", + " 'MutualInformation': 'Mutual Information',\n", + " 'ReliefF10': 'ReliefF 10NN',\n", + " 'ReliefF100': 'ReliefF 100NN',\n", + " 'SURFstar': 'SURF*',\n", + " 'MultiSURF': 'MultiSURF',\n", + " 'MultiSURFstar': 'MultiSURF*',\n", + " 'SWRFstar': 'SWRF*',\n", + " 'ABS_ReliefF10': 'ReliefF 10NN ABS',\n", + " 'ABS_ReliefF': 'ReliefF 100NN ABS',\n", + " 'ABS_MultiSURF': 'MultiSURF ABS',\n", + " 'ABS_MultiSURFstar': 'MultiSURF* ABS',\n", + " 'ABD_SWRFstar': 'SWRF* ABS',\n", + "}\n", + "\n", + "percentages_df = percentages_df.iloc[1:] # Drop the first row as this will always be 0\n", + "percentages_df_transposed = percentages_df.T # Transpose percentages_df to switch rows and columns for horizontal orientation\n", + "\n", + "# Reorder the DataFrame according to your defined RBA order\n", + "percentages_df_ordered = percentages_df_transposed.loc[rba_order] # The .loc indexer reindexes the DataFrame to the specified order; any missing labels will result in NaN rows\n", + "\n", + "# Define the tick labels as percentages of optimality\n", + "xtick_labels = ['Optimal', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']\n", + "# Generate a list of positions at which to place the x-tick labels, assuming they should be placed at even intervals\n", + "xtick_positions = np.linspace(start=0, stop=percentages_df_transposed.shape[1] - 0.13, num=len(xtick_labels))\n", + "\n", + "# Create the heatmap with the reordered DataFrame\n", + "plt.figure(figsize=(12, 7)) # Adjust the size as necessary\n", + "heatmap = sns.heatmap(percentages_df_ordered, annot=False, fmt=\".1f\", cmap=custom_cmap, cbar_kws={'label': 'Power (Frequency of Success)'})\n", + "\n", + "# Add horizontal lines manually between Methods\n", + "for i in range(percentages_df_ordered.shape[0] - 1):\n", + " heatmap.axhline(i + 1, color='black', linewidth=1.5)\n", + "\n", + "# Adding black border around the heatmap\n", + "for _, spine in heatmap.spines.items():\n", + " spine.set_visible(True)\n", + " spine.set_linewidth(1.5)\n", + " spine.set_edgecolor(\"black\")\n", + "\n", + "# Adding black border around the color bar in legend\n", + "cbar = heatmap.collections[0].colorbar\n", + "cbar.outline.set_linewidth(1.5)\n", + "cbar.outline.set_edgecolor(\"black\")\n", + "\n", + "# Set the title and axis labels appropriately\n", + "heatmap.set_title('a_20 Test Datasets Heatmap', fontsize = 16)\n", + "heatmap.set_xlabel('Predictive features in top % of ranked features', fontsize = 14)\n", + "heatmap.set_ylabel('Method', fontsize = 14)\n", + "\n", + "# Set the custom x-tick labels, positions, and fontsize\n", + "heatmap.set_xticks(xtick_positions)\n", + "heatmap.set_xticklabels(xtick_labels, rotation=0, fontsize=11)\n", + "\n", + "# Set the custom y-tick labels fontsize\n", + "new_ytick_labels = [rba_descriptive_names[rba] for rba in rba_order]\n", + "heatmap.set_yticklabels(new_ytick_labels, fontsize=11)\n", + "\n", + "# Save and show the Plot\n", + "plt.rcParams['font.sans-serif'] = 'Helvetica'\n", + "plt.rcParams['font.family'] = 'sans-serif'\n", + "plt.rcParams['pdf.fonttype'] = 42\n", + "plt.tight_layout() # Adjust the layout\n", + "# save_path = os.path.join(results_dir, 'XOR2_a20_ABS.pdf') # Save\n", + "# plt.savefig(save_path, format='pdf', bbox_inches='tight') # Save\n", + "plt.show() # Display the heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Below is the parsing code that goes through all result files and calculates the ranks for each feature for all replicates" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Consolidated rankings saved to: ./data/consolidated_rankings.csv\n" + ] + } + ], + "source": [ + "# Define the path to the 'Results' directory\n", + "results_dir = './data/' # Change this to the path of your 'Results' directory\n", + "\n", + "# Initialize a DataFrame to hold all rankings\n", + "all_rankings_df = pd.DataFrame()\n", + "\n", + "# Initialize a dictionary to hold the total number of features (N) for each RBA method\n", + "total_features_per_rba = {}\n", + "\n", + "# Iterate over each subfolder in the Results directory, each named for an RBA\n", + "for rba in os.listdir(results_dir):\n", + " rba_path = os.path.join(results_dir, rba)\n", + " if os.path.isdir(rba_path): # Ensure it's a directory\n", + " method_feature_counts = [] # To store feature counts for each dataset within this method\n", + " for file in os.listdir(rba_path):\n", + " if file.endswith('.txt'): # Ensure the file is a .txt file\n", + " file_path = os.path.join(rba_path, file)\n", + " \n", + " # Extract the dataset identifier from the file name\n", + " parts = file.split('_')\n", + " dataset_id = '_'.join(parts[-2].split('_')[:2])\n", + " \n", + " # Determine Ranks\n", + " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature'])\n", + " if rba == \"RandomShuffle\":\n", + " df['Rank'] = df.index + 1\n", + " else:\n", + " column_to_use = 'ABS_Feature_Importance' if \"ABS\" in rba else 'Feature_Importance'\n", + " df = pd.read_csv(file_path, sep='\\t', usecols=['Feature', column_to_use])\n", + " df.sort_values(by=column_to_use, ascending=False, inplace=True)\n", + " df.reset_index(drop=True, inplace=True)\n", + " df['Rank'] = df.index + 1\n", + " \n", + " # Store in a dataframe\n", + " predictive_df = df[df['Feature'].str.startswith('M')][['Feature', 'Rank']]\n", + " predictive_df['RBA'] = rba\n", + " predictive_df['Dataset'] = dataset_id\n", + " \n", + " all_rankings_df = pd.concat([all_rankings_df, predictive_df], ignore_index=True)\n", + " \n", + " # Add the feature count of this dataset to the list for this method\n", + " method_feature_counts.append(df['Feature'].nunique())\n", + " \n", + " # Store the maximum feature count encountered for this method as N\n", + " total_features_per_rba[rba] = max(method_feature_counts)\n", + "\n", + "# Now, you have both the rankings and the total number of features (N) for each RBA method\n", + "# You can use total_features_per_rba to access N for each method as needed\n", + "\n", + "# Specify the path to save the consolidated rankings file\n", + "save_path = os.path.join(results_dir, 'consolidated_rankings.csv')\n", + "all_rankings_df.to_csv(save_path, index=False)\n", + "\n", + "print(f\"Consolidated rankings saved to: {save_path}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### This code generates the ranking percentages that are needed to create the heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentages saved to percentages_df.csv.\n" + ] + } + ], + "source": [ + "# Assuming all_rankings_df is correctly prepared and contains 'RBA', 'Feature', 'Rank', 'Dataset'\n", + "\n", + "# Since all datasets have the same N, we can pick the N from any RBA method from total_features_per_rba\n", + "N = next(iter(total_features_per_rba.values()))\n", + "\n", + "# Step 1: Identify the lowest-ranked predictive feature for each dataset for each RBA\n", + "lowest_ranks = all_rankings_df.groupby(['RBA', 'Dataset'])['Rank'].max().reset_index()\n", + "\n", + "# Initialize a structure to hold the calculated percentages for each RBA method\n", + "percentages = {rba: [0] * N for rba in lowest_ranks['RBA'].unique()}\n", + "\n", + "# Step 2: Calculate percentages for each position for each RBA\n", + "for rba in percentages.keys():\n", + " rba_data = lowest_ranks[lowest_ranks['RBA'] == rba]\n", + " for position in range(1, N + 1):\n", + " # Count how many of the lowest ranks are better (lower number) than the current position\n", + " count_higher = rba_data[rba_data['Rank'] <= (position)].shape[0]\n", + " total_datasets = rba_data.shape[0] # Should be 30 per RBA if there are 30 datasets\n", + " percentages[rba][position - 1] = (count_higher / total_datasets) * 100\n", + "\n", + "# Convert the percentages to a DataFrame for visualization\n", + "percentages_df = pd.DataFrame(percentages, index=range(1, N + 1))\n", + "\n", + "# Save percentages_df to a CSV file\n", + "save_path_percentages = os.path.join(results_dir, 'percentages_df.csv')\n", + "percentages_df.to_csv(save_path_percentages, index_label='Ranking Position')\n", + "\n", + "print(\"Percentages saved to percentages_df.csv.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting code for heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1 extra bytes in post.stringData array\n", + "'created' timestamp seems very low; regarding as unix timestamp\n", + "Zapf NOT subset; don't know how to subset; dropped\n", + "feat NOT subset; don't know how to subset; dropped\n", + "meta NOT subset; don't know how to subset; dropped\n", + "morx NOT subset; don't know how to subset; dropped\n" + ] + }, + { + "data": { + "image/png": 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tLRwdHdGxY0ccPXpU2c4YG9f169fh4uKCmTNnKmWMcfFjjI3jl19+QZ06dWBpaYnKlStjyZIlyjbGuPgxxkWXnp6ON954A56enrCzs0Pr1q3xzz//KNsZY+O4cuUKevbsCRsbG9jb26NHjx64dOmSsp1xfnpP88yRnZ2Nt99+G66urtBqtWjTps0Tre7y6aef4osvvjDaMdALQMqI27dvS7169cTZ2Vk+/fRTOXLkiPzzzz+ycOFCcXBwkPr160tSUtJjtbVy5UrJfWgpKSly7ty54uq6iIgEBATIwIEDi3UfJe3OnTsyb948ASArV65UyuPj48Xe3l6GDRsmR44ckfXr14ujo6OEhYUpdVatWiVeXl5SqVIlWbBggV67YWFh8t1335XYcZRVvXv3Fg8PD9m0aZMcOXJEpk6dKgAkIiKCMTaSBg0aSJMmTWT37t3y999/S1BQkLi4uMjt27cZ42LQuXNnASAzZswQEd4rjGXlypWiVqsNfo4ePcoYG8nmzZvFwsJC+ffHnDlzBIBERkYyxkbw/vvv53kOq9Vq8fb2ZoyNJCQkRKpUqSJbt26VQ4cOSe/evaVcuXISHx/PGBtJdna2NGzYUJo0aSKRkZGyd+9eCQgIkOrVq0tGRgbj/JSK8swxZcoUcXZ2lg0bNsiRI0dk8ODB4uzsLDdv3hQRkcTEROnVq5c4OTlJmzZtJCYmRm+/rVq1kgcPHpTcwdIzr8wkUIYOHSoODg5y/vx5g23Hjh0TKysree211x6rrUcTKCXheUugzJs3T1QqlQAwuJlNnz5dqlatKllZWUrZhx9+KC4uLpKVlSVRUVFSoUIF2bRpk/z3v/+V6tWry7p160RE5MaNG+Lv7y85OTklfkxlybVr18TExER++uknvfLmzZtLhw4dGGMjOHTokACQqKgopezy5csCQH7//XfG2MiWLFkitWvXFldXVyWBwhgbR2hoqPTr109OnTql95ORkcEYG0mjRo0kNDRUr6xTp04ybdo0xtgIbty4YXD+/v333+Lk5CSLFy9mjI0gMzNTzM3N5euvv1bK0tLSRK1Wy/LlyxljI/njjz8EgMTGxipliYmJolKpZNWqVYzzUyjKM8edO3fE0tJS77y/f/++2Nvby9KlS0VEpFu3bjJ8+HCJioqSGTNmSL169ZS606ZNU+JP9LjKRALl5s2bYm5uLu+//36+dcaNGyeWlpYSERGh/JW+WbNmYm1tLY0bN5adO3eKiMisWbOUC1D316Pw8HABoGQXAciHH34o3bp1E2tra/Hy8pLIyEhZsGCBuLm5iaWlpXTu3FlvxMu+ffvE399fNBqNlCtXTjp06CBHjx5Vtj9vCZSEhAQ5fPiwHD582OBm1rRpUxk3bpxe/X/++Ud5WP34449l0qRJyrYlS5bIhAkTRERk/Pjxsm3btpI5iDIsOjpaPD099bLgIiLdu3eXunXrMsZGsGPHDmnevLncu3dPKbt27ZoAkLVr1zLGRnT8+HGxtbWVw4cPi6enp5JAYYyNo3379rJkyZI8tzHGRae7L+ROtubGGBePbt26ySuvvCIijLExJCUlCQD55ZdflLIHDx6ItbW1LF68mDE2kgULFoiDg4NBubOzswwfPpxxfgpFeebYvHmzAJAbN27o1enWrZt07dpVRES0Wq2kpKQo2xwcHCQpKUmuXLkibdu2LcYjo+dVmZgD5eDBg3jw4AHatGmTb53AwEBkZGTg9OnTAIBx48bhnXfewf79+9G4cWN06NABZ86cweuvv44PPvgAAHDu3Dk0b948z/bef/99dOzYEXv27EHFihXRoUMH/Prrr/jpp5+wefNmHDp0SGnn/v376NSpE3x8fLBnzx5s3rwZANC3b19jhqFMqVChAho0aIAGDRoYbIuNjUW1atX0yjw8PAAACQkJqFmzJv744w9cvnwZSUlJWLt2LWrWrInY2FjExcWhXbt2JXEIZVqjRo0QFxenF8eTJ09i+/btaNKkCWNsBG3atMHff/8NS0tLAEBqaireeecdODo6onXr1oyxkWRkZGDAgAGYOXOmwf2CMTaOU6dOYceOHahduzacnZ3Rvn17HDp0CABjbAzHjh0D8DCWjRs3hr29PRo3bowff/xRKWeMjWvVqlWIjIzEp59+CoAxNgYHBwe0bNkS8+fPx4ULF3Dv3j1MnToVWVlZ6Nq1K2NsJOXLl0dKSore3Izp6elISkpCQkIC4/wUivLMERsbC1tbWzg5ORnUSUhIAADUrFkTX375JbKysrB27VpYWFigXLlyeOedd/Dee+8Vz0HRc61MJFCSkpIAwODkz02r1QKAcsOaNWsWunbtivr16+OLL76Ah4cHPv/8czg4OKB8+fIAAC8vL1hZWeXZ3quvvooxY8agUaNGGDBgADIzM7Fy5Ur4+fkhKCgIrVu3ViYgunv3LiZMmIBPPvkEDRs2hK+vL4YOHYqzZ88aLQbPkpSUFGg0Gr0yGxsbZVvnzp3RoUMHeHp6onz58nBxccGIESMwY8YMhIWFlUaXy7zvv/8eL730EmxtbfH+++8zxkamm+xt5cqVmDx5MhwcHBhjI5kyZQocHBwwefJkg22McdGlpaXhypUryM7OxieffIL169fD1tYWfn5+OHDgAGNsBLdu3QLw8FyePHkytm/fjpdffhkDBw7EmjVrGGMju3v3LiZPnox3330XFSpUAMB7hbGEh4cjNjYWVatWhUajwaJFizBjxgxUq1aNMTaSTp06QaPR4M0330RKSgpu376NMWPGIDs7GwDPZWMrLJ55bdfVSUlJAQCsXLkSS5YsgYWFBcaOHYvVq1fjxIkTSE1NRcuWLYv/IOi5UyYSKPb29gCgZArzcvHiRQCAi4sLAMDPz0/ZZmJigqZNmz5RQqNevXrKf6vVagCAt7e3UmZubq7MgO3k5ISRI0fiiy++QEhICNq2bYsJEyY89r6eN/b29garIt27dw8A4OjoCABYvHgxkpOTcePGDfzyyy+Ijo6GpaUl6tevj/Pnz+Pll19GlSpVMHDgQNy4caPEj6GsiI2NRevWrREcHAxfX19ERUXBzc2NMTaypUuX4q+//sKMGTMwY8YMrFy5kjE2gi1btmDVqlX47rvvYGJi+L8Txrjo1Go1zp8/j02bNqF9+/Zo1aoVfv75Z1StWhWffvopY2wEutUcFixYgAEDBqBp06aYPXs2OnbsiOXLlzPGRrZo0SKo1WqEhoYqZYxx0V2/fh1BQUGoXbs2tm/fjgMHDmDKlCmYPXs21q9fzxgbibOzMzZs2IAtW7bAzs4Ojo6OSEpKQvPmzVG+fHnG2cgKi2de23V1dPHWxfXSpUtISEhAmzZt9BJWixcvRs2aNVG/fn1l5CFRQcpEAqVJkyYwMTHB3r17860TERGBihUrKsO4dMPyddLT06FSqR57n3mNTMnrAQAAoqOj4ePjg+joaAQEBGDu3LmYM2fOY+/reePu7o64uDi9Mt3ybZUrV1bKdEvHAsC7776rxKxbt27w9/fHli1bUL58eQwbNqxE+l3W7N69G40bN0ZcXBw2btyIzZs3w83NDQBjbAxHjx5FZGQkgIfxbNGiBcLCwtCuXTusW7eOMTaCLVu2ICUlBd7e3rC0tISlpSUuXryI+fPnw9LSkjE2AjMzM1SuXFnv/0+mpqaoU6cObt++zRgbge4PM7Vr19Yrr1u3Lq5evcoYG9H9+/fx2WefYcyYMTA3N1fKGeOi++WXX3D16lX89NNPaNu2LZo2bYoPP/wQ/v7++OabbxhjIwoMDMT58+eRkJCAGzdu4LfffkNsbCyaNm3KOBtZYfF0d3dXRgI9Wid3vFUqFdzd3WFqaoqdO3eiUqVK8Pb2xtq1axEeHo7vvvsOixcvxtSpU3Hw4MHiPix61pX2JCw6vXv3FkdHR7l+/brBtn/++UdMTEzk448/lsjISAEgGzZsULZnZGRIhQoVZOrUqSJiuApPXpPI5p6g6NHtIiIDBw6UgIAAEXm4AkLNmjX1+jRt2jS9fTxvk8jm9mi8ZsyYITVq1NCbJXz+/PlSpUqVPD+/YcMGmTJliog8nDA49+Rbd+7cERsbm2LqedmVnp4uFSpUkJYtW8qdO3cMtjPGRTdnzhyxtbWV+/fv65X7+/vLkCFDGGMjSExMNFhZo2LFijJ69Gg5deqUTJ8+nTEuoq+//lrc3NwkNTVVKXvw4IFUqVJFpk6dyvPYCJKTk0WtVsuqVav0ytu3by+9evVijI1ozZo1AkAuXLigV84YF90nn3wiAOTSpUt65R06dJD+/fszxkayc+dOqVatmly7dk0pi4iIEFNTU7lw4QLjXERP+syRkpIiVlZW8sMPPyjb09LSxMHBQb799luD9nNyciQwMFASExNF5OHEvbknaX/jjTdk4cKFRj8uer6UmQRKfHy8VK9eXapVqyZfffWVHD16VI4cOSLLli0TJycn6dWrl2RlZSkJlGrVqsmWLVvk0KFD0rdvX7G2tlaWFFu1apUAkL1790p6enqREygLFiwQKysr+fXXX+XIkSMyd+5ccXFxEQASHR0tIi9WAiUuLk60Wq2MHj1ajhw5IsuXLxdLS0u9OjpZWVni5+cnt2/fVsqqVq0qc+fOlaNHj8rYsWMlKCioJA6jTNmwYYOymtS5c+f0fq5cucIYG8H58+fF0tJS+vTpI3v27JGoqCh54403xMzMTPbv388YF5Pcq/AwxkV36dIlsbe3l65du8ru3btl//790rdvX7G1tZX4+HjG2EjeeustKV++vPzwww9y+PBhmT17tpiamkpUVBRjbEQDBw40+IOUCO8VxhAXFycajUb8/Pxk586dcvToUQkLCxNTU1PZsWMHY2wkSUlJ4ujoKH379pUDBw7Izz//LC4uLspKMYxz0TzNM8f48ePF1dVVNm/eLHv37pV27dqJl5eX3iqMOqtXr5Y5c+Yov3/77bdSp04d2bNnj2zZskUqVqwou3fvLt6DpGdemUmgiIjcvn1b3n77balRo4ao1WqxsbERX19fWblypWRnZ4uIKAmU5cuXS61atcTKykpatGghe/fuVdqJj48XHx8fsbCwkP379xc5gZKRkSFDhgwRW1tbcXJykrFjx0psbKx4eXlJxYoVReTFSqCIiOzdu1eaNm0qarVaKleurKy1/qgVK1bIokWL9MqOHTsmzZs3F41GI+3atZOLFy8WW9/LqgULFugtt537R3feMcZFt3XrViWGjo6O0rp1a4mMjFS2M8bGlzuBIsIYG0N0dLS0b99ebG1tRavVSps2beTQoUPKdsa46LKzsyUsLEzc3NzEwsJCGjRoIL/99puynTE2Dk9PTxk5cmSe2xjjojt27Jj06tVLnJ2dRavVSrNmzXgeF4MDBw7ISy+9JDY2NsoI+NzPEIzz03uaZ46MjAyZNGmSODk5iUajkc6dO0tcXJxB2/fv3xdfX1+9EZ05OTkyY8YMcXJykooVK8qyZcuK58DouaISESmBN4WMZteuXWjdujXOnTsHLy+v0u4OEREREREREb0AysQkskREREREREREZRkTKEREREREREREhXjmXuEhIiIiIiIiIippHIFCRERERERERFQIJlCIiIiIiIiIiArBBAoRERERERERUSGYQCEiIiIiIiIiKgQTKEREREREREREhWAChYieK9988w1UKlWeP3Z2dujSpQuOHDlSYv2Ji4uDSqXC7NmzlTKVSgU/Pz+jtD979myoVCrs2LHDKO09iYMHD6JZs2ZQq9UICQkp8f0/S4YOHQqVSoWYmJjS7opRnT9/HnPnzsWsWbNw7NixPOtERESgWbNmxdaHmJgYtG7dGtbW1ggKCiq2/Twp3b1o165dxb6vXbt2QaVS4ZtvvimwXk5ODt544w04OzvD1NQUFy9eLPa+ERERPU/MSrsDRETFoXnz5mjRooXy+4MHD3DmzBlERERg165d2LdvHxo0aFAqfQsNDUXVqlWf+HPBwcH4/vvvceHCBVSuXBkA0KJFC4SGhqJSpUpG7mXhpkyZgkOHDuHll19G27Zti3Vf7u7uMDMzQ1xcXLHup7i0b98e9vb2sLe3N1qbfn5+2LdvH0TEaG0+ib/++gtt27ZFeno6AGDu3Ln45Zdf0KtXL716s2fPxnvvvVds/QgLC8OuXbsQGBiInj17Ftt+ngd//vknFi9eDDc3N4wePRo2NjbFtq8vv/wSI0aMQHh4OIYOHVps+yEiIipJTKAQ0XOpY8eOeqM+dHT/qH/33XexadOmku8YgMWLFxutrY4dO6Jjx45Ga+9JXLp0CVqtttTi+CwZMGAABgwYUNrdMBoRwYgRI+Dt7Y2ff/4Z1tbWGDRoEMaMGYP27dtDq9UCAH7//XeYmJigU6dOxdaXS5cuAQDWrFkDJyenYtvP80AXq4kTJ+LNN98s5d4QERE9e/gKDxG9UIYNGwaNRoO9e/fmWyczMxM5OTkl2KtnU05ODmxtbUu7G0+F33HRxMXF4eTJk1iwYAG8vb3h7u6OFStW4Pr164iKilLqzZ49G3PmzCnWvui+Rzs7uyf+7L1794zdnTKtKLEqC16074uIiMoeJlCI6IViamoKS0tLpKWlKWWVK1dGgwYNcPr0aQQFBcHGxgYpKSkAgKysLHz88ceoVasWLC0t4ebmhrFjxyIxMdGg7X379iEgIAAajQZOTk4YPnw4bt++bVAvrzlQLl26hEGDBqF8+fLQarVo2LAhVqxYoTzwqFQqfP/99wCAKlWqIDAwEID+HCjx8fEwNTXN8/UgEYGHhwfKlSuH+/fvK+Vff/01GjduDCsrKzg7OyM4OBixsbEFxlA338LFixcRHx8PlUqlN0T/5s2bmDBhAtzd3WFpaYmaNWti/vz5yMzMNGjrxx9/hJ+fn/J6S8OGDbFw4UKlrm5fV69excWLF/XmkwkMDIRKpUJWVlae/Zs5c6ZSZozvODs7G8uWLUO9evWg0Wjg4OCAbt264ejRowXGCzCcA0XXx8WLF2Pjxo1o0qQJrKys4OrqijfffDPPWOno5tXZt28fABjE/9q1axg9ejTc3NygVqtRrVo1TJ8+HXfv3tVrR6VSoWPHjjh58iS6dOkCW1tbODk5YeTIkUhKSirweHTntYuLi1JWoUIFvW2bNm2ClZUV2rVrV2h88hIdHY1u3brBwcEBVlZWaNSoEf7zn/8oryzp4rB7924AgIWFhXJd5CV3zMPDw1G5cmW9UUFbtmxB+/bt4eTkBBsbG9SuXRuzZs3CnTt3lDq6fU6cOBF79+6Fv78/NBoNHB0dERISopxT+UlNTUWLFi1gYmKC1atXK+VpaWmYOXMmqlWrBrVajapVq2LatGkG3xnwMK7NmjVTzpfJkycjIyOj0HgGBgZi2LBhAICRI0dCpVLpvRK3ceNG+Pn5Ked2jx498M8//xi0c/nyZYwbNw5VqlSBpaUlKlWqhFdffRXR0dF6+xoxYgSAh0lrlUoFIO9rM/dncl/Pujlk1q9fj/nz58PV1RVvv/22Uv/48ePo1asXHBwcoNFo0KpVK6xZs8ag3dOnT6Nv375wc3ODpaUlfHx88MEHHyA7O7vQmBERERkQIqLnSHh4uACQWbNm5bk9Li5OAEitWrWUMk9PT/Hw8BBnZ2dp2LChjBw5UtLT0yUnJ0e6d+8uAKRevXoyYsQI6dKli5iamoqrq6vExcUpbezbt08sLCzEwsJCevfuLcHBwVK+fHmpVq2aQX8ASKtWrZTfz58/LxUqVBAzMzPp0aOHhISEiKenpwCQKVOmiIhIaGio+Pj4CAAZNmyYLF26VEREZs2aJQBk+/btIiLi7+8vAOT48eN6x33w4EEBICEhIUrZhAkTBIB4eXlJSEiI9O7dWywtLUWr1Up0dHS+MT537pyEhoaKjY2NaLVaCQ0Nle+//15ERBITE8XLy0sASNu2bWXUqFHSrFkzASABAQFy//59pZ0FCxYIAHF3d5fBgwfL0KFDxd3dXQBIcHCw3r60Wq3Y2NhIaGioREREiIhIQECAAJAHDx7o9S8yMlIAyIwZM4z6HY8dO1YASIsWLWTUqFHStWtXMTU1FRsbG7lw4UK+8RIRGTJkiACQc+fO6fXR19dXzMzMpEuXLjJy5Ehxc3MTAPLuu+/m21ZSUpKEhoZKxYoVBYBe/K9cuSKVKlUSExMT6dixo4wePVpatWolAKRu3bpy584dpR0A4u3tLXZ2dtKyZUsZOXKkNG3aVABInTp1JC0tLd8+pKamilqtlmnTpilln332mQCQmJgYycnJkYYNG8rOnTsLjEt+tm3bJmq1WrRarbz66qsyYsQIqV69ugCQIUOG6MVBF7PXX39duS7yoot548aNxcLCQrp16yYrVqwQEZE1a9YIAHF0dJT+/fvL8OHDpUaNGgJA/Pz8lDYuXLggAKRJkyZiYWEhrVu3llGjRil1Bw8erNTV3YsiIyNFRCQjI0OCgoIEgHz++edKvfT0dOUa8fX1lVGjRin1atWqJbdv31bqrlmzRlQqlWi1WhkwYID069dPbG1tlftMeHh4vse/dOlSadeunXJthoaGSlJSkoiILFy4UACIm5ubDBkyRPr37y92dnZiZmYmmzdvVtpITk5Wzrt27drJqFGjpH379mJiYiIWFhZy8uRJZV9t27ZV6oWGhup9B7mvTZ1Hr2dd/Bo3biw2NjbSp08fWbt2rYiI/Pnnn2JtbS3W1tbSu3dvee2116Ry5coCQKZPn660GRMTIzY2NmJrayvBwcEydOhQ5f40fvz4fGNFRESUHyZQiOi5kl8C5d69e7J//37lAXHBggXKNl2yIiQkRHJycpTyFStWKA+oucs3b94sAOTVV19VyurXry9mZmby119/KWUJCQlK0qOgBErXrl0FgPzxxx9KWWpqqlSvXl2srKwkNTVVREQGDhwoAPQe1h9NoOgeYufOnat3/NOnT9ert3XrVgEgffr00UtqREdHi1qtlhYtWuQf5Fxxc3Nz0yvr37+/AFAedHTeeustASDLly9XyqpXry6enp6SnJyslN29e1fc3d1FrVbrfd7NzU08PT31yp40gVKU7zg1NVXMzc3F19dXb1+bNm0yeGjLS34JFJVKJZs2bVLqXbx4UczMzKR69eoFticiSmIkt169egkA+fHHH/XKP/zwQwEgEydOVMoACACZNGmSXt3Ro0cLAHn//fcL3P8777wjAKRTp07Sp08fMTExkUGDBomIyLp16yQwMLDQY8hLRkaGuLq6iq2trZw5c0Ypz8zMVJJdGzZsUMrzOw8elTvmjyZ22rVrJzY2NnL58mWl7MGDB9K4cWMBIPHx8SLyvwQKAL1kzZ07d8TJyUmsra0lMzNTRPQTKA8ePJBu3boJAPnwww/19q27NhcuXKhXrruWdUmqe/fuiYuLi9jb2yvnkYjImTNnxNnZudAESu4+rVy5Uik7efKkmJmZSatWrSQlJUUpj4uLEycnJ3F3d1di+/333wsAWbx4sV67n3zyiQCQDz74QClbuXKlQZ+eJoGi0WiUxIzIw/PA09NTHB0d5ezZs0r5vXv3xN/fX1Qqlfz77796sd26datS7/79+9KiRQsxMzNT7q1ERESPiwkUInqu6P7RXdBPp06dlIcckf89XCcmJuq11aBBA9FoNJKRkWGwn5YtW4pGo5EHDx5IdHS0wV+fdX788ccCEyiJiYliYmIiAQEBBp/98ssvpXnz5srDw+MkUBITE8XU1NQgAeLj4yMVKlSQrKwsERHp0aOHAJArV64Y7HfAgAECQBISEgy25fZoAuXmzZtiamoqbdu2Nah79+5dUavV0qFDB6Vs4cKFeskDnZdeesnggdhYCZSn/Y4TExMFgPj4+Og9dGVlZUlERIQcO3bM4PO55ZdAyR2P3H2ysLAosD0RwwTK9evXxcTERBo2bGhQNzs7W1xcXMTFxUUpAyDm5uZ6D80iD0d2mJqa5tlObllZWTJ16lRxdHQUGxsbGTFihKSlpUlOTo7Ur19f/vzzT6VuZmamXL9+XS9JlZ9ff/1VAMgbb7xhsO3EiRMCQF555RWl7EkTKJ07dzbY9uWXX8qqVasMygcNGqT3vekSKD4+PgZ1ddeULtmiuxft3LlTuXZnzpyp95ns7GxxcHDIM2GWk5Mj7u7uUqNGDRH5X1zyGp00b968p06gTJw4UQDI3r17Deq//fbbAkBJDB84cEDmzZsnt27d0qv33//+1+CaM1YCZezYsXr1NmzYIAAkLCzMoI1t27YJAJk3b56IiIwZM8bgeEVETp8+LREREZKenp5nnIiIiPLDVXiI6Ln06DLGKpUKNjY28PX1RceOHZV38nWcnZ3h7Oys/H7v3j0cPXoU5cqVw9SpUw3aT0lJQVpaGq5evYqDBw8CAPz9/fPsR0EOHz6MnJycPOduGD58OIYPH17g5x/l7OyM1q1bY+fOnUhMTISLiwtOnz6N06dP4/XXX4epqSkA4O+//4aZmRkWLFhg0IZuXoQzZ84o81o8jqioKGRnZ+P69euYOHGiwXYrKyucOXNG+X3SpEnIycnB4cOHcerUKWVi0oIm+C2KonzHnp6e6Nq1K37//XfUqFEDPXv2ROvWrREUFFSkVZDq1KljUGZnZ4fMzExkZ2cr39fjiI6ORk5OTp4r3piYmKBRo0aIiIjArVu34ODgAACoW7euwVK2Dg4O8Pb2xrlz5wrcn6mpKebPn4/58+frla9duxZOTk546aWXAADz5s3DvHnzcPfuXXh4eODzzz9H165d821Xdz3ldRy1a9eGtbU1Tp06VWDfCpJXzHXX2b///osTJ04gLi4OZ8+exS+//JJnG7Vr1zYo003MqlvWWWfatGk4ePAgTExM0L9/f71t586dw61bt2Bra5vnNWNiYoKYmBhkZ2cX6T5TkL///hsA8N133xnMIaK7Xs+cOYMWLVqgWbNmaNasGZKSkhAREYELFy4gLi4OGzZseOr9F+bR70vX3/379xvETDe3la7fgwYNwldffYWRI0dizZo16NChA4KCglC/fn3UqFGj2PpMRETPLyZQiOi5lN8yxvkxNzfX+/3WrVsQEdy6dQuffvppvp+7e/euMuFmXskGjUZT4H51n839YF9U/fr1w44dO/D7779j+PDhWL9+PQDoTZh58+ZNZGVlFXpsT+LmzZsAgGPHjuHYsWN51smdENiwYQPGjRunTH7r6ekJX19feHl5FfrwXpBHJ5XVKcp3DAC//vorVqxYgVWrVuGzzz7DsmXLYGZmhi5dumDFihV6E6o+LktLy3y3yf9Plvq4dOdSxYoV89xubW0NAHoTjua37K9Go9GbbPhxiQjee+89rFixAgAQHh6OmTNnYsKECWjevDlWrVqFPn364OjRo/k+wBZ2HFZWVo81aWp+Hj0PAGDv3r0YPnw4zp49C5VKBXd3dzRp0gT169dXEhe5Pcn3dvDgQfj5+WHv3r0YP348du7cqWzTXTNxcXEFnoP37t0r0n2mILo+/Oc//8m3ju4aSE5OxujRo7FmzRrk5OTAzs4OtWrVQosWLYp0zQKPf93q+vvHH38U2l9fX18cOnQICxcuxO+//45t27YBACpVqoTZs2crk+oSERE9Lq7CQ0SUB90DSYMGDSAPX3fM86dOnTrKg+mNGzcM2klISChwP2q1GgDyXL1DRJCVlfXED9K9evWCubk5Nm3aBABYv349qlatqvdXao1GA3t7+wKPraBRAnnRxWzixIn5tql7+Dl9+jT69esHrVaL3bt3Iy0tDbGxsVi9ejVcXV2faL+PunXr1hP193G+Y+DhKi8TJkzAwYMHcePGDaxZswZdunTBxo0bMXjw4CL12RisrKwA5H/8ukSVbvQJgHwTEQkJCXr1HteaNWvg5uaGli1bAgAWLVqEYcOGYfHixejfvz82bdoET09PLFu27KmO4/79+7h16xbKly//xH3LT1JSErp3747k5GRs3rwZqampuHTpEtatWwcfH58itz948GDs3r0bHTt2RGRkJH766Sdlm+4c7NGjR4HnoFarLdJ9piC6PiQnJ+e7//HjxwMAQkND8fPPP2P69Om4cuUKkpOTsX//fqMkIp70ut2wYUO+/V27dq1Sv169evj2229x48YNREdHY+7cucjIyMDw4cOVlayIiIgeFxMoRER5sLe3h7u7O2JiYgyG5APA+++/j5CQEIgI6tWrBwDKcqq57dmzp8D96B7ODx06ZLBt0qRJMDc3x4kTJ56o7w4ODmjbti127NiBmJgYREVFGbw6ULduXSQnJ+PSpUsGn//iiy8QHByM5OTkJ9pv3bp1ASDPZX0zMzMxdOhQfPTRRwCAHTt2IDMzE7Nnz4a/v7+SSBIRvaVVC2JhYQHg4V/nc8trxEBenuQ7/vvvv9GjRw8cOHAAAODo6Ig+ffpgw4YNqFWrVqHfc0nQnUt//vmnwbZbt24hKioK9erV0xs9cezYMYO//MfGxiIhIQENGjR4ov3n5ORgzpw5mDNnjlJ24cIFvXbMzMxQp04dXLhw4amOY9u2bRCRIr2y8qi//voLt27dwsSJE9G5c2clUQEA58+fL3L7w4YNg4mJCRYtWgQzMzNMnjwZqampAIAaNWrAwsICx48fzzNR+vrrr+Ott94CgCLdZwpS0HW7du1aBAcHK8tvb968Gd7e3ggLC4Obm5tS73HjlN81e+fOHZw9e7bI/T158iSCg4Px22+/AXiYzH3jjTcA/O81trfffhtLly6FiBTb64JERPT8YgKFiCgfgwcPRmpqqt4DIfDwoWLWrFlITU2FSqVCQEAA3N3d8dNPP+H48eNKvZs3b+Ljjz8ucB81atRA48aN8dtvv+l99vz58/j666/h6empPFCamDy8Zec31D23V199Fenp6ZgwYQJExCCBohsx8fbbbyM7O1sp37dvHyZPnoyzZ8/C3t6+0P3kVq1aNbRs2RKRkZGIiIhQynNycjB16lR8++230Gq1AP73IJU7gaN7/SOvpI6JiYnBcXt4eACA8ooSAMTExOCrr7567D4/7ndsa2uLjRs3YuHChXr9SEhIQHx8/FO9vlNUj54P3t7eaNKkCXbs2IHt27fr1Z0/fz4ePHhgMKdOcnKy3jmq+65ycnLQr1+/J+rPzz//jMqVK6NZs2ZKWY0aNbBu3TrlHLt27Rp2795d4PwTPXv2hJWVFZYuXYqrV68q5ffv38dHH30EExMTo756kde5CABff/21UR+wa9asiTFjxiA+Ph7vvfcegIejbfr06YPY2FjltSedRYsWYenSpcp8TT169IC1tTU+//xzvbjExsZi5cqVT90v3b1g9uzZeomNU6dOYdy4cdi1axcqVaoE4GGsbt++rSSAdPufO3euQbt53a/c3d0BAFu3btXb18yZM/XuQwXp2bMntFotPv/8c71ka3JyMsaMGYMff/wRVapUAfBw1NWyZcsQHR2t14bu9yeZ44mIiAjAI+sfEhE94/JbxrggeS3HK/Jw6dqGDRsKAGnWrJm89tprEhgYKADEzc1NLl26pNSNiIgQMzMzsba2ln79+smIESPExcVFOnXqVOgyxgcOHBArKyvRarUSHBwsgwYNEjs7O1GpVPLrr78q9XRLcgYGBsqSJUtExHAVHp07d+6IWq0WAFKvXj2DY8vOzlb6Vrt2bQkJCZFOnTqJqamp2NrayuHDh58qbidOnJBy5cqJqampdOrUSUJCQqR27doCQIKCgpQVNuLj45V6PXv2lJCQEPHy8hIXFxdp3769soTwjRs3RETE19dXAEhwcLCy3POOHTsEgKjVaunatav07dtXNBqNslzso6vwFPU71q2y4uPjI0OHDpWBAwdKuXLlBIB88803BcYqv1V4Hmc1kvzolozu1q2brF69WkREoqKiRKvVipmZmXTr1k3GjBmjxO6ll17SaxOAVKhQQSwtLSUwMFBGjx4t9erVEwASEBBQ6P5zy87Olpo1a0pUVJRe+fr16wWA1K9fX0JCQsTV1VW0Wq1eXPOyfPlyASD29vYSHBwsI0aMkKpVq+a5ZPSTrsLzaMzT0tKUtjt06CAjRoyQevXqiVarlZ49ewoA6dq1q5w6dUpZhWfgwIEG7T/6HedexlgnKSlJHBwcxNzcXFldKyEhQSpVqqRc26+99pqy3HqdOnXkzp07yud1K9uUK1dOBg8eLEOHDhU7OzvlWn6aVXhEREaOHCkApGrVqjJkyBDp0aOHWFlZiYWFhURERCj1pk2bJgDEy8tLRo0aJV26dBELCwtltaJq1aopfdAtle7t7S0TJkxQ2vDz81PKBw0aJA0bNhQHBwdlValHV+F5tK8iIqtWrRITExMpV66c9O3bVwYOHCiurq4GqxRFR0eLqampco8YNWqUtGzZUgBIzZo15d69ewXGi4iI6FFMoBDRc8WYCRQRkZSUFHnzzTfFw8NDLCwsxNPTU0aPHi3Xrl0zqBsZGSm+vr6iVqvF1dVVJk2aJCkpKYUmUEREDh06JO3atRONRiOOjo7Stm1b2bNnj16dCxcuSKNGjcTc3Fx69uwpIvknUEREunfvrrek56MyMzMlLCxMvLy8xMLCQipWrCgDBw5UHgALk1/cYmJipF+/flKuXDmxsrKS2rVry9y5c/WWjhZ5+LDv7+8vGo1GXF1dZeTIkZKQkCBHjhwRDw8PsbW1latXr4qIyJYtW8TDw0PMzMxk0aJFShvh4eHi4+MjarVaqlWrJvPnz5fTp08/dgJF5PG/47t370poaKhUrlxZzM3NRaPRiK+vr6xdu7bQWBVHAiU6Olq8vb3FzMxMJk6cqJQfP35cevToIfb29qJWq8Xb21veeecdSUtL0/u87jzctWuXtGjRQqysrKRKlSoyffp0vaWaH8fq1aule/fueW4LDw+XqlWrirm5uTRv3lxZErcwGzdulJYtW4q1tbVoNBpp1qyZfP311wb1ippAERE5d+6cdO7cWWxtbcXJyUn69+8vZ8+elcuXL0vNmjXF2tpaDh48WOQEiojIkiVLBIC0bt1aKbt27ZqSdFWr1eLl5SVTpkwxWGJaRGTt2rVSr149sbCwkMqVK0tYWJhyzj9tAiUnJ0c+//xzqV27tlhYWIizs7N0797dICGWmZkpM2fOFA8PD7G2tpbmzZvLjz/+KCIio0ePFrVaLZMmTRIRkQcPHkjPnj3FyspKypUrp7QRHx8vr7zyitjb24utra107NhRjh07piz1/DgJFJGHSycHBASItbW12NnZSatWreTnn382qLd161YJCAgQW1tbMTMzE3d3dxk5cqSSnCUiInoSKpEnnJ2QiIiInnkqlQqtWrXiPBBEREREj4lzoBARERERERERFYIJFCIiIiIiIiKiQjCBQkRERERERERUCLPS7gARERGVPE6BRkRERPRkOAKFiIiIiIiIiKgQTKAQERERERERERWCCRQiIiIiIiIiokIwgUJEREREREREVAgmUIiIiIiIiIiICsEEChERERERERFRIZhAISIiIiIiIiIqBBMoRERERERERESFYAKFiIiIiIiIiKgQ/wdANEYpBuUpMAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create custom color scheme\n", + "custom_cmap = sns.color_palette('Oranges', n_colors=1000)[:800] + sns.color_palette('Blues', n_colors=1000)[800:]\n", + "\n", + "# Define your preferred order of the RBAs as a list\n", + "rba_order = [\n", + " 'RandomShuffle',\n", + " 'MutualInformation',\n", + " 'SURFstar',\n", + " 'MultiSURFstar',\n", + " 'SWRFstar',\n", + "]\n", + "\n", + "# Define a mapping from your RBA order to new descriptive names\n", + "rba_descriptive_names = {\n", + " 'RandomShuffle': 'Random Shuffle',\n", + " 'MutualInformation': 'Mutual Information',\n", + " 'ReliefF10': 'ReliefF 10NN',\n", + " 'ReliefF100': 'ReliefF 100NN',\n", + " 'MultiSURF': 'MultiSURF',\n", + " 'MultiSURFstar': 'MultiSURF*',\n", + " 'SURFstar': 'SURF*',\n", + " 'SWRFstar': 'SWRF*',\n", + " 'ABS_ReliefF10': 'ReliefF 10NN ABS',\n", + " 'ABS_ReliefF': 'ReliefF 100NN ABS',\n", + " 'ABS_MultiSURF': 'MultiSURF ABS',\n", + " 'ABS_MultiSURFstar': 'MultiSURF* ABS',\n", + " 'ABD_SWRFstar': 'SWRF* ABS',\n", + "}\n", + "\n", + "percentages_df = percentages_df.iloc[1:] # Drop the first row as this will always be 0\n", + "percentages_df_transposed = percentages_df.T # Transpose percentages_df to switch rows and columns for horizontal orientation\n", + "\n", + "# Reorder the DataFrame according to your defined RBA order\n", + "percentages_df_ordered = percentages_df_transposed.loc[rba_order] # The .loc indexer reindexes the DataFrame to the specified order; any missing labels will result in NaN rows\n", + "\n", + "# Define the tick labels as percentages of optimality\n", + "xtick_labels = ['Optimal', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']\n", + "# Generate a list of positions at which to place the x-tick labels, assuming they should be placed at even intervals\n", + "xtick_positions = np.linspace(start=0, stop=percentages_df_transposed.shape[1] - 0.13, num=len(xtick_labels))\n", + "\n", + "# Create the heatmap with the reordered DataFrame\n", + "plt.figure(figsize=(12, 7)) # Adjust the size as necessary\n", + "heatmap = sns.heatmap(percentages_df_ordered, annot=False, fmt=\".1f\", cmap=custom_cmap, cbar_kws={'label': 'Power (Frequency of Success)'})\n", + "\n", + "# Add horizontal lines manually between Methods\n", + "for i in range(percentages_df_ordered.shape[0] - 1):\n", + " heatmap.axhline(i + 1, color='black', linewidth=1.5)\n", + "\n", + "# Adding black border around the heatmap\n", + "for _, spine in heatmap.spines.items():\n", + " spine.set_visible(True)\n", + " spine.set_linewidth(1.5)\n", + " spine.set_edgecolor(\"black\")\n", + "\n", + "# Adding black border around the color bar in legend\n", + "cbar = heatmap.collections[0].colorbar\n", + "cbar.outline.set_linewidth(1.5)\n", + "cbar.outline.set_edgecolor(\"black\")\n", + "\n", + "# Set the title and axis labels appropriately\n", + "heatmap.set_title('a_20 Test Datasets Heatmap', fontsize = 16)\n", + "heatmap.set_xlabel('Predictive features in top % of ranked features', fontsize = 14)\n", + "heatmap.set_ylabel('Method', fontsize = 14)\n", + "\n", + "# Set the custom x-tick labels, positions, and fontsize\n", + "heatmap.set_xticks(xtick_positions)\n", + "heatmap.set_xticklabels(xtick_labels, rotation=0, fontsize=11)\n", + "\n", + "# Set the custom y-tick labels fontsize\n", + "new_ytick_labels = [rba_descriptive_names[rba] for rba in rba_order]\n", + "heatmap.set_yticklabels(new_ytick_labels, fontsize=11)\n", + "\n", + "# Save and show the Plot\n", + "plt.rcParams['font.sans-serif'] = 'Helvetica'\n", + "plt.rcParams['font.family'] = 'sans-serif'\n", + "plt.rcParams['pdf.fonttype'] = 42\n", + "plt.tight_layout() # Adjust the layout\n", + "save_path = os.path.join(results_dir, 'a_20_Testdataset_Heatmap.pdf') # Save\n", + "plt.savefig(save_path, format='pdf', bbox_inches='tight') # Save\n", + "plt.show() # Display the heatmap" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "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/demo/run_benchmark.py b/demo/run_benchmark.py new file mode 100644 index 0000000..549bdaf --- /dev/null +++ b/demo/run_benchmark.py @@ -0,0 +1,145 @@ +import os +import sys +import pandas as pd +import numpy as np +import argparse +import time + +from multiprocessing import Pool, cpu_count + +from sklearn.feature_selection import mutual_info_classif + +# Get the absolute path to the directory containing the package +package_path = os.path.abspath(os.path.join("", "..")) + +# Add the package path to sys.path +sys.path.insert(0, package_path) +from skrebate import SURFstar, MultiSURFstar, SWRFstar + +def find_txt_groups(root_dir): + # Grab all the folders that contain 30 txt files and exclude the file name that contains 'Results' + txt_groups = [] + + for root, dirs, files in os.walk(root_dir): + txt_files = [f for f in files if f.endswith('.txt') and 'Results' not in f] + + files_count = len(txt_files) + + # if files_count == 30 and 'Results' not in root: + # txt_groups.append(root) + # elif files_count != 0: + # print(f"{root} got {len(txt_files)} files") + if 'Results' not in root: + txt_groups.append(root) + elif files_count != 0: + print(f"{root} got {len(txt_files)} files") + + return txt_groups + + +def single_file_job(args): + # Run all the methods on a single data file + exp_dir, txt_file, result_dir, methods = args + + try: + print(f"\tProcessing with data file {txt_file}...") + if txt_file.endswith('.txt'): + data = pd.read_csv(os.path.join(exp_dir, txt_file), sep='\t') + elif txt_file.endswith('.csv'): + data = pd.read_csv(os.path.join(exp_dir, txt_file)) + else: + raise ValueError(f"Unsupported file format: {txt_file}") + features, labels = data.drop('class', axis=1).values, data['class'].values + + for method_name, (method, params) in methods.items(): + if method_name == "RandomShuffle": + importances = np.random.permutation(len(data.drop('class', axis=1).columns)) + elif method_name == "MutualInformation": + importances = mutual_info_classif(features, labels) + else: + fs = method(**params) + fs.fit(features, labels) + importances = fs.feature_importances_ + + results = pd.DataFrame({ + 'Feature': data.drop('class', axis=1).columns, + 'Feature_Importance': importances + }) + + base_name = os.path.splitext(os.path.basename(txt_file))[0] + results.to_csv(os.path.join(result_dir, exp_dir, method_name, f"{base_name}_Results.txt"), index=False, sep='\t') + return f"File {txt_file} processed successfully." + except Exception as e: + raise e # Uncomment this line to raise the exception after logging the error + return f"Error processing {txt_file}: {e}" + + +def main(data_dir, result_dir, methods, num_cpus, dry_run): + # Find all the folder that contains 30 data files + exp_dirs = find_txt_groups(data_dir) + print(f"Found {len(exp_dirs)} exps") + + if dry_run: + print("[Dry Run] The files in the following folders will be processed:") + for exp_dir in exp_dirs: + print(f"{exp_dir}") + return + + # Generate the configuration for all the jobs + args = [] + for exp_dir in exp_dirs: + + for method_name, _ in methods.items(): + os.makedirs(os.path.join(result_dir, exp_dir, method_name), exist_ok=True) + + txt_files = [f for f in os.listdir(exp_dir) if (f.endswith('.txt') or f.endswith('.csv'))] + + args += [(exp_dir, txt_file, result_dir, methods) for txt_file in txt_files] + + start_time = time.time() + + # results = [] + # for arg in args: + # results.append(single_file_job(arg)) # Test a single file job to ensure everything is working + + print(f"Parallel processing on {len(args)} tasks...") + with Pool(processes=num_cpus) as pool: + results = pool.map(single_file_job, args) + + elapsed_time = time.time() - start_time + print(f"\nTotal processing time: {elapsed_time:.2f} seconds") + + for result in results: + print(result) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Parallel processing for feature importance computation.") + # parser.add_argument("--data_dir", type=str, required=True, help="Path to the directory containing .txt files.") + # parser.add_argument("--result_dir", type=str, default='.', help="Path to the directory for saving results.") + parser.add_argument("--num_cpus", type=int, default=None, help="Number of CPU cores to use for multiprocessing.") + parser.add_argument("-n", "--dry_run", action="store_true", help="If set, perform a dry run without actual computation.") + args = parser.parse_args() + + if args.num_cpus is None: + num_cpus = cpu_count() + else: + num_cpus = args.num_cpus + print(f"Using cpu number: {num_cpus}") + + + ############Path and method setting############ + data_dir = "../data/" + result_dir = 'Results' + + methods = { + "RandomShuffle": (None, None), + "MutualInformation": (None, None), + "SURFstar": (SURFstar, {'n_features_to_select': 5}), + "MultiSURFstar": (MultiSURFstar, {'n_features_to_select': 5}), + "SWRFstar": (SWRFstar, {'n_features_to_select': 5}), + } + ############Path and method setting############ + + main(data_dir, result_dir, methods, args.num_cpus, args.dry_run) + diff --git a/docs/404.html b/docs/404.html index 672e09c..bb9b8be 100644 --- a/docs/404.html +++ b/docs/404.html @@ -1,153 +1,135 @@ - - - - - - - - - - - scikit-rebate - - - - - - - - - - - - - - -
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+ + + + + + + + + diff --git a/docs/assets/styles/custom.css b/docs/assets/styles/custom.css new file mode 100644 index 0000000..8ed0aab --- /dev/null +++ b/docs/assets/styles/custom.css @@ -0,0 +1,7 @@ +.wy-table-responsive table td, .wy-table-responsive table th { + white-space: normal !important; +} + +.wy-table-responsive { + overflow : visible !important; +} \ No newline at end of file diff --git a/docs/citing/index.html b/docs/citing/index.html index 3ebaf58..08eb6da 100644 --- a/docs/citing/index.html +++ b/docs/citing/index.html @@ -1,192 +1,171 @@ - - - - - - - - - - - Citing - scikit-rebate - - - - - - - - - - - - - - - - -
- - - - -
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If you use scikit-rebate or the MultiSURF algorithm in a scientific publication, please consider citing the following paper (currently available as a pre-print in arXiv):

-

Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods." arXiv preprint arXiv:1711.08477 (2017).

-

Alternatively a complete review of Relief-based algorithms is available at:

-

Urbanowicz, Ryan J., Melissa Meeker, William LaCava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." arXiv preprint arXiv:1711.08421 (2017).

-

To cite the original Relief paper:

-

Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." In Machine Learning Proceedings 1992, pp. 249-256. 1992.

-

To cite the original ReliefF paper:

-

Kononenko, Igor. "Estimating attributes: analysis and extensions of RELIEF." In European conference on machine learning, pp. 171-182. Springer, Berlin, Heidelberg, 1994.

-

To cite the original SURF paper:

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Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, and Jason H. Moore. "Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions." BioData mining 2, no. 1 (2009): 5.

-

To cite the original SURF* paper:

-

Greene, Casey S., Daniel S. Himmelstein, Jeff Kiralis, and Jason H. Moore. "The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 182-193. Springer, Berlin, Heidelberg, 2010.

-

To cite the original MultiSURF* paper:

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Granizo-Mackenzie, Delaney, and Jason H. Moore. "Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 1-10. Springer, Berlin, Heidelberg, 2013.

-

To cite the original TuRF paper:

-

Moore, Jason H., and Bill C. White. "Tuning ReliefF for genome-wide genetic analysis." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 166-175. Springer, Berlin, Heidelberg, 2007.

- -
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- - - GitHub - - - « Previous - - - Next » - - -
- - - - - - - + + + + + + + + Citing - scikit-rebate + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ + +

If you use scikit-rebate or the MultiSURF algorithm in a scientific publication, please consider citing the following paper:

+ +

Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods for bioinformatics data mining." Journal of Biomedical Informatics, 85:168–188, 2018.

+

Alternatively a complete review of Relief-based algorithms is available at:

+ +

Urbanowicz, Ryan J., Melissa Meeker, William LaCava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." Journal of Biomedical Informatics, 85:189–203, 2018.

+

To cite the original Relief paper:

+

Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." In Machine Learning Proceedings 1992, pp. 249-256. 1992.

+

To cite the original ReliefF paper:

+

Kononenko, Igor. "Estimating attributes: analysis and extensions of RELIEF." In European conference on machine learning, pp. 171-182. Springer, Berlin, Heidelberg, 1994.

+

To cite the original SURF paper:

+

Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, and Jason H. Moore. "Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions." BioData Mining 2, no. 1 (2009): 5.

+

To cite the original SURF* paper:

+

Greene, Casey S., Daniel S. Himmelstein, Jeff Kiralis, and Jason H. Moore. "The informative extremes: using both nearest and farthest individuals can improve relief algorithms in the domain of human genetics." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 182-193. Springer, Berlin, Heidelberg, 2010.

+

To cite the original MultiSURF* paper:

+

Granizo-Mackenzie, Delaney, and Jason H. Moore. "Multiple threshold spatially uniform relieff for the genetic analysis of complex human diseases." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 1-10. Springer, Berlin, Heidelberg, 2013.

+ +

To cite the original SWRF* paper:

+

Stokes, Matthew E., and Shyam Visweswaran. "Application of a spatially-weighted relief algorithm for ranking genetic predictors of disease." BioData Mining, 5:20, 2012.

+

To cite the original μ-Relief paper:

+

Aggarwal, Nitisha, Unmesh Shukla, G. J. Saxena, et al. "Mean based relief: An improved feature selection method based on relieff." Applied Intelligence, 53:23004–23028, 2023.

+

To cite the original TuRF paper:

+

Moore, Jason H., and Bill C. White. "Tuning ReliefF for genome-wide genetic analysis." In European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 166-175. Springer, Berlin, Heidelberg, 2007.

+ +
+
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+
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+ + + + GitHub + + + + « Previous + + + Next » + + +
+ + + + + + + + + diff --git a/docs/contributing/index.html b/docs/contributing/index.html index 34c3132..6af0e9a 100644 --- a/docs/contributing/index.html +++ b/docs/contributing/index.html @@ -1,302 +1,260 @@ - - - - - - - - - - - Contributing - scikit-rebate - - - - - - - - - - - - - - - - -
- - - - -
- - - - - -
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-
- -
-
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-
- -

We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to scikit-rebate, please file a new issue so we can discuss it.

-

Project layout

-

The latest stable release of scikit-rebate is on the master branch, whereas the latest version of scikit-rebate in development is on the development branch. Make sure you are looking at and working on the correct branch if you're looking to contribute code.

-

In terms of directory structure:

-
    -
  • All of scikit-rebate's code sources are in the skrebate directory
    • -
  • The documentation sources are in the docs_sources directory
    • -
  • The latest documentation build is in the docs directory
    • -
  • Unit tests for scikit-rebate are in the tests.py file
    • -
-

Make sure to familiarize yourself with the project layout before making any major contributions, and especially make sure to send all code changes to the development branch.

-

How to contribute

-

The preferred way to contribute to scikit-rebate is to fork the -main repository on -GitHub:

-
    -
  1. -

    Fork the project repository: - click on the 'Fork' button near the top of the page. This creates - a copy of the code under your account on the GitHub server.

    -
    2. -
  2. -

    Clone this copy to your local disk:

    -
      $ git clone git@github.com:YourLogin/scikit-rebate.git
-  $ cd scikit-rebate
-
    -
    3. -
  3. -

    Create a branch to hold your changes:

    -
      $ git checkout -b my-contribution
-
    -
    4. -
  4. -

    Make sure your local environment is setup correctly for development. Installation instructions are almost identical to the user instructions except that scikit-rebate should not be installed. If you have scikit-rebate installed on your computer, then make sure you are using a virtual environment that does not have scikit-rebate installed. Furthermore, you should make sure you have installed the nose package into your development environment so that you can test changes locally.

    -
      $ conda install nose
-
    -
    5. -
  5. -

    Start making changes on your newly created branch, remembering to never work on the master branch! Work on this copy on your computer using Git to do the version control.

    -
    6. -
  6. -

    Once some changes are saved locally, you can use your tweaked version of scikit-rebate by navigating to the project's base directory and running scikit-rebate in a script.

    -
    7. -
  7. -

    To check your changes haven't broken any existing tests and to check new tests you've added pass run the following (note, you must have the nose package installed within your dev environment for this to work):

    -
      $ nosetests -s -v
-
    -
    8. -
  8. -

    When you're done editing and local testing, run:

    -
      $ git add modified_files
-  $ git commit
-
    -
    9. -
-

to record your changes in Git, then push them to GitHub with:

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      $ git push -u origin my-contribution
-
-

Finally, go to the web page of your fork of the scikit-rebate repo, and click 'Pull Request' (PR) to send your changes to the maintainers for review. Make sure that you send your PR to the development branch, as the master branch is reserved for the latest stable release. This will start the CI server to check all the project's unit tests run and send an email to the maintainers.

-

(For details on the above look up the Git documentation on the web.)

-

Before submitting your pull request

-

Before you submit a pull request for your contribution, please work through this checklist to make sure that you have done everything necessary so we can efficiently review and accept your changes.

-

If your contribution changes scikit-rebate in any way:

-
    -
  • -

    Update the documentation so all of your changes are reflected there.

    -
    • -
  • -

    Update the README if anything there has changed.

    -
    • -
-

If your contribution involves any code changes:

-
    -
  • -

    Update the project unit tests to test your code changes.

    -
    • -
  • -

    Make sure that your code is properly commented with docstrings and comments explaining your rationale behind non-obvious coding practices.

    -
    • -
-

If your contribution requires a new library dependency:

-
    -
  • -

    Double-check that the new dependency is easy to install via pip or Anaconda and supports both Python 2 and 3. If the dependency requires a complicated installation, then we most likely won't merge your changes because we want to keep scikit-rebate easy to install.

    -
    • -
  • -

    Add a line to pip install the library to .travis_install.sh

    -
    • -
  • -

    Add a line to print the version of the library to .travis_install.sh

    -
    • -
  • -

    Similarly add a line to print the version of the library to .travis_test.sh

    -
    • -
-

Updating the documentation

-

We use mkdocs to manage our documentation. This allows us to write the docs in Markdown and compile them to HTML as needed. Below are a few useful commands to know when updating the documentation. Make sure that you are running them in the base repository directory.

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    -
  • -

    mkdocs serve: Hosts of a local version of the documentation that you can access at the provided URL. The local version will update automatically as you save changes to the documentation.

    -
    • -
  • -

    mkdocs build --clean: Creates a fresh build of the documentation in HTML. Always run this before deploying the documentation to GitHub.

    -
    • -
  • -

    mkdocs gh-deploy: Deploys the documentation to GitHub. If you're deploying on your fork of scikit-rebate, the online documentation should be accessible at http://<YOUR GITHUB USERNAME>.github.io/scikit-rebate/. Generally, you shouldn't need to run this command because you can view your changes with mkdocs serve.

    -
    • -
-

After submitting your pull request

-

After submitting your pull request, Travis-CI will automatically run unit tests on your changes and make sure that your updated code builds and runs on Python 2 and 3. We also use services that automatically check code quality and test coverage.

-

Check back shortly after submitting your pull request to make sure that your code passes these checks. If any of the checks come back with a red X, then do your best to address the errors.

- -
-
- - -
-
- -
- -
- -
- - - GitHub - - - « Previous - - - Next » - - -
- - - - - - - + + + + + + + + Contributing - scikit-rebate + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ +

We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension to scikit-rebate, please file a new issue so we can discuss it.

+

Project layout

+

The latest stable release of scikit-rebate is on the master branch, whereas the latest version of scikit-rebate in development is on the development branch. Make sure you are looking at and working on the correct branch if you're looking to contribute code.

+

In terms of directory structure:

+
    +
  • All of scikit-rebate's code sources are in the skrebate directory
    • +
  • The documentation sources are in the docs_sources directory
    • +
  • The latest documentation build is in the docs directory
    • +
  • Unit tests for scikit-rebate are in the tests.py file
    • +
+

Make sure to familiarize yourself with the project layout before making any major contributions, and especially make sure to send all code changes to the development branch.

+

How to contribute

+

The preferred way to contribute to scikit-rebate is to fork the +main repository on +GitHub:

+
    +
  1. +

    Fork the project repository: + click on the 'Fork' button near the top of the page. This creates + a copy of the code under your account on the GitHub server.

    +
    2. +
  2. +

    Clone this copy to your local disk:

    +
      $ git clone git@github.com:YourLogin/scikit-rebate.git
+  $ cd scikit-rebate
+
    +
    3. +
  3. +

    Create a branch to hold your changes:

    +
      $ git checkout -b my-contribution
+
    +
    4. +
  4. +

    Make sure your local environment is setup correctly for development. Installation instructions are almost identical to the user instructions except that scikit-rebate should not be installed. If you have scikit-rebate installed on your computer, then make sure you are using a virtual environment that does not have scikit-rebate installed. Furthermore, you should make sure you have installed the nose package into your development environment so that you can test changes locally.

    +
      $ conda install nose
+
    +
    5. +
  5. +

    Start making changes on your newly created branch, remembering to never work on the master branch! Work on this copy on your computer using Git to do the version control.

    +
    6. +
  6. +

    Once some changes are saved locally, you can use your tweaked version of scikit-rebate by navigating to the project's base directory and running scikit-rebate in a script.

    +
    7. +
  7. +

    To check your changes haven't broken any existing tests and to check new tests you've added pass run the following (note, you must have the nose package installed within your dev environment for this to work):

    +
      $ nosetests -s -v
+
    +
    8. +
  8. +

    When you're done editing and local testing, run:

    +
      $ git add modified_files
+  $ git commit
+
    +
    9. +
+

to record your changes in Git, then push them to GitHub with:

+
      $ git push -u origin my-contribution
+
+

Finally, go to the web page of your fork of the scikit-rebate repo, and click 'Pull Request' (PR) to send your changes to the maintainers for review. Make sure that you send your PR to the development branch, as the master branch is reserved for the latest stable release. This will start the CI server to check all the project's unit tests run and send an email to the maintainers.

+

(For details on the above look up the Git documentation on the web.)

+

Before submitting your pull request

+

Before you submit a pull request for your contribution, please work through this checklist to make sure that you have done everything necessary so we can efficiently review and accept your changes.

+

If your contribution changes scikit-rebate in any way:

+
    +
  • +

    Update the documentation so all of your changes are reflected there.

    +
    • +
  • +

    Update the README if anything there has changed.

    +
    • +
+

If your contribution involves any code changes:

+
    +
  • +

    Update the project unit tests to test your code changes.

    +
    • +
  • +

    Make sure that your code is properly commented with docstrings and comments explaining your rationale behind non-obvious coding practices.

    +
    • +
+

If your contribution requires a new library dependency:

+
    +
  • +

    Double-check that the new dependency is easy to install via pip or Anaconda and supports both Python 2 and 3. If the dependency requires a complicated installation, then we most likely won't merge your changes because we want to keep scikit-rebate easy to install.

    +
    • +
  • +

    Add a line to pip install the library to .travis_install.sh

    +
    • +
  • +

    Add a line to print the version of the library to .travis_install.sh

    +
    • +
  • +

    Similarly add a line to print the version of the library to .travis_test.sh

    +
    • +
+

Updating the documentation

+

We use mkdocs to manage our documentation. This allows us to write the docs in Markdown and compile them to HTML as needed. Below are a few useful commands to know when updating the documentation. Make sure that you are running them in the base repository directory.

+
    +
  • +

    mkdocs serve: Hosts of a local version of the documentation that you can access at the provided URL. The local version will update automatically as you save changes to the documentation.

    +
    • +
  • +

    mkdocs build --clean: Creates a fresh build of the documentation in HTML. Always run this before deploying the documentation to GitHub.

    +
    • +
  • +

    mkdocs gh-deploy: Deploys the documentation to GitHub. If you're deploying on your fork of scikit-rebate, the online documentation should be accessible at http://<YOUR GITHUB USERNAME>.github.io/scikit-rebate/. Generally, you shouldn't need to run this command because you can view your changes with mkdocs serve.

    +
    • +
+

After submitting your pull request

+

After submitting your pull request, Travis-CI will automatically run unit tests on your changes and make sure that your updated code builds and runs on Python 2 and 3. We also use services that automatically check code quality and test coverage.

+

Check back shortly after submitting your pull request to make sure that your code passes these checks. If any of the checks come back with a red X, then do your best to address the errors.

+ +
+
+ +
+
+ +
+ +
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Without and highlighting styles attached the + * formatting is broken. + * + * https://github.com/mkdocs/mkdocs/issues/319 + */ +.rst-content .no-highlight { + display: block; + padding: 0.5em; + color: #333; +} + + +/* + * Additions specific to the search functionality provided by MkDocs + */ + +.search-results { + margin-top: 23px; +} + +.search-results article { + border-top: 1px solid #E1E4E5; + padding-top: 24px; +} + +.search-results article:first-child { + border-top: none; +} + +form .search-query { + width: 100%; + border-radius: 50px; + padding: 6px 12px; + border-color: #D1D4D5; +} + +/* + * Improve inline code blocks within admonitions. + * + * https://github.com/mkdocs/mkdocs/issues/656 + */ + .rst-content .admonition code { + color: #404040; + border: 1px solid #c7c9cb; + border: 1px solid rgba(0, 0, 0, 0.2); + background: #f8fbfd; + background: rgba(255, 255, 255, 0.7); +} + +/* + * Account for wide tables which go off the side. + * Override borders to avoid weirdness on narrow tables. + * + * https://github.com/mkdocs/mkdocs/issues/834 + * https://github.com/mkdocs/mkdocs/pull/1034 + */ +.rst-content .section .docutils { + width: 100%; + overflow: auto; + display: block; + border: none; +} + +td, th { + border: 1px solid #e1e4e5 !important; + border-collapse: collapse; +} + +/* + * Without the following amendments, the navigation in the theme will be + * slightly cut off. This is due to the fact that the .wy-nav-side has a + * padding-bottom of 2em, which must not necessarily align with the font-size of + * 90 % on the .rst-current-version container, combined with the padding of 12px + * above and below. These amendments fix this in two steps: First, make sure the + * .rst-current-version container has a fixed height of 40px, achieved using + * line-height, and then applying a padding-bottom of 40px to this container. In + * a second step, the items within that container are re-aligned using flexbox. + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ + .wy-nav-side { + padding-bottom: 40px; +} + +/* For section-index only */ +.wy-menu-vertical .current-section p { + background-color: #e3e3e3; + color: #404040; +} + +/* + * The second step of above amendment: Here we make sure the items are aligned + * correctly within the .rst-current-version container. Using flexbox, we + * achieve it in such a way that it will look like the following: + * + * [No repo_name] + * Next >> // On the first page + * << Previous Next >> // On all subsequent pages + * + * [With repo_name] + * Next >> // On the first page + * << Previous Next >> // On all subsequent pages + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ +.rst-versions .rst-current-version { + padding: 0 12px; + display: flex; + font-size: initial; + justify-content: space-between; + align-items: center; + line-height: 40px; +} + +/* + * Please note that this amendment also involves removing certain inline-styles + * from the file ./mkdocs/themes/readthedocs/versions.html. + * + * https://github.com/mkdocs/mkdocs/issues/2012 + */ +.rst-current-version span { + flex: 1; + text-align: center; +} diff --git a/docs/fonts/fontawesome-webfont.eot b/docs/fonts/fontawesome-webfont.eot deleted file mode 100644 index 0662cb9..0000000 Binary files a/docs/fonts/fontawesome-webfont.eot and /dev/null differ diff --git a/docs/fonts/fontawesome-webfont.svg b/docs/fonts/fontawesome-webfont.svg deleted file mode 100644 index a4ba52c..0000000 --- a/docs/fonts/fontawesome-webfont.svg +++ /dev/null @@ -1,399 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/fonts/fontawesome-webfont.ttf b/docs/fonts/fontawesome-webfont.ttf deleted file mode 100644 index d365924..0000000 Binary files a/docs/fonts/fontawesome-webfont.ttf and /dev/null differ diff --git a/docs/fonts/fontawesome-webfont.woff b/docs/fonts/fontawesome-webfont.woff deleted file mode 100644 index b9bd17e..0000000 Binary files a/docs/fonts/fontawesome-webfont.woff and /dev/null differ diff --git a/docs/index.html b/docs/index.html index b5bbd17..1a79f8c 100644 --- a/docs/index.html +++ b/docs/index.html @@ -1,198 +1,172 @@ - - - - - - - - - - - Home - scikit-rebate - - - - - - - - - - - - - - - - -
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scikit-rebate is a scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. As of 5/7/18, this project is still under active development and we encourage you to check back on this repository regularly for updates.

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These algorithms excel at identifying features that are predictive of the outcome in supervised learning problems, and are especially good at identifying feature interactions that are normally overlooked by standard feature selection methods.

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The main benefit of Relief-based algorithms is that they identify feature interactions without having to exhaustively check every pairwise interaction, thus taking significantly less time than exhaustive pairwise search.

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Relief-based algorithms are commonly applied to genetic analyses, where epistasis (i.e., feature interactions) is common. However, the algorithms implemented in this package can be applied to almost any supervised classification data set and supports:

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    -
  • -

    A mix of categorical and/or continuous features

    -
    • -
  • -

    Data with missing values

    -
    • -
  • -

    Binary endpoints (i.e., classification)

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    • -
  • -

    Multi-class endpoints (i.e., classification)

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    • -
  • -

    Continuous endpoints (i.e., regression)

    -
    • -
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- - - GitHub - - - - Next » - - -
- - - - - - - - - + + + + + + + + scikit-rebate + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ +

scikit-rebate is a scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.

+ + +

These algorithms excel at identifying features that are predictive of the outcome in supervised learning problems, and are especially good at identifying feature interactions that are normally overlooked by standard feature selection methods.

+

The main benefit of Relief-based algorithms is that they identify feature interactions without having to exhaustively check every pairwise interaction, thus taking significantly less time than exhaustive pairwise search.

+

Relief-based algorithms are commonly applied to genetic analyses, where epistasis (i.e., feature interactions) is common. However, the algorithms implemented in this package can be applied to almost any supervised, structured data set and support:

+
    +
  • +

    A mix of categorical and/or continuous features

    +
    • +
  • +

    Data with missing values

    +
    • +
  • +

    Binary endpoints (i.e., classification)

    +
    • +
  • +

    Multi-class endpoints (i.e., classification)

    +
    • +
  • +

    Continuous endpoints (i.e., regression)

    +
    • +
+ +
+
+ +
+
+ +
+ +
+ +
+ + + + GitHub + + + + + Next » + + +
+ + + + + + + + + + + diff --git a/docs/installing/index.html b/docs/installing/index.html index 2b18317..67a301a 100644 --- a/docs/installing/index.html +++ b/docs/installing/index.html @@ -1,206 +1,173 @@ - - - - - - - - - - - Installation - scikit-rebate - - - - - - - - - - - - - - - - -
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scikit-rebate is built on top of the following existing Python packages:

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    NumPy

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    • -
  • -

    SciPy

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    • -
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    scikit-learn

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    • -
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All of the necessary Python packages can be installed via the Anaconda Python distribution, which we strongly recommend that you use. We also strongly recommend that you use Python 3 over Python 2 if you're given the choice.

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NumPy, SciPy, and scikit-learn can be installed in Anaconda via the command:

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conda install numpy scipy scikit-learn
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- -

Once the prerequisites are installed, you should be able to install scikit-rebate with a pip command:

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pip install skrebate
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- -

You can retrieve basic information about your installed version of skrebate with the following pip command:

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pip show skrebate
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- -

You can check that you have the most up to date pypi release of skrebate with the following pip command:

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pip install skrebate -U
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- -

Please file a new issue if you run into installation problems.

- -
-
- - -
-
- -
- -
- -
- - - GitHub - - - « Previous - - - Next » - - -
- - - - - - - + + + + + + + + Installation - scikit-rebate + + + + + + + + + + + + + + +
+ + +
+ +
+
+ +
+
+
+
+ +

scikit-rebate is built on top of the following existing Python packages:

+
    +
  • +

    NumPy

    +
    • +
  • +

    SciPy

    +
    • +
  • +

    scikit-learn

    +
    • +
+

All of the necessary Python packages can be installed via the Anaconda Python distribution, which we strongly recommend that you use. We also strongly recommend that you use Python 3 over Python 2 if you're given the choice.

+

NumPy, SciPy, and scikit-learn can be installed in Anaconda via the command:

+
conda install numpy scipy scikit-learn
+
+

Once the prerequisites are installed, you should be able to install scikit-rebate with a pip command:

+
pip install skrebate
+
+

You can retrieve basic information about your installed version of skrebate with the following pip command:

+
pip show skrebate
+
+

You can check that you have the most up-to-date pypi release of skrebate with the following pip command:

+
pip install skrebate -U
+
+

Please file a new issue if you run into installation problems.

+ +
+
+ +
+
+ +
+ +
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pmulhuw psadbw pshufw pf2iw pfnacc pfpnacc pi2fw pswapd maskmovdqu clflush movntdq movnti movntpd movdqa movdqu movdq2q movq2dq paddq pmuludq pshufd pshufhw pshuflw pslldq psrldq psubq punpckhqdq punpcklqdq addpd addsd andnpd andpd cmpeqpd cmpeqsd cmplepd cmplesd cmpltpd cmpltsd cmpneqpd cmpneqsd cmpnlepd cmpnlesd cmpnltpd cmpnltsd cmpordpd cmpordsd cmpunordpd cmpunordsd cmppd comisd cvtdq2pd cvtdq2ps cvtpd2dq cvtpd2pi cvtpd2ps cvtpi2pd cvtps2dq cvtps2pd cvtsd2si cvtsd2ss cvtsi2sd cvtss2sd cvttpd2pi cvttpd2dq cvttps2dq cvttsd2si divpd divsd maxpd maxsd minpd minsd movapd movhpd movlpd movmskpd movupd mulpd mulsd orpd shufpd sqrtpd sqrtsd subpd subsd ucomisd unpckhpd unpcklpd xorpd addsubpd addsubps haddpd haddps hsubpd hsubps lddqu movddup movshdup movsldup clgi stgi vmcall vmclear vmfunc vmlaunch vmload vmmcall vmptrld vmptrst vmread vmresume vmrun vmsave vmwrite vmxoff vmxon invept invvpid pabsb pabsw pabsd palignr phaddw phaddd phaddsw phsubw phsubd phsubsw pmaddubsw pmulhrsw pshufb psignb psignw psignd extrq insertq movntsd movntss lzcnt blendpd blendps blendvpd blendvps dppd dpps extractps insertps movntdqa mpsadbw packusdw pblendvb pblendw pcmpeqq pextrb pextrd pextrq phminposuw pinsrb pinsrd pinsrq pmaxsb pmaxsd pmaxud pmaxuw pminsb pminsd pminud pminuw pmovsxbw pmovsxbd pmovsxbq pmovsxwd pmovsxwq pmovsxdq pmovzxbw pmovzxbd pmovzxbq pmovzxwd pmovzxwq pmovzxdq pmuldq pmulld ptest roundpd roundps roundsd roundss crc32 pcmpestri pcmpestrm pcmpistri pcmpistrm pcmpgtq popcnt getsec pfrcpv pfrsqrtv movbe aesenc aesenclast aesdec aesdeclast aesimc aeskeygenassist vaesenc vaesenclast vaesdec vaesdeclast vaesimc vaeskeygenassist vaddpd vaddps vaddsd vaddss vaddsubpd vaddsubps vandpd vandps vandnpd vandnps vblendpd vblendps vblendvpd vblendvps vbroadcastss vbroadcastsd vbroadcastf128 vcmpeq_ospd vcmpeqpd vcmplt_ospd vcmpltpd vcmple_ospd vcmplepd vcmpunord_qpd vcmpunordpd vcmpneq_uqpd vcmpneqpd vcmpnlt_uspd vcmpnltpd vcmpnle_uspd vcmpnlepd vcmpord_qpd vcmpordpd vcmpeq_uqpd vcmpnge_uspd vcmpngepd vcmpngt_uspd vcmpngtpd vcmpfalse_oqpd vcmpfalsepd vcmpneq_oqpd vcmpge_ospd vcmpgepd vcmpgt_ospd vcmpgtpd vcmptrue_uqpd vcmptruepd vcmplt_oqpd vcmple_oqpd vcmpunord_spd vcmpneq_uspd vcmpnlt_uqpd vcmpnle_uqpd vcmpord_spd vcmpeq_uspd vcmpnge_uqpd vcmpngt_uqpd vcmpfalse_ospd vcmpneq_ospd vcmpge_oqpd vcmpgt_oqpd vcmptrue_uspd vcmppd vcmpeq_osps vcmpeqps vcmplt_osps vcmpltps vcmple_osps vcmpleps vcmpunord_qps vcmpunordps vcmpneq_uqps vcmpneqps vcmpnlt_usps vcmpnltps vcmpnle_usps vcmpnleps vcmpord_qps vcmpordps vcmpeq_uqps vcmpnge_usps vcmpngeps vcmpngt_usps vcmpngtps vcmpfalse_oqps vcmpfalseps vcmpneq_oqps vcmpge_osps vcmpgeps vcmpgt_osps vcmpgtps vcmptrue_uqps vcmptrueps vcmplt_oqps vcmple_oqps vcmpunord_sps vcmpneq_usps vcmpnlt_uqps vcmpnle_uqps vcmpord_sps vcmpeq_usps vcmpnge_uqps vcmpngt_uqps vcmpfalse_osps vcmpneq_osps vcmpge_oqps vcmpgt_oqps vcmptrue_usps vcmpps vcmpeq_ossd vcmpeqsd vcmplt_ossd vcmpltsd vcmple_ossd vcmplesd vcmpunord_qsd vcmpunordsd vcmpneq_uqsd vcmpneqsd vcmpnlt_ussd vcmpnltsd vcmpnle_ussd vcmpnlesd vcmpord_qsd vcmpordsd vcmpeq_uqsd vcmpnge_ussd vcmpngesd vcmpngt_ussd vcmpngtsd vcmpfalse_oqsd vcmpfalsesd vcmpneq_oqsd vcmpge_ossd vcmpgesd vcmpgt_ossd vcmpgtsd vcmptrue_uqsd vcmptruesd vcmplt_oqsd vcmple_oqsd vcmpunord_ssd vcmpneq_ussd vcmpnlt_uqsd vcmpnle_uqsd vcmpord_ssd vcmpeq_ussd vcmpnge_uqsd vcmpngt_uqsd vcmpfalse_ossd vcmpneq_ossd vcmpge_oqsd vcmpgt_oqsd vcmptrue_ussd vcmpsd vcmpeq_osss vcmpeqss vcmplt_osss vcmpltss vcmple_osss vcmpless vcmpunord_qss vcmpunordss vcmpneq_uqss vcmpneqss vcmpnlt_usss vcmpnltss vcmpnle_usss vcmpnless vcmpord_qss vcmpordss vcmpeq_uqss vcmpnge_usss vcmpngess vcmpngt_usss vcmpngtss vcmpfalse_oqss vcmpfalsess vcmpneq_oqss vcmpge_osss vcmpgess vcmpgt_osss vcmpgtss vcmptrue_uqss vcmptruess vcmplt_oqss vcmple_oqss vcmpunord_sss vcmpneq_usss vcmpnlt_uqss vcmpnle_uqss vcmpord_sss vcmpeq_usss vcmpnge_uqss vcmpngt_uqss vcmpfalse_osss vcmpneq_osss vcmpge_oqss vcmpgt_oqss vcmptrue_usss vcmpss vcomisd vcomiss vcvtdq2pd vcvtdq2ps vcvtpd2dq vcvtpd2ps vcvtps2dq vcvtps2pd vcvtsd2si vcvtsd2ss vcvtsi2sd vcvtsi2ss vcvtss2sd vcvtss2si vcvttpd2dq vcvttps2dq vcvttsd2si vcvttss2si vdivpd vdivps vdivsd vdivss vdppd vdpps vextractf128 vextractps vhaddpd vhaddps vhsubpd vhsubps vinsertf128 vinsertps vlddqu vldqqu vldmxcsr vmaskmovdqu vmaskmovps vmaskmovpd vmaxpd vmaxps vmaxsd vmaxss vminpd vminps vminsd vminss vmovapd vmovaps vmovd vmovq vmovddup vmovdqa vmovqqa vmovdqu vmovqqu vmovhlps vmovhpd vmovhps vmovlhps vmovlpd vmovlps vmovmskpd vmovmskps vmovntdq vmovntqq vmovntdqa vmovntpd vmovntps vmovsd vmovshdup vmovsldup vmovss vmovupd vmovups vmpsadbw vmulpd vmulps vmulsd vmulss vorpd vorps vpabsb vpabsw vpabsd vpacksswb vpackssdw vpackuswb vpackusdw vpaddb vpaddw vpaddd vpaddq vpaddsb vpaddsw vpaddusb vpaddusw vpalignr vpand vpandn vpavgb vpavgw vpblendvb vpblendw vpcmpestri vpcmpestrm vpcmpistri vpcmpistrm vpcmpeqb vpcmpeqw vpcmpeqd vpcmpeqq vpcmpgtb vpcmpgtw vpcmpgtd vpcmpgtq vpermilpd vpermilps vperm2f128 vpextrb vpextrw vpextrd vpextrq vphaddw vphaddd vphaddsw vphminposuw vphsubw vphsubd vphsubsw vpinsrb vpinsrw vpinsrd vpinsrq vpmaddwd vpmaddubsw vpmaxsb vpmaxsw vpmaxsd vpmaxub vpmaxuw vpmaxud vpminsb vpminsw vpminsd vpminub vpminuw vpminud vpmovmskb vpmovsxbw vpmovsxbd vpmovsxbq vpmovsxwd vpmovsxwq vpmovsxdq vpmovzxbw vpmovzxbd vpmovzxbq vpmovzxwd vpmovzxwq vpmovzxdq vpmulhuw vpmulhrsw vpmulhw vpmullw vpmulld vpmuludq vpmuldq vpor vpsadbw vpshufb vpshufd vpshufhw vpshuflw vpsignb vpsignw vpsignd vpslldq vpsrldq vpsllw vpslld vpsllq vpsraw vpsrad vpsrlw vpsrld vpsrlq vptest vpsubb vpsubw vpsubd vpsubq vpsubsb vpsubsw vpsubusb vpsubusw vpunpckhbw vpunpckhwd vpunpckhdq vpunpckhqdq vpunpcklbw vpunpcklwd vpunpckldq vpunpcklqdq vpxor vrcpps vrcpss vrsqrtps vrsqrtss vroundpd vroundps vroundsd vroundss vshufpd vshufps vsqrtpd vsqrtps vsqrtsd vsqrtss vstmxcsr vsubpd vsubps vsubsd vsubss vtestps vtestpd vucomisd vucomiss vunpckhpd vunpckhps vunpcklpd vunpcklps vxorpd vxorps vzeroall vzeroupper pclmullqlqdq pclmulhqlqdq pclmullqhqdq pclmulhqhqdq pclmulqdq vpclmullqlqdq vpclmulhqlqdq vpclmullqhqdq vpclmulhqhqdq vpclmulqdq vfmadd132ps vfmadd132pd vfmadd312ps vfmadd312pd vfmadd213ps vfmadd213pd vfmadd123ps vfmadd123pd vfmadd231ps vfmadd231pd vfmadd321ps vfmadd321pd vfmaddsub132ps vfmaddsub132pd vfmaddsub312ps vfmaddsub312pd vfmaddsub213ps vfmaddsub213pd vfmaddsub123ps vfmaddsub123pd vfmaddsub231ps vfmaddsub231pd vfmaddsub321ps vfmaddsub321pd vfmsub132ps vfmsub132pd vfmsub312ps vfmsub312pd vfmsub213ps vfmsub213pd vfmsub123ps vfmsub123pd vfmsub231ps vfmsub231pd vfmsub321ps vfmsub321pd vfmsubadd132ps vfmsubadd132pd vfmsubadd312ps vfmsubadd312pd vfmsubadd213ps vfmsubadd213pd vfmsubadd123ps vfmsubadd123pd vfmsubadd231ps vfmsubadd231pd vfmsubadd321ps vfmsubadd321pd vfnmadd132ps vfnmadd132pd vfnmadd312ps vfnmadd312pd vfnmadd213ps vfnmadd213pd vfnmadd123ps vfnmadd123pd vfnmadd231ps vfnmadd231pd vfnmadd321ps vfnmadd321pd vfnmsub132ps vfnmsub132pd vfnmsub312ps vfnmsub312pd vfnmsub213ps vfnmsub213pd vfnmsub123ps vfnmsub123pd vfnmsub231ps vfnmsub231pd vfnmsub321ps vfnmsub321pd vfmadd132ss vfmadd132sd vfmadd312ss vfmadd312sd vfmadd213ss vfmadd213sd vfmadd123ss vfmadd123sd vfmadd231ss vfmadd231sd vfmadd321ss vfmadd321sd vfmsub132ss vfmsub132sd vfmsub312ss vfmsub312sd vfmsub213ss vfmsub213sd vfmsub123ss vfmsub123sd vfmsub231ss vfmsub231sd vfmsub321ss vfmsub321sd vfnmadd132ss vfnmadd132sd vfnmadd312ss vfnmadd312sd vfnmadd213ss vfnmadd213sd vfnmadd123ss vfnmadd123sd vfnmadd231ss vfnmadd231sd vfnmadd321ss vfnmadd321sd vfnmsub132ss vfnmsub132sd vfnmsub312ss vfnmsub312sd vfnmsub213ss vfnmsub213sd vfnmsub123ss vfnmsub123sd vfnmsub231ss vfnmsub231sd vfnmsub321ss vfnmsub321sd rdfsbase rdgsbase rdrand wrfsbase wrgsbase vcvtph2ps vcvtps2ph adcx adox rdseed clac stac xstore xcryptecb xcryptcbc xcryptctr xcryptcfb xcryptofb montmul xsha1 xsha256 llwpcb slwpcb lwpval lwpins vfmaddpd vfmaddps vfmaddsd vfmaddss vfmaddsubpd vfmaddsubps vfmsubaddpd vfmsubaddps vfmsubpd vfmsubps vfmsubsd vfmsubss vfnmaddpd vfnmaddps vfnmaddsd vfnmaddss vfnmsubpd vfnmsubps vfnmsubsd vfnmsubss vfrczpd vfrczps vfrczsd vfrczss vpcmov vpcomb vpcomd vpcomq vpcomub vpcomud vpcomuq vpcomuw vpcomw vphaddbd vphaddbq vphaddbw vphadddq vphaddubd vphaddubq vphaddubw vphaddudq vphadduwd vphadduwq vphaddwd vphaddwq vphsubbw vphsubdq vphsubwd vpmacsdd vpmacsdqh vpmacsdql vpmacssdd vpmacssdqh vpmacssdql vpmacsswd vpmacssww vpmacswd vpmacsww vpmadcsswd vpmadcswd vpperm vprotb vprotd vprotq vprotw vpshab vpshad vpshaq vpshaw vpshlb vpshld vpshlq vpshlw vbroadcasti128 vpblendd vpbroadcastb vpbroadcastw vpbroadcastd vpbroadcastq vpermd vpermpd vpermps vpermq vperm2i128 vextracti128 vinserti128 vpmaskmovd vpmaskmovq vpsllvd vpsllvq vpsravd vpsrlvd vpsrlvq vgatherdpd vgatherqpd vgatherdps vgatherqps vpgatherdd vpgatherqd vpgatherdq vpgatherqq xabort xbegin xend xtest andn bextr blci blcic blsi blsic blcfill blsfill blcmsk blsmsk blsr blcs bzhi mulx pdep pext rorx sarx shlx shrx tzcnt tzmsk t1mskc valignd valignq vblendmpd vblendmps vbroadcastf32x4 vbroadcastf64x4 vbroadcasti32x4 vbroadcasti64x4 vcompresspd vcompressps vcvtpd2udq vcvtps2udq vcvtsd2usi vcvtss2usi vcvttpd2udq vcvttps2udq vcvttsd2usi vcvttss2usi vcvtudq2pd vcvtudq2ps vcvtusi2sd vcvtusi2ss vexpandpd vexpandps vextractf32x4 vextractf64x4 vextracti32x4 vextracti64x4 vfixupimmpd vfixupimmps vfixupimmsd vfixupimmss vgetexppd vgetexpps vgetexpsd vgetexpss vgetmantpd vgetmantps vgetmantsd vgetmantss vinsertf32x4 vinsertf64x4 vinserti32x4 vinserti64x4 vmovdqa32 vmovdqa64 vmovdqu32 vmovdqu64 vpabsq vpandd vpandnd vpandnq vpandq vpblendmd vpblendmq vpcmpltd vpcmpled vpcmpneqd vpcmpnltd vpcmpnled vpcmpd vpcmpltq vpcmpleq vpcmpneqq vpcmpnltq vpcmpnleq vpcmpq vpcmpequd vpcmpltud vpcmpleud vpcmpnequd vpcmpnltud vpcmpnleud vpcmpud vpcmpequq vpcmpltuq vpcmpleuq vpcmpnequq vpcmpnltuq vpcmpnleuq vpcmpuq vpcompressd vpcompressq vpermi2d vpermi2pd vpermi2ps vpermi2q vpermt2d vpermt2pd vpermt2ps vpermt2q vpexpandd vpexpandq vpmaxsq vpmaxuq vpminsq vpminuq vpmovdb vpmovdw vpmovqb vpmovqd vpmovqw vpmovsdb vpmovsdw vpmovsqb vpmovsqd vpmovsqw vpmovusdb vpmovusdw vpmovusqb vpmovusqd vpmovusqw vpord vporq vprold vprolq vprolvd vprolvq vprord vprorq vprorvd vprorvq vpscatterdd vpscatterdq vpscatterqd vpscatterqq vpsraq vpsravq vpternlogd vpternlogq vptestmd vptestmq vptestnmd vptestnmq vpxord vpxorq vrcp14pd vrcp14ps vrcp14sd vrcp14ss vrndscalepd vrndscaleps vrndscalesd vrndscaless vrsqrt14pd vrsqrt14ps vrsqrt14sd vrsqrt14ss vscalefpd vscalefps vscalefsd vscalefss vscatterdpd vscatterdps vscatterqpd vscatterqps vshuff32x4 vshuff64x2 vshufi32x4 vshufi64x2 kandnw kandw kmovw knotw kortestw korw kshiftlw kshiftrw kunpckbw kxnorw kxorw vpbroadcastmb2q vpbroadcastmw2d vpconflictd vpconflictq vplzcntd vplzcntq vexp2pd vexp2ps vrcp28pd vrcp28ps vrcp28sd vrcp28ss vrsqrt28pd vrsqrt28ps vrsqrt28sd vrsqrt28ss vgatherpf0dpd vgatherpf0dps vgatherpf0qpd vgatherpf0qps vgatherpf1dpd vgatherpf1dps vgatherpf1qpd vgatherpf1qps vscatterpf0dpd vscatterpf0dps vscatterpf0qpd vscatterpf0qps vscatterpf1dpd vscatterpf1dps vscatterpf1qpd vscatterpf1qps prefetchwt1 bndmk bndcl bndcu bndcn bndmov bndldx bndstx sha1rnds4 sha1nexte sha1msg1 sha1msg2 sha256rnds2 sha256msg1 sha256msg2 hint_nop0 hint_nop1 hint_nop2 hint_nop3 hint_nop4 hint_nop5 hint_nop6 hint_nop7 hint_nop8 hint_nop9 hint_nop10 hint_nop11 hint_nop12 hint_nop13 hint_nop14 hint_nop15 hint_nop16 hint_nop17 hint_nop18 hint_nop19 hint_nop20 hint_nop21 hint_nop22 hint_nop23 hint_nop24 hint_nop25 hint_nop26 hint_nop27 hint_nop28 hint_nop29 hint_nop30 hint_nop31 hint_nop32 hint_nop33 hint_nop34 hint_nop35 hint_nop36 hint_nop37 hint_nop38 hint_nop39 hint_nop40 hint_nop41 hint_nop42 hint_nop43 hint_nop44 hint_nop45 hint_nop46 hint_nop47 hint_nop48 hint_nop49 hint_nop50 hint_nop51 hint_nop52 hint_nop53 hint_nop54 hint_nop55 hint_nop56 hint_nop57 hint_nop58 hint_nop59 hint_nop60 hint_nop61 hint_nop62 hint_nop63",literal:"ip eip rip al ah bl bh cl ch dl dh sil dil bpl spl r8b r9b r10b r11b r12b r13b r14b r15b ax bx cx dx si di bp sp r8w r9w r10w r11w r12w r13w r14w r15w eax ebx ecx edx esi edi ebp esp eip r8d r9d r10d r11d r12d r13d r14d r15d rax rbx rcx rdx rsi rdi rbp rsp r8 r9 r10 r11 r12 r13 r14 r15 cs ds es fs gs ss st st0 st1 st2 st3 st4 st5 st6 st7 mm0 mm1 mm2 mm3 mm4 mm5 mm6 mm7 xmm0 xmm1 xmm2 xmm3 xmm4 xmm5 xmm6 xmm7 xmm8 xmm9 xmm10 xmm11 xmm12 xmm13 xmm14 xmm15 xmm16 xmm17 xmm18 xmm19 xmm20 xmm21 xmm22 xmm23 xmm24 xmm25 xmm26 xmm27 xmm28 xmm29 xmm30 xmm31 ymm0 ymm1 ymm2 ymm3 ymm4 ymm5 ymm6 ymm7 ymm8 ymm9 ymm10 ymm11 ymm12 ymm13 ymm14 ymm15 ymm16 ymm17 ymm18 ymm19 ymm20 ymm21 ymm22 ymm23 ymm24 ymm25 ymm26 ymm27 ymm28 ymm29 ymm30 ymm31 zmm0 zmm1 zmm2 zmm3 zmm4 zmm5 zmm6 zmm7 zmm8 zmm9 zmm10 zmm11 zmm12 zmm13 zmm14 zmm15 zmm16 zmm17 zmm18 zmm19 zmm20 zmm21 zmm22 zmm23 zmm24 zmm25 zmm26 zmm27 zmm28 zmm29 zmm30 zmm31 k0 k1 k2 k3 k4 k5 k6 k7 bnd0 bnd1 bnd2 bnd3 cr0 cr1 cr2 cr3 cr4 cr8 dr0 dr1 dr2 dr3 dr8 tr3 tr4 tr5 tr6 tr7 r0 r1 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FindMinimumCut FindMinValue FindPermutation FindPostmanTour FindProcessParameters FindRoot FindSequenceFunction FindSettings FindShortestPath FindShortestTour FindThreshold FindVertexCover FindVertexCut Fine FinishDynamic FiniteAbelianGroupCount FiniteGroupCount FiniteGroupData First FirstPassageTimeDistribution FischerGroupFi22 FischerGroupFi23 FischerGroupFi24Prime FisherHypergeometricDistribution FisherRatioTest FisherZDistribution Fit FitAll FittedModel FixedPoint FixedPointList FlashSelection Flat Flatten FlattenAt FlatTopWindow FlipView Floor FlushPrintOutputPacket Fold FoldList Font FontColor FontFamily FontForm FontName FontOpacity FontPostScriptName FontProperties FontReencoding FontSize FontSlant FontSubstitutions FontTracking FontVariations FontWeight For ForAll Format FormatRules FormatType FormatTypeAutoConvert FormatValues FormBox FormBoxOptions FortranForm Forward ForwardBackward Fourier FourierCoefficient FourierCosCoefficient FourierCosSeries FourierCosTransform FourierDCT FourierDCTFilter FourierDCTMatrix FourierDST FourierDSTMatrix FourierMatrix FourierParameters FourierSequenceTransform FourierSeries FourierSinCoefficient FourierSinSeries FourierSinTransform FourierTransform FourierTrigSeries FractionalBrownianMotionProcess FractionalPart FractionBox FractionBoxOptions FractionLine Frame FrameBox FrameBoxOptions Framed FrameInset FrameLabel Frameless FrameMargins FrameStyle FrameTicks FrameTicksStyle FRatioDistribution FrechetDistribution FreeQ FrequencySamplingFilterKernel FresnelC FresnelS Friday FrobeniusNumber FrobeniusSolve FromCharacterCode FromCoefficientRules FromContinuedFraction FromDate FromDigits FromDMS Front FrontEndDynamicExpression FrontEndEventActions FrontEndExecute FrontEndObject FrontEndResource FrontEndResourceString FrontEndStackSize FrontEndToken FrontEndTokenExecute FrontEndValueCache FrontEndVersion FrontFaceColor FrontFaceOpacity Full FullAxes FullDefinition FullForm FullGraphics FullOptions FullSimplify Function FunctionExpand FunctionInterpolation FunctionSpace FussellVeselyImportance GaborFilter GaborMatrix GaborWavelet GainMargins GainPhaseMargins Gamma GammaDistribution GammaRegularized GapPenalty Gather GatherBy GaugeFaceElementFunction GaugeFaceStyle GaugeFrameElementFunction GaugeFrameSize GaugeFrameStyle GaugeLabels GaugeMarkers GaugeStyle GaussianFilter GaussianIntegers GaussianMatrix GaussianWindow GCD GegenbauerC General GeneralizedLinearModelFit GenerateConditions GeneratedCell GeneratedParameters GeneratingFunction Generic GenericCylindricalDecomposition GenomeData GenomeLookup GeodesicClosing GeodesicDilation GeodesicErosion GeodesicOpening GeoDestination GeodesyData GeoDirection GeoDistance GeoGridPosition GeometricBrownianMotionProcess GeometricDistribution GeometricMean GeometricMeanFilter GeometricTransformation GeometricTransformation3DBox GeometricTransformation3DBoxOptions GeometricTransformationBox GeometricTransformationBoxOptions GeoPosition GeoPositionENU GeoPositionXYZ GeoProjectionData GestureHandler GestureHandlerTag Get GetBoundingBoxSizePacket GetContext GetEnvironment GetFileName GetFrontEndOptionsDataPacket GetLinebreakInformationPacket GetMenusPacket GetPageBreakInformationPacket Glaisher GlobalClusteringCoefficient GlobalPreferences GlobalSession Glow GoldenRatio GompertzMakehamDistribution GoodmanKruskalGamma GoodmanKruskalGammaTest Goto Grad Gradient GradientFilter GradientOrientationFilter Graph GraphAssortativity GraphCenter GraphComplement GraphData GraphDensity GraphDiameter GraphDifference GraphDisjointUnion GraphDistance GraphDistanceMatrix GraphElementData GraphEmbedding GraphHighlight GraphHighlightStyle GraphHub Graphics Graphics3D Graphics3DBox Graphics3DBoxOptions GraphicsArray GraphicsBaseline GraphicsBox GraphicsBoxOptions GraphicsColor GraphicsColumn GraphicsComplex GraphicsComplex3DBox GraphicsComplex3DBoxOptions GraphicsComplexBox GraphicsComplexBoxOptions GraphicsContents GraphicsData GraphicsGrid GraphicsGridBox GraphicsGroup GraphicsGroup3DBox GraphicsGroup3DBoxOptions GraphicsGroupBox GraphicsGroupBoxOptions GraphicsGrouping GraphicsHighlightColor GraphicsRow GraphicsSpacing GraphicsStyle GraphIntersection GraphLayout GraphLinkEfficiency GraphPeriphery GraphPlot GraphPlot3D GraphPower GraphPropertyDistribution GraphQ GraphRadius GraphReciprocity GraphRoot GraphStyle GraphUnion Gray GrayLevel GreatCircleDistance Greater GreaterEqual GreaterEqualLess GreaterFullEqual GreaterGreater GreaterLess GreaterSlantEqual GreaterTilde Green Grid GridBaseline GridBox GridBoxAlignment GridBoxBackground GridBoxDividers GridBoxFrame GridBoxItemSize GridBoxItemStyle GridBoxOptions GridBoxSpacings GridCreationSettings GridDefaultElement GridElementStyleOptions GridFrame GridFrameMargins GridGraph GridLines GridLinesStyle GroebnerBasis GroupActionBase GroupCentralizer GroupElementFromWord GroupElementPosition GroupElementQ GroupElements GroupElementToWord GroupGenerators GroupMultiplicationTable GroupOrbits GroupOrder GroupPageBreakWithin GroupSetwiseStabilizer GroupStabilizer GroupStabilizerChain Gudermannian GumbelDistribution HaarWavelet HadamardMatrix HalfNormalDistribution HamiltonianGraphQ HammingDistance HammingWindow HankelH1 HankelH2 HankelMatrix HannPoissonWindow HannWindow HaradaNortonGroupHN HararyGraph HarmonicMean HarmonicMeanFilter HarmonicNumber Hash HashTable Haversine HazardFunction Head HeadCompose Heads HeavisideLambda HeavisidePi HeavisideTheta HeldGroupHe HeldPart HelpBrowserLookup HelpBrowserNotebook HelpBrowserSettings HermiteDecomposition HermiteH HermitianMatrixQ HessenbergDecomposition Hessian HexadecimalCharacter Hexahedron HexahedronBox HexahedronBoxOptions HiddenSurface HighlightGraph HighlightImage HighpassFilter HigmanSimsGroupHS HilbertFilter HilbertMatrix Histogram Histogram3D HistogramDistribution HistogramList HistogramTransform HistogramTransformInterpolation HitMissTransform HITSCentrality HodgeDual HoeffdingD HoeffdingDTest Hold HoldAll HoldAllComplete HoldComplete HoldFirst HoldForm HoldPattern HoldRest HolidayCalendar HomeDirectory HomePage Horizontal HorizontalForm HorizontalGauge HorizontalScrollPosition HornerForm HotellingTSquareDistribution HoytDistribution HTMLSave Hue HumpDownHump HumpEqual HurwitzLerchPhi HurwitzZeta HyperbolicDistribution HypercubeGraph HyperexponentialDistribution Hyperfactorial Hypergeometric0F1 Hypergeometric0F1Regularized Hypergeometric1F1 Hypergeometric1F1Regularized Hypergeometric2F1 Hypergeometric2F1Regularized HypergeometricDistribution HypergeometricPFQ HypergeometricPFQRegularized HypergeometricU Hyperlink HyperlinkCreationSettings Hyphenation HyphenationOptions HypoexponentialDistribution HypothesisTestData I Identity IdentityMatrix If IgnoreCase Im Image Image3D Image3DSlices ImageAccumulate ImageAdd ImageAdjust ImageAlign ImageApply ImageAspectRatio ImageAssemble ImageCache ImageCacheValid ImageCapture ImageChannels ImageClip ImageColorSpace ImageCompose ImageConvolve ImageCooccurrence ImageCorners ImageCorrelate ImageCorrespondingPoints ImageCrop ImageData ImageDataPacket ImageDeconvolve ImageDemosaic ImageDifference ImageDimensions ImageDistance ImageEffect ImageFeatureTrack ImageFileApply ImageFileFilter ImageFileScan ImageFilter ImageForestingComponents ImageForwardTransformation ImageHistogram ImageKeypoints ImageLevels ImageLines ImageMargins ImageMarkers ImageMeasurements ImageMultiply ImageOffset ImagePad ImagePadding ImagePartition ImagePeriodogram ImagePerspectiveTransformation ImageQ ImageRangeCache ImageReflect ImageRegion ImageResize ImageResolution ImageRotate ImageRotated ImageScaled ImageScan ImageSize ImageSizeAction ImageSizeCache ImageSizeMultipliers ImageSizeRaw ImageSubtract ImageTake ImageTransformation ImageTrim ImageType ImageValue ImageValuePositions Implies Import ImportAutoReplacements ImportString ImprovementImportance In IncidenceGraph IncidenceList IncidenceMatrix IncludeConstantBasis IncludeFileExtension IncludePods IncludeSingularTerm Increment Indent IndentingNewlineSpacings IndentMaxFraction IndependenceTest IndependentEdgeSetQ IndependentUnit IndependentVertexSetQ Indeterminate IndexCreationOptions Indexed IndexGraph IndexTag Inequality InexactNumberQ InexactNumbers Infinity Infix Information Inherited InheritScope Initialization InitializationCell InitializationCellEvaluation InitializationCellWarning InlineCounterAssignments InlineCounterIncrements InlineRules Inner Inpaint Input InputAliases InputAssumptions InputAutoReplacements InputField InputFieldBox InputFieldBoxOptions InputForm InputGrouping InputNamePacket InputNotebook InputPacket InputSettings InputStream InputString InputStringPacket InputToBoxFormPacket Insert InsertionPointObject InsertResults Inset Inset3DBox Inset3DBoxOptions InsetBox InsetBoxOptions Install InstallService InString Integer IntegerDigits IntegerExponent IntegerLength IntegerPart IntegerPartitions IntegerQ Integers IntegerString Integral Integrate Interactive InteractiveTradingChart Interlaced Interleaving InternallyBalancedDecomposition InterpolatingFunction InterpolatingPolynomial Interpolation InterpolationOrder InterpolationPoints InterpolationPrecision Interpretation InterpretationBox InterpretationBoxOptions InterpretationFunction InterpretTemplate InterquartileRange Interrupt InterruptSettings Intersection Interval IntervalIntersection IntervalMemberQ IntervalUnion Inverse InverseBetaRegularized InverseCDF InverseChiSquareDistribution InverseContinuousWaveletTransform InverseDistanceTransform InverseEllipticNomeQ InverseErf InverseErfc InverseFourier InverseFourierCosTransform InverseFourierSequenceTransform InverseFourierSinTransform InverseFourierTransform InverseFunction InverseFunctions InverseGammaDistribution InverseGammaRegularized InverseGaussianDistribution InverseGudermannian InverseHaversine InverseJacobiCD InverseJacobiCN InverseJacobiCS InverseJacobiDC InverseJacobiDN InverseJacobiDS InverseJacobiNC InverseJacobiND InverseJacobiNS InverseJacobiSC InverseJacobiSD InverseJacobiSN InverseLaplaceTransform InversePermutation InverseRadon InverseSeries InverseSurvivalFunction InverseWaveletTransform InverseWeierstrassP InverseZTransform Invisible InvisibleApplication InvisibleTimes IrreduciblePolynomialQ IsolatingInterval IsomorphicGraphQ IsotopeData Italic Item ItemBox ItemBoxOptions ItemSize ItemStyle ItoProcess JaccardDissimilarity JacobiAmplitude Jacobian JacobiCD JacobiCN JacobiCS JacobiDC JacobiDN JacobiDS JacobiNC JacobiND JacobiNS JacobiP JacobiSC JacobiSD JacobiSN JacobiSymbol JacobiZeta JankoGroupJ1 JankoGroupJ2 JankoGroupJ3 JankoGroupJ4 JarqueBeraALMTest JohnsonDistribution Join Joined JoinedCurve JoinedCurveBox JoinForm JordanDecomposition JordanModelDecomposition K KagiChart KaiserBesselWindow KaiserWindow KalmanEstimator KalmanFilter KarhunenLoeveDecomposition KaryTree KatzCentrality KCoreComponents KDistribution KelvinBei KelvinBer KelvinKei KelvinKer KendallTau KendallTauTest KernelExecute KernelMixtureDistribution KernelObject Kernels Ket Khinchin KirchhoffGraph KirchhoffMatrix KleinInvariantJ KnightTourGraph KnotData KnownUnitQ KolmogorovSmirnovTest KroneckerDelta KroneckerModelDecomposition KroneckerProduct KroneckerSymbol KuiperTest KumaraswamyDistribution Kurtosis KuwaharaFilter Label Labeled LabeledSlider LabelingFunction LabelStyle LaguerreL LambdaComponents LambertW LanczosWindow LandauDistribution Language LanguageCategory LaplaceDistribution LaplaceTransform Laplacian LaplacianFilter LaplacianGaussianFilter Large Larger Last Latitude LatitudeLongitude LatticeData LatticeReduce Launch LaunchKernels LayeredGraphPlot LayerSizeFunction LayoutInformation LCM LeafCount LeapYearQ LeastSquares LeastSquaresFilterKernel Left LeftArrow LeftArrowBar LeftArrowRightArrow LeftDownTeeVector LeftDownVector LeftDownVectorBar LeftRightArrow LeftRightVector LeftTee LeftTeeArrow LeftTeeVector LeftTriangle LeftTriangleBar LeftTriangleEqual LeftUpDownVector LeftUpTeeVector LeftUpVector LeftUpVectorBar LeftVector LeftVectorBar LegendAppearance Legended LegendFunction LegendLabel LegendLayout LegendMargins LegendMarkers LegendMarkerSize LegendreP LegendreQ LegendreType Length LengthWhile LerchPhi Less LessEqual LessEqualGreater LessFullEqual LessGreater LessLess LessSlantEqual LessTilde LetterCharacter LetterQ Level LeveneTest LeviCivitaTensor LevyDistribution Lexicographic LibraryFunction LibraryFunctionError LibraryFunctionInformation LibraryFunctionLoad LibraryFunctionUnload LibraryLoad LibraryUnload LicenseID LiftingFilterData LiftingWaveletTransform LightBlue LightBrown LightCyan Lighter LightGray LightGreen Lighting LightingAngle LightMagenta LightOrange LightPink LightPurple LightRed LightSources LightYellow Likelihood Limit LimitsPositioning LimitsPositioningTokens LindleyDistribution Line Line3DBox LinearFilter LinearFractionalTransform LinearModelFit LinearOffsetFunction LinearProgramming LinearRecurrence LinearSolve LinearSolveFunction LineBox LineBreak LinebreakAdjustments LineBreakChart LineBreakWithin LineColor LineForm LineGraph LineIndent LineIndentMaxFraction LineIntegralConvolutionPlot LineIntegralConvolutionScale LineLegend LineOpacity LineSpacing LineWrapParts LinkActivate LinkClose LinkConnect LinkConnectedQ LinkCreate LinkError LinkFlush LinkFunction LinkHost LinkInterrupt LinkLaunch LinkMode LinkObject LinkOpen LinkOptions LinkPatterns LinkProtocol LinkRead LinkReadHeld LinkReadyQ Links LinkWrite LinkWriteHeld LiouvilleLambda List Listable ListAnimate ListContourPlot ListContourPlot3D ListConvolve ListCorrelate ListCurvePathPlot ListDeconvolve ListDensityPlot Listen ListFourierSequenceTransform ListInterpolation ListLineIntegralConvolutionPlot ListLinePlot ListLogLinearPlot ListLogLogPlot ListLogPlot ListPicker ListPickerBox ListPickerBoxBackground ListPickerBoxOptions ListPlay ListPlot ListPlot3D ListPointPlot3D ListPolarPlot ListQ ListStreamDensityPlot ListStreamPlot ListSurfacePlot3D ListVectorDensityPlot ListVectorPlot ListVectorPlot3D ListZTransform Literal LiteralSearch LocalClusteringCoefficient LocalizeVariables LocationEquivalenceTest LocationTest Locator LocatorAutoCreate LocatorBox LocatorBoxOptions LocatorCentering LocatorPane LocatorPaneBox LocatorPaneBoxOptions LocatorRegion Locked Log Log10 Log2 LogBarnesG LogGamma LogGammaDistribution LogicalExpand LogIntegral LogisticDistribution LogitModelFit LogLikelihood LogLinearPlot LogLogisticDistribution LogLogPlot LogMultinormalDistribution LogNormalDistribution LogPlot LogRankTest LogSeriesDistribution LongEqual Longest LongestAscendingSequence LongestCommonSequence LongestCommonSequencePositions LongestCommonSubsequence LongestCommonSubsequencePositions LongestMatch LongForm Longitude LongLeftArrow LongLeftRightArrow LongRightArrow Loopback LoopFreeGraphQ LowerCaseQ LowerLeftArrow LowerRightArrow LowerTriangularize LowpassFilter LQEstimatorGains LQGRegulator LQOutputRegulatorGains LQRegulatorGains LUBackSubstitution LucasL LuccioSamiComponents LUDecomposition LyapunovSolve LyonsGroupLy MachineID MachineName MachineNumberQ MachinePrecision MacintoshSystemPageSetup Magenta Magnification Magnify MainSolve MaintainDynamicCaches Majority MakeBoxes MakeExpression MakeRules MangoldtLambda ManhattanDistance Manipulate Manipulator MannWhitneyTest MantissaExponent Manual Map MapAll MapAt MapIndexed MAProcess MapThread MarcumQ MardiaCombinedTest MardiaKurtosisTest MardiaSkewnessTest MarginalDistribution MarkovProcessProperties Masking MatchingDissimilarity MatchLocalNameQ MatchLocalNames MatchQ Material MathematicaNotation MathieuC MathieuCharacteristicA MathieuCharacteristicB MathieuCharacteristicExponent MathieuCPrime MathieuGroupM11 MathieuGroupM12 MathieuGroupM22 MathieuGroupM23 MathieuGroupM24 MathieuS MathieuSPrime MathMLForm MathMLText Matrices MatrixExp MatrixForm MatrixFunction MatrixLog MatrixPlot MatrixPower MatrixQ MatrixRank Max MaxBend MaxDetect MaxExtraBandwidths MaxExtraConditions MaxFeatures MaxFilter Maximize MaxIterations MaxMemoryUsed MaxMixtureKernels MaxPlotPoints MaxPoints MaxRecursion MaxStableDistribution MaxStepFraction MaxSteps MaxStepSize MaxValue MaxwellDistribution McLaughlinGroupMcL Mean MeanClusteringCoefficient MeanDegreeConnectivity MeanDeviation MeanFilter MeanGraphDistance MeanNeighborDegree MeanShift MeanShiftFilter Median MedianDeviation MedianFilter Medium MeijerG MeixnerDistribution MemberQ MemoryConstrained MemoryInUse Menu MenuAppearance MenuCommandKey MenuEvaluator MenuItem MenuPacket MenuSortingValue MenuStyle MenuView MergeDifferences Mesh MeshFunctions MeshRange MeshShading MeshStyle Message MessageDialog MessageList MessageName MessageOptions MessagePacket Messages MessagesNotebook MetaCharacters MetaInformation Method MethodOptions MexicanHatWavelet MeyerWavelet Min MinDetect MinFilter MinimalPolynomial MinimalStateSpaceModel Minimize Minors MinRecursion MinSize MinStableDistribution Minus MinusPlus MinValue Missing MissingDataMethod MittagLefflerE MixedRadix MixedRadixQuantity MixtureDistribution Mod Modal Mode Modular ModularLambda Module Modulus MoebiusMu Moment Momentary MomentConvert MomentEvaluate MomentGeneratingFunction Monday Monitor MonomialList MonomialOrder MonsterGroupM MorletWavelet MorphologicalBinarize MorphologicalBranchPoints MorphologicalComponents MorphologicalEulerNumber MorphologicalGraph MorphologicalPerimeter MorphologicalTransform Most MouseAnnotation MouseAppearance MouseAppearanceTag MouseButtons Mouseover MousePointerNote MousePosition MovingAverage MovingMedian MoyalDistribution MultiedgeStyle MultilaunchWarning MultiLetterItalics MultiLetterStyle MultilineFunction Multinomial MultinomialDistribution MultinormalDistribution MultiplicativeOrder Multiplicity Multiselection MultivariateHypergeometricDistribution MultivariatePoissonDistribution MultivariateTDistribution N NakagamiDistribution NameQ Names NamespaceBox Nand NArgMax NArgMin NBernoulliB NCache NDSolve NDSolveValue Nearest NearestFunction NeedCurrentFrontEndPackagePacket NeedCurrentFrontEndSymbolsPacket NeedlemanWunschSimilarity Needs Negative NegativeBinomialDistribution NegativeMultinomialDistribution NeighborhoodGraph Nest NestedGreaterGreater NestedLessLess NestedScriptRules NestList NestWhile NestWhileList NevilleThetaC NevilleThetaD NevilleThetaN NevilleThetaS NewPrimitiveStyle NExpectation Next NextPrime NHoldAll NHoldFirst NHoldRest NicholsGridLines NicholsPlot NIntegrate NMaximize NMaxValue NMinimize NMinValue NominalVariables NonAssociative NoncentralBetaDistribution NoncentralChiSquareDistribution NoncentralFRatioDistribution NoncentralStudentTDistribution NonCommutativeMultiply NonConstants None NonlinearModelFit NonlocalMeansFilter NonNegative NonPositive Nor NorlundB Norm Normal NormalDistribution NormalGrouping Normalize NormalizedSquaredEuclideanDistance NormalsFunction NormFunction Not NotCongruent NotCupCap NotDoubleVerticalBar Notebook NotebookApply NotebookAutoSave NotebookClose NotebookConvertSettings NotebookCreate NotebookCreateReturnObject NotebookDefault NotebookDelete NotebookDirectory NotebookDynamicExpression NotebookEvaluate NotebookEventActions NotebookFileName NotebookFind NotebookFindReturnObject NotebookGet NotebookGetLayoutInformationPacket NotebookGetMisspellingsPacket NotebookInformation NotebookInterfaceObject NotebookLocate NotebookObject NotebookOpen NotebookOpenReturnObject NotebookPath NotebookPrint NotebookPut NotebookPutReturnObject NotebookRead NotebookResetGeneratedCells Notebooks NotebookSave NotebookSaveAs NotebookSelection NotebookSetupLayoutInformationPacket NotebooksMenu NotebookWrite NotElement NotEqualTilde NotExists NotGreater NotGreaterEqual NotGreaterFullEqual NotGreaterGreater NotGreaterLess NotGreaterSlantEqual NotGreaterTilde NotHumpDownHump NotHumpEqual NotLeftTriangle NotLeftTriangleBar NotLeftTriangleEqual NotLess NotLessEqual NotLessFullEqual NotLessGreater NotLessLess NotLessSlantEqual NotLessTilde NotNestedGreaterGreater NotNestedLessLess NotPrecedes NotPrecedesEqual NotPrecedesSlantEqual NotPrecedesTilde NotReverseElement NotRightTriangle NotRightTriangleBar NotRightTriangleEqual NotSquareSubset NotSquareSubsetEqual NotSquareSuperset NotSquareSupersetEqual NotSubset NotSubsetEqual NotSucceeds NotSucceedsEqual NotSucceedsSlantEqual NotSucceedsTilde NotSuperset NotSupersetEqual NotTilde NotTildeEqual NotTildeFullEqual NotTildeTilde NotVerticalBar NProbability NProduct NProductFactors NRoots NSolve NSum NSumTerms Null NullRecords NullSpace NullWords Number NumberFieldClassNumber NumberFieldDiscriminant NumberFieldFundamentalUnits NumberFieldIntegralBasis NumberFieldNormRepresentatives NumberFieldRegulator NumberFieldRootsOfUnity NumberFieldSignature NumberForm NumberFormat NumberMarks NumberMultiplier NumberPadding NumberPoint NumberQ NumberSeparator NumberSigns NumberString Numerator NumericFunction NumericQ NuttallWindow NValues NyquistGridLines NyquistPlot O ObservabilityGramian ObservabilityMatrix ObservableDecomposition ObservableModelQ OddQ Off Offset OLEData On ONanGroupON OneIdentity Opacity Open OpenAppend Opener OpenerBox OpenerBoxOptions OpenerView OpenFunctionInspectorPacket Opening OpenRead OpenSpecialOptions OpenTemporary OpenWrite Operate OperatingSystem OptimumFlowData Optional OptionInspectorSettings OptionQ Options OptionsPacket OptionsPattern OptionValue OptionValueBox OptionValueBoxOptions Or Orange Order OrderDistribution OrderedQ Ordering Orderless OrnsteinUhlenbeckProcess Orthogonalize Out Outer OutputAutoOverwrite OutputControllabilityMatrix OutputControllableModelQ OutputForm OutputFormData OutputGrouping OutputMathEditExpression OutputNamePacket OutputResponse OutputSizeLimit OutputStream Over OverBar OverDot Overflow OverHat Overlaps Overlay OverlayBox OverlayBoxOptions Overscript OverscriptBox OverscriptBoxOptions OverTilde OverVector OwenT OwnValues PackingMethod PaddedForm Padding PadeApproximant PadLeft PadRight PageBreakAbove PageBreakBelow PageBreakWithin PageFooterLines PageFooters PageHeaderLines PageHeaders PageHeight PageRankCentrality PageWidth PairedBarChart PairedHistogram PairedSmoothHistogram PairedTTest PairedZTest PaletteNotebook PalettePath Pane PaneBox PaneBoxOptions Panel PanelBox PanelBoxOptions Paneled PaneSelector PaneSelectorBox PaneSelectorBoxOptions PaperWidth ParabolicCylinderD ParagraphIndent ParagraphSpacing ParallelArray ParallelCombine ParallelDo ParallelEvaluate Parallelization Parallelize ParallelMap ParallelNeeds ParallelProduct ParallelSubmit ParallelSum ParallelTable ParallelTry Parameter ParameterEstimator ParameterMixtureDistribution ParameterVariables ParametricFunction ParametricNDSolve ParametricNDSolveValue ParametricPlot ParametricPlot3D ParentConnect ParentDirectory ParentForm Parenthesize ParentList ParetoDistribution Part PartialCorrelationFunction PartialD ParticleData Partition PartitionsP PartitionsQ ParzenWindow PascalDistribution PassEventsDown PassEventsUp Paste PasteBoxFormInlineCells PasteButton Path PathGraph PathGraphQ Pattern PatternSequence PatternTest PauliMatrix PaulWavelet Pause PausedTime PDF PearsonChiSquareTest PearsonCorrelationTest PearsonDistribution PerformanceGoal PeriodicInterpolation Periodogram PeriodogramArray PermutationCycles PermutationCyclesQ PermutationGroup PermutationLength PermutationList PermutationListQ PermutationMax PermutationMin PermutationOrder PermutationPower PermutationProduct PermutationReplace Permutations PermutationSupport Permute PeronaMalikFilter Perpendicular PERTDistribution PetersenGraph PhaseMargins Pi Pick PIDData PIDDerivativeFilter PIDFeedforward PIDTune Piecewise PiecewiseExpand PieChart PieChart3D PillaiTrace PillaiTraceTest Pink Pivoting PixelConstrained PixelValue PixelValuePositions Placed Placeholder PlaceholderReplace Plain PlanarGraphQ Play PlayRange Plot Plot3D Plot3Matrix PlotDivision PlotJoined PlotLabel PlotLayout PlotLegends PlotMarkers PlotPoints PlotRange PlotRangeClipping PlotRangePadding PlotRegion PlotStyle Plus PlusMinus Pochhammer PodStates PodWidth Point Point3DBox PointBox PointFigureChart PointForm PointLegend PointSize PoissonConsulDistribution PoissonDistribution PoissonProcess PoissonWindow PolarAxes PolarAxesOrigin PolarGridLines PolarPlot PolarTicks PoleZeroMarkers PolyaAeppliDistribution PolyGamma Polygon Polygon3DBox Polygon3DBoxOptions PolygonBox PolygonBoxOptions PolygonHoleScale PolygonIntersections PolygonScale PolyhedronData PolyLog PolynomialExtendedGCD PolynomialForm PolynomialGCD PolynomialLCM PolynomialMod PolynomialQ PolynomialQuotient PolynomialQuotientRemainder PolynomialReduce PolynomialRemainder Polynomials PopupMenu PopupMenuBox PopupMenuBoxOptions PopupView PopupWindow Position Positive PositiveDefiniteMatrixQ PossibleZeroQ Postfix PostScript Power PowerDistribution PowerExpand PowerMod PowerModList PowerSpectralDensity PowersRepresentations PowerSymmetricPolynomial Precedence PrecedenceForm Precedes PrecedesEqual PrecedesSlantEqual PrecedesTilde Precision PrecisionGoal PreDecrement PredictionRoot PreemptProtect PreferencesPath Prefix PreIncrement Prepend PrependTo PreserveImageOptions Previous PriceGraphDistribution PrimaryPlaceholder Prime PrimeNu PrimeOmega PrimePi PrimePowerQ PrimeQ Primes PrimeZetaP PrimitiveRoot PrincipalComponents PrincipalValue Print PrintAction PrintForm PrintingCopies PrintingOptions PrintingPageRange PrintingStartingPageNumber PrintingStyleEnvironment PrintPrecision PrintTemporary Prism PrismBox PrismBoxOptions PrivateCellOptions PrivateEvaluationOptions PrivateFontOptions PrivateFrontEndOptions PrivateNotebookOptions PrivatePaths Probability ProbabilityDistribution ProbabilityPlot ProbabilityPr ProbabilityScalePlot ProbitModelFit ProcessEstimator ProcessParameterAssumptions ProcessParameterQ ProcessStateDomain ProcessTimeDomain Product ProductDistribution ProductLog ProgressIndicator ProgressIndicatorBox ProgressIndicatorBoxOptions Projection Prolog PromptForm Properties Property PropertyList PropertyValue Proportion Proportional Protect Protected ProteinData Pruning PseudoInverse Purple Put PutAppend Pyramid PyramidBox PyramidBoxOptions QBinomial QFactorial QGamma QHypergeometricPFQ QPochhammer QPolyGamma QRDecomposition QuadraticIrrationalQ Quantile QuantilePlot Quantity QuantityForm QuantityMagnitude QuantityQ QuantityUnit Quartics QuartileDeviation Quartiles QuartileSkewness QueueingNetworkProcess QueueingProcess QueueProperties Quiet Quit Quotient QuotientRemainder RadialityCentrality RadicalBox RadicalBoxOptions RadioButton RadioButtonBar RadioButtonBox RadioButtonBoxOptions Radon RamanujanTau RamanujanTauL RamanujanTauTheta RamanujanTauZ Random RandomChoice RandomComplex RandomFunction RandomGraph RandomImage RandomInteger RandomPermutation RandomPrime RandomReal RandomSample RandomSeed RandomVariate RandomWalkProcess Range RangeFilter RangeSpecification RankedMax RankedMin Raster Raster3D Raster3DBox Raster3DBoxOptions RasterArray RasterBox RasterBoxOptions Rasterize RasterSize Rational RationalFunctions Rationalize Rationals Ratios Raw RawArray RawBoxes RawData RawMedium RayleighDistribution Re Read ReadList ReadProtected Real RealBlockDiagonalForm RealDigits RealExponent Reals Reap Record RecordLists RecordSeparators Rectangle RectangleBox RectangleBoxOptions RectangleChart RectangleChart3D RecurrenceFilter RecurrenceTable RecurringDigitsForm Red Reduce RefBox ReferenceLineStyle ReferenceMarkers ReferenceMarkerStyle Refine ReflectionMatrix ReflectionTransform Refresh RefreshRate RegionBinarize RegionFunction RegionPlot RegionPlot3D RegularExpression Regularization Reinstall Release ReleaseHold ReliabilityDistribution ReliefImage ReliefPlot Remove RemoveAlphaChannel RemoveAsynchronousTask Removed RemoveInputStreamMethod RemoveOutputStreamMethod RemoveProperty RemoveScheduledTask RenameDirectory RenameFile RenderAll RenderingOptions RenewalProcess RenkoChart Repeated RepeatedNull RepeatedString Replace ReplaceAll ReplaceHeldPart ReplaceImageValue ReplaceList ReplacePart ReplacePixelValue ReplaceRepeated Resampling Rescale RescalingTransform ResetDirectory ResetMenusPacket ResetScheduledTask Residue Resolve Rest Resultant ResumePacket Return ReturnExpressionPacket ReturnInputFormPacket ReturnPacket ReturnTextPacket Reverse ReverseBiorthogonalSplineWavelet ReverseElement ReverseEquilibrium ReverseGraph ReverseUpEquilibrium RevolutionAxis RevolutionPlot3D RGBColor RiccatiSolve RiceDistribution RidgeFilter RiemannR RiemannSiegelTheta RiemannSiegelZ Riffle Right RightArrow RightArrowBar RightArrowLeftArrow RightCosetRepresentative RightDownTeeVector RightDownVector RightDownVectorBar RightTee RightTeeArrow RightTeeVector RightTriangle RightTriangleBar RightTriangleEqual RightUpDownVector RightUpTeeVector RightUpVector RightUpVectorBar RightVector RightVectorBar RiskAchievementImportance RiskReductionImportance RogersTanimotoDissimilarity Root RootApproximant RootIntervals RootLocusPlot RootMeanSquare RootOfUnityQ RootReduce Roots RootSum Rotate RotateLabel RotateLeft RotateRight RotationAction RotationBox RotationBoxOptions RotationMatrix RotationTransform Round RoundImplies RoundingRadius Row RowAlignments RowBackgrounds RowBox RowHeights RowLines RowMinHeight RowReduce RowsEqual RowSpacings RSolve RudvalisGroupRu Rule RuleCondition RuleDelayed RuleForm RulerUnits Run RunScheduledTask RunThrough RuntimeAttributes RuntimeOptions RussellRaoDissimilarity SameQ SameTest SampleDepth SampledSoundFunction SampledSoundList SampleRate SamplingPeriod SARIMAProcess SARMAProcess SatisfiabilityCount SatisfiabilityInstances SatisfiableQ Saturday Save Saveable SaveAutoDelete SaveDefinitions SawtoothWave Scale Scaled ScaleDivisions ScaledMousePosition ScaleOrigin ScalePadding ScaleRanges ScaleRangeStyle ScalingFunctions ScalingMatrix ScalingTransform Scan ScheduledTaskActiveQ ScheduledTaskData ScheduledTaskObject ScheduledTasks SchurDecomposition ScientificForm ScreenRectangle ScreenStyleEnvironment ScriptBaselineShifts ScriptLevel ScriptMinSize ScriptRules ScriptSizeMultipliers Scrollbars ScrollingOptions ScrollPosition Sec Sech SechDistribution SectionGrouping SectorChart SectorChart3D SectorOrigin SectorSpacing SeedRandom Select Selectable SelectComponents SelectedCells SelectedNotebook Selection SelectionAnimate SelectionCell SelectionCellCreateCell SelectionCellDefaultStyle SelectionCellParentStyle SelectionCreateCell SelectionDebuggerTag SelectionDuplicateCell SelectionEvaluate SelectionEvaluateCreateCell SelectionMove SelectionPlaceholder SelectionSetStyle SelectWithContents SelfLoops SelfLoopStyle SemialgebraicComponentInstances SendMail Sequence SequenceAlignment SequenceForm SequenceHold SequenceLimit Series SeriesCoefficient SeriesData SessionTime Set SetAccuracy SetAlphaChannel SetAttributes Setbacks SetBoxFormNamesPacket SetDelayed SetDirectory SetEnvironment SetEvaluationNotebook SetFileDate SetFileLoadingContext SetNotebookStatusLine SetOptions SetOptionsPacket SetPrecision SetProperty SetSelectedNotebook SetSharedFunction SetSharedVariable SetSpeechParametersPacket SetStreamPosition SetSystemOptions Setter SetterBar SetterBox SetterBoxOptions Setting SetValue Shading Shallow ShannonWavelet ShapiroWilkTest Share Sharpen ShearingMatrix ShearingTransform ShenCastanMatrix Short ShortDownArrow Shortest ShortestMatch ShortestPathFunction ShortLeftArrow ShortRightArrow ShortUpArrow Show ShowAutoStyles ShowCellBracket ShowCellLabel ShowCellTags ShowClosedCellArea ShowContents ShowControls ShowCursorTracker ShowGroupOpenCloseIcon ShowGroupOpener ShowInvisibleCharacters ShowPageBreaks ShowPredictiveInterface ShowSelection ShowShortBoxForm ShowSpecialCharacters ShowStringCharacters ShowSyntaxStyles ShrinkingDelay ShrinkWrapBoundingBox SiegelTheta SiegelTukeyTest Sign Signature SignedRankTest SignificanceLevel SignPadding SignTest SimilarityRules SimpleGraph SimpleGraphQ Simplify Sin Sinc SinghMaddalaDistribution SingleEvaluation SingleLetterItalics SingleLetterStyle SingularValueDecomposition SingularValueList SingularValuePlot SingularValues Sinh SinhIntegral SinIntegral SixJSymbol Skeleton SkeletonTransform SkellamDistribution Skewness SkewNormalDistribution Skip SliceDistribution Slider Slider2D Slider2DBox Slider2DBoxOptions SliderBox SliderBoxOptions SlideView Slot SlotSequence Small SmallCircle Smaller SmithDelayCompensator SmithWatermanSimilarity SmoothDensityHistogram SmoothHistogram SmoothHistogram3D SmoothKernelDistribution SocialMediaData Socket SokalSneathDissimilarity Solve SolveAlways SolveDelayed Sort SortBy Sound SoundAndGraphics SoundNote SoundVolume Sow Space SpaceForm Spacer Spacings Span SpanAdjustments SpanCharacterRounding SpanFromAbove SpanFromBoth SpanFromLeft SpanLineThickness SpanMaxSize SpanMinSize SpanningCharacters SpanSymmetric SparseArray SpatialGraphDistribution Speak SpeakTextPacket SpearmanRankTest SpearmanRho Spectrogram SpectrogramArray Specularity SpellingCorrection SpellingDictionaries SpellingDictionariesPath SpellingOptions SpellingSuggestionsPacket Sphere SphereBox SphericalBesselJ SphericalBesselY SphericalHankelH1 SphericalHankelH2 SphericalHarmonicY SphericalPlot3D SphericalRegion SpheroidalEigenvalue SpheroidalJoiningFactor SpheroidalPS SpheroidalPSPrime SpheroidalQS SpheroidalQSPrime SpheroidalRadialFactor SpheroidalS1 SpheroidalS1Prime SpheroidalS2 SpheroidalS2Prime Splice SplicedDistribution SplineClosed SplineDegree SplineKnots SplineWeights Split SplitBy SpokenString Sqrt SqrtBox SqrtBoxOptions Square SquaredEuclideanDistance SquareFreeQ SquareIntersection SquaresR SquareSubset SquareSubsetEqual SquareSuperset SquareSupersetEqual SquareUnion SquareWave StabilityMargins StabilityMarginsStyle StableDistribution Stack StackBegin StackComplete StackInhibit StandardDeviation StandardDeviationFilter StandardForm Standardize StandbyDistribution Star StarGraph StartAsynchronousTask StartingStepSize StartOfLine StartOfString StartScheduledTask StartupSound StateDimensions StateFeedbackGains StateOutputEstimator StateResponse StateSpaceModel StateSpaceRealization StateSpaceTransform StationaryDistribution StationaryWaveletPacketTransform StationaryWaveletTransform StatusArea StatusCentrality StepMonitor StieltjesGamma StirlingS1 StirlingS2 StopAsynchronousTask StopScheduledTask StrataVariables StratonovichProcess StreamColorFunction StreamColorFunctionScaling StreamDensityPlot StreamPlot StreamPoints StreamPosition Streams StreamScale StreamStyle String StringBreak StringByteCount StringCases StringCount StringDrop StringExpression StringForm StringFormat StringFreeQ StringInsert StringJoin StringLength StringMatchQ StringPosition StringQ StringReplace StringReplaceList StringReplacePart StringReverse StringRotateLeft StringRotateRight StringSkeleton StringSplit StringTake StringToStream StringTrim StripBoxes StripOnInput StripWrapperBoxes StrokeForm StructuralImportance StructuredArray StructuredSelection StruveH StruveL Stub StudentTDistribution Style StyleBox StyleBoxAutoDelete StyleBoxOptions StyleData StyleDefinitions StyleForm StyleKeyMapping StyleMenuListing StyleNameDialogSettings StyleNames StylePrint StyleSheetPath Subfactorial Subgraph SubMinus SubPlus SubresultantPolynomialRemainders SubresultantPolynomials Subresultants Subscript SubscriptBox SubscriptBoxOptions Subscripted Subset SubsetEqual Subsets SubStar Subsuperscript SubsuperscriptBox SubsuperscriptBoxOptions Subtract SubtractFrom SubValues Succeeds SucceedsEqual SucceedsSlantEqual SucceedsTilde SuchThat Sum SumConvergence Sunday SuperDagger SuperMinus SuperPlus Superscript SuperscriptBox SuperscriptBoxOptions Superset SupersetEqual SuperStar Surd SurdForm SurfaceColor SurfaceGraphics SurvivalDistribution SurvivalFunction SurvivalModel SurvivalModelFit SuspendPacket SuzukiDistribution SuzukiGroupSuz SwatchLegend Switch Symbol SymbolName SymletWavelet Symmetric SymmetricGroup SymmetricMatrixQ SymmetricPolynomial SymmetricReduction Symmetrize SymmetrizedArray SymmetrizedArrayRules SymmetrizedDependentComponents SymmetrizedIndependentComponents SymmetrizedReplacePart SynchronousInitialization SynchronousUpdating Syntax SyntaxForm SyntaxInformation SyntaxLength SyntaxPacket SyntaxQ SystemDialogInput SystemException SystemHelpPath SystemInformation SystemInformationData SystemOpen SystemOptions SystemsModelDelay SystemsModelDelayApproximate SystemsModelDelete SystemsModelDimensions SystemsModelExtract SystemsModelFeedbackConnect SystemsModelLabels SystemsModelOrder SystemsModelParallelConnect SystemsModelSeriesConnect SystemsModelStateFeedbackConnect SystemStub Tab TabFilling Table TableAlignments TableDepth TableDirections TableForm TableHeadings TableSpacing TableView TableViewBox TabSpacings TabView TabViewBox TabViewBoxOptions TagBox TagBoxNote TagBoxOptions TaggingRules TagSet TagSetDelayed TagStyle 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