lightgbm-feature-importance-evaluator-zhoumath 0.1.0

Evaluate feature importance for LightGBM models using various methods.

lightgbm_feature_importance_evaluator_zhoumath

A Python package for evaluating feature importance in LightGBM models using various methods. This package is tailored for LightGBM users but also supports model-agnostic feature importance evaluation methods, such as permutation and drop-column importance.

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Key Features

• LightGBM-Specific Importance Methods:
  • Gain-based importance.
  • Split-based importance.
• Model-Agnostic Methods:
  • Permutation importance.
  • Drop-column importance.
• SHAP-Based Interpretability:
  • Local and global explanations using SHAP values.
• Cross-Validation Support:
  • Robust feature importance evaluation using stratified k-fold cross-validation.
• Feature Filtering:
  • Select important features based on a user-defined threshold.

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Installation

You can install the package directly from PyPI: pip install lightgbm_feature_importance_evaluator_zhoumath

Usage Here鈥檚 how to use lightgbm_feature_importance_evaluator_zhoumath step by step:

1. Import the Package from lightgbm_feature_importance_evaluator_zhoumath.evaluator import FeatureImportanceEvaluator from lightgbm import LGBMClassifier import pandas as pd

2. Prepare Your Dataset

Sample dataset

data = pd.DataFrame({ "feature1": [1, 2, 3, 4, 5], "feature2": [5, 4, 3, 2, 1], "feature3": [2, 3, 4, 5, 6], "target": [0, 1, 0, 1, 0], "timestamp": ["2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04", "2023-01-05"] })

3. Initialize the Evaluator evaluator = FeatureImportanceEvaluator( data=data, target_column="target", timestamp_column="timestamp", feature_columns=["feature1", "feature2", "feature3"], model=LGBMClassifier(), importance_method="gain", # Choose from 'gain', 'split', 'permutation', 'shap', 'drop_column' )

4. Evaluate Feature Importance importance_df = evaluator.evaluate_importance() print("Feature Importance:") print(importance_df)

5. Filter Features selected_features, sorted_importance = evaluator.get_filtered_features(importance_df) print("Selected Features:") print(selected_features)

Available Importance Methods: gain #Importance based on the gain (performance improvement) when a feature is used for splitting. split #Importance based on the frequency a feature is used for splitting. permutation #Model-agnostic importance based on the drop in performance when a feature is randomly shuffled. shap #Uses SHAP values to explain the contribution of each feature to predictions. drop_column #Measures the change in model performance when a feature is entirely removed from the dataset.

Customization Options Cross-Validation: Adjust the number of splits using n_splits. Repeat cross-validation multiple times using repeats. Importance Threshold: Filter features with importance above a specific threshold using importance_threshold. Date Filtering: Use start_date and end_date to filter data based on a time range.

Dependencies The package requires the following Python libraries: numpy pandas scikit-learn shap lightgbm Install them with: pip install -r requirements.txt

License This project is licensed under the MIT License - see the LICENSE file for details.

Contributing Contributions are welcome! If you have suggestions for improvements or new features, feel free to open an issue or submit a pull request.

Acknowledgments This package was developed to streamline the evaluation of feature importance in LightGBM models and simplify workflows for data scientists and machine learning practitioners.

Author Zhoushus Email: [e-mail] GitHub: https://github.com/shanghaizhoushus