ksfeatureselector

KSFeatureSelector

KSFeatureSelector is a robust and flexible Python package designed for selecting the most discriminatory features in both binary and multi-class classification problems using the Kolmogorov-Smirnov (K-S) test. It provides advanced options for handling multi-class scenarios and aggregating p-values.

Features

• Uses the K-S test to rank features by their ability to separate classes.
• Handles target variables with more than two categories (up to 10 classes internally).
• Flexible Comparison Strategies:
  • pairwise: Performs K-S tests between every unique pair of classes.
  • one-vs-rest: Compares each class against all other classes combined.
• Multiple P-Value Aggregation Methods:
  • fisher: Uses Fisher's combined probability test (default, generally recommended).
  • min: Takes the minimum p-value from all comparisons for a feature.
  • max: Takes the maximum p-value from all comparisons for a feature.
• Scikit-learn Style API: Offers a class-based interface (KSFeatureSelector with fit, transform) for seamless integration into machine learning pipelines.
• Convenience Function: Provides a simple select_ks_features wrapper for quick, one-off feature selection.
• Robust Validation & Warnings: Includes comprehensive input validation and issues UserWarning for data quality issues, such as categories with too few observations or insufficient samples for K-S tests.
• Pure Python: Built using pandas, scipy, and numpy.

Installation

.. code-block:: bash

pip install ksfeatureselector

For local installation:

.. code-block:: bash

pip install -e .

Usage

.. code-block:: python

from ksfeatureselector import select_ks_features

significant_features = select_ks_features( df, x_cols, y_var, top_p=0.01, aggregation_method='one-vs-rest', p_value_aggregation_method='min' ) print(f"Significant features (one-vs-rest, min p-value <= 0.01): {significant_features}")

Example 3: Select top 3 features using 'pairwise' comparison

and 'max' p-value aggregation

top_3_features_max_agg = select_ks_features( df, x_cols, y_var, top_n=3, aggregation_method='pairwise', p_value_aggregation_method='max' ) print(f"Top 3 features (pairwise, max p-value): {top_3_features_max_agg}")

Arguments

• df (pd.DataFrame):
  The input DataFrame containing feature columns and the binary target column.

• x_cols (List[str]):
  A list of column names in df representing the features you want to evaluate.

• y_var (str):
  The name of the column in df representing the binary target variable (0/1 or similar).

• top_p (float, optional):
  If provided, only features with a K-S test p-value less than top_p will be selected.

• top_n (int, optional):
  If provided, the top n features with the lowest p-values will be selected.

  .. note::

  You can use either top_p or top_n, or both. If both are given, the function will apply top_p first, and then take the top n from that filtered list.

License

MIT License

Author

V Subrahmanya Raghu Ram Kishore Parupudi Email: [e-mail]