Usage
=====

Overview
--------

The ``moosefs`` package provides a customizable, ensemble‑based feature selection pipeline that balances predictive performance and stability. The core entry is ``FeatureSelectionPipeline``.

Getting Started
---------------

.. code-block:: python

    from moosefs import FeatureSelectionPipeline

    # data is a pandas.DataFrame with a 'target' column
    fs_methods = ["f_statistic_selector", "random_forest_selector", "svm_selector"]
    merging_strategy = "union_of_intersections_merger"

    pipeline = FeatureSelectionPipeline(
        data=data,
        fs_methods=fs_methods,
        merging_strategy=merging_strategy,
        num_repeats=5,
        task="classification",
        num_features_to_select=10,
    )
    best, repeat, group = pipeline.run()
    print(best)

Extendability
-------------

Custom selectors can subclass ``FeatureSelector`` and implement ``compute_scores``:

.. code-block:: python

    import numpy as np
    from moosefs.feature_selectors.base_selector import FeatureSelector

    class MySelector(FeatureSelector):
        name = "MySelector"

        def __init__(self, task: str, num_features_to_select: int):
            super().__init__(task, num_features_to_select)

        def compute_scores(self, X, y):
            # return a 1D numpy array of per‑feature scores
            return np.random.rand(X.shape[1])