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"""Functions for doing feature selection during preprocessing."""
from typing import Optional, cast
import numpy as np
from numpy import ndarray
from numpy.typing import NDArray
from .utils import ArrayLike
def run_feature_selection(
X: ndarray,
y: ndarray,
select_k_features: int,
random_state: Optional[np.random.RandomState] = None,
) -> NDArray[np.bool_]:
"""
Find most important features.
Uses a gradient boosting tree regressor as a proxy for finding
the k most important features in X, returning indices for those
features as output.
"""
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import SelectFromModel
clf = RandomForestRegressor(
n_estimators=100, max_depth=3, random_state=random_state
)
clf.fit(X, y)
selector = SelectFromModel(
clf, threshold=-np.inf, max_features=select_k_features, prefit=True
)
return cast(NDArray[np.bool_], selector.get_support(indices=False))
# Function has not been removed only due to usage in module tests
def _handle_feature_selection(
X: ndarray,
select_k_features: Optional[int],
y: ndarray,
variable_names: ArrayLike[str],
):
if select_k_features is not None:
selection = run_feature_selection(X, y, select_k_features)
print(f"Using features {[variable_names[i] for i in selection]}")
X = X[:, selection]
else:
selection = None
return X, selection
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