8000 [MRG+1] MAINT: Simplify n_features_to_select logic in RFECV by MechCoder · Pull Request #6569 · scikit-learn/scikit-learn · GitHub
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[MRG+1] MAINT: Simplify n_features_to_select logic in RFECV #6569

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Merged
merged 1 commit into from
Mar 23, 2016
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[MRG+1] MAINT: Simplify n_features_to_select logic in RFECV #6569

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15 changes: 6 additions & 9 deletions sklearn/feature_selection/rfe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -421,14 +421,11 @@ def fit(self, X, y):
func(rfe, self.estimator, X, y, train, test, scorer)
for train, test in cv.split(X, y))

scores = np.sum(scores, axis=0)[::-1]
# The index in 'scores' when 'n_features' features are selected
n_feature_index = np.ceil((n_features - n_features_to_select) /
float(self.step))
n_features_to_select = max(n_features_to_select,
n_features - ((n_feature_index -
np.argmax(scores)) *
self.step))
scores = np.sum(scores, axis=0)
n_features_to_select = max(
n_features - (np.argmax(scores) * self.step),
n_features_to_select)

# Re-execute an elimination with best_k over the whole set
rfe = RFE(estimator=self.estimator,
n_features_to_select=n_features_to_select, step=self.step)
Expand All @@ -444,5 +441,5 @@ def fit(self, X, y):

# Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
# here, the scores are normalized by get_n_splits(X, y)
self.grid_scores_ = scores / cv.get_n_splits(X, y)
self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y)
return self
0