E5EC [MRG+1] MAINT: Simplify n_features_to_select logic in RFECV by MechCoder · Pull Request #6569 · scikit-learn/scikit-learn · GitHub
[go: up one dir, main page]
Skip to content

[MRG+1] MAINT: Simplify n_features_to_select logic in RFECV #6569

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Mar 23, 2016
Merged

[MRG+1] MAINT: Simplify n_features_to_select logic in RFECV #6569

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 6 additions & 9 deletions sklearn/feature_selection/rfe.py
View file
Open in desktop
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