i used pipeline and grid_search to select the best parameters and then used these parameters to fit the best pipeline ('best_pipe'). However since the feature_selection (SelectKBest) is in the pipeline there has been no fit applied to SelectKBest.
I need to know the feature names of the 'k' selected features. Any ideas how to retrieve them? Thank you in advance
from sklearn import (cross_validation, feature_selection, pipeline,
preprocessing, linear_model, grid_search)
folds = 5
split = cross_validation.StratifiedKFold(target, n_folds=folds, shuffle = False, random_state = 0)
scores = []
for k, (train, test) in enumerate(split):
X_train, X_test, y_train, y_test = X.ix[train], X.ix[test], y.ix[train], y.ix[test]
top_feat = feature_selection.SelectKBest()
pipe = pipeline.Pipeline([('scaler', preprocessing.StandardScaler()),
('feat', top_feat),
('clf', linear_model.LogisticRegression())])
K = [40, 60, 80, 100]
C = [1.0, 0.1, 0.01, 0.001, 0.0001, 0.00001]
penalty = ['l1', 'l2']
param_grid = [{'feat__k': K,
'clf__C': C,
'clf__penalty': penalty}]
scoring = 'precision'
gs = grid_search.GridSearchCV(estimator=pipe, param_grid = param_grid, scoring = scoring)
gs.fit(X_train, y_train)
best_score = gs.best_score_
scores.append(best_score)
print "Fold: {} {} {:.4f}".format(k+1, scoring, best_score)
print gs.best_params_
best_pipe = pipeline.Pipeline([('scale', preprocessing.StandardScaler()),
('feat', feature_selection.SelectKBest(k=80)),
('clf', linear_model.LogisticRegression(C=.0001, penalty='l2'))])
best_pipe.fit(X_train, y_train)
best_pipe.predict(X_test)
You can access the feature selector by name in best_pipe
:
features = best_pipe.named_steps['feat']
Then you can call transform()
on an index array to get the names of the selected columns:
X.columns[features.transform(np.arange(len(X.columns)))]
The output here will be the eighty column names selected in the pipeline.
Jake's answer totally works. But depending on what feature selector your using, there's another option that I think is cleaner. This one worked for me:
X.columns[features.get_support()]
It gave me an identical answer to Jake's answer. And you can see more about it in the docs, but get_support
returns an array of true/false values for whether or not the column was used. Also, it's worth noting that X
must be of identical shape to the training data used on the feature selector.
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