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Extend functionality of Sequential Feature Selector to allow repeating cross-validation. #272

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Extend functionality of Sequential Feature Selector to allow repeating cross-validation. #272

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a46e200
Added n_cv_repeats parameter and extended functionality with Repeated…
nd26 Oct 31, 2017
2a5c9c5
Changed tabs to spaces + added parameter to constructor
nd26 Nov 1, 2017
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25 changes: 21 additions & 4 deletions mlxtend/feature_selection/sequential_feature_selector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,15 +18,19 @@
from sklearn.base import BaseEstimator
from sklearn.base import MetaEstimatorMixin
from ..externals.name_estimators import _name_estimators
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_score, RepeatedKFold, RepeatedStratifiedKFold
from sklearn.externals.joblib import Parallel, delayed


def _calc_score(selector, X, y, indices):
if selector.cv:
if selector.n_cv_repeats > 0:
if selector._estimator_type == 'classifier':
cv_folds = RepeatedStratifiedKFold(n_splits=selector.cv, n_repeats=selector.n_cv_repeats)
else:
cv_folds = RepeatedKFold(n_splits=selector.cv, n_repeats=selector.n_cv_repeats)
scores = cross_val_score(selector.est_,
X[:, indices], y,
cv=selector.cv,
cv=selector.cv if selector.n_cv_repeats == 0 else cv_folds,
scoring=selector.scorer,
n_jobs=1,
pre_dispatch=selector.pre_dispatch)
Expand Down Expand Up @@ -103,6 +107,11 @@ class SequentialFeatureSelector(BaseEstimator, MetaEstimatorMixin):
if False. Set to False if the estimator doesn't
implement scikit-learn's set_params and get_params methods.
In addition, it is required to set cv=0, and n_jobs=1.
n_cv_repeats : int (default = 0)
The number of times cross-validation will be repeated. If 0 then it's
not repeated. Negative numbers raise an exception. Uses Scikit-learn
RepeatedStratifiedKFold for a classifier or RepeatedKFold otherwise.


Attributes
----------
Expand All @@ -125,7 +134,8 @@ def __init__(self, estimator, k_features=1,
verbose=0, scoring=None,
cv=5, n_jobs=1,
pre_dispatch='2*n_jobs',
clone_estimator=True):
clone_estimator=True,
n_cv_repeats = 0):

self.estimator = estimator
self.k_features = k_features
Expand All @@ -149,6 +159,13 @@ def __init__(self, estimator, k_features=1,
self.est_ = self.estimator
self.scoring = scoring

self.n_cv_repeats = n_cv_repeats
if self.n_cv_repeats < 0:
raise AttributeError('Number of cross-validation repeats should be >= 0.')
if not self.cv and self.n_cv_repeats > 0:
raise AttributeError('Cannot repeat cross-validation when it\'s set to 0.')


if scoring is None:
if self.est_._estimator_type == 'classifier':
scoring = 'accuracy'
Expand Down