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@@ -214,13 +214,12 @@
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        cv = check_cv(self.cv, y, is_classifier(self.estimator))
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        feature_importances = []  # type: List
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        base_scores = []  # type: List[float]
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        weights = fit_params.get('sample_weight', None)
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        fit_params.pop('sample_weight', None)
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        weights = fit_params.pop('sample_weight', None)
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        fold_fit_params = fit_params.copy()
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        for train, test in cv.split(X, y, groups):
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            if weights is None:
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                est = clone(self.estimator).fit(X[train], y[train], **fit_params)
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            else:
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                est = clone(self.estimator).fit(X[train], y[train], sample_weight=weights[train], **fit_params)
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            if weights is not None:
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                fold_fit_params['sample_weight'] = weights[train]
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            est = clone(self.estimator).fit(X[train], y[train], **fold_fit_params)
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            score_func = partial(self.scorer_, est)
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            _base_score, _importances = self._get_score_importances(
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                score_func, X[test], y[test])
Files Coverage
eli5 97.33%
Project Totals (49 files) 97.33%
1070.5
TRAVIS_PYTHON_VERSION=3.5
TRAVIS_OS_NAME=linux
TOXENV=py35-nodeps
1070.1
TRAVIS_PYTHON_VERSION=2.7
TRAVIS_OS_NAME=linux
TOXENV=py27
1070.4
TRAVIS_PYTHON_VERSION=3.5
TRAVIS_OS_NAME=linux
TOXENV=py35-extra
1070.2
TRAVIS_PYTHON_VERSION=2.7
TRAVIS_OS_NAME=linux
TOXENV=py27-extra
1070.7
TRAVIS_PYTHON_VERSION=3.6
TRAVIS_OS_NAME=linux
TOXENV=py36-extra
1070.3
TRAVIS_PYTHON_VERSION=3.5
TRAVIS_OS_NAME=linux
TOXENV=py35
1070.6
TRAVIS_PYTHON_VERSION=3.6
TRAVIS_OS_NAME=linux
TOXENV=py36
1070.8
TRAVIS_PYTHON_VERSION=3.6
TRAVIS_OS_NAME=linux
TOXENV=py36-legacy
1070.11
TRAVIS_PYTHON_VERSION=3.7
TRAVIS_OS_NAME=linux
TOXENV=py37-nodeps
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comment:
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  layout: "header, diff, tree"
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coverage:
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  status:
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    project: false
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