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set force_all_finite=False for SelectFromModel.transform #11026

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set force_all_finite=False for SelectFromModel.transform #11026

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d0b11c4
set force_all_finite=True for SelectFromModel.transform
alexpantyukhin Apr 24, 2018
26ef4d0
fix flake8
alexpantyukhin Apr 24, 2018
2642357
add test
alexpantyukhin Apr 25, 2018
f628d5e
add test nan
alexpantyukhin Apr 27, 2018
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6 changes: 5 additions & 1 deletion sklearn/feature_selection/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,11 @@ def transform(self, X):
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.
"""
X = check_array(X, accept_sparse='csr')
return self._transform(X)

def _transform(self, X, force_all_finite=True):
X = check_array(X, force_all_finite=force_all_finite,
accept_sparse='csr')
mask = self.get_support()
if not mask.any():
warn("No features were selected: either the data is"
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3 changes: 3 additions & 0 deletions sklearn/feature_selection/from_model.py
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Expand Up @@ -143,6 +143,9 @@ def _get_support_mask(self):
threshold = _calculate_threshold(estimator, scores, self.threshold)
return scores >= threshold

def transform(self, X):
return self._transform(X, False)

def fit(self, X, y=None, **fit_params):
"""Fit the SelectFromModel meta-transformer.

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15 changes: 15 additions & 0 deletions sklearn/feature_selection/tests/test_from_model.py
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@@ -1,5 +1,6 @@
import numpy as np

from sklearn.impute import SimpleImputer
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
Expand Down Expand Up @@ -198,3 +199,17 @@ def test_threshold_without_refitting():
# Set a higher threshold to filter out more features.
model.threshold = "1.0 * mean"
assert_greater(X_transform.shape[1], model.transform(data).shape[1])


def test_transform_accepts_infinite_data():
# Test that transform doesn't check for np.inf and np.nan values.
est = SimpleImputer(strategy='mean', missing_values='NaN')
model = SelectFromModel(estimator=est)
data[2, :] = np.nan
model.fit(data, y)

X_len = len(model.get_support())
X = np.arange(X_len).reshape(1, X_len)
X.fill(np.inf)

model.transform(X)