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[MRG+1] add docs that C can receive array in RandomizedLogisticRegression #6537

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Merged
merged 10 commits into from
Feb 18, 2017
Merged

[MRG+1] add docs that C can receive array in RandomizedLogisticRegression #6537

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4db495b
doc: state that parameter C can receive an array
pianomania Mar 14, 2016
3535c00
add more details in doc, and check the dim of C
pianomania Mar 16, 2016
8c23d91
doc: state that parameter C can receive an array
pianomania Mar 14, 2016
3f6ea47
add more details in doc, and check the dim of C
pianomania Mar 16, 2016
7e44df4
a little modification
pianomania Feb 17, 2017
d4c7714
add double backticks
pianomania Feb 17, 2017
96deb31
minor modifications and make line length less than 79 characters
pianomania Feb 17, 2017
19a4dc6
remove the backslash and correct typos
pianomania Feb 17, 2017
dc85f4e
meet PEP8's E128 requirement
pianomania Feb 17, 2017
da90617
use .format and add a test
pianomania Feb 18, 2017
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11 changes: 10 additions & 1 deletion sklearn/linear_model/randomized_l1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,6 +354,11 @@ def _randomized_logistic(X, y, weights, mask, C=1., verbose=False,
X *= (1 - weights)

C = np.atleast_1d(np.asarray(C, dtype=np.float64))
if C.ndim > 1:
raise ValueError("C should be 1-dimensional array-like, "
"but got a {}-dimensional array-like instead: {}."
.format(C.ndim, C))

scores = np.zeros((X.shape[1], len(C)), dtype=np.bool)

for this_C, this_scores in zip(C, scores.T):
Expand Down Expand Up @@ -381,8 +386,12 @@ class RandomizedLogisticRegression(BaseRandomizedLinearModel):

Parameters
----------
C : float, optional, default=1
C : float or array-like of shape [n_reg_parameter], optional, default=1
The regularization parameter C in the LogisticRegression.
When C is an array, fit will take each regularization parameter in C
one by one for LogisticRegression and store results for each one
in ``all_scores_``, where columns and rows represent corresponding
reg_parameters and features.

scaling : float, optional, default=0.5
The s parameter used to randomly scale the penalty of different
Expand Down
3 changes: 3 additions & 0 deletions sklearn/linear_model/tests/test_randomized_l1.py
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Expand Up @@ -100,6 +100,9 @@ def test_randomized_logistic():
feature_scores = clf.fit(X, y).scores_
assert_array_equal(np.argsort(F), np.argsort(feature_scores))

clf = RandomizedLogisticRegression(verbose=False, C=[[1., 0.5]])
assert_raises(ValueError, clf.fit, X, y)


def test_randomized_logistic_sparse():
# Check randomized sparse logistic regression on sparse data
Expand Down