[MRG+1] add docs that C can receive array in RandomizedLogisticRegression (scikit-learn#6537)

* doc: state that parameter C can receive an array

* add more details in doc, and check the dim of C

* doc: state that parameter C can receive an array

* add more details in doc, and check the dim of C

* a little modification

* minor modifications and make line length less than 79 characters

* remove the backslash and correct typos

* meet PEP8's E128 requirement

* use .format and add a test

Author: pianomania

sklearn/linear_model/randomized_l1.py

@@ -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):
@@ -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

sklearn/linear_model/tests/test_randomized_l1.py

@@ -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