ENH: make LinearSVC copyiable

GaelVaroquaux · GaelVaroquaux · commit 5bf5266eb2c3 · 2012-05-06T19:04:19.000+02:00
Fixes scikit-learn#820
diff --git a/sklearn/svm/base.py b/sklearn/svm/base.py
@@ -755,6 +755,11 @@ def _validate_for_predict(self, X):
             raise ValueError(
                 "cannot use sparse input in %r trained on dense data"
                 % type(self).__name__)
+        if not self.raw_coef_.flags['F_CONTIGUOUS']:
+            warnings.warn('Coefficients are the fortran-contiguous. '
+                          'Copying them.', RuntimeWarning,
+                          stacklevel=3)
+            self.raw_coef_ = np.asfortranarray(self.raw_coef_)
         return X
 
     def _get_intercept_(self):
diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py
@@ -3,11 +3,13 @@
 
 TODO: remove hard coded numerical results when possible
 """
+import copy
+import warnings
 
 import numpy as np
 from numpy.testing import assert_array_equal, assert_array_almost_equal, \
                           assert_almost_equal
-from nose.tools import assert_raises, assert_true
+from nose.tools import assert_raises, assert_true, assert_equal
 
 from sklearn import svm, linear_model, datasets, metrics, base
 from sklearn.datasets.samples_generator import make_classification
@@ -151,7 +153,7 @@ def test_precomputed():
     assert_almost_equal(np.mean(pred == iris.target), .99, decimal=2)
 
 
-def test_SVR():
+def test_svr():
     """
     Test Support Vector Regression
     """
@@ -598,6 +600,15 @@ def test_linearsvc_verbose():
     os.dup2(stdout, 1)  # restore original stdout
 
 
+def test_linearsvc_deepcopy():
+    rng = check_random_state(0)
+    clf = svm.LinearSVC()
+    clf.fit(rng.rand(10, 2), rng.randint(0, 2, size=10))
+    with warnings.catch_warnings(record=True) as warn_queue:
+        copy.deepcopy(clf).predict(rng.rand(2))
+    assert_equal(len(warn_queue), 1)
+
+
 def test_svc_clone_with_callable_kernel():
     a = svm.SVC(kernel=lambda x, y: np.dot(x, y.T), probability=True)
     b = base.clone(a)
