def test_grid_search_sparse_score_func():
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
clf = LinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
cv.set_params(refit=False).fit(X_[:180], y_[:180])
y_pred = cv.predict(X_[180:])
C = cv.best_estimator.C
X_ = sp.csr_matrix(X_)
clf = SparseLinearSVC()
cv = GridSearchCV(clf, {'C': [0.1, 1.0]}, score_func=f1_score)
# XXX: set refit to False due to a random bug when True (default)
cv.set_params(refit=False).fit(X_[:180], y_[:180])
y_pred2 = cv.predict(X_[180:])
C2 = cv.best_estimator.C
assert_array_equal(y_pred, y_pred2)
assert_equal(C, C2)
