def test_grid_search(self):
# Test that the best estimator contains the right value for foo_param
clf = MockClassifier()
grid_search = ATGridSearchCV(clf, {'foo_param': [1, 2, 3]},
verbose=3,
webserver_url=self.live_server_url)
# make sure it selects the smallest parameter in case of ties
old_stdout = sys.stdout
sys.stdout = StringIO()
wait(grid_search.fit(X, y))
sys.stdout = old_stdout
self.assertIn(grid_search.best_estimator_.foo_param, [2, 3])
_mock_sort = partial(_sort_grid_scores, param='foo_param')
for idx, tup in enumerate(
sorted(grid_search.grid_scores_, key=cmp_to_key(_mock_sort))):
self.assertEqual(tup[0], {'foo_param': idx + 1},
'%d%s' % (idx, tup))
# Smoke test the score etc:
grid_search.score(X, y)
grid_search.predict_proba(X)
grid_search.decision_function(X)
grid_search.transform(X)
# Test exception handling on scoring
grid_search.scoring = 'sklearn'
self.assertRaises(ValueError, grid_search.fit, X, y)
def test_transform_inverse_transform_round_trip(self):
clf = MockClassifier()
grid_search = ATGridSearchCV(clf, {'foo_param': [1, 2, 3]},
verbose=3,
webserver_url=self.live_server_url)
wait(grid_search.fit(X, y))
X_round_trip = grid_search.inverse_transform(grid_search.transform(X))
assert_array_equal(X, X_round_trip)