def test_pandas_input(self):
# check cross_val_score doesn't destroy pandas dataframe
types = [(MockDataFrame, MockDataFrame)]
try:
from pandas import Series, DataFrame
types.append((DataFrame, Series))
except ImportError:
pass
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
for InputFeatureType, TargetType in types:
# X dataframe, y series
X_df, y_ser = InputFeatureType(X), TargetType(y)
clf = CheckingClassifier(
check_X=lambda x: isinstance(x, InputFeatureType),
check_y=lambda x: isinstance(x, TargetType),
)
grid_search = TuneGridSearchCV(clf, {"foo_param": [1, 2, 3]})
grid_search.fit(X_df, y_ser).score(X_df, y_ser)
grid_search.predict(X_df)
self.assertTrue(hasattr(grid_search, "cv_results_"))
def test_y_as_list(self):
# Pass y as list in dcv.GridSearchCV
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(check_y=lambda x: isinstance(x, list))
cv = KFold(n_splits=3)
grid_search = TuneGridSearchCV(clf, {"foo_param": [1, 2, 3]}, cv=cv)
grid_search.fit(X, y.tolist()).score(X, y)
self.assertTrue(hasattr(grid_search, "cv_results_"))
def test_gridsearch_nd(self):
# Pass X as list in dcv.GridSearchCV
X_4d = np.arange(10 * 5 * 3 * 2).reshape(10, 5, 3, 2)
y_3d = np.arange(10 * 7 * 11).reshape(10, 7, 11)
clf = CheckingClassifier(
check_X=lambda x: x.shape[1:] == (5, 3, 2),
check_y=lambda x: x.shape[1:] == (7, 11),
)
grid_search = TuneGridSearchCV(clf, {"foo_param": [1, 2, 3]}, cv=3)
grid_search.fit(X_4d, y_3d).score(X, y)
self.assertTrue(hasattr(grid_search, "cv_results_"))
def test_local_mode(self):
# Pass X as list in dcv.GridSearchCV
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(check_X=lambda x: isinstance(x, list))
cv = KFold(n_splits=3)
with patch.object(ray, "init", wraps=ray.init) as wrapped_init:
grid_search = TuneGridSearchCV(
clf, {"foo_param": [1, 2, 3]}, n_jobs=1, cv=cv)
grid_search.fit(X.tolist(), y).score(X, y)
self.assertTrue(hasattr(grid_search, "cv_results_"))
self.assertTrue(wrapped_init.call_args[1]["local_mode"])
def test_hyperparameter_searcher_with_fit_params(self, name, cls, kwargs):
X = np.arange(100).reshape(10, 10)
y = np.array([0] * 5 + [1] * 5)
clf = CheckingClassifier(expected_fit_params=["spam", "eggs"])
pipe = Pipeline([("clf", clf)])
searcher = cls(pipe, {"clf__foo_param": [1, 2, 3]}, cv=2, **kwargs)
# The CheckingClassifer generates an assertion error if
# a parameter is missing or has length != len(X).
with self.assertRaises(AssertionError) as exc:
searcher.fit(X, y, clf__spam=np.ones(10))
self.assertTrue("Expected fit parameter(s) ['eggs'] not seen." in str(
exc.exception))
searcher.fit(X, y, clf__spam=np.ones(10), clf__eggs=np.zeros(10))
