def test_logistic_regression():
with cupy_using_allocator(dummy_allocator):
X_train, X_test, y_train, y_test = \
small_classification_dataset(np.float32)
y_train = y_train.astype(np.float32)
y_test = y_test.astype(np.float32)
culog = LogisticRegression()
culog.fit(X_train, y_train)
culog.predict(X_train)
def test_base_n_features_in(datatype, use_integer_n_features):
X_train, _, _, _ = small_classification_dataset(datatype)
integer_n_features = 8
clf = cuml.Base()
if use_integer_n_features:
clf._set_n_features_in(integer_n_features)
assert clf.n_features_in_ == integer_n_features
else:
clf._set_n_features_in(X_train)
assert clf.n_features_in_ == X_train.shape[1]
def test_logistic_regression_model_default(dtype):
X_train, X_test, y_train, y_test = small_classification_dataset(dtype)
y_train = y_train.astype(dtype)
y_test = y_test.astype(dtype)
culog = cuLog()
culog.fit(X_train, y_train)
sklog = skLog(multi_class="auto")
sklog.fit(X_train, y_train)
assert culog.score(X_test, y_test) >= sklog.score(X_test, y_test) - 0.022
