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