def test_classification_manual_tuning_correct(data_fixture, request): data = request.getfixturevalue(data_fixture) data.features = Scaling().fit(data.features).apply(data.features) train_data, test_data = train_test_data_setup(data=data) knn = Model(model_type='knn') model, _ = knn.fit(data=train_data) test_predicted = knn.predict(fitted_model=model, data=test_data) knn_for_tune = Model(model_type='knn') knn_for_tune.params = {'n_neighbors': 1} model, _ = knn_for_tune.fit(data=train_data) test_predicted_tuned = knn_for_tune.predict(fitted_model=model, data=test_data) assert not np.array_equal(test_predicted, test_predicted_tuned)
def test_pca_manual_tuning_correct(data_fixture, request): data = request.getfixturevalue(data_fixture) data.features = Scaling().fit(data.features).apply(data.features) train_data, test_data = train_test_data_setup(data=data) pca = Model(model_type='pca_data_model') model, _ = pca.fit(data=train_data) test_predicted = pca.predict(fitted_model=model, data=test_data) pca_for_tune = Model(model_type='pca_data_model') pca_for_tune.params = { 'svd_solver': 'randomized', 'iterated_power': 'auto', 'dim_reduction_expl_thr': 0.7, 'dim_reduction_min_expl': 0.001 } model, _ = pca_for_tune.fit(data=train_data) test_predicted_tuned = pca_for_tune.predict(fitted_model=model, data=test_data) assert not np.array_equal(test_predicted, test_predicted_tuned)