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)
