def test_regressor_custom_cascade_layer_workflow_in_memory(y_train):
model = CascadeForestRegressor()
n_estimators = 4
estimators = [DecisionTreeRegressor() for _ in range(n_estimators)]
model.set_estimator(estimators) # set custom base estimators
predictor = DecisionTreeRegressor()
model.set_predictor(predictor)
model.fit(X_train_reg, y_train)
y_pred_before = model.predict(X_test_reg)
# Save and Reload
model.save(save_dir)
model = CascadeForestRegressor()
model.load(save_dir)
# Predictions after loading
y_pred_after = model.predict(X_test_reg)
# Make sure the same predictions before and after model serialization
assert_array_equal(y_pred_before, y_pred_after)
assert (model.get_estimator(0, 0, "custom") is
model._get_layer(0).estimators_["0-0-custom"].estimator_)
model.clean() # clear the buffer
shutil.rmtree(save_dir)
def test_model_properties_after_fitting():
"""Check the model properties after fitting a deep forest model."""
model = CascadeForestRegressor(**toy_kwargs)
model.fit(X_train, y_train)
assert len(model) == model.n_layers_
assert model[0] is model._get_layer(0)
with pytest.raises(ValueError) as excinfo:
model._get_layer(model.n_layers_)
assert "The layer index should be in the range" in str(excinfo.value)
with pytest.raises(RuntimeError) as excinfo:
model._set_layer(0, None)
assert "already exists in the internal container" in str(excinfo.value)
with pytest.raises(ValueError) as excinfo:
model._get_binner(model.n_layers_ + 1)
assert "The binner index should be in the range" in str(excinfo.value)
with pytest.raises(RuntimeError) as excinfo:
model._set_binner(0, None)
assert "already exists in the internal container" in str(excinfo.value)
