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_custom_base_estimator_invalid_n_splits():
model = CascadeForestRegressor()
n_estimators = 4
estimators = [DecisionTreeClassifier() for _ in range(n_estimators)]
with pytest.raises(ValueError) as excinfo:
model.set_estimator(estimators, n_splits=1)
assert "should be at least 2" in str(excinfo.value)
def test_custom_base_estimator_missing_predict():
class tmp_estimator:
def __init__(self):
pass
def fit(self, X, y):
pass
model = CascadeForestRegressor()
with pytest.raises(AttributeError) as excinfo:
model.set_estimator([tmp_estimator()])
assert "The `predict` method" in str(excinfo.value)
with pytest.raises(AttributeError) as excinfo:
model.set_predictor(tmp_estimator())
assert "The `predict` method of the predictor" in str(excinfo.value)