def test_model_workflow_partial_mode():
"""Run the workflow of deep forest with a local buffer."""
case_kwargs = copy.deepcopy(kwargs)
case_kwargs.update({"partial_mode": True})
model = CascadeForestClassifier(**case_kwargs)
model.fit(X_train, y_train)
# Predictions before saving
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier(**case_kwargs)
model.load(save_dir)
# Predictions after loading
y_pred_after = model.predict(X_test)
# Make sure the same predictions before and after model serialization
assert_array_equal(y_pred_before, y_pred_after)
model.clean() # clear the buffer
shutil.rmtree(save_dir)
def test_model_workflow_in_memory(backend):
"""Run the workflow of deep forest with in-memory mode."""
case_kwargs = copy.deepcopy(kwargs)
case_kwargs.update({"partial_mode": False})
case_kwargs.update({"backend": backend})
model = CascadeForestClassifier(**case_kwargs)
model.fit(X_train, y_train)
# Test feature_importances_
if backend == "sklearn":
model.get_layer_feature_importances(0)
else:
with pytest.raises(RuntimeError) as excinfo:
model.get_layer_feature_importances(0)
assert "Please use the sklearn backend" in str(excinfo.value)
# Predictions before saving
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier(**case_kwargs)
model.load(save_dir)
# Make sure the same predictions before and after model serialization
y_pred_after = model.predict(X_test)
assert_array_equal(y_pred_before, y_pred_after)
shutil.rmtree(save_dir)
def test_custom_cascade_layer_workflow_partial_mode():
model = CascadeForestClassifier(partial_mode=True)
n_estimators = 4
estimators = [DecisionTreeClassifier() for _ in range(n_estimators)]
model.set_estimator(estimators) # set custom base estimators
predictor = DecisionTreeClassifier()
model.set_predictor(predictor)
model.fit(X_train, y_train)
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier()
model.load(save_dir)
# Predictions after loading
y_pred_after = model.predict(X_test)
# Make sure the same predictions before and after model serialization
assert_array_equal(y_pred_before, y_pred_after)
model.clean() # clear the buffer
shutil.rmtree(save_dir)
def test_model_workflow_in_memory(backend):
"""Run the workflow of deep forest with in-memory mode."""
case_kwargs = copy.deepcopy(kwargs)
case_kwargs.update({"partial_mode": False})
case_kwargs.update({"backend": backend})
model = CascadeForestClassifier(**case_kwargs)
model.fit(X_train, y_train)
# Predictions before saving
y_pred_before = model.predict(X_test)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier(**case_kwargs)
model.load(save_dir)
# Make sure the same predictions before and after model serialization
y_pred_after = model.predict(X_test)
assert_array_equal(y_pred_before, y_pred_after)
shutil.rmtree(save_dir)
def test_classifier_custom_cascade_layer_workflow_in_memory():
model = CascadeForestClassifier()
n_estimators = 4
estimators = [DecisionTreeClassifier() for _ in range(n_estimators)]
model.set_estimator(estimators) # set custom base estimators
predictor = DecisionTreeClassifier()
model.set_predictor(predictor)
model.fit(X_train_clf, y_train_clf)
y_pred_before = model.predict(X_test_clf)
# Save and Reload
model.save(save_dir)
model = CascadeForestClassifier()
model.load(save_dir)
# Predictions after loading
y_pred_after = model.predict(X_test_clf)
# 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)