def test_model_properties_after_fitting():
"""Check the model properties after fitting a deep forest model."""
model = CascadeForestClassifier(**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)
# Test the hook on forest estimator
assert (
model.get_forest(0, 0, "rf")
is model._get_layer(0).estimators_["0-0-rf"].estimator_
)
with pytest.raises(ValueError) as excinfo:
model.get_forest(model.n_layers_, 0, "rf")
assert "`layer_idx` should be in the range" in str(excinfo.value)
with pytest.raises(ValueError) as excinfo:
model.get_forest(0, model.n_estimators, "rf")
assert "`est_idx` should be in the range" in str(excinfo.value)
with pytest.raises(ValueError) as excinfo:
model.get_forest(0, 0, "Unknown")
assert "`forest_type` should be one of" in str(excinfo.value)
X, y = shap.datasets.iris()
# print("X", X.head())
# print("y", y)
feature_names = X.columns
X = np.array(X)
y = np.array(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = CascadeForestClassifier(backend="sklearn")
model.fit(X_train, y_train)
forest = model.get_forest(0, 0, "rf")
explainer = shap.TreeExplainer(forest)
shap_values = explainer.shap_values(X_test)
# shap_values = np.array(explainer.shap_values(X_train))
# print(shap_values.shape)
# print(shap_values[2].shape)
# shap.summary_plot(shap_values[2], X_train)
#
#
# clf = RandomForestClassifier(n_estimators=100)
# clf.fit(X_train, y_train)
# explainer = shap.TreeExplainer(clf)
# shap_values = np.array(explainer.shap_values(X_test))
# shap.summary_plot(shap_values[0],X_train)