if (not alg.name.startswith("TPOT") and
not alg.name.startswith("AutoSklearn") and
not alg.name.startswith("XGBoost")):
if alg.type == 'classification':
model_explainer = shap.KernelExplainer(model.predict_proba, dataframe_train)
if alg.type == 'regression' or alg.type == 'anomaly':
model_explainer = shap.KernelExplainer(model.predict, dataframe_train)
else:
print("Model explainer not supported for the selected algorithm!")
else:
print("Model explainer not supported on GPU!")
# -------------------------------------------------------------
# Check if sampling is enabled for AutoSklearn
#
if alg.sampling:
model.refit(dataframe_train.values.copy(), dataframe_label.values.ravel().copy())
# -------------------------------------------------------------
# Get the fitted model from TPOT
#
if alg.name == 'TPOT_Regressor' or alg.name == 'TPOT_Classifier':
model = model.fitted_pipeline_
else:
# -------------------------------------------------------------
# Non-supervised algorithms
#
if NVIDIA_RAPIDS_ENABLED:
model.fit(dataframe_train)
else:
model.fit(dataframe_train.values)
model_explainer = shap.KernelExplainer(model.predict, dataframe_train)
# if is_labeled_data and alg.automl:
