model_explainability_monitor = ModelExplainabilityMonitor(
    role=role,
    sagemaker_session=sagemaker_session,
    max_runtime_in_seconds=1800,
)

model_explainability_baselining_job_result_uri = f"{baseline_results_uri}/model_explainability"
model_explainability_data_config = DataConfig(
    s3_data_input_path=validation_dataset,
    s3_output_path=model_explainability_baselining_job_result_uri,
    label=label_header,
    headers=all_headers,
    dataset_type=dataset_type,
)

# Here use the mean value of test dataset as SHAP baseline
test_dataframe = pd.read_csv(test_dataset, header=None)
shap_baseline = [list(test_dataframe.mean())]

shap_config = SHAPConfig(
    baseline=shap_baseline,
    num_samples=100,
    agg_method="mean_abs",
    save_local_shap_values=False,
)

model_explainability_monitor.suggest_baseline(
    data_config=model_explainability_data_config,
    model_config=model_config,
    explainability_config=shap_config,
)
print(f"ModelExplainabilityMonitor baselining job: {model_explainability_monitor.latest_baselining_job_name}")