MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/score_explain.py

import pandas as pd
import joblib
from azureml.core.model import Model
from azureml.train.automl.runtime.automl_explain_utilities import (
    automl_setup_model_explanations,
)
import scipy as sp


def init():

    global automl_model
    global scoring_explainer

    # Retrieve the path to the model file using the model name
    # Assume original model is named original_prediction_model
    automl_model_path = Model.get_model_path("automl_model")
    scoring_explainer_path = Model.get_model_path("scoring_explainer")

    automl_model = joblib.load(automl_model_path)
    scoring_explainer = joblib.load(scoring_explainer_path)


def is_multi_dimensional(matrix):
    if hasattr(matrix, "ndim") and matrix.ndim > 1:
        return True
    if hasattr(matrix, "shape") and matrix.shape[1]:
        return True
    return False


def convert_matrix(matrix):
    if sp.sparse.issparse(matrix):
        matrix = matrix.todense()
    if is_multi_dimensional(matrix):
        matrix = matrix.tolist()
    return matrix


def run(raw_data):
    # Get predictions and explanations for each data point
    data = pd.read_json(raw_data, orient="records")
    # Make prediction
    predictions = automl_model.predict(data)
    # Setup for inferencing explanations
    automl_explainer_setup_obj = automl_setup_model_explanations(
        automl_model, X_test=data, task="regression"
    )
    # Retrieve model explanations for engineered explanations
    engineered_local_importance_values = scoring_explainer.explain(
        automl_explainer_setup_obj.X_test_transform
    )
    engineered_local_importance_values = convert_matrix(
        engineered_local_importance_values
    )

    # Retrieve model explanations for raw explanations
    raw_local_importance_values = scoring_explainer.explain(
        automl_explainer_setup_obj.X_test_transform, get_raw=True
    )
    raw_local_importance_values = convert_matrix(raw_local_importance_values)

    # You can return any data type as long as it is JSON-serializable
    return {
        "predictions": predictions.tolist(),
        "engineered_local_importance_values": engineered_local_importance_values,
        "raw_local_importance_values": raw_local_importance_values,
    }