DataRobot Python API Get Prediction Explanations

get_prediction_explanations.py

def upload_dataset_from_catalog(self, dataset):
    import json
    import time
    # client = dr.Client()
    res = client.post(client.endpoint + "/projects/{}/predictionDatasets/datasetUploads/".format(self.id), json={"datasetId": dataset.id})
    dataset_id = json.loads(res.text)['datasetId']
    while dataset_id not in [ds.id for ds in self.get_datasets()]:
        time.sleep(1)
    return dataset_id
def get_prediction_explanations(self, dataset_id):
    project = dr.Project.get(self.project_id)
    if project.advanced_options['shap_only_mode'] == False:
        try:
            dr.PredictionExplanationsInitialization.get(project.id, self.id)
        except dr.errors.ClientError as e:
            if e.json['message'] == 'No prediction explanations initialization found for model':
                print('Initialising Explanations...')
                while True:
                    try:
                        pei_job = dr.PredictionExplanationsInitialization.create(project.id, self.id)
                        pei_job.wait_for_completion()
                    except dr.errors.ClientError as e:
                        if e.json['message'] == 'A prerequisite was not satisfied: Feature impact has not been computed for the model. Run feature impact for this model first.':
                            print('Calculating Feature Impact...')
                            self.get_or_request_feature_impact()
                        else:
                            raise(e)
                    else:
                        print('Explanations Initialised')
                        break
            else:
                raise(e)
        print('Calculation Explanations...')
        while True:
            try:
                pe_job = dr.PredictionExplanations.create(project.id, self.id, dataset_id)
                pe = pe_job.get_result_when_complete()
            except dr.errors.ClientError as e:
                if e.json['message'] == 'Predictions must first be generated for this dataset':
                    print('Calculating Predictions...')
                    p_job = self.request_predictions(dataset_id)
                    p_job.get_result_when_complete()
                else:
                    raise(e)
            else:
                print('Explanations Done')
                break
        return pe.get_all_as_dataframe()
    else:
        shap_matrix_job = dr.models.ShapMatrix.create(project.id, self.id, dataset_id)
        shap_matrix = shap_matrix_job.get_result_when_complete()
        return shap_matrix.get_as_dataframe()
dr.Project.upload_dataset_from_catalog = upload_dataset_from_catalog
dr.Model.get_prediction_explanations = get_prediction_explanations