Prediction Explanations

To compute prediction explanations you need to have feature impact computed for a model, and predictions for an uploaded dataset computed with a selected model.

Computing prediction explanations is a resource-intensive task, but you can configure it with maximum explanations per row and prediction value thresholds to speed up the process.

Quick Reference

import datarobot as dr
# Get project
my_projects = dr.Project.list()
project = my_projects[0]
# Get model
models = project.get_models()
model = models[0]
# Compute feature impact
feature_impacts = model.get_or_request_feature_impact()
# Upload dataset
dataset = project.upload_dataset('./data_to_predict.csv')
# Compute predictions
predict_job = model.request_predictions(
# Initialize prediction explanations
pei_job = dr.PredictionExplanationsInitialization.create(,
# Compute prediction explanations with default parameters
pe_job = dr.PredictionExplanations.create(,,
pe = pe_job.get_result_when_complete()
# Iterate through predictions with prediction explanations
for row in pe.get_rows():
# download to a CSV file

List Prediction Explanations

You can use the PredictionExplanations.list() method to return a list of prediction explanations computed for a project’s models:

import datarobot as dr
prediction_explanations = dr.PredictionExplanations.list('58591727100d2b57196701b3')
>>> [PredictionExplanations(id=585967e7100d2b6afc93b13b,
pe = prediction_explanations[0]

>>> u'58591727100d2b57196701b3'
>>> u'585932c5100d2b7c298b8acf'

You can pass following parameters to filter the result:

  • model_id – str, used to filter returned prediction explanations by model_id.
  • limit – int, limit for number of items returned, default: no limit.
  • offset – int, number of items to skip, default: 0.

List Prediction Explanations Example:

project_id = '58591727100d2b57196701b3'
model_id = '585932c5100d2b7c298b8acf'
dr.PredictionExplanations.list(project_id, model_id=model_id, limit=20, offset=100)

Initialize Prediction Explanations

In order to compute prediction explanations you have to initialize it for a particular model.

dr.PredictionExplanationsInitialization.create(project_id, model_id)

Compute Prediction Explanations

If all prerequisites are in place, you can compute prediction explanations in the following way:

import datarobot as dr
project_id = '5506fcd38bd88f5953219da0'
model_id = '5506fcd98bd88f1641a720a3'
dataset_id = '5506fcd98bd88a8142b725c8'
pe_job = dr.PredictionExplanations.create(project_id, model_id, dataset_id,
                               max_explanations=2, threshold_low=0.2, threshold_high=0.8)
pe = pe_job.get_result_when_complete()


  • max_explanations are the maximum number of prediction explanations to compute for each row.
  • threshold_low and threshold_high are thresholds for the value of the prediction of the row. Prediction explanations will be computed for a row if the row’s prediction value is higher than threshold_high or lower than threshold_low. If no thresholds are specified, prediction explanations will be computed for all rows.

Retrieving Prediction Explanations

You have three options for retrieving prediction explanations.


PredictionExplanations.get_all_as_dataframe() and PredictionExplanations.download_to_csv() reformat prediction explanations to match the schema of CSV file downloaded from UI (RowId, Prediction, Explanation 1 Strength, Explanation 1 Feature, Explanation 1 Value, …, Explanation N Strength, Explanation N Feature, Explanation N Value)

Get prediction explanations rows one by one as PredictionExplanationsRow objects:

import datarobot as dr
project_id = '5506fcd38bd88f5953219da0'
prediction_explanations_id = '5506fcd98bd88f1641a720a3'
pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id)
for row in pe.get_rows():

Get all rows as pandas.DataFrame:

import datarobot as dr
project_id = '5506fcd38bd88f5953219da0'
prediction_explanations_id = '5506fcd98bd88f1641a720a3'
pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id)
prediction_explanations_df = pe.get_all_as_dataframe()

Download all rows to a file as CSV document:

import datarobot as dr
project_id = '5506fcd38bd88f5953219da0'
prediction_explanations_id = '5506fcd98bd88f1641a720a3'
pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id)

Adjusted Predictions In Prediction Explanations

In some projects such as insurance projects, the prediction adjusted by exposure is more useful compared with raw prediction. For example, the raw prediction (e.g. claim counts) is divided by exposure (e.g. time) in the project with exposure column. The adjusted prediction provides insights with regard to the predicted claim counts per unit of time. To include that information, set exclude_adjusted_predictions to False in correspondent method calls.

import datarobot as dr
project_id = '5506fcd38bd88f5953219da0'
prediction_explanations_id = '5506fcd98bd88f1641a720a3'
pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id)
pe.download_to_csv('prediction_explanations.csv', exclude_adjusted_predictions=False)
prediction_explanations_df = pe.get_all_as_dataframe(exclude_adjusted_predictions=False)

Deprecated Reason Codes Interface

This feature was previously referred to using the Reason Codes API. This interface is now deprecated and should be replaced with the Prediction Explanations interface.

SHAP based prediction explanations

You can request SHAP based prediction explanations using previously uploaded scoring dataset for models that support SHAP. Unlike for XEMP prediction explanations you do not need to have feature impact computed for a model, and predictions for an uploaded dataset.

See datarobot.models.ShapMatrix.create() reference for a description of the types of parameters that can be passed in.

import datarobot as dr
project_id = '5ea6d3354cfad121cf33a5e1'
model_id = '5ea6d38b4cfad121cf33a60d'
project = dr.Project.get(project_id)
model = dr.Model.get(project=project_id, model_id=model_id)
# check if model supports SHAP
model_capabilities = model.get_supported_capabilities()
>>> True
# upload dataset to generate prediction explanations
dataset_from_path = project.upload_dataset('./data_to_predict.csv')

shap_matrix_job = ShapMatrix.create(project_id=project_id, model_id=model_id,
>>> Job(shapMatrix, status=inprogress)
# wait for job to finish
shap_matrix = shap_matrix_job.get_result_when_complete()
>>> ShapMatrix(id='5ea84b624cfad1361c53f65d', project_id='5ea6d3354cfad121cf33a5e1', model_id='5ea6d38b4cfad121cf33a60d', dataset_id='5ea84b464cfad1361c53f655')

# retrieve SHAP matrix as pandas.DataFrame
df = shap_matrix.get_as_dataframe()

# list as available SHAP matrices for a project
shap_matrices = dr.ShapMatrix.list(project_id)
>>> [ShapMatrix(id='5ea84b624cfad1361c53f65d', project_id='5ea6d3354cfad121cf33a5e1', model_id='5ea6d38b4cfad121cf33a60d', dataset_id='5ea84b464cfad1361c53f655')]

shap_matrix = shap_matrices[0]
# retrieve SHAP matrix as pandas.DataFrame
df = shap_matrix.get_as_dataframe()