datarobot-predict 1.0.1

DataRobot Prediction Library

About

DataRobot Prediction Library is a Python library for making predictions using various prediction methods supported by DataRobot. The intention is to provide a common interface for making predictions, making it easy to swap out the underlying implementation.

For more info, see the DataRobot Documentation.

Setup

Prerequisites

• Python 3.7 or greater
• Scoring Code requires Java Runtime Environment 8 or higher
• Scoring Code models generated on DataRobot 7.3 and later are supported

Installation

$ pip install datarobot-predict

Usage

Scoring Code

To get started, instantiate a ScoringCodeModel with a path to a jar file

from datarobot_predict.scoring_code import ScoringCodeModel

model = ScoringCodeModel("model.jar")

To get predictions from the model, pass a pandas DataFrame to the predict method

result_df = model.predict(df)

The Scoring Code jar file can be downloaded using the DataRobot Python Client. This example shows how to fetch Scoring Code from a deployment and use it to make predictions

# pip install datarobot

import datarobot as dr
from datarobot_predict.scoring_code import ScoringCodeModel

dr.Client(endpoint="https://app.datarobot.com/api/v2", token="<API_TOKEN>")

deployment = dr.Deployment.get(deployment_id="<DEPLOYMENT_ID>")
deployment.download_scoring_code("model.jar")

model = ScoringCodeModel("model.jar")
result_df = model.predict(df)

Prediction Explanations

To compute Prediction Explanations, it is required that the Scoring Code model has Prediction Explanations enabled. For more info, see the DataRobot docs page about Scoring Code download.

To compute explanations, set max_explanations to a positive value

df_with_explanations = model.predict(df, max_explanations=3)

Time Series

Forecast point predictions are returned by default if no other arguments are provided for a Time Series Model. The forecast point can be specified using the forecast_point parameter or auto-detected.

result_df = model.predict(df, forecast_point=datetime.datetime(1958, 6, 1))

To do historical predictions, set time_series_type accordingly

from datarobot_predict.scoring_code import TimeSeriesType

result_df = model.predict(
    df,
    time_series_type=TimeSeriesType.HISTORICAL,
    predictions_start_date=datetime.datetime(2020, 1, 1),
    predictions_end_date=datetime.datetime(2022, 6, 1),
)

The date column in the input is expected to be a string in the same date format used when the model was trained.

Prediction Intervals

To compute Prediction Intervals, it is required that the Scoring Code model has Prediction Intervals enabled. For more info, see the DataRobot docs page about Scoring Code download.

Prediction intervals are computed when prediction_intervals_length is set to a positive value

result_df = model.predict(df, prediction_intervals_length=3)