DataRobot Prediction Library
Project description
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.
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)
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