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توضیحات

DataRobot Prediction Library

ویژگی	مقدار
سیستم عامل	-
نام فایل	datarobot-predict-1.1.0
نام	datarobot-predict
نسخه کتابخانه	1.1.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	DataRobot
ایمیل نویسنده	support@datarobot.com
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/datarobot-predict/
مجوز	Apache-2.0

# 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](https://docs.datarobot.com/). ## License [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) # 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](https://pypi.org/project/datarobot/). 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) ``` ### Feature types The column types of the input DataFrame can affect the predicted values. A typical way to read a DataFrame from csv is to simply use ``` df = pd.read_csv("input.csv") ``` This will cause pandas to auto-detect column types from the input. If the detected column type is incompatible with the DataRobot feature type this can cause problems. An example of this is the following boolean-like csv column: ``` column_name FALSE TRUE FALSE ``` DataRobot will detect this as a categorical feature because it has missing values. Pandas will detect it as boolean and read it into the following DataFrame: ``` column_name False True nan False ``` When this column has to be converted to categorical, the values will no longer be capitalized which means they will not match the expected `TRUE` or `FALSE` values. The solution to this is to use appropritate types when reading from csv. For the example above we could read the column as categorical ``` df = pd.read_csv("input.csv", dtype={"column_name": "category"}) ``` A more general workaround is to read all or some columns as raw strings ``` df = pd.read_csv("input.csv", dtype=str) # use str type for all columns df = pd.read_csv("input.csv", dtype={"column_name": "str"}) # use str type for column_name ``` This will force the parsing of values to be performed by logic in the Scoring Code JAR which will do it consistently with DataRobot. ### 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](https://docs.datarobot.com/en/docs/predictions/port-pred/scoring-code/sc-download-leaderboard.html#leaderboard-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](https://docs.datarobot.com/en/docs/predictions/port-pred/scoring-code/sc-download-leaderboard.html#leaderboard-download). Prediction intervals are computed when `prediction_intervals_length` is set to a positive value ``` result_df = model.predict(df, prediction_intervals_length=3) ```

نیازمندی

مقدار	نام
>=0.15	pandas
>=7,<9	click
>=0.10.9.7,<0.11.0.0	py4j

زبان مورد نیاز

مقدار	نام
>=3.7,<3.12	Python

نحوه نصب

نصب پکیج whl datarobot-predict-1.1.0:

pip install datarobot-predict-1.1.0.whl

نصب پکیج tar.gz datarobot-predict-1.1.0:

pip install datarobot-predict-1.1.0.tar.gz