Batch predictions¶

score()¶

Create new batch prediction job, upload the scoring dataset and return a batch prediction job.

The default intake and output options are both localFile which requires the caller to pass the file parameter and either download the results using the download() method afterwards or pass a path to a file where the scored data will be downloaded to afterwards.

Variables

Added in version 2.22.

If using localFile output, wait this many seconds for the download to become
available. See download().

* download_read_timeout (Optional[int], default 660) –

Added in version 2.22.

If using localFile output, wait this many seconds for the server to respond
between chunks.

* upload_read_timeout (Optional[int], default 600) –

Added in version 2.28.

If using localFile intake, wait this many seconds for the server to respond
after whole dataset upload.

* prediction_threshold (Optional[float]) –

Added in version 3.4.0.

Threshold is the point that sets the class boundary for a predicted value. The model
classifies an observation below the threshold as FALSE, and an observation above the
threshold as TRUE. In other words, DataRobot automatically assigns the positive class
label to any prediction exceeding the threshold.
This value can be set between 0.0 and 1.0.

Returns

Return type: BatchPredictionJob

apply_time_series_data_prep_and_score()¶

Prepare the dataset with time series data prep, create new batch prediction job, upload the scoring dataset, and return a batch prediction job.

The supported intake_settings are of type localFile or dataset.

For timeseries_settings of type forecast the forecast_point must be specified.

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

Added in version v3.1.

Variables

Returns

Return type: BatchPredictionJob

Raises

score_to_file()¶

Create new batch prediction job, upload the scoring dataset and download the scored CSV file concurrently.

Will block until the entire file is scored.

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

Variables

Returns

Return type: BatchPredictionJob

apply_time_series_data_prep_and_score_to_file()¶

Prepare the input dataset with time series data prep. Then, create a new batch prediction job using the prepared AI catalog item as input and concurrently download the scored CSV file.

The function call will return when the entire file is scored.

For timeseries_settings of type forecast the forecast_point must be specified.

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

Added in version v3.1.

Variables

Returns

Return type: BatchPredictionJob

Raises

score_s3()¶

Create new batch prediction job, with a scoring dataset from S3 and writing the result back to S3.

This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job)

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

Variables

Returns

Return type: BatchPredictionJob

score_azure()¶

Create new batch prediction job, with a scoring dataset from Azure blob storage and writing the result back to Azure blob storage.

This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job).

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

Variables

Returns

Return type: BatchPredictionJob

score_gcp()¶

Create new batch prediction job, with a scoring dataset from Google Cloud Storage and writing the result back to one.

This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job).

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

Variables

Returns

Return type: BatchPredictionJob

score_from_existing()¶

Create a new batch prediction job based on the settings from a previously created one

Variables

Returns

Return type: BatchPredictionJob

score_pandas()¶

Run a batch prediction job, with a scoring dataset from a pandas dataframe. The output from the prediction will be joined to the passed DataFrame and returned.

Use columnNamesRemapping to drop or rename columns in the output

This method blocks until the job has completed or raises an exception on errors.

Refer to the datarobot.models.BatchPredictionJob.score() method for details on the other kwargs parameters.

Variables

Return type: Tuple[BatchPredictionJob, DataFrame]

Returns

score_with_leaderboard_model()¶

Creates a new batch prediction job for a Leaderboard model by uploading the scoring dataset. Returns a batch prediction job.

The default intake and output options are both localFile, which requires the caller to pass the file parameter and either download the results using the download() method afterwards or pass a path to a file where the scored data will be downloaded to.

Variables

Added in version 2.22.

If using localFile output, wait this many seconds for the download to become
available. See download().

* download_read_timeout (int (optional, default 660)) –

Added in version 2.22.

If using localFile output, wait this many seconds for the server to respond
between chunks.

* upload_read_timeout (int (optional, default 600)) –

Added in version 2.28.

If using localFile intake, wait this many seconds for the server to respond
after whole dataset upload.

* prediction_threshold (Optional[float]) –

Added in version 3.4.0.

Threshold is the point that sets the class boundary for a predicted value. The model
classifies an observation below the threshold as FALSE, and an observation above the
threshold as TRUE. In other words, DataRobot automatically assigns the positive class
label to any prediction exceeding the threshold.
This value can be set between 0.0 and 1.0.

Returns

Return type: BatchPredictionJob

get()¶

Get batch prediction job

Variables

Returns

Return type: BatchPredictionJob

download()¶

Downloads the CSV result of a prediction job

Variables

Added in version 2.22.

Seconds to wait for the download to become available.

The download will not be available before the job has started processing.
In case other jobs are occupying the queue, processing may not start
immediately.

If the timeout is reached, the job will be aborted and RuntimeError
is raised.

Set to -1 to wait infinitely.

* read_timeout (int (optional, default 660)) –

Added in version 2.22.

Seconds to wait for the server to respond between chunks.

Return type: None

delete()¶

Cancel this job. If this job has not finished running, it will be removed and canceled.

Return type: None

get_status()¶

Get status of batch prediction job

Returns

Return type: BatchPredictionJob status data

list_by_status()¶

Get jobs collection for specific set of statuses

Variables

Returns

Return type: BatchPredictionJob statuses

get()¶

Get batch prediction job definition

Variables

Returns

Return type: BatchPredictionJobDefinition

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.get('5a8ac9ab07a57a0001be501f')
>>> definition
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

list()¶

Get job all definitions

Parameters

Returns

Return type: List[BatchPredictionJobDefinition]

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.list()
>>> definition
[
    BatchPredictionJobDefinition(60912e09fd1f04e832a575c1),
    BatchPredictionJobDefinition(6086ba053f3ef731e81af3ca)
]

create()¶

Creates a new batch prediction job definition to be run either at scheduled interval or as a manual run.

Variables

Returns

Return type: BatchPredictionJobDefinition

>>> import datarobot as dr
>>> job_spec = {
...    "num_concurrent": 4,
...    "deployment_id": "foobar",
...    "intake_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...    "output_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...}
>>> schedule = {
...    "day_of_week": [
...        1
...    ],
...    "month": [
...        "*"
...    ],
...    "hour": [
...        16
...    ],
...    "minute": [
...        0
...    ],
...    "day_of_month": [
...        1
...    ]
...}
>>> definition = BatchPredictionJobDefinition.create(
...    enabled=False,
...    batch_prediction_job=job_spec,
...    name="some_definition_name",
...    schedule=schedule
... )
>>> definition
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

update()¶

Updates a job definition with the changed specs.

Takes the same input as create()

Variables

Returns

Return type: BatchPredictionJobDefinition

>>> import datarobot as dr
>>> job_spec = {
...    "num_concurrent": 5,
...    "deployment_id": "foobar_new",
...    "intake_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...    "output_settings": {
...        "url": "s3://foobar/123",
...        "type": "s3",
...        "format": "csv"
...    },
...}
>>> schedule = {
...    "day_of_week": [
...        1
...    ],
...    "month": [
...        "*"
...    ],
...    "hour": [
...        "*"
...    ],
...    "minute": [
...        30, 59
...    ],
...    "day_of_month": [
...        1, 2, 6
...    ]
...}
>>> definition = BatchPredictionJobDefinition.create(
...    enabled=False,
...    batch_prediction_job=job_spec,
...    name="updated_definition_name",
...    schedule=schedule
... )
>>> definition
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

run_on_schedule()¶

Sets the run schedule of an already created job definition.

If the job was previously not enabled, this will also set the job to enabled.

Variables

Returns

Return type: BatchPredictionJobDefinition

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.create('...')
>>> schedule = {
...    "day_of_week": [
...        1
...    ],
...    "month": [
...        "*"
...    ],
...    "hour": [
...        "*"
...    ],
...    "minute": [
...        30, 59
...    ],
...    "day_of_month": [
...        1, 2, 6
...    ]
...}
>>> definition.run_on_schedule(schedule)
BatchPredictionJobDefinition(60912e09fd1f04e832a575c1)

run_once()¶

Manually submits a batch prediction job to the queue, based off of an already created job definition.

Returns

Return type: BatchPredictionJob

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.create('...')
>>> job = definition.run_once()
>>> job.wait_for_completion()

delete()¶

Deletes the job definition and disables any future schedules of this job if any. If a scheduled job is currently running, this will not be cancelled.

>>> import datarobot as dr
>>> definition = dr.BatchPredictionJobDefinition.get('5a8ac9ab07a57a0001be501f')
>>> definition.delete()

Return type: None

from_job()¶

Transforms a generic Job into a PredictJob

Parameters

Returns

Return type: PredictJob

Raises

get()¶

Fetches one PredictJob. If the job finished, raises PendingJobFinished exception.

Parameters

Returns

Return type: PredictJob

Raises

get_predictions()¶

Fetches finished predictions from the job used to generate them.

Parameters

Returns

Return type: pandas.DataFrame

Raises

cancel()¶

Cancel this job. If this job has not finished running, it will be removed and canceled.

Return type: None

get_result()¶

Parameters

Returns

Return type: object

Raises

get_result_when_complete()¶

Parameters

Returns

Return type: object

Raises

refresh()¶

Update this object with the latest job data from the server.

Return type: None

wait_for_completion()¶

Waits for job to complete.

Parameters

Return type: None

get()¶

Retrieve information about a dataset uploaded for predictions

Parameters

Returns

Return type: PredictionDataset

delete()¶

Delete a dataset uploaded for predictions

Will also delete predictions made using this dataset and cancel any predict jobs using this dataset.

Return type: None