Job

class datarobot.models.Job

Tracks asynchronous work being done within a project

Variables:

id (int) – the id of the job
project_id (str) – the id of the project the job belongs to
status (str) – the status of the job - will be one of datarobot.enums.QUEUE_STATUS
job_type (str) – what kind of work the job is doing - will be one of datarobot.enums.JOB_TYPE
is_blocked (bool) – if true, the job is blocked (cannot be executed) until its dependencies are resolved

classmethod get(project_id, job_id)

Fetches one job.

Parameters:

project_id (str) – The identifier of the project in which the job resides
job_id (str) – The job id

Returns:

job – The job

Return type:

Job

Raises:

AsyncFailureError – Querying this resource gave a status code other than 200 or 303

cancel(): Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)

Parameters:: params (dict or None) – Query parameters to be added to request to get results.

Notes

For featureEffects, source param is required to define source, otherwise the default is training.

Returns:

result –

Return type depends on the job type

for model jobs, a Model is returned
for predict jobs, a pandas.DataFrame (with predictions) is returned
for featureImpact jobs, a list of dicts by default (see with_metadata parameter of the FeatureImpactJob class and its get() method).
for primeRulesets jobs, a list of Rulesets
for primeModel jobs, a PrimeModel
for primeDownloadValidation jobs, a PrimeFile
for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization
for predictionExplanations jobs, a PredictionExplanations
for featureEffects, a FeatureEffects.

Return type:

object

Raises:

JobNotFinished – If the job is not finished, the result is not available.
AsyncProcessUnsuccessfulError – If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)

Parameters:

max_wait (Optional[int]) – How long to wait for the job to finish.
params (dict, optional) – Query parameters to be added to request.

Returns:

result – Return type is the same as would be returned by Job.get_result.

Return type:

object

Raises:

AsyncTimeoutError – If the job does not finish in time
AsyncProcessUnsuccessfulError – If the job errored or was aborted

refresh(): Update this object with the latest job data from the server.

wait_for_completion(max_wait=600)

Waits for job to complete.

Parameters:: max_wait (Optional[int]) – How long to wait for the job to finish.
Return type:: None

class datarobot.models.TrainingPredictionsJob

classmethod get(project_id, job_id, model_id=None, data_subset=None)

Fetches one training predictions job.

The resulting TrainingPredictions object will be annotated with model_id and data_subset.

Parameters:

project_id (str) – The identifier of the project in which the job resides
job_id (str) – The job id
model_id (str) – The identifier of the model used for computing training predictions
data_subset (dr.enums.DATA_SUBSET, optional) – Data subset used for computing training predictions

Returns:

job – The job

Return type:

TrainingPredictionsJob

refresh(): Update this object with the latest job data from the server.

cancel(): Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)

Parameters:: params (dict or None) – Query parameters to be added to request to get results.

Notes

For featureEffects, source param is required to define source, otherwise the default is training.

Returns:

result –

Return type depends on the job type

for model jobs, a Model is returned
for predict jobs, a pandas.DataFrame (with predictions) is returned
for featureImpact jobs, a list of dicts by default (see with_metadata parameter of the FeatureImpactJob class and its get() method).
for primeRulesets jobs, a list of Rulesets
for primeModel jobs, a PrimeModel
for primeDownloadValidation jobs, a PrimeFile
for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization
for predictionExplanations jobs, a PredictionExplanations
for featureEffects, a FeatureEffects.

Return type:

object

Raises:

JobNotFinished – If the job is not finished, the result is not available.
AsyncProcessUnsuccessfulError – If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)

Parameters:

max_wait (Optional[int]) – How long to wait for the job to finish.
params (dict, optional) – Query parameters to be added to request.

Returns:

result – Return type is the same as would be returned by Job.get_result.

Return type:

object

Raises:

AsyncTimeoutError – If the job does not finish in time
AsyncProcessUnsuccessfulError – If the job errored or was aborted

wait_for_completion(max_wait=600)

Waits for job to complete.

Parameters:: max_wait (Optional[int]) – How long to wait for the job to finish.
Return type:: None

class datarobot.models.ShapMatrixJob

classmethod get(project_id, job_id, model_id=None, dataset_id=None)

Fetches one SHAP matrix job.

Parameters:

project_id (str) – The identifier of the project in which the job resides
job_id (str) – The job identifier
model_id (str) – The identifier of the model used for computing prediction explanations
dataset_id (str) – The identifier of the dataset against which prediction explanations should be computed

Returns:

job – The job

Return type:

ShapMatrixJob

Raises:

AsyncFailureError – Querying this resource gave a status code other than 200 or 303

refresh()

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

Return type:: None

cancel(): Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)

Parameters:: params (dict or None) – Query parameters to be added to request to get results.

Notes

For featureEffects, source param is required to define source, otherwise the default is training.

Returns:

result –

Return type depends on the job type

for model jobs, a Model is returned
for predict jobs, a pandas.DataFrame (with predictions) is returned
for featureImpact jobs, a list of dicts by default (see with_metadata parameter of the FeatureImpactJob class and its get() method).
for primeRulesets jobs, a list of Rulesets
for primeModel jobs, a PrimeModel
for primeDownloadValidation jobs, a PrimeFile
for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization
for predictionExplanations jobs, a PredictionExplanations
for featureEffects, a FeatureEffects.

Return type:

object

Raises:

JobNotFinished – If the job is not finished, the result is not available.
AsyncProcessUnsuccessfulError – If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)

Parameters:

max_wait (Optional[int]) – How long to wait for the job to finish.
params (dict, optional) – Query parameters to be added to request.

Returns:

result – Return type is the same as would be returned by Job.get_result.

Return type:

object

Raises:

AsyncTimeoutError – If the job does not finish in time
AsyncProcessUnsuccessfulError – If the job errored or was aborted

wait_for_completion(max_wait=600)

Waits for job to complete.

Parameters:: max_wait (Optional[int]) – How long to wait for the job to finish.
Return type:: None

class datarobot.models.FeatureImpactJob

Custom Feature Impact job to handle different return value structures.

The original implementation had just the the data and the new one also includes some metadata.

In general, we aim to keep the number of Job classes low by just utilizing the job_type attribute to control any specific formatting; however in this case when we needed to support a new representation with the _same_ job_type, customizing the behavior of _make_result_from_location allowed us to achieve our ends without complicating the _make_result_from_json method.

classmethod get(project_id, job_id, with_metadata=False)

Fetches one job.

Parameters:

project_id (str) – The identifier of the project in which the job resides
job_id (str) – The job id
with_metadata (bool) – To make this job return the metadata (i.e. the full object of the completed resource) set the with_metadata flag to True.

Returns:

job – The job

Return type:

Job

Raises:

AsyncFailureError – Querying this resource gave a status code other than 200 or 303

cancel(): Cancel this job. If this job has not finished running, it will be removed and canceled.

get_result(params=None)

Parameters:: params (dict or None) – Query parameters to be added to request to get results.

Notes

For featureEffects, source param is required to define source, otherwise the default is training.

Returns:

result –

Return type depends on the job type

for model jobs, a Model is returned
for predict jobs, a pandas.DataFrame (with predictions) is returned
for featureImpact jobs, a list of dicts by default (see with_metadata parameter of the FeatureImpactJob class and its get() method).
for primeRulesets jobs, a list of Rulesets
for primeModel jobs, a PrimeModel
for primeDownloadValidation jobs, a PrimeFile
for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization
for predictionExplanations jobs, a PredictionExplanations
for featureEffects, a FeatureEffects.

Return type:

object

Raises:

JobNotFinished – If the job is not finished, the result is not available.
AsyncProcessUnsuccessfulError – If the job errored or was aborted

get_result_when_complete(max_wait=600, params=None)

Parameters:

max_wait (Optional[int]) – How long to wait for the job to finish.
params (dict, optional) – Query parameters to be added to request.

Returns:

result – Return type is the same as would be returned by Job.get_result.

Return type:

object

Raises:

AsyncTimeoutError – If the job does not finish in time
AsyncProcessUnsuccessfulError – If the job errored or was aborted

refresh(): Update this object with the latest job data from the server.

wait_for_completion(max_wait=600)

Waits for job to complete.

Parameters:: max_wait (Optional[int]) – How long to wait for the job to finish.
Return type:: None