Data exports¶

class datarobot.models.deployment.data_exports.PredictionDataExport¶

A prediction data export.

Added in version v3.4.

• Variables:
  • id (str) – The ID of the prediction data export.
  • model_id (str) – The ID of the model (or null if not specified).
  • created_at (datetime) – Prediction data export creation timestamp.
  • period (Period) – A prediction data time range definition.
  • status (ExportStatus) – A prediction data export processing state.
  • error (ExportError) – Error description, appears when prediction data export job failed (status is FAILED).
  • batches (ExportBatches) – Metadata associated with exported batch.
  • deployment_id (str) – The ID of the deployment.

classmethod list(deployment_id, status=None, model_id=None, batch=None, offset=0, limit=100)¶

Retrieve a list of prediction data exports.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • model_id (Optional[str]) – The ID of the model used for prediction data export.
  • status (Optional[ExportStatus]) – A prediction data export processing state.
  • batch (Optional[bool]) – If true, only return batch exports. If false, only return real-time exports. If not provided, return both real-time and batch exports.
  • limit (Optional[int]) – The maximum number of objects to return. The default is 100 (0 means no limit).
  • offset (Optional[int]) – The starting offset of the results. The default is 0.
• Returns: prediction_data_exports – A list of PredictionDataExport objects.
• Return type: list

from datarobot.models.deployment import PredictionDataExport

prediction_data_exports = PredictionDataExport.list(deployment_id='5c939e08962d741e34f609f0')

classmethod get(deployment_id, export_id)¶

Retrieve a single prediction data export.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • export_id (str) – The ID of the prediction data export.
• Returns: prediction_data_export – A prediction data export.
• Return type: PredictionDataExport

from datarobot.models.deployment import PredictionDataExport

prediction_data_export = PredictionDataExport.get(
    deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b'
    )

classmethod create(deployment_id, start, end, model_id=None, batch_ids=None, max_wait=600)¶

Create a deployment prediction data export. Waits until ready and fetches PredictionDataExport after the export finishes. This method is blocking.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • start (Union[datetime, str]) – Inclusive start of the time range.
  • end (Union[datetime, str]) – Exclusive end of the time range.
  • model_id (Optional[str]) – The ID of the model.
  • batch_ids (Optional[List[str]]) – IDs of batches to export. Null for real-time data exports.
  • max_wait (int,) – Seconds to wait for successful resolution.
• Returns: prediction_data_export – A prediction data export.
• Return type: PredictionDataExport

from datetime import datetime, timedelta
from datarobot.models.deployment import PredictionDataExport

now=datetime.now()
prediction_data_export = PredictionDataExport.create(
    deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now
    )

fetch_data()¶

Return data from prediction export as datarobot Dataset.

• Returns: prediction_datasets – List of datasets for a given export, most often it is just one.
• Return type: List[Dataset]

from datarobot.models.deployment import PredictionDataExport

prediction_data_export = PredictionDataExport.get(
    deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b'
    )
prediction_datasets = prediction_data_export.fetch_data()

class datarobot.models.deployment.data_exports.ActualsDataExport¶

An actuals data export.

Added in version v3.4.

• Variables:
  • id (str) – The ID of the actuals data export.
  • model_id (str) – The ID of the model (or null if not specified).
  • created_at (datetime) – Actuals data export creation timestamp.
  • period (Period) – A actuals data time range definition.
  • status (ExportStatus) – A data export processing state.
  • error (ExportError) – Error description, appears when actuals data export job failed (status is FAILED).
  • only_matched_predictions (bool) – If true, exports actuals with matching predictions only.
  • deployment_id (str) – The ID of the deployment.

classmethod list(deployment_id, status=None, offset=0, limit=100)¶

Retrieve a list of actuals data exports.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • status (Optional[ExportStatus]) – Actuals data export processing state.
  • limit (Optional[int]) – The maximum number of objects to return. The default is 100 (0 means no limit).
  • offset (Optional[int]) – The starting offset of the results. The default is 0.
• Returns: actuals_data_exports – A list of ActualsDataExport objects.
• Return type: list

from datarobot.models.deployment import ActualsDataExport

actuals_data_exports = ActualsDataExport.list(deployment_id='5c939e08962d741e34f609f0')

classmethod get(deployment_id, export_id)¶

Retrieve a single actuals data export.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • export_id (str) – The ID of the actuals data export.
• Returns: actuals_data_export – An actuals data export.
• Return type: ActualsDataExport

from datarobot.models.deployment import ActualsDataExport

actuals_data_export = ActualsDataExport.get(
    deployment_id='5c939e08962d741e34f609f0', export_id='65fb0a6c9bb187781cfdea36'
    )

classmethod create(deployment_id, start, end, model_id=None, only_matched_predictions=None, max_wait=600)¶

Create a deployment actuals data export. Waits until ready and fetches ActualsDataExport after the export finishes. This method is blocking.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • start (Union[datetime, str]) – Inclusive start of the time range.
  • end (Union[datetime, str]) – Exclusive end of the time range.
  • model_id (Optional[str]) – The ID of the model.
  • only_matched_predictions (Optional[bool]) – If true, exports actuals with matching predictions only.
  • max_wait (int) – Seconds to wait for successful resolution.
• Returns: actuals_data_export – An actuals data export.
• Return type: ActualsDataExport

from datetime import datetime, timedelta
from datarobot.models.deployment import ActualsDataExport

now=datetime.now()
actuals_data_export = ActualsDataExport.create(
    deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now
    )

fetch_data()¶

Return data from actuals export as datarobot Dataset.

• Returns: actuals_datasets – List of datasets for a given export, most often it is just one.
• Return type: List[Dataset]

from datarobot.models.deployment import ActualsDataExport

actuals_data_export = ActualsDataExport.get(
    deployment_id='5c939e08962d741e34f609f0', export_id='65fb0a6c9bb187781cfdea36'
    )
actuals_datasets = actuals_data_export.fetch_data()

class datarobot.models.deployment.data_exports.TrainingDataExport¶

A training data export.

Added in version v3.4.

• Variables:
  • id (str) – The ID of the training data export.
  • model_id (str) – The ID of the model (or null if not specified).
  • model_package_id (str) – The ID of the model package.
  • created_at (datetime) – Training data export creation timestamp.
  • deployment_id (str) – The ID of the deployment.

classmethod list(deployment_id)¶

Retrieve a list of successful training data exports.

• Parameters: deployment_id (str) – The ID of the deployment.
• Returns: training_data_exports – A list of TrainingDataExport objects.
• Return type: list

from datarobot.models.deployment import TrainingDataExport

training_data_exports = TrainingDataExport.list(deployment_id='5c939e08962d741e34f609f0')

classmethod get(deployment_id, export_id)¶

Retrieve a single training data export.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • export_id (str) – The ID of the training data export.
• Returns: training_data_export – A training data export.
• Return type: TrainingDataExport

from datarobot.models.deployment import TrainingDataExport
training_data_export = TrainingDataExport.get(
    deployment_id='5c939e08962d741e34f609f0', export_id='65fbf2356124f1daa3acc522'
    )

classmethod create(deployment_id, model_id=None, max_wait=600)¶

Create a single training data export. Waits until ready and fetches TrainingDataExport after the export finishes. This method is blocking.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • model_id (Optional[str]) – The ID of the model.
  • max_wait (int) – Seconds to wait for successful resolution.
• Return type: str
• Returns:

  • dataset_id (str) – A created dataset with training data.

    Examples * -------- * code-block:: python – from datarobot.models.deployment import TrainingDataExport dataset_id = TrainingDataExport.create(deployment_id=’5c939e08962d741e34f609f0’)

fetch_data()¶

Return data from training data export as datarobot Dataset.

• Returns: training_dataset – A datasets for a given export.
• Return type: Dataset

from datarobot.models.deployment import TrainingDataExport

training_data_export = TrainingDataExport.get(
    deployment_id='5c939e08962d741e34f609f0', export_id='65fbf2356124f1daa3acc522'
    )
training_data_export = training_data_export.fetch_data()

class datarobot.models.deployment.data_exports.DataQualityExport¶

A data quality export record.

Added in version v3.6.

• Variables:
  • association_id (str) – The association ID of the data quality export.
  • timestamp (datetime) – The data quality export creation timestamp.
  • deployment_id (str) – The ID of the deployment.
  • prompt (Optional[str]) – The LLM prompt of the data quality export.
  • predicted_value (str) – The predicted value of the data quality export.
  • actual_value (Optional[str]) – The actual value (if available) of the data quality export.
  • context (List[Dict[str, str]]) – Context data (context and link data) for the contexts associated with the data quality export.
  • metrics (List[Dict[str, Any]]) – Custom-metrics data for the data quality export.

classmethod list(deployment_id, start, end, model_id=None, prediction_pattern=None, prompt_pattern=None, actual_pattern=None, order_by=None, order_metric=None, filter_metric=None, filter_value=None, offset=0, limit=100)¶

Retrieve a list of data-quality export records for a given deployment.

Added in version v3.6.

• Parameters:
  • deployment_id (str) – The ID of the deployment.
  • start (Union[str, datetime]) – The earliest time of the objects to return.
  • end (Union[str, datetime]) – The latest time of the objects to return.
  • model_id (Optional[str]) – The ID of the model.
  • prediction_pattern (Optional[str]) – The keywords to search in a predicted value for a text generation target.
  • prompt_pattern (Optional[str]) – The keywords to search in a prompt value for a text generation target.
  • actual_pattern (Optional[str]) – The keywords to search in an actual value for a text generation target.
  • order_by (Optional[str]) – The field to sort by (e.g. associationId, timestamp, or customMetrics). Use a leading ‘-’ to indicate descending order. When ordering by a custom-metric, must also specify ‘order_metric’. The default is None, which equates to ‘-timestamp’.
  • order_metric (Optional[str]) – When ‘order_by’ is a custom-metric, this specifies the custom-metric name or ID to use for ordering. The default is None.
  • filter_metric (Optional[str]) – Specifies the metric name or ID to use for matching. Must also use ‘filter_value’ to specify the value that must be matched. The default is None.
  • filter_value (Optional[str]) – Specifies the value associated with ‘filter_metric’ that must be matched. The default is None.
  • offset (Optional[int]) – The starting offset of the results. The default is 0.
  • limit (Optional[int]) – The maximum number of objects to return. The default is 100 (which is maximum).
• Returns: data_quality_exports – A list of DataQualityExport objects.
• Return type: list

from datarobot.models.deployment import DataQualityExport

data_quality_exports = DataQualityExport.list(
    deployment_id='5c939e08962d741e34f609f0', start_time='2024-07-01', end_time='2024-08-01
)