Data Exports

class datarobot.models.deployment.data_exports.PredictionDataExport(id, period, created_at, model_id, status, data=None, error=None, batches=None, deployment_id=None)

A prediction data export.

Added in version v3.4.

Attributes:

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: list: A list of PredictionDataExport objects.

Return type:

List[PredictionDataExport]

Examples

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: PredictionDataExport: A prediction data export.

Return type:

PredictionDataExport

Examples

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: PredictionDataExport: A prediction data export.

Return type:

PredictionDataExport

Examples

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[Dataset]: List of datasets for a given export, most often it is just one.

Return type:

List[Dataset]

Examples

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(id, period, created_at, model_id, status, data=None, error=None, only_matched_predictions=None, deployment_id=None)

An actuals data export.

Added in version v3.4.

Attributes:

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: list: A list of ActualsDataExport objects.

Return type:

List[ActualsDataExport]

Examples

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: ActualsDataExport: An actuals data export.

Return type:

ActualsDataExport

Examples

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: ActualsDataExport: An actuals data export.

Return type:

ActualsDataExport

Examples

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[Dataset]: List of datasets for a given export, most often it is just one.

Return type:

List[Dataset]

Examples

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(id, created_at, model_id, model_package_id, data=None, deployment_id=None)

A training data export.

Added in version v3.4.

Attributes:

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: list: A list of TrainingDataExport objects.

Return type:

List[TrainingDataExport]

Examples

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: TrainingDataExport: A training data export.

Return type:

TrainingDataExport

Examples

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.

Returns:

dataset_id: str: A created dataset with training data.

Return type:

str

Examples

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: Dataset: A datasets for a given export.

Return type:

Dataset

Examples

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(association_id, timestamp, prompt, predicted_value, actual_value=None, context=None, metrics=None, deployment_id=None)

A data quality export record.

Added in version v3.6.

Attributes:

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. :rtype: List[DataQualityExport]

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: list: A list of DataQualityExport objects.

Examples

from datarobot.models.deployment import DataQualityExport

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