describe_batch_predictions ¶

def describe_batch_predictions(
    self,
    *,
    config_overrides: Optional[MachineLearningClientConfig] = None,
    filter_variable: Optional[
        "capo_machine_learning.types.batch_prediction_filter_variable.BatchPredictionFilterVariable"
    ] = None,
    eq: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    gt: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    lt: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    ge: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    le: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    ne: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    prefix: Optional[
        "capo_machine_learning.types.comparator_value.ComparatorValue"
    ] = None,
    sort_order: Optional["capo_machine_learning.types.sort_order.SortOrder"] = None,
    next_token: Optional[
        "capo_machine_learning.types.string_type.StringType"
    ] = None,
    limit: Optional["capo_machine_learning.types.page_limit.PageLimit"] = None,
) -> "capo_machine_learning.types.describe_batch_predictions_output.DescribeBatchPredictionsOutput":
    """<p>Returns a list of <code>BatchPrediction</code> operations that match the search criteria in the request.</p>

    Args:
        filter_variable: <p>Use one of the following variables to filter a list of <code>BatchPrediction</code>:</p> <ul> <li> <p> <code>CreatedAt</code> - Sets the search criteria to the <code>BatchPrediction</code> creation date.</p> </li> <li> <p> <code>Status</code> - Sets the search criteria to the <code>BatchPrediction</code> status.</p> </li> <li> <p> <code>Name</code> - Sets the search criteria to the contents of the <code>BatchPrediction</code> <b> </b> <code>Name</code>.</p> </li> <li> <p> <code>IAMUser</code> - Sets the search criteria to the user account that invoked the <code>BatchPrediction</code> creation.</p> </li> <li> <p> <code>MLModelId</code> - Sets the search criteria to the <code>MLModel</code> used in the <code>BatchPrediction</code>.</p> </li> <li> <p> <code>DataSourceId</code> - Sets the search criteria to the <code>DataSource</code> used in the <code>BatchPrediction</code>.</p> </li> <li> <p> <code>DataURI</code> - Sets the search criteria to the data file(s) used in the <code>BatchPrediction</code>. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.</p> </li> </ul>
        eq: <p>The equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that exactly match the value specified with <code>EQ</code>.</p>
        gt: <p>The greater than operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are greater than the value specified with <code>GT</code>.</p>
        lt: <p>The less than operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are less than the value specified with <code>LT</code>.</p>
        ge: <p>The greater than or equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are greater than or equal to the value specified with <code>GE</code>. </p>
        le: <p>The less than or equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are less than or equal to the value specified with <code>LE</code>.</p>
        ne: <p>The not equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values not equal to the value specified with <code>NE</code>.</p>
        prefix: <p>A string that is found at the beginning of a variable, such as <code>Name</code> or <code>Id</code>.</p> <p>For example, a <code>Batch Prediction</code> operation could have the <code>Name</code> <code>2014-09-09-HolidayGiftMailer</code>. To search for this <code>BatchPrediction</code>, select <code>Name</code> for the <code>FilterVariable</code> and any of the following strings for the <code>Prefix</code>: </p> <ul> <li> <p>2014-09</p> </li> <li> <p>2014-09-09</p> </li> <li> <p>2014-09-09-Holiday</p> </li> </ul>
        sort_order: <p>A two-value parameter that determines the sequence of the resulting list of <code>MLModel</code>s.</p> <ul> <li> <p> <code>asc</code> - Arranges the list in ascending order (A-Z, 0-9).</p> </li> <li> <p> <code>dsc</code> - Arranges the list in descending order (Z-A, 9-0).</p> </li> </ul> <p>Results are sorted by <code>FilterVariable</code>.</p>
        next_token: <p>An ID of the page in the paginated results.</p>
        limit: <p>The number of pages of information to include in the result. The range of acceptable values is <code>1</code> through <code>100</code>. The default value is <code>100</code>.</p>

    Raises:
        capo_machine_learning.errors.internal_server_exception.InternalServerException: <p>An error on the server occurred when trying to process a request.</p>
        capo_machine_learning.errors.invalid_input_exception.InvalidInputException: <p>An error on the client occurred. Typically, the cause is an invalid input value.</p>
        capo_machine_learning.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_machine_learning.types.describe_batch_predictions_input.DescribeBatchPredictionsInput]",
    ) -> OperationResponse[
        "capo_machine_learning.types.describe_batch_predictions_output.DescribeBatchPredictionsOutput"
    ]:
        import capo_machine_learning._operations.amazon_ml_20141212.describe_batch_predictions

        output, http_response = (
            capo_machine_learning._operations.amazon_ml_20141212.describe_batch_predictions.describe_batch_predictions(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeBatchPredictions")
    )
    input_: capo_machine_learning.types.describe_batch_predictions_input.DescribeBatchPredictionsInput = {}
    if filter_variable is not None:
        input_["filter_variable"] = filter_variable
    if eq is not None:
        input_["eq"] = eq
    if gt is not None:
        input_["gt"] = gt
    if lt is not None:
        input_["lt"] = lt
    if ge is not None:
        input_["ge"] = ge
    if le is not None:
        input_["le"] = le
    if ne is not None:
        input_["ne"] = ne
    if prefix is not None:
        input_["prefix"] = prefix
    if sort_order is not None:
        input_["sort_order"] = sort_order
    if next_token is not None:
        input_["next_token"] = next_token
    if limit is not None:
        input_["limit"] = limit

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output