list_predictors ¶

def list_predictors(
    self,
    *,
    config_overrides: Optional[forecastClientConfig] = None,
    next_token: Optional["capo_forecast.types.next_token.NextToken"] = None,
    max_results: Optional["capo_forecast.types.max_results.MaxResults"] = None,
    filters: Optional["capo_forecast.types.filters.Filters"] = None,
) -> "capo_forecast.types.list_predictors_response.ListPredictorsResponse":
    """<p>Returns a list of predictors created using the <a>CreateAutoPredictor</a> or <a>CreatePredictor</a> operations. For each predictor, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). </p> <p>You can retrieve the complete set of properties by using the ARN with the <a>DescribeAutoPredictor</a> and <a>DescribePredictor</a> operations. You can filter the list using an array of <a>Filter</a> objects.</p>

    Args:
        next_token: <p>If the result of the previous request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.</p>
        max_results: <p>The number of items to return in the response.</p>
        filters: <p>An array of filters. For each filter, you provide a condition and a match statement. The condition is either <code>IS</code> or <code>IS_NOT</code>, which specifies whether to include or exclude the predictors that match the statement from the list, respectively. The match statement consists of a key and a value.</p> <p> <b>Filter properties</b> </p> <ul> <li> <p> <code>Condition</code> - The condition to apply. Valid values are <code>IS</code> and <code>IS_NOT</code>. To include the predictors that match the statement, specify <code>IS</code>. To exclude matching predictors, specify <code>IS_NOT</code>.</p> </li> <li> <p> <code>Key</code> - The name of the parameter to filter on. Valid values are <code>DatasetGroupArn</code> and <code>Status</code>.</p> </li> <li> <p> <code>Value</code> - The value to match.</p> </li> </ul> <p>For example, to list all predictors whose status is ACTIVE, you would specify:</p> <p> <code>"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]</code> </p>

    Raises:
        capo_forecast.errors.invalid_input_exception.InvalidInputException: <p>We can't process the request because it includes an invalid value or a value that exceeds the valid range.</p>
        capo_forecast.errors.invalid_next_token_exception.InvalidNextTokenException: <p>The token is not valid. Tokens expire after 24 hours.</p>
        capo_forecast.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_forecast.types.list_predictors_request.ListPredictorsRequest]",
    ) -> OperationResponse[
        "capo_forecast.types.list_predictors_response.ListPredictorsResponse"
    ]:
        import capo_forecast._operations.amazon_forecast.list_predictors

        output, http_response = (
            capo_forecast._operations.amazon_forecast.list_predictors.list_predictors(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListPredictors")
    )
    input_: capo_forecast.types.list_predictors_request.ListPredictorsRequest = {}
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if filters is not None:
        input_["filters"] = filters

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