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GetEventPredictionRequest

Source code in docs_src/capo_frauddetector/types/get_event_prediction_request.py 
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class GetEventPredictionRequest(TypedDict, closed=True):
    detector_id: "capo_frauddetector.types.string.string"
    """<p>The detector ID.</p>"""
    detector_version_id: NotRequired[
        "capo_frauddetector.types.whole_number_version_string.wholeNumberVersionString"
    ]
    """<p>The detector version ID.</p>"""
    event_id: "capo_frauddetector.types.string.string"
    """<p>The unique ID used to identify the event.</p>"""
    event_type_name: "capo_frauddetector.types.string.string"
    """<p>The event type associated with the detector specified for the prediction.</p>"""
    entities: "capo_frauddetector.types.list_of_entities.listOfEntities"
    """<p>The entity type (associated with the detector's event type) and specific entity ID representing who performed the event. If an entity id is not available, use "UNKNOWN."</p>"""
    event_timestamp: (
        "capo_frauddetector.types.utc_timestamp_iso8601.utcTimestampISO8601"
    )
    """<p>Timestamp that defines when the event under evaluation occurred. The timestamp must be specified using ISO 8601 standard in UTC.</p>"""
    event_variables: "capo_frauddetector.types.event_variable_map.EventVariableMap"
    """<p>Names of the event type's variables you defined in Amazon Fraud Detector to represent data elements and their corresponding values for the event you are sending for evaluation.</p> <important> <p>You must provide at least one eventVariable</p> </important> <p>To ensure most accurate fraud prediction and to simplify your data preparation, Amazon Fraud Detector will replace all missing variables or values as follows:</p> <p> <b>For Amazon Fraud Detector trained models:</b> </p> <p>If a null value is provided explicitly for a variable or if a variable is missing, model will replace the null value or the missing variable (no variable name in the eventVariables map) with calculated default mean/medians for numeric variables and with special values for categorical variables.</p> <p> <b>For imported SageMaker models:</b> </p> <p>If a null value is provided explicitly for a variable, the model and rules will use “null” as the value. If a variable is not provided (no variable name in the eventVariables map), model and rules will use the default value that is provided for the variable. </p>"""
    external_model_endpoint_data_blobs: NotRequired[
        "capo_frauddetector.types.external_model_endpoint_data_blob_map.ExternalModelEndpointDataBlobMap"
    ]
    """<p>The Amazon SageMaker model endpoint input data blobs.</p>"""

detector_id instance-attribute 

detector_id: string

The detector ID.

detector_version_id instance-attribute 

detector_version_id: NotRequired[wholeNumberVersionString]

The detector version ID.

entities instance-attribute 

entities: listOfEntities

The entity type (associated with the detector's event type) and specific entity ID representing who performed the event. If an entity id is not available, use "UNKNOWN."

event_id instance-attribute 

event_id: string

The unique ID used to identify the event.

event_timestamp instance-attribute 

event_timestamp: utcTimestampISO8601

Timestamp that defines when the event under evaluation occurred. The timestamp must be specified using ISO 8601 standard in UTC.

event_type_name instance-attribute 

event_type_name: string

The event type associated with the detector specified for the prediction.

event_variables instance-attribute 

event_variables: EventVariableMap

Names of the event type's variables you defined in Amazon Fraud Detector to represent data elements and their corresponding values for the event you are sending for evaluation.

You must provide at least one eventVariable

To ensure most accurate fraud prediction and to simplify your data preparation, Amazon Fraud Detector will replace all missing variables or values as follows:

For Amazon Fraud Detector trained models:

If a null value is provided explicitly for a variable or if a variable is missing, model will replace the null value or the missing variable (no variable name in the eventVariables map) with calculated default mean/medians for numeric variables and with special values for categorical variables.

For imported SageMaker models:

If a null value is provided explicitly for a variable, the model and rules will use “null” as the value. If a variable is not provided (no variable name in the eventVariables map), model and rules will use the default value that is provided for the variable.

external_model_endpoint_data_blobs instance-attribute 

external_model_endpoint_data_blobs: NotRequired[ExternalModelEndpointDataBlobMap]

The Amazon SageMaker model endpoint input data blobs.