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tfma.metrics.ExampleCount( name: Text = EXAMPLE_COUNT_NAME )
Note that although the example_count is independent of the model, this metric will be associated with a model for consistency with other metrics.
Always disable confidence intervals for ExampleCount.
Confidence intervals capture uncertainty in a metric if it were computed on more examples. For ExampleCount, this sort of uncertainty is not meaningful, so confidence intervals are disabled.
computations( eval_config: Optional[
tfma.EvalConfig] = None, schema: Optional[schema_pb2.Schema] = None, model_names: Optional[List[Text]] = None, output_names: Optional[List[Text]] = None, sub_keys: Optional[List[Optional[SubKey]]] = None, aggregation_type: Optional[AggregationType] = None, class_weights: Optional[Dict[int, float]] = None, query_key: Optional[Text] = None, is_diff: Optional[bool] = False ) ->
Creates computations associated with metric.
get_config() -> Dict[Text, Any]
Returns serializable config.