tfma.metrics.specs_from_metrics

Returns specs for tf.keras.metrics/losses or tfma.metrics classes.

tfma.metrics.specs_from_metrics(
    metrics: Union[List[_TFOrTFMAMetric], Dict[Text, List[_TFOrTFMAMetric]]],
    model_names: Optional[List[Text]] = None,
    output_names: Optional[List[Text]] = None,
    binarize: Optional[tfma.BinarizationOptions] = None,
    aggregate: Optional[tfma.AggregationOptions] = None,
    query_key: Optional[Text] = None,
    include_example_count: Optional[bool] = None,
    include_weighted_example_count: Optional[bool] = None
) -> List[tfma.MetricsSpec]

Examples:

metrics_specs = specs_from_metrics([ tf.keras.metrics.BinaryAccuracy(), tf.keras.metrics.AUC(), tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), tfma.metrics.MeanLabel(), tfma.metrics.MeanPrediction() ... ])

metrics_specs = specs_from_metrics({ 'output1': [ tf.keras.metrics.BinaryAccuracy(), tf.keras.metrics.AUC(), tfma.metrics.MeanLabel(), tfma.metrics.MeanPrediction() ... ], 'output2': [ tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), ] })

Args
`metrics`	List of tf.keras.metrics.Metric, tf.keras.losses.Loss, or tfma.metrics.Metric. For multi-output models a dict of dicts may be passed where the first dict is indexed by the output_name.
`model_names`	Optional model names (if multi-model evaluation).
`output_names`	Optional output names (if multi-output models). If the metrics are a dict this should not be set.
`binarize`	Optional settings for binarizing multi-class/multi-label metrics.
`aggregate`	Optional settings for aggregating multi-class/multi-label metrics.
`query_key`	Optional query key for query/ranking based metrics.
`include_example_count`	True to add example_count metric. Default is True.
`include_weighted_example_count`	True to add weighted_example_count metric. Default is True. A weighted example count will be added per output for multi-output models.

tfma.metrics.specs_from_metrics

Examples:

Args