tfma.metrics.CalibrationPlot

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Calibration plot.

Inherits From: Metric

tfma.metrics.CalibrationPlot(
    num_buckets: int = DEFAULT_NUM_BUCKETS,
    left: float = None,
    right: float = None,
    name: Text = CALIBRATION_PLOT_NAME
)

num_buckets Number of buckets to use when creating the plot. Defaults to 1000.
left Left boundary of plot. Defaults to 0.0 when a schema is not provided.
right Right boundary of plot. Defaults to 1.0 when a schema is not provided.
name Plot name.

compute_confidence_interval Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.

Methods

computations

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computations(
    eval_config: Optional[config.EvalConfig] = None,
    schema: Optional[schema_pb2.Schema] = None,
    model_names: Optional[List[Text]] = None,
    output_names: Optional[List[Text]] = None,
    sub_keys: Optional[List[SubKey]] = None,
    class_weights: Optional[Dict[int, float]] = None,
    query_key: Optional[Text] = None,
    is_diff: Optional[bool] = False
) -> MetricComputations

Creates computations associated with metric.

get_config

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get_config() -> Dict[Text, Any]

Returns serializable config.

is_model_independent

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is_model_independent() -> bool

Returns true if the metric does not depend on a model.