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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
)
Args | |
---|---|
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. |
Attributes | |
---|---|
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
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
) -> tfma.metrics.MetricComputations
Creates computations associated with metric.
get_config
get_config() -> tfma.types.Extracts
Returns serializable config.