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sdv.metrics.tabular.MultiSingleColumnMetric

class sdv.metrics.tabular.MultiSingleColumnMetric(single_column_metric=None, **single_column_metric_kwargs)[source]

SingleTableMetric subclass that applies a SingleColumnMetric on each column.

This class can either be used by creating a subclass that inherits from it and sets the SingleColumn Metric as the single_column_metric attribute, or by creating an instance of this class passing the underlying SingleColumn metric as an argument.

name

Name to use when reports about this metric are printed.

Type

str

goal

The goal of this metric.

Type

sdmetrics.goal.Goal

min_value

Minimum value or values that this metric can take.

Type

Union[float, tuple[float]]

max_value

Maximum value or values that this metric can take.

Type

Union[float, tuple[float]]

single_column_metric

SingleColumn metric to apply.

Type

sdmetrics.single_column.base.SingleColumnMetric

field_types

Field types to which the SingleColumn metric will be applied.

Type

dict

__init__(single_column_metric=None, **single_column_metric_kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([single_column_metric])

Initialize self.

compute(real_data, synthetic_data[, metadata])

Compute this metric.

compute_breakdown(real_data, synthetic_data)

Compute this metric broken down by column.

get_subclasses([include_parents])

Recursively find subclasses of this metric.

normalize(raw_score)

Return the raw_score as is, since it is already normalized.

Attributes

field_types

single_column_metric

single_column_metric_kwargs