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You are looking at the documentation for an older version of the SDV! We are no longer supporting or maintaining this version of the software
Click here to go to the new docs pages.
sdv.metrics.timeseries.
TimeSeriesDetectionMetric
Base class for Machine Learning Detection based metrics on time series.
These metrics build a Machine Learning Classifier that learns to tell the synthetic data apart from the real data, which later on is evaluated using Cross Validation.
The output of the metric is one minus the average ROC AUC score obtained.
name
Name to use when reports about this metric are printed.
str
goal
The goal of this metric.
sdmetrics.goal.Goal
min_value
Minimum value or values that this metric can take.
Union[float, tuple[float]]
max_value
Maximum value or values that this metric can take.
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__()
Initialize self.
compute(real_data, synthetic_data[, …])
compute
Compute this metric.
get_subclasses([include_parents])
get_subclasses
Recursively find subclasses of this metric.
normalize(raw_score)
normalize
Return the raw_score as is, since it is already normalized.
Attributes