sdv.metrics.tabular

SingleTableMetric()	Base class for metrics that apply to single tables.
SingleTableMetric.get_subclasses([…])	Recursively find subclasses of this metric.
MultiColumnPairsMetric(column_pairs_metric, …)	SingleTableMetric subclass that applies a ColumnPairsMetric on each possible column pair.
MultiColumnPairsMetric.get_subclasses([…])	Recursively find subclasses of this metric.
MultiSingleColumnMetric([single_column_metric])	SingleTableMetric subclass that applies a SingleColumnMetric on each column.
MultiSingleColumnMetric.get_subclasses([…])	Recursively find subclasses of this metric.

Single Table BayesianNetwork Metrics

BNLikelihood()	BayesianNetwork Likelihood Single Table metric.
BNLikelihood.get_subclasses([include_parents])	Recursively find subclasses of this metric.
BNLikelihood.compute(real_data, synthetic_data)	Compute this metric.
BNLogLikelihood()	BayesianNetwork Log Likelihood Single Table metric.
BNLogLikelihood.get_subclasses([include_parents])	Recursively find subclasses of this metric.
BNLogLikelihood.compute(real_data, …[, …])	Compute this metric.

Single Table Statistical Metrics

CSTest([single_column_metric])	MultiSingleColumnMetric based on SingleColumn CSTest.
CSTest.get_subclasses([include_parents])	Recursively find subclasses of this metric.
CSTest.compute(real_data, synthetic_data[, …])	Compute this metric.
KSTest([single_column_metric])	MultiSingleColumnMetric based on SingleColumn KSTest.
KSTest.get_subclasses([include_parents])	Recursively find subclasses of this metric.
KSTest.compute(real_data, synthetic_data[, …])	Compute this metric.
KSTestExtended([single_column_metric])	KSTest variation that transforms everything to numerical before comparing the tables.
KSTestExtended.get_subclasses([include_parents])	Recursively find subclasses of this metric.
KSTestExtended.compute(real_data, synthetic_data)	Compute this metric.
ContinuousKLDivergence(column_pairs_metric, …)	MultiColumnPairsMetric based on ColumnPairs ContinuousKLDivergence.
ContinuousKLDivergence.get_subclasses([…])	Recursively find subclasses of this metric.
ContinuousKLDivergence.compute(real_data, …)	Compute this metric.
DiscreteKLDivergence(column_pairs_metric, …)	MultiColumnPairsMetric based on ColumnPairs DiscreteKLDivergence.
DiscreteKLDivergence.get_subclasses([…])	Recursively find subclasses of this metric.
DiscreteKLDivergence.compute(real_data, …)	Compute this metric.

Single Table GaussianMixture Metrics

GMLogLikelihood()	GaussianMixture Single Table metric.
GMLogLikelihood.get_subclasses([include_parents])	Recursively find subclasses of this metric.
GMLogLikelihood.compute(real_data, …[, …])	Compute this metric.

Single Table Detection Metrics

DetectionMetric()	Base class for Machine Learning Detection based metrics on single tables.
DetectionMetric.get_subclasses([include_parents])	Recursively find subclasses of this metric.
ScikitLearnClassifierDetectionMetric()	Base class for Detection metrics build using Scikit Learn Classifiers.
ScikitLearnClassifierDetectionMetric.get_subclasses([…])	Recursively find subclasses of this metric.
LogisticDetection()	ScikitLearnClassifierDetectionMetric based on a LogisticRegression.
LogisticDetection.get_subclasses([…])	Recursively find subclasses of this metric.
LogisticDetection.compute(real_data, …[, …])	Compute this metric.
SVCDetection()	ScikitLearnClassifierDetectionMetric based on a SVC.
SVCDetection.get_subclasses([include_parents])	Recursively find subclasses of this metric.
SVCDetection.compute(real_data, synthetic_data)	Compute this metric.

Single Table Efficacy Metrics

MLEfficacyMetric()	Base class for Machine Learning Efficacy metrics on single tables.
MLEfficacyMetric.get_subclasses([…])	Recursively find subclasses of this metric.
BinaryEfficacyMetric()	Base class for Binary Classification Efficacy metrics.
BinaryEfficacyMetric.get_subclasses([…])	Recursively find subclasses of this metric.
BinaryDecisionTreeClassifier()	Binary DecisionTreeClassifier Efficacy based metric.
BinaryDecisionTreeClassifier.get_subclasses([…])	Recursively find subclasses of this metric.
BinaryDecisionTreeClassifier.compute(…[, …])	Compute this metric.
BinaryAdaBoostClassifier()	Binary AdaBoostClassifier Efficacy based metric.
BinaryAdaBoostClassifier.get_subclasses([…])	Recursively find subclasses of this metric.
BinaryAdaBoostClassifier.compute(real_data, …)	Compute this metric.
BinaryLogisticRegression()	Binary LogisticRegression Efficacy based metric.
BinaryLogisticRegression.get_subclasses([…])	Recursively find subclasses of this metric.
BinaryLogisticRegression.compute(real_data, …)	Compute this metric.
BinaryMLPClassifier()	Binary MLPClassifier Efficacy based metric.
BinaryMLPClassifier.get_subclasses([…])	Recursively find subclasses of this metric.
BinaryMLPClassifier.compute(real_data, …)	Compute this metric.
MulticlassEfficacyMetric()	Base class for Multiclass Classification Efficacy Metrics.
MulticlassEfficacyMetric()	Base class for Multiclass Classification Efficacy Metrics.
MulticlassDecisionTreeClassifier()	Multiclass DecisionTreeClassifier Efficacy based metric.
MulticlassDecisionTreeClassifier.get_subclasses([…])	Recursively find subclasses of this metric.
MulticlassDecisionTreeClassifier.compute(…)	Compute this metric.
MulticlassMLPClassifier()	Multiclass MLPClassifier Efficacy based metric.
MulticlassMLPClassifier.get_subclasses([…])	Recursively find subclasses of this metric.
MulticlassMLPClassifier.compute(real_data, …)	Compute this metric.
RegressionEfficacyMetric()	RegressionEfficacy base class.
RegressionEfficacyMetric()	RegressionEfficacy base class.
LinearRegression()	LinearRegression Efficacy based metric.
LinearRegression.get_subclasses([…])	Recursively find subclasses of this metric.
LinearRegression.compute(real_data, …[, …])	Compute this metric.
MLPRegressor()	MLPRegressor Efficacy based metric.
MLPRegressor.get_subclasses([include_parents])	Recursively find subclasses of this metric.
MLPRegressor.compute(real_data, synthetic_data)	Compute this metric.

Single Table Privacy Metrics

CategoricalPrivacyMetric()	Base class for Categorical Privacy metrics on single tables.
CategoricalPrivacyMetric.get_subclasses([…])	Recursively find subclasses of this metric.
NumericalPrivacyMetric()	Base class for Numerical Privacy metrics on single tables.
NumericalPrivacyMetric.get_subclasses([…])	Recursively find subclasses of this metric.
CategoricalCAP()	The Categorical CAP privacy metric.
CategoricalCAP.get_subclasses([include_parents])	Recursively find subclasses of this metric.
CategoricalCAP.compute(real_data, synthetic_data)	Compute this metric.
CategoricalZeroCAP()	The Categorical 0CAP privacy metric.
CategoricalZeroCAP.get_subclasses([…])	Recursively find subclasses of this metric.
CategoricalZeroCAP.compute(real_data, …[, …])	Compute this metric.
CategoricalGeneralizedCAP()	The GeneralizedCAP privacy metric.
CategoricalGeneralizedCAP.get_subclasses([…])	Recursively find subclasses of this metric.
CategoricalGeneralizedCAP.compute(real_data, …)	Compute this metric.
CategoricalKNN()	The Categorical KNN privacy metric.
CategoricalKNN.get_subclasses([include_parents])	Recursively find subclasses of this metric.
CategoricalKNN.compute(real_data, synthetic_data)	Compute this metric.
CategoricalNB()	The Categorical NaiveBaysian privacy metric.
CategoricalNB.get_subclasses([include_parents])	Recursively find subclasses of this metric.
CategoricalNB.compute(real_data, synthetic_data)	Compute this metric.
CategoricalRF()	The Categorical RF privacy metric.
CategoricalRF.get_subclasses([include_parents])	Recursively find subclasses of this metric.
CategoricalRF.compute(real_data, synthetic_data)	Compute this metric.
CategoricalSVM()	The Categorical SVM privacy metric.
CategoricalSVM.get_subclasses([include_parents])	Recursively find subclasses of this metric.
CategoricalSVM.compute(real_data, synthetic_data)	Compute this metric.
NumericalMLP()	The Multi-layer Perceptron regression privacy metric.
NumericalMLP.get_subclasses([include_parents])	Recursively find subclasses of this metric.
NumericalMLP.compute(real_data, synthetic_data)	Compute this metric.
NumericalLR()	The Numerical Linear Regression privacy metric.
NumericalLR.get_subclasses([include_parents])	Recursively find subclasses of this metric.
NumericalLR.compute(real_data, synthetic_data)	Compute this metric.
NumericalSVR()	The Numerical Support-vector Regression privacy metric.
NumericalSVR.get_subclasses([include_parents])	Recursively find subclasses of this metric.
NumericalSVR.compute(real_data, synthetic_data)	Compute this metric.
CategoricalEnsemble()	The Categorical Ensemble privacy metric.
CategoricalEnsemble.get_subclasses([…])	Recursively find subclasses of this metric.
CategoricalEnsemble.compute(real_data, …)	Compute this metric.
NumericalRadiusNearestNeighbor()	The Radius Nearest Neighbor privacy metric.
NumericalRadiusNearestNeighbor.get_subclasses([…])	Recursively find subclasses of this metric.
NumericalRadiusNearestNeighbor.compute(…)	Compute this metric.

sdv.metrics sdv.metrics.tabular.SingleTableMetric