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.