API Reference#

This is the class and function reference of mlquantify. Please refer to the full user guide for further details, as the raw specifications of classes and functions may not be enough to give full guidelines on their use. For reference on concepts repeated across the API, see glossary.

Object	Description
`get_config`	`mlquantify`
`set_config`	`mlquantify`
`config_context`	`mlquantify`
`BaseQuantifier`	`mlquantify.base`
`MetaquantifierMixin`	`mlquantify.base`
`ProtocolMixin`	`mlquantify.base`
`AggregationMixin`	`mlquantify.base_aggregative`
`SoftPredictionMixin`	`mlquantify.base_aggregative`
`CrispPredictionMixin`	`mlquantify.base_aggregative`
`Calibrator`	`mlquantify.calibration`
`ClassifierCalibrator`	`mlquantify.calibration`
`QuantifierCalibrator`	`mlquantify.calibration`
`BaseComposeQuantifier`	`mlquantify.compose`
`LinearComposeQuantifier`	`mlquantify.compose`
`LikelihoodComposeQuantifier`	`mlquantify.compose`
`ComposeQuantifier`	`mlquantify.compose`
`BaseConfidenceRegion`	`mlquantify.confidence`
`ConfidenceInterval`	`mlquantify.confidence`
`ConfidenceEllipseSimplex`	`mlquantify.confidence`
`ConfidenceEllipseCLR`	`mlquantify.confidence`
`construct_confidence_region`	`mlquantify.confidence`
`CC`	`mlquantify.counting`
`PCC`	`mlquantify.counting`
`ThresholdAdjustment`	`mlquantify.counting`
`TAC`	`mlquantify.counting`
`TX`	`mlquantify.counting`
`TMAX`	`mlquantify.counting`
`T50`	`mlquantify.counting`
`MS`	`mlquantify.counting`
`MS2`	`mlquantify.counting`
`FM`	`mlquantify.counting`
`GACC`	`mlquantify.counting`
`GPACC`	`mlquantify.counting`
`evaluate_thresholds`	`mlquantify.counting`
`compute_tpr`	`mlquantify.counting`
`compute_fpr`	`mlquantify.counting`
`compute_table`	`mlquantify.counting`
`CDE`	`mlquantify.likelihood`
`EMQ`	`mlquantify.likelihood`
`MLPE`	`mlquantify.likelihood`
`BaseLoss`	`mlquantify.losses`
`DistanceLoss`	`mlquantify.losses`
`LeastSquaresLoss`	`mlquantify.losses`
`HellingerSurrogateLoss`	`mlquantify.losses`
`EnergyLoss`	`mlquantify.losses`
`NegativeLogLikelihoodLoss`	`mlquantify.losses`
`MixtureNegativeLogLikelihoodLoss`	`mlquantify.losses`
`RegularizedMixtureNLLLoss`	`mlquantify.losses`
`normalize_distribution`	`mlquantify.losses`
`get_loss`	`mlquantify.losses`
`BaseMatchingQuantifier`	`mlquantify.matching`
`MatchingHistogramQuantifier`	`mlquantify.matching`
`DyS`	`mlquantify.matching`
`HDy`	`mlquantify.matching`
`HDx`	`mlquantify.matching`
`SORD`	`mlquantify.matching`
`MatchingKernelQuantifier`	`mlquantify.matching`
`MMD_RKHS`	`mlquantify.matching`
`KDEyQuantifier`	`mlquantify.matching`
`KDEyML`	`mlquantify.matching`
`KDEyHD`	`mlquantify.matching`
`KDEyCS`	`mlquantify.matching`
`GKDEyML`	`mlquantify.matching`
`GHDx`	`mlquantify.matching`
`GHDy`	`mlquantify.matching`
`SMM`	`mlquantify.matching`
`EDy`	`mlquantify.matching`
`EDx`	`mlquantify.matching`
`EnsembleQ`	`mlquantify.meta`
`QuaDapt`	`mlquantify.meta`
`AggregativeBootstrap`	`mlquantify.meta`
`AE`	`mlquantify.metrics`
`SE`	`mlquantify.metrics`
`MAE`	`mlquantify.metrics`
`MSE`	`mlquantify.metrics`
`KLD`	`mlquantify.metrics`
`RAE`	`mlquantify.metrics`
`NAE`	`mlquantify.metrics`
`NRAE`	`mlquantify.metrics`
`NKLD`	`mlquantify.metrics`
`NMD`	`mlquantify.metrics`
`RNOD`	`mlquantify.metrics`
`VSE`	`mlquantify.metrics`
`CvM_L1`	`mlquantify.metrics`
`GridSearchQ`	`mlquantify.model_selection`
`BaseProtocol`	`mlquantify.model_selection`
`APP`	`mlquantify.model_selection`
`NPP`	`mlquantify.model_selection`
`UPP`	`mlquantify.model_selection`
`PPP`	`mlquantify.model_selection`
`binary_quantifier`	`mlquantify.multiclass`
`BinaryQuantifier`	`mlquantify.multiclass`
`PWK`	`mlquantify.neighbors`
`QuaNet`	`mlquantify.neural`
`BaseRepresentation`	`mlquantify.representations`
`HistogramRepresentation`	`mlquantify.representations`
`KDERepresentation`	`mlquantify.representations`
`DistanceRepresentation`	`mlquantify.representations`
`KernelMeanRepresentation`	`mlquantify.representations`
`PredictionRepresentation`	`mlquantify.representations`
`HardPredictionRepresentation`	`mlquantify.representations`
`SoftPredictionRepresentation`	`mlquantify.representations`
`solve_binary`	`mlquantify.solvers`
`ternary_search`	`mlquantify.solvers`
`solve_simplex`	`mlquantify.solvers`
`minimize_prevalence`	`mlquantify.solvers`
`minimize_prevalence_blocks`	`mlquantify.solvers`
`get_prev_from_labels`	`mlquantify.utils`
`normalize_prevalence`	`mlquantify.utils`
`load_quantifier`	`mlquantify.utils`
`make_prevs`	`mlquantify.utils`
`apply_cross_validation`	`mlquantify.utils`
`simplex_uniform_kraemer`	`mlquantify.utils`
`simplex_grid_sampling`	`mlquantify.utils`
`simplex_uniform_sampling`	`mlquantify.utils`
`get_indexes_with_prevalence`	`mlquantify.utils`