Welcome to QuaPy’s documentation!
QuaPy is a Python-based open-source framework for quantification.
This document contains the API of the modules included in QuaPy.
Installation
pip install quapy
GitHub
QuaPy is hosted in GitHub at https://github.com/HLT-ISTI/QuaPy
Contents
- quapy
- quapy package
- Subpackages
- quapy.classification package
- Submodules
- quapy.classification.calibration module
BCTSCalibrationNBVSCalibrationRecalibratedProbabilisticClassifierRecalibratedProbabilisticClassifierBaseRecalibratedProbabilisticClassifierBase.classes_RecalibratedProbabilisticClassifierBase.fit()RecalibratedProbabilisticClassifierBase.fit_cv()RecalibratedProbabilisticClassifierBase.fit_tr_val()RecalibratedProbabilisticClassifierBase.predict()RecalibratedProbabilisticClassifierBase.predict_proba()
TSCalibrationVSCalibration
- quapy.classification.methods module
- quapy.classification.neural module
- quapy.classification.svmperf module
- Module contents
- quapy.data package
- Submodules
- quapy.data.base module
DatasetLabelledCollectionLabelledCollection.XLabelledCollection.XpLabelledCollection.XyLabelledCollection.binaryLabelledCollection.counts()LabelledCollection.join()LabelledCollection.kFCV()LabelledCollection.load()LabelledCollection.n_classesLabelledCollection.pLabelledCollection.prevalence()LabelledCollection.sampling()LabelledCollection.sampling_from_index()LabelledCollection.sampling_index()LabelledCollection.split_random()LabelledCollection.split_stratified()LabelledCollection.stats()LabelledCollection.uniform_sampling()LabelledCollection.uniform_sampling_index()LabelledCollection.y
- quapy.data.datasets module
- quapy.data.preprocessing module
- quapy.data.reader module
- Module contents
- quapy.method package
- Submodules
- quapy.method.aggregative module
ACCAdjustedClassifyAndCountAggregativeCrispQuantifierAggregativeMedianEstimatorAggregativeQuantifierAggregativeQuantifier.aggregate()AggregativeQuantifier.aggregation_fit()AggregativeQuantifier.classes_AggregativeQuantifier.classifierAggregativeQuantifier.classifier_fit_predict()AggregativeQuantifier.classify()AggregativeQuantifier.fit()AggregativeQuantifier.quantify()AggregativeQuantifier.val_splitAggregativeQuantifier.val_split_
AggregativeSoftQuantifierBinaryAggregativeQuantifierCCClassifyAndCountDMyDistributionMatchingYDySEMQExpectationMaximizationQuantifierHDyHellingerDistanceYOneVsAllAggregativePACCPCCProbabilisticAdjustedClassifyAndCountProbabilisticClassifyAndCountSLDSMMnewELM()newSVMAE()newSVMKLD()newSVMQ()newSVMRAE()KDEBaseKDEyCSKDEyHDKDEyMLQuaNetModuleQuaNetTrainermae_loss()MAXMSMS2T50ThresholdOptimizationX
- quapy.method.base module
- quapy.method.meta module
- quapy.method.non_aggregative module
- Module contents
- quapy.classification package
- Submodules
- quapy.error module
absolute_error()acc_error()acce()ae()f1_error()f1e()from_name()kld()mae()mean_absolute_error()mean_normalized_absolute_error()mean_normalized_relative_absolute_error()mean_relative_absolute_error()mkld()mnae()mnkld()mnrae()mrae()mse()nae()nkld()normalized_absolute_error()normalized_relative_absolute_error()nrae()rae()relative_absolute_error()se()smooth()
- quapy.evaluation module
- quapy.functional module
HellingerDistance()TopsoeDistance()adjusted_quantification()argmin_prevalence()as_binary_prevalence()check_prevalence_vector()get_divergence()get_nprevpoints_approximation()linear_search()normalize_prevalence()num_prevalence_combinations()optim_minimize()prevalence_from_labels()prevalence_from_probabilities()prevalence_linspace()strprev()uniform_prevalence_sampling()uniform_simplex_sampling()
- quapy.model_selection module
- quapy.plot module
- quapy.protocol module
- quapy.util module
- Module contents
- Subpackages
- quapy package