Bases: BaseEstimator, ClassifierMixin
A wrapper for the SVM-perf package by Thorsten Joachims. When using losses for quantification, the source code has to be patched. See @@ -821,6 +821,8 @@ for further details.
C – trade-off between training error and margin (default 0.01)
verbose – set to True to print svm-perf std outputs
loss – the loss to optimize for. Available losses are “01”, “f1”, “kld”, “nkld”, “q”, “qacc”, “qf1”, “qgm”, “mae”, “mrae”.
host_folder – directory where to store the trained model; set to None (default) for using a tmp directory +(temporal directories are automatically deleted)
Set the hyper-parameters for svm-perf. Currently, only the C parameter is supported
-parameters – a **kwargs dictionary {‘C’: <float>}
-Extract one sample determined by the given parameters
+Realizes the sample given the index of the instances.
params – all the necessary parameters to generate a sample
+index – indexes of the instances to select
one sample (the same sample has to be generated for the same parameters)
+an instance of qp.data.LabelledCollection
This function has to return all the necessary parameters to replicate the samples
+Return all the necessary parameters to replicate the samples as according to the APP protocol.
a list of parameters, each of which serves to deterministically generate a sample
+a list of indexes that realize the APP sampling
Indicates the total number of samples that the protocol generates.
+Returns the number of samples that will be generated
The number of samples to generate if known, or None otherwise.
+int
domainA –
domainB –
sample_size –
repeats –
domainA – one domain, an object of qp.data.LabelledCollection
domainB – another domain, an object of qp.data.LabelledCollection
sample_size – integer, the number of instances in each sample; if None (default) then it is taken from +qp.environ[“SAMPLE_SIZE”]. If this is not set, a ValueError exception is raised.
repeats – int, number of samples to draw for every mixture rate
prevalence – the prevalence to preserv along the mixtures. If specified, should be an array containing one prevalence value (positive float) for each class and summing up to one. If not specified, the prevalence will be taken from the domain A (default).
Extract one sample determined by the given parameters
+Realizes the sample given a pair of indexes of the instances from A and B.
params – all the necessary parameters to generate a sample
+indexes – indexes of the instances to select from A and B
one sample (the same sample has to be generated for the same parameters)
+an instance of qp.data.LabelledCollection
This function has to return all the necessary parameters to replicate the samples
+Return all the necessary parameters to replicate the samples as according to the this protocol.
a list of parameters, each of which serves to deterministically generate a sample
+a list of zipped indexes (from A and B) that realize the sampling
Indicates the total number of samples that the protocol generates.
+Returns the number of samples that will be generated (equals to “repeats * mixture_points”)
The number of samples to generate if known, or None otherwise.
+int
Extract one sample determined by the given parameters
+Realizes the sample given the index of the instances.
params – all the necessary parameters to generate a sample
+index – indexes of the instances to select
one sample (the same sample has to be generated for the same parameters)
+an instance of qp.data.LabelledCollection
This function has to return all the necessary parameters to replicate the samples
+Return all the necessary parameters to replicate the samples as according to the NPP protocol.
a list of parameters, each of which serves to deterministically generate a sample
+a list of indexes that realize the NPP sampling
Indicates the total number of samples that the protocol generates.
+Returns the number of samples that will be generated (equals to “repeats”)
The number of samples to generate if known, or None otherwise.
+int
Bases: object
Protocols that generate samples from a qp.data.LabelledCollection object.
Returns a collator function, i.e., a function that prepares the yielded data
+return_type – either ‘sample_prev’ (default) if the collator is requested to yield tuples of
+(sample, prevalence), or ‘labelled_collection’ when it is requested to yield instances of
+qp.data.LabelledCollection
the collator function (a callable function that takes as input an instance of
+qp.data.LabelledCollection)
Returns the labelled collection on which this protocol acts.
+an object of type qp.data.LabelledCollection
Returns a copy of this protocol that acts on a modified version of the original
+qp.data.LabelledCollection in which the original instances have been replaced
+with the outputs of a classifier for each instance. (This is convenient for speeding-up
+the evaluation procedures for many samples, by pre-classifying the instances in advance.)
pre_classifications – the predictions issued by a classifier, typically an array-like +with shape (n_instances,) when the classifier is a hard one, or with shape +(n_instances, n_classes) when the classifier is a probabilistic one.
in_place – whether or not to apply the modification in-place or in a new copy (default).
a copy of this protocol
+Extract one sample determined by the given parameters
+Realizes the sample given the index of the instances.
params – all the necessary parameters to generate a sample
+index – indexes of the instances to select
one sample (the same sample has to be generated for the same parameters)
+an instance of qp.data.LabelledCollection
This function has to return all the necessary parameters to replicate the samples
+Return all the necessary parameters to replicate the samples as according to the USimplexPP protocol.
a list of parameters, each of which serves to deterministically generate a sample
+a list of indexes that realize the USimplexPP sampling
Indicates the total number of samples that the protocol generates.
+Returns the number of samples that will be generated (equals to “repeats”)
The number of samples to generate if known, or None otherwise.
+int
Bases: AggregativeQuantifier, BinaryQuantifier
Class of Explicit Loss Minimization (ELM) quantifiers. -Quantifiers based on ELM represent a family of methods based on structured output learning; -these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss -measure. This implementation relies on -Joachims’ SVM perf structured output -learning algorithm, which has to be installed and patched for the purpose (see this -script).
-svmperf_base – path to the folder containing the binary files of SVM perf
loss – the loss to optimize (see quapy.classification.svmperf.SVMperf.valid_losses)
kwargs – rest of SVM perf’s parameters
Implements the aggregation of label predictions.
-classif_predictions – np.ndarray of label predictions
-np.ndarray of shape (n_classes,) with class prevalence estimates.
-Provides the label predictions for the given instances. The predictions should respect the format expected by
-aggregate(), i.e., posterior probabilities for probabilistic quantifiers, or crisp predictions for
-non-probabilistic quantifiers
instances – array-like
-np.ndarray of shape (n_instances,) with label predictions
-Trains the aggregative quantifier
-data – a quapy.data.base.LabelledCollection consisting of the training data
fit_classifier – whether or not to train the learner (default is True). Set to False if the -learner has been trained outside the quantifier.
self
-alias of EMQ
Bases: OneVsAllGeneric, AggregativeQuantifier
Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary quantifier for each class, and then l1-normalizes the outputs so that the @@ -1029,108 +953,6 @@ learner has been trained outside the quantifier.
Bases: ELM
SVM(AE), which attempts to minimize Absolute Error as first used by -Moreo and Sebastiani, 2021. -Equivalent to:
->>> ELM(svmperf_base, loss='mae', **kwargs)
-svmperf_base – path to the folder containing the binary files of SVM perf
kwargs – rest of SVM perf’s parameters
Bases: ELM
SVM(KLD), which attempts to minimize the Kullback-Leibler Divergence as proposed by -Esuli et al. 2015. -Equivalent to:
->>> ELM(svmperf_base, loss='kld', **kwargs)
-svmperf_base – path to the folder containing the binary files of SVM perf
kwargs – rest of SVM perf’s parameters
Bases: ELM
SVM(NKLD), which attempts to minimize a version of the the Kullback-Leibler Divergence normalized -via the logistic function, as proposed by -Esuli et al. 2015. -Equivalent to:
->>> ELM(svmperf_base, loss='nkld', **kwargs)
-svmperf_base – path to the folder containing the binary files of SVM perf
kwargs – rest of SVM perf’s parameters
Bases: ELM
SVM(Q), which attempts to minimize the Q loss combining a classification-oriented loss and a -quantification-oriented loss, as proposed by -Barranquero et al. 2015. -Equivalent to:
->>> ELM(svmperf_base, loss='q', **kwargs)
-svmperf_base – path to the folder containing the binary files of SVM perf
kwargs – rest of SVM perf’s parameters
Bases: ELM
SVM(RAE), which attempts to minimize Relative Absolute Error as first used by -Moreo and Sebastiani, 2021. -Equivalent to:
->>> ELM(svmperf_base, loss='mrae', **kwargs)
-svmperf_base – path to the folder containing the binary files of SVM perf
kwargs – rest of SVM perf’s parameters
Explicit Loss Minimization (ELM) quantifiers. +Quantifiers based on ELM represent a family of methods based on structured output learning; +these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss +measure. This implementation relies on +Joachims’ SVM perf structured output +learning algorithm, which has to be installed and patched for the purpose (see this +script). +This function equivalent to:
+>>> CC(SVMperf(svmperf_base, loss, C))
+svmperf_base – path to the folder containing the binary files of SVM perf; if set to None (default) +this path will be obtained from qp.environ[‘SVMPERF_HOME’]
loss – the loss to optimize (see quapy.classification.svmperf.SVMperf.valid_losses)
C – trade-off between training error and margin (default 0.01)
returns an instance of CC set to work with SVMperf (with loss and C set properly) as the +underlying classifier
+SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Absolute Error as first used by +Moreo and Sebastiani, 2021. +Equivalent to:
+>>> CC(SVMperf(svmperf_base, loss='mae', C=C))
+Quantifiers based on ELM represent a family of methods based on structured output learning; +these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss +measure. This implementation relies on +Joachims’ SVM perf structured output +learning algorithm, which has to be installed and patched for the purpose (see this +script). +This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
+svmperf_base – path to the folder containing the binary files of SVM perf; if set to None (default) +this path will be obtained from qp.environ[‘SVMPERF_HOME’]
C – trade-off between training error and margin (default 0.01)
returns an instance of CC set to work with SVMperf (with loss and C set properly) as the +underlying classifier
+SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Kullback-Leibler Divergence +normalized via the logistic function, as proposed by +Esuli et al. 2015. +Equivalent to:
+>>> CC(SVMperf(svmperf_base, loss='nkld', C=C))
+Quantifiers based on ELM represent a family of methods based on structured output learning; +these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss +measure. This implementation relies on +Joachims’ SVM perf structured output +learning algorithm, which has to be installed and patched for the purpose (see this +script). +This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
+svmperf_base – path to the folder containing the binary files of SVM perf; if set to None (default) +this path will be obtained from qp.environ[‘SVMPERF_HOME’]
C – trade-off between training error and margin (default 0.01)
returns an instance of CC set to work with SVMperf (with loss and C set properly) as the +underlying classifier
+SVM(Q) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Q loss combining a +classification-oriented loss and a quantification-oriented loss, as proposed by +Barranquero et al. 2015. +Equivalent to:
+>>> CC(SVMperf(svmperf_base, loss='q', C=C))
+Quantifiers based on ELM represent a family of methods based on structured output learning; +these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss +measure. This implementation relies on +Joachims’ SVM perf structured output +learning algorithm, which has to be installed and patched for the purpose (see this +script). +This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
+svmperf_base – path to the folder containing the binary files of SVM perf; if set to None (default) +this path will be obtained from qp.environ[‘SVMPERF_HOME’]
C – trade-off between training error and margin (default 0.01)
returns an instance of CC set to work with SVMperf (with loss and C set properly) as the +underlying classifier
+SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Relative Absolute Error as first +used by Moreo and Sebastiani, 2021. +Equivalent to:
+>>> CC(SVMperf(svmperf_base, loss='mrae', C=C))
+Quantifiers based on ELM represent a family of methods based on structured output learning; +these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss +measure. This implementation relies on +Joachims’ SVM perf structured output +learning algorithm, which has to be installed and patched for the purpose (see this +script). +This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
+svmperf_base – path to the folder containing the binary files of SVM perf; if set to None (default) +this path will be obtained from qp.environ[‘SVMPERF_HOME’]
C – trade-off between training error and margin (default 0.01)
returns an instance of CC set to work with SVMperf (with loss and C set properly) as the +underlying classifier
+Bases: OneVsAll, BaseQuantifier
Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.
@@ -1343,8 +1321,8 @@ quantifier for each class, and then l1-normalizes the outputs so that the class