diff --git a/quapy/method/__init__.py b/quapy/method/__init__.py
index b5bbd72..88acd16 100644
--- a/quapy/method/__init__.py
+++ b/quapy/method/__init__.py
@@ -8,7 +8,7 @@ AGGREGATIVE_METHODS = {
agg.AdjustedClassifyAndCount,
agg.ProbabilisticClassifyAndCount,
agg.ProbabilisticAdjustedClassifyAndCount,
- agg.ExplicitLossMinimisationBinary,
+ agg.ExplicitLossMinimisation,
agg.ExpectationMaximizationQuantifier,
agg.HellingerDistanceY
}
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index f2d2756..92918f5 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -60,7 +60,7 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier):
"""
Abstract class for quantification methods that base their estimations on the aggregation of posterior probabilities
as returned by a probabilistic classifier. Aggregative Probabilistic Quantifiers thus extend Aggregative
- Quantifiersimplement by implementing a _posterior_probabilities_ method returning values in [0,1] -- the posterior
+ Quantifiers by implementing a _posterior_probabilities_ method returning values in [0,1] -- the posterior
probabilities.
"""
@@ -224,9 +224,8 @@ class ExpectationMaximizationQuantifier(AggregativeProbabilisticQuantifier):
MAX_ITER = 1000
EPSILON = 1e-4
- def __init__(self, learner, verbose=False):
+ def __init__(self, learner):
self.learner = learner
- self.verbose = verbose
def fit(self, data: LabelledCollection, fit_learner=True, *args):
self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True)
@@ -234,10 +233,10 @@ class ExpectationMaximizationQuantifier(AggregativeProbabilisticQuantifier):
return self
def aggregate(self, classif_posteriors, epsilon=EPSILON):
- return self.EM(self.train_prevalence, classif_posteriors, self.verbose, epsilon)
+ return self.EM(self.train_prevalence, classif_posteriors, epsilon)
@classmethod
- def EM(cls, tr_prev, posterior_probabilities, verbose=False, epsilon=EPSILON):
+ def EM(cls, tr_prev, posterior_probabilities, epsilon=EPSILON):
Px = posterior_probabilities
Ptr = np.copy(tr_prev)
qs = np.copy(Ptr) # qs (the running estimate) is initialized as the training prevalence
@@ -256,10 +255,6 @@ class ExpectationMaximizationQuantifier(AggregativeProbabilisticQuantifier):
converged = True
qs_prev_ = qs
- s += 1
-
- if verbose:
- print('-'*80)
if not converged:
raise UserWarning('the method has reached the maximum number of iterations; it might have not converged')
@@ -317,10 +312,69 @@ class HellingerDistanceY(AggregativeProbabilisticQuantifier):
return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2))
+class ExplicitLossMinimisation(AggregativeQuantifier):
+
+ def __init__(self, svmperf_base, loss, **kwargs):
+ self.svmperf_base = svmperf_base
+ self.loss = loss
+ self.kwargs = kwargs
+
+ def fit(self, data: LabelledCollection, fit_learner=True, *args):
+ assert data.binary, f'{self.__class__.__name__} works only on problems of binary classification' \
+ f'Use the class OneVsAll to enable {self.__class__.__name__} work on single-label data.'
+ assert fit_learner, 'the method requires that fit_learner=True'
+ self.learner = SVMperf(self.svmperf_base, loss=self.loss, **self.kwargs).fit(data.instances, data.labels)
+ return self
+
+ def aggregate(self, classif_predictions:np.ndarray, *args):
+ return F.prevalence_from_labels(classif_predictions, self.learner.n_classes_)
+
+ def classify(self, X, y=None):
+ return self.learner.predict(X)
+
+
+
+class SVMQ(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMQ, self).__init__(svmperf_base, loss='q', **kwargs)
+
+
+class SVMKLD(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMKLD, self).__init__(svmperf_base, loss='kld', **kwargs)
+
+
+class SVMNKLD(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMNKLD, self).__init__(svmperf_base, loss='nkld', **kwargs)
+
+
+class SVMAE(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMAE, self).__init__(svmperf_base, loss='mae', **kwargs)
+
+
+class SVMRAE(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMRAE, self).__init__(svmperf_base, loss='mrae', **kwargs)
+
+
+CC = ClassifyAndCount
+ACC = AdjustedClassifyAndCount
+PCC = ProbabilisticClassifyAndCount
+PACC = ProbabilisticAdjustedClassifyAndCount
+ELM = ExplicitLossMinimisation
+EMQ = ExpectationMaximizationQuantifier
+HDy = HellingerDistanceY
+
+
class OneVsAll(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 class prevelences sum up to 1.
+ This variant was used, along with the ExplicitLossMinimization quantifier in
+ Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
+ Social Network Analysis and Mining6(19), 1–22 (2016)
"""
def __init__(self, binary_quantifier, n_jobs=-1):
@@ -379,84 +433,4 @@ class OneVsAll(AggregativeQuantifier):
def _delayed_binary_fit(self, c, data, **kwargs):
bindata = LabelledCollection(data.instances, data.labels == c, n_classes=2)
- self.dict_binary_quantifiers[c].fit(bindata, **kwargs)
-
-
-# class ExplicitLossMinimisation(AggregativeQuantifier):
-# """
-# A variant of Explicit Loss Minimisation based on SVMperf that works also on single-label data. It uses one binary
-# quantifier for each class and then l1-normalizes the class predictions so that they sum up to one.
-# This variant was used in Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
-# Social Network Analysis and Mining6(19), 1–22 (2016)
-# """
-#
-# def __init__(self, svmperf_base, loss, **kwargs):
-# self.svmperf_base = svmperf_base
-# self.loss = loss
-# self.kwargs = kwargs
-#
-# def fit(self, data: LabelledCollection, fit_learner=True, *args):
-# assert fit_learner, 'the method requires that fit_learner=True'
-# self.learner = ExplicitLossMinimisationBinary(self.svmperf_base, self.loss, **self.kwargs)
-# if not data.binary:
-# self.learner = OneVsAll(self.learner, n_jobs=-1)
-# return self.learner.fit(data, *args)
-#
-# def aggregate(self, instances, *args):
-# return self.learner.aggregate(instances, *args)
-
-
-class ExplicitLossMinimisationBinary(AggregativeQuantifier):
-
- def __init__(self, svmperf_base, loss, **kwargs):
- self.svmperf_base = svmperf_base
- self.loss = loss
- self.kwargs = kwargs
-
- def fit(self, data: LabelledCollection, fit_learner=True, *args):
- assert data.binary, f'{self.__class__.__name__} works only on problems of binary classification'
- assert fit_learner, 'the method requires that fit_learner=True'
- self.learner = SVMperf(self.svmperf_base, loss=self.loss, **self.kwargs).fit(data.instances, data.labels)
- return self
-
- def aggregate(self, classif_predictions:np.ndarray, *args):
- return F.prevalence_from_labels(classif_predictions, self.learner.n_classes_)
-
- def classify(self, X, y=None):
- return self.learner.predict(X)
-
-
-
-class SVMQ(ExplicitLossMinimisationBinary):
- def __init__(self, svmperf_base, **kwargs):
- super(SVMQ, self).__init__(svmperf_base, loss='q', **kwargs)
-
-
-class SVMKLD(ExplicitLossMinimisationBinary):
- def __init__(self, svmperf_base, **kwargs):
- super(SVMKLD, self).__init__(svmperf_base, loss='kld', **kwargs)
-
-
-class SVMNKLD(ExplicitLossMinimisationBinary):
- def __init__(self, svmperf_base, **kwargs):
- super(SVMNKLD, self).__init__(svmperf_base, loss='nkld', **kwargs)
-
-
-class SVMAE(ExplicitLossMinimisationBinary):
- def __init__(self, svmperf_base, **kwargs):
- super(SVMAE, self).__init__(svmperf_base, loss='mae', **kwargs)
-
-
-class SVMRAE(ExplicitLossMinimisationBinary):
- def __init__(self, svmperf_base, **kwargs):
- super(SVMRAE, self).__init__(svmperf_base, loss='mrae', **kwargs)
-
-
-CC = ClassifyAndCount
-ACC = AdjustedClassifyAndCount
-PCC = ProbabilisticClassifyAndCount
-PACC = ProbabilisticAdjustedClassifyAndCount
-ELM = ExplicitLossMinimisationBinary
-EMQ = ExpectationMaximizationQuantifier
-HDy = HellingerDistanceY
-
+ self.dict_binary_quantifiers[c].fit(bindata, **kwargs)
\ No newline at end of file