refactoring the aggregative quantifiers
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@ -44,12 +44,11 @@ class AggregativeQuantifier(ABC, BaseQuantifier):
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learner has been trained outside the quantifier.
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:return: self
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"""
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classif_predictions = self.classification_fit(data, fit_classifier)
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classif_predictions = self.classifier_fit_predict(data, fit_classifier)
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self.aggregation_fit(classif_predictions)
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return self
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@abstractmethod
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def classification_fit(self, data: LabelledCollection, fit_classifier=True):
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def classifier_fit_predict(self, data: LabelledCollection, fit_classifier=True, predict_on=None):
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"""
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Trains the classifier if requested (`fit_classifier=True`) and generate the necessary predictions to
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train the aggregation function.
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@ -57,11 +56,62 @@ class AggregativeQuantifier(ABC, BaseQuantifier):
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:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
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:param fit_classifier: whether to train the learner (default is True). Set to False if the
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learner has been trained outside the quantifier.
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:param predict_on: specifies the set on which predictions need to be issued. This parameter can
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be specified as None (default) to indicate no prediction is needed; a float in (0, 1) to
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indicate the proportion of instances to be used for predictions (the remainder is used for
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training); an integer >1 to indicate that the predictions must be generated via k-fold
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cross-validation, using this integer as k; or the data sample itself on which to generate
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the predictions.
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"""
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...
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assert isinstance(fit_classifier, bool), 'unexpected type for "fit_classifier", must be boolean'
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self.__check_classifier()
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if predict_on is None:
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if fit_classifier:
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self.classifier.fit(*data.Xy)
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predictions = None
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elif isinstance(predict_on, float):
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if fit_classifier:
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if not (0. < predict_on < 1.):
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raise ValueError(f'proportion {predict_on=} out of range, must be in (0,1)')
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train, val = data.split_stratified(train_prop=(1 - predict_on))
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self.classifier.fit(*train.Xy)
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predictions = (self.classify(val.X), val.y)
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else:
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raise ValueError(f'wrong type for predict_on: since fit_classifier=False, '
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f'the set on which predictions have to be issued must be '
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f'explicitly indicated')
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elif isinstance(predict_on, LabelledCollection):
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if fit_classifier:
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self.classifier.fit(*data.Xy)
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predictions = (self.classify(predict_on.X), predict_on.y)
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elif isinstance(predict_on, int):
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if fit_classifier:
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if not predict_on > 1:
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raise ValueError(f'invalid value {predict_on} in fit. '
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f'Specify a integer >1 for kFCV estimation.')
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predictions = cross_val_predict(
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classifier, *data.Xy, cv=predict_on, n_jobs=self.n_jobs, method=self.__classifier_method())
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self.classifier.fit(*data.Xy)
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else:
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raise ValueError(f'wrong type for predict_on: since fit_classifier=False, '
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f'the set on which predictions have to be issued must be '
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f'explicitly indicated')
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else:
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raise ValueError(
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f'error: param "predict_on" ({type(predict_on)}) not understood; '
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f'use either a float indicating the split proportion, or a '
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f'tuple (X,y) indicating the validation partition')
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return predictions
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@abstractmethod
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def aggregation_fit(self, classif_predictions):
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def aggregation_fit(self, classif_predictions: LabelledCollection):
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"""
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Trains the aggregation function.
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@ -99,6 +149,13 @@ class AggregativeQuantifier(ABC, BaseQuantifier):
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"""
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return self.classifier.predict(instances)
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@property
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def __classifier_method(self):
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return 'predict'
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def __check_classifier(self, adapt_if_necessary=False):
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assert hasattr(self.classifier, 'predict')
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def quantify(self, instances):
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"""
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Generate class prevalence estimates for the sample's instances by aggregating the label predictions generated
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@ -142,106 +199,20 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier, ABC):
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def classify(self, instances):
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return self.classifier.predict_proba(instances)
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@property
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def __classifier_method(self):
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return 'predict_proba'
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# Helper
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# ------------------------------------
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def _ensure_probabilistic(classifier):
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if not hasattr(classifier, 'predict_proba'):
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print(f'The learner {classifier.__class__.__name__} does not seem to be probabilistic. '
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f'The learner will be calibrated.')
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classifier = CalibratedClassifierCV(classifier, cv=5)
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return classifier
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def _training_helper(classifier,
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data: LabelledCollection,
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fit_classifier: bool = True,
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ensure_probabilistic=False,
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val_split: Union[LabelledCollection, float] = None):
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"""
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Training procedure common to all Aggregative Quantifiers.
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:param classifier: the learner to be fit
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:param data: the data on which to fit the learner. If requested, the data will be split before fitting the learner.
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:param fit_classifier: whether or not to fit the learner (if False, then bypasses any action)
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:param ensure_probabilistic: if True, guarantees that the resulting classifier implements predict_proba (if the
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learner is not probabilistic, then a CalibratedCV instance of it is trained)
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:param val_split: if specified as a float, indicates the proportion of training instances that will define the
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validation split (e.g., 0.3 for using 30% of the training set as validation data); if specified as a
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LabelledCollection, represents the validation split itself
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:return: the learner trained on the training set, and the unused data (a _LabelledCollection_ if train_val_split>0
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or None otherwise) to be used as a validation set for any subsequent parameter fitting
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"""
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if fit_classifier:
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if ensure_probabilistic:
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classifier = _ensure_probabilistic(classifier)
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if val_split is not None:
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if isinstance(val_split, float):
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if not (0 < val_split < 1):
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raise ValueError(f'train/val split {val_split} out of range, must be in (0,1)')
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train, unused = data.split_stratified(train_prop=1 - val_split)
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elif isinstance(val_split, LabelledCollection):
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train = data
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unused = val_split
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def __check_classifier(self, adapt_if_necessary=False):
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if not hasattr(self.classifier, 'predict_proba'):
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if adapt_if_necessary:
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print(f'warning: The learner {self.classifier.__class__.__name__} does not seem to be '
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f'probabilistic. The learner will be calibrated (using CalibratedClassifierCV).')
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self.classifier = CalibratedClassifierCV(self.classifier, cv=5)
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else:
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raise ValueError(
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f'param "val_split" ({type(val_split)}) not understood; use either a float indicating the split '
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'proportion, or a LabelledCollection indicating the validation split')
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else:
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train, unused = data, None
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if isinstance(classifier, BaseQuantifier):
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classifier.fit(train)
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else:
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classifier.fit(*train.Xy)
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else:
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if ensure_probabilistic:
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if not hasattr(classifier, 'predict_proba'):
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raise AssertionError('error: the learner cannot be calibrated since fit_classifier is set to False')
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unused = None
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if isinstance(val_split, LabelledCollection):
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unused = val_split
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return classifier, unused
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def cross_generate_predictions(
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data,
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classifier,
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val_split,
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probabilistic,
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fit_classifier,
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n_jobs
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):
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n_jobs = qp._get_njobs(n_jobs)
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if isinstance(val_split, int):
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assert fit_classifier == True, \
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'the parameters for the adjustment cannot be estimated with kFCV with fit_classifier=False'
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if probabilistic:
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classifier = _ensure_probabilistic(classifier)
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predict = 'predict_proba'
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else:
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predict = 'predict'
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y_pred = cross_val_predict(classifier, *data.Xy, cv=val_split, n_jobs=n_jobs, method=predict)
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class_count = data.counts()
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# fit the learner on all data
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classifier.fit(*data.Xy)
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y = data.y
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classes = data.classes_
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else:
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classifier, val_data = _training_helper(
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classifier, data, fit_classifier, ensure_probabilistic=probabilistic, val_split=val_split
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)
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y_pred = classifier.predict_proba(val_data.instances) if probabilistic else classifier.predict(val_data.instances)
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y = val_data.labels
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classes = val_data.classes_
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class_count = val_data.counts()
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return classifier, y, y_pred, classes, class_count
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raise AssertionError(f'error: The learner {self.classifier.__class__.__name__} does not '
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f'seem to be probabilistic. The learner cannot be calibrated since '
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f'fit_classifier is set to False')
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# Methods
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@ -257,19 +228,7 @@ class CC(AggregativeQuantifier):
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def __init__(self, classifier: BaseEstimator):
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self.classifier = classifier
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def classification_fit(self, data: LabelledCollection, fit_classifier=True):
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"""
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Trains the classifier unless `fit_classifier` is False, in which case, the classifier is assumed to
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be already fit and there is nothing else to do.
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:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
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:param fit_classifier: if False, the classifier is assumed to be fit
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:return: self
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"""
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self.classifier, _ = _training_helper(self.classifier, data, fit_classifier)
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return None
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def aggregation_fit(self, classif_predictions: np.ndarray):
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def aggregation_fit(self, classif_predictions: LabelledCollection):
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"""
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Nothing to do here!
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@ -307,33 +266,11 @@ class ACC(AggregativeQuantifier):
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self.val_split = val_split
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self.n_jobs = qp._get_njobs(n_jobs)
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def classification_fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None):
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def aggregation_fit(self, classif_predictions: LabelledCollection):
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"""
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Trains the classifier and generates, optionally through a cross-validation procedure, the predictions
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needed for estimating the misclassification rates matrix.
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Estimates the misclassification rates.
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:param data: the training set
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:param fit_classifier: set to False to bypass the training (the learner is assumed to be already fit)
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:param val_split: either a float in (0,1) indicating the proportion of training instances to use for
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validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection
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indicating the validation set itself, or an int indicating the number `k` of folds to be used in `k`-fold
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cross validation to estimate the parameters
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:return: self
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"""
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if val_split is None:
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val_split = self.val_split
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self.classifier, true_labels, pred_labels, classes, class_count = cross_generate_predictions(
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data, self.classifier, val_split, probabilistic=False, fit_classifier=fit_classifier, n_jobs=self.n_jobs
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)
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return (true_labels, pred_labels)
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def aggregation_fit(self, classif_predictions):
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"""
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Nothing to do here!
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:param classif_predictions: this is actually None
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:param classif_predictions: classifier predictions with true labels
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"""
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true_labels, pred_labels = classif_predictions
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self.cc = CC(self.classifier)
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@ -393,11 +330,7 @@ class PCC(AggregativeProbabilisticQuantifier):
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def __init__(self, classifier: BaseEstimator):
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self.classifier = classifier
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def classification_fit(self, data: LabelledCollection, fit_classifier=True):
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self.classifier, _ = _training_helper(self.classifier, data, fit_classifier, ensure_probabilistic=True)
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return self
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def aggregation_fit(self, classif_predictions: np.ndarray):
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def aggregation_fit(self, classif_predictions: LabelledCollection):
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"""
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Nothing to do here!
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@ -429,33 +362,11 @@ class PACC(AggregativeProbabilisticQuantifier):
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self.val_split = val_split
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self.n_jobs = qp._get_njobs(n_jobs)
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def classification_fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None):
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def aggregation_fit(self, classif_predictions: LabelledCollection):
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"""
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Trains the soft classifier and generates, optionally through a cross-validation procedure, the posterior
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probabilities needed for estimating the misclassification rates matrix.
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Estimates the misclassification rates
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:param data: the training set
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:param fit_classifier: set to False to bypass the training (the learner is assumed to be already fit)
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:param val_split: either a float in (0,1) indicating the proportion of training instances to use for
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validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection
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indicating the validation set itself, or an int indicating the number `k` of folds to be used in `k`-fold
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cross validation to estimate the parameters
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:return: self
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"""
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if val_split is None:
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val_split = self.val_split
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self.classifier, true_labels, posteriors, classes, class_count = cross_generate_predictions(
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data, self.classifier, val_split, probabilistic=True, fit_classifier=fit_classifier, n_jobs=self.n_jobs
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)
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return (true_labels, posteriors)
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def aggregation_fit(self, classif_predictions):
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"""
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Nothing to do here!
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:param classif_predictions: this is actually None
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:param classif_predictions: classifier predictions with true labels
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"""
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true_labels, posteriors = classif_predictions
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self.pcc = PCC(self.classifier)
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@ -509,7 +420,7 @@ class EMQ(AggregativeProbabilisticQuantifier):
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self.exact_train_prev = exact_train_prev
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self.recalib = recalib
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def classification_fit(self, data: LabelledCollection, fit_classifier=True):
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def classifier_fit_predict(self, data: LabelledCollection, fit_classifier=True):
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self.classifier, true_labels, posteriors, classes, class_count = cross_generate_predictions(
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data, self.classifier, val_split, probabilistic=True, fit_classifier=fit_classifier, n_jobs=self.n_jobs
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)
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@ -842,7 +753,7 @@ class DMy(AggregativeProbabilisticQuantifier):
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distributions = np.cumsum(distributions, axis=1)
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return distributions
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def classification_fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, LabelledCollection] = None):
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def classifier_fit_predict(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, LabelledCollection] = None):
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"""
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Trains the classifier (if requested) and generates the validation distributions out of the training data.
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The validation distributions have shape `(n, ch, nbins)`, with `n` the number of classes, `ch` the number of
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@ -63,7 +63,7 @@ def newOneVsAll(binary_quantifier, n_jobs=None):
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return OneVsAllGeneric(binary_quantifier, n_jobs)
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class OneVsAllGeneric(OneVsAll,BaseQuantifier):
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class OneVsAllGeneric(OneVsAll, BaseQuantifier):
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"""
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Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary
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quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.
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