from abc import abstractmethod from copy import deepcopy from typing import Union import numpy as np from joblib import Parallel, delayed from sklearn.base import BaseEstimator from sklearn.calibration import CalibratedClassifierCV from sklearn.metrics import confusion_matrix from sklearn.model_selection import StratifiedKFold from tqdm import tqdm import quapy as qp import quapy.functional as F from quapy.classification.svmperf import SVMperf from quapy.data import LabelledCollection from quapy.method.base import BaseQuantifier, BinaryQuantifier # Abstract classes # ------------------------------------ class AggregativeQuantifier(BaseQuantifier): """ Abstract class for quantification methods that base their estimations on the aggregation of classification results. Aggregative Quantifiers thus implement a _classify_ method and maintain a _learner_ attribute. """ @abstractmethod def fit(self, data: LabelledCollection, fit_learner=True): ... @property def learner(self): return self.learner_ @learner.setter def learner(self, value): self.learner_ = value def classify(self, instances): return self.learner.predict(instances) def quantify(self, instances): classif_predictions = self.classify(instances) return self.aggregate(classif_predictions) @abstractmethod def aggregate(self, classif_predictions:np.ndarray): ... def get_params(self, deep=True): return self.learner.get_params() def set_params(self, **parameters): self.learner.set_params(**parameters) @property def n_classes(self): return len(self.classes) @property def classes(self): return self.learner.classes_ @property def aggregative(self): return True 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 Quantifiers by implementing a _posterior_probabilities_ method returning values in [0,1] -- the posterior probabilities. """ def posterior_probabilities(self, instances): return self.learner.predict_proba(instances) def predict_proba(self, instances): return self.posterior_probabilities(instances) def quantify(self, instances): classif_posteriors = self.posterior_probabilities(instances) return self.aggregate(classif_posteriors) def set_params(self, **parameters): if isinstance(self.learner, CalibratedClassifierCV): parameters = {'base_estimator__'+k:v for k,v in parameters.items()} self.learner.set_params(**parameters) @property def probabilistic(self): return True # Helper # ------------------------------------ def training_helper(learner, data: LabelledCollection, fit_learner: bool = True, ensure_probabilistic=False, val_split:Union[LabelledCollection, float]=None): """ Training procedure common to all Aggregative Quantifiers. :param learner: the learner to be fit :param data: the data on which to fit the learner. If requested, the data will be split before fitting the learner. :param fit_learner: whether or not to fit the learner (if False, then bypasses any action) :param ensure_probabilistic: if True, guarantees that the resulting classifier implements predict_proba (if the learner is not probabilistic, then a CalibratedCV instance of it is trained) :param val_split: if specified as a float, indicates the proportion of training instances that will define the validation split (e.g., 0.3 for using 30% of the training set as validation data); if specified as a LabelledCollection, represents the validation split itself :return: the learner trained on the training set, and the unused data (a _LabelledCollection_ if train_val_split>0 or None otherwise) to be used as a validation set for any subsequent parameter fitting """ if fit_learner: if ensure_probabilistic: if not hasattr(learner, 'predict_proba'): print(f'The learner {learner.__class__.__name__} does not seem to be probabilistic. ' f'The learner will be calibrated.') learner = CalibratedClassifierCV(learner, cv=5) if val_split is not None: if isinstance(val_split, float): if not (0 < val_split < 1): raise ValueError(f'train/val split {val_split} out of range, must be in (0,1)') train, unused = data.split_stratified(train_prop=1-val_split) elif val_split.__class__.__name__ == LabelledCollection.__name__: #isinstance(val_split, LabelledCollection): train = data unused = val_split else: raise ValueError(f'param "val_split" ({type(val_split)}) not understood; use either a float indicating the split ' 'proportion, or a LabelledCollection indicating the validation split') else: train, unused = data, None if isinstance(learner, BaseQuantifier): learner.fit(train) else: learner.fit(train.instances, train.labels) else: if ensure_probabilistic: if not hasattr(learner, 'predict_proba'): raise AssertionError('error: the learner cannot be calibrated since fit_learner is set to False') unused = data return learner, unused # Methods # ------------------------------------ class CC(AggregativeQuantifier): """ The most basic Quantification method. One that simply classifies all instances and countes how many have been attributed each of the classes in order to compute class prevalence estimates. """ def __init__(self, learner:BaseEstimator): self.learner = learner def fit(self, data: LabelledCollection, fit_learner=True): """ Trains the Classify & Count method unless _fit_learner_ is False, in which case it is assumed to be already fit. :param data: training data :param fit_learner: if False, the classifier is assumed to be fit :return: self """ self.learner, _ = training_helper(self.learner, data, fit_learner) return self def aggregate(self, classif_predictions): return F.prevalence_from_labels(classif_predictions, self.n_classes) class ACC(AggregativeQuantifier): def __init__(self, learner:BaseEstimator, val_split=0.4): self.learner = learner self.val_split = val_split def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, int, LabelledCollection]=None): """ Trains a ACC quantifier :param data: the training set :param fit_learner: set to False to bypass the training (the learner is assumed to be already fit) :param val_split: either a float in (0,1) indicating the proportion of training instances to use for validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection indicating the validation set itself, or an int indicating the number k of folds to be used in kFCV to estimate the parameters :return: self """ if val_split is None: val_split = self.val_split if isinstance(val_split, int): # kFCV estimation of parameters y, y_ = [], [] kfcv = StratifiedKFold(n_splits=val_split) pbar = tqdm(kfcv.split(*data.Xy), total=val_split) for k, (training_idx, validation_idx) in enumerate(pbar): pbar.set_description(f'{self.__class__.__name__} fitting fold {k}') training = data.sampling_from_index(training_idx) validation = data.sampling_from_index(validation_idx) learner, val_data = training_helper(self.learner, training, fit_learner, val_split=validation) y_.append(learner.predict(val_data.instances)) y.append(val_data.labels) y = np.concatenate(y) y_ = np.concatenate(y_) class_count = data.counts() # fit the learner on all data self.learner.fit(*data.Xy) else: self.learner, val_data = training_helper(self.learner, data, fit_learner, val_split=val_split) y_ = self.learner.predict(val_data.instances) y = val_data.labels class_count = val_data.counts() self.cc = CC(self.learner) # estimate the matrix with entry (i,j) being the estimate of P(yi|yj), that is, the probability that a # document that belongs to yj ends up being classified as belonging to yi self.Pte_cond_estim_ = confusion_matrix(y, y_).T / class_count return self def classify(self, data): return self.cc.classify(data) def aggregate(self, classif_predictions): prevs_estim = self.cc.aggregate(classif_predictions) return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim) @classmethod def solve_adjustment(cls, PteCondEstim, prevs_estim): # solve for the linear system Ax = B with A=PteCondEstim and B = prevs_estim A = PteCondEstim B = prevs_estim try: adjusted_prevs = np.linalg.solve(A, B) adjusted_prevs = np.clip(adjusted_prevs, 0, 1) adjusted_prevs /= adjusted_prevs.sum() except np.linalg.LinAlgError: adjusted_prevs = prevs_estim # no way to adjust them! return adjusted_prevs class PCC(AggregativeProbabilisticQuantifier): def __init__(self, learner: BaseEstimator): self.learner = learner def fit(self, data: LabelledCollection, fit_learner=True): self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True) return self def aggregate(self, classif_posteriors): return F.prevalence_from_probabilities(classif_posteriors, binarize=False) class PACC(AggregativeProbabilisticQuantifier): def __init__(self, learner: BaseEstimator, val_split=0.4): self.learner = learner self.val_split = val_split def fit(self, data: LabelledCollection, fit_learner=True, val_split:Union[float, int, LabelledCollection]=None): """ Trains a PACC quantifier :param data: the training set :param fit_learner: set to False to bypass the training (the learner is assumed to be already fit) :param val_split: either a float in (0,1) indicating the proportion of training instances to use for validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection indicating the validation set itself, or an int indicating the number k of folds to be used in kFCV to estimate the parameters :return: self """ if val_split is None: val_split = self.val_split if isinstance(val_split, int): # kFCV estimation of parameters y, y_ = [], [] kfcv = StratifiedKFold(n_splits=val_split) pbar = tqdm(kfcv.split(*data.Xy), total=val_split) for k, (training_idx, validation_idx) in enumerate(pbar): pbar.set_description(f'{self.__class__.__name__} fitting fold {k}') training = data.sampling_from_index(training_idx) validation = data.sampling_from_index(validation_idx) learner, val_data = training_helper( self.learner, training, fit_learner, ensure_probabilistic=True, val_split=validation) y_.append(learner.predict_proba(val_data.instances)) y.append(val_data.labels) y = np.concatenate(y) y_ = np.vstack(y_) # fit the learner on all data self.learner.fit(*data.Xy) else: self.learner, val_data = training_helper( self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split) y_ = self.learner.predict_proba(val_data.instances) y = val_data.labels self.pcc = PCC(self.learner) # estimate the matrix with entry (i,j) being the estimate of P(yi|yj), that is, the probability that a # document that belongs to yj ends up being classified as belonging to yi confusion = np.empty(shape=(data.n_classes, data.n_classes)) for yi in range(data.n_classes): confusion[yi] = y_[y==yi].mean(axis=0) self.Pte_cond_estim_ = confusion.T return self def aggregate(self, classif_posteriors): prevs_estim = self.pcc.aggregate(classif_posteriors) return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim) def classify(self, data): return self.pcc.classify(data) class EMQ(AggregativeProbabilisticQuantifier): MAX_ITER = 1000 EPSILON = 1e-4 def __init__(self, learner: BaseEstimator): self.learner = learner def fit(self, data: LabelledCollection, fit_learner=True): self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True) self.train_prevalence = F.prevalence_from_labels(data.labels, self.n_classes) return self def aggregate(self, classif_posteriors, epsilon=EPSILON): priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon) return priors def predict_proba(self, instances, epsilon=EPSILON): classif_posteriors = self.learner.predict_proba(instances) priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon) return posteriors @classmethod 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 s, converged = 0, False qs_prev_ = None while not converged and s < EMQ.MAX_ITER: # E-step: ps is Ps(y=+1|xi) ps_unnormalized = (qs / Ptr) * Px ps = ps_unnormalized / ps_unnormalized.sum(axis=1).reshape(-1,1) # M-step: qs_pos is Ps+1(y=+1) qs = ps.mean(axis=0) if qs_prev_ is not None and qp.error.mae(qs, qs_prev_) < epsilon and s>10: converged = True qs_prev_ = qs s += 1 if not converged: #raise UserWarning('the method has reached the maximum number of iterations; it might have not converged') print('[warning] the method has reached the maximum number of iterations; it might have not converged') return qs, ps class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier): """ Implementation of the method based on the Hellinger Distance y (HDy) proposed by González-Castro, V., Alaiz-Rodrı́guez, R., and Alegre, E. (2013). Class distribution estimation based on the Hellinger distance. Information Sciences, 218:146–164. """ def __init__(self, learner: BaseEstimator, val_split=0.4): self.learner = learner self.val_split = val_split def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, LabelledCollection]=None): """ Trains a HDy quantifier :param data: the training set :param fit_learner: set to False to bypass the training (the learner is assumed to be already fit) :param val_split: either a float in (0,1) indicating the proportion of training instances to use for validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection indicating the validation set itself :return: self """ if val_split is None: val_split = self.val_split self._check_binary(data, self.__class__.__name__) self.learner, validation = training_helper( self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split) Px = self.posterior_probabilities(validation.instances)[:,1] # takes only the P(y=+1|x) self.Pxy1 = Px[validation.labels == 1] self.Pxy0 = Px[validation.labels == 0] # pre-compute the histogram for positive and negative examples self.bins = np.linspace(10, 110, 11, dtype=int) #[10, 20, 30, ..., 100, 110] self.Pxy1_density = {bins: np.histogram(self.Pxy1, bins=bins, range=(0, 1), density=True)[0] for bins in self.bins} self.Pxy0_density = {bins: np.histogram(self.Pxy0, bins=bins, range=(0, 1), density=True)[0] for bins in self.bins} return self def aggregate(self, classif_posteriors): # "In this work, the number of bins b used in HDx and HDy was chosen from 10 to 110 in steps of 10, # and the final estimated a priori probability was taken as the median of these 11 estimates." # (González-Castro, et al., 2013). Px = classif_posteriors[:,1] # takes only the P(y=+1|x) prev_estimations = [] #for bins in np.linspace(10, 110, 11, dtype=int): #[10, 20, 30, ..., 100, 110] #Pxy0_density, _ = np.histogram(self.Pxy0, bins=bins, range=(0, 1), density=True) #Pxy1_density, _ = np.histogram(self.Pxy1, bins=bins, range=(0, 1), density=True) for bins in self.bins: Pxy0_density = self.Pxy0_density[bins] Pxy1_density = self.Pxy1_density[bins] Px_test, _ = np.histogram(Px, bins=bins, range=(0, 1), density=True) prev_selected, min_dist = None, None for prev in F.prevalence_linspace(n_prevalences=100, repeat=1, smooth_limits_epsilon=0.0): Px_train = prev*Pxy1_density + (1 - prev)*Pxy0_density hdy = F.HellingerDistance(Px_train, Px_test) if prev_selected is None or hdy < min_dist: prev_selected, min_dist = prev, hdy prev_estimations.append(prev_selected) pos_class_prev = np.median(prev_estimations) return np.asarray([1-pos_class_prev, pos_class_prev]) class ELM(AggregativeQuantifier, BinaryQuantifier): def __init__(self, svmperf_base, loss, **kwargs): self.svmperf_base = svmperf_base self.loss = loss self.kwargs = kwargs self.learner = SVMperf(self.svmperf_base, loss=self.loss, **self.kwargs) def fit(self, data: LabelledCollection, fit_learner=True): self._check_binary(data, self.__class__.__name__) assert fit_learner, 'the method requires that fit_learner=True' self.learner.fit(data.instances, data.labels) return self def aggregate(self, classif_predictions:np.ndarray): return F.prevalence_from_labels(classif_predictions, self.learner.n_classes_) def classify(self, X, y=None): return self.learner.predict(X) class SVMQ(ELM): def __init__(self, svmperf_base, **kwargs): super(SVMQ, self).__init__(svmperf_base, loss='q', **kwargs) class SVMKLD(ELM): def __init__(self, svmperf_base, **kwargs): super(SVMKLD, self).__init__(svmperf_base, loss='kld', **kwargs) class SVMNKLD(ELM): def __init__(self, svmperf_base, **kwargs): super(SVMNKLD, self).__init__(svmperf_base, loss='nkld', **kwargs) class SVMAE(ELM): def __init__(self, svmperf_base, **kwargs): super(SVMAE, self).__init__(svmperf_base, loss='mae', **kwargs) class SVMRAE(ELM): def __init__(self, svmperf_base, **kwargs): super(SVMRAE, self).__init__(svmperf_base, loss='mrae', **kwargs) ClassifyAndCount = CC AdjustedClassifyAndCount = ACC ProbabilisticClassifyAndCount = PCC ProbabilisticAdjustedClassifyAndCount = PACC ExpectationMaximizationQuantifier = EMQ HellingerDistanceY = HDy ExplicitLossMinimisation = ELM 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 Mining 6(19), 1–22 (2016) """ def __init__(self, binary_quantifier, n_jobs=-1): self.binary_quantifier = binary_quantifier self.n_jobs = n_jobs def fit(self, data: LabelledCollection, fit_learner=True): assert not data.binary, \ f'{self.__class__.__name__} expect non-binary data' assert isinstance(self.binary_quantifier, BaseQuantifier), \ f'{self.binary_quantifier} does not seem to be a Quantifier' assert fit_learner==True, 'fit_learner must be True' self.dict_binary_quantifiers = {c: deepcopy(self.binary_quantifier) for c in data.classes_} self.__parallel(self._delayed_binary_fit, data) return self def classify(self, instances): # returns a matrix of shape (n,m) with n the number of instances and m the number of classes. The entry # (i,j) is a binary value indicating whether instance i belongs to class j. The binary classifications are # independent of each other, meaning that an instance can end up be attributed to 0, 1, or more classes. classif_predictions_bin = self.__parallel(self._delayed_binary_classification, instances) return classif_predictions_bin.T def posterior_probabilities(self, instances): # returns a matrix of shape (n,m,2) with n the number of instances and m the number of classes. The entry # (i,j,1) (resp. (i,j,0)) is a value in [0,1] indicating the posterior probability that instance i belongs # (resp. does not belong) to class j. # The posterior probabilities are independent of each other, meaning that, in general, they do not sum # up to one. if not self.binary_quantifier.probabilistic: raise NotImplementedError(f'{self.__class__.__name__} does not implement posterior_probabilities because ' f'the base quantifier {self.binary_quantifier.__class__.__name__} is not ' f'probabilistic') posterior_predictions_bin = self.__parallel(self._delayed_binary_posteriors, instances) return np.swapaxes(posterior_predictions_bin, 0, 1) def aggregate(self, classif_predictions_bin): if self.probabilistic: assert classif_predictions_bin.shape[1]==self.n_classes and classif_predictions_bin.shape[2]==2, \ 'param classif_predictions_bin does not seem to be a valid matrix (ndarray) of posterior ' \ 'probabilities (2 dimensions) for each document (row) and class (columns)' else: assert set(np.unique(classif_predictions_bin)).issubset({0,1}), \ 'param classif_predictions_bin does not seem to be a valid matrix (ndarray) of binary ' \ 'predictions for each document (row) and class (columns)' prevalences = self.__parallel(self._delayed_binary_aggregate, classif_predictions_bin) return F.normalize_prevalence(prevalences) def quantify(self, X): if self.probabilistic: predictions = self.posterior_probabilities(X) else: predictions = self.classify(X) return self.aggregate(predictions) def __parallel(self, func, *args, **kwargs): return np.asarray( Parallel(n_jobs=self.n_jobs, backend='threading')( delayed(func)(c, *args, **kwargs) for c in self.classes ) ) @property def classes(self): return sorted(self.dict_binary_quantifiers.keys()) def set_params(self, **parameters): self.binary_quantifier.set_params(**parameters) def get_params(self, deep=True): return self.binary_quantifier.get_params() def _delayed_binary_classification(self, c, X): return self.dict_binary_quantifiers[c].classify(X) def _delayed_binary_posteriors(self, c, X): return self.dict_binary_quantifiers[c].posterior_probabilities(X) def _delayed_binary_aggregate(self, c, classif_predictions): # the estimation for the positive class prevalence return self.dict_binary_quantifiers[c].aggregate(classif_predictions[:, c])[1] def _delayed_binary_fit(self, c, data): bindata = LabelledCollection(data.instances, data.labels == c, n_classes=2) self.dict_binary_quantifiers[c].fit(bindata) @property def binary(self): return False @property def probabilistic(self): return self.binary_quantifier.probabilistic