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