forked from moreo/QuaPy
436 lines
18 KiB
Python
436 lines
18 KiB
Python
import numpy as np
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from copy import deepcopy
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import functional as F
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import error
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from method.base import BaseQuantifier
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from quapy.classification.svmperf import SVMperf
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from quapy.data import LabelledCollection
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from sklearn.metrics import confusion_matrix
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from sklearn.calibration import CalibratedClassifierCV
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from joblib import Parallel, delayed
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from abc import abstractmethod
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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, *args): ...
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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, *args):
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classif_predictions = self.classify(instances)
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return self.aggregate(classif_predictions, *args)
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@abstractmethod
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def aggregate(self, classif_predictions:np.ndarray, *args): ...
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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, data):
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return self.learner.predict_proba(data)
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def quantify(self, instances, *args):
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classif_posteriors = self.posterior_probabilities(instances)
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return self.aggregate(classif_posteriors, *args)
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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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train_val_split=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 train_val_split: if specified, indicates the proportion of training instances on which to fit the learner
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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 train_val_split is not None:
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if not (0 < train_val_split < 1):
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raise ValueError(f'train/val split {train_val_split} out of range, must be in (0,1)')
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train, unused = data.split_stratified(train_prop=train_val_split)
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else:
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train, unused = data, None
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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 ClassifyAndCount(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):
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self.learner = learner
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def fit(self, data: LabelledCollection, fit_learner=True, *args):
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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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:param args: unused
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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, *args):
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return F.prevalence_from_labels(classif_predictions, self.n_classes)
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class AdjustedClassifyAndCount(AggregativeQuantifier):
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def __init__(self, learner):
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self.learner = learner
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def fit(self, data: LabelledCollection, fit_learner=True, train_val_split=0.6):
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self.learner, validation = training_helper(self.learner, data, fit_learner, train_val_split=train_val_split)
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self.cc = ClassifyAndCount(self.learner)
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y_ = self.classify(validation.instances)
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y = validation.labels
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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 / validation.counts()
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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, *args):
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prevs_estim = self.cc.aggregate(classif_predictions)
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return AdjustedClassifyAndCount.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 ProbabilisticClassifyAndCount(AggregativeProbabilisticQuantifier):
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def __init__(self, learner):
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self.learner = learner
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def fit(self, data : LabelledCollection, fit_learner=True, *args):
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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, *args):
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return F.prevalence_from_probabilities(classif_posteriors, binarize=False)
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class ProbabilisticAdjustedClassifyAndCount(AggregativeProbabilisticQuantifier):
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def __init__(self, learner):
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self.learner = learner
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def fit(self, data: LabelledCollection, fit_learner=True, train_val_split=0.6):
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self.learner, validation = training_helper(
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self.learner, data, fit_learner, ensure_probabilistic=True, train_val_split=train_val_split
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)
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self.pcc = ProbabilisticClassifyAndCount(self.learner)
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y_ = self.classify(validation.instances)
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y = validation.labels
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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 / validation.counts()
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return self
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def aggregate(self, classif_posteriors, *args):
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prevs_estim = self.pcc.aggregate(classif_posteriors)
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return AdjustedClassifyAndCount.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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def soft_classify(self, data):
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return self.pcc.posterior_probabilities(data)
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class ExpectationMaximizationQuantifier(AggregativeProbabilisticQuantifier):
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MAX_ITER = 1000
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EPSILON = 1e-4
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def __init__(self, learner):
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self.learner = learner
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def fit(self, data: LabelledCollection, fit_learner=True, *args):
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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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return self.EM(self.train_prevalence, classif_posteriors, epsilon)
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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 < ExpectationMaximizationQuantifier.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 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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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
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class HellingerDistanceY(AggregativeProbabilisticQuantifier):
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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):
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self.learner = learner
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def fit(self, data: LabelledCollection, fit_learner=True, train_val_split=0.6):
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assert data.binary, f'{self.__class__.__name__} works only on problems of binary classification. ' \
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f'Use the class OneVsAll to enable {self.__class__.__name__} work on single-label data.'
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self.learner, validation = training_helper(
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self.learner, data, fit_learner, ensure_probabilistic=True, train_val_split=train_val_split)
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Px = self.posterior_probabilities(validation.instances)
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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, *args):
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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
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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 = HellingerDistanceY.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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@classmethod
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def HellingerDistance(cls, P, Q):
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return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2))
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class ExplicitLossMinimisation(AggregativeQuantifier):
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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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def fit(self, data: LabelledCollection, fit_learner=True, *args):
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assert data.binary, f'{self.__class__.__name__} works only on problems of binary classification' \
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f'Use the class OneVsAll to enable {self.__class__.__name__} work on single-label data.'
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assert fit_learner, 'the method requires that fit_learner=True'
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self.learner = SVMperf(self.svmperf_base, loss=self.loss, **self.kwargs).fit(data.instances, data.labels)
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return self
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def aggregate(self, classif_predictions:np.ndarray, *args):
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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(ExplicitLossMinimisation):
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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(ExplicitLossMinimisation):
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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(ExplicitLossMinimisation):
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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(ExplicitLossMinimisation):
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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(ExplicitLossMinimisation):
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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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CC = ClassifyAndCount
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ACC = AdjustedClassifyAndCount
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PCC = ProbabilisticClassifyAndCount
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PACC = ProbabilisticAdjustedClassifyAndCount
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ELM = ExplicitLossMinimisation
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EMQ = ExpectationMaximizationQuantifier
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HDy = HellingerDistanceY
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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 Mining6(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, **kwargs):
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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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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, **kwargs)
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return self
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def classify(self, instances):
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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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def aggregate(self, classif_predictions_bin, *args):
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assert set(np.unique(classif_predictions_bin)) == {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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return F.normalize_prevalence(prevalences)
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def quantify(self, X, *args):
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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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)
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)
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@property
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def classes(self):
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return sorted(self.dict_binary_quantifiers.keys())
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def set_params(self, **parameters):
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self.binary_quantifier.set_params(**parameters)
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def get_params(self, deep=True):
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return self.binary_quantifier.get_params()
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def _delayed_binary_classification(self, c, X):
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return self.dict_binary_quantifiers[c].classify(X)
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def _delayed_binary_quantify(self, c, X):
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return self.dict_binary_quantifiers[c].quantify(X)[1] # the estimation for the positive class prevalence
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def _delayed_binary_aggregate(self, c, classif_predictions):
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return self.dict_binary_quantifiers[c].aggregate(classif_predictions[:,c])[1] # the estimation for the positive class prevalence
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def _delayed_binary_fit(self, c, data, **kwargs):
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bindata = LabelledCollection(data.instances, data.labels == c, n_classes=2)
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self.dict_binary_quantifiers[c].fit(bindata, **kwargs) |