forked from moreo/QuaPy
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
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from quapy.method.base import BaseQuantifier
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import numpy as np
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from distribution_matching.method.kdey import KDEBase
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import quapy as qp
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from quapy.data import LabelledCollection
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import quapy.functional as F
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from sklearn.preprocessing import StandardScaler
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class KDExML(BaseQuantifier, KDEBase):
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def __init__(self, bandwidth=0.1, standardize=True):
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self._check_bandwidth(bandwidth)
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self.bandwidth = bandwidth
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self.standardize = standardize
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def fit(self, data: LabelledCollection):
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X, y = data.Xy
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if self.standardize:
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self.scaler = StandardScaler()
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X = self.scaler.fit_transform(X)
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self.mix_densities = self.get_mixture_components(X, y, data.n_classes, self.bandwidth)
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return self
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def quantify(self, X):
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"""
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Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood
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of the data (i.e., that minimizes the negative log-likelihood)
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:param X: instances in the sample
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:return: a vector of class prevalence estimates
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"""
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epsilon = 1e-10
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n_classes = len(self.mix_densities)
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if self.standardize:
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X = self.scaler.transform(X)
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test_densities = [self.pdf(kde_i, X) for kde_i in self.mix_densities]
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def neg_loglikelihood(prev):
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test_mixture_likelihood = sum(prev_i * dens_i for prev_i, dens_i in zip (prev, test_densities))
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test_loglikelihood = np.log(test_mixture_likelihood + epsilon)
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return -np.sum(test_loglikelihood)
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return F.optim_minimize(neg_loglikelihood, n_classes)
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