forked from moreo/QuaPy
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.svm import LinearSVC
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from NewMethods.fgsld.fine_grained_sld import FineGrainedSLD
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from method.aggregative import EMQ, CC
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from quapy.data import LabelledCollection
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from quapy.method.base import BaseQuantifier
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import quapy as qp
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import quapy.functional as F
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from sklearn.linear_model import LogisticRegression
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class FakeFGLSD(BaseQuantifier):
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def __init__(self, learner, nbins, isomerous):
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self.learner = learner
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self.nbins = nbins
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self.isomerous = isomerous
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def fit(self, data: LabelledCollection):
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self.Xtr, self.ytr = data.Xy
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self.learner.fit(self.Xtr, self.ytr)
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return self
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def quantify(self, instances):
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tr_priors = F.prevalence_from_labels(self.ytr, n_classes=2)
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fgsld = FineGrainedSLD(self.Xtr, instances, self.ytr, tr_priors, self.learner, n_bins=self.nbins)
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priors, posteriors = fgsld.run(self.isomerous)
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return priors
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def get_params(self, deep=True):
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pass
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def set_params(self, **parameters):
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pass
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qp.environ['SAMPLE_SIZE'] = 500
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dataset = qp.datasets.fetch_reviews('hp')
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qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
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training = dataset.training
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test = dataset.test
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cls = CalibratedClassifierCV(LinearSVC())
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method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
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for model, model_name in [
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(CC(cls), 'CC'),
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(FakeFGLSD(cls, nbins=1, isomerous=False), 'FGSLD-1'),
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(FakeFGLSD(cls, nbins=2, isomerous=False), 'FGSLD-2'),
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#(FakeFGLSD(cls, nbins=5, isomerous=False), 'FGSLD-5'),
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#(FakeFGLSD(cls, nbins=10, isomerous=False), 'FGSLD-10'),
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#(FakeFGLSD(cls, nbins=50, isomerous=False), 'FGSLD-50'),
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#(FakeFGLSD(cls, nbins=100, isomerous=False), 'FGSLD-100'),
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# (FakeFGLSD(cls, nbins=1, isomerous=False), 'FGSLD-1'),
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#(FakeFGLSD(cls, nbins=10, isomerous=True), 'FGSLD-10-ISO'),
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# (FakeFGLSD(cls, nbins=50, isomerous=False), 'FGSLD-50'),
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(EMQ(cls), 'SLD'),
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]:
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print('running ', model_name)
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model.fit(training)
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true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
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model, test, qp.environ['SAMPLE_SIZE'], n_repetitions=10, n_prevpoints=21, n_jobs=-1
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)
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method_names.append(model_name)
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true_prevs.append(true_prev)
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estim_prevs.append(estim_prev)
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tr_prevs.append(training.prevalence())
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, train_prev=tr_prevs[0], savepath='./plot_fglsd.png')
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