forked from moreo/QuaPy
46 lines
2.1 KiB
Python
46 lines
2.1 KiB
Python
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import LinearSVC
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import quapy as qp
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import quapy.functional as F
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# load a textual binary dataset and create a tfidf bag of words
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train_path = './datasets/reviews/kindle/train.txt'
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test_path = './datasets/reviews/kindle/test.txt'
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dataset = qp.Dataset.load(train_path, test_path, qp.reader.from_text)
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dataset.training = dataset.training.sampling(1000, 0.4, 0.6)
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dataset.test = dataset.test.sampling(500, 0.6, 0.4)
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qp.preprocessing.text2tfidf(dataset, inplace=True)
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qp.preprocessing.reduce_columns(dataset, min_df=10, inplace=True)
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# load a sparse matrix ternary dataset
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#train_path = './datasets/twitter/train/sst.train+dev.feature.txt'
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#test_path = './datasets/twitter/test/sst.test.feature.txt'
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#dataset = qp.Dataset.load(train_path, test_path, qp.reader.from_sparse)
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#dataset.training = dataset.training.sampling(500, 0.3, 0.2, 0.5)
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#dataset.test = dataset.test.sampling(500, 0.2, 0.5, 0.3)
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# training a quantifier
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learner = LogisticRegression()
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# q = qp.method.aggregative.ClassifyAndCount(learner)
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# q = qp.method.aggregative.AdjustedClassifyAndCount(learner)
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# q = qp.method.aggregative.AdjustedClassifyAndCount(learner)
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# q = qp.method.aggregative.ProbabilisticClassifyAndCount(learner)
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# q = qp.method.aggregative.ProbabilisticAdjustedClassifyAndCount(learner)
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# q = qp.method.aggregative.ExpectationMaximizationQuantifier(learner)
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# q = qp.method.aggregative.ExplicitLossMinimisation(svmperf_base='./svm_perf_quantification', loss='q', verbose=0, C=1000)
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# q = qp.method.aggregative.SVMQ(svmperf_base='./svm_perf_quantification', verbose=0, C=1000)
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q = qp.method.aggregative.HDy(learner)
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q.fit(dataset.training)
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# estimating class prevalences
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prevalences_estim = q.quantify(dataset.test.instances)
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prevalences_true = dataset.test.prevalence()
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# evaluation (one single prediction)
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error = qp.error.mae(prevalences_true, prevalences_estim)
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print(f'method {q.__class__.__name__}')
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print(f'true prevalence {F.strprev(prevalences_true)}')
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print(f'estim prevalence {F.strprev(prevalences_estim)}')
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print(f'MAE={error:.3f}')
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