54 lines
2.1 KiB
Python
54 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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SAMPLE_SIZE=500
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binary = False
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if binary:
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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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qp.preprocessing.text2tfidf(dataset, inplace=True)
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qp.preprocessing.reduce_columns(dataset, min_df=10, inplace=True)
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else:
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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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# training a quantifier
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learner = LogisticRegression()
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model = qp.method.aggregative.ClassifyAndCount(learner)
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# model = qp.method.aggregative.AdjustedClassifyAndCount(learner)
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# model = qp.method.aggregative.AdjustedClassifyAndCount(learner)
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# model = qp.method.aggregative.ProbabilisticClassifyAndCount(learner)
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# model = qp.method.aggregative.ProbabilisticAdjustedClassifyAndCount(learner)
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# model = qp.method.aggregative.ExpectationMaximizationQuantifier(learner)
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model.fit(dataset.training)
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# estimating class prevalences
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prevalences_estim = model.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 {model.__class__.__name__}')
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print(f'Evaluation in test (1 eval)')
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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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true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(model, dataset.test, SAMPLE_SIZE)
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qp.error.SAMPLE_SIZE=SAMPLE_SIZE
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print(f'Evaluation according to the artificial sampling protocol ({len(true_prev)} evals)')
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for error in qp.error.QUANTIFICATION_ERROR:
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score = error(true_prev, estim_prev)
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print(f'{error.__name__}={score:.5f}')
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