33 lines
1023 B
Python
33 lines
1023 B
Python
import numpy
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import pytest
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.svm import LinearSVC
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import quapy as qp
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from quapy.method import AGGREGATIVE_METHODS
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datasets = [pytest.param(qp.datasets.fetch_twitter('hcr'), id='hcr'),
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pytest.param(qp.datasets.fetch_UCIDataset('ionosphere'), id='ionosphere')]
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learners = [LogisticRegression, MultinomialNB, LinearSVC]
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('aggregative_method', AGGREGATIVE_METHODS)
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@pytest.mark.parametrize('learner', learners)
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def test_aggregative_methods(dataset, aggregative_method, learner):
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model = aggregative_method(learner())
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if model.binary and not dataset.binary:
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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