refactored unittests
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import unittest
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class ImportTest(unittest.TestCase):
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def test_import(self):
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import quapy as qp
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self.assertIsNotNone(qp.__version__)
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if __name__ == '__main__':
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unittest.main()
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import unittest
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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import quapy.functional as F
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from quapy.method.aggregative import PCC
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from quapy.data.datasets import *
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class TestDatasets(unittest.TestCase):
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def new_quantifier(self):
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return PCC(LogisticRegression(C=0.001, max_iter=100))
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def _check_dataset(self, dataset):
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q = self.new_quantifier()
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print(f'testing method {q} in {dataset.name}...', end='')
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q.fit(dataset.training)
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estim_prevalences = q.quantify(dataset.test.instances)
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self.assertTrue(F.check_prevalence_vector(estim_prevalences))
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print(f'[done]')
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def _check_samples(self, gen, q, max_samples_test=5, vectorizer=None):
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for X, p in gen():
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if vectorizer is not None:
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X = vectorizer.transform(X)
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estim_prevalences = q.quantify(X)
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self.assertTrue(F.check_prevalence_vector(estim_prevalences))
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max_samples_test -= 1
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if max_samples_test == 0:
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break
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def test_reviews(self):
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for dataset_name in REVIEWS_SENTIMENT_DATASETS:
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print(f'loading dataset {dataset_name}...', end='')
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dataset = fetch_reviews(dataset_name, tfidf=True, min_df=10)
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dataset.stats()
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dataset.reduce()
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print(f'[done]')
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self._check_dataset(dataset)
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def test_twitter(self):
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for dataset_name in TWITTER_SENTIMENT_DATASETS_TEST:
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print(f'loading dataset {dataset_name}...', end='')
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dataset = fetch_twitter(dataset_name, min_df=10)
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dataset.stats()
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dataset.reduce()
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print(f'[done]')
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self._check_dataset(dataset)
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def test_UCIBinaryDataset(self):
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for dataset_name in UCI_BINARY_DATASETS:
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try:
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print(f'loading dataset {dataset_name}...', end='')
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dataset = fetch_UCIBinaryDataset(dataset_name)
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dataset.stats()
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dataset.reduce()
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print(f'[done]')
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self._check_dataset(dataset)
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except FileNotFoundError as fnfe:
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if dataset_name == 'pageblocks.5' and fnfe.args[0].find(
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'If this is the first time you attempt to load this dataset') > 0:
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print('The pageblocks.5 dataset requires some hand processing to be usable; skipping this test.')
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continue
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def test_UCIMultiDataset(self):
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for dataset_name in UCI_MULTICLASS_DATASETS:
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print(f'loading dataset {dataset_name}...', end='')
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dataset = fetch_UCIMulticlassDataset(dataset_name)
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dataset.stats()
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n_classes = dataset.n_classes
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uniform_prev = F.uniform_prevalence(n_classes)
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dataset.training = dataset.training.sampling(100, *uniform_prev)
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dataset.test = dataset.test.sampling(100, *uniform_prev)
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print(f'[done]')
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self._check_dataset(dataset)
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def test_lequa2022(self):
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for dataset_name in LEQUA2022_VECTOR_TASKS:
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print(f'loading dataset {dataset_name}...', end='')
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train, gen_val, gen_test = fetch_lequa2022(dataset_name)
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train.stats()
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n_classes = train.n_classes
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train = train.sampling(100, *F.uniform_prevalence(n_classes))
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q = self.new_quantifier()
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q.fit(train)
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self._check_samples(gen_val, q, max_samples_test=5)
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self._check_samples(gen_test, q, max_samples_test=5)
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for dataset_name in LEQUA2022_TEXT_TASKS:
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print(f'loading dataset {dataset_name}...', end='')
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train, gen_val, gen_test = fetch_lequa2022(dataset_name)
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train.stats()
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n_classes = train.n_classes
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train = train.sampling(100, *F.uniform_prevalence(n_classes))
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tfidf = TfidfVectorizer()
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train.instances = tfidf.fit_transform(train.instances)
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q = self.new_quantifier()
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q.fit(train)
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self._check_samples(gen_val, q, max_samples_test=5, vectorizer=tfidf)
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self._check_samples(gen_test, q, max_samples_test=5, vectorizer=tfidf)
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def test_IFCB(self):
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print(f'loading dataset IFCB.')
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for mod_sel in [False, True]:
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train, gen = fetch_IFCB(single_sample_train=True, for_model_selection=mod_sel)
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train.stats()
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n_classes = train.n_classes
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train = train.sampling(100, *F.uniform_prevalence(n_classes))
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q = self.new_quantifier()
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q.fit(train)
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self._check_samples(gen, q, max_samples_test=5)
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if __name__ == '__main__':
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unittest.main()
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import unittest
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from sklearn.linear_model import LogisticRegression
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from quapy.method import AGGREGATIVE_METHODS, BINARY_METHODS
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from quapy.method.aggregative import *
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import inspect
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class HierarchyTestCase(unittest.TestCase):
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def test_aggregative(self):
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lr = LogisticRegression()
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for m in AGGREGATIVE_METHODS:
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self.assertEqual(isinstance(m(lr), AggregativeQuantifier), True)
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def test_inspect_aggregative(self):
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import quapy.method.aggregative as methods
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members = inspect.getmembers(methods)
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classes = set([cls for name, cls in members if inspect.isclass(cls)])
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quantifiers = [cls for cls in classes if issubclass(cls, BaseQuantifier)]
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quantifiers = [cls for cls in quantifiers if issubclass(cls, AggregativeQuantifier)]
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quantifiers = [cls for cls in quantifiers if not inspect.isabstract(cls) ]
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for cls in quantifiers:
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self.assertIn(cls, AGGREGATIVE_METHODS)
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def test_binary(self):
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lr = LogisticRegression()
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for m in BINARY_METHODS:
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self.assertEqual(isinstance(m(lr), BinaryQuantifier), True)
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def test_probabilistic(self):
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lr = LogisticRegression()
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for m in [CC(lr), ACC(lr)]:
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self.assertEqual(isinstance(m, AggregativeCrispQuantifier), True)
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self.assertEqual(isinstance(m, AggregativeSoftQuantifier), False)
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for m in [PCC(lr), PACC(lr)]:
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self.assertEqual(isinstance(m, AggregativeCrispQuantifier), False)
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self.assertEqual(isinstance(m, AggregativeSoftQuantifier), True)
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if __name__ == '__main__':
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unittest.main()
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import itertools
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import unittest
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from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from quapy.method.aggregative import ACC
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from quapy.method.meta import Ensemble
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from quapy.method import AGGREGATIVE_METHODS, BINARY_METHODS, NON_AGGREGATIVE_METHODS
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from quapy.functional import check_prevalence_vector
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class TestMethods(unittest.TestCase):
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tiny_dataset_multiclass = qp.datasets.fetch_UCIMulticlassDataset('academic-success').reduce(n_test=10)
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tiny_dataset_binary = qp.datasets.fetch_UCIBinaryDataset('ionosphere').reduce(n_test=10)
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datasets = [tiny_dataset_binary, tiny_dataset_multiclass]
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def test_aggregative(self):
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for dataset in TestMethods.datasets:
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learner = LogisticRegression()
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learner.fit(*dataset.training.Xy)
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for model in AGGREGATIVE_METHODS:
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if not dataset.binary and model in BINARY_METHODS:
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print(f'skipping the test of binary model {model.__name__} on multiclass dataset {dataset.name}')
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continue
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q = model(learner)
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print('testing', q)
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q.fit(dataset.training, fit_classifier=False)
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estim_prevalences = q.quantify(dataset.test.X)
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self.assertTrue(check_prevalence_vector(estim_prevalences))
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def test_non_aggregative(self):
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for dataset in TestMethods.datasets:
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for model in NON_AGGREGATIVE_METHODS:
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if not dataset.binary and model in BINARY_METHODS:
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print(f'skipping the test of binary model {model.__name__} on multiclass dataset {dataset.name}')
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continue
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q = model()
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print(f'testing {q} on dataset {dataset.name}')
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q.fit(dataset.training)
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estim_prevalences = q.quantify(dataset.test.X)
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self.assertTrue(check_prevalence_vector(estim_prevalences))
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def test_ensembles(self):
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qp.environ['SAMPLE_SIZE'] = 10
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base_quantifier = ACC(LogisticRegression())
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for dataset, policy in itertools.product(TestMethods.datasets, Ensemble.VALID_POLICIES):
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if not dataset.binary and policy == 'ds':
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print(f'skipping the test of binary policy ds on non-binary dataset {dataset}')
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continue
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print(f'testing {base_quantifier} on dataset {dataset.name} with {policy=}')
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ensemble = Ensemble(quantifier=base_quantifier, size=3, policy=policy, n_jobs=-1)
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ensemble.fit(dataset.training)
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estim_prevalences = ensemble.quantify(dataset.test.instances)
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self.assertTrue(check_prevalence_vector(estim_prevalences))
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def test_quanet(self):
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try:
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import quapy.classification.neural
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except ModuleNotFoundError:
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print('the torch package is not installed; skipping unit test for QuaNet')
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return
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qp.environ['SAMPLE_SIZE'] = 10
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# load the kindle dataset as text, and convert words to numerical indexes
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dataset = qp.datasets.fetch_reviews('kindle', pickle=True).reduce()
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qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
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from quapy.classification.neural import CNNnet
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cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)
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from quapy.classification.neural import NeuralClassifierTrainer
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learner = NeuralClassifierTrainer(cnn, device='cpu')
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from quapy.method.meta import QuaNet
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model = QuaNet(learner, device='cpu', n_epochs=2, tr_iter_per_poch=10, va_iter_per_poch=10, patience=2)
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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self.assertTrue(check_prevalence_vector(estim_prevalences))
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if __name__ == '__main__':
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unittest.main()
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