122 lines
4.5 KiB
Python
122 lines
4.5 KiB
Python
"""
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Integration tests for dataset fetchers and large external resources.
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This module is intentionally excluded from default ``unittest`` discovery by
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using an ``integration_*.py`` filename.
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"""
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import os
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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.data.datasets import *
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from quapy.method.aggregative import PCC
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class IntegrationDatasetsTest(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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train, test = dataset.reduce().train_test
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q = self.new_quantifier()
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if len(train) > 500:
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train = train.sampling(500)
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q.fit(*dataset.training.Xy)
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estim_prevalences = q.predict(dataset.test.instances)
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self.assertTrue(F.check_prevalence_vector(estim_prevalences))
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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.predict(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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dataset = fetch_reviews(dataset_name, tfidf=True, min_df=10)
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dataset.reduce()
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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[:1]:
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dataset = fetch_twitter(dataset_name, min_df=10)
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dataset.reduce()
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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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dataset = fetch_UCIBinaryDataset(dataset_name)
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dataset.reduce()
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self._check_dataset(dataset)
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def test_UCIMultiDataset(self):
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for dataset_name in UCI_MULTICLASS_DATASETS:
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dataset = fetch_UCIMulticlassDataset(dataset_name)
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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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self._check_dataset(dataset)
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def test_lequa2022(self):
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if os.environ.get('QUAPY_TESTS_OMIT_LARGE_DATASETS'):
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return
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for dataset_name in LEQUA2022_VECTOR_TASKS:
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train, gen_val, gen_test = fetch_lequa2022(dataset_name)
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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.Xy)
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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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train, gen_val, gen_test = fetch_lequa2022(dataset_name)
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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.Xy)
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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_lequa2024(self):
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if os.environ.get('QUAPY_TESTS_OMIT_LARGE_DATASETS'):
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return
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for task in LEQUA2024_TASKS:
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train, gen_val, gen_test = fetch_lequa2024(task, merge_T3=True)
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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.Xy)
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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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def test_IFCB(self):
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if os.environ.get('QUAPY_TESTS_OMIT_LARGE_DATASETS'):
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return
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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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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.Xy)
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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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