forked from moreo/QuaPy
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
import pytest
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from quapy.data.datasets import REVIEWS_SENTIMENT_DATASETS, TWITTER_SENTIMENT_DATASETS_TEST, \
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TWITTER_SENTIMENT_DATASETS_TRAIN, UCI_BINARY_DATASETS, LEQUA2022_TASKS, UCI_MULTICLASS_DATASETS,\
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fetch_reviews, fetch_twitter, fetch_UCIBinaryDataset, fetch_lequa2022, fetch_UCIMulticlassLabelledCollection
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@pytest.mark.parametrize('dataset_name', REVIEWS_SENTIMENT_DATASETS)
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def test_fetch_reviews(dataset_name):
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dataset = fetch_reviews(dataset_name)
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print(f'Dataset {dataset_name}')
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print('Training set stats')
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dataset.training.stats()
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print('Test set stats')
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dataset.test.stats()
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@pytest.mark.parametrize('dataset_name', TWITTER_SENTIMENT_DATASETS_TEST + TWITTER_SENTIMENT_DATASETS_TRAIN)
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def test_fetch_twitter(dataset_name):
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try:
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dataset = fetch_twitter(dataset_name)
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except ValueError as ve:
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if dataset_name == 'semeval' and ve.args[0].startswith(
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'dataset "semeval" can only be used for model selection.'):
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dataset = fetch_twitter(dataset_name, for_model_selection=True)
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print(f'Dataset {dataset_name}')
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print('Training set stats')
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dataset.training.stats()
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print('Test set stats')
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@pytest.mark.parametrize('dataset_name', UCI_BINARY_DATASETS)
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def test_fetch_UCIDataset(dataset_name):
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try:
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dataset = fetch_UCIBinaryDataset(dataset_name)
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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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return
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print(f'Dataset {dataset_name}')
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print('Training set stats')
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dataset.training.stats()
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print('Test set stats')
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@pytest.mark.parametrize('dataset_name', UCI_MULTICLASS_DATASETS)
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def test_fetch_UCIMultiDataset(dataset_name):
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dataset = fetch_UCIMulticlassLabelledCollection(dataset_name)
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print(f'Dataset {dataset_name}')
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print('Training set stats')
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dataset.stats()
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print('Test set stats')
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@pytest.mark.parametrize('dataset_name', LEQUA2022_TASKS)
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def test_fetch_lequa2022(dataset_name):
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train, gen_val, gen_test = fetch_lequa2022(dataset_name)
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print(train.stats())
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print('Val:', gen_val.total())
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print('Test:', gen_test.total())
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