43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
"""
|
|
Integration tests for optional or resource-heavy method end-to-end checks.
|
|
|
|
This module is intentionally excluded from default ``unittest`` discovery by
|
|
using an ``integration_*.py`` filename.
|
|
"""
|
|
|
|
import unittest
|
|
|
|
import quapy as qp
|
|
from quapy.functional import check_prevalence_vector
|
|
|
|
|
|
class IntegrationMethodsTest(unittest.TestCase):
|
|
|
|
def test_quanet(self):
|
|
try:
|
|
import quapy.classification.neural
|
|
except ModuleNotFoundError:
|
|
print('the torch package is not installed; skipping integration test for QuaNet')
|
|
return
|
|
|
|
qp.environ['SAMPLE_SIZE'] = 10
|
|
|
|
dataset = qp.datasets.fetch_reviews('kindle', pickle=True).reduce()
|
|
qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
|
|
|
|
from quapy.classification.neural import CNNnet
|
|
from quapy.classification.neural import NeuralClassifierTrainer
|
|
from quapy.method.meta import QuaNet
|
|
|
|
cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)
|
|
learner = NeuralClassifierTrainer(cnn, device='cpu')
|
|
model = QuaNet(learner, device='cpu', n_epochs=2, tr_iter_per_poch=10, va_iter_per_poch=10, patience=2)
|
|
|
|
model.fit(*dataset.training.Xy)
|
|
estim_prevalences = model.predict(dataset.test.instances)
|
|
self.assertTrue(check_prevalence_vector(estim_prevalences))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|