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QuaPy
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Tests

This commit is contained in:
Andrea Esuli 2021-04-30 17:22:58 +02:00
parent 44cec7a046
commit 8f284e540a
2 changed files with 25 additions and 8 deletions
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20
quapy/tests/test_datasets.py
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@ -6,13 +6,27 @@ from quapy.data.datasets import REVIEWS_SENTIMENT_DATASETS, TWITTER_SENTIMENT_DA
@pytest.mark.parametrize('dataset_name', REVIEWS_SENTIMENT_DATASETS) @pytest.mark.parametrize('dataset_name', REVIEWS_SENTIMENT_DATASETS)
def test_fetch_reviews(dataset_name): def test_fetch_reviews(dataset_name):
fetch_reviews(dataset_name) dataset = fetch_reviews(dataset_name)
print(dataset.n_classes, len(dataset.training), len(dataset.test))
@pytest.mark.parametrize('dataset_name', TWITTER_SENTIMENT_DATASETS_TEST + TWITTER_SENTIMENT_DATASETS_TRAIN) @pytest.mark.parametrize('dataset_name', TWITTER_SENTIMENT_DATASETS_TEST + TWITTER_SENTIMENT_DATASETS_TRAIN)
def test_fetch_twitter(dataset_name): def test_fetch_twitter(dataset_name):
fetch_twitter(dataset_name) try:
dataset = fetch_twitter(dataset_name)
except ValueError as ve:
if dataset_name == 'semeval' and ve.args[0].startswith(
'dataset "semeval" can only be used for model selection.'):
dataset = fetch_twitter(dataset_name, for_model_selection=True)
print(dataset.n_classes, len(dataset.training), len(dataset.test))
@pytest.mark.parametrize('dataset_name', UCI_DATASETS) @pytest.mark.parametrize('dataset_name', UCI_DATASETS)
def test_fetch_UCIDataset(dataset_name): def test_fetch_UCIDataset(dataset_name):
fetch_UCIDataset(dataset_name) try:
dataset = fetch_UCIDataset(dataset_name)
except FileNotFoundError as fnfe:
if dataset_name == 'pageblocks.5' and fnfe.args[0].find(
'If this is the first time you attempt to load this dataset') > 0:
return
print(dataset.n_classes, len(dataset.training), len(dataset.test))

11
quapy/tests/test_methods.py
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@ -5,20 +5,23 @@ from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC from sklearn.svm import LinearSVC
import quapy as qp import quapy as qp
from quapy.method import AGGREGATIVE_METHODS
datasets = [qp.datasets.fetch_twitter('semeval16')] datasets = [pytest.param(qp.datasets.fetch_twitter('hcr'), id='hcr'),
pytest.param(qp.datasets.fetch_UCIDataset('ionosphere'), id='ionosphere')]
aggregative_methods = [qp.method.aggregative.CC, qp.method.aggregative.ACC, qp.method.aggregative.ELM]
learners = [LogisticRegression, MultinomialNB, LinearSVC] learners = [LogisticRegression, MultinomialNB, LinearSVC]
@pytest.mark.parametrize('dataset', datasets) @pytest.mark.parametrize('dataset', datasets)
@pytest.mark.parametrize('aggregative_method', aggregative_methods) @pytest.mark.parametrize('aggregative_method', AGGREGATIVE_METHODS)
@pytest.mark.parametrize('learner', learners) @pytest.mark.parametrize('learner', learners)
def test_aggregative_methods(dataset, aggregative_method, learner): def test_aggregative_methods(dataset, aggregative_method, learner):
model = aggregative_method(learner()) model = aggregative_method(learner())
if model.binary and not dataset.binary:
return
model.fit(dataset.training) model.fit(dataset.training)
estim_prevalences = model.quantify(dataset.test.instances) estim_prevalences = model.quantify(dataset.test.instances)