Tests

quapy/tests/test_datasets.py

@@ -6,13 +6,27 @@ from quapy.data.datasets import REVIEWS_SENTIMENT_DATASETS, TWITTER_SENTIMENT_DA
 
 @pytest.mark.parametrize('dataset_name', REVIEWS_SENTIMENT_DATASETS)
 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)
 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):
-    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))

quapy/tests/test_methods.py

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