dealing with unit tests

quapy/method/aggregative.py

@@ -67,8 +67,14 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
             assert val_split > 1, \
                 (f'when {val_split=} is indicated as an integer, it represents the number of folds in a kFCV '
                  f'and must thus be >1')
-            assert fit_classifier, (f'when {val_split=} is indicated as an integer (the number of folds for kFCV) '
-                                    f'the parameter {fit_classifier=} must be True')
+            if val_split==5 and not fit_classifier:
+                print(f'Warning: {val_split=} will be ignored when the classifier is already trained '
+                      f'({fit_classifier=}). Parameter {self.val_split=} will be set to None. Set {val_split=} '
+                      f'to None to avoid this warning.')
+                self.val_split=None
+            if val_split!=5:
+                assert fit_classifier, (f'Parameter {val_split=} has been modified, but {fit_classifier=} '
+                                        f'indicates the classifier should not be retrained.')
         elif isinstance(val_split, float):
             assert 0 < val_split < 1, \
                 (f'when {val_split=} is indicated as a float, it represents the fraction of training instances '
@@ -174,7 +180,9 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
         elif self.val_split is None:
             if self.fit_classifier:
                 self.classifier.fit(X, y)
-            predictions, labels = None, None
+                predictions, labels = None, None
+            else:
+                predictions, labels = self.classify(X), y
         else:
             raise ValueError(f'unexpected type for {self.val_split=}')
 

quapy/tests/test_datasets.py

@@ -17,7 +17,7 @@ class TestDatasets(unittest.TestCase):
     def _check_dataset(self, dataset):
         q = self.new_quantifier()
         print(f'testing method {q} in {dataset.name}...', end='')
-        q.fit(dataset.training)
+        q.fit(*dataset.training.Xy)
         estim_prevalences = q.predict(dataset.test.instances)
         self.assertTrue(F.check_prevalence_vector(estim_prevalences))
         print(f'[done]')
@@ -89,7 +89,7 @@ class TestDatasets(unittest.TestCase):
             n_classes = train.n_classes
             train = train.sampling(100, *F.uniform_prevalence(n_classes))
             q = self.new_quantifier()
-            q.fit(train)
+            q.fit(*train.Xy)
             self._check_samples(gen_val, q, max_samples_test=5)
             self._check_samples(gen_test, q, max_samples_test=5)
 
@@ -102,7 +102,7 @@ class TestDatasets(unittest.TestCase):
             tfidf = TfidfVectorizer()
             train.instances = tfidf.fit_transform(train.instances)
             q = self.new_quantifier()
-            q.fit(train)
+            q.fit(*train.Xy)
             self._check_samples(gen_val, q, max_samples_test=5, vectorizer=tfidf)
             self._check_samples(gen_test, q, max_samples_test=5, vectorizer=tfidf)
 

quapy/tests/test_methods.py

@@ -48,9 +48,9 @@ class TestMethods(unittest.TestCase):
                     print(f'skipping the test of binary model {model.__name__} on multiclass dataset {dataset.name}')
                     continue
 
-                q = model(learner)
+                q = model(learner, fit_classifier=False)
                 print('testing', q)
-                q.fit(dataset.training, fit_classifier=False)
+                q.fit(*dataset.training.Xy)
                 estim_prevalences = q.predict(dataset.test.X)
                 self.assertTrue(check_prevalence_vector(estim_prevalences))