collator functions in protocols for preparing the outputs

quapy/data/base.py

@@ -63,10 +63,9 @@ class LabelledCollection:
         """
         return self.instances.shape[0]
 
-    @property
     def prevalence(self):
         """
-        Returns the prevalence, or relative frequency, of the classes of interest.
+        Returns the prevalence, or relative frequency, of the classes in the codeframe.
 
         :return: a np.ndarray of shape `(n_classes)` with the relative frequencies of each class, in the same order
             as listed by `self.classes_`
@@ -75,7 +74,7 @@ class LabelledCollection:
 
     def counts(self):
         """
-        Returns the number of instances for each of the classes of interest.
+        Returns the number of instances for each of the classes in the codeframe.
 
         :return: a np.ndarray of shape `(n_classes)` with the number of instances of each class, in the same order
             as listed by `self.classes_`
@@ -252,7 +251,8 @@ class LabelledCollection:
     @property
     def Xp(self):
         """
-        Gets the instances and the true prevalence. This is useful when implementing evaluation protocols
+        Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from
+        a `LabelledCollection` object.
 
         :return: a tuple `(instances, prevalence)` from this collection
         """
@@ -420,6 +420,16 @@ class Dataset:
         """
         return len(self.vocabulary)
 
+    @property
+    def train_test(self):
+        """
+        Alias to `self.training` and `self.test`
+
+        :return: the training and test collections
+        :return: the training and test collections
+        """
+        return self.training, self.test
+
     def stats(self, show):
         """
         Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:

quapy/protocol.py

@@ -135,13 +135,13 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
     :param random_seed: allows replicating samples across runs (default None)
     """
 
-    def __init__(self, data:LabelledCollection, sample_size, n_prevalences=21, repeats=10, random_seed=None):
+    def __init__(self, data:LabelledCollection, sample_size, n_prevalences=21, repeats=10, random_seed=None, return_type='sample_prev'):
         super(APP, self).__init__(random_seed)
         self.data = data
         self.sample_size = sample_size
         self.n_prevalences = n_prevalences
         self.repeats = repeats
-        self.set_collator(collator_fn=lambda x: (x.instances, x.prevalence()))
+        self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
 
     def prevalence_grid(self):
         """
@@ -192,13 +192,13 @@ class NPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
     :param random_seed: allows replicating samples across runs (default None)
     """
 
-    def __init__(self, data:LabelledCollection, sample_size, repeats=100, random_seed=None):
+    def __init__(self, data:LabelledCollection, sample_size, repeats=100, random_seed=None, return_type='sample_prev'):
         super(NPP, self).__init__(random_seed)
         self.data = data
         self.sample_size = sample_size
         self.repeats = repeats
         self.random_seed = random_seed
-        self.set_collator(collator_fn=lambda x: (x.instances, x.prevalence()))
+        self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
 
     def samples_parameters(self):
         indexes = []
@@ -229,13 +229,13 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol)
     :param random_seed: allows replicating samples across runs (default None)
     """
 
-    def __init__(self, data: LabelledCollection, sample_size, repeats=100, random_seed=None):
+    def __init__(self, data: LabelledCollection, sample_size, repeats=100, random_seed=None, return_type='sample_prev'):
         super(USimplexPP, self).__init__(random_seed)
         self.data = data
         self.sample_size = sample_size
         self.repeats = repeats
         self.random_seed = random_seed
-        self.set_collator(collator_fn=lambda x: (x.instances, x.prevalence()))
+        self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
 
     def samples_parameters(self):
         indexes = []
@@ -339,7 +339,7 @@ class CovariateShiftPP(AbstractStochasticSeededProtocol):
         indexesA, indexesB = indexes
         sampleA = self.A.sampling_from_index(indexesA)
         sampleB = self.B.sampling_from_index(indexesB)
-        return sampleA+sampleB
+        return (sampleA+sampleB).Xp
 
     def total(self):
         return self.repeats * len(self.mixture_points)

quapy/tests/test_datasets.py

@@ -46,9 +46,7 @@ def test_fetch_UCIDataset(dataset_name):
 
 @pytest.mark.parametrize('dataset_name', LEQUA2022_TASKS)
 def test_fetch_lequa2022(dataset_name):
-    fetch_lequa2022(dataset_name)
-    # dataset = fetch_lequa2022(dataset_name)
-    # print(f'Dataset {dataset_name}')
-    # print('Training set stats')
-    # dataset.training.stats()
-    # print('Test set stats')
+    train, gen_val, gen_test = fetch_lequa2022(dataset_name)
+    print(train.stats())
+    print('Val:', gen_val.total())
+    print('Test:', gen_test.total())

quapy/tests/test_protocols.py

@@ -12,8 +12,8 @@ def mock_labelled_collection(prefix=''):
 
 def samples_to_str(protocol):
     samples_str = ""
-    for sample in protocol():
-        samples_str += f'{sample.instances}\t{sample.labels}\t{sample.prevalence()}\n'
+    for instances, prev in protocol():
+        samples_str += f'{instances}\t{prev}\n'
     return samples_str