Added classes_ property to all quantifiers.

TODO.txt

@@ -17,14 +17,12 @@ Current issues:
 In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the
 negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as
 an instance of single-label with 2 labels. Check
-Add classnames to LabelledCollection? This should improve visualization of reports
 Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps)
 OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers
 Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed
 
 Improvements:
 ==========================================
-Clarify whether QuaNet is an aggregative method or not.
 Explore the hyperparameter "number of bins" in HDy
 Rename EMQ to SLD ?
 Parallelize the kFCV in ACC and PACC?

quapy/method/aggregative.py

@@ -53,10 +53,10 @@ class AggregativeQuantifier(BaseQuantifier):
 
     @property
     def n_classes(self):
-        return len(self.classes)
+        return len(self.classes_)
 
     @property
-    def classes(self):
+    def classes_(self):
         return self.learner.classes_
 
     @property

quapy/method/base.py

@@ -19,6 +19,10 @@ class BaseQuantifier(metaclass=ABCMeta):
     @abstractmethod
     def get_params(self, deep=True): ...
 
+    @abstractmethod
+    @property
+    def classes_(self): ...
+
     # these methods allows meta-learners to reimplement the decision based on their constituents, and not
     # based on class structure
     @property

quapy/method/meta.py

@@ -186,6 +186,10 @@ class Ensemble(BaseQuantifier):
         order = np.argsort(dist)
         return _select_k(predictions, order, k=self.red_size)
 
+    @property
+    def classes_(self):
+        return self.base_quantifier.classes_
+
     @property
     def binary(self):
         return self.base_quantifier.binary

quapy/method/neural.py

@@ -58,6 +58,7 @@ class QuaNetTrainer(BaseQuantifier):
         self.device = torch.device(device)
 
         self.__check_params_colision(self.quanet_params, self.learner.get_params())
+        self._classes_ = None
 
     def fit(self, data: LabelledCollection, fit_learner=True):
         """
@@ -67,6 +68,7 @@ class QuaNetTrainer(BaseQuantifier):
         :param fit_learner: if true, trains the classifier on a split containing 40% of the data
         :return: self
         """
+        self._classes_ = data.classes_
         classifier_data, unused_data = data.split_stratified(0.4)
         train_data, valid_data = unused_data.split_stratified(0.66)  # 0.66 split of 60% makes 40% and 20%
 
@@ -256,6 +258,10 @@ class QuaNetTrainer(BaseQuantifier):
         import shutil
         shutil.rmtree(self.checkpointdir, ignore_errors=True)
 
+    @property
+    def classes_(self):
+        return self._classes_
+
 
 def mae_loss(output, target):
     return torch.mean(torch.abs(output - target))

quapy/method/non_aggregative.py

@@ -2,18 +2,22 @@ from quapy.data import LabelledCollection
 from .base import BaseQuantifier
 
 
-
 class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
 
     def __init__(self, **kwargs):
-        pass
+        self._classes_ = None
 
     def fit(self, data: LabelledCollection, *args):
+        self._classes_ = data.classes_
         self.estimated_prevalence = data.prevalence()
 
     def quantify(self, documents, *args):
         return self.estimated_prevalence
 
+    @property
+    def classes_(self):
+        return self._classes_
+
     def get_params(self):
         pass
 

quapy/model_selection.py

@@ -4,7 +4,6 @@ from copy import deepcopy
 from typing import Union, Callable
 
 import quapy as qp
-import quapy.functional as F
 from quapy.data.base import LabelledCollection
 from quapy.evaluation import artificial_sampling_prediction
 from quapy.method.aggregative import BaseQuantifier
@@ -118,6 +117,7 @@ class GridSearchQ(BaseQuantifier):
             def handler(signum, frame):
                 self.sout('timeout reached')
                 raise TimeoutError()
+
             signal.signal(signal.SIGALRM, handler)
 
         self.sout(f'starting optimization with n_jobs={n_jobs}')
@@ -175,6 +175,10 @@ class GridSearchQ(BaseQuantifier):
     def quantify(self, instances):
         return self.best_model_.quantify(instances)
 
+    @property
+    def classes_(self):
+        return self.best_model_.classes_
+
     def set_params(self, **parameters):
         self.param_grid = parameters
 
@@ -185,4 +189,3 @@ class GridSearchQ(BaseQuantifier):
         if hasattr(self, 'best_model_'):
             return self.best_model_
         raise ValueError('best_model called before fit')
-