bugfix, some methods modified h

ClassifierAccuracy/experiments.py

@@ -1,8 +1,8 @@
 from ClassifierAccuracy.util.commons import *
 from ClassifierAccuracy.util.plotting import plot_diagonal
 
-PROBLEM = 'multiclass'
+PROBLEM = 'binary'
-ORACLE = True
+ORACLE = False
 basedir = PROBLEM+('-oracle' if ORACLE else '')
 
 
@@ -14,6 +14,10 @@ elif PROBLEM == 'multiclass':
     qp.environ['SAMPLE_SIZE'] = 250
     NUM_TEST = 1000
     gen_datasets = gen_multi_datasets
+elif PROBLEM == 'tweet':
+    qp.environ['SAMPLE_SIZE'] = 100
+    NUM_TEST = 1000
+    gen_datasets = gen_tweet_datasets
 
 
 for (cls_name, h), (dataset_name, (L, V, U)) in itertools.product(gen_classifiers(), gen_datasets()):

ClassifierAccuracy/models_multiclass.py

@@ -65,7 +65,7 @@ class CAPContingencyTable(ClassifierAccuracyPrediction):
             the errors in quantification performance
         :return: float
         """
-        cont_table = self.predict_ct(X, oracle)
+        cont_table = self.predict_ct(X, oracle_prev)
         raw_acc = self.acc(cont_table)
         norm_acc = np.clip(raw_acc, 0, 1)
         return norm_acc
@@ -140,7 +140,7 @@ class ContTableTransferCAP(CAPContingencyTableQ):
     def fit(self, val: LabelledCollection):
         y_hat = self.h.predict(val.X)
         y_true = val.y
-        self.cont_table = confusion_matrix(y_true, y_pred=y_hat, labels=val.classes_, normalize='all')
+        self.cont_table = confusion_matrix(y_true=y_true, y_pred=y_hat, labels=val.classes_, normalize='all')
         self.train_prev = val.prevalence()
         self.quantifier_fit(val)
         return self
@@ -332,7 +332,7 @@ class PabloCAP(ClassifierAccuracyPrediction):
     def __init__(self, h, acc_fn, q_class, n_val_samples=100, aggr='mean'):
         self.h = h
         self.acc = acc_fn
-        self.q = q_class(h)
+        self.q = q_class(deepcopy(h))
         self.n_val_samples = n_val_samples
         self.aggr = aggr
         assert aggr in ['mean', 'median'], 'unknown aggregation function, use mean or median'

ClassifierAccuracy/util/commons.py

@@ -17,7 +17,7 @@ from ClassifierAccuracy.util.tabular import Table
 from quapy.method.aggregative import EMQ, ACC, KDEyML
 
 from quapy.data import LabelledCollection
-from quapy.data.datasets import fetch_UCIMulticlassLabelledCollection, UCI_MULTICLASS_DATASETS, fetch_lequa2022
+from quapy.data.datasets import fetch_UCIMulticlassLabelledCollection, UCI_MULTICLASS_DATASETS, fetch_lequa2022, TWITTER_SENTIMENT_DATASETS_TEST
 from quapy.data.datasets import fetch_reviews
 
 
@@ -45,6 +45,9 @@ def gen_multi_datasets(only_names=False)-> [str,[LabelledCollection,LabelledColl
             yield dataset_name, split(dataset)
 
     # yields the 20 newsgroups dataset
+    if only_names:
+        yield "20news", None
+    else:
         train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
         test  = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))
         tfidf = TfidfVectorizer(min_df=5, sublinear_tf=True)
@@ -56,10 +59,23 @@ def gen_multi_datasets(only_names=False)-> [str,[LabelledCollection,LabelledColl
         yield "20news", (T, V, U)
 
     # yields the T1B@LeQua2022 (training) dataset
+    if only_names:
+        yield "T1B-LeQua2022", None
+    else:
         train, _, _ = fetch_lequa2022(task='T1B')
         yield "T1B-LeQua2022", split(train)
 
 
+def gen_tweet_datasets(only_names=False)-> [str,[LabelledCollection,LabelledCollection,LabelledCollection]]:
+    for dataset_name in TWITTER_SENTIMENT_DATASETS_TEST:
+        if only_names:
+            yield dataset_name, None
+        else:
+            data = qp.datasets.fetch_twitter(dataset_name, min_df=3, pickle=True)
+            T, V = data.training.split_stratified(0.5, random_state=0)
+            U = data.test
+            yield dataset_name, (T, V, U)
+
 
 def gen_bin_datasets(only_names=False) -> [str,[LabelledCollection,LabelledCollection,LabelledCollection]]:
     if only_names:
@@ -104,7 +120,7 @@ def gen_CAP_cont_table(h)->[str,CAPContingencyTable]:
     #yield 'QuAcc(EMQ)nxn-noX', QuAccNxN(h, acc_fn, EMQ(LogisticRegression()), add_posteriors=True, add_X=False)
     #yield 'QuAcc(EMQ)nxn', QuAccNxN(h, acc_fn, EMQ(LogisticRegression()))
     #yield 'QuAcc(EMQ)nxn-MC', QuAccNxN(h, acc_fn, EMQ(LogisticRegression()), add_maxconf=True)
-    yield 'QuAcc(EMQ)nxn-NE', QuAccNxN(h, acc_fn, EMQ(LogisticRegression()), add_negentropy=True)
+    # yield 'QuAcc(EMQ)nxn-NE', QuAccNxN(h, acc_fn, EMQ(LogisticRegression()), add_negentropy=True)
     #yield 'QuAcc(EMQ)nxn-MIS', QuAccNxN(h, acc_fn, EMQ(LogisticRegression()), add_maxinfsoft=True)
     #yield 'QuAcc(EMQ)1xn2', QuAcc1xN2(h, acc_fn, EMQ(LogisticRegression()))
     #yield 'QuAcc(EMQ)1xn2', QuAcc1xN2(h, acc_fn, EMQ(LogisticRegression()))

quapy/data/base.py

@@ -123,7 +123,7 @@ class LabelledCollection:
         if len(prevs) == self.n_classes - 1:
             prevs = prevs + (1 - sum(prevs),)
         assert len(prevs) == self.n_classes, 'unexpected number of prevalences'
-        assert sum(prevs) == 1, f'prevalences ({prevs}) wrong range (sum={sum(prevs)})'
+        assert np.isclose(sum(prevs), 1), f'prevalences ({prevs}) wrong range (sum={sum(prevs)})'
 
         # Decide how many instances should be taken for each class in order to satisfy the requested prevalence
         # accurately, and the number of instances in the sample (exactly). If int(size * prevs[i]) (which is