merged

TODO.txt

@@ -6,3 +6,6 @@ Add prediction - artificial sampling
 Add readers for typical datasets used in Quantification
 Add NAE, NRAE
 Add "measures for evaluating ordinal"?
+Document methods with paper references
+The parallel training in svmperf seems not to work
+

quapy/error.py

@@ -2,6 +2,7 @@ from sklearn.metrics import f1_score
 import numpy as np
 
 
+
 SAMPLE_SIZE = None
 
 

quapy/functional.py

@@ -77,6 +77,7 @@ def normalize_prevalence(prevalences):
         return np.ones_like(prevalences) / prevalences.size
 
 
+
 def num_prevalence_combinations(nclasses:int, nprevpoints:int, nrepeats:int):
     """
     Computes the number of prevalence combinations in the nclasses-dimensional simplex if nprevpoints equally distant
@@ -118,3 +119,4 @@ def get_nprevpoints_approximation(nclasses, nrepeats, combinations_budget):
             return nprevpoints-1
         else:
             nprevpoints+=1
+

quapy/method/aggregative.py

@@ -337,6 +337,10 @@ class OneVsAll(AggregativeQuantifier):
                 delayed(self._delayed_binary_predict)(c, self.class_method, X) for c in self.classes
             )
         )
+<<<<<<< HEAD
+=======
+        print('one vs all: ', prevalences)
+>>>>>>> 2361186a01c53e744f4291e2e2299700216ff139
         return F.normalize_prevalence(prevalences)
 
     @property

quapy/utils/util.py

@@ -5,6 +5,7 @@ import contextlib
 import numpy as np
 
 
+
 def get_parallel_slices(n_tasks, n_jobs=-1):
     if n_jobs == -1:
         n_jobs = multiprocessing.cpu_count()
@@ -32,4 +33,3 @@ def temp_seed(seed):
     finally:
         np.random.set_state(state)
 
-

test.py

@@ -3,6 +3,7 @@ from sklearn.svm import LinearSVC
 import quapy as qp
 import quapy.functional as F
 
+
 SAMPLE_SIZE=500
 binary = False
 
@@ -28,6 +29,7 @@ model = qp.method.aggregative.ClassifyAndCount(learner)
 # model = qp.method.aggregative.ProbabilisticClassifyAndCount(learner)
 # model = qp.method.aggregative.ProbabilisticAdjustedClassifyAndCount(learner)
 # model = qp.method.aggregative.ExpectationMaximizationQuantifier(learner)
+
 model.fit(dataset.training)
 
 # estimating class prevalences
@@ -51,3 +53,4 @@ print(f'Evaluation according to the artificial sampling protocol ({len(true_prev
 for error in qp.error.QUANTIFICATION_ERROR:
     score = error(true_prev, estim_prev)
     print(f'{error.__name__}={score:.5f}')
+