reminders

CHANGE_LOG.txt

@@ -1,6 +1,8 @@
 Change Log 0.1.10
 -----------------
 
+CLEAN TODO-FILE
+
 - Base code Refactor:
     - Removing coupling between LabelledCollection and quantification methods. E.g.:
         def fit(data:LabelledCollection): -> def fit(X, y):

TODO.txt

@@ -1,3 +1,18 @@
+Add the fix suggested by Alexander:
+
+For a more general application, I would maybe first stablish a per-class threshold value of plausible prevalence
+based on the number of actual positives and the required sample size; e.g., for sample_size=100 and actual
+positives [10, 100, 500] -> [0.1, 1.0, 1.0], meaning that class 0 can be sampled at most at 0.1 prevalence, while
+the others can be sampled up to 1. prevalence. Then, when a prevalence value is requested, e.g., [0.33, 0.33, 0.33],
+we may either clip each value and normalize (as you suggest for the extreme case, e.g., [0.1, 0.33, 0.33]/sum) or
+scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum.
+- This affects LabelledCollection
+- This functionality should be accessible via sampling protocols and evaluation functions
+
+Solve the pre-trained classifier issues. An example is the coptic-codes script I did, which needed a mock_lr to
+work for having access to classes_; think also the case in which the precomputed outputs are already generated
+as in the unifying problems code.
+
 Para quitar el labelledcollection de los métodos:
 
 - El follón viene por la semántica confusa de fit en agregativos, que recibe 3 parámetros:

quapy/tests/test_methods.py

@@ -106,7 +106,7 @@ class TestMethods(unittest.TestCase):
         from quapy.method.meta import QuaNet
         model = QuaNet(learner, device='cpu', n_epochs=2, tr_iter_per_poch=10, va_iter_per_poch=10, patience=2)
 
-        model.fit(dataset.training)
+        model.fit(*dataset.training.Xy)
         estim_prevalences = model.predict(dataset.test.instances)
         self.assertTrue(check_prevalence_vector(estim_prevalences))
 
@@ -114,7 +114,7 @@ class TestMethods(unittest.TestCase):
         for dataset in TestMethods.datasets:
             for q in COMPOSABLE_METHODS:
                 print('testing', q)
-                q.fit(dataset.training)
+                q.fit(*dataset.training.Xy)
                 estim_prevalences = q.predict(dataset.test.X)
                 self.assertTrue(check_prevalence_vector(estim_prevalences))