Merge branch 'master' of github.com:HLT-ISTI/QuaPy

quapy/plot.py

@@ -9,9 +9,9 @@ import math
 
 import quapy as qp
 
-plt.rcParams['figure.figsize'] = [12, 8]
+plt.rcParams['figure.figsize'] = [10, 6]
 plt.rcParams['figure.dpi'] = 200
-plt.rcParams['font.size'] = 16
+plt.rcParams['font.size'] = 18
 
 
 def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,

quapy/protocol.py

@@ -214,18 +214,30 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
     :param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
     :param random_state: allows replicating samples across runs (default 0, meaning that the sequence of samples
         will be the same every time the protocol is called)
+    :param sanity_check: int, raises an exception warning the user that the number of examples to be generated exceed
+        this number; set to None for skipping this check
     :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or
         to "labelled_collection" to get instead instances of LabelledCollection
     """
 
-    def __init__(self, data:LabelledCollection, sample_size=None, n_prevalences=21, repeats=10,
+    def __init__(self, data: LabelledCollection, sample_size=None, n_prevalences=21, repeats=10,
-                 smooth_limits_epsilon=0, random_state=0, return_type='sample_prev'):
+                 smooth_limits_epsilon=0, random_state=0, sanity_check=10000, return_type='sample_prev'):
         super(APP, self).__init__(random_state)
         self.data = data
         self.sample_size = qp._get_sample_size(sample_size)
         self.n_prevalences = n_prevalences
         self.repeats = repeats
         self.smooth_limits_epsilon = smooth_limits_epsilon
+        if not ((isinstance(sanity_check, int) and sanity_check>0) or sanity_check is None):
+            raise ValueError('param "sanity_check" must either be None or a positive integer')
+        if isinstance(sanity_check, int):
+            n = F.num_prevalence_combinations(n_prevpoints=n_prevalences, n_classes=data.n_classes, n_repeats=repeats)
+            if n > sanity_check:
+                raise RuntimeError(
+                    f"Abort: the number of samples that will be generated by {self.__class__.__name__} ({n}) "
+                    f"exceeds the maximum number of allowed samples ({sanity_check = }). Set 'sanity_check' to "
+                    f"None for bypassing this check, or to a higher number.")
+
         self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
 
     def prevalence_grid(self):

quapy/tests/test_protocols.py

@@ -1,5 +1,7 @@
 import unittest
 import numpy as np
+
+import quapy.functional
 from quapy.data import LabelledCollection
 from quapy.protocol import APP, NPP, UPP, DomainMixer, AbstractStochasticSeededProtocol
 
@@ -19,6 +21,17 @@ def samples_to_str(protocol):
 
 class TestProtocols(unittest.TestCase):
 
+    def test_app_sanity_check(self):
+        data = mock_labelled_collection()
+        n_prevpoints = 101
+        repeats = 10
+        with self.assertRaises(RuntimeError):
+            p = APP(data, sample_size=5, n_prevalences=n_prevpoints, repeats=repeats, random_state=42)
+        n_combinations = \
+            quapy.functional.num_prevalence_combinations(n_prevpoints, n_classes=data.n_classes, n_repeats=repeats)
+        p = APP(data, sample_size=5, n_prevalences=n_prevpoints, random_state=42, sanity_check=n_combinations)
+        p = APP(data, sample_size=5, n_prevalences=n_prevpoints, random_state=42, sanity_check=None)
+
     def test_app_replicate(self):
         data = mock_labelled_collection()
         p = APP(data, sample_size=5, n_prevalences=11, random_state=42)