Minor changes

Code/MonoMultiViewClassifiers/ExecClassif.py

@@ -236,6 +236,7 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory,
 
 
 def genMetricsScores(results, trueLabels, metrics):
+    """Used to add all the metrics scores to the multiclass result structure  for each clf and each iteration"""
 
     logging.debug("Start:\t Getting multiclass scores for each metric")
 
@@ -255,8 +256,10 @@ def genMetricsScores(results, trueLabels, metrics):
 
 
 def getErrorOnLabels(multiclassResults, multiclassLabels):
+    """Used to add all the arrays showing on which example there is an error for each clf and each iteration"""
 
     logging.debug("Start:\t Getting errors on each example for each classifier")
+
     for iterIndex, iterResults in enumerate(multiclassResults):
         for classifierName, classifierResults in iterResults.items():
             errorOnExamples = classifierResults["labels"] == multiclassLabels
@@ -268,6 +271,7 @@ def getErrorOnLabels(multiclassResults, multiclassLabels):
 
 
 def publishMulticlassResults(multiclassResults):
+    # TODO : figure and folder organization
     pass
 
 
@@ -294,12 +298,22 @@ def analyzeMulticlass(results, statsIter, nbExamples, nbLabels, multiclassLabels
     return multiclassResults
 
 
-def getResults(results, statsIter, nbMulticlass, argumentDictionaries, multiclassLabels):
+def analyzeBiclass(results):
+    # TODO
+    return ""
+
+
+def analyzeIter(results):
+    # TODO
+    pass
+
+
+def getResults(results, statsIter, nbMulticlass, argumentDictionaries, multiclassLabels, metrics):
     if statsIter > 1:
         if nbMulticlass > 1:
-            # TODO : analyze biclass results
-            multiclassResults = analyzeMulticlass(results, statsIter, argumentDictionaries, multiclassLabels)
-            analyzerIter(multiclassResults)
+            analyzeBiclass(results)
+            multiclassResults = analyzeMulticlass(results, statsIter, argumentDictionaries, multiclassLabels, metrics)
+            analyzeIter(multiclassResults)
         else:
             biclassResults = analyzeBiclass(results)
             analyzeIter(biclassResults)
@@ -401,7 +415,7 @@ def execBenchmark(nbCores, statsIter, nbMulticlass, argumentsDictionaries, multi
     # Do everything with flagging
 
     logging.debug("Start:\t Analyzing preds")
-    # getResults(results, statsIter, nbMulticlass, argumentsDictionaries, multiclassLabels)
+    # getResults(results, statsIter, nbMulticlass, argumentsDictionaries, multiclassLabels, metrics)
     logging.debug("Done:\t Analyzing preds")
 
     return results

Code/Tests/test_ExecClassif.py

@@ -253,6 +253,7 @@ class Test_genMetricsScores(unittest.TestCase):
         cls.assertEqual(0, multiclassResults[1]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
         cls.assertEqual(cls.score_to_get_f1, multiclassResults[1]["cheese_is_no_disease"]["metricsScores"]["f1_score"])
 
+
 class Test_getErrorOnLabels(unittest.TestCase):
 
     @classmethod
@@ -267,6 +268,24 @@ class Test_getErrorOnLabels(unittest.TestCase):
         multiclassResults = ExecClassif.getErrorOnLabels(cls.multiclassResults, cls.true_labels)
         np.testing.assert_array_equal(np.array([1, 0, 1, 1, 1, 1, 0, 0, 0]),
                                       multiclassResults[0]["chicken_is_heaven"]["errorOnExample"])
+
+    def test_full(cls):
+        cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels},
+                                  "cheese_is_no_disease": {"labels": cls.wrong_labels}},
+                                 {"chicken_is_heaven": {"labels": cls.wrong_labels},
+                                  "cheese_is_no_disease": {"labels": cls.wrong_labels}},
+                                 ]
+        multiclassResults = ExecClassif.getErrorOnLabels(cls.multiclassResults, cls.true_labels)
+        np.testing.assert_array_equal(np.array([1, 0, 1, 1, 1, 1, 0, 0, 0]),
+                                      multiclassResults[0]["chicken_is_heaven"]["errorOnExample"])
+        np.testing.assert_array_equal(np.array([0, 0, 0, 0, 0, 0, 0, 0, 0]),
+                                      multiclassResults[1]["cheese_is_no_disease"]["errorOnExample"])
+
+    def test_type(cls):
+        multiclassResults = ExecClassif.getErrorOnLabels(cls.multiclassResults, cls.true_labels)
+        cls.assertEqual(type(multiclassResults[0]["chicken_is_heaven"]["errorOnExample"][0]), np.int64)
+        np.testing.assert_array_equal(np.array([1, 0, 1, 1, 1, 1, 0, 0, 0]),
+                                      multiclassResults[0]["chicken_is_heaven"]["errorOnExample"])
 #
 # class Essai(unittest.TestCase):
 #

Readme.md

-# Mono- and Multi-view classification benchmark
+[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)
 [![Build Status](https://travis-ci.com/babau1/multiview-machine-learning-omis.svg?token=pjoowx3poApRRtwqHTpd&branch=master)](https://travis-ci.com/babau1/multiview-machine-learning-omis)
+# Mono- and Multi-view classification benchmark
 
 This project aims to be an easy-to use solution to run a prior benchmark on a dataset and evaluate mono- & multi-view algorithms capacity to classify it correctly.