diff --git a/Code/MonoMultiViewClassifiers/ExecClassif.py b/Code/MonoMultiViewClassifiers/ExecClassif.py
index 621dbb6b9169206f83b4640e091ff84d0a4c90a6..fe4694beef9e6b4800e49204730856e859b0bb5d 100644
--- a/Code/MonoMultiViewClassifiers/ExecClassif.py
+++ b/Code/MonoMultiViewClassifiers/ExecClassif.py
@@ -235,17 +235,26 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory,
return results, labelAnalysis
-def genMetricsScores(results, trueLabels, metrics):
+def getClassificationIndices(argumentsDictionaries, iterIndex):
+
+ for argumentsDictionary in argumentsDictionaries:
+ if argumentsDictionary["flag"][0]==iterIndex:
+
+
+
+
+def genMetricsScores(results, trueLabels, metrics, argumentsDictionaries):
"""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")
-
+ # TODO : Metric score for train and test
for metric in metrics:
metricModule = getattr(Metrics, metric[0])
for iterIndex, iterResults in enumerate(results):
for classifierName, resultDictionary in iterResults.items():
if not "metricsScores" in resultDictionary:
results[iterIndex][classifierName]["metricsScores"]={}
+ classificationIndices = getClassificationIndices(argumentsDictionaries, iterIndex)
score = metricModule.score(trueLabels,resultDictionary["labels"])
results[iterIndex][classifierName]["metricsScores"][metric[0]] = score
@@ -270,12 +279,67 @@ def getErrorOnLabels(multiclassResults, multiclassLabels):
return multiclassResults
-def publishMulticlassResults(multiclassResults):
+def autolabel(rects, ax):
+ """Used to print scores on top of the bars"""
+ for rect in rects:
+ height = rect.get_height()
+ ax.text(rect.get_x() + rect.get_width() / 2., 1.01 * height,
+ "%.2f" % height,
+ ha='center', va='bottom')
+
+def publishMulticlassResults(multiclassResults, metrics, statsIter, argumentDictionaries, minSize=10):
+ # mono, multi = multiclassResults
+ directory = argumentDictionaries["diretory"] # TODO : care that's fake
+ for iterIndex in range(statsIter):
+ for metric in metrics:
+ logging.debug("Start:\t Multiclass score graph generation for "+metric[0])
+ classifiersNames = []
+ validationScores = []
+ trainScores = []
+ for classifierName in multiclassResults[iterIndex].keys():
+ classifiersNames.append(classifierName)
+ validationScores.append(multiclassResults[iterIndex][classifierName]["metricsScore"][metric[0]]["validation"])
+ trainScores.append(multiclassResults[iterIndex][classifierName]["metricsScore"][metric[0]]["train"])
+ nbResults = len(validationScores)
+ # nbResults = len(mono) + len(multi)
+ # validationScores = [float(res[1][2][metric[0]][1]) for res in mono]
+ # validationScores += [float(scores[metric[0]][1]) for a, b, scores, c in multi]
+ # trainScores = [float(res[1][2][metric[0]][0]) for res in mono]
+ # trainScores += [float(scores[metric[0]][0]) for a, b, scores, c in multi]
+
+ validationScores = np.array(validationScores)
+ trainScores = np.array(trainScores)
+ names = np.array(names)
+ size = nbResults
+ if nbResults < minSize:
+ size = minSize
+ figKW = {"figsize" : (size, 3.0/4*size+2.0)}
+ f, ax = plt.subplots(nrows=1, ncols=1, **figKW)
+ barWidth= 0.35
+ sorted_indices = np.argsort(validationScores)
+ validationScores = validationScores[sorted_indices]
+ trainScores = trainScores[sorted_indices]
+ names = names[sorted_indices]
+
+ ax.set_title(metric[0] + "\n on validation set for each classifier")
+ rects = ax.bar(range(nbResults), validationScores, barWidth, color="r", )
+ rect2 = ax.bar(np.arange(nbResults) + barWidth, trainScores, barWidth, color="0.7", )
+ autolabel(rects, ax)
+ autolabel(rect2, ax)
+ ax.legend((rects[0], rect2[0]), ('Test', 'Train'))
+ ax.set_ylim(-0.1, 1.1)
+ ax.set_xticks(np.arange(nbResults) + barWidth)
+ ax.set_xticklabels(names, rotation="vertical")
+ plt.tight_layout()
+ f.savefig(directory + time.strftime("%Y%m%d-%H%M%S") + "-" + name + "-" + metric[0] + ".png")
+ plt.close()
+ logging.debug("Done:\t Multiclass score graph generation for " + metric[0])
# TODO : figure and folder organization
pass
-def analyzeMulticlass(results, statsIter, nbExamples, nbLabels, multiclassLabels, metrics):
+def analyzeMulticlass(results, statsIter, argumentDictionaries, nbExamples, nbLabels, multiclassLabels, metrics):
+ """Used to tranform one versus one results in multiclass results and to publish it"""
multiclassResults = [{} for _ in range(statsIter)]
for iterIndex in range(statsIter):
for flag, resMono, resMulti in results:
@@ -292,9 +356,9 @@ def analyzeMulticlass(results, statsIter, nbExamples, nbLabels, multiclassLabels
for iterIndex, multiclassiterResult in enumerate(multiclassResults):
for key, value in multiclassiterResult.items():
multiclassResults[iterIndex][key] = {"labels": np.argmax(value, axis=1)}
- multiclassResults = genMetricsScores(multiclassResults, multiclassLabels, metrics)
+ multiclassResults = genMetricsScores(multiclassResults, multiclassLabels, metrics, argumentDictionaries)
multiclassResults = getErrorOnLabels(multiclassResults, multiclassLabels)
- publishMulticlassResults(multiclassResults)
+ publishMulticlassResults(multiclassResults, metrics, statsIter, argumentDictionaries)
return multiclassResults
@@ -450,7 +514,7 @@ def execClassif(arguments):
multiclassLabels, labelsIndices, oldIndicesMulticlass = Multiclass.genMulticlassLabels(DATASET.get("Labels").value, multiclassMethod)
- classificationIndices = execution.genSplits(statsIter, oldIndicesMulticlass, multiclassLabels, args.CL_split, statsIterRandomStates, multiclassMethod)
+ classificationIndices = execution.genSplits(statsIter, oldIndicesMulticlass, DATASET.get("Labels").value, args.CL_split, statsIterRandomStates, multiclassMethod)
kFolds = execution.genKFolds(statsIter, args.CL_nbFolds, statsIterRandomStates)
diff --git a/Code/MonoMultiViewClassifiers/ResultAnalysis.py b/Code/MonoMultiViewClassifiers/ResultAnalysis.py
index 6e1ba8f21f422cdc8d36a64ee7cb7f8a15cf16a9..53c51485005308ac14321f33aa8f3f05f85b6c80 100644
--- a/Code/MonoMultiViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMultiViewClassifiers/ResultAnalysis.py
@@ -60,17 +60,17 @@ def resultAnalysis(benchmark, results, name, times, metrics, directory, minSize=
validationScores = np.array(validationScores)
trainScores = np.array(trainScores)
names = np.array(names)
+ sorted_indices = np.argsort(validationScores)
+ validationScores = validationScores[sorted_indices]
+ trainScores = trainScores[sorted_indices]
+ names = names[sorted_indices]
+
size = nbResults
if nbResults < minSize:
size = minSize
figKW = {"figsize" : (size, 3.0/4*size+2.0)}
f, ax = plt.subplots(nrows=1, ncols=1, **figKW)
barWidth= 0.35
- sorted_indices = np.argsort(validationScores)
- validationScores = validationScores[sorted_indices]
- trainScores = trainScores[sorted_indices]
- names = names[sorted_indices]
-
ax.set_title(metric[0] + "\n on validation set for each classifier")
rects = ax.bar(range(nbResults), validationScores, barWidth, color="r", )
rect2 = ax.bar(np.arange(nbResults) + barWidth, trainScores, barWidth, color="0.7", )
diff --git a/Code/Tests/Test_MonoviewClassifiers/test_compatibility.py b/Code/Tests/Test_MonoviewClassifiers/test_compatibility.py
index 82c5932a6d788c6799a1f6f2eb82777384110344..9b7ea4accb148457d59700f5eed0a893b556691b 100644
--- a/Code/Tests/Test_MonoviewClassifiers/test_compatibility.py
+++ b/Code/Tests/Test_MonoviewClassifiers/test_compatibility.py
@@ -73,6 +73,7 @@ class Test_fit(unittest.TestCase):
res = monoview_classifier_module.fit(cls.dataset, cls.labels, cls.random_state, **cls.args)
cls.assertIn("predict", dir(res), "fit must return an object able to predict")
+
class Test_paramsToSet(unittest.TestCase):
def test_inputs(self):
@@ -93,3 +94,16 @@ class Test_paramsToSet(unittest.TestCase):
self.assertEqual(type(res), list)
self.assertEqual(len(res), 2)
self.assertEqual(type(res[0]), list)
+
+# class Test_getKWARGS(unittest.TestCase):
+#
+# # TODO : Find a way to enter the right args
+#
+# def test_inputs(self):
+# for fileName in os.listdir("Code/MonoMultiViewClassifiers/MonoviewClassifiers"):
+# if fileName[-3:] == ".py" and fileName != "__init__.py":
+# monoview_classifier_module = getattr(MonoviewClassifiers, fileName[:-3])
+# with self.assertRaises(TypeError, msg="getKWARGS must have 1 positional args") as catcher:
+# monoview_classifier_module.getKWARGS()
+# monoview_classifier_module.getKWARGS([1],2)
+