diff --git a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
index e06e196d064be6843745336e7792ffda1016d037..80ac9dc5c9066f57ca84592581eaefb1e2db4f71 100644
--- a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
+++ b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
@@ -358,7 +358,7 @@ def execOneBenchmarkMonoCore(DATASET=None, LABELS_DICTIONARY=None, directory=Non
metrics=metrics, nIter=args.CL_HPS_iter, **arguments)]
logging.debug("Done:\t Multiview benchmark")
- return [flag, resultsMonoview, resultsMultiview]
+ return [flag, resultsMonoview + resultsMultiview]
def execBenchmark(nbCores, statsIter, nbMulticlass, benchmarkArgumentsDictionaries, classificationIndices, directories,
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
index 71f0840fc0ebef79ab22ab906c7d459016a1bfbb..9f4260d720b010fdcfb60965230847542a8eaba1 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
@@ -187,6 +187,8 @@ class MonoviewResult(object):
self.y_test_multiclass_pred = y_test_multiclass_pred
self.test_folds_preds = test_folds_preds
+ def get_classifier_name(self):
+ return self.classifier_name+"-"+self.view_name
diff --git a/multiview_platform/MonoMultiViewClassifiers/Multiview/MultiviewUtils.py b/multiview_platform/MonoMultiViewClassifiers/Multiview/MultiviewUtils.py
index 5093007d8fa5db2d392ed8a0f597b6509c023b64..d06972b7a7ee06c3c5c5c248d5c2a04e841d39da 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Multiview/MultiviewUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Multiview/MultiviewUtils.py
@@ -1,8 +1,15 @@
+from .. import MultiviewClassifiers
+
class MultiviewResult(object):
def __init__(self, classifier_name, classifier_config,
metrics_scores, full_labels, test_labels_multiclass):
self.classifier_name = classifier_name
self.classifier_config = classifier_config
self.metrics_scores = metrics_scores
- self.full_labels = full_labels
- self.test_labels_multiclass = test_labels_multiclass
\ No newline at end of file
+ self.full_labels_pred = full_labels
+ self.y_test_multiclass_pred = test_labels_multiclass
+
+ def get_classifier_name(self):
+ multiviewClassifierPackage = getattr(MultiviewClassifiers, self.classifier_name)
+ multiviewClassifierModule = getattr(multiviewClassifierPackage, self.classifier_name + "Module")
+ return multiviewClassifierModule.genName(self.classifier_config)
\ No newline at end of file
diff --git a/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py b/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py
index 6c0cee191479bc4696af58e7ebacb1ff14992031..9374f47fbb9aed8fff3c95cc5bd221f6cc1f02ab 100644
--- a/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py
+++ b/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py
@@ -59,65 +59,56 @@ def autolabel(rects, ax, set=1, std=None):
ha='center', va='bottom', size="small")
-def getMetricsScoresBiclass(metrics, monoviewResults, multiviewResults):
+def getMetricsScoresBiclass(metrics, results):
r"""Used to extract metrics scores in case of biclass classification
Parameters
----------
metrics : list of lists
The metrics names with configuration metrics[i][0] = name of metric i
- monoviewResults : list of MonoviewResult objects
+ results : list of MonoviewResult and MultiviewResults objects
A list containing all the resluts for all the monoview experimentations.
- set : integer
- 1 means the test scores, anything else means the train score
- std: None or array
- The standard deviations in the case of statsIter results.
Returns
-------
+ metricsScores : dict of dict of list
+ Regroups all the scores for each metrics for each classifier and for the train and test sets.
+ organized as :
+ -`metricScores[metric_name]["classifiersNames"]` is a list of all the classifiers available for this metric,
+ -`metricScores[metric_name]["trainScores"]` is a list of all the available classifiers scores on the train set,
+ -`metricScores[metric_name]["testScores"]` is a list of all the available classifiers scores on the test set.
"""
metricsScores = {}
+
for metric in metrics:
classifiersNames = []
trainScores = []
testScores = []
- for classifierResult in monoviewResults:
- trainScores.append(classifierResult.metrics_scores[metric[0]][0])
- testScores.append(classifierResult.metrics_scores[metric[0]][1])
- classifiersNames.append(classifierResult.classifier_name+"-"+classifierResult.view_name)
- for classifierResult in multiviewResults:
+
+ for classifierResult in results:
trainScores.append(classifierResult.metrics_scores[metric[0]][0])
testScores.append(classifierResult.metrics_scores[metric[0]][1])
- multiviewClassifierPackage = getattr(MultiviewClassifiers, classifierResult.classifier_name)
- multiviewClassifierModule = getattr(multiviewClassifierPackage, classifierResult.classifier_name+"Module")
- classifiersNames.append(multiviewClassifierModule.genName(classifierResult.classifier_config))
+ classifiersNames.append(classifierResult.get_classifier_name())
+
metricsScores[metric[0]] = {"classifiersNames": classifiersNames,
"trainScores": trainScores,
"testScores": testScores}
return metricsScores
-def getExampleErrorsBiclass(usedBenchmarkArgumentDictionary, monoviewResults, multiviewResults):
+def getExampleErrorsBiclass(usedBenchmarkArgumentDictionary, results):
exampleErrors = {}
trueLabels = usedBenchmarkArgumentDictionary["labels"]
- for classifierResult in monoviewResults:
- classifierName = classifierResult.classifier_name+"-"+classifierResult.view_name
+
+ for classifierResult in results:
+ classifierName = classifierResult.get_classifier_name()
predictedLabels = classifierResult.full_labels_pred
errorOnExamples = predictedLabels==trueLabels
errorOnExamples = errorOnExamples.astype(int)
unseenExamples = np.where(trueLabels==-100)[0]
errorOnExamples[unseenExamples]=-100
exampleErrors[classifierName] = errorOnExamples
- for classifierResult in multiviewResults:
- multiviewClassifierPackage = getattr(MultiviewClassifiers, classifierResult.classifier_name)
- multiviewClassifierModule = getattr(multiviewClassifierPackage, classifierResult.classifier_name+"Module")
- classifierName = multiviewClassifierModule.genName(classifierResult.classifier_config)
- predictedLabels = classifierResult.full_labels
- errorOnExamples = predictedLabels==trueLabels
- errorOnExamples = errorOnExamples.astype(int)
- unseenExamples = np.where(trueLabels==-100)[0]
- errorOnExamples[unseenExamples]=-100
- exampleErrors[classifierName] = errorOnExamples
+
return exampleErrors
@@ -219,8 +210,7 @@ def publishExampleErrors(exampleErrors, directory, databaseName, labelsNames, mi
def analyzeBiclass(results, benchmarkArgumentDictionaries, statsIter, metrics):
logging.debug("Srart:\t Analzing all biclass resuls")
biclassResults = [{} for _ in range(statsIter)]
- for result in results:
- flag = result[0]
+ for flag, result in results:
iteridex = flag[0]
classifierPositive = flag[1][0]
classifierNegative = flag[1][1]
@@ -229,10 +219,8 @@ def analyzeBiclass(results, benchmarkArgumentDictionaries, statsIter, metrics):
for benchmarkArgumentDictionary in benchmarkArgumentDictionaries:
if benchmarkArgumentDictionary["flag"]==flag:
usedBenchmarkArgumentDictionary = benchmarkArgumentDictionary
- monoviewResults = result[1]
- multiviewResults = result[2]
- metricsScores = getMetricsScoresBiclass(metrics, monoviewResults, multiviewResults)
- exampleErrors = getExampleErrorsBiclass(usedBenchmarkArgumentDictionary, monoviewResults, multiviewResults)
+ metricsScores = getMetricsScoresBiclass(metrics, result)
+ exampleErrors = getExampleErrorsBiclass(usedBenchmarkArgumentDictionary, result)
directory = usedBenchmarkArgumentDictionary["directory"]
databaseName = usedBenchmarkArgumentDictionary["args"].name
labelsNames = [usedBenchmarkArgumentDictionary["LABELS_DICTIONARY"][0],
@@ -380,15 +368,18 @@ def analyzeMulticlass(results, statsIter, benchmarkArgumentDictionaries, nbExamp
metrics, classificationIndices, directories):
"""Used to transform one versus one results in multiclass results and to publish it"""
multiclassResults = [{} for _ in range(statsIter)]
- for flag, resMono, resMulti in results:
+
+ for flag, result in results:
iterIndex = flag[0]
classifierPositive = flag[1][0]
classifierNegative = flag[1][1]
+
for benchmarkArgumentDictionary in benchmarkArgumentDictionaries:
if benchmarkArgumentDictionary["flag"] == flag:
trainIndices, testIndices, testMulticlassIndices = benchmarkArgumentDictionary["classificationIndices"]
- for classifierResult in resMono:
- classifierName = classifierResult.classifier_name+"-"+classifierResult.view_name
+
+ for classifierResult in result:
+ classifierName = classifierResult.get_classifier_name()
if classifierName not in multiclassResults[iterIndex]:
multiclassResults[iterIndex][classifierName] = np.zeros((nbExamples, nbLabels),dtype=int)
for exampleIndex in trainIndices:
@@ -404,31 +395,6 @@ def analyzeMulticlass(results, statsIter, benchmarkArgumentDictionaries, nbExamp
else:
multiclassResults[iterIndex][classifierName][exampleIndex, classifierNegative] += 1
- for classifierResult in resMulti:
- multiviewClassifierPackage = getattr(MultiviewClassifiers, classifierResult.classifier_name)
- multiviewClassifierModule = getattr(multiviewClassifierPackage, classifierResult.classifier_name+"Module")
- classifierName = multiviewClassifierModule.genName(classifierResult.classifier_config)
- if classifierName not in multiclassResults[iterIndex]:
- multiclassResults[iterIndex][classifierName] = np.zeros((nbExamples,nbLabels),dtype=int)
- for exampleIndex in trainIndices:
- label = classifierResult.full_labels[exampleIndex]
- if label == 1:
- multiclassResults[iterIndex][classifierName][exampleIndex, classifierPositive] += 1
- else:
- multiclassResults[iterIndex][classifierName][exampleIndex, classifierNegative] += 1
- for multiclassIndex, exampleIndex in enumerate(testMulticlassIndices):
- label = classifierResult.test_labels_multiclass[multiclassIndex]
- if label == 1:
- multiclassResults[iterIndex][classifierName][exampleIndex, classifierPositive] += 1
- else:
- multiclassResults[iterIndex][classifierName][exampleIndex, classifierNegative] += 1
- # for exampleIndex, label in enumerate(classifierResult[3]):
- # if label == 1:
- # multiclassResults[iterIndex][classifierName][exampleIndex, classifierPositive] += 1
- # else:
- # multiclassResults[iterIndex][classifierName][exampleIndex, classifierNegative] += 1
-
-
for iterIndex, multiclassiterResult in enumerate(multiclassResults):
for key, value in multiclassiterResult.items():
multiclassResults[iterIndex][key] = {"labels": np.argmax(value, axis=1)}