diff --git a/multiview_platform/MonoMultiViewClassifiers/Multiview/ExecMultiview.py b/multiview_platform/MonoMultiViewClassifiers/Multiview/ExecMultiview.py
index 1f2a5d10e6ac9d176bc9e8fec1e3823fd0bd0abe..f711aca712edbe74cefda02959d87eef7214dd76 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Multiview/ExecMultiview.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Multiview/ExecMultiview.py
@@ -9,6 +9,7 @@ import numpy as np
from ..utils import HyperParameterSearch
from ..utils.Dataset import getShape
from .. import MultiviewClassifiers
+from .MultiviewUtils import MultiviewResult
# Author-Info
@@ -157,7 +158,8 @@ def ExecMultiview(directory, DATASET, name, classificationIndices, KFolds, nbCor
learningRate, name, imagesAnalysis)
logging.debug("Start:\t Saving preds")
- return CL_type, classificationKWARGS, metricsScores, fullLabels, testLabelsMulticlass
+ return MultiviewResult(CL_type, classificationKWARGS, metricsScores, fullLabels, testLabelsMulticlass)
+ # return CL_type, classificationKWARGS, metricsScores, fullLabels, testLabelsMulticlass
if __name__ == "__main__":
diff --git a/multiview_platform/MonoMultiViewClassifiers/Multiview/MultiviewUtils.py b/multiview_platform/MonoMultiViewClassifiers/Multiview/MultiviewUtils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5093007d8fa5db2d392ed8a0f597b6509c023b64
--- /dev/null
+++ b/multiview_platform/MonoMultiViewClassifiers/Multiview/MultiviewUtils.py
@@ -0,0 +1,8 @@
+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
diff --git a/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py b/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py
index 7fbf98ff97c6050ab7a08c837856a0173988187d..6c0cee191479bc4696af58e7ebacb1ff14992031 100644
--- a/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py
+++ b/multiview_platform/MonoMultiViewClassifiers/ResultAnalysis.py
@@ -66,8 +66,8 @@ def getMetricsScoresBiclass(metrics, monoviewResults, multiviewResults):
----------
metrics : list of lists
The metrics names with configuration metrics[i][0] = name of metric i
- monoviewResults : list of
- The ax.
+ monoviewResults : list of MonoviewResult 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
@@ -86,11 +86,11 @@ def getMetricsScoresBiclass(metrics, monoviewResults, multiviewResults):
testScores.append(classifierResult.metrics_scores[metric[0]][1])
classifiersNames.append(classifierResult.classifier_name+"-"+classifierResult.view_name)
for classifierResult in multiviewResults:
- trainScores.append(classifierResult[2][metric[0]][0])
- testScores.append(classifierResult[2][metric[0]][1])
- multiviewClassifierPackage = getattr(MultiviewClassifiers, classifierResult[0])
- multiviewClassifierModule = getattr(multiviewClassifierPackage, classifierResult[0]+"Module")
- classifiersNames.append(multiviewClassifierModule.genName(classifierResult[1]))
+ 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))
metricsScores[metric[0]] = {"classifiersNames": classifiersNames,
"trainScores": trainScores,
"testScores": testScores}
@@ -109,10 +109,10 @@ def getExampleErrorsBiclass(usedBenchmarkArgumentDictionary, monoviewResults, mu
errorOnExamples[unseenExamples]=-100
exampleErrors[classifierName] = errorOnExamples
for classifierResult in multiviewResults:
- multiviewClassifierPackage = getattr(MultiviewClassifiers, classifierResult[0])
- multiviewClassifierModule = getattr(multiviewClassifierPackage, classifierResult[0]+"Module")
- classifierName = multiviewClassifierModule.genName(classifierResult[1])
- predictedLabels = classifierResult[3]
+ 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]
@@ -405,19 +405,19 @@ def analyzeMulticlass(results, statsIter, benchmarkArgumentDictionaries, nbExamp
multiclassResults[iterIndex][classifierName][exampleIndex, classifierNegative] += 1
for classifierResult in resMulti:
- multiviewClassifierPackage = getattr(MultiviewClassifiers, classifierResult[0])
- multiviewClassifierModule = getattr(multiviewClassifierPackage, classifierResult[0]+"Module")
- classifierName = multiviewClassifierModule.genName(classifierResult[1])
+ 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[3][exampleIndex]
+ 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[4][multiclassIndex]
+ label = classifierResult.test_labels_multiclass[multiclassIndex]
if label == 1:
multiclassResults[iterIndex][classifierName][exampleIndex, classifierPositive] += 1
else: