diff --git a/Code/ExecClassif.py b/Code/ExecClassif.py
index 64b6dde4c6466cabfdc7c486cbbabbed3df1c388..dab41d2a8884f06ad89ef53c16c7501cbdb145fa 100644
--- a/Code/ExecClassif.py
+++ b/Code/ExecClassif.py
@@ -3,6 +3,7 @@ import pkgutil
import Multiview
from Multiview.ExecMultiview import ExecMultiview
from Monoview.ExecClassifMonoView import ExecMonoview
+import Multiview.GetMultiviewDb as DB
import Monoview
import os
import time
@@ -130,6 +131,14 @@ logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=lo
if args.log:
logging.getLogger().addHandler(logging.StreamHandler())
+getDatabase = getattr(DB, "get" + args.name + "DB" + args.type[1:])
+DATASET, LABELS_DICTIONARY = getDatabase(args.views, args.pathF, args.name, len(args.CL_classes), args.CL_classes)
+datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
+NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
+views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW)]
+NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
+
+
logging.info("Begginging")
benchmark = {}
if args.CL_type.split(":")==["Benchmark"]:
@@ -184,28 +193,30 @@ KNNKWARGS = {"classifier__n_neighbors": map(float,args.CL_KNN_neigh.split(":"))}
argumentDictionaries = {"Monoview":{}, "Multiview":[]}
-if benchmark["Monoview"]:
- for view in args.views.split(":"):
- argumentDictionaries["Monoview"][str(view)] = []
- for classifier in benchmark["Monoview"]:
- arguments = {classifier+"KWARGS": globals()[classifier+"KWARGS"], "feat":view, "fileFeat": args.fileFeat,
- "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": classifier,
- classifier+"KWARGS": globals()[classifier+"KWARGS"]}
- argumentDictionaries["Monoview"][str(view)].append(arguments)
-
-bestClassifiers = []
-bestClassifiersConfigs = []
-for viewArguments in argumentDictionaries["Monoview"].values():
- resultsMonoview = Parallel(n_jobs=nbCores)(
- delayed(ExecMonoview)(args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=True,
- **arguments)
- for arguments in viewArguments)
- accuracies = [result[1] for result in resultsMonoview]
- classifiersNames = [result[0] for result in resultsMonoview]
- classifiersConfigs = [result[2] for result in resultsMonoview]
- bestClassifiers.append(classifiersNames[np.argmax(np.array(accuracies))])
- bestClassifiersConfigs.append(classifiersConfigs[np.argmax(np.array(accuracies))])
-
+# if benchmark["Monoview"]:
+# for view in args.views.split(":"):
+# argumentDictionaries["Monoview"][str(view)] = []
+# for classifier in benchmark["Monoview"]:
+# arguments = {classifier+"KWARGS": globals()[classifier+"KWARGS"], "feat":view, "fileFeat": args.fileFeat,
+# "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": classifier,
+# classifier+"KWARGS": globals()[classifier+"KWARGS"]}
+# argumentDictionaries["Monoview"][str(view)].append(arguments)
+#
+# bestClassifiers = []
+# bestClassifiersConfigs = []
+# for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].values()):
+# resultsMonoview = Parallel(n_jobs=nbCores)(
+# delayed(ExecMonoview)(DATASET.get("View"+str(viewIndex)).value, DATASET.get("labels").value, args.name,
+# args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=True,
+# **arguments)
+# for arguments in viewArguments)
+# accuracies = [result[1] for result in resultsMonoview]
+# classifiersNames = [result[0] for result in resultsMonoview]
+# classifiersConfigs = [result[2] for result in resultsMonoview]
+# bestClassifiers.append(classifiersNames[np.argmax(np.array(accuracies))])
+# bestClassifiersConfigs.append(classifiersConfigs[np.argmax(np.array(accuracies))])
+bestClassifiers = ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]
+bestClassifiersConfigs = [["1"],["1"],["1"],["1"]]
if benchmark["Multiview"]:
if benchmark["Multiview"]["Fusion"]:
if benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
@@ -249,8 +260,8 @@ if benchmark["Multiview"]:
argumentDictionaries["Multiview"].append(arguments)
resultsMultiview = Parallel(n_jobs=nbCores)(
- delayed(ExecMultiview)(args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=True,
- **arguments)
+ delayed(ExecMultiview)(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
+ LABELS_DICTIONARY, gridSearch=True, **arguments)
for arguments in argumentDictionaries["Multiview"])
# for classifierType, argumentsList in argumentDictionaries.iteritems():
diff --git a/Code/Monoview/ExecClassifMonoView.py b/Code/Monoview/ExecClassifMonoView.py
index c7ff0f799c43461df450c66084a97f86a8b306e8..04a271e7ea188ee11d08703307e8e5bc7a401f7c 100644
--- a/Code/Monoview/ExecClassifMonoView.py
+++ b/Code/Monoview/ExecClassifMonoView.py
@@ -30,7 +30,7 @@ __date__ = 2016-03-25
### Argument Parser
-def ExecMonoview(name, learningRate, nbFolds, nbCores, databaseType, path, gridSearch=True, **kwargs):
+def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path, gridSearch=True, **kwargs):
t_start = time.time()
directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
feat = kwargs["feat"]
@@ -44,20 +44,6 @@ def ExecMonoview(name, learningRate, nbFolds, nbCores, databaseType, path, gridS
logging.debug("### Main Programm for Classification MonoView")
logging.debug("### Classification - Database:" + str(name) + " Feature:" + str(feat) + " train_size:" + str(learningRate) + ", CrossValidation k-folds:" + str(nbFolds) + ", cores:" + str(nbCores)+", algorithm : "+CL_type)
- # Read the features
- logging.debug("Start:\t Read " + databaseType + " Files")
-
- if databaseType == ".csv":
- X = np.genfromtxt(path + fileFeat, delimiter=';')
- Y = np.genfromtxt(path + fileCL, delimiter=';')
- elif databaseType == ".hdf5":
- dataset = h5py.File(path + name + ".hdf5", "r")
- viewsDict = dict((dataset.get("/View"+str(viewIndex)+"/name").value, viewIndex) for viewIndex in range(dataset.get("nbView").value))
- X = dataset["View"+str(viewsDict[feat])+"/matrix"][...]
- Y = dataset["Labels/labelsArray"][...]
-
- logging.debug("Info:\t Shape of Feature:" + str(X.shape) + ", Length of classLabels vector:" + str(Y.shape))
- logging.debug("Done:\t Read CSV Files")
# Calculate Train/Test data
logging.debug("Start:\t Determine Train/Test split")
@@ -204,7 +190,23 @@ if __name__=='__main__':
if(args.log):
logging.getLogger().addHandler(logging.StreamHandler())
+
+ # Read the features
+ logging.debug("Start:\t Read " + args.type + " Files")
+
+ if args.databaseType == ".csv":
+ X = np.genfromtxt(args.pathF + args.fileFeat, delimiter=';')
+ Y = np.genfromtxt(args.pathF + args.fileCL, delimiter=';')
+ elif args.type == ".hdf5":
+ dataset = h5py.File(args.pathF + args.name + ".hdf5", "r")
+ viewsDict = dict((dataset.get("View"+str(viewIndex)).attrs["name"], viewIndex) for viewIndex in range(dataset.get("Metadata").attrs["nbView"]))
+ X = dataset["View"+str(viewsDict[args.feat])][...]
+ Y = dataset["labels"][...]
+
+ logging.debug("Info:\t Shape of Feature:" + str(X.shape) + ", Length of classLabels vector:" + str(Y.shape))
+ logging.debug("Done:\t Read CSV Files")
+
arguments = {"RandomForestKWARGS": RandomForestKWARGS, "SVCKWARGS": SVCKWARGS,
"DecisionTreeKWARGS": DecisionTreeKWARGS, "SGDKWARGS": SGDKWARGS, "feat":args.feat,
"fileFeat": args.fileFeat, "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": args.CL_type}
- ExecMonoview(args.name, args.CL_split, args.CL_CV, args.CL_Cores, args.type, args.pathF, **arguments)
+ ExecMonoview(X, Y, args.name, args.CL_split, args.CL_CV, args.CL_Cores, args.type, args.pathF, **arguments)
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3131e2bdc54911acefa5e89f3578fa40d98e26d0
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-1.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.478260869565;0.0416666666667;0.328571428571
+Oui;0.239130434783;0.0416666666667;0.171428571429
+All;0.717391304348;0.0833333333333;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fb1b91d89d6e5b5c3699b4e7cb2a684e5bffe1f0
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-2.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;;0.314285714286
+Oui;0.295454545455;;0.185714285714
+All;0.795454545455;;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c37d7e485cc7ddf323cd5b821ca5723a81483d1a
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-3.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;;0.342857142857
+Oui;0.229166666667;;0.157142857143
+All;0.729166666667;;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..34b9ef9be476fab7bc35cf71be85d5779d8ccb5f
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix-4.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.346153846154;0.444444444444;0.371428571429
+Oui;0.0769230769231;0.277777777778;0.128571428571
+All;0.423076923077;0.722222222222;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5b1479a8486b209c1c853234efadc4111ccdf8d6
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrix.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.326923076923;0.5;0.371428571429
+Oui;0.115384615385;0.166666666667;0.128571428571
+All;0.442307692308;0.666666666667;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrixImg-1.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrixImg-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..ec5e4d4b6499932ee5b4157ef62fb822e5307728
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new file mode 100644
index 0000000000000000000000000000000000000000..031281b78bb76d8990add7c3e76379aafb7f6339
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index 0000000000000000000000000000000000000000..67961b566905b77656562a4a9b3f8dbcda7de94e
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrixImg-4.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrixImg-4.png
new file mode 100644
index 0000000000000000000000000000000000000000..5609e6ce14d5373908ce28b9b588c9927805fc52
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrixImg.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-ConfMatrixImg.png
new file mode 100644
index 0000000000000000000000000000000000000000..82b334a676c5b38bba9d5555af9a53459c2f95d8
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..95e361c4614c748dee37c87784c3ea5a77779c44
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-1.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.666666666667;0.95652173913;0.785714285714;23.0
+Oui;0.5;0.0833333333333;0.142857142857;12.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f218eca7089c36145f3b6aec95539d28bd7a2bcd
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-2.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.628571428571;1.0;0.771929824561;22.0
+Oui;0.0;0.0;0.0;13.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..45417716354c392e85f7f1960ac02a701a0b7df6
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-3.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.685714285714;1.0;0.813559322034;24.0
+Oui;0.0;0.0;0.0;11.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5e4458236c29426da4fd5451214994565c4cfe2d
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report-4.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.818181818182;0.692307692308;0.75;26.0
+Oui;0.384615384615;0.555555555556;0.454545454545;9.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7ae1fa919134aab2f8ae0f8b0edb7c9d7b9283cf
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Report.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.739130434783;0.653846153846;0.69387755102;26.0
+Oui;0.25;0.333333333333;0.285714285714;9.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Score-1.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Score-1.png
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index 0000000000000000000000000000000000000000..23722959a153adee8c0f6e47059aaf1beca02f00
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index 0000000000000000000000000000000000000000..61d5c7690d46b99f0bbeb4ab54a8faa6000576be
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index 0000000000000000000000000000000000000000..d2b477a5a2273aeddab9c93fb7745d0d62f43450
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Score.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Score.png
new file mode 100644
index 0000000000000000000000000000000000000000..196fc87d498172cb775f51e341e9e5e6851caf6f
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5e8d3d23cdeba763f3b4c7499b8389359675dc2f
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.657142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.4642857142857142
+5;Mean of F1-Score of top 20 classes by F1-Score;0.4642857142857142
+6;Mean of F1-Score of top 30 classes by F1-Score;0.4642857142857142
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..819494183b9f75326814335457a36af802ab1c93
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.628571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.38596491228070173
+5;Mean of F1-Score of top 20 classes by F1-Score;0.38596491228070173
+6;Mean of F1-Score of top 30 classes by F1-Score;0.38596491228070173
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fb3b57b1ad14692b305fb2473f3d50e46bee21e3
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-3.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.685714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.4067796610169492
+5;Mean of F1-Score of top 20 classes by F1-Score;0.4067796610169492
+6;Mean of F1-Score of top 30 classes by F1-Score;0.4067796610169492
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cfc8c4a817bdd3149cf25401b0d66bf0f5c9d639
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats-4.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.657142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.6022727272727273
+5;Mean of F1-Score of top 20 classes by F1-Score;0.6022727272727273
+6;Mean of F1-Score of top 30 classes by F1-Score;0.6022727272727273
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats.csv
new file mode 100644
index 0000000000000000000000000000000000000000..313fe8a1ee553538eeef8930ae567e360ff9ff49
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Clinic-Stats.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.571428571429
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.4897959183673469
+5;Mean of F1-Score of top 20 classes by F1-Score;0.4897959183673469
+6;Mean of F1-Score of top 30 classes by F1-Score;0.4897959183673469
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1fa13645401c42d5721bd2f473646149dfacfbbe
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-1.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.483333333333;0.1;0.428571428571
+Oui;0.0833333333333;0.0;0.0714285714286
+All;0.566666666667;0.1;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5a3f315d5d5c894a39428bce1e45c0424d12a1cf
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-2.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;0.0;0.371428571429
+Oui;0.0576923076923;0.333333333333;0.128571428571
+All;0.557692307692;0.333333333333;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-3.csv
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index 0000000000000000000000000000000000000000..a1ebf0595614fb1080995c8b426071a80d4817ce
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-3.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;;0.385714285714
+Oui;0.148148148148;;0.114285714286
+All;0.648148148148;;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-4.csv
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index 0000000000000000000000000000000000000000..fafad4465902c0b1accc078589cb489013e13ad7
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix-4.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;0.0;0.285714285714
+Oui;0.2;0.233333333333;0.214285714286
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrix.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;;0.314285714286
+Oui;0.295454545455;;0.185714285714
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrixImg-1.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-ConfMatrixImg-1.png
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-1.csv
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index 0000000000000000000000000000000000000000..8a160c6b94c4dc1dffb92f44b5f3ae4b448e6cbc
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-1.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.852941176471;0.966666666667;0.90625;30.0
+Oui;0.0;0.0;0.0;5.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-2.csv
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index 0000000000000000000000000000000000000000..883dde245783b9903be30b8acb1f5dfb74a075de
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-2.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.896551724138;1.0;0.945454545455;26.0
+Oui;1.0;0.666666666667;0.8;9.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-3.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-3.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.771428571429;1.0;0.870967741935;27.0
+Oui;0.0;0.0;0.0;8.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-4.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report-4.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.714285714286;1.0;0.833333333333;20.0
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Report.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.628571428571;1.0;0.771929824561;22.0
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Score-3.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Score-3.png
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Score-4.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Score-4.png
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Score.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Score.png
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..60369ed171b55f177c773b946e3e173be6f38f1b
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.453125
+5;Mean of F1-Score of top 20 classes by F1-Score;0.453125
+6;Mean of F1-Score of top 30 classes by F1-Score;0.453125
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-2.csv
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index 0000000000000000000000000000000000000000..bb4e312c3ae16c8c73b35eee27293b3144ff4d63
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.914285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8727272727272728
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8727272727272728
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8727272727272728
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..69922632bbd248d18b1b00a3ab99d7757879e62a
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-3.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.771428571429
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.435483870967742
+5;Mean of F1-Score of top 20 classes by F1-Score;0.435483870967742
+6;Mean of F1-Score of top 30 classes by F1-Score;0.435483870967742
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a73beda40854ac8b1487704df6af7012ab527484
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats-4.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.771428571429
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7348484848484849
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7348484848484849
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7348484848484849
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats.csv
new file mode 100644
index 0000000000000000000000000000000000000000..819494183b9f75326814335457a36af802ab1c93
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-Methyl-Stats.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.628571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.38596491228070173
+5;Mean of F1-Score of top 20 classes by F1-Score;0.38596491228070173
+6;Mean of F1-Score of top 30 classes by F1-Score;0.38596491228070173
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..55ae5b075404ea9415d3ee31186eec1024e9c18c
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-1.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.462962962963;0.125;0.385714285714
+Oui;0.0555555555556;0.3125;0.114285714286
+All;0.518518518519;0.4375;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-2.csv
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index 0000000000000000000000000000000000000000..8b48b232b4edf96f91ca89a452fc69725fe9004b
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-2.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.462962962963;0.125;0.385714285714
+Oui;0.037037037037;0.375;0.114285714286
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-3.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-3.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.425925925926;0.25;0.385714285714
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-4.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-4.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.433333333333;0.4;0.428571428571
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-5.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-5.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix-5.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.423076923077;0.222222222222;0.371428571429
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-ConfMatrix.csv
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+;Non;Oui;All
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-1.csv
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@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.892857142857;0.925925925926;0.909090909091;27.0
+Oui;0.714285714286;0.625;0.666666666667;8.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-2.csv
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@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.925925925926;0.925925925926;0.925925925926;27.0
+Oui;0.75;0.75;0.75;8.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-3.csv
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@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.92;0.851851851852;0.884615384615;27.0
+Oui;0.6;0.75;0.666666666667;8.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-4.csv
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+;Precision;Recall;F1;Support
+Non;0.962962962963;0.866666666667;0.912280701754;30.0
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-5.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report-5.csv
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+;Precision;Recall;F1;Support
+Non;1.0;0.846153846154;0.916666666667;26.0
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Report.csv
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+;Precision;Recall;F1;Support
+Non;1.0;0.8;0.888888888889;25.0
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-1.csv
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--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7878787878787878
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7878787878787878
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7878787878787878
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-2.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.885714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.837962962962963
+5;Mean of F1-Score of top 20 classes by F1-Score;0.837962962962963
+6;Mean of F1-Score of top 30 classes by F1-Score;0.837962962962963
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-3.csv
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--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-3.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7756410256410255
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7756410256410255
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7756410256410255
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-4.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-4.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7638326585695008
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7638326585695008
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7638326585695008
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-5.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-5.csv
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index 0000000000000000000000000000000000000000..be7df7ca3ba374ddf4200288040d20b9096b8a42
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats-5.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.885714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
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+5;Mean of F1-Score of top 20 classes by F1-Score;0.8674242424242424
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8674242424242424
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats.csv
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index 0000000000000000000000000000000000000000..bf63027079c9e6ed9c224c804ba841815a777019
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-MiRNA_-Stats.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
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+5;Mean of F1-Score of top 20 classes by F1-Score;0.8444444444444446
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8444444444444446
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-1.csv
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index 0000000000000000000000000000000000000000..ea73b222cdd2fd655afe4142538273d25dc21c07
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-1.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.464285714286;0.142857142857;0.4
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+All;0.553571428571;0.285714285714;0.5
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-2.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-2.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.458333333333;0.0909090909091;0.342857142857
+Oui;0.0416666666667;0.409090909091;0.157142857143
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-3.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-3.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.42;0.2;0.357142857143
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-4.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix-4.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-ConfMatrix.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-1.csv
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index 0000000000000000000000000000000000000000..010d9917f3841e9d0f2b357225619630cb45c5ab
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-1.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.838709677419;0.928571428571;0.881355932203;28.0
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-2.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-2.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.916666666667;0.916666666667;0.916666666667;24.0
+Oui;0.818181818182;0.818181818182;0.818181818182;11.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-3.csv
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+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-3.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.875;0.84;0.857142857143;25.0
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report-4.csv
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@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e3e4bbe522ce5cde1ea05710a18dd592e95701ce
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Report.csv
@@ -0,0 +1,3 @@
+;Precision;Recall;F1;Support
+Non;0.884615384615;0.884615384615;0.884615384615;26.0
+Oui;0.666666666667;0.666666666667;0.666666666667;9.0
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-1.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..80c3a2e0c39cdd1cd20b8a2ceebb60a9c64f9792
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-2.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-2.png
new file mode 100644
index 0000000000000000000000000000000000000000..6e947567c37ce2075410660dbd536461db01192b
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-3.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-3.png
new file mode 100644
index 0000000000000000000000000000000000000000..0028f0eb6cd3004379d8ca100d4bd0a4c131e338
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-4.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score-4.png
new file mode 100644
index 0000000000000000000000000000000000000000..1f46f04b7e90b76ff97dc30347c4160430142f15
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diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score.png b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score.png
new file mode 100644
index 0000000000000000000000000000000000000000..6a62a5b930aeead4c116d1b1d9368c8e9eaf632a
Binary files /dev/null and b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Score.png differ
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-1.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-1.csv
new file mode 100644
index 0000000000000000000000000000000000000000..94937fae057ec88b3a6b8c94cbe145f22129677a
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.6224961479198767
+5;Mean of F1-Score of top 20 classes by F1-Score;0.6224961479198767
+6;Mean of F1-Score of top 30 classes by F1-Score;0.6224961479198767
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-2.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-2.csv
new file mode 100644
index 0000000000000000000000000000000000000000..be7df7ca3ba374ddf4200288040d20b9096b8a42
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.885714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8674242424242424
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8674242424242424
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8674242424242424
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-3.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-3.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4d47e5bf6e3be8f048844078df8e7edbae440e6c
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-3.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7619047619047619
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7619047619047619
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7619047619047619
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-4.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2d60df03ee29d988117314b9166acae57b8527b5
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats-4.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8057713651498335
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8057713651498335
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8057713651498335
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats.csv b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e0555824ca01d3107bb9fe860c222a925c504c54
--- /dev/null
+++ b/Code/Monoview/Results-ClassMonoView/2016_08_23-CMV-MultiOmic-RNASeq-Stats.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7756410256410255
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7756410256410255
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7756410256410255
diff --git a/Code/Multiview/ExecMultiview.py b/Code/Multiview/ExecMultiview.py
index aef7169e2a6545a0b8f3f2df3c44e78eef39c1a3..ebc61a5bf3b3ddb37c202cc2eff975ccd8aa8310 100644
--- a/Code/Multiview/ExecMultiview.py
+++ b/Code/Multiview/ExecMultiview.py
@@ -15,12 +15,16 @@ import logging
import time
-def ExecMultiview(name, learningRate, nbFolds, nbCores, databaseType, path, gridSearch=False, **kwargs):
+def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, path, LABELS_DICTIONARY, gridSearch=False, **kwargs):
+
+ datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
+ NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
+ views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW)]
+ NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
CL_type = kwargs["CL_type"]
views = kwargs["views"]
NB_VIEW = kwargs["NB_VIEW"]
- NB_CLASS = kwargs["NB_CLASS"]
LABELS_NAMES = kwargs["LABELS_NAMES"]
MumboKWARGS = kwargs["MumboKWARGS"]
FusionKWARGS = kwargs["FusionKWARGS"]
@@ -30,23 +34,9 @@ def ExecMultiview(name, learningRate, nbFolds, nbCores, databaseType, path, grid
logging.info("### Classification - Database : " + str(name) + " ; Views : " + ", ".join(views) +
" ; Algorithm : " + CL_type + " ; Cores : " + str(nbCores))
-
-
- logging.info("Start:\t Read " + str.upper(databaseType[1:]) + " Database Files for " + name)
-
- getDatabase = getattr(DB, "get" + name + "DB" + databaseType[1:])
- DATASET, LABELS_DICTIONARY = getDatabase(views, path, name, NB_CLASS, LABELS_NAMES)
- datasetLength = DATASET["/datasetLength"][...]
- NB_VIEW = DATASET.get("nbView").value
- views = [str(DATASET["/View"+str(viewIndex)+"/name"][...]) for viewIndex in range(NB_VIEW)]
- NB_CLASS = DATASET.get("nbClass").value
-
- logging.info("Info:\t Labels used: " + ", ".join(LABELS_DICTIONARY.values()))
- logging.info("Info:\t Length of dataset:" + str(datasetLength))
-
for viewIndex in range(NB_VIEW):
- logging.info("Info:\t Shape of " + str(DATASET["/View"+str(viewIndex)+"/name"][...]) + " :" + str(
- DATASET["View" + str(viewIndex) + "/shape"][...]))
+ logging.info("Info:\t Shape of " + str(DATASET.get("View"+str(viewIndex)).attrs["name"]) + " :" + str(
+ DATASET.get("View"+str(viewIndex)).shape))
logging.info("Done:\t Read Database Files")
@@ -58,7 +48,7 @@ def ExecMultiview(name, learningRate, nbFolds, nbCores, databaseType, path, grid
logging.info("Start:\t Determine "+str(nbFolds)+" folds")
if nbFolds != 1:
- kFolds = DB.getKFoldIndices(nbFolds, DATASET["/Labels/labelsArray"][...], NB_CLASS, learningIndices)
+ kFolds = DB.getKFoldIndices(nbFolds, DATASET.get("labels")[...], NB_CLASS, learningIndices)
else:
kFolds = [[], range(datasetLength)]
@@ -99,7 +89,7 @@ def ExecMultiview(name, learningRate, nbFolds, nbCores, databaseType, path, grid
logging.info("\tStart:\t Fold number " + str(foldIdx + 1))
trainIndices = [index for index in range(datasetLength) if index not in fold]
DATASET_LENGTH = len(trainIndices)
- classifier = classifierClass(NB_VIEW, DATASET_LENGTH, DATASET.get("/Labels/labelsArray").value, NB_CORES=nbCores, **initKWARGS)
+ classifier = classifierClass(NB_VIEW, DATASET_LENGTH, DATASET.get("labels").value, NB_CORES=nbCores, **initKWARGS)
classifier.fit_hdf5(DATASET, trainIndices=trainIndices)
kFoldClassifier.append(classifier)
@@ -255,7 +245,17 @@ if __name__=='__main__':
"LABELS_NAMES": args.CL_classes.split(":"),
"FusionKWARGS": FusionKWARGS,
"MumboKWARGS": MumboKWARGS}
- ExecMultiview(args.name, args.CL_split, args.CL_nbFolds, args.CL_cores, args.type, args.pathF, gridSearch=True, **arguments)
+
+ logging.info("Start:\t Read " + str.upper(args.type[1:]) + " Database Files for " + args.name)
+
+ getDatabase = getattr(DB, "get" + args.name + "DB" + args.type[1:])
+ DATASET, LABELS_DICTIONARY = getDatabase(views, args.pathF, args.name, NB_CLASS, LABELS_NAMES)
+
+ logging.info("Info:\t Labels used: " + ", ".join(LABELS_DICTIONARY.values()))
+ logging.info("Info:\t Length of dataset:" + str(DATASET.get("Metadata").attrs["datasetlength"]))
+
+ ExecMultiview(DATASET, args.name, args.CL_split, args.CL_nbFolds, args.CL_cores, args.type, args.pathF,
+ LABELS_DICTIONARY, gridSearch=True, **arguments)
diff --git a/Code/Multiview/Fusion/Fusion.py b/Code/Multiview/Fusion/Fusion.py
index 80e4fa2f74ac11d9df943fa650705906fa55aefc..d9b53ec8deb569fddf793d5f7c5c9aa8b2329fbb 100644
--- a/Code/Multiview/Fusion/Fusion.py
+++ b/Code/Multiview/Fusion/Fusion.py
@@ -1,14 +1,16 @@
from Methods import *
+
def gridSearch_hdf5(DATASET, classifiersNames):
bestSettings = []
for classifierIndex, classifierName in enumerate(classifiersNames):
classifierModule = globals()[classifierName] # Permet d'appeler une fonction avec une string
classifierMethod = getattr(classifierModule, "gridSearch")
- bestSettings.append(classifierMethod(DATASET["/View"+str(classifierIndex)+"/matrix"][...],
- DATASET["/Labels/labelsArray"][...]))
+ bestSettings.append(classifierMethod(DATASET.get("View"+str(classifierIndex))[...],
+ DATASET.get("labels")[...]))
return bestSettings
+
class Fusion:
def __init__(self, NB_VIEW, DATASET_LENGTH, CLASS_LABELS, NB_CORES=1,**kwargs):
fusionType = kwargs['fusionType']
@@ -32,7 +34,7 @@ class Fusion:
def predict_hdf5(self, DATASET, usedIndices=None):
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
predictedLabels = self.classifier.predict_hdf5(DATASET, usedIndices=usedIndices)
else:
diff --git a/Code/Multiview/Fusion/Methods/EarlyFusion.py b/Code/Multiview/Fusion/Methods/EarlyFusion.py
index 7cc9e3051d46b77a42ba442b928226c61b4a888f..761d1b4ce61953577e68ba4b48a0289cc33eaa97 100644
--- a/Code/Multiview/Fusion/Methods/EarlyFusion.py
+++ b/Code/Multiview/Fusion/Methods/EarlyFusion.py
@@ -16,13 +16,13 @@ class EarlyFusionClassifier(object):
def makeMonoviewData_hdf5(self, DATASET, weights=None, usedIndices=None):
if not usedIndices:
- uesdIndices = range(DATASET.get("datasetLength").value)
- NB_VIEW = DATASET.get("nbView").value
+ uesdIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
+ NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
if type(weights)=="NoneType":
weights = np.array([1/NB_VIEW for i in range(NB_VIEW)])
if sum(weights)!=1:
weights = weights/sum(weights)
- self.monoviewData = np.concatenate([weights[viewIndex]*DATASET["/View"+str(viewIndex)+"/matrix"][usedIndices, :]
+ self.monoviewData = np.concatenate([weights[viewIndex]*DATASET.get("View"+str(viewIndex))[usedIndices, :]
for viewIndex in np.arange(NB_VIEW)], axis=1)
@@ -35,17 +35,17 @@ class WeightedLinear(EarlyFusionClassifier):
def fit_hdf5(self, DATASET, trainIndices=None):
if not trainIndices:
- trainIndices = range(DATASET.get("datasetLength").value)
+ trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
self.makeMonoviewData_hdf5(DATASET, weights=self.weights, usedIndices=trainIndices)
monoviewClassifierModule = getattr(MonoviewClassifiers, self.monoviewClassifierName)
- self.monoviewClassifier = monoviewClassifierModule.fit(self.monoviewData, DATASET["/Labels/labelsArray"][trainIndices],
+ self.monoviewClassifier = monoviewClassifierModule.fit(self.monoviewData, DATASET.get("labels")[trainIndices],
NB_CORES=self.nbCores,
**dict((str(configIndex),config) for configIndex,config in
enumerate(self.monoviewClassifiersConfig)))
def predict_hdf5(self, DATASET, usedIndices=None):
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
self.makeMonoviewData_hdf5(DATASET, weights=self.weights, usedIndices=usedIndices)
predictedLabels = self.monoviewClassifier.predict(self.monoviewData)
diff --git a/Code/Multiview/Fusion/Methods/LateFusion.py b/Code/Multiview/Fusion/Methods/LateFusion.py
index 5b01eb72deb8652072765c38b77fcd7795e488e5..962f51b07e8af4c32b6ec5e5f4ffc95908456238 100644
--- a/Code/Multiview/Fusion/Methods/LateFusion.py
+++ b/Code/Multiview/Fusion/Methods/LateFusion.py
@@ -33,12 +33,12 @@ class LateFusionClassifier(object):
def fit_hdf5(self, DATASET, trainIndices=None):
if trainIndices == None:
- trainIndices = range(DATASET.get("datasetLength").value)
- nbView = DATASET.get("nbView").value
+ trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
+ nbView = DATASET.get("Metadata").attrs["nbView"]
self.monoviewClassifiers = Parallel(n_jobs=self.nbCores)(
delayed(fifMonoviewClassifier)(self.monoviewClassifiersNames[viewIndex],
- DATASET["/View"+str(viewIndex)+"/matrix"][trainIndices, :],
- DATASET["/Labels/labelsArray"][trainIndices],
+ DATASET.get("View"+str(viewIndex))[trainIndices, :],
+ DATASET.get("labels")[trainIndices],
self.monoviewClassifiersConfigs[viewIndex])
for viewIndex in range(nbView))
@@ -53,13 +53,13 @@ class WeightedLinear(LateFusionClassifier):
# Normalize weights ?
# weights = weights/float(max(weights))
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
predictedLabels = []
- viewScores = np.zeros((DATASET.get("nbView").value, len(usedIndices), DATASET.get("nbClass").value))
- for viewIndex in range(DATASET.get("nbView").value):
+ viewScores = np.zeros((DATASET.get("Metadata").attrs["nbView"], len(usedIndices), DATASET.get("Metadata").attrs["nbClass"]))
+ for viewIndex in range(DATASET.get("Metadata").attrs["nbView"]):
viewScores[viewIndex] = self.monoviewClassifiers[viewIndex].predict_proba(
- DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
+ DATASET.get("View" + str(viewIndex))[usedIndices])
for currentIndex, usedIndex in enumerate(usedIndices):
predictedLabel = np.argmax(np.array(
[max(viewScore) * weight for viewScore, weight in zip(viewScores[:, currentIndex], self.weights)],
@@ -92,13 +92,13 @@ class SVMForLinear(LateFusionClassifier):
def fit_hdf5(self, DATASET, trainIndices=None):
if trainIndices == None:
- trainIndices = range(DATASET.get("datasetLength").value)
- nbViews = DATASET.get("nbView").value
+ trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
+ nbViews = DATASET.get("Metadata").attrs["nbView"]
for viewIndex in range(nbViews):
monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex])
self.monoviewClassifiers.append(
- monoviewClassifier.fit(DATASET["/View" + str(viewIndex) + "/matrix"][trainIndices],
- DATASET["/Labels/labelsArray"][trainIndices],
+ monoviewClassifier.fit(DATASET.get("View" + str(viewIndex))[trainIndices],
+ DATASET.get("labels")[trainIndices],
NB_CORES=self.nbCores,
**dict((str(configIndex), config) for configIndex, config in
enumerate(self.monoviewClassifiersConfigs[viewIndex]
@@ -109,13 +109,13 @@ class SVMForLinear(LateFusionClassifier):
# Normalize weights ?
# weights = weights/float(max(weights))
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
- monoviewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int)
- for viewIndex in range(DATASET.get("nbView").value):
+ monoviewDecisions = np.zeros((len(usedIndices), DATASET.get("Metadata").attrs["nbView"]), dtype=int)
+ for viewIndex in range(DATASET.get("Metadata").attrs["nbView"]):
monoviewClassifier = getattr(MonoviewClassifiers, self.monoviewClassifiersNames[viewIndex])
monoviewDecisions[:, viewIndex] = self.monoviewClassifiers[viewIndex].predict(
- DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
+ DATASET.get("View" + str(viewIndex))[usedIndices])
predictedLabels = self.SVMClassifier.predict(monoviewDecisions)
else:
predictedLabels = []
@@ -123,12 +123,12 @@ class SVMForLinear(LateFusionClassifier):
def SVMForLinearFusionFit(self, DATASET, usedIndices=None):
self.SVMClassifier = OneVsOneClassifier(SVC())
- monoViewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int)
- for viewIndex in range(DATASET.get("nbView").value):
+ monoViewDecisions = np.zeros((len(usedIndices), DATASET.get("Metadata").attrs["nbView"]), dtype=int)
+ for viewIndex in range(DATASET.get("Metadata").attrs["nbView"]):
monoViewDecisions[:, viewIndex] = self.monoviewClassifiers[viewIndex].predict(
- DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
+ DATASET.get("View" + str(viewIndex))[usedIndices])
- self.SVMClassifier.fit(monoViewDecisions, DATASET["/Labels/labelsArray"][usedIndices])
+ self.SVMClassifier.fit(monoViewDecisions, DATASET.get("labels")[usedIndices])
def getConfig(self, fusionMethodConfig, monoviewClassifiersNames,monoviewClassifiersConfigs):
configString = "with SVM for linear \n\t-With monoview classifiers : "
@@ -148,20 +148,20 @@ class MajorityVoting(LateFusionClassifier):
def predict_hdf5(self, DATASET, usedIndices=None):
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
datasetLength = len(usedIndices)
- votes = np.zeros((datasetLength, DATASET.get("nbClass").value), dtype=int)
- monoViewDecisions = np.zeros((len(usedIndices), DATASET.get("nbView").value), dtype=int)
- for viewIndex in range(DATASET.get("nbView").value):
+ votes = np.zeros((datasetLength, DATASET.get("Metadata").attrs["nbClass"]), dtype=int)
+ monoViewDecisions = np.zeros((len(usedIndices),DATASET.get("Metadata").attrs["nbView"]), dtype=int)
+ for viewIndex in range(DATASET.get("Metadata").attrs["nbView"]):
monoViewDecisions[:, viewIndex] = self.monoviewClassifiers[viewIndex].predict(
- DATASET["/View" + str(viewIndex) + "/matrix"][usedIndices])
+ DATASET.get("View" + str(viewIndex))[usedIndices])
for exampleIndex in range(datasetLength):
for featureClassification in monoViewDecisions[exampleIndex, :]:
votes[exampleIndex, featureClassification] += 1
nbMaximum = len(np.where(votes[exampleIndex] == max(votes[exampleIndex]))[0])
try:
- assert nbMaximum != DATASET.get("nbView").value
+ assert nbMaximum != DATASET.get("Metadata").attrs["nbView"]
except:
print "Majority voting can't decide, each classifier has voted for a different class"
raise
@@ -198,16 +198,14 @@ class BayesianInference(LateFusionClassifier):
def predict_hdf5(self, DATASET, usedIndices=None):
nbView = DATASET.get("nbView").value
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if sum(self.weights)!=1.0:
self.weights = self.weights/sum(self.weights)
if usedIndices:
- viewScores = np.zeros((nbView, len(usedIndices), DATASET.get("nbClass").value))
+ viewScores = np.zeros((nbView, len(usedIndices), DATASET.get("Metadata").attrs["nbClass"]))
for viewIndex in range(nbView):
- viewScores[viewIndex] = np.power(self.monoviewClassifiers[viewIndex].predict_proba(DATASET["/View" +
- str(viewIndex) +
- "/matrix"]
+ viewScores[viewIndex] = np.power(self.monoviewClassifiers[viewIndex].predict_proba(DATASET.get("View" + str(viewIndex))
[usedIndices]),
self.weights[viewIndex])
predictedLabels = np.argmax(np.prod(viewScores, axis=1), axis=1)
diff --git a/Code/Multiview/GetMultiviewDb.py b/Code/Multiview/GetMultiviewDb.py
index c231b59c36c2c0a26b90395f218127a4131a8ef5..8a9dfa82e61ea17cdd3152f2068f277d19fbe749 100644
--- a/Code/Multiview/GetMultiviewDb.py
+++ b/Code/Multiview/GetMultiviewDb.py
@@ -114,8 +114,8 @@ def isUseful (labelSupports, index, CLASS_LABELS, labelDict):
def splitDataset(DATASET, LEARNING_RATE, DATASET_LENGTH):
- LABELS = DATASET["/Labels/labelsArray"][...]
- NB_CLASS = int(DATASET["/nbClass"][...])
+ LABELS = DATASET.get("labels")[...]
+ NB_CLASS = int(DATASET["Metadata"].attrs["nbClass"])
validationIndices = extractRandomTrainingSet(LABELS, 1-LEARNING_RATE, DATASET_LENGTH, NB_CLASS)
validationIndices.sort()
return validationIndices
@@ -174,7 +174,6 @@ def getDbfromCSV(path):
for file in files:
if file[-8:]=='plus.csv' and file[:7]=='sample1':
- print 'poulet'
X = open(path+file)
for x, i in zip(X, range(20)):
DATA[0, i+20] = np.array([float(coord) for coord in x.strip().split('\t')])
@@ -254,7 +253,7 @@ def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, LABELS_NAMES):
def getCaltechDBcsv(views, pathF, nameDB, NB_CLASS, LABELS_NAMES):
- DATASET = h5py.File(nameDB+".hdf5", "w")
+ datasetFile = h5py.File(nameDB+".hdf5", "w")
fullLabels = np.genfromtxt(pathF + nameDB + '-ClassLabels.csv', delimiter=';').astype(int)
if len(set(fullLabels))>NB_CLASS:
labelsAvailable = list(set(fullLabels))
@@ -267,19 +266,19 @@ def getCaltechDBcsv(views, pathF, nameDB, NB_CLASS, LABELS_NAMES):
for viewIndex, view in enumerate(views):
viewFile = pathF + nameDB + "-" + view + '.csv'
viewMatrix = np.array(np.genfromtxt(viewFile, delimiter=';'))[usedIndices, :]
- DATASET["/View"+str(viewIndex)+"/matrix"] = viewMatrix
- DATASET["/View"+str(viewIndex)+"/name"] = view
- DATASET["/View"+str(viewIndex)+"/shape"] = viewMatrix.shape
+ datasetFile["/View"+str(viewIndex)+"/matrix"] = viewMatrix
+ datasetFile["/View"+str(viewIndex)+"/name"] = view
+ datasetFile["/View"+str(viewIndex)+"/shape"] = viewMatrix.shape
- DATASET["/Labels/labelsArray"] = fullLabels[usedIndices]
+ datasetFile["/Labels/labelsArray"] = fullLabels[usedIndices]
labelsNamesFile = open(pathF+nameDB+'-ClassLabels-Description.csv')
labelsDictionary = dict((classIndice, labelName) for (classIndice, labelName) in [(int(line.strip().split(";")[0]),
line.strip().split(";")[1])
for lineIndex, line in labelsNamesFile if int(line.strip().split(";")[0]) in labelsUsed])
- DATASET["/datasetLength"] = len(DATASET["/Labels/labelsArray"][...])
- DATASET["/nbView"] = len(views)
- DATASET["/nbClass"] = len(set(DATASET["/Labels/labelsArray"][...]))
+ datasetFile["/datasetLength"] = len(datasetFile["/Labels/labelsArray"][...])
+ datasetFile["/nbView"] = len(views)
+ datasetFile["/nbClass"] = len(set(datasetFile["/Labels/labelsArray"][...]))
# keptLabelsIndices = [labelIndice for labelIndice, labelName in labelsDictionary.items() if labelName in LABELS_NAMES]
# maxNumbreOfClasses = len(labelsDictionary)
#
@@ -293,125 +292,131 @@ def getCaltechDBcsv(views, pathF, nameDB, NB_CLASS, LABELS_NAMES):
# elif len(LABELS_NAMES) > NB_CLASS:
# keptLabelsIndices = keptLabelsIndices[:NB_CLASS]
#
- # DATASET = {}
+ # datasetFile = {}
#
# for featureIndex in range(len(fullDataset)):
- # DATASET[featureIndex]=np.array([fullDataset[exampleIndice] for exampleIndice in range(datasetLength) if fullClasslabels[exampleIndice] in keptLabelsIndices])
+ # datasetFile[featureIndex]=np.array([fullDataset[exampleIndice] for exampleIndice in range(datasetLength) if fullClasslabels[exampleIndice] in keptLabelsIndices])
#
# CLASS_LABELS = np.array([keptLabelsIndices.index(classLabel) for classLabel in fullClasslabels if classLabel in keptLabelsIndices])
# DATASET_LENGTH = len(CLASS_LABELS)
#
# LABELS_DICTIONARY = dict((keptLabelsIndices.index(classLabel), labelsDictionary[classLabel]) for classLabel in keptLabelsIndices)
- return DATASET, labelsDictionary
+ return datasetFile, labelsDictionary
def getMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
- datasetFile = h5py.File(path+"MultiOmicDataset.hdf5", "w")
+ datasetFile = h5py.File(path+"MultiOmic.hdf5", "w")
logging.debug("Start:\t Getting Methylation Data")
methylData = np.genfromtxt(path+"matching_methyl.csv", delimiter=',')
- datasetFile["/View0/matrix"] = methylData
- datasetFile["/View0/name"] = "Methyl"
- datasetFile["/View0/shape"] = methylData.shape
+ methylDset = datasetFile.create_dataset("View0", methylData.shape)
+ methylDset[...] = methylData
+ methylDset.attrs["name"] = "Methyl"
logging.debug("Done:\t Getting Methylation Data")
logging.debug("Start:\t Getting MiRNA Data")
mirnaData = np.genfromtxt(path+"matching_mirna.csv", delimiter=',')
- datasetFile["/View1/matrix"] = mirnaData
- datasetFile["/View1/name"] = "MiRNA_"
- datasetFile["/View1/shape"] = mirnaData.shape
+ mirnaDset = datasetFile.create_dataset("View1", mirnaData.shape)
+ mirnaDset[...] = mirnaData
+ mirnaDset.attrs["name"]="MiRNA_"
logging.debug("Done:\t Getting MiRNA Data")
logging.debug("Start:\t Getting RNASeq Data")
rnaseqData = np.genfromtxt(path+"matching_rnaseq.csv", delimiter=',')
- datasetFile["/View2/matrix"] = rnaseqData
- datasetFile["/View2/name"] = "RNASeq"
- datasetFile["/View2/shape"] = rnaseqData.shape
+ rnaseqDset = datasetFile.create_dataset("View2", rnaseqData.shape)
+ rnaseqDset[...] = rnaseqData
+ rnaseqDset.attrs["name"]="RANSeq"
logging.debug("Done:\t Getting RNASeq Data")
logging.debug("Start:\t Getting Clinical Data")
clinical = np.genfromtxt(path+"clinicalMatrix.csv", delimiter=',')
- datasetFile["/View3/matrix"] = clinical
- datasetFile["/View3/name"] = "Clinic"
- datasetFile["/View3/shape"] = clinical.shape
+ clinicalDset = datasetFile.create_dataset("View3", clinical.shape)
+ clinicalDset[...] = clinical
+ clinicalDset.attrs["name"] = "Clinic"
logging.debug("Done:\t Getting Clinical Data")
labelFile = open(path+'brca_labels_triple-negatif.csv')
- LABELS = np.array([int(line.strip().split(',')[1]) for line in labelFile])
- datasetFile["/Labels/labelsArray"] = LABELS
-
- datasetFile["/nbView"] = 4
- datasetFile["/nbClass"] = 2
- datasetFile["/datasetLength"] = len(datasetFile["/Labels/labelsArray"])
+ labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])
+ labelsDset = datasetFile.create_dataset("labels", labels.shape)
+ labelsDset[...] = labels
+ labelsDset.attrs["name"] = "Labels"
+
+ metaDataGrp = datasetFile.create_group("Metadata")
+ metaDataGrp.attrs["nbView"] = 4
+ metaDataGrp.attrs["nbClass"] = 2
+ metaDataGrp.attrs["datasetLength"] = len(labels)
labelDictionary = {0:"No", 1:"Yes"}
+ datasetFile.close()
+ datasetFile = h5py.File(path+"MultiOmic.hdf5", "r")
# datasetFile = getPseudoRNASeq(datasetFile)
return datasetFile, labelDictionary
def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
- datasetFile = h5py.File(path+"ModifiedMultiOmicDataset.hdf5", "w")
+ datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "w")
logging.debug("Start:\t Getting Methylation Data")
methylData = np.genfromtxt(path+"matching_methyl.csv", delimiter=',')
- datasetFile["/View0/matrix"] = methylData
- datasetFile["/View0/name"] = "Methyl_"
- datasetFile["/View0/shape"] = methylData.shape
+ methylDset = datasetFile.create_dataset("View0", methylData.shape)
+ methylDset[...] = methylData
+ methylDset.attrs["name"] = "Methyl_"
logging.debug("Done:\t Getting Methylation Data")
logging.debug("Start:\t Getting MiRNA Data")
mirnaData = np.genfromtxt(path+"matching_mirna.csv", delimiter=',')
- datasetFile["/View1/matrix"] = mirnaData
- datasetFile["/View1/name"] = "MiRNA__"
- datasetFile["/View1/shape"] = mirnaData.shape
+ mirnaDset = datasetFile.create_dataset("View1", mirnaData.shape)
+ mirnaDset[...] = mirnaData
+ mirnaDset.attrs["name"]="MiRNA__"
logging.debug("Done:\t Getting MiRNA Data")
logging.debug("Start:\t Getting RNASeq Data")
rnaseqData = np.genfromtxt(path+"matching_rnaseq.csv", delimiter=',')
- datasetFile["/View2/matrix"] = rnaseqData
- datasetFile["/View2/name"] = "RNASeq_"
- datasetFile["/View2/shape"] = rnaseqData.shape
+ rnaseqDset = datasetFile.create_dataset("View2", rnaseqData.shape)
+ rnaseqDset[...] = rnaseqData
+ rnaseqDset.attrs["name"]="RANSeq_"
logging.debug("Done:\t Getting RNASeq Data")
logging.debug("Start:\t Getting Clinical Data")
clinical = np.genfromtxt(path+"clinicalMatrix.csv", delimiter=',')
- datasetFile["/View3/matrix"] = clinical
- datasetFile["/View3/name"] = "Clinic_"
- datasetFile["/View3/shape"] = clinical.shape
+ clinicalDset = datasetFile.create_dataset("View3", clinical.shape)
+ clinicalDset[...] = clinical
+ clinicalDset.attrs["name"] = "Clinic_"
logging.debug("Done:\t Getting Clinical Data")
- logging.debug("Start:\t Getting Labels")
labelFile = open(path+'brca_labels_triple-negatif.csv')
- LABELS = np.array([int(line.strip().split(',')[1]) for line in labelFile])
- datasetFile["/Labels/labelsArray"] = LABELS
- logging.debug("Done:\t Getting Labels")
-
- logging.debug("Start:\t Getting Data Shape")
- datasetFile["/nbView"] = 5
- datasetFile["/nbClass"] = 2
- datasetFile["/datasetLength"] = len(datasetFile["/Labels/labelsArray"])
+ labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])
+ labelsDset = datasetFile.create_dataset("labels", labels.shape)
+ labelsDset[...] = labels
+ labelsDset.attrs["name"] = "Labels"
+
+ metaDataGrp = datasetFile.create_group("Metadata")
+ metaDataGrp.attrs["nbView"] = 4
+ metaDataGrp.attrs["nbClass"] = 2
+ metaDataGrp.attrs["datasetLength"] = len(labels)
labelDictionary = {0:"No", 1:"Yes"}
- logging.debug("Done:\t Getting Data Shape")
logging.debug("Start:\t Getting Modified RNASeq Data")
- RNASeq = datasetFile["View2/matrix"][...]
- modifiedRNASeq = np.zeros((datasetFile.get("datasetLength/").value, datasetFile["View2/shape"][1]), dtype=int)
+ RNASeq = datasetFile["View2"][...]
+ modifiedRNASeq = np.zeros((datasetFile.get("Metadata").attrs["datasetLength"], datasetFile.get("View2").shape[1]), dtype=int)
for exampleindice, exampleArray in enumerate(RNASeq):
RNASeqDictionary = dict((index, value) for index, value in enumerate(exampleArray))
sorted_x = sorted(RNASeqDictionary.items(), key=operator.itemgetter(1))
modifiedRNASeq[exampleindice] = np.array([index for (index, value) in sorted_x], dtype=int)
- datasetFile["/View4/matrix"] = modifiedRNASeq
- datasetFile["/View4/name"] = "MRNASeq"
- datasetFile["/View4/shape"] = modifiedRNASeq.shape
+ mrnaseqDset = datasetFile.create_dataset("View4", modifiedRNASeq.shape, data=modifiedRNASeq)
+ mrnaseqDset.attrs["name"] = "MRNASeq"
logging.debug("Done:\t Getting Modified RNASeq Data")
+ datasetFile.close()
+ datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "r")
+
return datasetFile, labelDictionary
def getModifiedMultiOmicDBhdf5(features, path, name, NB_CLASS, LABELS_NAMES):
- datasetFile = h5py.File(path+"ModifiedMultiOmicDataset.hdf5", "r")
+ datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "r")
labelDictionary = {0:"No", 1:"Yes"}
return datasetFile, labelDictionary
@@ -443,7 +448,7 @@ def getPseudoRNASeq(dataset):
def getMultiOmicDBhdf5(features, path, name, NB_CLASS, LABELS_NAMES):
- datasetFile = h5py.File(path+"MultiOmicDataset.hdf5", "r")
+ datasetFile = h5py.File(path+"MultiOmic.hdf5", "r")
labelDictionary = {0:"No", 1:"Yes"}
return datasetFile, labelDictionary
diff --git a/Code/Multiview/Mumbo/Mumbo.py b/Code/Multiview/Mumbo/Mumbo.py
index c66f17d8ae23b69b87a5aee4f26d7a437c697f48..912d6ebab17eb2160e0c69ec9d179bdbe808d900 100644
--- a/Code/Multiview/Mumbo/Mumbo.py
+++ b/Code/Multiview/Mumbo/Mumbo.py
@@ -43,8 +43,8 @@ def gridSearch_hdf5(DATASET, classifiersNames):
for classifierIndex, classifierName in enumerate(classifiersNames):
classifierModule = globals()[classifierName] # Permet d'appeler une fonction avec une string
classifierMethod = getattr(classifierModule, "gridSearch")
- bestSettings.append(classifierMethod(DATASET["/View"+str(classifierIndex)+"/matrix"][...],
- DATASET["/Labels/labelsArray"][...]))
+ bestSettings.append(classifierMethod(DATASET.get("View"+str(classifierIndex))[...],
+ DATASET.get("labels")[...]))
return bestSettings
@@ -124,11 +124,11 @@ class Mumbo:
def fit_hdf5(self, DATASET, trainIndices=None):
# Initialization
if not trainIndices:
- trainIndices = range(DATASET.get("datasetLength").value)
- NB_CLASS = DATASET["/nbClass"][...]
- NB_VIEW = DATASET["/nbView"][...]
+ trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
+ NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
+ NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
DATASET_LENGTH = len(trainIndices)
- LABELS = DATASET["/Labels/labelsArray"][trainIndices]
+ LABELS = DATASET["labels"][trainIndices]
# costMatrices, \
# generalCostMatrix, fs, ds, edges, alphas, \
# predictions, generalAlphas, generalFs = initialize(NB_CLASS, NB_VIEW,
@@ -161,7 +161,7 @@ class Mumbo:
self.updateCostmatrices(NB_VIEW, DATASET_LENGTH, NB_CLASS, LABELS)
bestView, edge = self.chooseView(NB_VIEW, LABELS, DATASET_LENGTH)
self.bestViews[self.iterIndex] = bestView
- logging.debug("\t\t\t Best view : \t\t"+DATASET["/View"+str(bestView)+"/name"][...])
+ logging.debug("\t\t\t Best view : \t\t"+DATASET["View"+str(bestView)].attrs["name"])
if areBad.all():
self.generalAlphas[self.iterIndex] = 0.
else:
@@ -174,9 +174,9 @@ class Mumbo:
# finalFs = computeFinalFs(DATASET_LENGTH, NB_CLASS, generalAlphas, predictions, bestViews, LABELS, NB_ITER)
def predict_hdf5(self, DATASET, usedIndices=None):
- NB_CLASS = DATASET.get("nbClass").value
+ NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
DATASET_LENGTH = len(usedIndices)
predictedLabels = np.zeros(DATASET_LENGTH)
@@ -231,11 +231,11 @@ class Mumbo:
iterIndex = self.iterIndex
trainedClassifiersAndLabels = Parallel(n_jobs=NB_JOBS)(
delayed(trainWeakClassifier_hdf5)(classifiersNames[viewIndex],
- DATASET["/View"+str(viewIndex)+"/matrix"][trainIndices, :],
- DATASET["/Labels/labelsArray"][trainIndices],
+ DATASET.get("View"+str(viewIndex))[trainIndices, :],
+ DATASET.get("labels")[trainIndices],
DATASET_LENGTH,
viewIndex, classifiersConfigs[viewIndex],
- str(DATASET["/View"+str(viewIndex)+"/name"][...]), iterIndex, costMatrices)
+ DATASET.get("View"+str(viewIndex)).attrs["name"], iterIndex, costMatrices)
for viewIndex in range(NB_VIEW))
for viewIndex, (classifier, labelsArray, isBad, averageAccuracy) in enumerate(trainedClassifiersAndLabels):
@@ -439,7 +439,7 @@ class Mumbo:
def classifyMumbobyIter_hdf5(self, DATASET, usedIndices=None, NB_CLASS=2):
if usedIndices == None:
- usedIndices = range(DATASET.get("datasetLength").value)
+ usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
DATASET_LENGTH = len(usedIndices)
predictedLabels = np.zeros((DATASET_LENGTH, self.nbIter))
@@ -449,7 +449,7 @@ class Mumbo:
votesByIter = np.zeros((DATASET_LENGTH, NB_CLASS))
for usedExampleIndex, exampleIndex in enumerate(usedIndices):
- data = np.array([np.array(DATASET["/View" + str(int(view)) + "/matrix"][exampleIndex, :])])
+ data = np.array([np.array(DATASET.get("View" + str(int(view)))[exampleIndex, :])])
votesByIter[usedExampleIndex, int(classifier.predict(data))] += alpha[view]
votes[usedExampleIndex] = votes[usedExampleIndex] + np.array(votesByIter[usedExampleIndex])
predictedLabels[usedExampleIndex, iterIndex] = np.argmax(votes[usedExampleIndex])
diff --git a/Code/Multiview/Mumbo/analyzeResults.py b/Code/Multiview/Mumbo/analyzeResults.py
index 7002d6e1d07c268c132a8a47b1ea8aa0b1a4489e..8d32066b5acc42666c21c70f4295eed8b6af4d4a 100644
--- a/Code/Multiview/Mumbo/analyzeResults.py
+++ b/Code/Multiview/Mumbo/analyzeResults.py
@@ -25,7 +25,7 @@ def plotAccuracyByIter(trainAccuracy, testAccuracy, validationAccuracy, NB_ITER,
titleString = ""
for view, classifierConfig in zip(features, classifierAnalysis):
titleString += "\n" + view + " : " + classifierConfig
- titleString+="Best view = " + features[int(mainView)]
+ titleString+="\nBest view = " + features[int(mainView)]
ax1.set_title("Accuracy depending on iteration", fontsize=20)
plt.text(0.5, 1.08, titleString,
@@ -63,7 +63,7 @@ def classifyMumbobyIter_hdf5(usedIndices, DATASET, classifiers, alphas, views, N
votesByIter = np.zeros((DATASET_LENGTH, NB_CLASS))
for usedExampleIndex, exampleIndex in enumerate(usedIndices):
- data = np.array([np.array(DATASET["/View" + str(int(view)) + "/matrix"][exampleIndex, :])])
+ data = np.array([np.array(DATASET.get("View" + str(int(view)))[exampleIndex, :])])
votesByIter[usedExampleIndex, int(classifier.predict(data))] += alpha
votes[usedExampleIndex] = votes[usedExampleIndex] + np.array(votesByIter[usedExampleIndex])
predictedLabels[usedExampleIndex, iterIndex] = np.argmax(votes[usedExampleIndex])
@@ -79,13 +79,13 @@ def error(testLabels, computedLabels):
def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
DATASET, initKWARGS, LEARNING_RATE, LABELS_DICTIONARY, views, NB_CORES, times, kFolds, databaseName,
nbFolds, validationIndices):
- CLASS_LABELS = DATASET["/Labels/labelsArray"][...]
+ CLASS_LABELS = DATASET.get("labels")[...]
NB_ITER, classifierNames, classifierConfigs = initKWARGS.values()
- nbView = DATASET.get("nbView").value
- viewNames = [DATASET.get("/View"+str(viewIndex)+"/name").value for viewIndex in range(nbView)]
+ nbView = DATASET.get("Metadata").attrs["nbView"]
+ viewNames = [DATASET.get("View"+str(viewIndex)).attrs["name"] for viewIndex in range(nbView)]
- DATASET_LENGTH = DATASET.get("datasetLength").value-len(validationIndices)
- NB_CLASS = DATASET.get("nbClass").value
+ DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]-len(validationIndices)
+ NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
kFoldPredictedTrainLabelsByIter = []
kFoldPredictedTestLabelsByIter = []
kFoldPredictedValidationLabelsByIter = []
@@ -179,7 +179,7 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
str(kFoldAccuracyOnTrainByIter[foldIdx][iterIndex]) + '\n\t\t\tAccuracy on test : ' + \
str(kFoldAccuracyOnTestByIter[foldIdx][iterIndex]) + '\n\t\t\tAccuracy on validation : '+\
str(kFoldAccuracyOnValidationByIter[foldIdx][iterIndex]) + '\n\t\t\tSelected View : ' + \
- str(DATASET["/View"+str(int(kFoldBestViews[foldIdx][iterIndex]))+"/name"][...])
+ str(DATASET["View"+str(int(kFoldBestViews[foldIdx][iterIndex]))].attrs["name"])
stringAnalysis += "\n\t\t- Mean : \n\t\t\t Accuracy on train : " + str(
np.array(kFoldAccuracyOnTrainByIter)[:, iterIndex].mean()) + \
"\n\t\t\t Accuracy on test : " + str(np.array(kFoldAccuracyOnTestByIter)[:, iterIndex].mean())
diff --git a/Code/Multiview/Results/20160823-095233-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-095233-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..a48d9eb2ffdf0f85e0465f12416c79e120f3f3f5
--- /dev/null
+++ b/Code/Multiview/Results/20160823-095233-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-23 09:52:33,036 INFO: ### Main Programm for Multiview Classification
+2016-08-23 09:52:33,036 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 09:52:33,036 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 09:52:33,135 DEBUG: Start: Getting Methylation Data
+2016-08-23 09:52:46,092 DEBUG: Done: Getting Methylation Data
+2016-08-23 09:52:46,092 DEBUG: Start: Getting MiRNA Data
+2016-08-23 09:52:46,602 DEBUG: Done: Getting MiRNA Data
+2016-08-23 09:52:46,602 DEBUG: Start: Getting RNASeq Data
diff --git a/Code/Multiview/Results/20160823-095734-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-095734-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..c37799d18f26366b3c7a4e4441d21f8301926094
--- /dev/null
+++ b/Code/Multiview/Results/20160823-095734-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-23 09:57:34,864 INFO: ### Main Programm for Multiview Classification
+2016-08-23 09:57:34,864 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 09:57:34,864 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 09:57:34,871 DEBUG: Start: Getting Methylation Data
+2016-08-23 09:57:47,844 DEBUG: Done: Getting Methylation Data
+2016-08-23 09:57:47,844 DEBUG: Start: Getting MiRNA Data
+2016-08-23 09:57:48,349 DEBUG: Done: Getting MiRNA Data
+2016-08-23 09:57:48,349 DEBUG: Start: Getting RNASeq Data
diff --git a/Code/Multiview/Results/20160823-100003-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-100003-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..7942e10e852b8670ca5921038e7ab58d1c8584c9
--- /dev/null
+++ b/Code/Multiview/Results/20160823-100003-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,11 @@
+2016-08-23 10:00:03,590 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:00:03,591 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 10:00:03,593 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 10:00:03,596 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:00:17,099 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:00:17,102 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:00:17,613 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:00:17,615 DEBUG: Start: Getting RNASeq Data
+2016-08-23 10:00:57,371 DEBUG: Done: Getting RNASeq Data
+2016-08-23 10:00:57,441 DEBUG: Start: Getting Clinical Data
+2016-08-23 10:00:57,685 DEBUG: Done: Getting Clinical Data
diff --git a/Code/Multiview/Results/20160823-100209-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-100209-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..5dfa9429137486d5d60f8b9f05aa51280939fe54
--- /dev/null
+++ b/Code/Multiview/Results/20160823-100209-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,4 @@
+2016-08-23 10:02:09,222 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:02:09,224 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 10:02:09,225 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 10:02:09,239 DEBUG: Start: Getting Methylation Data
diff --git a/Code/Multiview/Results/20160823-100355-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-100355-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..1be1b75a46808499f8b1c7cd8350e3ccf1d513ab
--- /dev/null
+++ b/Code/Multiview/Results/20160823-100355-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,11 @@
+2016-08-23 10:03:55,104 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:03:55,106 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 10:03:55,108 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 10:03:55,111 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:04:07,386 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:04:07,390 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:04:07,898 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:04:07,900 DEBUG: Start: Getting RNASeq Data
+2016-08-23 10:04:48,716 DEBUG: Done: Getting RNASeq Data
+2016-08-23 10:04:48,795 DEBUG: Start: Getting Clinical Data
+2016-08-23 10:04:48,946 DEBUG: Done: Getting Clinical Data
diff --git a/Code/Multiview/Results/20160823-100549-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-100549-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..01683a2f8499fc9056e2a27387b12bdbd77b1668
--- /dev/null
+++ b/Code/Multiview/Results/20160823-100549-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,12 @@
+2016-08-23 10:05:49,567 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:05:49,569 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 10:05:49,571 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 10:05:49,573 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:06:02,626 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:06:02,631 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:06:03,136 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:06:03,138 DEBUG: Start: Getting RNASeq Data
+2016-08-23 10:06:42,875 DEBUG: Done: Getting RNASeq Data
+2016-08-23 10:06:42,951 DEBUG: Start: Getting Clinical Data
+2016-08-23 10:06:43,040 DEBUG: Done: Getting Clinical Data
+2016-08-23 10:06:43,074 DEBUG: Start: Getting Modified RNASeq Data
diff --git a/Code/Multiview/Results/20160823-100728-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-100728-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..adb5887180bd1fe087fe21ca83842db001f3d3fa
--- /dev/null
+++ b/Code/Multiview/Results/20160823-100728-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,12 @@
+2016-08-23 10:07:28,724 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:07:28,726 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 10:07:28,727 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 10:07:28,741 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:07:41,773 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:07:41,777 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:07:42,306 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:07:42,308 DEBUG: Start: Getting RNASeq Data
+2016-08-23 10:08:23,162 DEBUG: Done: Getting RNASeq Data
+2016-08-23 10:08:23,200 DEBUG: Start: Getting Clinical Data
+2016-08-23 10:08:23,271 DEBUG: Done: Getting Clinical Data
+2016-08-23 10:08:23,305 DEBUG: Start: Getting Modified RNASeq Data
diff --git a/Code/Multiview/Results/20160823-101021-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-101021-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..f7643c001f51913c4113a9305de442e9bf9a720f
--- /dev/null
+++ b/Code/Multiview/Results/20160823-101021-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,12 @@
+2016-08-23 10:10:21,670 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:10:21,674 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 10:10:21,678 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 10:10:21,700 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:10:34,807 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:10:34,811 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:10:35,320 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:10:35,321 DEBUG: Start: Getting RNASeq Data
+2016-08-23 10:11:16,285 DEBUG: Done: Getting RNASeq Data
+2016-08-23 10:11:16,320 DEBUG: Start: Getting Clinical Data
+2016-08-23 10:11:16,385 DEBUG: Done: Getting Clinical Data
+2016-08-23 10:11:16,419 DEBUG: Start: Getting Modified RNASeq Data
diff --git a/Code/Multiview/Results/20160823-101135-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/Multiview/Results/20160823-101135-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..4bea59113251b12fb3368ef513a8b02eb9ff7963
--- /dev/null
+++ b/Code/Multiview/Results/20160823-101135-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,13 @@
+2016-08-23 10:11:35,535 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:11:35,537 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
+2016-08-23 10:11:35,539 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-23 10:11:35,551 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:11:48,585 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:11:48,589 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:11:49,095 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:11:49,096 DEBUG: Start: Getting RNASeq Data
+2016-08-23 10:12:28,932 DEBUG: Done: Getting RNASeq Data
+2016-08-23 10:12:29,078 DEBUG: Start: Getting Clinical Data
+2016-08-23 10:12:29,238 DEBUG: Done: Getting Clinical Data
+2016-08-23 10:12:29,275 DEBUG: Start: Getting Modified RNASeq Data
+2016-08-23 10:13:18,889 DEBUG: Done: Getting Modified RNASeq Data
diff --git a/Code/Multiview/Results/20160823-101459-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log b/Code/Multiview/Results/20160823-101459-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..e342741951a893a6556747ed89fb07819d645129
--- /dev/null
+++ b/Code/Multiview/Results/20160823-101459-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-23 10:14:59,031 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:14:59,031 INFO: ### Classification - Database : MultiOmic ; Views : RGB, HOG, SIFT ; Algorithm : Mumbo ; Cores : 1
+2016-08-23 10:14:59,031 INFO: Start: Read CSV Database Files for MultiOmic
+2016-08-23 10:14:59,041 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:15:12,871 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:15:12,872 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:15:13,397 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:15:13,398 DEBUG: Start: Getting RNASeq Data
diff --git a/Code/Multiview/Results/20160823-101527-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log b/Code/Multiview/Results/20160823-101527-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..74fb901d350771d0c879e7bc5e4aa8f722731b0f
--- /dev/null
+++ b/Code/Multiview/Results/20160823-101527-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
@@ -0,0 +1,11 @@
+2016-08-23 10:15:27,507 INFO: ### Main Programm for Multiview Classification
+2016-08-23 10:15:27,509 INFO: ### Classification - Database : MultiOmic ; Views : RGB, HOG, SIFT ; Algorithm : Mumbo ; Cores : 1
+2016-08-23 10:15:27,510 INFO: Start: Read CSV Database Files for MultiOmic
+2016-08-23 10:15:27,517 DEBUG: Start: Getting Methylation Data
+2016-08-23 10:15:39,849 DEBUG: Done: Getting Methylation Data
+2016-08-23 10:15:39,853 DEBUG: Start: Getting MiRNA Data
+2016-08-23 10:15:40,334 DEBUG: Done: Getting MiRNA Data
+2016-08-23 10:15:40,335 DEBUG: Start: Getting RNASeq Data
+2016-08-23 10:16:20,802 DEBUG: Done: Getting RNASeq Data
+2016-08-23 10:16:20,841 DEBUG: Start: Getting Clinical Data
+2016-08-23 10:16:20,991 DEBUG: Done: Getting Clinical Data
diff --git a/Code/Multiview/run.py b/Code/Multiview/run.py
index 7307756a31457afef17fa71dca9d4c971f0c2606..3c60436dbb6dcad9e53111d2fb6af820873f767f 100644
--- a/Code/Multiview/run.py
+++ b/Code/Multiview/run.py
@@ -1,6 +1,6 @@
# coding=utf-8
import os
-os.system('python ExecMultiview.py -log --name MultiOmic --type .hdf5 --views Methyl:MiRNA:RNASEQ:Clinical --pathF /home/bbauvin/Documents/Data/Data_multi_omics/ --CL_split 0.7 --CL_nbFolds 2 --CL_nb_class 2 --CL_classes Positive:Negative --CL_type Fusion --CL_cores 4 --FU_type EarlyFusion --FU_method WeightedLinear')
+os.system('python ExecMultiview.py -log --name ModifiedMultiOmic --type .csv --views Methyl:MiRNA:RNASEQ:Clinical --pathF /home/bbauvin/Documents/Data/Data_multi_omics/ --CL_split 0.7 --CL_nbFolds 2 --CL_nb_class 2 --CL_classes Positive:Negative --CL_type Fusion --CL_cores 4 --FU_type EarlyFusion --FU_method WeightedLinear')
# /donnees/pj_bdd_bbauvin/Data_multi_omics/
#
# /home/bbauvin/Documents/Data/Data_multi_omics/
diff --git a/Code/Results/20160823-105758-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/Results/20160823-105758-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..6cc1f43c53701ec3f0d4b17dd2ccd8f283b154f7
--- /dev/null
+++ b/Code/Results/20160823-105758-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,661 @@
+2016-08-23 10:57:59,005 INFO: Begginging
+2016-08-23 10:57:59,439 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:57:59,439 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2016-08-23 10:57:59,439 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:57:59,465 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
+2016-08-23 10:57:59,465 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
+2016-08-23 10:57:59,465 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:57:59,465 DEBUG: Start: Classification
+2016-08-23 10:58:06,426 DEBUG: Info: Time for Classification: 6.67265105247[s]
+2016-08-23 10:58:06,426 DEBUG: Done: Classification
+2016-08-23 10:58:06,451 DEBUG: Start: Statistic Results
+2016-08-23 10:58:06,451 DEBUG: Info: Classification report:
+2016-08-23 10:58:06,460 DEBUG:
+ precision recall f1-score support
+
+ Non 1.00 0.80 0.89 25
+ Oui 0.67 1.00 0.80 10
+
+avg / total 0.90 0.86 0.86 35
+
+2016-08-23 10:58:06,462 DEBUG: Info: Statistics:
+2016-08-23 10:58:06,493 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.857142857143
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.844444
+5 Mean of F1-Score of top 20 classes by F1-Score 0.844444
+6 Mean of F1-Score of top 30 classes by F1-Score 0.844444
+2016-08-23 10:58:06,493 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:08,420 DEBUG: Done: Statistic Results
+2016-08-23 10:58:08,420 DEBUG: Start: Plot Result
+2016-08-23 10:58:08,640 DEBUG: Done: Plot Result
+2016-08-23 10:58:08,652 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:08,652 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2016-08-23 10:58:08,652 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:08,665 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
+2016-08-23 10:58:08,665 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
+2016-08-23 10:58:08,665 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:08,665 DEBUG: Start: Classification
+2016-08-23 10:58:18,001 DEBUG: Info: Time for Classification: 9.3463408947[s]
+2016-08-23 10:58:18,002 DEBUG: Done: Classification
+2016-08-23 10:58:18,484 DEBUG: Start: Statistic Results
+2016-08-23 10:58:18,484 DEBUG: Info: Classification report:
+2016-08-23 10:58:18,485 DEBUG:
+ precision recall f1-score support
+
+ Non 0.89 0.93 0.91 27
+ Oui 0.71 0.62 0.67 8
+
+avg / total 0.85 0.86 0.85 35
+
+2016-08-23 10:58:18,487 DEBUG: Info: Statistics:
+2016-08-23 10:58:18,494 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.857142857143
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.787879
+5 Mean of F1-Score of top 20 classes by F1-Score 0.787879
+6 Mean of F1-Score of top 30 classes by F1-Score 0.787879
+2016-08-23 10:58:18,494 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:18,798 DEBUG: Done: Statistic Results
+2016-08-23 10:58:18,799 DEBUG: Start: Plot Result
+2016-08-23 10:58:19,485 DEBUG: Done: Plot Result
+2016-08-23 10:58:19,494 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:19,494 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2016-08-23 10:58:19,494 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:19,506 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
+2016-08-23 10:58:19,507 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
+2016-08-23 10:58:19,507 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:19,507 DEBUG: Start: Classification
+2016-08-23 10:58:26,047 DEBUG: Info: Time for Classification: 6.5495569706[s]
+2016-08-23 10:58:26,047 DEBUG: Done: Classification
+2016-08-23 10:58:26,055 DEBUG: Start: Statistic Results
+2016-08-23 10:58:26,056 DEBUG: Info: Classification report:
+2016-08-23 10:58:26,056 DEBUG:
+ precision recall f1-score support
+
+ Non 0.93 0.93 0.93 27
+ Oui 0.75 0.75 0.75 8
+
+avg / total 0.89 0.89 0.89 35
+
+2016-08-23 10:58:26,058 DEBUG: Info: Statistics:
+2016-08-23 10:58:26,066 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.885714285714
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.837963
+5 Mean of F1-Score of top 20 classes by F1-Score 0.837963
+6 Mean of F1-Score of top 30 classes by F1-Score 0.837963
+2016-08-23 10:58:26,066 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:26,354 DEBUG: Done: Statistic Results
+2016-08-23 10:58:26,354 DEBUG: Start: Plot Result
+2016-08-23 10:58:26,569 DEBUG: Done: Plot Result
+2016-08-23 10:58:26,578 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:26,578 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2016-08-23 10:58:26,578 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:26,591 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
+2016-08-23 10:58:26,591 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
+2016-08-23 10:58:26,591 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:26,591 DEBUG: Start: Classification
+2016-08-23 10:58:27,979 DEBUG: Info: Time for Classification: 1.39624905586[s]
+2016-08-23 10:58:27,979 DEBUG: Done: Classification
+2016-08-23 10:58:27,984 DEBUG: Start: Statistic Results
+2016-08-23 10:58:27,984 DEBUG: Info: Classification report:
+2016-08-23 10:58:27,985 DEBUG:
+ precision recall f1-score support
+
+ Non 0.92 0.85 0.88 27
+ Oui 0.60 0.75 0.67 8
+
+avg / total 0.85 0.83 0.83 35
+
+2016-08-23 10:58:27,988 DEBUG: Info: Statistics:
+2016-08-23 10:58:27,997 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.828571428571
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.775641
+5 Mean of F1-Score of top 20 classes by F1-Score 0.775641
+6 Mean of F1-Score of top 30 classes by F1-Score 0.775641
+2016-08-23 10:58:27,997 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:28,309 DEBUG: Done: Statistic Results
+2016-08-23 10:58:28,309 DEBUG: Start: Plot Result
+2016-08-23 10:58:28,550 DEBUG: Done: Plot Result
+2016-08-23 10:58:28,559 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:28,559 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SVC
+2016-08-23 10:58:28,559 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:28,571 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
+2016-08-23 10:58:28,571 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
+2016-08-23 10:58:28,571 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:28,571 DEBUG: Start: Classification
+2016-08-23 10:58:36,064 DEBUG: Info: Time for Classification: 7.50162100792[s]
+2016-08-23 10:58:36,064 DEBUG: Done: Classification
+2016-08-23 10:58:36,204 DEBUG: Start: Statistic Results
+2016-08-23 10:58:36,205 DEBUG: Info: Classification report:
+2016-08-23 10:58:36,205 DEBUG:
+ precision recall f1-score support
+
+ Non 0.96 0.87 0.91 30
+ Oui 0.50 0.80 0.62 5
+
+avg / total 0.90 0.86 0.87 35
+
+2016-08-23 10:58:36,207 DEBUG: Info: Statistics:
+2016-08-23 10:58:36,215 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.857142857143
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.763833
+5 Mean of F1-Score of top 20 classes by F1-Score 0.763833
+6 Mean of F1-Score of top 30 classes by F1-Score 0.763833
+2016-08-23 10:58:36,215 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:36,505 DEBUG: Done: Statistic Results
+2016-08-23 10:58:36,505 DEBUG: Start: Plot Result
+2016-08-23 10:58:36,847 DEBUG: Done: Plot Result
+2016-08-23 10:58:36,880 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:36,880 DEBUG: ### Classification - Database:MultiOmic Feature:RNASeq train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2016-08-23 10:58:36,881 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:36,881 DEBUG: Info: Shape X_train:(312, 1046), Length of y_train:312
+2016-08-23 10:58:36,881 DEBUG: Info: Shape X_test:(35, 1046), Length of y_test:35
+2016-08-23 10:58:36,881 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:36,882 DEBUG: Start: Classification
+2016-08-23 10:58:37,080 DEBUG: Info: Time for Classification: 0.196507930756[s]
+2016-08-23 10:58:37,080 DEBUG: Done: Classification
+2016-08-23 10:58:37,081 DEBUG: Start: Statistic Results
+2016-08-23 10:58:37,082 DEBUG: Info: Classification report:
+2016-08-23 10:58:37,083 DEBUG:
+ precision recall f1-score support
+
+ Non 0.88 0.88 0.88 26
+ Oui 0.67 0.67 0.67 9
+
+avg / total 0.83 0.83 0.83 35
+
+2016-08-23 10:58:37,084 DEBUG: Info: Statistics:
+2016-08-23 10:58:37,091 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.828571428571
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.775641
+5 Mean of F1-Score of top 20 classes by F1-Score 0.775641
+6 Mean of F1-Score of top 30 classes by F1-Score 0.775641
+2016-08-23 10:58:37,092 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:37,376 DEBUG: Done: Statistic Results
+2016-08-23 10:58:37,376 DEBUG: Start: Plot Result
+2016-08-23 10:58:37,582 DEBUG: Done: Plot Result
+2016-08-23 10:58:37,583 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:37,584 DEBUG: ### Classification - Database:MultiOmic Feature:RNASeq train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2016-08-23 10:58:37,584 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:37,584 DEBUG: Info: Shape X_train:(312, 1046), Length of y_train:312
+2016-08-23 10:58:37,585 DEBUG: Info: Shape X_test:(35, 1046), Length of y_test:35
+2016-08-23 10:58:37,585 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:37,585 DEBUG: Start: Classification
+2016-08-23 10:58:37,903 DEBUG: Info: Time for Classification: 0.316462993622[s]
+2016-08-23 10:58:37,903 DEBUG: Done: Classification
+2016-08-23 10:58:37,919 DEBUG: Start: Statistic Results
+2016-08-23 10:58:37,920 DEBUG: Info: Classification report:
+2016-08-23 10:58:37,921 DEBUG:
+ precision recall f1-score support
+
+ Non 0.84 0.93 0.88 28
+ Oui 0.50 0.29 0.36 7
+
+avg / total 0.77 0.80 0.78 35
+
+2016-08-23 10:58:37,922 DEBUG: Info: Statistics:
+2016-08-23 10:58:37,929 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.8
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.622496
+5 Mean of F1-Score of top 20 classes by F1-Score 0.622496
+6 Mean of F1-Score of top 30 classes by F1-Score 0.622496
+2016-08-23 10:58:37,929 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:38,209 DEBUG: Done: Statistic Results
+2016-08-23 10:58:38,209 DEBUG: Start: Plot Result
+2016-08-23 10:58:38,432 DEBUG: Done: Plot Result
+2016-08-23 10:58:38,433 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:38,433 DEBUG: ### Classification - Database:MultiOmic Feature:RNASeq train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2016-08-23 10:58:38,433 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:38,434 DEBUG: Info: Shape X_train:(312, 1046), Length of y_train:312
+2016-08-23 10:58:38,434 DEBUG: Info: Shape X_test:(35, 1046), Length of y_test:35
+2016-08-23 10:58:38,434 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:38,434 DEBUG: Start: Classification
+2016-08-23 10:58:41,426 DEBUG: Info: Time for Classification: 2.98975014687[s]
+2016-08-23 10:58:41,426 DEBUG: Done: Classification
+2016-08-23 10:58:41,443 DEBUG: Start: Statistic Results
+2016-08-23 10:58:41,443 DEBUG: Info: Classification report:
+2016-08-23 10:58:41,444 DEBUG:
+ precision recall f1-score support
+
+ Non 0.92 0.92 0.92 24
+ Oui 0.82 0.82 0.82 11
+
+avg / total 0.89 0.89 0.89 35
+
+2016-08-23 10:58:41,446 DEBUG: Info: Statistics:
+2016-08-23 10:58:41,453 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.885714285714
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.867424
+5 Mean of F1-Score of top 20 classes by F1-Score 0.867424
+6 Mean of F1-Score of top 30 classes by F1-Score 0.867424
+2016-08-23 10:58:41,453 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:41,744 DEBUG: Done: Statistic Results
+2016-08-23 10:58:41,744 DEBUG: Start: Plot Result
+2016-08-23 10:58:41,973 DEBUG: Done: Plot Result
+2016-08-23 10:58:41,974 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:41,974 DEBUG: ### Classification - Database:MultiOmic Feature:RNASeq train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2016-08-23 10:58:41,974 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:41,975 DEBUG: Info: Shape X_train:(312, 1046), Length of y_train:312
+2016-08-23 10:58:41,975 DEBUG: Info: Shape X_test:(35, 1046), Length of y_test:35
+2016-08-23 10:58:41,975 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:41,975 DEBUG: Start: Classification
+2016-08-23 10:58:42,070 DEBUG: Info: Time for Classification: 0.0909140110016[s]
+2016-08-23 10:58:42,070 DEBUG: Done: Classification
+2016-08-23 10:58:42,072 DEBUG: Start: Statistic Results
+2016-08-23 10:58:42,072 DEBUG: Info: Classification report:
+2016-08-23 10:58:42,074 DEBUG:
+ precision recall f1-score support
+
+ Non 0.88 0.84 0.86 25
+ Oui 0.64 0.70 0.67 10
+
+avg / total 0.81 0.80 0.80 35
+
+2016-08-23 10:58:42,076 DEBUG: Info: Statistics:
+2016-08-23 10:58:42,086 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.8
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.761905
+5 Mean of F1-Score of top 20 classes by F1-Score 0.761905
+6 Mean of F1-Score of top 30 classes by F1-Score 0.761905
+2016-08-23 10:58:42,087 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:42,421 DEBUG: Done: Statistic Results
+2016-08-23 10:58:42,421 DEBUG: Start: Plot Result
+2016-08-23 10:58:42,694 DEBUG: Done: Plot Result
+2016-08-23 10:58:42,695 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:42,695 DEBUG: ### Classification - Database:MultiOmic Feature:RNASeq train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SVC
+2016-08-23 10:58:42,695 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:42,696 DEBUG: Info: Shape X_train:(312, 1046), Length of y_train:312
+2016-08-23 10:58:42,696 DEBUG: Info: Shape X_test:(35, 1046), Length of y_test:35
+2016-08-23 10:58:42,696 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:42,696 DEBUG: Start: Classification
+2016-08-23 10:58:52,919 DEBUG: Info: Time for Classification: 10.2208080292[s]
+2016-08-23 10:58:52,919 DEBUG: Done: Classification
+2016-08-23 10:58:52,924 DEBUG: Start: Statistic Results
+2016-08-23 10:58:52,924 DEBUG: Info: Classification report:
+2016-08-23 10:58:52,925 DEBUG:
+ precision recall f1-score support
+
+ Non 0.92 0.89 0.91 27
+ Oui 0.67 0.75 0.71 8
+
+avg / total 0.86 0.86 0.86 35
+
+2016-08-23 10:58:52,927 DEBUG: Info: Statistics:
+2016-08-23 10:58:52,934 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.857142857143
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.805771
+5 Mean of F1-Score of top 20 classes by F1-Score 0.805771
+6 Mean of F1-Score of top 30 classes by F1-Score 0.805771
+2016-08-23 10:58:52,934 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:58:53,223 DEBUG: Done: Statistic Results
+2016-08-23 10:58:53,223 DEBUG: Start: Plot Result
+2016-08-23 10:58:53,432 DEBUG: Done: Plot Result
+2016-08-23 10:58:53,462 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:58:53,462 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2016-08-23 10:58:53,463 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:58:53,524 DEBUG: Info: Shape X_train:(312, 73599), Length of y_train:312
+2016-08-23 10:58:53,524 DEBUG: Info: Shape X_test:(35, 73599), Length of y_test:35
+2016-08-23 10:58:53,524 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:58:53,524 DEBUG: Start: Classification
+2016-08-23 10:59:12,961 DEBUG: Info: Time for Classification: 19.4953379631[s]
+2016-08-23 10:59:12,961 DEBUG: Done: Classification
+2016-08-23 10:59:12,964 DEBUG: Start: Statistic Results
+2016-08-23 10:59:12,964 DEBUG: Info: Classification report:
+2016-08-23 10:59:12,965 DEBUG:
+ precision recall f1-score support
+
+ Non 0.74 0.65 0.69 26
+ Oui 0.25 0.33 0.29 9
+
+avg / total 0.61 0.57 0.59 35
+
+2016-08-23 10:59:12,967 DEBUG: Info: Statistics:
+2016-08-23 10:59:12,974 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.571428571429
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.489796
+5 Mean of F1-Score of top 20 classes by F1-Score 0.489796
+6 Mean of F1-Score of top 30 classes by F1-Score 0.489796
+2016-08-23 10:59:12,974 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:59:13,251 DEBUG: Done: Statistic Results
+2016-08-23 10:59:13,251 DEBUG: Start: Plot Result
+2016-08-23 10:59:13,458 DEBUG: Done: Plot Result
+2016-08-23 10:59:13,487 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:59:13,487 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2016-08-23 10:59:13,487 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:59:13,523 DEBUG: Info: Shape X_train:(312, 73599), Length of y_train:312
+2016-08-23 10:59:13,523 DEBUG: Info: Shape X_test:(35, 73599), Length of y_test:35
+2016-08-23 10:59:13,523 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:59:13,524 DEBUG: Start: Classification
+2016-08-23 10:59:40,117 DEBUG: Info: Time for Classification: 26.6264278889[s]
+2016-08-23 10:59:40,117 DEBUG: Done: Classification
+2016-08-23 10:59:41,459 DEBUG: Start: Statistic Results
+2016-08-23 10:59:41,459 DEBUG: Info: Classification report:
+2016-08-23 10:59:41,460 DEBUG:
+ precision recall f1-score support
+
+ Non 0.67 0.96 0.79 23
+ Oui 0.50 0.08 0.14 12
+
+avg / total 0.61 0.66 0.57 35
+
+2016-08-23 10:59:41,462 DEBUG: Info: Statistics:
+2016-08-23 10:59:41,469 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.657142857143
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.464286
+5 Mean of F1-Score of top 20 classes by F1-Score 0.464286
+6 Mean of F1-Score of top 30 classes by F1-Score 0.464286
+2016-08-23 10:59:41,469 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:59:41,749 DEBUG: Done: Statistic Results
+2016-08-23 10:59:41,749 DEBUG: Start: Plot Result
+2016-08-23 10:59:43,291 DEBUG: Done: Plot Result
+2016-08-23 10:59:43,321 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:59:43,321 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2016-08-23 10:59:43,321 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:59:43,357 DEBUG: Info: Shape X_train:(312, 73599), Length of y_train:312
+2016-08-23 10:59:43,357 DEBUG: Info: Shape X_test:(35, 73599), Length of y_test:35
+2016-08-23 10:59:43,358 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:59:43,358 DEBUG: Start: Classification
+2016-08-23 10:59:58,108 DEBUG: Info: Time for Classification: 14.7840790749[s]
+2016-08-23 10:59:58,108 DEBUG: Done: Classification
+2016-08-23 10:59:58,127 DEBUG: Start: Statistic Results
+2016-08-23 10:59:58,128 DEBUG: Info: Classification report:
+2016-08-23 10:59:58,142 DEBUG:
+ precision recall f1-score support
+
+ Non 0.63 1.00 0.77 22
+ Oui 0.00 0.00 0.00 13
+
+avg / total 0.40 0.63 0.49 35
+
+2016-08-23 10:59:58,147 DEBUG: Info: Statistics:
+2016-08-23 10:59:58,157 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.628571428571
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0.5
+4 Mean of F1-Score of top 10 classes by F1-Score 0.385965
+5 Mean of F1-Score of top 20 classes by F1-Score 0.385965
+6 Mean of F1-Score of top 30 classes by F1-Score 0.385965
+2016-08-23 10:59:58,157 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 10:59:58,457 DEBUG: Done: Statistic Results
+2016-08-23 10:59:58,457 DEBUG: Start: Plot Result
+2016-08-23 10:59:58,679 DEBUG: Done: Plot Result
+2016-08-23 10:59:58,709 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 10:59:58,709 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2016-08-23 10:59:58,709 DEBUG: Start: Determine Train/Test split
+2016-08-23 10:59:58,745 DEBUG: Info: Shape X_train:(312, 73599), Length of y_train:312
+2016-08-23 10:59:58,745 DEBUG: Info: Shape X_test:(35, 73599), Length of y_test:35
+2016-08-23 10:59:58,745 DEBUG: Done: Determine Train/Test split
+2016-08-23 10:59:58,745 DEBUG: Start: Classification
+2016-08-23 11:00:01,208 DEBUG: Info: Time for Classification: 2.49507904053[s]
+2016-08-23 11:00:01,208 DEBUG: Done: Classification
+2016-08-23 11:00:01,217 DEBUG: Start: Statistic Results
+2016-08-23 11:00:01,217 DEBUG: Info: Classification report:
+2016-08-23 11:00:01,218 DEBUG:
+ precision recall f1-score support
+
+ Non 0.69 1.00 0.81 24
+ Oui 0.00 0.00 0.00 11
+
+avg / total 0.47 0.69 0.56 35
+
+2016-08-23 11:00:01,220 DEBUG: Info: Statistics:
+2016-08-23 11:00:01,228 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.685714285714
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0.5
+4 Mean of F1-Score of top 10 classes by F1-Score 0.40678
+5 Mean of F1-Score of top 20 classes by F1-Score 0.40678
+6 Mean of F1-Score of top 30 classes by F1-Score 0.40678
+2016-08-23 11:00:01,228 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:00:01,541 DEBUG: Done: Statistic Results
+2016-08-23 11:00:01,541 DEBUG: Start: Plot Result
+2016-08-23 11:00:01,827 DEBUG: Done: Plot Result
+2016-08-23 11:00:01,859 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:00:01,859 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SVC
+2016-08-23 11:00:01,859 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:00:01,897 DEBUG: Info: Shape X_train:(312, 73599), Length of y_train:312
+2016-08-23 11:00:01,897 DEBUG: Info: Shape X_test:(35, 73599), Length of y_test:35
+2016-08-23 11:00:01,897 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:00:01,897 DEBUG: Start: Classification
+2016-08-23 11:00:34,135 DEBUG: Info: Time for Classification: 32.2736110687[s]
+2016-08-23 11:00:34,136 DEBUG: Done: Classification
+2016-08-23 11:00:34,724 DEBUG: Start: Statistic Results
+2016-08-23 11:00:34,724 DEBUG: Info: Classification report:
+2016-08-23 11:00:34,725 DEBUG:
+ precision recall f1-score support
+
+ Non 0.82 0.69 0.75 26
+ Oui 0.38 0.56 0.45 9
+
+avg / total 0.71 0.66 0.67 35
+
+2016-08-23 11:00:34,727 DEBUG: Info: Statistics:
+2016-08-23 11:00:34,734 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.657142857143
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.602273
+5 Mean of F1-Score of top 20 classes by F1-Score 0.602273
+6 Mean of F1-Score of top 30 classes by F1-Score 0.602273
+2016-08-23 11:00:34,734 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:00:35,019 DEBUG: Done: Statistic Results
+2016-08-23 11:00:35,019 DEBUG: Start: Plot Result
+2016-08-23 11:00:35,811 DEBUG: Done: Plot Result
+2016-08-23 11:00:35,838 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:00:35,838 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2016-08-23 11:00:35,838 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:00:35,839 DEBUG: Info: Shape X_train:(312, 127), Length of y_train:312
+2016-08-23 11:00:35,839 DEBUG: Info: Shape X_test:(35, 127), Length of y_test:35
+2016-08-23 11:00:35,839 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:00:35,839 DEBUG: Start: Classification
+2016-08-23 11:00:35,880 DEBUG: Info: Time for Classification: 0.0384650230408[s]
+2016-08-23 11:00:35,880 DEBUG: Done: Classification
+2016-08-23 11:00:35,881 DEBUG: Start: Statistic Results
+2016-08-23 11:00:35,882 DEBUG: Info: Classification report:
+2016-08-23 11:00:35,882 DEBUG:
+ precision recall f1-score support
+
+ Non 0.63 1.00 0.77 22
+ Oui 0.00 0.00 0.00 13
+
+avg / total 0.40 0.63 0.49 35
+
+2016-08-23 11:00:35,884 DEBUG: Info: Statistics:
+2016-08-23 11:00:35,891 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.628571428571
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0.5
+4 Mean of F1-Score of top 10 classes by F1-Score 0.385965
+5 Mean of F1-Score of top 20 classes by F1-Score 0.385965
+6 Mean of F1-Score of top 30 classes by F1-Score 0.385965
+2016-08-23 11:00:35,891 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:00:36,167 DEBUG: Done: Statistic Results
+2016-08-23 11:00:36,167 DEBUG: Start: Plot Result
+2016-08-23 11:00:36,373 DEBUG: Done: Plot Result
+2016-08-23 11:00:36,374 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:00:36,374 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2016-08-23 11:00:36,374 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:00:36,375 DEBUG: Info: Shape X_train:(312, 127), Length of y_train:312
+2016-08-23 11:00:36,375 DEBUG: Info: Shape X_test:(35, 127), Length of y_test:35
+2016-08-23 11:00:36,375 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:00:36,375 DEBUG: Start: Classification
+2016-08-23 11:00:36,442 DEBUG: Info: Time for Classification: 0.0647449493408[s]
+2016-08-23 11:00:36,442 DEBUG: Done: Classification
+2016-08-23 11:00:36,445 DEBUG: Start: Statistic Results
+2016-08-23 11:00:36,445 DEBUG: Info: Classification report:
+2016-08-23 11:00:36,446 DEBUG:
+ precision recall f1-score support
+
+ Non 0.85 0.97 0.91 30
+ Oui 0.00 0.00 0.00 5
+
+avg / total 0.73 0.83 0.78 35
+
+2016-08-23 11:00:36,448 DEBUG: Info: Statistics:
+2016-08-23 11:00:36,455 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.828571428571
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0.5
+4 Mean of F1-Score of top 10 classes by F1-Score 0.453125
+5 Mean of F1-Score of top 20 classes by F1-Score 0.453125
+6 Mean of F1-Score of top 30 classes by F1-Score 0.453125
+2016-08-23 11:00:36,456 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:00:36,744 DEBUG: Done: Statistic Results
+2016-08-23 11:00:36,744 DEBUG: Start: Plot Result
+2016-08-23 11:00:36,951 DEBUG: Done: Plot Result
+2016-08-23 11:00:36,952 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:00:36,952 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2016-08-23 11:00:36,952 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:00:36,952 DEBUG: Info: Shape X_train:(312, 127), Length of y_train:312
+2016-08-23 11:00:36,952 DEBUG: Info: Shape X_test:(35, 127), Length of y_test:35
+2016-08-23 11:00:36,953 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:00:36,953 DEBUG: Start: Classification
+2016-08-23 11:00:39,168 DEBUG: Info: Time for Classification: 2.21314096451[s]
+2016-08-23 11:00:39,168 DEBUG: Done: Classification
+2016-08-23 11:00:39,177 DEBUG: Start: Statistic Results
+2016-08-23 11:00:39,177 DEBUG: Info: Classification report:
+2016-08-23 11:00:39,178 DEBUG:
+ precision recall f1-score support
+
+ Non 0.90 1.00 0.95 26
+ Oui 1.00 0.67 0.80 9
+
+avg / total 0.92 0.91 0.91 35
+
+2016-08-23 11:00:39,180 DEBUG: Info: Statistics:
+2016-08-23 11:00:39,187 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.914285714286
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.872727
+5 Mean of F1-Score of top 20 classes by F1-Score 0.872727
+6 Mean of F1-Score of top 30 classes by F1-Score 0.872727
+2016-08-23 11:00:39,187 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:00:39,476 DEBUG: Done: Statistic Results
+2016-08-23 11:00:39,476 DEBUG: Start: Plot Result
+2016-08-23 11:00:39,690 DEBUG: Done: Plot Result
+2016-08-23 11:00:39,691 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:00:39,691 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2016-08-23 11:00:39,691 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:00:39,691 DEBUG: Info: Shape X_train:(312, 127), Length of y_train:312
+2016-08-23 11:00:39,692 DEBUG: Info: Shape X_test:(35, 127), Length of y_test:35
+2016-08-23 11:00:39,692 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:00:39,692 DEBUG: Start: Classification
+2016-08-23 11:00:39,757 DEBUG: Info: Time for Classification: 0.0622010231018[s]
+2016-08-23 11:00:39,757 DEBUG: Done: Classification
+2016-08-23 11:00:39,758 DEBUG: Start: Statistic Results
+2016-08-23 11:00:39,759 DEBUG: Info: Classification report:
+2016-08-23 11:00:39,759 DEBUG:
+ precision recall f1-score support
+
+ Non 0.77 1.00 0.87 27
+ Oui 0.00 0.00 0.00 8
+
+avg / total 0.60 0.77 0.67 35
+
+2016-08-23 11:00:39,761 DEBUG: Info: Statistics:
+2016-08-23 11:00:39,769 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.771428571429
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0.5
+4 Mean of F1-Score of top 10 classes by F1-Score 0.435484
+5 Mean of F1-Score of top 20 classes by F1-Score 0.435484
+6 Mean of F1-Score of top 30 classes by F1-Score 0.435484
+2016-08-23 11:00:39,769 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:00:40,097 DEBUG: Done: Statistic Results
+2016-08-23 11:00:40,097 DEBUG: Start: Plot Result
+2016-08-23 11:00:40,305 DEBUG: Done: Plot Result
+2016-08-23 11:00:40,306 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:00:40,306 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SVC
+2016-08-23 11:00:40,307 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:00:40,307 DEBUG: Info: Shape X_train:(312, 127), Length of y_train:312
+2016-08-23 11:00:40,307 DEBUG: Info: Shape X_test:(35, 127), Length of y_test:35
+2016-08-23 11:00:40,307 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:00:40,307 DEBUG: Start: Classification
+2016-08-23 11:02:01,155 DEBUG: Info: Time for Classification: 80.8456330299[s]
+2016-08-23 11:02:01,156 DEBUG: Done: Classification
+2016-08-23 11:02:01,158 DEBUG: Start: Statistic Results
+2016-08-23 11:02:01,158 DEBUG: Info: Classification report:
+2016-08-23 11:02:01,159 DEBUG:
+ precision recall f1-score support
+
+ Non 0.71 1.00 0.83 20
+ Oui 1.00 0.47 0.64 15
+
+avg / total 0.84 0.77 0.75 35
+
+2016-08-23 11:02:01,161 DEBUG: Info: Statistics:
+2016-08-23 11:02:01,168 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.771428571429
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.734848
+5 Mean of F1-Score of top 20 classes by F1-Score 0.734848
+6 Mean of F1-Score of top 30 classes by F1-Score 0.734848
+2016-08-23 11:02:01,168 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:02:01,456 DEBUG: Done: Statistic Results
+2016-08-23 11:02:01,456 DEBUG: Start: Plot Result
+2016-08-23 11:02:01,662 DEBUG: Done: Plot Result
diff --git a/Code/Results/20160823-110509-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/Results/20160823-110509-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..22ce34e83c357a1cd5ff2508c33c11ccaec9eca8
--- /dev/null
+++ b/Code/Results/20160823-110509-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,41 @@
+2016-08-23 11:05:09,064 INFO: Begginging
+2016-08-23 11:05:09,081 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:05:09,081 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2016-08-23 11:05:09,081 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:05:09,095 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
+2016-08-23 11:05:09,095 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
+2016-08-23 11:05:09,095 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:05:09,095 DEBUG: Start: Classification
+2016-08-23 11:05:16,229 DEBUG: Info: Time for Classification: 7.14391708374[s]
+2016-08-23 11:05:16,229 DEBUG: Done: Classification
+2016-08-23 11:05:16,231 DEBUG: Start: Statistic Results
+2016-08-23 11:05:16,231 DEBUG: Info: Classification report:
+2016-08-23 11:05:16,232 DEBUG:
+ precision recall f1-score support
+
+ Non 1.00 0.85 0.92 26
+ Oui 0.69 1.00 0.82 9
+
+avg / total 0.92 0.89 0.89 35
+
+2016-08-23 11:05:16,234 DEBUG: Info: Statistics:
+2016-08-23 11:05:16,241 DEBUG:
+ Statistic Values
+0 Accuracy score on test 0.885714285714
+1 Top 10 classes by F1-Score [Non, Oui]
+2 Worst 10 classes by F1-Score [Oui, Non]
+3 Ratio of classes with F1-Score==0 of all classes 0
+4 Mean of F1-Score of top 10 classes by F1-Score 0.867424
+5 Mean of F1-Score of top 20 classes by F1-Score 0.867424
+6 Mean of F1-Score of top 30 classes by F1-Score 0.867424
+2016-08-23 11:05:16,241 DEBUG: Info: Calculate Confusionmatrix
+2016-08-23 11:05:16,795 DEBUG: Done: Statistic Results
+2016-08-23 11:05:16,795 DEBUG: Start: Plot Result
+2016-08-23 11:05:17,004 DEBUG: Done: Plot Result
+2016-08-23 11:05:17,014 DEBUG: ### Main Programm for Classification MonoView
+2016-08-23 11:05:17,014 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2016-08-23 11:05:17,015 DEBUG: Start: Determine Train/Test split
+2016-08-23 11:05:17,028 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
+2016-08-23 11:05:17,028 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
+2016-08-23 11:05:17,029 DEBUG: Done: Determine Train/Test split
+2016-08-23 11:05:17,029 DEBUG: Start: Classification
diff --git a/Code/Results/20160823-110659-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/Results/20160823-110659-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..2bf73ae2ab968cfbd4912acb1641f0f5010c3e25
--- /dev/null
+++ b/Code/Results/20160823-110659-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,3 @@
+2016-08-23 11:06:59,550 INFO: Begginging
+2016-08-23 11:06:59,554 INFO: ### Main Programm for Multiview Classification
+2016-08-23 11:06:59,555 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
diff --git a/Code/Results/20160823-110859-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/Results/20160823-110859-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..69aa2366c346f089f1450f3bf488ab929b85a9bd
--- /dev/null
+++ b/Code/Results/20160823-110859-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,9 @@
+2016-08-23 11:08:59,084 INFO: Begginging
+2016-08-23 11:08:59,088 INFO: ### Main Programm for Multiview Classification
+2016-08-23 11:08:59,089 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-23 11:08:59,089 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-23 11:08:59,089 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-23 11:08:59,090 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-23 11:08:59,090 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-23 11:08:59,090 INFO: Done: Read Database Files
+2016-08-23 11:08:59,090 INFO: Start: Determine validation split for ratio 0.9
diff --git a/Code/Results/20160823-111036-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/Results/20160823-111036-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..8ca5253ced3d2c47fe838556497b79c2b4ea98be
--- /dev/null
+++ b/Code/Results/20160823-111036-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,11 @@
+2016-08-23 11:10:36,299 INFO: Begginging
+2016-08-23 11:10:36,303 INFO: ### Main Programm for Multiview Classification
+2016-08-23 11:10:36,303 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-23 11:10:36,303 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-23 11:10:36,304 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-23 11:10:36,304 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-23 11:10:36,304 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-23 11:10:36,305 INFO: Done: Read Database Files
+2016-08-23 11:10:36,305 INFO: Start: Determine validation split for ratio 0.9
+2016-08-23 11:10:36,307 INFO: Done: Determine validation split
+2016-08-23 11:10:36,307 INFO: Start: Determine 2 folds
diff --git a/Code/Results/20160823-111124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/Results/20160823-111124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..a3bfd6e3a1d63bdf6590e54e99b1831f06712329
--- /dev/null
+++ b/Code/Results/20160823-111124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-23 11:11:24,815 INFO: Begginging
+2016-08-23 11:11:24,818 INFO: ### Main Programm for Multiview Classification
+2016-08-23 11:11:24,818 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-23 11:11:24,819 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-23 11:11:24,819 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-23 11:11:24,820 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-23 11:11:24,820 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-23 11:11:24,820 INFO: Done: Read Database Files
+2016-08-23 11:11:24,820 INFO: Start: Determine validation split for ratio 0.9
+2016-08-23 11:11:24,823 INFO: Done: Determine validation split
+2016-08-23 11:11:24,823 INFO: Start: Determine 2 folds
+2016-08-23 11:11:24,849 INFO: Info: Length of Learning Sets: 157
+2016-08-23 11:11:24,849 INFO: Info: Length of Testing Sets: 156
+2016-08-23 11:11:24,849 INFO: Info: Length of Validation Set: 34
+2016-08-23 11:11:24,849 INFO: Done: Determine folds
+2016-08-23 11:11:24,849 INFO: Start: Learning with Fusion and 2 folds
+2016-08-23 11:11:24,849 INFO: Start: Gridsearching best settings for monoview classifiers