Resolved randomized search for multicore issue

Code/MonoMutliViewClassifiers/ExecClassif.py

@@ -79,9 +79,9 @@ groupClass.add_argument('--CL_algos_multiview', metavar='STRING', action='store'
                         help='Determine which multiview classifier to use, separate with : if multiple, if empty, considering all', default='')
 groupClass.add_argument('--CL_cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
                         default=1)
-groupClass.add_argument('--CL_metrics', metavar='STRING', action='store',
-                        help='Determine which metric to use, separate with ":" if multiple, if empty, considering all, '
-                             'first one will be used for gridsearch', default='')
+groupClass.add_argument('--CL_metrics', metavar='STRING', action='store', nargs="+",
+                        help='Determine which metrics to use, separate metric and configuration with ":". If multiple, separate with space. If no metric is specified, considering all with accuracy for classification '
+                             'first one will be used for classification', default=[''])
 groupClass.add_argument('--CL_GS_iter', metavar='INT', action='store',
                         help='Determine how many Randomized grid search tests to do', type=int, default=30)
 groupClass.add_argument('--CL_NoGS', action='store_false',
@@ -202,11 +202,12 @@ if nbCores>1:
 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"]
-metrics = args.CL_metrics.split(":")
-if metrics == [""]:
-    metrics = [["accuracy_score", None]]
-metric = metrics[0]
-
+metrics = [metric.split(":") for metric in args.CL_metrics]
+if metrics == [[""]]:
+    metricsNames = [name for _, name, isPackage
+                    in pkgutil.iter_modules(['Metrics']) if not isPackage]
+    metrics = [[metricName, None] for metricName in metricsNames]
+print metrics
 
 logging.info("Start:\t Finding all available mono- & multiview algorithms")
 benchmark = {"Monoview":{}, "Multiview":[]}
@@ -256,25 +257,24 @@ fusionMethodConfig = [["0.25", "0.25", "0.25", "0.25"], "b"]
 mumboClassifierConfig = "a"
 mumboclassifierNames = "a"
 
-RandomForestKWARGS = {"0":map(int, args.CL_RF_trees.split())[0], "1":map(int, args.CL_RF_max_depth.split(":"))[0]}
-SVMLinearKWARGS = {"0":map(int, args.CL_SVML_C.split(":"))[0]}
-SVMRBFKWARGS = {"0":map(int, args.CL_SVMR_C.split(":"))[0]}
-SVMPolyKWARGS = {"0":map(int, args.CL_SVMP_C.split(":"))[0], '1':map(int, args.CL_SVMP_deg.split(":"))[0]}
-DecisionTreeKWARGS = {"0":map(int, args.CL_DT_depth.split(":"))[0]}
-SGDKWARGS = {"2": map(float, args.CL_SGD_alpha.split(":"))[0], "1": args.CL_SGD_penalty.split(":")[0],
+RandomForestKWARGSInit = {"0":map(int, args.CL_RF_trees.split())[0], "1":map(int, args.CL_RF_max_depth.split(":"))[0]}
+SVMLinearKWARGSInit = {"0":map(int, args.CL_SVML_C.split(":"))[0]}
+SVMRBFKWARGSInit = {"0":map(int, args.CL_SVMR_C.split(":"))[0]}
+SVMPolyKWARGSInit = {"0":map(int, args.CL_SVMP_C.split(":"))[0], '1':map(int, args.CL_SVMP_deg.split(":"))[0]}
+DecisionTreeKWARGSInit = {"0":map(int, args.CL_DT_depth.split(":"))[0]}
+SGDKWARGSInit = {"2": map(float, args.CL_SGD_alpha.split(":"))[0], "1": args.CL_SGD_penalty.split(":")[0],
              "0":args.CL_SGD_loss.split(":")[0]}
-KNNKWARGS = {"0": map(float, args.CL_KNN_neigh.split(":"))[0]}
-AdaboostKWARGS = {"0": args.CL_Ada_n_est.split(":")[0], "1": args.CL_Ada_b_est.split(":")[0]}
+KNNKWARGSInit = {"0": map(float, args.CL_KNN_neigh.split(":"))[0]}
+AdaboostKWARGSInit = {"0": args.CL_Ada_n_est.split(":")[0], "1": args.CL_Ada_b_est.split(":")[0]}
 
 dataBaseTime = time.time()-start
 argumentDictionaries = {"Monoview": {}, "Multiview": []}
-print benchmark
 try:
     if benchmark["Monoview"]:
         argumentDictionaries["Monoview"] = []
         for view in views:
             for classifier in benchmark["Monoview"]:
-                arguments = {"args":{classifier+"KWARGS": globals()[classifier+"KWARGS"], "feat":view, "fileFeat": args.fileFeat,
+                arguments = {"args":{classifier+"KWARGS": globals()[classifier+"KWARGSInit"], "feat":view, "fileFeat": args.fileFeat,
                                      "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": classifier}, "viewIndex":views.index(view)}
                 argumentDictionaries["Monoview"].append(arguments)
 except:
@@ -287,7 +287,7 @@ if nbCores>1:
     for stepIndex in range(int(math.ceil(float(nbExperiments)/nbCores))):
         resultsMonoview+=(Parallel(n_jobs=nbCores)(
                 delayed(ExecMonoview_multicore)(args.name, args.CL_split, args.CL_nbFolds, coreIndex, args.type, args.pathF, gridSearch=gridSearch,
-                                      metric=metric, nIter=args.CL_GS_iter, **argumentDictionaries["Monoview"][coreIndex+stepIndex*nbCores])
+                                                metrics=metrics, nIter=args.CL_GS_iter, **argumentDictionaries["Monoview"][coreIndex + stepIndex * nbCores])
                 for coreIndex in range(min(nbCores, nbExperiments - (stepIndex + 1) * nbCores))))
     accuracies = [[result[1][1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
     classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
@@ -300,7 +300,7 @@ else:
     resultsMonoview+=([ExecMonoview(DATASET.get("View"+str(arguments["viewIndex"])),
                                     DATASET.get("labels").value, args.name,
                                     args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
-                                             gridSearch=gridSearch, metric=metric, nIter=args.CL_GS_iter,
+                                    gridSearch=gridSearch, metrics=metrics, nIter=args.CL_GS_iter,
                                     **arguments)
                                 for arguments in argumentDictionaries["Monoview"]])
 
@@ -357,7 +357,7 @@ try:
                                              "LABELS_NAMES": args.CL_classes.split(":"),
                                              "FusionKWARGS": {"fusionType":"EarlyFusion", "fusionMethod":method,
                                                               "classifiersNames": [classifier],
-                                                              "classifiersConfigs": [globals()[classifier+"KWARGS"]],
+                                                              "classifiersConfigs": [globals()[classifier+"KWARGSInit"]],
                                                               'fusionMethodConfig': fusionMethodConfig}}
                                 argumentDictionaries["Multiview"].append(arguments)
                 except:

Code/MonoMutliViewClassifiers/Metrics/f1_score.py

@@ -27,7 +27,7 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["3"]
     except:
-        average = "binary"
+        average = "micro"
     score = metric(y_true, y_pred, sample_weight=sample_weight, labels=labels, pos_label=pos_label, average=average)
     return score
 
@@ -48,7 +48,7 @@ def get_scorer(**kwargs):
     try:
         average = kwargs["3"]
     except:
-        average = "binary"
+        average = "micro"
     return make_scorer(metric, greater_is_better=True, sample_weight=sample_weight, labels=labels,
                        pos_label=pos_label, average=average)
 
@@ -69,7 +69,7 @@ def getConfig(**kwargs):
     try:
         average = kwargs["3"]
     except:
-        average = "binary"
+        average = "micro"
     configString = "F1 score using "+str(sample_weight)+" as sample_weights, "+str(labels)+" as labels, "+str(pos_label)\
                    +" as pos_label, "+average+" as average (higher is better)"
     return configString
\ No newline at end of file

Code/MonoMutliViewClassifiers/Metrics/fbeta_score.py

@@ -26,7 +26,7 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["4"]
     except:
-        average = "binary"
+        average = "micro"
     score = metric(y_true, y_pred, beta, sample_weight=sample_weight, labels=labels, pos_label=pos_label, average=average)
     return score
 
@@ -51,7 +51,7 @@ def get_scorer(**kwargs):
     try:
         average = kwargs["4"]
     except:
-        average = "binary"
+        average = "micro"
     return make_scorer(metric, greater_is_better=True, beta=beta, sample_weight=sample_weight, labels=labels,
                        pos_label=pos_label, average=average)
 
@@ -76,7 +76,7 @@ def getConfig(**kwargs):
     try:
         average = kwargs["3"]
     except:
-        average = "binary"
+        average = "micro"
     configString = "F-beta score using "+str(sample_weight)+" as sample_weights, "+str(labels)+" as labels, "+str(pos_label) \
                    +" as pos_label, "+average+" as average, "+str(beta)+" as beta (higher is better)"
     return configString
\ No newline at end of file

Code/MonoMutliViewClassifiers/Metrics/roc_auc_score.py

@@ -14,7 +14,7 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["1"]
     except:
-        average = "binary"
+        average = "micro"
     score = metric(y_true, y_pred, sample_weight=sample_weight, average=average)
     return score
 
@@ -27,7 +27,7 @@ def get_scorer(**kwargs):
     try:
         average = kwargs["1"]
     except:
-        average = "binary"
+        average = "micro"
     return make_scorer(metric, greater_is_better=True, sample_weight=sample_weight, average=average)
 
 
@@ -39,6 +39,6 @@ def getConfig(**kwargs):
     try:
         average = kwargs["3"]
     except:
-        average = "binary"
+        average = "micro"
     configString = "ROS AUC score using "+str(sample_weight)+" as sample_weights, "+average+" as average (higher is better)"
     return configString
\ No newline at end of file

Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py

@@ -33,7 +33,6 @@ def ExecMonoview_multicore(name, learningRate, nbFolds, datasetFileIndex, databa
                            metrics=[["accuracy_score", None]], nIter=30, **args):
     DATASET = h5py.File(path+name+str(datasetFileIndex)+".hdf5", "r")
     kwargs = args["args"]
-
     views = [DATASET.get("View"+str(viewIndex)).attrs["name"] for viewIndex in range(DATASET.get("Metadata").attrs["nbView"])]
     neededViewIndex = views.index(kwargs["feat"])
     X = DATASET.get("View"+str(neededViewIndex))
@@ -45,7 +44,6 @@ def ExecMonoview_multicore(name, learningRate, nbFolds, datasetFileIndex, databa
 
 def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path, gridSearch=True,
                 metrics=[["accuracy_score", None]], nIter=30, **args):
-
     try:
         kwargs = args["args"]
     except:
@@ -189,7 +187,7 @@ if __name__=='__main__':
     groupClass.add_argument('--CL_Cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int, default=1)
     groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Split ratio for train and test', type=float, default=0.9)
     groupClass.add_argument('--CL_metrics', metavar='STRING', action='store',
-                        help='Determine which metric to use, separate with ":" if multiple, if empty, considering all', default='')
+                        help='Determine which metrics to use, separate with ":" if multiple, if empty, considering all', default='')
 
 
     groupClassifier = parser.add_argument_group('Classifier Config')

Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py

@@ -38,6 +38,7 @@ def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
 
 def execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, feat, CL_type, clKWARGS, classLabelsNames,
             shape, y_train, y_train_pred, y_test, y_test_pred, time):
+    print metrics
     metricsScores = {}
     metricModule = getattr(Metrics, metrics[0][0])
     train = metricModule.score(y_train, y_train_pred)

Code/MonoMutliViewClassifiers/Results/20160906-110031-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log

+2016-09-06 11:00:31,349 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 11:00:31,350 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\ 
+2016-09-06 11:00:40,496 DEBUG: Start:	 Creating datasets for multiprocessing

Code/MonoMutliViewClassifiers/Results/20160906-110243-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log

+2016-09-06 11:02:43,168 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 11:02:43,168 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\ 

Code/MonoMutliViewClassifiers/Results/20160906-110257-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log

+2016-09-06 11:02:57,719 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 11:02:57,720 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\ 
+2016-09-06 11:03:04,610 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 11:03:04,612 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 11:03:05,483 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:03:05,483 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 11:03:05,483 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:03:05,539 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 11:03:05,539 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 11:03:05,539 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:03:05,539 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:03:06,099 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:03:06,099 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 11:03:06,099 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:03:06,130 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 11:03:06,130 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 11:03:06,130 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:03:06,131 DEBUG: Start:	 RandomSearch best settings with 1 iterations

Code/MonoMutliViewClassifiers/Results/20160906-110344Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 0.971074380165
+accuracy_score on test : 0.828571428571
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 3
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.971074380165
+		- Score on test : 0.828571428571
+
+
+ Classification took 0:00:09
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110346Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.819047619048
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.819047619048
+
+
+ Classification took 0:00:11
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110346Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.921487603306
+accuracy_score on test : 0.847619047619
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 3
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.921487603306
+		- Score on test : 0.847619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110353Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.892561983471
+accuracy_score on test : 0.87619047619
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.892561983471
+		- Score on test : 0.87619047619
+
+
+ Classification took 0:00:02
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110356Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.72380952381
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.735537190083
+		- Score on test : 0.72380952381
+
+
+ Classification took 0:00:03
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110403Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.895238095238
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5123
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.895238095238
+
+
+ Classification took 0:00:09
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110416Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 0.938016528926
+accuracy_score on test : 0.819047619048
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 3
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.938016528926
+		- Score on test : 0.819047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110416Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.72380952381
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5123
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.735537190083
+		- Score on test : 0.72380952381
+
+
+ Classification took 0:00:11
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110416Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.895238095238
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5123
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.895238095238
+
+
+ Classification took 0:00:11
\ No newline at end of file

Code/MonoMutliViewClassifiers/Results/20160906-110417Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt

+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file