added criterion for random forest

Code/MonoMutliViewClassifiers/ExecClassif.py

@@ -447,6 +447,8 @@ groupRF.add_argument('--CL_RandomForest_trees', metavar='INT', type=int, action=
                      default=25)
 groupRF.add_argument('--CL_RandomForest_max_depth', metavar='INT', type=int, action='store', help='Max depth for the trees',
                      default=5)
+groupRF.add_argument('--CL_RandomForest_criterion', metavar='STRING', action='store', help='Criterion for the trees',
+                     default="entropy")
 
 groupSVMLinear = parser.add_argument_group('Linear SVM arguments')
 groupSVMLinear.add_argument('--CL_SVMLinear_C', metavar='INT', type=int, action='store', help='Penalty parameter used',
@@ -474,7 +476,7 @@ groupDT.add_argument('--CL_DecisionTree_depth', metavar='INT', type=int, action=
 groupDT.add_argument('--CL_DecisionTree_criterion', metavar='STRING', action='store',
                      help='Determine max depth for Decision Trees', default="entropy")
 groupDT.add_argument('--CL_DecisionTree_splitter', metavar='STRING', action='store',
-                     help='Determine max depth for Decision Trees', default="random")
+                     help='Determine criterion for Decision Trees', default="random")
 
 
 groupSGD = parser.add_argument_group('SGD arguments')

Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py

@@ -17,7 +17,8 @@ def canProbas():
 def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
     num_estimators = int(kwargs['0'])
     maxDepth = int(kwargs['1'])
-    classifier = RandomForestClassifier(n_estimators=num_estimators, max_depth=maxDepth, n_jobs=NB_CORES)
+    criterion = kwargs["2"]
+    classifier = RandomForestClassifier(n_estimators=num_estimators, max_depth=maxDepth, criterion=criterion, n_jobs=NB_CORES)
     classifier.fit(DATASET, CLASS_LABELS)
     return classifier
 
@@ -29,13 +30,16 @@ def getKWARGS(kwargsList):
             kwargsDict['0'] = int(kwargValue)
         elif kwargName == "CL_RandomForest_max_depth":
             kwargsDict['1'] = kwargValue
+        elif kwargName == "CL_RandomForest_criterion":
+            kwargsDict['2'] = kwargValue
     return kwargsDict
 
 
 def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_rf = Pipeline([('classifier', RandomForestClassifier())])
     param_rf = {"classifier__n_estimators": randint(1, 30),
-                "classifier__max_depth":randint(1, 30)}
+                "classifier__max_depth":randint(1, 30),
+                "classifier__criterion":["gini", "entropy"]}
     metricModule = getattr(Metrics, metric[0])
     if metric[1]!=None:
         metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
@@ -46,12 +50,13 @@ def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_s
     rf_detector = grid_rf.fit(X_train, y_train)
 
     desc_estimators = [rf_detector.best_params_["classifier__n_estimators"],
-                       rf_detector.best_params_["classifier__max_depth"]]
+                       rf_detector.best_params_["classifier__max_depth"],
+                       rf_detector.best_params_["classifier__criterion"]]
     return desc_estimators
 
 
 def getConfig(config):
     try:
-        return "\n\t\t- Random Forest with num_esimators : "+str(config[0])+", max_depth : "+str(config[1])
+        return "\n\t\t- Random Forest with num_esimators : "+str(config[0])+", max_depth : "+str(config[1])+ ", criterion : "+config[2]
     except:
-        return "\n\t\t- Random Forest with num_esimators : "+str(config["0"])+", max_depth : "+str(config["1"])
+        return "\n\t\t- Random Forest with num_esimators : "+str(config["0"])+", max_depth : "+str(config["1"])+ ", criterion : "+config["2"]