modified gridsearch name for all monoview classifiers

Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py

@@ -32,7 +32,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
+def randomizedSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
     pipeline = Pipeline([('classifier', AdaBoostClassifier())])
 
     param= {"classifier__n_estimators": randint(1, 15),

Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py

@@ -28,7 +28,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30 ):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30 ):
     pipeline_KNN = Pipeline([('classifier', KNeighborsClassifier())])
     param_KNN = {"classifier__n_neighbors": randint(1, 50)}
     metricModule = getattr(Metrics, metric[0])

Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py

@@ -32,7 +32,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+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)}

Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py

@@ -60,7 +60,7 @@ def getKWARGS(kwargsList):
 
 
 
-def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
+def randomizedSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
 
     metricModule = getattr(Metrics, metric[0])
     if metric[1]!=None:

Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py

@@ -37,7 +37,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SGD = Pipeline([('classifier', SGDClassifier())])
     losses = ['log', 'modified_huber']
     penalties = ["l1", "l2", "elasticnet"]

Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py

@@ -28,7 +28,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SVMLinear = Pipeline([('classifier', SVC(kernel="linear", max_iter=1000))])
     param_SVMLinear = {"classifier__C":randint(1, 10000)}
     metricModule = getattr(Metrics, metric[0])

Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py

@@ -31,7 +31,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SVMPoly = Pipeline([('classifier', SVC(kernel="poly", max_iter=1000))])
     param_SVMPoly = {"classifier__C": randint(1, 10000), "classifier__degree":randint(1, 30)}
     metricModule = getattr(Metrics, metric[0])

Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py

@@ -29,7 +29,7 @@ def getKWARGS(kwargsList):
     return kwargsDict
 
 
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+def randomizedSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
     pipeline_SVMRBF = Pipeline([('classifier', SVC(kernel="rbf", max_iter=1000))])
     param_SVMRBF = {"classifier__C": randint(1, 10000)}
     metricModule = getattr(Metrics, metric[0])