Using adaboost with svm as weak learner [fails]

gistfile1.py

def cvalidate():
    targetset = np.genfromtxt(open('trainLabels.csv','r'), dtype='f16')
    y = [x for x in targetset]

    trainset = np.genfromtxt(open('train.csv','r'), delimiter=',', dtype='f16')
    X = np.array([x for x in trainset])
    
    X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.3, random_state = 0)

    X_train, X_test = decomposition_pca(X_train, X_test)

    parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
    c_range = 10.0 ** np.arange(6.5,7.5,.25)
    gamma_range = 10.0 ** np.arange(-2.5,0.5,.25)
    parameters = {'kernel':['rbf'], 'C':c_range,  'gamma':gamma_range} 
    svr = SVC(probability=True)

    clf = grid_search.GridSearchCV(svr, parameters)


    bdt = AdaBoostClassifier(base_estimator = clf,
                         algorithm="SAMME",
                         n_estimators=200)
    #bdt = AdaBoostClassifier(base_estimator=SVC(probability=True))

    bdt.fit(X_train, y_train)
    

    print bdt.score(X_test, y_test)