def ROC_calc(ML_alg):
	global ML_fpr_dict, ML_tpr_dict, ML_roc_auc_dict
    ##Binarize data
    Train = Target_train
    Test = Target_test
    Train = label_binarize(Train, classes=list(range(1, 10, 1)))
    Test = label_binarize(Test, classes=list(range(1, 10, 1)))
    n_classes = Train.shape[1]
 
    ##Learn and predict
    ML_alg.fit(Feature_train, Train)
    Target_alg = ML_alg.predict(Feature_test)
 
    # Compute ROC curve and ROC area for each class
    ML_fpr_dict = dict()
    ML_tpr_dict = dict()
    ML_roc_auc_dict = dict()
    for i in range(n_classes):
        ML_fpr_dict[i], ML_tpr_dict[i], _ = roc_curve(Test[:, i], Target_alg[:, i])
        ML_roc_auc_dict[i] = auc(ML_fpr_dict[i], ML_tpr_dict[i])

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