MTMKL.py 文件源码

def getAUC(self,test_tasks):
        mean_tpr = 0.0
        mean_fpr = np.linspace(0, 1, 100)
        for t in range(self.n_tasks):
            X_t, Y_t = self.extractTaskData(self.train_tasks,t)
            X_test_t, Y_test_t = self.extractTaskData(test_tasks, t)

            overallKernel = self.constructKernelFunction(t)

            self.classifiers[t] = SVC(C=self.C, kernel=overallKernel, probability=True, max_iter=self.max_iter_internal, tol=self.tolerance)
            probas_ = self.classifiers[t].fit(X_t, Y_t).predict_proba(X_test_t)
            fpr, tpr, thresholds = roc_curve(Y_test_t, probas_[:, 1])

            mean_tpr += interp(mean_fpr, fpr, tpr)
            mean_tpr[0] = 0.0

        mean_tpr /= self.n_tasks
        mean_tpr[-1] = 1.0
        mean_auc = auc(mean_fpr, mean_tpr)

        return mean_auc, mean_fpr, mean_tpr