roc_auc.py 文件源码

def perform(self, node, inputs, output_storage):
        """
        Calculate ROC AUC score.

        Parameters
        ----------
        node : Apply instance
            Symbolic inputs and outputs.
        inputs : list
            Sequence of inputs.
        output_storage : list
            List of mutable 1-element lists.
        """
        if roc_auc_score is None:
            raise RuntimeError("Could not import from sklearn.")
        y_true, y_score = inputs
        y_true = np.argmax(y_true, axis=1)
        y_score = np.argmax(y_score, axis=1)
        try:
            TP = np.sum(y_true[y_score==1]==1)*1. #/ sum(y_true)
            FP = np.sum(y_true[y_score==1]==0)*1. #/ (y_true.shape[0]-sum(y_true))
            #TN = np.sum(truey[predy==0]==0)*1. / (truey.shape[0]-sum(truey))
            FN = np.sum(y_true[y_score==0]==1)*1. #/ sum(y_true)
            #prec = TP / (TP+FP+1e-6)
            #reca = TP / (TP+FN+1e-6)
            #f1 = 2*prec*reca / (prec+reca+1e-6)
            f1 = 2*TP / (2*TP +FP +FN)
        except ValueError:
            f1 = np.nan
        #rvalue = np.array((roc_auc, prec, reca, f1))
        #[0][0]
        output_storage[0][0] = theano._asarray(f1, dtype=config.floatX)