Python scipy.stats 模块，mannwhitneyu() 实例源码

def compute_test_accuracy(X_test, Y_test, model, prediction_type, cellgroup_map_array):

    prediction = model.predict(X_test)
    auc = []

    if prediction_type=="cellgroup":

        prediction = np.dot(prediction, cellgroup_map_array)
        Y_test = np.dot(Y_test, cellgroup_map_array)

    mask = ~np.logical_or(Y_test.sum(1)==0, Y_test.sum(1)==Y_test.shape[1])

    for y,pred in zip(Y_test.T,prediction.T):
        pos = np.logical_and(mask, y==1)
        neg = np.logical_and(mask, y==0)
        try:
            U = stats.mannwhitneyu(pred[pos], pred[neg])[0]
            auc.append(1.-U/(np.count_nonzero(pos)*np.count_nonzero(neg)))
        except ValueError:
            auc.append(0.5)

    return auc

def on_epoch_end(self, epoch, logs={}):

        values = []
        prediction = self.model.predict(self.model.validation_data[0])
        Y = self.model.validation_data[1][0]

        if self.prediction_type=="cellgroup":

            prediction = np.dot(prediction, self.map_array)
            Y = np.dot(Y, self.map_array)

        mask = ~np.logical_or(Y.sum(1)==0, Y.sum(1)==Y.shape[1])

        for y,pred in zip(Y.T,prediction.T):
            pos = np.logical_and(mask, y==1)
            neg = np.logical_and(mask, y==0)
            try:
                U = stats.mannwhitneyu(pred[pos], pred[neg])[0]
                values.append(1.-U/(np.count_nonzero(pos)*np.count_nonzero(neg)))
            except ValueError:
                values.append(0.5)

        self.values.append(values)
        epoch_time = time.time()-self.current_time
        self.logger.log_this("epoch_%d_auroc: %s (%ds)"%(epoch, ' '.join(['%.4f'%v for v in self.values[-1]]), int(epoch_time)))
        self.current_time = time.time()