relatedness.py 文件源码

def run(self):
        self.nepoch = 0
        bestpr = -1
        early_stop_count = 0
        r = np.arange(1, 6)
        stop_train = False

        # Preparing data
        trainX, trainy, devX, devy, testX, testy = self.prepare_data(
            self.train['X'], self.train['y'],
            self.valid['X'], self.valid['y'],
            self.test['X'], self.test['y'])

        # Training
        while not stop_train and self.nepoch <= self.maxepoch:
            self.trainepoch(trainX, trainy, nepoches=50)
            yhat = np.dot(self.predict_proba(devX), r)
            pr = pearsonr(yhat, self.devscores)[0]
            # early stop on Pearson
            if pr > bestpr:
                bestpr = pr
                bestmodel = copy.deepcopy(self.model)
            elif self.early_stop:
                if early_stop_count >= 3:
                    stop_train = True
                early_stop_count += 1
        self.model = bestmodel

        yhat = np.dot(self.predict_proba(testX), r)

        return bestpr, yhat