def run()

in senteval/tools/ranking.py [0:0]

• 73 lines of code
• 9 McCabe index (conditional complexity)

    def run(self):
        self.nepoch = 0
        bestdevscore = -1
        early_stop_count = 0
        stop_train = False

        # Preparing data
        logging.info('prepare data')
        trainTxt, trainImg, devTxt, devImg, testTxt, testImg = \
            self.prepare_data(self.train['sentfeat'], self.train['imgfeat'],
                              self.valid['sentfeat'], self.valid['imgfeat'],
                              self.test['sentfeat'], self.test['imgfeat'])

        # Training
        while not stop_train and self.nepoch <= self.maxepoch:
            logging.info('start epoch')
            self.trainepoch(trainTxt, trainImg, devTxt, devImg, nepoches=1)
            logging.info('Epoch {0} finished'.format(self.nepoch))

            results = {'i2t': {'r1': 0, 'r5': 0, 'r10': 0, 'medr': 0},
                       't2i': {'r1': 0, 'r5': 0, 'r10': 0, 'medr': 0},
                       'dev': bestdevscore}
            score = 0
            for i in range(5):
                devTxt_i = devTxt[i*5000:(i+1)*5000]
                devImg_i = devImg[i*5000:(i+1)*5000]
                # Compute dev ranks img2txt
                r1_i2t, r5_i2t, r10_i2t, medr_i2t = self.i2t(devImg_i,
                                                             devTxt_i)
                results['i2t']['r1'] += r1_i2t / 5
                results['i2t']['r5'] += r5_i2t / 5
                results['i2t']['r10'] += r10_i2t / 5
                results['i2t']['medr'] += medr_i2t / 5
                logging.info("Image to text: {0}, {1}, {2}, {3}"
                             .format(r1_i2t, r5_i2t, r10_i2t, medr_i2t))
                # Compute dev ranks txt2img
                r1_t2i, r5_t2i, r10_t2i, medr_t2i = self.t2i(devImg_i,
                                                             devTxt_i)
                results['t2i']['r1'] += r1_t2i / 5
                results['t2i']['r5'] += r5_t2i / 5
                results['t2i']['r10'] += r10_t2i / 5
                results['t2i']['medr'] += medr_t2i / 5
                logging.info("Text to Image: {0}, {1}, {2}, {3}"
                             .format(r1_t2i, r5_t2i, r10_t2i, medr_t2i))
                score += (r1_i2t + r5_i2t + r10_i2t +
                          r1_t2i + r5_t2i + r10_t2i) / 5

            logging.info("Dev mean Text to Image: {0}, {1}, {2}, {3}".format(
                        results['t2i']['r1'], results['t2i']['r5'],
                        results['t2i']['r10'], results['t2i']['medr']))
            logging.info("Dev mean Image to text: {0}, {1}, {2}, {3}".format(
                        results['i2t']['r1'], results['i2t']['r5'],
                        results['i2t']['r10'], results['i2t']['medr']))

            # early stop on Pearson
            if score > bestdevscore:
                bestdevscore = score
                bestmodel = copy.deepcopy(self.model)
            elif self.early_stop:
                if early_stop_count >= 3:
                    stop_train = True
                early_stop_count += 1
        self.model = bestmodel

        # Compute test for the 5 splits
        results = {'i2t': {'r1': 0, 'r5': 0, 'r10': 0, 'medr': 0},
                   't2i': {'r1': 0, 'r5': 0, 'r10': 0, 'medr': 0},
                   'dev': bestdevscore}
        for i in range(5):
            testTxt_i = testTxt[i*5000:(i+1)*5000]
            testImg_i = testImg[i*5000:(i+1)*5000]
            # Compute test ranks img2txt
            r1_i2t, r5_i2t, r10_i2t, medr_i2t = self.i2t(testImg_i, testTxt_i)
            results['i2t']['r1'] += r1_i2t / 5
            results['i2t']['r5'] += r5_i2t / 5
            results['i2t']['r10'] += r10_i2t / 5
            results['i2t']['medr'] += medr_i2t / 5
            # Compute test ranks txt2img
            r1_t2i, r5_t2i, r10_t2i, medr_t2i = self.t2i(testImg_i, testTxt_i)
            results['t2i']['r1'] += r1_t2i / 5
            results['t2i']['r5'] += r5_t2i / 5
            results['t2i']['r10'] += r10_t2i / 5
            results['t2i']['medr'] += medr_t2i / 5

        return bestdevscore, results['i2t']['r1'], results['i2t']['r5'], \
                             results['i2t']['r10'], results['i2t']['medr'], \
                             results['t2i']['r1'], results['t2i']['r5'], \
                             results['t2i']['r10'], results['t2i']['medr']