recommenders/models/newsrec/io/mind_all_iterator.py [130:196]:
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    def init_behaviors(self, behaviors_file):
        """Init behavior logs given behaviors file.

        Args:
            behaviors_file (str): path of behaviors file
        """
        self.histories = []
        self.imprs = []
        self.labels = []
        self.impr_indexes = []
        self.uindexes = []

        with tf.io.gfile.GFile(behaviors_file, "r") as rd:
            impr_index = 0
            for line in rd:
                uid, time, history, impr = line.strip("\n").split(self.col_spliter)[-4:]

                history = [self.nid2index[i] for i in history.split()]
                history = [0] * (self.his_size - len(history)) + history[
                    : self.his_size
                ]

                impr_news = [self.nid2index[i.split("-")[0]] for i in impr.split()]
                label = [int(i.split("-")[1]) for i in impr.split()]
                uindex = self.uid2index[uid] if uid in self.uid2index else 0

                self.histories.append(history)
                self.imprs.append(impr_news)
                self.labels.append(label)
                self.impr_indexes.append(impr_index)
                self.uindexes.append(uindex)
                impr_index += 1

    def parser_one_line(self, line):
        """Parse one string line into feature values.

        Args:
            line (str): a string indicating one instance.

        Yields:
            list: Parsed results including label, impression id , user id,
            candidate_title_index, clicked_title_index,
            candidate_ab_index, clicked_ab_index,
            candidate_vert_index, clicked_vert_index,
            candidate_subvert_index, clicked_subvert_index,
        """
        if self.npratio > 0:
            impr_label = self.labels[line]
            impr = self.imprs[line]

            poss = []
            negs = []

            for news, click in zip(impr, impr_label):
                if click == 1:
                    poss.append(news)
                else:
                    negs.append(news)

            for p in poss:
                candidate_title_index = []
                impr_index = []
                user_index = []
                label = [1] + [0] * self.npratio

                n = newsample(negs, self.npratio)
                candidate_title_index = self.news_title_index[[p] + n]
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recommenders/models/newsrec/io/mind_iterator.py [103:167]:
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    def init_behaviors(self, behaviors_file):
        """init behavior logs given behaviors file.

        Args:
        behaviors_file: path of behaviors file
        """
        self.histories = []
        self.imprs = []
        self.labels = []
        self.impr_indexes = []
        self.uindexes = []

        with tf.io.gfile.GFile(behaviors_file, "r") as rd:
            impr_index = 0
            for line in rd:
                uid, time, history, impr = line.strip("\n").split(self.col_spliter)[-4:]

                history = [self.nid2index[i] for i in history.split()]
                history = [0] * (self.his_size - len(history)) + history[
                    : self.his_size
                ]

                impr_news = [self.nid2index[i.split("-")[0]] for i in impr.split()]
                label = [int(i.split("-")[1]) for i in impr.split()]
                uindex = self.uid2index[uid] if uid in self.uid2index else 0

                self.histories.append(history)
                self.imprs.append(impr_news)
                self.labels.append(label)
                self.impr_indexes.append(impr_index)
                self.uindexes.append(uindex)
                impr_index += 1

    def parser_one_line(self, line):
        """Parse one behavior sample into feature values.
        if npratio is larger than 0, return negtive sampled result.

        Args:
            line (int): sample index.

        Yields:
            list: Parsed results including label, impression id , user id,
            candidate_title_index, clicked_title_index.
        """
        if self.npratio > 0:
            impr_label = self.labels[line]
            impr = self.imprs[line]

            poss = []
            negs = []

            for news, click in zip(impr, impr_label):
                if click == 1:
                    poss.append(news)
                else:
                    negs.append(news)

            for p in poss:
                candidate_title_index = []
                impr_index = []
                user_index = []
                label = [1] + [0] * self.npratio

                n = newsample(negs, self.npratio)
                candidate_title_index = self.news_title_index[[p] + n]
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