text: # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  This file loads and preprocesses the dataset for metapath2vec model.
  """
  
  import sys
  import os
  import glob
  import numpy as np
  import tqdm
  import time
  import logging
  import random
  from pgl import heter_graph
  import pickle as pkl
  
  
  class Dataset(object):
      """Implementation of Dataset class
  
      This is a simple implementation of loading and processing dataset for metapath2vec model.
  
      Args:
          config: dict, some configure parameters.
      """
  
      NEGATIVE_TABLE_SIZE = 1e8
  
      def __init__(self, config):
          self.config = config
          self.walk_files = os.path.join(config['input_path'],
                                         config['walk_path'])
          self.word2id_file = os.path.join(config['input_path'],
                                           config['word2id_file'])
  
          self.word2freq = {}
          self.word2id = {}
          self.id2word = {}
          self.sentences_count = 0
          self.token_count = 0
          self.negatives = []
          self.discards = []
  
          logging.info('reading sentences')
          self.read_words()
          logging.info('initializing discards')
          self.initDiscards()
          logging.info('initializing negatives')
          self.initNegatives()
  
      def read_words(self):
          """Read words(nodes) from walk files which are produced by sampler.
          """
          word_freq = dict()
          for walk_file in glob.glob(self.walk_files):
              with open(walk_file, 'r') as reader:
                  for walk in reader:
                      walk = walk.strip().split()
                      if len(walk) > 1:
                          self.sentences_count += 1
                          for word in walk:
                              if int(word) >= self.config[
                                      'paper_start_index']:  # remove paper
                                  continue
                              else:
                                  self.token_count += 1
                                  word_freq[word] = word_freq.get(word, 0) + 1
  
          wid = 0
          logging.info('Read %d sentences.' % self.sentences_count)
          logging.info('Read %d words.' % self.token_count)
          logging.info('%d words have been sampled.' % len(word_freq))
          for w, c in word_freq.items():
              if c < self.config['min_count']:
                  continue
              self.word2id[w] = wid
              self.id2word[wid] = w
              self.word2freq[wid] = c
              wid += 1
  
          self.word_count = len(self.word2id)
          logging.info(
              '%d words displayed less than %d(min_count) have been discarded.' %
              (len(word_freq) - len(self.word2id), self.config['min_count']))
  
          pkl.dump(self.word2id, open(self.word2id_file, 'wb'))
  
      def initDiscards(self):
          """Get a frequency table for sub-sampling.
          """
          t = 0.0001
          f = np.array(list(self.word2freq.values())) / self.token_count
          self.discards = np.sqrt(t / f) + (t / f)
  
      def initNegatives(self):
          """Get a table for negative sampling
          """
          pow_freq = np.array(list(self.word2freq.values()))**0.75
          words_pow = sum(pow_freq)
          ratio = pow_freq / words_pow
          count = np.round(ratio * Dataset.NEGATIVE_TABLE_SIZE)
          for wid, c in enumerate(count):
              self.negatives += [wid] * int(c)
          self.negatives = np.array(self.negatives)
          np.random.shuffle(self.negatives)
          self.sampling_prob = ratio
  
      def getNegatives(self, size):
          """Get negative samples from negative samling table.
          """
          return np.random.choice(self.negatives, size)
  
      def walk_from_files(self, walkpath_files):
          """Generate walks from files.
          """
          bucket = []
          for filename in walkpath_files:
              with open(filename) as reader:
                  for line in reader:
                      words = line.strip().split()
                      words = [
                          w for w in words
                          if int(w) < self.config['paper_start_index']
                      ]
                      if len(words) > 1:
                          word_ids = [
                              self.word2id[w] for w in words if w in self.word2id
                          ]
                          bucket.append(word_ids)
                          if len(bucket) == self.config['batch_size']:
                              yield bucket
                              bucket = []
          if len(bucket):
              yield bucket
  
      def pairs_generator(self, walkpath_files):
          """Generate train pairs(src, pos, negs) for training model.
          """
  
          def wrapper():
              """wrapper for multiprocess calling.
              """
              for walks in self.walk_from_files(walkpath_files):
                  res = self.gen_pairs(walks)
                  yield res
  
          return wrapper
  
      def gen_pairs(self, walks):
          """Generate train pairs data for training model.
          """
          src = []
          pos = []
          negs = []
          skip_window = self.config['win_size'] // 2
          for walk in walks:
              for i in range(len(walk)):
                  for j in range(1, skip_window + 1):
                      if i - j >= 0:
                          src.append(walk[i])
                          pos.append(walk[i - j])
                          negs.append(
                              self.getNegatives(size=self.config['neg_num']))
                      if i + j < len(walk):
                          src.append(walk[i])
                          pos.append(walk[i + j])
                          negs.append(
                              self.getNegatives(size=self.config['neg_num']))
  
          src = np.array(src, dtype=np.int64).reshape(-1, 1, 1)
          pos = np.array(pos, dtype=np.int64).reshape(-1, 1, 1)
          negs = np.expand_dims(np.array(negs, dtype=np.int64), -1)
          return {"src": src, "pos": pos, "negs": negs}
  
  
  if __name__ == "__main__":
      config = {
          'input_path': './data/out_aminer_CPAPC/',
          'walk_path': 'aminer_walks_CPAPC_500num_100len/*',
          'author_label_file': 'author_label.txt',
          'venue_label_file': 'venue_label.txt',
          'remapping_author_label_file': 'multi_class_author_label.txt',
          'remapping_venue_label_file': 'multi_class_venue_label.txt',
          'word2id_file': 'word2id.pkl',
          'win_size': 7,
          'neg_num': 5,
          'min_count': 2,
          'batch_size': 1,
      }
  
      log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
      logging.basicConfig(level=getattr(logging, 'INFO'), format=log_format)
  
      dataset = Dataset(config)
  