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data_load_ml.py
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data_load_ml.py
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from __future__ import print_function
from hyperparams import Hyperparams as hp
import tensorflow as tf
import random
def load_user_vocab():
user_ids = [line.strip() for line in open(hp.user_ids_file, 'r').read().splitlines()]
user2idx = {int(user): idx for idx, user in enumerate(user_ids)}
idx2user = {idx: int(user) for idx, user in enumerate(user_ids)}
return user2idx, idx2user
def load_item_vocab():
item_ids = [line.strip() for line in open(hp.item_ids_file, 'r').read().splitlines()]
item2idx = {int(item): idx for idx, item in enumerate(item_ids)}
idx2item = {idx: int(item) for idx, item in enumerate(item_ids)}
return item2idx, idx2item
##########################################
def load_gen_data(file_path):
user2idx, _ = load_user_vocab()
item2idx, _ = load_item_vocab()
USER, CARD, CARD_IDX, ITEM_CAND, ITEM_POS = [], [], [], [], []
with open(file_path, 'r') as fin:
for line in fin:
strs = line.strip().split('\t')
USER.append(user2idx[int(strs[0])])
card_ = [item2idx[int(x)] for x in strs[1].split(',')]
CARD.append(card_)
item_cand_ = sorted([item2idx[int(x)] for x in strs[2].split(',')])
ITEM_CAND.append(item_cand_) # sorted
ITEM_POS.append(card_[0])
item_cand_idx_map = {}
for idx, item in enumerate(item_cand_):
item_cand_idx_map[item] = idx
card_idx_ = [item_cand_idx_map[item] for item in card_]
CARD_IDX.append(card_idx_)
'''
tmp = set(strs[2].split(','))
tmp.remove(strs[1].split(',')[0])
tmp = list(tmp)
random.shuffle(tmp)
ITEM_CAND_NEG.append([item2idx[int(x)] for x in tmp])
'''
return USER, CARD, CARD_IDX, ITEM_CAND, ITEM_POS
def get_gen_batch_data(is_training=True):
# Load data
if is_training:
USER, CARD, CARD_IDX, ITEM_CAND, ITEM_POS = load_gen_data(hp.gen_data_train)
batch_size = hp.batch_size
print('Load generator training data done!')
else:
USER, CARD, CARD_IDX, ITEM_CAND, ITEM_POS = load_gen_data(hp.gen_data_test)
batch_size = hp.batch_size
print('Load generator testing data done!')
# calc total batch count
num_batch = len(USER) // batch_size
# Convert to tensor
USER = tf.convert_to_tensor(USER, tf.int32) # [batch_size]
CARD = tf.convert_to_tensor(CARD, tf.int32) # [batch_size, 4]
CARD_IDX = tf.convert_to_tensor(CARD_IDX, tf.int32) # [batch_size, 4]
ITEM_CAND = tf.convert_to_tensor(ITEM_CAND, tf.int32) # [batch_size, 20]
ITEM_POS = tf.convert_to_tensor(ITEM_POS, tf.int32) # [batch_size]
# ITEM_CAND_NEG = tf.convert_to_tensor(ITEM_CAND_NEG, tf.int32) # [batch_size, 19]
# Create Queues
input_queues = tf.train.slice_input_producer([USER, CARD, CARD_IDX, ITEM_CAND, ITEM_POS])
# create batch queues
user, card, card_idx, item_cand, item_pos = \
tf.train.shuffle_batch(input_queues,
num_threads=8,
batch_size=batch_size,
capacity=batch_size * 64,
min_after_dequeue=batch_size * 32,
allow_smaller_final_batch=False)
# card_neg = tf.random_crop(item_cand_neg, size=[hp.batch_size, hp.res_length])
return user, card, card_idx, item_cand, item_pos, num_batch
#####################################
def load_dis_data(file_path):
user2idx, _ = load_user_vocab()
item2idx, _ = load_item_vocab()
USER, CARD, LABEL = [], [], []
with open(file_path, 'r') as fin:
for line in fin:
strs = line.strip().split('\t')
USER.append(user2idx[int(strs[0])])
card = [item2idx[int(x)] for x in strs[1].split(',')]
random.shuffle(card) # shuffled
CARD.append(card)
LABEL.append(float(strs[2]))
return USER, CARD, LABEL
def get_dis_batch_data(is_training=True):
# Load data
if is_training:
USER, CARD, LABEL = load_dis_data(hp.dis_data_train)
batch_size = hp.batch_size
print('Load discriminator training data done!')
else:
USER, CARD, LABEL = load_dis_data(hp.dis_data_test)
batch_size = hp.batch_size
print('Load discriminator testing data done!')
# calc total batch count
num_batch = len(USER) // batch_size
# Convert to tensor
USER = tf.convert_to_tensor(USER, tf.int32) # [batch_size]
CARD = tf.convert_to_tensor(CARD, tf.int32) # [batch_size, 4]
LABEL = tf.convert_to_tensor(LABEL, tf.float32) # [batch_size]
# Create Queues
input_queues = tf.train.slice_input_producer([USER, CARD, LABEL])
# create batch queues
user, card, label = \
tf.train.shuffle_batch(input_queues,
num_threads=8,
batch_size=batch_size,
capacity=batch_size * 64,
min_after_dequeue=batch_size * 32,
allow_smaller_final_batch=False)
return user, card, label, num_batch
if __name__ == "__main__":
user, card, card_idx, item_cand, item_pos, num_batch = get_gen_batch_data(is_training=True)
print(user)
print(card)
print(card_idx)
print(item_cand)
print(item_pos)
print(str(num_batch))
user, card, label, num_batch = get_dis_batch_data(is_training=True)
print(user)
print(card)
print(label)