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[ModelZoo] Support Co_Action Network #344
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32ce9e7
[ModelZoo] Support Co_Action Network
aiden-law-tian d6d5be5
[ModelZoo] Support Co_Action Network
aiden-law-tian b438c64
[ModelZoo] Support Co_Action Net
aiden-law-tian c5df688
[ModelZoo] Support FNN
aiden-law-tian 4e4b400
[ModelZoo] Support Co_Action Network
aiden-law-tian b613472
[ModelZoo] Support FNN
aiden-law-tian 0cc1389
[ModelZoo] Support FwFM
aiden-law-tian 6421ff4
[ModelZoo] Support PNN
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# Co-Action Network | ||
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Implementation of paper "CAN: Revisiting Feature Co-Action for Click Through Rate Prediction". | ||
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paper: [arxiv (to be released)]() | ||
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## Installation | ||
dependences: | ||
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tensorflow:1.4.1 | ||
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python: 2.7 | ||
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higher version of tensorflow and python3 will be supported soon! | ||
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## Getting Started | ||
training: | ||
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CUDA_VISIBLE_DEVICES=0 python train.py train {model} | ||
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model: CAN,Cartesion,PNN, etc. (check the train.py) | ||
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## Citation | ||
## Contact | ||
## License |
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# Co-Action Network | ||
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Implementation of paper "CAN: Revisiting Feature Co-Action for Click Through Rate Prediction". | ||
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paper: [arxiv (to be released)]() | ||
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## Installation | ||
dependences: | ||
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tensorflow:1.4.1 | ||
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python: 2.7 | ||
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higher version of tensorflow and python3 will be supported soon! | ||
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## Getting Started | ||
training: | ||
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CUDA_VISIBLE_DEVICES=0 python train.py train {model} | ||
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model: CAN,Cartesion,PNN, etc. (check the train.py) | ||
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## Citation | ||
## Contact | ||
## License |
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export PATH="~/anaconda4/bin:$PATH" | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 移除本地开发变量 |
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wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Books.json.gz | ||
wget http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Books.json.gz | ||
gunzip reviews_Books.json.gz | ||
gunzip meta_Books.json.gz | ||
python script/process_data.py meta_Books.json reviews_Books.json | ||
python script/local_aggretor.py | ||
python script/split_by_user.py | ||
python script/generate_voc.py |
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import tensorflow as tf | ||
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def dice(_x, axis=-1, epsilon=0.000000001, name=''): | ||
with tf.variable_scope(name, reuse=tf.AUTO_REUSE): | ||
alphas = tf.get_variable('alpha'+name, _x.get_shape()[-1], | ||
initializer=tf.constant_initializer(0.0), | ||
dtype=tf.float32) | ||
input_shape = list(_x.get_shape()) | ||
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reduction_axes = list(range(len(input_shape))) | ||
del reduction_axes[axis] | ||
broadcast_shape = [1] * len(input_shape) | ||
broadcast_shape[axis] = input_shape[axis] | ||
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# case: train mode (uses stats of the current batch) | ||
mean = tf.reduce_mean(_x, axis=reduction_axes) | ||
brodcast_mean = tf.reshape(mean, broadcast_shape) | ||
std = tf.reduce_mean(tf.square(_x - brodcast_mean) + epsilon, axis=reduction_axes) | ||
std = tf.sqrt(std) | ||
brodcast_std = tf.reshape(std, broadcast_shape) | ||
x_normed = (_x - brodcast_mean) / (brodcast_std + epsilon) | ||
# x_normed = tf.layers.batch_normalization(_x, center=False, scale=False) | ||
x_p = tf.sigmoid(x_normed) | ||
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return alphas * (1.0 - x_p) * _x + x_p * _x | ||
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def parametric_relu(_x): | ||
alphas = tf.get_variable('alpha', _x.get_shape()[-1], | ||
initializer=tf.constant_initializer(0.0), | ||
dtype=tf.float32) | ||
pos = tf.nn.relu(_x) | ||
neg = alphas * (_x - abs(_x)) * 0.5 | ||
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return pos + neg |
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ckpt = tf.train.get_checkpoint_state("./ckpt_path/").model_checkpoint_path | ||
saver = tf.train.import_meta_graph(ckpt+'.meta') | ||
variables = tf.trainable_variables() | ||
total_parameters = 0 | ||
for variable in variables: | ||
shape = variable.get_shape() | ||
variable_parameters = 1 | ||
for dim in shape: | ||
variable_parameters *= dim.value | ||
total_parameters += variable_parameters | ||
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import numpy | ||
import json | ||
import _pickle as cPickle | ||
import random | ||
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import gzip | ||
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import data.script.shuffle | ||
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def unicode_to_utf8(d): | ||
return dict((key.encode("UTF-8"), value) for (key,value) in d.items()) | ||
def dict_unicode_to_utf8(d): | ||
return dict(((key[0].encode("UTF-8"), key[1].encode("UTF-8")), value) for (key,value) in d.items()) | ||
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def load_dict(filename): | ||
try: | ||
with open(filename, 'rb') as f: | ||
return unicode_to_utf8(json.load(f)) | ||
except: | ||
try: | ||
with open(filename, 'rb') as f: | ||
return unicode_to_utf8(cPickle.load(f)) | ||
except: | ||
with open(filename, 'rb') as f: | ||
return dict_unicode_to_utf8(cPickle.load(f)) | ||
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def fopen(filename, mode='r'): | ||
if filename.endswith('.gz'): | ||
return gzip.open(filename, mode) | ||
return open(filename, mode) | ||
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class DataIterator: | ||
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def __init__(self, source, | ||
uid_voc, | ||
mid_voc, | ||
cat_voc, | ||
batch_size=128, | ||
maxlen=100, | ||
skip_empty=False, | ||
shuffle_each_epoch=False, | ||
sort_by_length=True, | ||
max_batch_size=20, | ||
minlen=None, | ||
label_type=1): | ||
if shuffle_each_epoch: | ||
self.source_orig = source | ||
self.source = shuffle.main(self.source_orig, temporary=True) | ||
else: | ||
self.source = fopen(source, 'r') | ||
self.source_dicts = [] | ||
for source_dict in [uid_voc, mid_voc, cat_voc, '../CAN/data/item_carte_voc.pkl', '../CAN/data/cate_carte_voc.pkl']: | ||
self.source_dicts.append(load_dict(source_dict)) | ||
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f_meta = open("../CAN/data/item-info", "r") | ||
meta_map = {} | ||
for line in f_meta: | ||
arr = line.strip().split("\t") | ||
if arr[0] not in meta_map: | ||
meta_map[arr[0]] = arr[1] | ||
self.meta_id_map ={} | ||
for key in meta_map: | ||
val = meta_map[key] | ||
if key in self.source_dicts[1]: | ||
mid_idx = self.source_dicts[1][key] | ||
else: | ||
mid_idx = 0 | ||
if val in self.source_dicts[2]: | ||
cat_idx = self.source_dicts[2][val] | ||
else: | ||
cat_idx = 0 | ||
self.meta_id_map[mid_idx] = cat_idx | ||
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f_review = open("../CAN/data/reviews-info", "r") | ||
self.mid_list_for_random = [] | ||
for line in f_review: | ||
arr = line.strip().split("\t") | ||
tmp_idx = 0 | ||
if arr[1] in self.source_dicts[1]: | ||
tmp_idx = self.source_dicts[1][arr[1]] | ||
self.mid_list_for_random.append(tmp_idx) | ||
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self.batch_size = batch_size | ||
self.maxlen = maxlen | ||
self.minlen = minlen | ||
self.skip_empty = skip_empty | ||
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self.n_uid = len(self.source_dicts[0]) | ||
self.n_mid = len(self.source_dicts[1]) | ||
self.n_cat = len(self.source_dicts[2]) | ||
self.n_carte = [len(self.source_dicts[3]), len(self.source_dicts[4])] | ||
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self.shuffle = shuffle_each_epoch | ||
self.sort_by_length = sort_by_length | ||
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self.source_buffer = [] | ||
self.k = batch_size * max_batch_size | ||
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self.end_of_data = False | ||
self.label_type = label_type | ||
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def get_n(self): | ||
return self.n_uid, self.n_mid, self.n_cat, self.n_carte | ||
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def __iter__(self): | ||
return self | ||
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def reset(self): | ||
if self.shuffle: | ||
self.source= shuffle.main(self.source_orig, temporary=True) | ||
else: | ||
self.source.seek(0) | ||
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def __next__(self): | ||
if self.end_of_data: | ||
self.end_of_data = False | ||
self.reset() | ||
raise StopIteration | ||
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source = [] | ||
target = [] | ||
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if len(self.source_buffer) == 0: | ||
for k_ in range(self.k): | ||
ss = self.source.readline() | ||
if ss == "": | ||
break | ||
self.source_buffer.append(ss.strip("\n").split("\t")) | ||
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# sort by history behavior length | ||
if self.sort_by_length: | ||
his_length = numpy.array([len(s[4].split("")) for s in self.source_buffer]) | ||
tidx = his_length.argsort() | ||
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_sbuf = [self.source_buffer[i] for i in tidx] | ||
self.source_buffer = _sbuf | ||
else: | ||
self.source_buffer.reverse() | ||
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if len(self.source_buffer) == 0: | ||
self.end_of_data = False | ||
self.reset() | ||
raise StopIteration | ||
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try: | ||
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# actual work here | ||
while True: | ||
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# read from source file and map to word index | ||
try: | ||
ss = self.source_buffer.pop() | ||
except IndexError: | ||
break | ||
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uid = self.source_dicts[0][ss[1]] if ss[1] in self.source_dicts[0] else 0 | ||
mid = self.source_dicts[1][ss[2]] if ss[2] in self.source_dicts[1] else 0 | ||
cat = self.source_dicts[2][ss[3]] if ss[3] in self.source_dicts[2] else 0 | ||
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tmp = [] | ||
item_carte = [] | ||
for fea in ss[4].split(""): | ||
m = self.source_dicts[1][fea] if fea in self.source_dicts[1] else 0 | ||
tmp.append(m) | ||
i_c = self.source_dicts[3][(ss[2], fea)] if (ss[2], fea) in self.source_dicts[3] else 0 | ||
item_carte.append(i_c) | ||
mid_list = tmp | ||
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tmp1 = [] | ||
cate_carte = [] | ||
for fea in ss[5].split(""): | ||
c = self.source_dicts[2][fea] if fea in self.source_dicts[2] else 0 | ||
tmp1.append(c) | ||
c_c = self.source_dicts[4][(ss[3], fea)] if (ss[3], fea) in self.source_dicts[4] else 0 | ||
cate_carte.append(c_c) | ||
cat_list = tmp1 | ||
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# read from source file and map to word index | ||
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if self.minlen != None: | ||
if len(mid_list) <= self.minlen: | ||
continue | ||
if self.skip_empty and (not mid_list): | ||
continue | ||
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noclk_mid_list = [] | ||
noclk_cat_list = [] | ||
for pos_mid in mid_list: | ||
noclk_tmp_mid = [] | ||
noclk_tmp_cat = [] | ||
noclk_index = 0 | ||
while True: | ||
noclk_mid_indx = random.randint(0, len(self.mid_list_for_random)-1) | ||
noclk_mid = self.mid_list_for_random[noclk_mid_indx] | ||
if noclk_mid == pos_mid: | ||
continue | ||
noclk_tmp_mid.append(noclk_mid) | ||
noclk_tmp_cat.append(self.meta_id_map[noclk_mid]) | ||
noclk_index += 1 | ||
if noclk_index >= 5: | ||
break | ||
noclk_mid_list.append(noclk_tmp_mid) | ||
noclk_cat_list.append(noclk_tmp_cat) | ||
carte_list = [item_carte, cate_carte] | ||
source.append([uid, mid, cat, mid_list, cat_list, noclk_mid_list, noclk_cat_list, carte_list]) | ||
if self.label_type == 1: | ||
target.append([float(ss[0])]) | ||
else: | ||
target.append([float(ss[0]), 1-float(ss[0])]) | ||
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if len(source) >= self.batch_size or len(target) >= self.batch_size: | ||
break | ||
except IOError: | ||
self.end_of_data = True | ||
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# all sentence pairs in maxibatch filtered out because of length | ||
if len(source) == 0 or len(target) == 0: | ||
source, target = self.next() | ||
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return source, target | ||
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以deeprec,python3.6为标准来写