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体验新版 GitCode,发现更多精彩内容 >>
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dee19f6e
编写于
11月 11, 2018
作者:
Q
Qiao Longfei
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差异文件
add preprocess.py
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b3943336
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5 changed file
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+70
-13
fluid/PaddleRec/word2vec/README.cn.md
fluid/PaddleRec/word2vec/README.cn.md
+1
-5
fluid/PaddleRec/word2vec/README.md
fluid/PaddleRec/word2vec/README.md
+1
-5
fluid/PaddleRec/word2vec/data/preprocess.py
fluid/PaddleRec/word2vec/data/preprocess.py
+64
-0
fluid/PaddleRec/word2vec/reader.py
fluid/PaddleRec/word2vec/reader.py
+4
-2
fluid/PaddleRec/word2vec/requirements.txt
fluid/PaddleRec/word2vec/requirements.txt
+0
-1
未找到文件。
fluid/PaddleRec/word2vec/README.cn.md
浏览文件 @
dee19f6e
...
...
@@ -15,11 +15,7 @@
```
## 运行环境
需要先安装PaddlePaddle Fluid,然后运行:
```
shell
pip
install
-r
requirements.txt
```
需要先安装PaddlePaddle Fluid
## 数据集
本文使用的是Kaggle公司举办的
[
展示广告竞赛
](
https://www.kaggle.com/c/criteo-display-ad-challenge/
)
中所使用的Criteo数据集。
...
...
fluid/PaddleRec/word2vec/README.md
浏览文件 @
dee19f6e
...
...
@@ -20,11 +20,7 @@ factorization machines, please refer to the paper [factorization
machines
](
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
)
## Environment
You should install PaddlePaddle Fluid first, and run:
```
shell
pip
install
-r
requirements.txt
```
You should install PaddlePaddle Fluid first.
## Dataset
This example uses Criteo dataset which was used for the
[
Display Advertising
...
...
fluid/PaddleRec/word2vec/data/preprocess.py
0 → 100644
浏览文件 @
dee19f6e
# -*- coding: utf-8 -*
import
re
import
argparse
def
parse_args
():
parser
=
argparse
.
ArgumentParser
(
description
=
"Paddle Fluid word2 vector preprocess"
)
parser
.
add_argument
(
'--data_path'
,
type
=
str
,
required
=
True
,
help
=
"The path of training dataset"
)
parser
.
add_argument
(
'--dict_path'
,
type
=
str
,
default
=
'./dict'
,
help
=
"The path of generated dict"
)
parser
.
add_argument
(
'--freq'
,
type
=
int
,
default
=
5
,
help
=
"If the word count is less then freq, it will be removed from dict"
)
return
parser
.
parse_args
()
def
preprocess
(
data_path
,
dict_path
,
freq
):
"""
proprocess the data, generate dictionary and save into dict_path.
:param data_path: the input data path.
:param dict_path: the generated dict path. the data in dict is "word count"
:param freq:
:return:
"""
# word to count
word_count
=
dict
()
with
open
(
data_path
)
as
f
:
for
line
in
f
:
line
=
line
.
lower
()
line
=
re
.
sub
(
"[^0-9a-z ]"
,
""
,
line
)
words
=
line
.
split
()
for
item
in
words
:
if
item
in
word_count
:
word_count
[
item
]
=
word_count
[
item
]
+
1
else
:
word_count
[
item
]
=
1
item_to_remove
=
[]
for
item
in
word_count
:
if
word_count
[
item
]
<=
freq
:
item_to_remove
.
append
(
item
)
for
item
in
item_to_remove
:
del
word_count
[
item
]
with
open
(
dict_path
,
'w+'
)
as
f
:
for
k
,
v
in
word_count
.
items
():
f
.
write
(
str
(
k
)
+
" "
+
str
(
v
)
+
'
\n
'
)
if
__name__
==
"__main__"
:
args
=
parse_args
()
preprocess
(
args
.
data_path
,
args
.
dict_path
,
args
.
freq
)
fluid/PaddleRec/word2vec/reader.py
浏览文件 @
dee19f6e
...
...
@@ -6,13 +6,16 @@ import random
from
collections
import
Counter
"""
refs: https://github.com/NELSONZHAO/zhihu/blob/master/skip_gram/Skip-Gram-English-Corpus.ipynb
text8 dataset
http://mattmahoney.net/dc/textdata.html
"""
with
open
(
'data/text8.txt'
)
as
f
:
text
=
f
.
read
()
# 定义函数来完成数据的预处理
def
preprocess
(
text
,
freq
=
5
):
'''
对文本进行预处理
...
...
@@ -52,7 +55,6 @@ print(words[:20])
# 构建映射表
vocab
=
set
(
words
)
vocab_to_int
=
{
w
:
c
for
c
,
w
in
enumerate
(
vocab
)}
int_to_vocab
=
{
c
:
w
for
c
,
w
in
enumerate
(
vocab
)}
dict_size
=
len
(
set
(
words
))
...
...
fluid/PaddleRec/word2vec/requirements.txt
已删除
100644 → 0
浏览文件 @
b3943336
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