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96a2e44a
编写于
3月 06, 2017
作者:
Q
qiaolongfei
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
optimize seq2seq-dataset
上级
37806792
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
229 addition
and
258 deletion
+229
-258
demo/sentiment/preprocess.py
demo/sentiment/preprocess.py
+160
-6
python/paddle/v2/dataset/wmt14.py
python/paddle/v2/dataset/wmt14.py
+69
-80
python/paddle/v2/dataset/wmt14_util.py
python/paddle/v2/dataset/wmt14_util.py
+0
-172
未找到文件。
demo/sentiment/preprocess.py
浏览文件 @
96a2e44a
...
...
@@ -12,22 +12,176 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
os
import
sys
import
random
import
operator
import
numpy
as
np
from
subprocess
import
Popen
,
PIPE
from
os.path
import
join
as
join_path
from
optparse
import
OptionParser
from
os.path
import
join
as
join_path
from
subprocess
import
Popen
,
PIPE
import
numpy
as
np
from
paddle.utils.preprocess_util
import
*
from
paddle.utils.preprocess_util
import
save_list
,
DatasetCreater
"""
Usage: run following command to show help message.
python preprocess.py -h
"""
class
SeqToSeqDatasetCreater
(
DatasetCreater
):
"""
A class to process data for sequence to sequence application.
"""
def
__init__
(
self
,
data_path
,
output_path
):
"""
data_path: the path to store the train data, test data and gen data
output_path: the path to store the processed dataset
"""
DatasetCreater
.
__init__
(
self
,
data_path
)
self
.
gen_dir_name
=
'gen'
self
.
gen_list_name
=
'gen.list'
self
.
output_path
=
output_path
def
concat_file
(
self
,
file_path
,
file1
,
file2
,
output_path
,
output
):
"""
Concat file1 and file2 to be one output file
The i-th line of output = i-th line of file1 + '
\t
' + i-th line of file2
file_path: the path to store file1 and file2
output_path: the path to store output file
"""
file1
=
os
.
path
.
join
(
file_path
,
file1
)
file2
=
os
.
path
.
join
(
file_path
,
file2
)
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
'paste '
+
file1
+
' '
+
file2
+
' > '
+
output
)
def
cat_file
(
self
,
dir_path
,
suffix
,
output_path
,
output
):
"""
Cat all the files in dir_path with suffix to be one output file
dir_path: the base directory to store input file
suffix: suffix of file name
output_path: the path to store output file
"""
cmd
=
'cat '
file_list
=
os
.
listdir
(
dir_path
)
file_list
.
sort
()
for
file
in
file_list
:
if
file
.
endswith
(
suffix
):
cmd
+=
os
.
path
.
join
(
dir_path
,
file
)
+
' '
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
cmd
+
'> '
+
output
)
def
build_dict
(
self
,
file_path
,
dict_path
,
dict_size
=-
1
):
"""
Create the dictionary for the file, Note that
1. Valid characters include all printable characters
2. There is distinction between uppercase and lowercase letters
3. There is 3 special token:
<s>: the start of a sequence
<e>: the end of a sequence
<unk>: a word not included in dictionary
file_path: the path to store file
dict_path: the path to store dictionary
dict_size: word count of dictionary
if is -1, dictionary will contains all the words in file
"""
if
not
os
.
path
.
exists
(
dict_path
):
dictory
=
dict
()
with
open
(
file_path
,
"r"
)
as
fdata
:
for
line
in
fdata
:
line
=
line
.
split
(
'
\t
'
)
for
line_split
in
line
:
words
=
line_split
.
strip
().
split
()
for
word
in
words
:
if
word
not
in
dictory
:
dictory
[
word
]
=
1
else
:
dictory
[
word
]
+=
1
output
=
open
(
dict_path
,
"w+"
)
output
.
write
(
'<s>
\n
<e>
\n
<unk>
\n
'
)
count
=
3
for
key
,
value
in
sorted
(
dictory
.
items
(),
key
=
lambda
d
:
d
[
1
],
reverse
=
True
):
output
.
write
(
key
+
"
\n
"
)
count
+=
1
if
count
==
dict_size
:
break
self
.
dict_size
=
count
def
create_dataset
(
self
,
dict_size
=-
1
,
mergeDict
=
False
,
suffixes
=
[
'.src'
,
'.trg'
]):
"""
Create seqToseq dataset
"""
# dataset_list and dir_list has one-to-one relationship
train_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
train_dir_name
)
test_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
test_dir_name
)
gen_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
gen_dir_name
)
dataset_list
=
[
train_dataset
,
test_dataset
,
gen_dataset
]
train_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
train_dir_name
)
test_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
test_dir_name
)
gen_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
gen_dir_name
)
dir_list
=
[
train_dir
,
test_dir
,
gen_dir
]
# create directory
for
dir
in
dir_list
:
if
not
os
.
path
.
exists
(
dir
):
os
.
makedirs
(
dir
)
# checkout dataset should be parallel corpora
suffix_len
=
len
(
suffixes
[
0
])
for
dataset
in
dataset_list
:
file_list
=
os
.
listdir
(
dataset
)
if
len
(
file_list
)
%
2
==
1
:
raise
RuntimeError
(
"dataset should be parallel corpora"
)
file_list
.
sort
()
for
i
in
range
(
0
,
len
(
file_list
),
2
):
if
file_list
[
i
][:
-
suffix_len
]
!=
file_list
[
i
+
1
][:
-
suffix_len
]:
raise
RuntimeError
(
"source and target file name should be equal"
)
# cat all the files with the same suffix in dataset
for
suffix
in
suffixes
:
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
+
suffix
self
.
cat_file
(
dataset
,
suffix
,
dataset
,
outname
)
# concat parallel corpora and create file.list
print
'concat parallel corpora for dataset'
id
=
0
list
=
[
'train.list'
,
'test.list'
,
'gen.list'
]
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
self
.
concat_file
(
dataset
,
outname
+
suffixes
[
0
],
outname
+
suffixes
[
1
],
dir_list
[
id
],
outname
)
save_list
([
os
.
path
.
join
(
dir_list
[
id
],
outname
)],
os
.
path
.
join
(
self
.
output_path
,
list
[
id
]))
id
+=
1
# build dictionary for train data
dict
=
[
'src.dict'
,
'trg.dict'
]
dict_path
=
[
os
.
path
.
join
(
self
.
output_path
,
dict
[
0
]),
os
.
path
.
join
(
self
.
output_path
,
dict
[
1
])
]
if
mergeDict
:
outname
=
os
.
path
.
join
(
train_dir
,
train_dataset
.
split
(
'/'
)[
-
1
])
print
'build src dictionary for train data'
self
.
build_dict
(
outname
,
dict_path
[
0
],
dict_size
)
print
'build trg dictionary for train data'
os
.
system
(
'cp '
+
dict_path
[
0
]
+
' '
+
dict_path
[
1
])
else
:
outname
=
os
.
path
.
join
(
train_dataset
,
self
.
train_dir_name
)
for
id
in
range
(
0
,
2
):
suffix
=
suffixes
[
id
]
print
'build '
+
suffix
[
1
:]
+
' dictionary for train data'
self
.
build_dict
(
outname
+
suffix
,
dict_path
[
id
],
dict_size
)
print
'dictionary size is'
,
self
.
dict_size
def
save_dict
(
dict
,
filename
,
is_reverse
=
True
):
"""
Save dictionary into file.
...
...
python/paddle/v2/dataset/wmt14.py
浏览文件 @
96a2e44a
...
...
@@ -14,103 +14,92 @@
"""
wmt14 dataset
"""
import
os
import
os.path
import
tarfile
import
paddle.v2.dataset.common
from
wmt14_util
import
SeqToSeqDatasetCreater
__all__
=
[
'train'
,
'test'
,
'build_dict'
]
URL_DEV_TEST
=
'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
MD5_DEV_TEST
=
'7d7897317ddd8ba0ae5c5fa7248d3ff5'
# this is a small set of data for test. The original data is too large and will be add later.
URL_TRAIN
=
'http://
paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data
/wmt14.tgz'
MD5_TRAIN
=
'
7373473f86016f1f48037c9c340a2d5b
'
URL_TRAIN
=
'http://
localhost:8989
/wmt14.tgz'
MD5_TRAIN
=
'
a755315dd01c2c35bde29a744ede23a6
'
START
=
"<s>"
END
=
"<e>"
UNK
=
"<unk>"
UNK_IDX
=
2
DEFAULT_DATA_DIR
=
"./data"
ORIGIN_DATA_DIR
=
"wmt14"
INNER_DATA_DIR
=
"pre-wmt14"
SRC_DICT
=
INNER_DATA_DIR
+
"/src.dict"
TRG_DICT
=
INNER_DATA_DIR
+
"/trg.dict"
TRAIN_FILE
=
INNER_DATA_DIR
+
"/train/train"
def
__process_data__
(
data_path
,
dict_size
=
None
):
downloaded_data
=
os
.
path
.
join
(
data_path
,
ORIGIN_DATA_DIR
)
if
not
os
.
path
.
exists
(
downloaded_data
):
# 1. download and extract tgz.
with
tarfile
.
open
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
))
as
tf
:
tf
.
extractall
(
data_path
)
# 2. process data file to intermediate format.
processed_data
=
os
.
path
.
join
(
data_path
,
INNER_DATA_DIR
)
if
not
os
.
path
.
exists
(
processed_data
):
dict_size
=
dict_size
or
-
1
data_creator
=
SeqToSeqDatasetCreater
(
downloaded_data
,
processed_data
)
data_creator
.
create_dataset
(
dict_size
,
mergeDict
=
False
)
def
__read_to_dict__
(
dict_path
,
count
):
with
open
(
dict_path
,
"r"
)
as
fin
:
def
__read_to_dict__
(
tar_file
,
dict_size
):
def
__to_dict__
(
fd
,
size
):
out_dict
=
dict
()
for
line_count
,
line
in
enumerate
(
f
in
):
if
line_count
<
=
count
:
for
line_count
,
line
in
enumerate
(
f
d
):
if
line_count
<
size
:
out_dict
[
line
.
strip
()]
=
line_count
else
:
break
return
out_dict
def
__reader__
(
file_name
,
src_dict
,
trg_dict
):
with
open
(
file_name
,
'r'
)
as
f
:
for
line_count
,
line
in
enumerate
(
f
):
line_split
=
line
.
strip
().
split
(
'
\t
'
)
if
len
(
line_split
)
!=
2
:
continue
src_seq
=
line_split
[
0
]
# one source sequence
src_words
=
src_seq
.
split
()
src_ids
=
[
src_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
[
START
]
+
src_words
+
[
END
]
return
out_dict
with
tarfile
.
open
(
tar_file
,
mode
=
'r'
)
as
f
:
names
=
[
each_item
.
name
for
each_item
in
f
if
each_item
.
name
.
endswith
(
"src.dict"
)
]
assert
len
(
names
)
==
1
src_dict
=
__to_dict__
(
f
.
extractfile
(
names
[
0
]),
dict_size
)
names
=
[
each_item
.
name
for
each_item
in
f
if
each_item
.
name
.
endswith
(
"trg.dict"
)
]
assert
len
(
names
)
==
1
trg_dict
=
__to_dict__
(
f
.
extractfile
(
names
[
0
]),
dict_size
)
return
src_dict
,
trg_dict
def
reader_creator
(
tar_file
,
file_name
,
dict_size
):
def
reader
():
src_dict
,
trg_dict
=
__read_to_dict__
(
tar_file
,
dict_size
)
with
tarfile
.
open
(
tar_file
,
mode
=
'r'
)
as
f
:
names
=
[
each_item
.
name
for
each_item
in
f
if
each_item
.
name
.
endswith
(
file_name
)
]
trg_seq
=
line_split
[
1
]
# one target sequence
trg_words
=
trg_seq
.
split
()
trg_ids
=
[
trg_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
trg_words
]
# remove sequence whose length > 80 in training mode
if
len
(
src_ids
)
>
80
or
len
(
trg_ids
)
>
80
:
continue
trg_ids_next
=
trg_ids
+
[
trg_dict
[
END
]]
trg_ids
=
[
trg_dict
[
START
]]
+
trg_ids
yield
src_ids
,
trg_ids
,
trg_ids_next
def
train
(
data_dir
=
None
,
dict_size
=
None
):
data_dir
=
data_dir
or
DEFAULT_DATA_DIR
__process_data__
(
data_dir
,
dict_size
)
src_lang_dict
=
os
.
path
.
join
(
data_dir
,
SRC_DICT
)
trg_lang_dict
=
os
.
path
.
join
(
data_dir
,
TRG_DICT
)
train_file_name
=
os
.
path
.
join
(
data_dir
,
TRAIN_FILE
)
default_dict_size
=
len
(
open
(
src_lang_dict
,
"r"
).
readlines
())
if
dict_size
>
default_dict_size
:
raise
ValueError
(
"dict_dim should not be larger then the "
"length of word dict"
)
real_dict_dim
=
dict_size
or
default_dict_size
src_dict
=
__read_to_dict__
(
src_lang_dict
,
real_dict_dim
)
trg_dict
=
__read_to_dict__
(
trg_lang_dict
,
real_dict_dim
)
return
lambda
:
__reader__
(
train_file_name
,
src_dict
,
trg_dict
)
for
name
in
names
:
for
line
in
f
.
extractfile
(
name
):
line_split
=
line
.
strip
().
split
(
'
\t
'
)
if
len
(
line_split
)
!=
2
:
continue
src_seq
=
line_split
[
0
]
# one source sequence
src_words
=
src_seq
.
split
()
src_ids
=
[
src_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
[
START
]
+
src_words
+
[
END
]
]
trg_seq
=
line_split
[
1
]
# one target sequence
trg_words
=
trg_seq
.
split
()
trg_ids
=
[
trg_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
trg_words
]
# remove sequence whose length > 80 in training mode
if
len
(
src_ids
)
>
80
or
len
(
trg_ids
)
>
80
:
continue
trg_ids_next
=
trg_ids
+
[
trg_dict
[
END
]]
trg_ids
=
[
trg_dict
[
START
]]
+
trg_ids
yield
src_ids
,
trg_ids
,
trg_ids_next
return
reader
def
train
(
dict_size
):
return
reader_creator
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
),
'train/train'
,
dict_size
)
def
test
(
dict_size
):
return
reader_creator
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
),
'test/test'
,
dict_size
)
python/paddle/v2/dataset/wmt14_util.py
已删除
100644 → 0
浏览文件 @
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# Copyright (c) 2016 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.
import
os
from
paddle.utils.preprocess_util
import
save_list
,
DatasetCreater
class
SeqToSeqDatasetCreater
(
DatasetCreater
):
"""
A class to process data for sequence to sequence application.
"""
def
__init__
(
self
,
data_path
,
output_path
):
"""
data_path: the path to store the train data, test data and gen data
output_path: the path to store the processed dataset
"""
DatasetCreater
.
__init__
(
self
,
data_path
)
self
.
gen_dir_name
=
'gen'
self
.
gen_list_name
=
'gen.list'
self
.
output_path
=
output_path
def
concat_file
(
self
,
file_path
,
file1
,
file2
,
output_path
,
output
):
"""
Concat file1 and file2 to be one output file
The i-th line of output = i-th line of file1 + '
\t
' + i-th line of file2
file_path: the path to store file1 and file2
output_path: the path to store output file
"""
file1
=
os
.
path
.
join
(
file_path
,
file1
)
file2
=
os
.
path
.
join
(
file_path
,
file2
)
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
'paste '
+
file1
+
' '
+
file2
+
' > '
+
output
)
def
cat_file
(
self
,
dir_path
,
suffix
,
output_path
,
output
):
"""
Cat all the files in dir_path with suffix to be one output file
dir_path: the base directory to store input file
suffix: suffix of file name
output_path: the path to store output file
"""
cmd
=
'cat '
file_list
=
os
.
listdir
(
dir_path
)
file_list
.
sort
()
for
file
in
file_list
:
if
file
.
endswith
(
suffix
):
cmd
+=
os
.
path
.
join
(
dir_path
,
file
)
+
' '
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
cmd
+
'> '
+
output
)
def
build_dict
(
self
,
file_path
,
dict_path
,
dict_size
=-
1
):
"""
Create the dictionary for the file, Note that
1. Valid characters include all printable characters
2. There is distinction between uppercase and lowercase letters
3. There is 3 special token:
<s>: the start of a sequence
<e>: the end of a sequence
<unk>: a word not included in dictionary
file_path: the path to store file
dict_path: the path to store dictionary
dict_size: word count of dictionary
if is -1, dictionary will contains all the words in file
"""
if
not
os
.
path
.
exists
(
dict_path
):
dictory
=
dict
()
with
open
(
file_path
,
"r"
)
as
fdata
:
for
line
in
fdata
:
line
=
line
.
split
(
'
\t
'
)
for
line_split
in
line
:
words
=
line_split
.
strip
().
split
()
for
word
in
words
:
if
word
not
in
dictory
:
dictory
[
word
]
=
1
else
:
dictory
[
word
]
+=
1
output
=
open
(
dict_path
,
"w+"
)
output
.
write
(
'<s>
\n
<e>
\n
<unk>
\n
'
)
count
=
3
for
key
,
value
in
sorted
(
dictory
.
items
(),
key
=
lambda
d
:
d
[
1
],
reverse
=
True
):
output
.
write
(
key
+
"
\n
"
)
count
+=
1
if
count
==
dict_size
:
break
self
.
dict_size
=
count
def
create_dataset
(
self
,
dict_size
=-
1
,
mergeDict
=
False
,
suffixes
=
[
'.src'
,
'.trg'
]):
"""
Create seqToseq dataset
"""
# dataset_list and dir_list has one-to-one relationship
train_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
train_dir_name
)
test_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
test_dir_name
)
gen_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
gen_dir_name
)
dataset_list
=
[
train_dataset
,
test_dataset
,
gen_dataset
]
train_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
train_dir_name
)
test_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
test_dir_name
)
gen_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
gen_dir_name
)
dir_list
=
[
train_dir
,
test_dir
,
gen_dir
]
# create directory
for
dir
in
dir_list
:
if
not
os
.
path
.
exists
(
dir
):
os
.
makedirs
(
dir
)
# checkout dataset should be parallel corpora
suffix_len
=
len
(
suffixes
[
0
])
for
dataset
in
dataset_list
:
file_list
=
os
.
listdir
(
dataset
)
if
len
(
file_list
)
%
2
==
1
:
raise
RuntimeError
(
"dataset should be parallel corpora"
)
file_list
.
sort
()
for
i
in
range
(
0
,
len
(
file_list
),
2
):
if
file_list
[
i
][:
-
suffix_len
]
!=
file_list
[
i
+
1
][:
-
suffix_len
]:
raise
RuntimeError
(
"source and target file name should be equal"
)
# cat all the files with the same suffix in dataset
for
suffix
in
suffixes
:
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
+
suffix
self
.
cat_file
(
dataset
,
suffix
,
dataset
,
outname
)
# concat parallel corpora and create file.list
print
'concat parallel corpora for dataset'
id
=
0
list
=
[
'train.list'
,
'test.list'
,
'gen.list'
]
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
self
.
concat_file
(
dataset
,
outname
+
suffixes
[
0
],
outname
+
suffixes
[
1
],
dir_list
[
id
],
outname
)
save_list
([
os
.
path
.
join
(
dir_list
[
id
],
outname
)],
os
.
path
.
join
(
self
.
output_path
,
list
[
id
]))
id
+=
1
# build dictionary for train data
dict
=
[
'src.dict'
,
'trg.dict'
]
dict_path
=
[
os
.
path
.
join
(
self
.
output_path
,
dict
[
0
]),
os
.
path
.
join
(
self
.
output_path
,
dict
[
1
])
]
if
mergeDict
:
outname
=
os
.
path
.
join
(
train_dir
,
train_dataset
.
split
(
'/'
)[
-
1
])
print
'build src dictionary for train data'
self
.
build_dict
(
outname
,
dict_path
[
0
],
dict_size
)
print
'build trg dictionary for train data'
os
.
system
(
'cp '
+
dict_path
[
0
]
+
' '
+
dict_path
[
1
])
else
:
outname
=
os
.
path
.
join
(
train_dataset
,
self
.
train_dir_name
)
for
id
in
range
(
0
,
2
):
suffix
=
suffixes
[
id
]
print
'build '
+
suffix
[
1
:]
+
' dictionary for train data'
self
.
build_dict
(
outname
+
suffix
,
dict_path
[
id
],
dict_size
)
print
'dictionary size is'
,
self
.
dict_size
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