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体验新版 GitCode,发现更多精彩内容 >>
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c4d61454
编写于
9月 06, 2018
作者:
Z
Zeng Jinle
提交者:
GitHub
9月 06, 2018
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差异文件
Merge pull request #1230 from kuke/update_eval
Upload eval for douban data
上级
4aa817ec
17b3f6cb
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
114 addition
and
8 deletion
+114
-8
fluid/deep_attention_matching_net/douban/test.sh
fluid/deep_attention_matching_net/douban/test.sh
+3
-2
fluid/deep_attention_matching_net/douban/train.sh
fluid/deep_attention_matching_net/douban/train.sh
+2
-0
fluid/deep_attention_matching_net/model.py
fluid/deep_attention_matching_net/model.py
+1
-1
fluid/deep_attention_matching_net/test_and_evaluate.py
fluid/deep_attention_matching_net/test_and_evaluate.py
+9
-1
fluid/deep_attention_matching_net/train_and_evaluate.py
fluid/deep_attention_matching_net/train_and_evaluate.py
+13
-4
fluid/deep_attention_matching_net/ubuntu/train.sh
fluid/deep_attention_matching_net/ubuntu/train.sh
+1
-0
fluid/deep_attention_matching_net/utils/douban_evaluation.py
fluid/deep_attention_matching_net/utils/douban_evaluation.py
+85
-0
未找到文件。
fluid/deep_attention_matching_net/douban/test.sh
浏览文件 @
c4d61454
export
CUDA_VISIBLE_DEVICES
=
0,1,2,3
python
-u
test_and_evaluate.py
--use_cuda
\
python
-u
../test_and_evaluate.py
--use_cuda
\
--ext_eval
\
--data_path
./data/data.pkl
\
--save_path
./
\
--save_path
./
eval_10000
\
--model_path
models/step_10000
\
--batch_size
100
\
--vocab_size
172130
\
...
...
fluid/deep_attention_matching_net/douban/train.sh
浏览文件 @
c4d61454
export
CUDA_VISIBLE_DEVICES
=
0,1,2,3
python
-u
../train_and_evaluate.py
--use_cuda
\
--data_path
./data/data.pkl
\
--ext_eval
\
--word_emb_init
./data/word_embedding.pkl
\
--save_path
./models
\
--batch_size
100
\
--vocab_size
172130
\
...
...
fluid/deep_attention_matching_net/model.py
浏览文件 @
c4d61454
...
...
@@ -131,6 +131,6 @@ class Net(object):
sim
=
fluid
.
layers
.
concat
(
input
=
sim_turns
,
axis
=
2
)
# for douban
final_info
=
layers
.
cnn_3d
(
sim
,
16
,
16
)
final_info
=
layers
.
cnn_3d
(
sim
,
32
,
16
)
loss
,
logits
=
layers
.
loss
(
final_info
,
label
)
return
loss
,
logits
fluid/deep_attention_matching_net/test_and_evaluate.py
浏览文件 @
c4d61454
...
...
@@ -8,7 +8,6 @@ import paddle.fluid as fluid
import
utils.reader
as
reader
import
cPickle
as
pickle
from
utils.util
import
print_arguments
import
utils.evaluation
as
eva
from
model
import
Net
...
...
@@ -50,6 +49,10 @@ def parse_args():
'--use_cuda'
,
action
=
'store_true'
,
help
=
'If set, use cuda for training.'
)
parser
.
add_argument
(
'--ext_eval'
,
action
=
'store_true'
,
help
=
'If set, use MAP, MRR ect for evaluation.'
)
parser
.
add_argument
(
'--max_turn_num'
,
type
=
int
,
...
...
@@ -147,6 +150,11 @@ def test(args):
train_data
,
val_data
,
test_data
=
pickle
.
load
(
open
(
args
.
data_path
,
'rb'
))
print
(
"finish loading data ..."
)
if
args
.
ext_eval
:
import
utils.douban_evaluation
as
eva
else
:
import
utils.evaluation
as
eva
test_batches
=
reader
.
build_batches
(
test_data
,
data_conf
)
test_batch_num
=
len
(
test_batches
[
"response"
])
...
...
fluid/deep_attention_matching_net/train_and_evaluate.py
浏览文件 @
c4d61454
...
...
@@ -8,7 +8,6 @@ import paddle.fluid as fluid
import
utils.reader
as
reader
import
cPickle
as
pickle
from
utils.util
import
print_arguments
import
utils.evaluation
as
eva
from
model
import
Net
...
...
@@ -34,7 +33,7 @@ def parse_args():
parser
.
add_argument
(
'--data_path'
,
type
=
str
,
default
=
"data/
ubuntu/
data_small.pkl"
,
default
=
"data/data_small.pkl"
,
help
=
'Path to training data. (default: %(default)s)'
)
parser
.
add_argument
(
'--save_path'
,
...
...
@@ -45,6 +44,10 @@ def parse_args():
'--use_cuda'
,
action
=
'store_true'
,
help
=
'If set, use cuda for training.'
)
parser
.
add_argument
(
'--ext_eval'
,
action
=
'store_true'
,
help
=
'If set, use MAP, MRR ect for evaluation.'
)
parser
.
add_argument
(
'--max_turn_num'
,
type
=
int
,
...
...
@@ -74,7 +77,7 @@ def parse_args():
'--_EOS_'
,
type
=
int
,
default
=
28270
,
help
=
'The id for end of sentence in vocabulary.'
)
help
=
'The id for
the
end of sentence in vocabulary.'
)
parser
.
add_argument
(
'--stack_num'
,
type
=
int
,
...
...
@@ -140,9 +143,15 @@ def train(args):
main_program
=
test_program
,
share_vars_from
=
train_exe
)
if
args
.
ext_eval
:
import
utils.douban_evaluation
as
eva
else
:
import
utils.evaluation
as
eva
if
args
.
word_emb_init
is
not
None
:
print
(
"start loading word embedding init ..."
)
word_emb
=
pickle
.
load
(
open
(
args
.
word_emb_init
,
'rb'
)).
astype
(
'float32'
)
word_emb
=
np
.
array
(
pickle
.
load
(
open
(
args
.
word_emb_init
,
'rb'
))).
astype
(
'float32'
)
print
(
"finish loading word embedding init ..."
)
print
(
"start loading data ..."
)
...
...
fluid/deep_attention_matching_net/ubuntu/train.sh
浏览文件 @
c4d61454
export
CUDA_VISIBLE_DEVICES
=
0,1,2,3
python
-u
../train_and_evaluate.py
--use_cuda
\
--data_path
./data/data.pkl
\
--word_emb_init
./data/word_embedding.pkl
\
--save_path
./models
\
--batch_size
100
\
--vocab_size
434512
\
...
...
fluid/deep_attention_matching_net/utils/douban_evaluation.py
0 → 100644
浏览文件 @
c4d61454
import
sys
import
numpy
as
np
from
sklearn.metrics
import
average_precision_score
def
mean_average_precision
(
sort_data
):
#to do
count_1
=
0
sum_precision
=
0
for
index
in
range
(
len
(
sort_data
)):
if
sort_data
[
index
][
1
]
==
1
:
count_1
+=
1
sum_precision
+=
1.0
*
count_1
/
(
index
+
1
)
return
sum_precision
/
count_1
def
mean_reciprocal_rank
(
sort_data
):
sort_lable
=
[
s_d
[
1
]
for
s_d
in
sort_data
]
assert
1
in
sort_lable
return
1.0
/
(
1
+
sort_lable
.
index
(
1
))
def
precision_at_position_1
(
sort_data
):
if
sort_data
[
0
][
1
]
==
1
:
return
1
else
:
return
0
def
recall_at_position_k_in_10
(
sort_data
,
k
):
sort_lable
=
[
s_d
[
1
]
for
s_d
in
sort_data
]
select_lable
=
sort_lable
[:
k
]
return
1.0
*
select_lable
.
count
(
1
)
/
sort_lable
.
count
(
1
)
def
evaluation_one_session
(
data
):
sort_data
=
sorted
(
data
,
key
=
lambda
x
:
x
[
0
],
reverse
=
True
)
m_a_p
=
mean_average_precision
(
sort_data
)
m_r_r
=
mean_reciprocal_rank
(
sort_data
)
p_1
=
precision_at_position_1
(
sort_data
)
r_1
=
recall_at_position_k_in_10
(
sort_data
,
1
)
r_2
=
recall_at_position_k_in_10
(
sort_data
,
2
)
r_5
=
recall_at_position_k_in_10
(
sort_data
,
5
)
return
m_a_p
,
m_r_r
,
p_1
,
r_1
,
r_2
,
r_5
def
evaluate
(
file_path
):
sum_m_a_p
=
0
sum_m_r_r
=
0
sum_p_1
=
0
sum_r_1
=
0
sum_r_2
=
0
sum_r_5
=
0
i
=
0
total_num
=
0
with
open
(
file_path
,
'r'
)
as
infile
:
for
line
in
infile
:
if
i
%
10
==
0
:
data
=
[]
tokens
=
line
.
strip
().
split
(
'
\t
'
)
data
.
append
((
float
(
tokens
[
0
]),
int
(
tokens
[
1
])))
if
i
%
10
==
9
:
total_num
+=
1
m_a_p
,
m_r_r
,
p_1
,
r_1
,
r_2
,
r_5
=
evaluation_one_session
(
data
)
sum_m_a_p
+=
m_a_p
sum_m_r_r
+=
m_r_r
sum_p_1
+=
p_1
sum_r_1
+=
r_1
sum_r_2
+=
r_2
sum_r_5
+=
r_5
i
+=
1
#print('total num: %s' %total_num)
#print('MAP: %s' %(1.0*sum_m_a_p/total_num))
#print('MRR: %s' %(1.0*sum_m_r_r/total_num))
#print('P@1: %s' %(1.0*sum_p_1/total_num))
return
(
1.0
*
sum_m_a_p
/
total_num
,
1.0
*
sum_m_r_r
/
total_num
,
1.0
*
sum_p_1
/
total_num
,
1.0
*
sum_r_1
/
total_num
,
1.0
*
sum_r_2
/
total_num
,
1.0
*
sum_r_5
/
total_num
)
if
__name__
==
'__main__'
:
result
=
evaluate
(
sys
.
argv
[
1
])
for
r
in
result
:
print
(
r
)
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