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ffa9fb04
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体验新版 GitCode,发现更多精彩内容 >>
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ffa9fb04
编写于
8月 02, 2020
作者:
X
Xiaoguang Li
提交者:
lixiaoguang
8月 02, 2020
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add lhs sampling into tuning module
上级
a230d0bd
变更
2
隐藏空白更改
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并排
Showing
2 changed file
with
221 addition
and
0 deletion
+221
-0
analysis/optimizer/knob_sampling_manager.py
analysis/optimizer/knob_sampling_manager.py
+215
-0
analysis/optimizer/optimizer.py
analysis/optimizer/optimizer.py
+6
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未找到文件。
analysis/optimizer/knob_sampling_manager.py
0 → 100644
浏览文件 @
ffa9fb04
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# Copyright (c) 2019 Huawei Technologies Co., Ltd.
# A-Tune is licensed under the Mulan PSL v2.
# You can use this software according to the terms and conditions of the Mulan PSL v2.
# You may obtain a copy of Mulan PSL v2 at:
# http://license.coscl.org.cn/MulanPSL2
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR
# PURPOSE.
# See the Mulan PSL v2 for more details.
# Create: 2020-07-30
"""
This class is used to perform lhs(Latin hypercube sampling), to get 'balanced' sampling configuration and its performance
"""
import
logging
import
numpy
as
np
import
lhsmdu
import
sys
import
os
LOGGER
=
logging
.
getLogger
(
__name__
)
class
KnobSampling
(
object
):
"""knob sampling"""
def
__init__
(
self
,
p_nob
,
split_count
=
5
):
option_range
=
[]
if
p_nob
[
'dtype'
]
==
'string'
:
option_range
.
extend
(
p_nob
[
'options'
])
ref_val
=
str
(
p_nob
[
'ref'
])
for
i
in
range
(
len
(
option_range
)):
if
option_range
[
i
]
==
ref_val
and
i
!=
0
:
option_range
[
i
]
=
option_range
[
0
]
option_range
[
0
]
=
ref_val
elif
p_nob
[
'dtype'
]
==
'int'
or
p_nob
[
'dtype'
]
==
'float'
:
items
=
p_nob
[
'items'
]
if
items
is
not
None
:
for
item
in
items
:
option_range
.
append
(
str
(
item
))
step
=
1
if
p_nob
[
'range'
]
is
not
None
:
if
'step'
in
p_nob
.
keys
():
if
p_nob
[
'dtype'
]
==
'int'
:
step
=
int
((
p_nob
[
'range'
][
1
]
-
p_nob
[
'range'
][
0
])
/
split_count
)
elif
p_nob
[
'dtype'
]
==
'float'
:
step
=
float
((
p_nob
[
'range'
][
1
]
-
p_nob
[
'range'
][
0
])
/
split_count
)
item_val
=
p_nob
[
'range'
][
0
]
for
i
in
range
(
split_count
):
option_range
.
append
(
str
(
item_val
))
item_val
+=
step
if
str
(
p_nob
[
'ref'
])
not
in
option_range
:
option_range
.
append
(
str
(
p_nob
[
'ref'
]))
self
.
option_range
=
option_range
class
KnobSamplingManager
(
object
):
"""knob sample manager"""
def
__init__
(
self
,
knobs
,
child_conn
,
sample_count
,
split_count
,
algorithm
=
'lhs'
):
option_range_list
=
[]
name_list
=
[]
for
p_nob
in
knobs
:
knob_sampling
=
KnobSampling
(
p_nob
,
split_count
)
option_range_list
.
append
(
knob_sampling
.
option_range
)
name_list
.
append
(
p_nob
[
'name'
])
self
.
_option_range_list
=
option_range_list
self
.
_knobs
=
knobs
self
.
_name_list
=
name_list
self
.
_child_conn
=
child_conn
self
.
_sample_count
=
sample_count
self
.
_algorithm
=
algorithm
self
.
_is_discrete
=
[]
self
.
_value_count
=
[]
self
.
_value_min
=
[]
for
i
in
range
(
len
(
self
.
_option_range_list
)):
option_range
=
self
.
_option_range_list
[
i
]
if
isinstance
(
option_range
,
list
):
self
.
_is_discrete
.
append
(
True
)
self
.
_value_count
.
append
(
float
(
len
(
option_range
)))
self
.
_value_min
.
append
(
float
(
0
))
else
:
assert
(
isinstance
(
option_range
,
tuple
))
self
.
_is_discrete
.
append
(
False
)
self
.
_value_count
.
append
(
float
(
option_range
[
1
]
-
option_range
[
0
]))
self
.
_value_min
=
float
(
option_range
[
0
])
def
get_rate_samples
(
self
):
"""
Note: return type is matrix, access with rates[i, j] NOT rates[i][j]
"""
if
self
.
_algorithm
==
'lhs'
:
rates
=
lhsmdu
.
sample
(
len
(
self
.
_option_range_list
),
self
.
_sample_count
)
return
rates
elif
self
.
_algorithm
==
'mcs'
:
rates
=
lhsmdu
.
createRandomStandardUniformMatrix
(
\
len
(
self
.
_option_range_list
),
self
.
_sample_count
)
return
rates
else
:
rates
=
lhsmdu
.
sample
(
len
(
self
.
_option_range_list
),
self
.
_sample_count
)
return
rates
def
get_sample_from_rate
(
self
,
dim
,
rate
):
"""return the sample depend on rate"""
assert
(
dim
<
len
(
self
.
_option_range_list
))
if
self
.
_is_discrete
[
dim
]
==
True
:
index
=
int
(
self
.
_value_count
[
dim
]
*
rate
)
return
self
.
_option_range_list
[
dim
][
index
]
else
:
return
(
self
.
_value_min
[
dim
]
+
self
.
_value_count
[
dim
]
*
rate
)
def
get_knob_samples
(
self
):
"""get knob samples"""
rates
=
self
.
get_rate_samples
()
LOGGER
.
info
(
'Get lhs rates:%s'
,
rates
)
knob_samples
=
[]
for
i
in
range
(
self
.
_sample_count
):
knob_sample
=
[]
for
dim
in
range
(
len
(
self
.
_option_range_list
)):
rate
=
rates
[
dim
,
i
]
# Be carefull
sample
=
self
.
get_sample_from_rate
(
dim
,
rate
)
knob_sample
.
append
(
sample
)
knob_samples
.
append
(
knob_sample
)
LOGGER
.
info
(
'Get lhs samples: %s'
,
knob_samples
)
return
knob_samples
def
get_knob_samples_horizontal
(
self
):
"""get knob samples in horizontal"""
rates
=
self
.
get_rate_samples
()
LOGGER
.
info
(
rates
)
knob_samples
=
[]
for
dim
in
range
(
len
(
self
.
_option_range_list
)):
knob_sample
=
[]
for
i
in
range
(
self
.
_sample_count
):
rate
=
rates
[
dim
,
i
]
# Be carefull
sample
=
self
.
get_sample_from_rate
(
dim
,
rate
)
knob_sample
.
append
(
sample
)
knob_samples
.
append
(
knob_sample
)
return
knob_samples
def
construct_one_knob_sample
(
self
,
knob_samples
,
index
):
"""construct one knob sample"""
return
knob_samples
[
index
]
def
test_performance_one_knob_sample
(
self
,
knob_samples
,
index
):
"""test performance of one knob sample"""
set_knob_val_vec
=
self
.
construct_one_knob_sample
(
knob_samples
,
index
)
iterResult
=
{}
params
=
{}
for
i
in
range
(
len
(
set_knob_val_vec
)):
knob_val
=
set_knob_val_vec
[
i
]
knob_name
=
self
.
_name_list
[
i
]
if
self
.
_knobs
[
i
][
'dtype'
]
==
'int'
:
params
[
knob_name
]
=
int
(
knob_val
)
elif
self
.
_knobs
[
i
][
'dtype'
]
==
'float'
:
params
[
knob_name
]
=
float
(
knob_val
)
elif
self
.
_knobs
[
i
][
'dtype'
]
==
'string'
:
params
[
knob_name
]
=
knob_val
iterResult
[
"param"
]
=
params
self
.
_child_conn
.
send
(
iterResult
)
result
=
self
.
_child_conn
.
recv
()
x_num
=
0.0
eval_list
=
result
.
split
(
','
)
for
value
in
eval_list
:
num
=
float
(
value
)
x_num
=
x_num
+
num
performance
=
x_num
LOGGER
.
info
(
'knob sample: %s, result: %s'
,
set_knob_val_vec
,
performance
)
return
performance
def
do_knob_sampling_test
(
self
,
knob_samples
):
"""test knob sampling"""
results
=
[]
for
index
in
range
(
self
.
_sample_count
):
result
=
self
.
test_performance_one_knob_sample
(
knob_samples
,
index
)
results
.
append
(
result
)
return
results
def
get_best_params
(
self
,
knob_samples
,
results
):
"""get best_params"""
np_results
=
np
.
array
(
results
)
best_index
=
np
.
argmin
(
np_results
)
set_knob_val_vec
=
self
.
construct_one_knob_sample
(
knob_samples
,
best_index
)
params
=
{}
for
i
in
range
(
len
(
set_knob_val_vec
)):
knob_val
=
set_knob_val_vec
[
i
]
knob_name
=
self
.
_name_list
[
i
]
if
self
.
_knobs
[
i
][
'dtype'
]
==
'int'
:
params
[
knob_name
]
=
int
(
knob_val
)
elif
self
.
_knobs
[
i
][
'dtype'
]
==
'float'
:
params
[
knob_name
]
=
float
(
knob_val
)
elif
self
.
_knobs
[
i
][
'dtype'
]
==
'string'
:
params
[
knob_name
]
=
knob_val
return
params
def
get_option_index
(
self
,
option
):
"""return the index of the option"""
option_range_list
=
self
.
_option_range_list
option_index
=
[]
for
i
in
range
(
len
(
option
)):
val
=
option
[
i
]
index
=
option_range_list
[
i
].
index
(
val
)
option_index
.
append
(
index
)
return
option_index
def
get_options_index
(
self
,
options
):
"""return the options's index"""
options_index
=
[]
for
option
in
options
:
option_index
=
self
.
get_option_index
(
option
)
options_index
.
append
(
option_index
)
return
options_index
analysis/optimizer/optimizer.py
浏览文件 @
ffa9fb04
...
...
@@ -24,6 +24,7 @@ from sklearn.linear_model import Lasso
from
sklearn.preprocessing
import
StandardScaler
from
analysis.optimizer.abtest_tuning_manager
import
ABtestTuningManager
from
analysis.optimizer.knob_sampling_manager
import
KnobSamplingManager
LOGGER
=
logging
.
getLogger
(
__name__
)
...
...
@@ -217,6 +218,11 @@ class Optimizer(multiprocessing.Process):
options
,
performance
=
abtuning_manager
.
do_abtest_tuning_abtest
()
params
=
abtuning_manager
.
get_best_params
()
options
=
abtuning_manager
.
get_options_index
(
options
)
# convert string option into index
elif
self
.
engine
==
'lhs'
:
knobsampling_manager
=
KnobSamplingManager
(
self
.
knobs
,
self
.
child_conn
,
self
.
max_eval
,
self
.
split_count
)
options
=
knobsampling_manager
.
get_knob_samples
()
performance
=
knobsampling_manager
.
do_knob_sampling_test
(
options
)
params
=
knobsampling_manager
.
get_best_params
(
options
,
performance
)
LOGGER
.
info
(
"Minimization procedure has been completed."
)
except
ValueError
as
value_error
:
LOGGER
.
error
(
'Value Error: %s'
,
repr
(
value_error
))
...
...
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