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04eaf75c
编写于
2月 19, 2017
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add getValue to some evaluators.
上级
39feacb0
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
198 addition
and
39 deletion
+198
-39
paddle/gserver/evaluators/Evaluator.cpp
paddle/gserver/evaluators/Evaluator.cpp
+113
-36
paddle/gserver/evaluators/Evaluator.h
paddle/gserver/evaluators/Evaluator.h
+41
-3
paddle/gserver/gradientmachines/NeuralNetwork.cpp
paddle/gserver/gradientmachines/NeuralNetwork.cpp
+38
-0
paddle/gserver/gradientmachines/NeuralNetwork.h
paddle/gserver/gradientmachines/NeuralNetwork.h
+4
-0
paddle/utils/Error.h
paddle/utils/Error.h
+2
-0
未找到文件。
paddle/gserver/evaluators/Evaluator.cpp
浏览文件 @
04eaf75c
...
...
@@ -538,12 +538,15 @@ double RankAucEvaluator::calcRankAuc(real* outputData,
:
aucTmp
/
(
clickSum
*
noClickSum
);
}
std
::
string
RankAucEvaluator
::
getTypeImpl
()
const
{
return
"rankauc"
;
}
// class PrecisionRecallEvaluator
REGISTER_EVALUATOR
(
precision_recall
,
PrecisionRecallEvaluator
);
void
PrecisionRecallEvaluator
::
start
()
{
Evaluator
::
start
();
statsInfo_
.
clear
();
values_
.
clear
();
}
real
PrecisionRecallEvaluator
::
evalImp
(
std
::
vector
<
Argument
>&
arguments
)
{
...
...
@@ -603,7 +606,9 @@ real PrecisionRecallEvaluator::evalImp(std::vector<Argument>& arguments) {
return
0
;
}
void
PrecisionRecallEvaluator
::
printStats
(
std
::
ostream
&
os
)
const
{
template
<
typename
T1
,
typename
T2
>
void
PrecisionRecallEvaluator
::
printStatsHelper
(
T1
labelCallback
,
T2
microAvgCallback
)
const
{
int
label
=
config_
.
positive_label
();
if
(
label
!=
-
1
)
{
CHECK
(
label
>=
0
&&
label
<
(
int
)
statsInfo_
.
size
())
...
...
@@ -612,9 +617,7 @@ void PrecisionRecallEvaluator::printStats(std::ostream& os) const {
double
precision
=
calcPrecision
(
statsInfo_
[
label
].
TP
,
statsInfo_
[
label
].
FP
);
double
recall
=
calcRecall
(
statsInfo_
[
label
].
TP
,
statsInfo_
[
label
].
FN
);
os
<<
"positive_label="
<<
label
<<
" precision="
<<
precision
<<
" recall="
<<
recall
<<
" F1-score="
<<
calcF1Score
(
precision
,
recall
);
labelCallback
(
label
,
precision
,
recall
,
calcF1Score
(
precision
,
recall
));
return
;
}
...
...
@@ -636,21 +639,45 @@ void PrecisionRecallEvaluator::printStats(std::ostream& os) const {
macroAvgPrecision
/=
numLabels
;
macroAvgRecall
/=
numLabels
;
double
macroAvgF1Score
=
calcF1Score
(
macroAvgPrecision
,
macroAvgRecall
);
os
<<
"macro-average-precision="
<<
macroAvgPrecision
<<
" macro-average-recall="
<<
macroAvgRecall
<<
" macro-average-F1-score="
<<
macroAvgF1Score
;
double
microAvgPrecision
=
calcPrecision
(
microTotalTP
,
microTotalFP
);
double
microAvgRecall
=
calcPrecision
(
microTotalTP
,
microTotalFN
);
double
microAvgF1Score
=
calcF1Score
(
microAvgPrecision
,
microAvgRecall
);
if
(
!
isMultiBinaryLabel_
)
{
// precision and recall are equal in this case
os
<<
" micro-average-precision="
<<
microAvgPrecision
;
}
else
{
os
<<
" micro-average-precision="
<<
microAvgPrecision
<<
" micro-average-recall="
<<
microAvgRecall
<<
" micro-average-F1-score="
<<
microAvgF1Score
;
}
microAvgCallback
(
macroAvgPrecision
,
macroAvgRecall
,
macroAvgF1Score
,
isMultiBinaryLabel_
,
microAvgPrecision
,
microAvgRecall
,
microAvgF1Score
);
}
void
PrecisionRecallEvaluator
::
printStats
(
std
::
ostream
&
os
)
const
{
this
->
printStatsHelper
(
[
&
os
](
int
label
,
double
precision
,
double
recall
,
double
f1
)
{
os
<<
"positive_label="
<<
label
<<
" precision="
<<
precision
<<
" recall="
<<
recall
<<
" F1-score="
<<
f1
;
},
[
&
os
](
double
macroAvgPrecision
,
double
macroAvgRecall
,
double
macroAvgF1Score
,
bool
isMultiBinaryLabel
,
double
microAvgPrecision
,
double
microAvgRecall
,
double
microAvgF1Score
)
{
os
<<
"macro-average-precision="
<<
macroAvgPrecision
<<
" macro-average-recall="
<<
macroAvgRecall
<<
" macro-average-F1-score="
<<
macroAvgF1Score
;
if
(
!
isMultiBinaryLabel
)
{
// precision and recall are equal in this case
os
<<
" micro-average-precision="
<<
microAvgPrecision
;
}
else
{
os
<<
" micro-average-precision="
<<
microAvgPrecision
<<
" micro-average-recall="
<<
microAvgRecall
<<
" micro-average-F1-score="
<<
microAvgF1Score
;
}
});
}
void
PrecisionRecallEvaluator
::
calcStatsInfo
(
const
MatrixPtr
&
output
,
...
...
@@ -731,6 +758,69 @@ void PrecisionRecallEvaluator::calcStatsInfoMulti(const MatrixPtr& output,
}
}
void
PrecisionRecallEvaluator
::
storeLocalValues
()
const
{
if
(
this
->
values_
.
size
()
==
0
)
{
this
->
printStatsHelper
(
[
this
](
int
label
,
double
precision
,
double
recall
,
double
f1
)
{
values_
[
"positive_label"
]
=
(
double
)
label
;
values_
[
"precision"
]
=
precision
;
values_
[
"recal"
]
=
recall
;
values_
[
"F1-score"
]
=
f1
;
},
[
this
](
double
macroAvgPrecision
,
double
macroAvgRecall
,
double
macroAvgF1Score
,
bool
isMultiBinaryLabel
,
double
microAvgPrecision
,
double
microAvgRecall
,
double
microAvgF1Score
)
{
values_
[
"macro-average-precision"
]
=
macroAvgPrecision
;
values_
[
"macro-average-recall"
]
=
macroAvgRecall
;
values_
[
"macro-average-F1-score"
]
=
macroAvgF1Score
;
if
(
!
isMultiBinaryLabel
)
{
// precision and recall are equal in this case
values_
[
"micro-average-precision"
]
=
microAvgPrecision
;
}
else
{
values_
[
"micro-average-precision"
]
=
microAvgPrecision
;
values_
[
"micro-average-recall"
]
=
microAvgRecall
;
values_
[
"micro-average-F1-score"
]
=
microAvgF1Score
;
}
});
}
}
void
PrecisionRecallEvaluator
::
getNames
(
std
::
vector
<
std
::
string
>*
names
)
{
this
->
storeLocalValues
();
names
->
clear
();
names
->
reserve
(
this
->
values_
.
size
());
for
(
auto
it
=
this
->
values_
.
begin
();
it
!=
this
->
values_
.
end
();
++
it
)
{
names
->
push_back
(
this
->
config_
.
name
()
+
"."
+
it
->
first
);
}
}
real
PrecisionRecallEvaluator
::
getValue
(
const
std
::
string
&
name
,
Error
*
err
)
const
{
this
->
storeLocalValues
();
auto
it
=
this
->
values_
.
find
(
name
);
if
(
it
!=
this
->
values_
.
end
()
&&
err
!=
nullptr
)
{
*
err
=
Error
(
"No such key %s"
,
name
.
c_str
());
return
.0
f
;
}
return
it
->
second
;
}
std
::
string
PrecisionRecallEvaluator
::
getType
(
const
std
::
string
&
name
,
Error
*
err
)
const
{
this
->
storeLocalValues
();
auto
it
=
this
->
values_
.
find
(
name
);
if
(
it
!=
this
->
values_
.
end
()
&&
err
!=
nullptr
)
{
*
err
=
Error
(
"No such key %s"
,
name
.
c_str
());
return
""
;
}
return
"precision_recall"
;
}
void
PrecisionRecallEvaluator
::
distributeEval
(
ParameterClient2
*
client
)
{
size_t
size
=
4
*
statsInfo_
.
size
();
double
*
buf
=
new
double
[
size
];
...
...
@@ -874,6 +964,8 @@ void PnpairEvaluator::calc(std::vector<PredictionResult>& predictArray) {
<<
" calc total special pair: "
<<
special
;
}
std
::
string
PnpairEvaluator
::
getTypeImpl
()
const
{
return
"pnpair"
;
}
ClassRegistrar
<
Evaluator
>
Evaluator
::
registrar_
;
Evaluator
*
Evaluator
::
create
(
const
EvaluatorConfig
&
config
)
{
Evaluator
*
evaluator
=
registrar_
.
createByType
(
config
.
type
());
...
...
@@ -901,27 +993,12 @@ public:
virtual
void
eval
(
const
NeuralNetwork
&
nn
)
{
for
(
const
std
::
string
&
name
:
config_
.
input_layers
())
{
const
Argument
&
argu
=
nn
.
getLayer
(
name
)
->
getOutput
();
if
(
argu
.
value
)
{
std
::
ostringstream
os
;
argu
.
value
->
print
(
os
);
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" value matrix:
\n
"
<<
os
.
str
();
}
if
(
argu
.
ids
)
{
std
::
ostringstream
os
;
argu
.
ids
->
print
(
os
,
argu
.
ids
->
getSize
());
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" ids vector:
\n
"
<<
os
.
str
();
}
if
(
auto
startPos
=
argu
.
sequenceStartPositions
)
{
std
::
ostringstream
os
;
startPos
->
getVector
(
false
)
->
print
(
os
,
startPos
->
getSize
());
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" sequence pos vector:
\n
"
<<
os
.
str
();
}
if
(
auto
subStartPos
=
argu
.
subSequenceStartPositions
)
{
std
::
ostringstream
os
;
subStartPos
->
getVector
(
false
)
->
print
(
os
,
subStartPos
->
getSize
());
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" sub-sequence pos vector:
\n
"
<<
os
.
str
();
std
::
vector
<
std
::
tuple
<
std
::
string
,
std
::
string
>>
out
;
auto
err
=
nn
.
getLayerOutputValue
(
name
,
&
out
);
err
.
check
();
for
(
auto
&
each
:
out
)
{
LOG
(
INFO
)
<<
"layer="
<<
name
<<
std
::
get
<
0
>
(
each
)
<<
":
\n
"
<<
std
::
get
<
1
>
(
each
);
}
}
}
...
...
paddle/gserver/evaluators/Evaluator.h
浏览文件 @
04eaf75c
...
...
@@ -132,6 +132,20 @@ public:
return
this
->
getValueImpl
();
}
virtual
std
::
string
getValueStr
(
const
std
::
string
&
name
,
paddle
::
Error
*
err
=
nullptr
)
const
{
paddle
::
Error
localErr
;
if
(
err
==
nullptr
)
{
err
=
&
localErr
;
}
real
result
=
this
->
getValue
(
name
,
err
);
if
(
!
err
->
isOK
())
{
return
""
;
}
else
{
return
std
::
to_string
(
result
);
}
}
virtual
std
::
string
getType
(
const
std
::
string
&
name
,
paddle
::
Error
*
err
=
nullptr
)
const
{
if
(
name
!=
config_
.
name
()
&&
err
!=
nullptr
)
{
...
...
@@ -142,7 +156,9 @@ public:
}
protected:
virtual
real
getValueImpl
()
const
{
return
.0
f
;
}
virtual
real
getValueImpl
()
const
{
return
numSamples_
!=
.0
?
totalScore_
/
numSamples_
:
.0
;
}
virtual
std
::
string
getTypeImpl
()
const
{
return
"base"
;
}
...
...
@@ -261,6 +277,10 @@ private:
real
*
clickData
,
real
*
pvData
,
size_t
size
);
// Evaluator interface
protected:
std
::
string
getTypeImpl
()
const
;
};
/**
* @brief precision, recall and f1 score Evaluator
...
...
@@ -310,6 +330,9 @@ private:
IVectorPtr
cpuLabel_
;
MatrixPtr
cpuWeight_
;
template
<
typename
T1
,
typename
T2
>
void
printStatsHelper
(
T1
labelCallback
,
T2
microAvgCallback
)
const
;
void
calcStatsInfo
(
const
MatrixPtr
&
output
,
const
IVectorPtr
&
label
,
const
MatrixPtr
&
weight
);
...
...
@@ -341,6 +364,15 @@ private:
return
0
;
}
}
mutable
std
::
unordered_map
<
std
::
string
,
real
>
values_
;
void
storeLocalValues
()
const
;
// Evaluator interface
public:
void
getNames
(
std
::
vector
<
std
::
string
>*
names
);
real
getValue
(
const
std
::
string
&
name
,
Error
*
err
)
const
;
std
::
string
getType
(
const
std
::
string
&
name
,
Error
*
err
)
const
;
};
/*
...
...
@@ -387,8 +419,7 @@ public:
virtual
void
finish
()
{
calc
(
predictArray_
);
}
virtual
void
printStats
(
std
::
ostream
&
os
)
const
{
os
<<
" pos/neg"
<<
"="
<<
pairArray_
[
0
]
/
((
pairArray_
[
1
]
<=
0
)
?
1.0
:
pairArray_
[
1
]);
os
<<
" pos/neg="
<<
this
->
getValueImpl
();
}
virtual
void
distributeEval
(
ParameterClient2
*
client
)
{
...
...
@@ -404,6 +435,13 @@ private:
IVectorPtr
cpuLabel_
;
IVectorPtr
cpuInfo_
;
MatrixPtr
cpuWeight_
;
// Evaluator interface
protected:
real
getValueImpl
()
const
{
return
pairArray_
[
0
]
/
((
pairArray_
[
1
]
<=
0
)
?
1.0
:
pairArray_
[
1
]);
}
std
::
string
getTypeImpl
()
const
;
};
}
// namespace paddle
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @
04eaf75c
...
...
@@ -405,4 +405,42 @@ NeuralNetwork* NeuralNetwork::newNeuralNetwork(const std::string& name,
}
}
Error
NeuralNetwork
::
getLayerOutputValue
(
const
std
::
string
&
layerName
,
std
::
vector
<
std
::
tuple
<
std
::
string
,
std
::
string
>>*
out
)
const
{
auto
&
layers
=
this
->
config_
.
layers
();
auto
it
=
std
::
find_if
(
layers
.
begin
(),
layers
.
end
(),
[
&
layerName
](
const
LayerConfig
&
conf
)
{
return
conf
.
name
()
==
layerName
;
});
if
(
it
==
layers
.
end
())
{
return
Error
(
"Cannot find layer %s"
,
layerName
.
c_str
());
}
auto
&
layer
=
this
->
getLayer
(
layerName
);
out
->
reserve
(
4
);
auto
&
argu
=
layer
->
getOutput
();
if
(
argu
.
value
)
{
std
::
ostringstream
os
;
argu
.
value
->
print
(
os
);
out
->
push_back
({
"value"
,
os
.
str
()});
}
if
(
argu
.
ids
)
{
std
::
ostringstream
os
;
argu
.
ids
->
print
(
os
,
argu
.
ids
->
getSize
());
out
->
push_back
({
"ids"
,
os
.
str
()});
}
if
(
auto
startPos
=
argu
.
sequenceStartPositions
)
{
std
::
ostringstream
os
;
startPos
->
getVector
(
false
)
->
print
(
os
,
startPos
->
getSize
());
out
->
push_back
({
"sequence pos"
,
os
.
str
()});
}
if
(
auto
subStartPos
=
argu
.
subSequenceStartPositions
)
{
std
::
ostringstream
os
;
subStartPos
->
getVector
(
false
)
->
print
(
os
,
subStartPos
->
getSize
());
out
->
push_back
({
"sub-sequence pos"
,
os
.
str
()});
}
return
Error
();
}
}
// namespace paddle
paddle/gserver/gradientmachines/NeuralNetwork.h
浏览文件 @
04eaf75c
...
...
@@ -128,6 +128,10 @@ public:
static
NeuralNetwork
*
newNeuralNetwork
(
const
std
::
string
&
name
=
""
,
NeuralNetwork
*
rootNetwork
=
nullptr
);
inline
Error
__must_check
getLayerOutputValue
(
const
std
::
string
&
layerName
,
std
::
vector
<
std
::
tuple
<
std
::
string
,
std
::
string
>>*
out
)
const
;
protected:
/**
* The constructor of NeuralNetwork.
...
...
paddle/utils/Error.h
浏览文件 @
04eaf75c
...
...
@@ -116,6 +116,8 @@ public:
*/
operator
bool
()
const
{
return
msg_
==
nullptr
;
}
bool
isOK
()
const
{
return
*
this
;
}
/**
* @brief check this status by glog.
* @note It is a temp method used during cleaning Paddle code. It will be
...
...
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