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24d988ce
编写于
10月 27, 2017
作者:
武
武毅
提交者:
GitHub
10月 27, 2017
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Merge pull request #4063 from typhoonzero/auc_op
Auc op
上级
2000cafe
63309941
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3
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3 changed file
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paddle/operators/auc_op.cc
paddle/operators/auc_op.cc
+85
-0
paddle/operators/auc_op.h
paddle/operators/auc_op.h
+135
-0
python/paddle/v2/framework/tests/test_auc_op.py
python/paddle/v2/framework/tests/test_auc_op.py
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paddle/operators/auc_op.cc
0 → 100644
浏览文件 @
24d988ce
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include "paddle/operators/auc_op.h"
namespace
paddle
{
namespace
operators
{
class
AucOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Inference"
),
"Input of Inference must be initialized."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input of Label must be initialized."
);
auto
inference_dim
=
ctx
->
GetInputDim
(
"Inference"
);
auto
label_dim
=
ctx
->
GetInputDim
(
"Label"
);
PADDLE_ENFORCE_EQ
(
inference_dim
,
label_dim
,
"inference and label should have same shape"
);
ctx
->
SetOutputDim
(
"AUC"
,
{
1
});
ctx
->
ShareLoD
(
"Inference"
,
/*->*/
"AUC"
);
}
};
class
AucOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
AucOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Inference"
,
"A floating point tensor of arbitrary shape and whose values"
"are in the range [0, 1]."
);
AddInput
(
"Label"
,
"A tensor whose shape matches "
"Inference. Will be cast to bool."
);
// TODO(typhoonzero): support weight input
AddOutput
(
"AUC"
,
"A scalar representing the "
"current area-under-curve."
);
AddAttr
<
std
::
string
>
(
"curve"
,
"Curve type, can be 'ROC' or 'PR'."
)
.
SetDefault
(
"ROC"
);
AddAttr
<
int
>
(
"num_thresholds"
,
"The number of thresholds to use when discretizing the"
" roc curve."
)
.
SetDefault
(
200
);
AddComment
(
R"DOC(Computes the AUC according forward output and label.
Best to use for binary classification evaluations.
If input label contains values other than 0 and 1, it will be cast
to bool.
You can find the definations here:
https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
Possible curves are:
- ROC: Receiver operating characteristic
- PR: Precision Recall
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_WITHOUT_GRADIENT
(
auc
,
ops
::
AucOp
,
ops
::
AucOpMaker
);
REGISTER_OP_CPU_KERNEL
(
auc
,
ops
::
AucKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/auc_op.h
0 → 100644
浏览文件 @
24d988ce
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
Place
,
typename
T
>
class
AucKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
inference
=
ctx
.
Input
<
Tensor
>
(
"Inference"
);
auto
*
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
auto
*
auc
=
ctx
.
Output
<
Tensor
>
(
"AUC"
);
float
*
auc_data
=
auc
->
mutable_data
<
float
>
(
ctx
.
GetPlace
());
std
::
string
curve
=
ctx
.
Attr
<
std
::
string
>
(
"curve"
);
int
num_thresholds
=
ctx
.
Attr
<
int
>
(
"num_thresholds"
);
std
::
vector
<
float
>
thresholds_list
;
thresholds_list
.
reserve
(
num_thresholds
);
for
(
int
i
=
1
;
i
<
num_thresholds
-
1
;
i
++
)
{
thresholds_list
[
i
]
=
(
float
)
i
/
(
num_thresholds
-
1
);
}
const
float
kEpsilon
=
1e-7
;
thresholds_list
[
0
]
=
0.0
f
-
kEpsilon
;
thresholds_list
[
num_thresholds
-
1
]
=
1.0
f
+
kEpsilon
;
size_t
num_samples
=
inference
->
numel
();
const
T
*
inference_data
=
inference
->
data
<
T
>
();
Tensor
label_casted
;
label_casted
.
Resize
(
label
->
dims
());
bool
*
label_casted_data
=
label_casted
.
mutable_data
<
bool
>
(
ctx
.
GetPlace
());
const
int
*
label_data
=
label
->
data
<
int
>
();
// cast label_data to bool
for
(
size_t
i
=
0
;
i
<
num_samples
;
i
++
)
{
label_casted_data
[
i
]
=
static_cast
<
bool
>
(
label_data
[
i
]);
}
// Create local tensor for storing the curve: TP, FN, TN, FP
// TODO(typhoonzero): use eigen op to caculate these values.
Tensor
true_positive
,
false_positive
,
true_negative
,
false_negative
;
true_positive
.
Resize
({
num_thresholds
});
false_negative
.
Resize
({
num_thresholds
});
true_negative
.
Resize
({
num_thresholds
});
false_positive
.
Resize
({
num_thresholds
});
int
*
tp_data
=
true_positive
.
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
fn_data
=
false_negative
.
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
tn_data
=
true_negative
.
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
fp_data
=
false_positive
.
mutable_data
<
int
>
(
ctx
.
GetPlace
());
for
(
int
idx_thresh
=
0
;
idx_thresh
<
num_thresholds
;
idx_thresh
++
)
{
// caculate TP, FN, TN, FP for current thresh
int
tp
=
0
,
fn
=
0
,
tn
=
0
,
fp
=
0
;
for
(
size_t
i
=
0
;
i
<
num_samples
;
i
++
)
{
if
(
label_casted_data
[
i
])
{
if
(
inference_data
[
i
]
>=
(
thresholds_list
[
idx_thresh
]))
{
tp
++
;
}
else
{
fn
++
;
}
}
else
{
if
(
inference_data
[
i
]
>=
(
thresholds_list
[
idx_thresh
]))
{
fp
++
;
}
else
{
tn
++
;
}
}
}
// store rates
tp_data
[
idx_thresh
]
=
tp
;
fn_data
[
idx_thresh
]
=
fn
;
tn_data
[
idx_thresh
]
=
tn
;
fp_data
[
idx_thresh
]
=
fp
;
}
// epsilon to avoid divide by zero.
float
epsilon
=
1e-6
;
// Riemann sum to caculate auc.
Tensor
tp_rate
,
fp_rate
,
rec_rate
;
tp_rate
.
Resize
({
num_thresholds
});
fp_rate
.
Resize
({
num_thresholds
});
rec_rate
.
Resize
({
num_thresholds
});
float
*
tp_rate_data
=
tp_rate
.
mutable_data
<
float
>
(
ctx
.
GetPlace
());
float
*
fp_rate_data
=
fp_rate
.
mutable_data
<
float
>
(
ctx
.
GetPlace
());
float
*
rec_rate_data
=
rec_rate
.
mutable_data
<
float
>
(
ctx
.
GetPlace
());
for
(
int
i
=
0
;
i
<
num_thresholds
;
i
++
)
{
tp_rate_data
[
i
]
=
((
float
)
tp_data
[
i
]
+
epsilon
)
/
(
tp_data
[
i
]
+
fn_data
[
i
]
+
epsilon
);
fp_rate_data
[
i
]
=
(
float
)
fp_data
[
i
]
/
(
fp_data
[
i
]
+
tn_data
[
i
]
+
epsilon
);
rec_rate_data
[
i
]
=
((
float
)
tp_data
[
i
]
+
epsilon
)
/
(
tp_data
[
i
]
+
fp_data
[
i
]
+
epsilon
);
}
*
auc_data
=
0.0
f
;
if
(
curve
==
"ROC"
)
{
for
(
int
i
=
0
;
i
<
num_thresholds
-
1
;
i
++
)
{
auto
dx
=
fp_rate_data
[
i
]
-
fp_rate_data
[
i
+
1
];
auto
y
=
(
tp_rate_data
[
i
]
+
tp_rate_data
[
i
+
1
])
/
2.0
f
;
*
auc_data
=
*
auc_data
+
dx
*
y
;
}
}
else
if
(
curve
==
"PR"
)
{
for
(
int
i
=
1
;
i
<
num_thresholds
;
i
++
)
{
auto
dx
=
tp_rate_data
[
i
]
-
tp_rate_data
[
i
-
1
];
auto
y
=
(
rec_rate_data
[
i
]
+
rec_rate_data
[
i
-
1
])
/
2.0
f
;
*
auc_data
=
*
auc_data
+
dx
*
y
;
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_auc_op.py
0 → 100644
浏览文件 @
24d988ce
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestAucOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"auc"
pred
=
np
.
random
.
random
((
128
)).
astype
(
"float32"
)
labels
=
np
.
random
.
randint
(
0
,
2
,
(
128
,
))
num_thresholds
=
200
self
.
inputs
=
{
'Inference'
:
pred
,
'Label'
:
labels
}
self
.
attrs
=
{
'curve'
:
'ROC'
,
'num_thresholds'
:
num_thresholds
}
# NOTE: sklearn use a different way to generate thresholds
# which will cause the result differs slightly:
# from sklearn.metrics import roc_curve, auc
# fpr, tpr, thresholds = roc_curve(labels, pred)
# auc_value = auc(fpr, tpr)
# we caculate AUC again using numpy for testing
kepsilon
=
1e-7
# to account for floating point imprecisions
thresholds
=
[(
i
+
1
)
*
1.0
/
(
num_thresholds
-
1
)
for
i
in
range
(
num_thresholds
-
2
)]
thresholds
=
[
0.0
-
kepsilon
]
+
thresholds
+
[
1.0
+
kepsilon
]
# caculate TP, FN, TN, FP count
tp_list
=
np
.
ndarray
((
num_thresholds
,
))
fn_list
=
np
.
ndarray
((
num_thresholds
,
))
tn_list
=
np
.
ndarray
((
num_thresholds
,
))
fp_list
=
np
.
ndarray
((
num_thresholds
,
))
for
idx_thresh
,
thresh
in
enumerate
(
thresholds
):
tp
,
fn
,
tn
,
fp
=
0
,
0
,
0
,
0
for
i
,
lbl
in
enumerate
(
labels
):
if
lbl
:
if
pred
[
i
]
>=
thresh
:
tp
+=
1
else
:
fn
+=
1
else
:
if
pred
[
i
]
>=
thresh
:
fp
+=
1
else
:
tn
+=
1
tp_list
[
idx_thresh
]
=
tp
fn_list
[
idx_thresh
]
=
fn
tn_list
[
idx_thresh
]
=
tn
fp_list
[
idx_thresh
]
=
fp
epsilon
=
1e-6
tpr
=
(
tp_list
.
astype
(
"float32"
)
+
epsilon
)
/
(
tp_list
+
fn_list
+
epsilon
)
fpr
=
fp_list
.
astype
(
"float32"
)
/
(
fp_list
+
tn_list
+
epsilon
)
rec
=
(
tp_list
.
astype
(
"float32"
)
+
epsilon
)
/
(
tp_list
+
fp_list
+
epsilon
)
x
=
fpr
[:
num_thresholds
-
1
]
-
fpr
[
1
:]
y
=
(
tpr
[:
num_thresholds
-
1
]
+
tpr
[
1
:])
/
2.0
auc_value
=
np
.
sum
(
x
*
y
)
self
.
outputs
=
{
'AUC'
:
auc_value
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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