/
metric_op.py
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/
metric_op.py
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# Copyright (c) 2019 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.
"""
Contrib layers just related to metric.
"""
from __future__ import print_function
import warnings
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.initializer import Normal, Constant
from paddle.fluid.framework import Variable
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.layers import nn
__all__ = ['ctr_metric_bundle']
def ctr_metric_bundle(input, label):
"""
ctr related metric layer
This function help compute the ctr related metrics: RMSE, MAE, predicted_ctr, q_value.
To compute the final values of these metrics, we should do following computations using
total instance number:
MAE = local_abserr / instance number
RMSE = sqrt(local_sqrerr / instance number)
predicted_ctr = local_prob / instance number
q = local_q / instance number
Note that if you are doing distribute job, you should all reduce these metrics and instance
number first
Args:
input(Variable): A floating-point 2D Variable, values are in the range
[0, 1]. Each row is sorted in descending order. This
input should be the output of topk. Typically, this
Variable indicates the probability of each label.
label(Variable): A 2D int Variable indicating the label of the training
data. The height is batch size and width is always 1.
Returns:
local_sqrerr(Variable): Local sum of squared error
local_abserr(Variable): Local sum of abs error
local_prob(Variable): Local sum of predicted ctr
local_q(Variable): Local sum of q value
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
label = fluid.layers.data(name="label", shape=[1], dtype="int32")
predict = fluid.layers.sigmoid(fluid.layers.fc(input=data, size=1))
auc_out = fluid.contrib.layers.ctr_metric_bundle(input=predict, label=label)
"""
assert input.shape == label.shape
helper = LayerHelper("ctr_metric_bundle", **locals())
local_abserr = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1])
local_sqrerr = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1])
local_prob = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1])
local_q = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1])
local_pos_num = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1])
local_ins_num = helper.create_global_variable(
persistable=True, dtype='float32', shape=[1])
tmp_res_elesub = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1])
tmp_res_sigmoid = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1])
tmp_ones = helper.create_global_variable(
persistable=False, dtype='float32', shape=[-1])
batch_prob = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1])
batch_abserr = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1])
batch_sqrerr = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1])
batch_q = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1])
batch_pos_num = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1])
batch_ins_num = helper.create_global_variable(
persistable=False, dtype='float32', shape=[1])
for var in [
local_abserr, batch_abserr, local_sqrerr, batch_sqrerr, local_prob,
batch_prob, local_q, batch_q, batch_pos_num, batch_ins_num,
local_pos_num, local_ins_num
]:
helper.set_variable_initializer(
var, Constant(
value=0.0, force_cpu=True))
helper.append_op(
type="elementwise_sub",
inputs={"X": [input],
"Y": [label]},
outputs={"Out": [tmp_res_elesub]})
helper.append_op(
type="squared_l2_norm",
inputs={"X": [tmp_res_elesub]},
outputs={"Out": [batch_sqrerr]})
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_sqrerr],
"Y": [local_sqrerr]},
outputs={"Out": [local_sqrerr]})
helper.append_op(
type="l1_norm",
inputs={"X": [tmp_res_elesub]},
outputs={"Out": [batch_abserr]})
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_abserr],
"Y": [local_abserr]},
outputs={"Out": [local_abserr]})
helper.append_op(
type="reduce_sum", inputs={"X": [input]},
outputs={"Out": [batch_prob]})
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_prob],
"Y": [local_prob]},
outputs={"Out": [local_prob]})
helper.append_op(
type="sigmoid",
inputs={"X": [input]},
outputs={"Out": [tmp_res_sigmoid]})
helper.append_op(
type="reduce_sum",
inputs={"X": [tmp_res_sigmoid]},
outputs={"Out": [batch_q]})
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_q],
"Y": [local_q]},
outputs={"Out": [local_q]})
helper.append_op(
type="reduce_sum",
inputs={"X": [label]},
outputs={"Out": [batch_pos_num]})
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_pos_num],
"Y": [local_pos_num]},
outputs={"Out": [local_pos_num]})
helper.append_op(
type='fill_constant_batch_size_like',
inputs={"Input": label},
outputs={'Out': [tmp_ones]},
attrs={
'shape': [-1, 1],
'dtype': tmp_ones.dtype,
'value': float(1.0),
})
helper.append_op(
type="reduce_sum",
inputs={"X": [tmp_ones]},
outputs={"Out": [batch_ins_num]})
helper.append_op(
type="elementwise_add",
inputs={"X": [batch_ins_num],
"Y": [local_ins_num]},
outputs={"Out": [local_ins_num]})
return local_sqrerr, local_abserr, local_prob, local_q, local_pos_num, local_ins_num