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189 lines
6.9 KiB
189 lines
6.9 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Contrib layers just related to metric.
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"""
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from __future__ import print_function
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import warnings
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from paddle.fluid.layer_helper import LayerHelper
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from paddle.fluid.initializer import Normal, Constant
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from paddle.fluid.framework import Variable
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.layers import nn
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__all__ = ['ctr_metric_bundle']
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def ctr_metric_bundle(input, label):
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"""
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ctr related metric layer
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This function help compute the ctr related metrics: RMSE, MAE, predicted_ctr, q_value.
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To compute the final values of these metrics, we should do following computations using
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total instance number:
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MAE = local_abserr / instance number
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RMSE = sqrt(local_sqrerr / instance number)
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predicted_ctr = local_prob / instance number
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q = local_q / instance number
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Note that if you are doing distribute job, you should all reduce these metrics and instance
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number first
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Args:
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input(Variable): A floating-point 2D Variable, values are in the range
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[0, 1]. Each row is sorted in descending order. This
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input should be the output of topk. Typically, this
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Variable indicates the probability of each label.
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label(Variable): A 2D int Variable indicating the label of the training
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data. The height is batch size and width is always 1.
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Returns:
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local_sqrerr(Variable): Local sum of squared error
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local_abserr(Variable): Local sum of abs error
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local_prob(Variable): Local sum of predicted ctr
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local_q(Variable): Local sum of q value
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
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label = fluid.layers.data(name="label", shape=[1], dtype="int32")
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predict = fluid.layers.sigmoid(fluid.layers.fc(input=data, size=1))
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auc_out = fluid.contrib.layers.ctr_metric_bundle(input=predict, label=label)
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"""
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assert input.shape == label.shape
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helper = LayerHelper("ctr_metric_bundle", **locals())
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local_abserr = helper.create_global_variable(
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persistable=True, dtype='float32', shape=[1])
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local_sqrerr = helper.create_global_variable(
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persistable=True, dtype='float32', shape=[1])
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local_prob = helper.create_global_variable(
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persistable=True, dtype='float32', shape=[1])
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local_q = helper.create_global_variable(
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persistable=True, dtype='float32', shape=[1])
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local_pos_num = helper.create_global_variable(
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persistable=True, dtype='float32', shape=[1])
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local_ins_num = helper.create_global_variable(
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persistable=True, dtype='float32', shape=[1])
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tmp_res_elesub = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[-1])
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tmp_res_sigmoid = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[-1])
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tmp_ones = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[-1])
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batch_prob = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[1])
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batch_abserr = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[1])
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batch_sqrerr = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[1])
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batch_q = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[1])
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batch_pos_num = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[1])
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batch_ins_num = helper.create_global_variable(
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persistable=False, dtype='float32', shape=[1])
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for var in [
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local_abserr, batch_abserr, local_sqrerr, batch_sqrerr, local_prob,
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batch_prob, local_q, batch_q, batch_pos_num, batch_ins_num,
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local_pos_num, local_ins_num
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]:
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helper.set_variable_initializer(
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var, Constant(
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value=0.0, force_cpu=True))
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helper.append_op(
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type="elementwise_sub",
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inputs={"X": [input],
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"Y": [label]},
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outputs={"Out": [tmp_res_elesub]})
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helper.append_op(
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type="squared_l2_norm",
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inputs={"X": [tmp_res_elesub]},
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outputs={"Out": [batch_sqrerr]})
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helper.append_op(
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type="elementwise_add",
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inputs={"X": [batch_sqrerr],
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"Y": [local_sqrerr]},
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outputs={"Out": [local_sqrerr]})
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helper.append_op(
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type="l1_norm",
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inputs={"X": [tmp_res_elesub]},
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outputs={"Out": [batch_abserr]})
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helper.append_op(
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type="elementwise_add",
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inputs={"X": [batch_abserr],
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"Y": [local_abserr]},
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outputs={"Out": [local_abserr]})
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helper.append_op(
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type="reduce_sum", inputs={"X": [input]},
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outputs={"Out": [batch_prob]})
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helper.append_op(
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type="elementwise_add",
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inputs={"X": [batch_prob],
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"Y": [local_prob]},
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outputs={"Out": [local_prob]})
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helper.append_op(
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type="sigmoid",
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inputs={"X": [input]},
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outputs={"Out": [tmp_res_sigmoid]})
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helper.append_op(
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type="reduce_sum",
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inputs={"X": [tmp_res_sigmoid]},
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outputs={"Out": [batch_q]})
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helper.append_op(
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type="elementwise_add",
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inputs={"X": [batch_q],
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"Y": [local_q]},
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outputs={"Out": [local_q]})
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helper.append_op(
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type="reduce_sum",
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inputs={"X": [label]},
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outputs={"Out": [batch_pos_num]})
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helper.append_op(
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type="elementwise_add",
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inputs={"X": [batch_pos_num],
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"Y": [local_pos_num]},
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outputs={"Out": [local_pos_num]})
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helper.append_op(
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type='fill_constant_batch_size_like',
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inputs={"Input": label},
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outputs={'Out': [tmp_ones]},
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attrs={
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'shape': [-1, 1],
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'dtype': tmp_ones.dtype,
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'value': float(1.0),
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})
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helper.append_op(
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type="reduce_sum",
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inputs={"X": [tmp_ones]},
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outputs={"Out": [batch_ins_num]})
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helper.append_op(
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type="elementwise_add",
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inputs={"X": [batch_ins_num],
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"Y": [local_ins_num]},
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outputs={"Out": [local_ins_num]})
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return local_sqrerr, local_abserr, local_prob, local_q, local_pos_num, local_ins_num
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