# 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 tensor __all__ = ['ctr_metric_bundle'] def ctr_metric_bundle(input, label, ins_tag_weight=None): """ 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(Tensor): A floating-point 2D Tensor, values are in the range [0, 1]. Each row is sorted in descending order. This input should be the output of topk. Typically, this Tensor indicates the probability of each label. label(Tensor): A 2D int Tensor indicating the label of the training data. The height is batch size and width is always 1. ins_tag_weight(Tensor): A 2D int Tensor indicating the ins_tag_weight of the training data. 1 means real data, 0 means fake data. A LoDTensor or Tensor with type float32,float64. Returns: local_sqrerr(Tensor): Local sum of squared error local_abserr(Tensor): Local sum of abs error local_prob(Tensor): Local sum of predicted ctr local_q(Tensor): Local sum of q value Examples 1: .. code-block:: python import paddle paddle.enable_static() data = paddle.static.data(name="data", shape=[32, 32], dtype="float32") label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32") predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(input=data, size=1)) auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label) Examples 2: .. code-block:: python import paddle paddle.enable_static() data = paddle.static.data(name="data", shape=[32, 32], dtype="float32") label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32") predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(input=data, size=1)) ins_tag_weight = paddle.static.data(name='ins_tag', shape=[-1,16], lod_level=0, dtype='int64') auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label, ins_tag_weight=ins_tag_weight) """ if ins_tag_weight is None: ins_tag_weight = tensor.fill_constant(shape=[1, 1], dtype="float32", value=1.0) 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="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]}) #if data is fake, return 0 inputs_slice = {'Input': ins_tag_weight} attrs = {'axes': [0]} attrs['starts'] = [0] attrs['ends'] = [1] helper.append_op(type="slice", inputs=inputs_slice, attrs=attrs, outputs={"Out": ins_tag_weight}) axis = helper.kwargs.get('axis', 0) helper.append_op(type="elementwise_mul", inputs={ "X": [batch_ins_num], "Y": [ins_tag_weight] }, outputs={"Out": [batch_ins_num]}, attrs={'axis': axis}) helper.append_op(type="elementwise_add", inputs={ "X": [batch_ins_num], "Y": [local_ins_num] }, outputs={"Out": [local_ins_num]}) helper.append_op(type="elementwise_mul", inputs={ "X": [batch_q], "Y": [ins_tag_weight] }, outputs={"Out": [batch_q]}, attrs={'axis': axis}) helper.append_op(type="elementwise_add", inputs={ "X": [batch_q], "Y": [local_q] }, outputs={"Out": [local_q]}) return local_sqrerr, local_abserr, local_prob, local_q, local_pos_num, local_ins_num