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114 lines
4.1 KiB
114 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <unordered_map>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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template <typename DeviceContext, typename T>
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class PositiveNegativePairKernel : public framework::OpKernel<T> {
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public:
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struct PredictionResult {
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PredictionResult(T score, T label, T weight)
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: score(score), label(label), weight(weight) {}
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T score;
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T label;
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T weight;
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};
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void Compute(const framework::ExecutionContext& context) const override {
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auto score_t = context.Input<Tensor>("Score");
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auto label_t = context.Input<Tensor>("Label");
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auto query_t = context.Input<Tensor>("QueryID");
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auto acc_positive_t = context.Input<Tensor>("AccumulatePositivePair");
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auto acc_negative_t = context.Input<Tensor>("AccumulateNegativePair");
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auto acc_neutral_t = context.Input<Tensor>("AccumulateNeutralPair");
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auto positive_t = context.Output<Tensor>("PositivePair");
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auto negative_t = context.Output<Tensor>("NegativePair");
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auto neutral_t = context.Output<Tensor>("NeutralPair");
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auto weight_t = context.Input<Tensor>("Weight");
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auto score = score_t->data<T>();
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auto label = label_t->data<T>();
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auto query = query_t->data<int64_t>();
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const T* weight = nullptr;
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if (weight_t != nullptr) {
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weight = weight_t->data<T>();
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}
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T* positive = positive_t->mutable_data<T>(context.GetPlace());
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T* negative = negative_t->mutable_data<T>(context.GetPlace());
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T* neutral = neutral_t->mutable_data<T>(context.GetPlace());
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auto score_dim = score_t->dims();
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auto batch_size = score_dim[0];
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auto width = score_dim[1];
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auto column = context.Attr<int32_t>("column");
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if (column < 0) {
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column += width;
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}
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// construct document instances for each query: Query => List[<score#0,
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// label#0, weight#0>, ...]
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std::unordered_map<int64_t, std::vector<PredictionResult>> predictions;
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for (auto i = 0; i < batch_size; ++i) {
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if (predictions.find(query[i]) == predictions.end()) {
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predictions.emplace(
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std::make_pair(query[i], std::vector<PredictionResult>()));
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}
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predictions[query[i]].emplace_back(score[i * width + column], label[i],
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weight_t != nullptr ? weight[i] : 1.0);
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}
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// for each query, accumulate pair counts
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T pos = 0, neg = 0, neu = 0;
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if (acc_positive_t != nullptr && acc_negative_t != nullptr &&
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acc_neutral_t != nullptr) {
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pos = acc_positive_t->data<T>()[0];
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neg = acc_negative_t->data<T>()[0];
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neu = acc_neutral_t->data<T>()[0];
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}
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auto evaluate_one_list = [&pos, &neg,
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&neu](std::vector<PredictionResult> vec) {
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for (auto ite1 = vec.begin(); ite1 != vec.end(); ++ite1) {
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for (auto ite2 = ite1 + 1; ite2 != vec.end(); ++ite2) {
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if (ite1->label == ite2->label) { // labels are equal, ignore.
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continue;
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}
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T w = (ite1->weight + ite2->weight) * 0.5;
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if (ite1->score == ite2->score) {
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neu += w;
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}
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(ite1->score - ite2->score) * (ite1->label - ite2->label) > 0.0
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? pos += w
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: neg += w;
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}
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}
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};
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for (auto prediction : predictions) {
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evaluate_one_list(prediction.second);
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}
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*positive = pos;
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*negative = neg;
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*neutral = neu;
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}
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};
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} // namespace operators
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} // namespace paddle
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