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Refactor AFT metric using GPU-CPU reducer
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,185 +1,8 @@ | ||
| /*! | ||
| * Copyright 2019 by Contributors | ||
| * \file survival_metric.cc | ||
| * \brief Metrics for survival analysis | ||
| * \author Avinash Barnwal, Hyunsu Cho and Toby Hocking | ||
| * Copyright 2020 XGBoost contributors | ||
| */ | ||
| // Dummy file to keep the CUDA conditional compile trick. | ||
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| #include <rabit/rabit.h> | ||
| #include <xgboost/metric.h> | ||
| #include <xgboost/host_device_vector.h> | ||
| #include <dmlc/registry.h> | ||
| #include <cmath> | ||
| #include <memory> | ||
| #include <vector> | ||
| #include <limits> | ||
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| #include "xgboost/json.h" | ||
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| #include "../common/math.h" | ||
| #include "../common/survival_util.h" | ||
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| using AFTParam = xgboost::common::AFTParam; | ||
| using ProbabilityDistributionType = xgboost::common::ProbabilityDistributionType; | ||
| template <typename Distribution> | ||
| using AFTLoss = xgboost::common::AFTLoss<Distribution>; | ||
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| namespace xgboost { | ||
| namespace metric { | ||
| // tag the this file, used by force static link later. | ||
| DMLC_REGISTRY_FILE_TAG(survival_metric); | ||
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| struct EvalIntervalRegressionAccuracy : public Metric { | ||
| public: | ||
| explicit EvalIntervalRegressionAccuracy(const char* param) {} | ||
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| bst_float Eval(const HostDeviceVector<bst_float> &preds, | ||
| const MetaInfo &info, | ||
| bool distributed) override { | ||
| CHECK_NE(info.labels_lower_bound_.Size(), 0U) | ||
| << "y_lower cannot be empty"; | ||
| CHECK_NE(info.labels_upper_bound_.Size(), 0U) | ||
| << "y_higher cannot be empty"; | ||
| CHECK_EQ(preds.Size(), info.labels_lower_bound_.Size()); | ||
| CHECK_EQ(preds.Size(), info.labels_upper_bound_.Size()); | ||
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| const auto& yhat = preds.HostVector(); | ||
| const auto& y_lower = info.labels_lower_bound_.HostVector(); | ||
| const auto& y_upper = info.labels_upper_bound_.HostVector(); | ||
| const auto& weights = info.weights_.HostVector(); | ||
| const bool is_null_weight = weights.empty(); | ||
| CHECK_LE(yhat.size(), static_cast<size_t>(std::numeric_limits<omp_ulong>::max())) | ||
| << "yhat is too big"; | ||
| const omp_ulong nsize = static_cast<omp_ulong>(yhat.size()); | ||
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| double acc_sum = 0.0; | ||
| double weight_sum = 0.0; | ||
| #pragma omp parallel for \ | ||
| firstprivate(nsize, is_null_weight) shared(weights, y_lower, y_upper, yhat) \ | ||
| reduction(+:acc_sum, weight_sum) | ||
| for (omp_ulong i = 0; i < nsize; ++i) { | ||
| const double pred = std::exp(yhat[i]); | ||
| const double w = is_null_weight ? 1.0 : weights[i]; | ||
| if (pred >= y_lower[i] && pred <= y_upper[i]) { | ||
| acc_sum += 1.0; | ||
| } | ||
| weight_sum += w; | ||
| } | ||
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| double dat[2]{acc_sum, weight_sum}; | ||
| if (distributed) { | ||
| rabit::Allreduce<rabit::op::Sum>(dat, 2); | ||
| } | ||
| return static_cast<bst_float>(dat[0] / dat[1]); | ||
| } | ||
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| const char* Name() const override { | ||
| return "interval-regression-accuracy"; | ||
| } | ||
| }; | ||
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| /*! \brief Negative log likelihood of Accelerated Failure Time model */ | ||
| struct EvalAFT : public Metric { | ||
| public: | ||
| explicit EvalAFT(const char* param) {} | ||
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| void Configure(const Args& args) override { | ||
| param_.UpdateAllowUnknown(args); | ||
| } | ||
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| void SaveConfig(Json* p_out) const override { | ||
| auto& out = *p_out; | ||
| out["name"] = String(this->Name()); | ||
| out["aft_loss_param"] = ToJson(param_); | ||
| } | ||
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| void LoadConfig(Json const& in) override { | ||
| FromJson(in["aft_loss_param"], ¶m_); | ||
| } | ||
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| template <typename Distribution> | ||
| inline void EvalImpl( | ||
| const std::vector<float>& weights, const std::vector<float>& y_lower, | ||
| const std::vector<float>& y_upper, const std::vector<float>& yhat, | ||
| omp_ulong nsize, bool is_null_weight, double aft_loss_distribution_scale, | ||
| double* out_nloglik_sum, double* out_weight_sum) { | ||
| double nloglik_sum = 0.0; | ||
| double weight_sum = 0.0; | ||
| #pragma omp parallel for \ | ||
| shared(weights, y_lower, y_upper, yhat) reduction(+:nloglik_sum, weight_sum) | ||
| for (omp_ulong i = 0; i < nsize; ++i) { | ||
| // If weights are empty, data is unweighted so we use 1.0 everywhere | ||
| const double w = is_null_weight ? 1.0 : weights[i]; | ||
| const double loss | ||
| = AFTLoss<Distribution>::Loss(y_lower[i], y_upper[i], yhat[i], aft_loss_distribution_scale); | ||
| nloglik_sum += loss; | ||
| weight_sum += w; | ||
| } | ||
| *out_nloglik_sum = nloglik_sum; | ||
| *out_weight_sum = weight_sum; | ||
| } | ||
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| bst_float Eval(const HostDeviceVector<bst_float> &preds, | ||
| const MetaInfo &info, | ||
| bool distributed) override { | ||
| CHECK_NE(info.labels_lower_bound_.Size(), 0U) | ||
| << "y_lower cannot be empty"; | ||
| CHECK_NE(info.labels_upper_bound_.Size(), 0U) | ||
| << "y_higher cannot be empty"; | ||
| CHECK_EQ(preds.Size(), info.labels_lower_bound_.Size()); | ||
| CHECK_EQ(preds.Size(), info.labels_upper_bound_.Size()); | ||
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| /* Compute negative log likelihood for each data point and compute weighted average */ | ||
| const auto& yhat = preds.HostVector(); | ||
| const auto& y_lower = info.labels_lower_bound_.HostVector(); | ||
| const auto& y_upper = info.labels_upper_bound_.HostVector(); | ||
| const auto& weights = info.weights_.HostVector(); | ||
| const bool is_null_weight = weights.empty(); | ||
| const float aft_loss_distribution_scale = param_.aft_loss_distribution_scale; | ||
| CHECK_LE(yhat.size(), static_cast<size_t>(std::numeric_limits<omp_ulong>::max())) | ||
| << "yhat is too big"; | ||
| const omp_ulong nsize = static_cast<omp_ulong>(yhat.size()); | ||
| double nloglik_sum, weight_sum; | ||
| switch (param_.aft_loss_distribution) { | ||
| case ProbabilityDistributionType::kNormal: | ||
| EvalImpl<common::NormalDistribution>(weights, y_lower, y_upper, yhat, nsize, is_null_weight, | ||
| aft_loss_distribution_scale, &nloglik_sum, &weight_sum); | ||
| break; | ||
| case ProbabilityDistributionType::kLogistic: | ||
| EvalImpl<common::LogisticDistribution>(weights, y_lower, y_upper, yhat, nsize, is_null_weight, | ||
| aft_loss_distribution_scale, &nloglik_sum, | ||
| &weight_sum); | ||
| break; | ||
| case ProbabilityDistributionType::kExtreme: | ||
| EvalImpl<common::ExtremeDistribution>(weights, y_lower, y_upper, yhat, nsize, is_null_weight, | ||
| aft_loss_distribution_scale, &nloglik_sum, &weight_sum); | ||
| break; | ||
| default: | ||
| LOG(FATAL) << "Unrecognized probability distribution type"; | ||
| } | ||
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| double dat[2]{nloglik_sum, weight_sum}; | ||
| if (distributed) { | ||
| rabit::Allreduce<rabit::op::Sum>(dat, 2); | ||
| } | ||
| return static_cast<bst_float>(dat[0] / dat[1]); | ||
| } | ||
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| const char* Name() const override { | ||
| return "aft-nloglik"; | ||
| } | ||
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| private: | ||
| AFTParam param_; | ||
| }; | ||
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| XGBOOST_REGISTER_METRIC(AFT, "aft-nloglik") | ||
| .describe("Negative log likelihood of Accelerated Failure Time model.") | ||
| .set_body([](const char* param) { return new EvalAFT(param); }); | ||
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| XGBOOST_REGISTER_METRIC(IntervalRegressionAccuracy, "interval-regression-accuracy") | ||
| .describe("") | ||
| .set_body([](const char* param) { return new EvalIntervalRegressionAccuracy(param); }); | ||
|
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| } // namespace metric | ||
| } // namespace xgboost | ||
| #if !defined(XGBOOST_USE_CUDA) | ||
| #include "survival_metric.cu" | ||
| #endif // !defined(XGBOOST_USE_CUDA) |
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