6f16f0ef58
* Fix various typos * Add override to functions that are overridden gcc gives warnings about functions that are being overridden by not being marked as oveirridden. This fixes it. * Use bst_float consistently Use bst_float for all the variables that involve weight, leaf value, gradient, hessian, gain, loss_chg, predictions, base_margin, feature values. In some cases, when due to additions and so on the value can take a larger value, double is used. This ensures that type conversions are minimal and reduces loss of precision.
348 lines
13 KiB
C++
348 lines
13 KiB
C++
/*!
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* Copyright 2015 by Contributors
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* \file regression.cc
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* \brief Definition of single-value regression and classification objectives.
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* \author Tianqi Chen, Kailong Chen
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*/
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#include <dmlc/omp.h>
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#include <xgboost/logging.h>
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#include <xgboost/objective.h>
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#include <vector>
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#include <algorithm>
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#include <utility>
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#include "../common/math.h"
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namespace xgboost {
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namespace obj {
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DMLC_REGISTRY_FILE_TAG(regression_obj);
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// common regressions
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// linear regression
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struct LinearSquareLoss {
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static bst_float PredTransform(bst_float x) { return x; }
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static bool CheckLabel(bst_float x) { return true; }
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static bst_float FirstOrderGradient(bst_float predt, bst_float label) { return predt - label; }
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static bst_float SecondOrderGradient(bst_float predt, bst_float label) { return 1.0f; }
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static bst_float ProbToMargin(bst_float base_score) { return base_score; }
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static const char* LabelErrorMsg() { return ""; }
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static const char* DefaultEvalMetric() { return "rmse"; }
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};
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// logistic loss for probability regression task
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struct LogisticRegression {
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static bst_float PredTransform(bst_float x) { return common::Sigmoid(x); }
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static bool CheckLabel(bst_float x) { return x >= 0.0f && x <= 1.0f; }
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static bst_float FirstOrderGradient(bst_float predt, bst_float label) { return predt - label; }
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static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
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const float eps = 1e-16f;
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return std::max(predt * (1.0f - predt), eps);
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}
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static bst_float ProbToMargin(bst_float base_score) {
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CHECK(base_score > 0.0f && base_score < 1.0f)
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<< "base_score must be in (0,1) for logistic loss";
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return -std::log(1.0f / base_score - 1.0f);
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}
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static const char* LabelErrorMsg() {
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return "label must be in [0,1] for logistic regression";
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}
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static const char* DefaultEvalMetric() { return "rmse"; }
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};
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// logistic loss for binary classification task.
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struct LogisticClassification : public LogisticRegression {
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static const char* DefaultEvalMetric() { return "error"; }
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};
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// logistic loss, but predict un-transformed margin
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struct LogisticRaw : public LogisticRegression {
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static bst_float PredTransform(bst_float x) { return x; }
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static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
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predt = common::Sigmoid(predt);
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return predt - label;
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}
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static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
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const float eps = 1e-16f;
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predt = common::Sigmoid(predt);
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return std::max(predt * (1.0f - predt), eps);
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}
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static const char* DefaultEvalMetric() { return "auc"; }
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};
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struct RegLossParam : public dmlc::Parameter<RegLossParam> {
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float scale_pos_weight;
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// declare parameters
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DMLC_DECLARE_PARAMETER(RegLossParam) {
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DMLC_DECLARE_FIELD(scale_pos_weight).set_default(1.0f).set_lower_bound(0.0f)
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.describe("Scale the weight of positive examples by this factor");
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}
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};
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// regression loss function
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template<typename Loss>
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class RegLossObj : public ObjFunction {
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public:
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void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
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param_.InitAllowUnknown(args);
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}
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void GetGradient(const std::vector<bst_float> &preds,
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const MetaInfo &info,
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int iter,
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std::vector<bst_gpair> *out_gpair) override {
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CHECK_NE(info.labels.size(), 0) << "label set cannot be empty";
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CHECK_EQ(preds.size(), info.labels.size())
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<< "labels are not correctly provided"
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<< "preds.size=" << preds.size() << ", label.size=" << info.labels.size();
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out_gpair->resize(preds.size());
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// check if label in range
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bool label_correct = true;
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// start calculating gradient
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const omp_ulong ndata = static_cast<omp_ulong>(preds.size());
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#pragma omp parallel for schedule(static)
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for (omp_ulong i = 0; i < ndata; ++i) {
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bst_float p = Loss::PredTransform(preds[i]);
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bst_float w = info.GetWeight(i);
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if (info.labels[i] == 1.0f) w *= param_.scale_pos_weight;
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if (!Loss::CheckLabel(info.labels[i])) label_correct = false;
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out_gpair->at(i) = bst_gpair(Loss::FirstOrderGradient(p, info.labels[i]) * w,
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Loss::SecondOrderGradient(p, info.labels[i]) * w);
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}
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if (!label_correct) {
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LOG(FATAL) << Loss::LabelErrorMsg();
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}
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}
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const char* DefaultEvalMetric() const override {
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return Loss::DefaultEvalMetric();
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}
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void PredTransform(std::vector<bst_float> *io_preds) override {
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std::vector<bst_float> &preds = *io_preds;
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const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
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#pragma omp parallel for schedule(static)
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for (bst_omp_uint j = 0; j < ndata; ++j) {
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preds[j] = Loss::PredTransform(preds[j]);
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}
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}
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bst_float ProbToMargin(bst_float base_score) const override {
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return Loss::ProbToMargin(base_score);
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}
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protected:
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RegLossParam param_;
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};
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// register the objective functions
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DMLC_REGISTER_PARAMETER(RegLossParam);
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XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:linear")
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.describe("Linear regression.")
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.set_body([]() { return new RegLossObj<LinearSquareLoss>(); });
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XGBOOST_REGISTER_OBJECTIVE(LogisticRegression, "reg:logistic")
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.describe("Logistic regression for probability regression task.")
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.set_body([]() { return new RegLossObj<LogisticRegression>(); });
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XGBOOST_REGISTER_OBJECTIVE(LogisticClassification, "binary:logistic")
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.describe("Logistic regression for binary classification task.")
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.set_body([]() { return new RegLossObj<LogisticClassification>(); });
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XGBOOST_REGISTER_OBJECTIVE(LogisticRaw, "binary:logitraw")
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.describe("Logistic regression for classification, output score before logistic transformation")
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.set_body([]() { return new RegLossObj<LogisticRaw>(); });
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// declare parameter
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struct PoissonRegressionParam : public dmlc::Parameter<PoissonRegressionParam> {
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float max_delta_step;
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DMLC_DECLARE_PARAMETER(PoissonRegressionParam) {
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DMLC_DECLARE_FIELD(max_delta_step).set_lower_bound(0.0f).set_default(0.7f)
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.describe("Maximum delta step we allow each weight estimation to be." \
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" This parameter is required for possion regression.");
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}
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};
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// poisson regression for count
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class PoissonRegression : public ObjFunction {
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public:
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// declare functions
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void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
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param_.InitAllowUnknown(args);
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}
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void GetGradient(const std::vector<bst_float> &preds,
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const MetaInfo &info,
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int iter,
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std::vector<bst_gpair> *out_gpair) override {
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CHECK_NE(info.labels.size(), 0) << "label set cannot be empty";
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CHECK_EQ(preds.size(), info.labels.size()) << "labels are not correctly provided";
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out_gpair->resize(preds.size());
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// check if label in range
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bool label_correct = true;
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// start calculating gradient
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const omp_ulong ndata = static_cast<omp_ulong>(preds.size()); // NOLINT(*)
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#pragma omp parallel for schedule(static)
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for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
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bst_float p = preds[i];
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bst_float w = info.GetWeight(i);
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bst_float y = info.labels[i];
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if (y >= 0.0f) {
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out_gpair->at(i) = bst_gpair((std::exp(p) - y) * w,
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std::exp(p + param_.max_delta_step) * w);
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} else {
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label_correct = false;
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}
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}
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CHECK(label_correct) << "PoissonRegression: label must be nonnegative";
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}
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void PredTransform(std::vector<bst_float> *io_preds) override {
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std::vector<bst_float> &preds = *io_preds;
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const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
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#pragma omp parallel for schedule(static)
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for (long j = 0; j < ndata; ++j) { // NOLINT(*)
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preds[j] = std::exp(preds[j]);
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}
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}
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void EvalTransform(std::vector<bst_float> *io_preds) override {
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PredTransform(io_preds);
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}
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bst_float ProbToMargin(bst_float base_score) const override {
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return std::log(base_score);
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}
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const char* DefaultEvalMetric(void) const override {
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return "poisson-nloglik";
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}
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private:
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PoissonRegressionParam param_;
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};
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// register the objective functions
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DMLC_REGISTER_PARAMETER(PoissonRegressionParam);
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XGBOOST_REGISTER_OBJECTIVE(PoissonRegression, "count:poisson")
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.describe("Possion regression for count data.")
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.set_body([]() { return new PoissonRegression(); });
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// gamma regression
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class GammaRegression : public ObjFunction {
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public:
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// declare functions
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void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
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}
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void GetGradient(const std::vector<bst_float> &preds,
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const MetaInfo &info,
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int iter,
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std::vector<bst_gpair> *out_gpair) override {
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CHECK_NE(info.labels.size(), 0) << "label set cannot be empty";
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CHECK_EQ(preds.size(), info.labels.size()) << "labels are not correctly provided";
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out_gpair->resize(preds.size());
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// check if label in range
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bool label_correct = true;
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// start calculating gradient
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const omp_ulong ndata = static_cast<omp_ulong>(preds.size()); // NOLINT(*)
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#pragma omp parallel for schedule(static)
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for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
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bst_float p = preds[i];
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bst_float w = info.GetWeight(i);
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bst_float y = info.labels[i];
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if (y >= 0.0f) {
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out_gpair->at(i) = bst_gpair((1 - y / std::exp(p)) * w, y / std::exp(p) * w);
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} else {
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label_correct = false;
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}
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}
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CHECK(label_correct) << "GammaRegression: label must be positive";
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}
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void PredTransform(std::vector<bst_float> *io_preds) override {
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std::vector<bst_float> &preds = *io_preds;
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const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
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#pragma omp parallel for schedule(static)
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for (long j = 0; j < ndata; ++j) { // NOLINT(*)
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preds[j] = std::exp(preds[j]);
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}
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}
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void EvalTransform(std::vector<bst_float> *io_preds) override {
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PredTransform(io_preds);
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}
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bst_float ProbToMargin(bst_float base_score) const override {
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return std::log(base_score);
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}
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const char* DefaultEvalMetric(void) const override {
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return "gamma-nloglik";
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}
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};
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// register the objective functions
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XGBOOST_REGISTER_OBJECTIVE(GammaRegression, "reg:gamma")
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.describe("Gamma regression for severity data.")
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.set_body([]() { return new GammaRegression(); });
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// declare parameter
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struct TweedieRegressionParam : public dmlc::Parameter<TweedieRegressionParam> {
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float tweedie_variance_power;
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DMLC_DECLARE_PARAMETER(TweedieRegressionParam) {
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DMLC_DECLARE_FIELD(tweedie_variance_power).set_range(1.0f, 2.0f).set_default(1.5f)
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.describe("Tweedie variance power. Must be between in range [1, 2).");
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}
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};
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// tweedie regression
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class TweedieRegression : public ObjFunction {
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public:
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// declare functions
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void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
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param_.InitAllowUnknown(args);
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}
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void GetGradient(const std::vector<bst_float> &preds,
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const MetaInfo &info,
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int iter,
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std::vector<bst_gpair> *out_gpair) override {
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CHECK_NE(info.labels.size(), 0) << "label set cannot be empty";
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CHECK_EQ(preds.size(), info.labels.size()) << "labels are not correctly provided";
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out_gpair->resize(preds.size());
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// check if label in range
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bool label_correct = true;
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// start calculating gradient
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const omp_ulong ndata = static_cast<omp_ulong>(preds.size()); // NOLINT(*)
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#pragma omp parallel for schedule(static)
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for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
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bst_float p = preds[i];
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bst_float w = info.GetWeight(i);
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bst_float y = info.labels[i];
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float rho = param_.tweedie_variance_power;
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if (y >= 0.0f) {
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bst_float grad = -y * std::exp((1 - rho) * p) + std::exp((2 - rho) * p);
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bst_float hess = -y * (1 - rho) * \
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std::exp((1 - rho) * p) + (2 - rho) * std::exp((2 - rho) * p);
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out_gpair->at(i) = bst_gpair(grad * w, hess * w);
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} else {
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label_correct = false;
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}
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}
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CHECK(label_correct) << "TweedieRegression: label must be nonnegative";
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}
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void PredTransform(std::vector<bst_float> *io_preds) override {
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std::vector<bst_float> &preds = *io_preds;
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const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
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#pragma omp parallel for schedule(static)
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for (long j = 0; j < ndata; ++j) { // NOLINT(*)
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preds[j] = std::exp(preds[j]);
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}
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}
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const char* DefaultEvalMetric(void) const override {
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std::ostringstream os;
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os << "tweedie-nloglik@" << param_.tweedie_variance_power;
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std::string metric = os.str();
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return metric.c_str();
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}
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private:
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TweedieRegressionParam param_;
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};
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// register the objective functions
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DMLC_REGISTER_PARAMETER(TweedieRegressionParam);
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XGBOOST_REGISTER_OBJECTIVE(TweedieRegression, "reg:tweedie")
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.describe("Tweedie regression for insurance data.")
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.set_body([]() { return new TweedieRegression(); });
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} // namespace obj
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} // namespace xgboost
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