xentropy_objective.hpp

#ifndef LIGHTGBM_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_
#define LIGHTGBM_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_
 
#include <LightGBM/objective_function.h>
#include <LightGBM/meta.h>
 
#include <LightGBM/utils/common.h>
 
#include <cstring>
#include <cmath>
 
/*
 * Implements gradients and hessians for the following point losses.
 * Target y is anything in interval [0, 1].
 *
 * (1) CrossEntropy; "xentropy";
 *
 * loss(y, p, w) = { -(1-y)*log(1-p)-y*log(p) }*w,
 * with probability p = 1/(1+exp(-f)), where f is being boosted
 *
 * ConvertToOutput: f -> p
 *
 * (2) CrossEntropyLambda; "xentlambda"
 *
 * loss(y, p, w) = -(1-y)*log(1-p)-y*log(p),
 * with p = 1-exp(-lambda*w), lambda = log(1+exp(f)), f being boosted, and w > 0
 *
 * ConvertToOutput: f -> lambda
 *
 * (1) and (2) are the same if w=1; but outputs still differ.
 *
 */
 
namespace LightGBM {
class CrossEntropy: public ObjectiveFunction {
public:
  explicit CrossEntropy(const Config&) {
  }
 
  explicit CrossEntropy(const std::vector<std::string>&) {
  }
 
  ~CrossEntropy() {}
 
  void Init(const Metadata& metadata, data_size_t num_data) override {
    num_data_ = num_data;
    label_ = metadata.label();
    weights_ = metadata.weights();
 
    CHECK_NOTNULL(label_);
    Common::CheckElementsIntervalClosed<label_t>(label_, 0.0f, 1.0f, num_data_, GetName());
    Log::Info("[%s:%s]: (objective) labels passed interval [0, 1] check",  GetName(), __func__);
 
    if (weights_ != nullptr) {
      label_t minw;
      double sumw;
      Common::ObtainMinMaxSum(weights_, num_data_, &minw, (label_t*)nullptr, &sumw);
      if (minw < 0.0f) {
        Log::Fatal("[%s]: at least one weight is negative", GetName());
      }
      if (sumw == 0.0f) {
        Log::Fatal("[%s]: sum of weights is zero", GetName());
      }
    }
  }
 
  void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {
    if (weights_ == nullptr) {
      // compute pointwise gradients and hessians with implied unit weights
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double z = 1.0f / (1.0f + std::exp(-score[i]));
        gradients[i] = static_cast<score_t>(z - label_[i]);
        hessians[i] = static_cast<score_t>(z * (1.0f - z));
      }
    } else {
      // compute pointwise gradients and hessians with given weights
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double z = 1.0f / (1.0f + std::exp(-score[i]));
        gradients[i] = static_cast<score_t>((z - label_[i]) * weights_[i]);
        hessians[i] = static_cast<score_t>(z * (1.0f - z) * weights_[i]);
      }
    }
  }
 
  const char* GetName() const override {
    return "xentropy";
  }
 
  // convert score to a probability
  void ConvertOutput(const double* input, double* output) const override {
    output[0] = 1.0f / (1.0f + std::exp(-input[0]));
  }
 
  std::string ToString() const override {
    std::stringstream str_buf;
    str_buf << GetName();
    return str_buf.str();
  }
 
  // implement custom average to boost from (if enabled among options)
  double BoostFromScore(int) const override {
    double suml = 0.0f;
    double sumw = 0.0f;
    if (weights_ != nullptr) {
      #pragma omp parallel for schedule(static) reduction(+:suml, sumw)
      for (data_size_t i = 0; i < num_data_; ++i) {
        suml += label_[i] * weights_[i];
        sumw += weights_[i];
      }
    } else {
      sumw = static_cast<double>(num_data_);
      #pragma omp parallel for schedule(static) reduction(+:suml)
      for (data_size_t i = 0; i < num_data_; ++i) {
        suml += label_[i];
      }
    }
    double pavg = suml / sumw;
    pavg = std::min(pavg, 1.0 - kEpsilon);
    pavg = std::max<double>(pavg, kEpsilon);
    double initscore = std::log(pavg / (1.0f - pavg));
    Log::Info("[%s:%s]: pavg = %f -> initscore = %f",  GetName(), __func__, pavg, initscore);
    return initscore;
  }
 
private:
  data_size_t num_data_;
  const label_t* label_;
  const label_t* weights_;
};
 
class CrossEntropyLambda: public ObjectiveFunction {
public:
  explicit CrossEntropyLambda(const Config&) {
    min_weight_ = max_weight_ = 0.0f;
  }
 
  explicit CrossEntropyLambda(const std::vector<std::string>&) {
  }
 
  ~CrossEntropyLambda() {}
 
  void Init(const Metadata& metadata, data_size_t num_data) override {
    num_data_ = num_data;
    label_ = metadata.label();
    weights_ = metadata.weights();
 
    CHECK_NOTNULL(label_);
    Common::CheckElementsIntervalClosed<label_t>(label_, 0.0f, 1.0f, num_data_, GetName());
    Log::Info("[%s:%s]: (objective) labels passed interval [0, 1] check",  GetName(), __func__);
 
    if (weights_ != nullptr) {
      Common::ObtainMinMaxSum(weights_, num_data_, &min_weight_, &max_weight_, (label_t*)nullptr);
      if (min_weight_ <= 0.0f) {
        Log::Fatal("[%s]: at least one weight is non-positive", GetName());
      }
 
      // Issue an info statement about this ratio
      double weight_ratio = max_weight_ / min_weight_;
      Log::Info("[%s:%s]: min, max weights = %f, %f; ratio = %f",
                GetName(), __func__,
                min_weight_, max_weight_,
                weight_ratio);
    } else {
      // all weights are implied to be unity; no need to do anything
    }
  }
 
  void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {
    if (weights_ == nullptr) {
      // compute pointwise gradients and hessians with implied unit weights; exactly equivalent to CrossEntropy with unit weights
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double z = 1.0f / (1.0f + std::exp(-score[i]));
        gradients[i] = static_cast<score_t>(z - label_[i]);
        hessians[i] = static_cast<score_t>(z * (1.0f - z));
      }
    } else {
      // compute pointwise gradients and hessians with given weights
      #pragma omp parallel for schedule(static)
      for (data_size_t i = 0; i < num_data_; ++i) {
        const double w = weights_[i];
        const double y = label_[i];
        const double epf = std::exp(score[i]);
        const double hhat = std::log(1.0f + epf);
        const double z = 1.0f - std::exp(-w*hhat);
        const double enf = 1.0f / epf;  // = std::exp(-score[i]);
        gradients[i] = static_cast<score_t>((1.0f - y / z) * w / (1.0f + enf));
        const double c = 1.0f / (1.0f - z);
        double d = 1.0f + epf;
        const double a = w * epf / (d * d);
        d = c - 1.0f;
        const double b = (c / (d * d) ) * (1.0f + w * epf - c);
        hessians[i] = static_cast<score_t>(a * (1.0f + y * b));
      }
    }
  }
 
  const char* GetName() const override {
    return "xentlambda";
  }
 
  //
  // ATTENTION: the function output is the "normalized exponential parameter" lambda > 0, not the probability
  //
  // If this code would read: output[0] = 1.0f / (1.0f + std::exp(-input[0]));
  // The output would still not be the probability unless the weights are unity.
  //
  // Let z = 1 / (1 + exp(-f)), then prob(z) = 1-(1-z)^w, where w is the weight for the specific point.
  //
 
  void ConvertOutput(const double* input, double* output) const override {
    output[0] = std::log(1.0f + std::exp(input[0]));
  }
 
  std::string ToString() const override {
    std::stringstream str_buf;
    str_buf << GetName();
    return str_buf.str();
  }
 
  double BoostFromScore(int) const override {
    double suml = 0.0f;
    double sumw = 0.0f;
    if (weights_ != nullptr) {
      #pragma omp parallel for schedule(static) reduction(+:suml, sumw)
      for (data_size_t i = 0; i < num_data_; ++i) {
        suml += label_[i] * weights_[i];
        sumw += weights_[i];
      }
    } else {
      sumw = static_cast<double>(num_data_);
      #pragma omp parallel for schedule(static) reduction(+:suml)
      for (data_size_t i = 0; i < num_data_; ++i) {
        suml += label_[i];
      }
    }
    double havg = suml / sumw;
    double initscore = std::log(std::exp(havg) - 1.0f);
    Log::Info("[%s:%s]: havg = %f -> initscore = %f",  GetName(), __func__, havg, initscore);
    return initscore;
  }
 
private:
  data_size_t num_data_;
  const label_t* label_;
  const label_t* weights_;
  label_t min_weight_;
  label_t max_weight_;
};
 
}  // end namespace LightGBM
 
#endif   // end #ifndef LIGHTGBM_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_