EVOLUTION-MANAGER

Edit File: poisson-loss.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_POISSON_LOSS_H_
#define TENSORFLOW_CORE_KERNELS_POISSON_LOSS_H_

#include <cmath>

#include "tensorflow/core/kernels/loss.h"
#include "tensorflow/core/lib/core/errors.h"

namespace tensorflow {

class PoissonLossUpdater : public DualLossUpdater {
 public:
  // Update is found by a Newton algorithm (see readme.md).
  double ComputeUpdatedDual(const int num_loss_partitions, const double label,
                            const double example_weight,
                            const double current_dual, const double wx,
                            const double weighted_example_norm) const final {
    // Newton algorithm converges quadratically so 10 steps will be largely
    // enough to achieve a very good precision
    static const int newton_total_steps = 10;
    // Initialize the Newton optimization at x such that
    // exp(x) = label - current_dual
    const double y_minus_a = label - current_dual;
    double x = (y_minus_a > 0) ? log(y_minus_a) : 0;
    for (int i = 0; i < newton_total_steps; ++i) {
      x = NewtonStep(x, num_loss_partitions, label, wx, example_weight,
                     weighted_example_norm, current_dual);
    }
    return label - exp(x);
  }

// Dual of poisson loss function.
  // https://en.wikipedia.org/wiki/Convex_conjugate
  double ComputeDualLoss(const double current_dual, const double example_label,
                         const double example_weight) const final {
    // Dual of the poisson loss function is
    // (y-a)*(log(y-a)-1), where a is the dual variable.
    // It is defined only for a<y.
    const double y_minus_a = example_label - current_dual;
    if (y_minus_a == 0.0) {
      // (y-a)*(log(y-a)-1) approaches 0 as y-a approaches 0.
      return 0.0;
    }
    if (y_minus_a < 0.0) {
      return std::numeric_limits<double>::max();
    }
    return y_minus_a * (log(y_minus_a) - 1) * example_weight;
  }

double ComputePrimalLoss(const double wx, const double example_label,
                           const double example_weight) const final {
    return (exp(wx) - wx * example_label) * example_weight;
  }

double PrimalLossDerivative(const double wx, const double label,
                              const double example_weight) const final {
    return (exp(wx) - label) * example_weight;
  }

// TODO(chapelle): We need to introduce a maximum_prediction parameter,
  // expose that parameter to the user and have this method return
  // 1.0/maximum_prediction.
  // Setting this at 1 for now, it only impacts the adaptive sampling.
  double SmoothnessConstant() const final { return 1; }

Status ConvertLabel(float* const example_label) const final {
    if (*example_label < 0.0) {
      return errors::InvalidArgument(
          "Only non-negative labels can be used with the Poisson log loss. "
          "Found example with label: ", *example_label);
    }
    return Status::OK();
  }

private:
  // One Newton step (see readme.md).
  double NewtonStep(const double x, const int num_loss_partitions,
                    const double label, const double wx,
                    const double example_weight,
                    const double weighted_example_norm,
                    const double current_dual) const {
    const double expx = exp(x);
    const double numerator =
        x - wx - num_loss_partitions * weighted_example_norm *
        example_weight * (label - current_dual - expx);
    const double denominator =
       1 + num_loss_partitions * weighted_example_norm * example_weight * expx;
    return x - numerator / denominator;
  }
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_LOGISTIC_LOSS_H_