EVOLUTION-MANAGER

Edit File: smooth-hinge-loss.h

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

#ifndef TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
#define TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_

#include <limits>

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

namespace tensorflow {

class SmoothHingeLossUpdater : public DualLossUpdater {
 public:
  // Computes the updated dual variable (corresponding) to a single example. The
  // updated dual value maximizes the objective function of the dual
  // optimization problem associated with smooth hinge loss. The computations
  // are detailed in readme.md.
  double ComputeUpdatedDual(const int num_partitions, const double label,
                            const double example_weight,
                            const double current_dual, const double wx,
                            const double weighted_example_norm) const final {
    // Intuitively there are 3 cases:
    // a. new optimal value of the dual variable falls within the admissible
    // range [0, 1]. In this case we set new dual to this value.
    // b. new optimal value is < 0. Then, because of convexity, the optimal
    // valid value for new dual = 0
    // c. new optimal value > 1.0. Then new optimal value should be set to 1.0.
    const double candidate_optimal_dual =
        current_dual +
        (label - wx - gamma * current_dual) /
            (num_partitions * example_weight * weighted_example_norm + gamma);
    if (label * candidate_optimal_dual < 0) {
      return 0.0;
    }
    if (label * candidate_optimal_dual > 1.0) {
      return label;
    }
    return candidate_optimal_dual;
  }

double ComputeDualLoss(const double current_dual, const double example_label,
                         const double example_weight) const final {
    // For binary classification, there are 2 conjugate functions, one per
    // label value (-1 and 1).
    const double y_alpha = current_dual * example_label;  // y \alpha
    if (y_alpha < 0 || y_alpha > 1.0) {
      return std::numeric_limits<double>::max();
    }
    return (-y_alpha + 0.5 * gamma * current_dual * current_dual) *
           example_weight;
  }

double ComputePrimalLoss(const double wx, const double example_label,
                           const double example_weight) const final {
    const double y_wx = example_label * wx;
    if (y_wx >= 1) return 0;
    if (y_wx <= 1 - gamma) return (1 - y_wx - gamma / 2) * example_weight;
    return (1 - y_wx) * (1 - y_wx) * example_weight * 0.5 / gamma;
  }

// Converts binary example labels from 0.0 or 1.0 to -1.0 or 1.0 respectively
  // as expected by smooth hinge loss.
  Status ConvertLabel(float* const example_label) const final {
    if (*example_label == 0.0) {
      *example_label = -1;
      return Status::OK();
    }
    if (*example_label == 1.0) {
      return Status::OK();
    }
    return errors::InvalidArgument(
        "Only labels of 0.0 or 1.0 are supported right now. "
        "Found example with label: ",
        *example_label);
  }

double PrimalLossDerivative(const double wx, const double label,
                              const double example_weight) const final {
    if (label * wx >= 1) {
      return 0;
    }
    if (label * wx <= 1 - gamma) {
      return -label;
    }
    return (wx - label) / gamma;
  }

double SmoothnessConstant() const final { return gamma; }

private:
  // Smoothness constant of smooth hinge loss
  // TODO(sibyl-Aix6ihai): expose this parameter
  const double gamma = 1;
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_SMOOTH_HINGE_LOSS_H_
// TENSORFLOW_KERNELS_SMOOTH_HINGE_LOSS_H_