logloss Class Reference

Inheritance diagram for logloss:

Public Member Functions
std::string	getType ()
float	getLoss (shared_data *, float prediction, float label)
float	getUpdate (float prediction, float label, float update_scale, float pred_per_update)
float	getUnsafeUpdate (float prediction, float label, float update_scale)
float	wexpmx (float x)
float	getRevertingWeight (shared_data *, float prediction, float eta_t)
float	first_derivative (shared_data *, float prediction, float label)
float	getSquareGrad (float prediction, float label)
float	second_derivative (shared_data *, float prediction, float label)
Public Member Functions inherited from loss_function
virtual	~loss_function ()

Detailed Description

Definition at line 157 of file loss_functions.cc.

Member Function Documentation

first_derivative()

float logloss::first_derivative ( shared_data *  , float  prediction, float  label  )

inlinevirtual

Implements loss_function.

Definition at line 211 of file loss_functions.cc.

References correctedExp.

  {
    float v = -label / (1 + correctedExp(label * prediction));
    return v;
  }

getLoss()

float logloss::getLoss ( shared_data *  , float  prediction, float  label  )

inlinevirtual

Implements loss_function.

Definition at line 162 of file loss_functions.cc.

References correctedExp, and f.

  {
    if (label != -1.f && label != 1.f)
      std::cout << "You are using label " << label << " not -1 or 1 as loss function expects!" << std::endl;
    return log(1 + correctedExp(-label * prediction));
  }

getRevertingWeight()

float logloss::getRevertingWeight ( shared_data *  , float  prediction, float  eta_t  )

inlinevirtual

Implements loss_function.

Definition at line 205 of file loss_functions.cc.

References correctedExp.

  {
    float z = -fabs(prediction);
    return (1 - z - correctedExp(z)) / eta_t;
  }

getSquareGrad()

float logloss::getSquareGrad ( float  prediction, float  label  )

inlinevirtual

Implements loss_function.

Definition at line 217 of file loss_functions.cc.

References squaredloss::first_derivative().

  {
    float d = first_derivative(nullptr, prediction, label);
    return d * d;
  }

getType()

std::string logloss::getType ( )

inlinevirtual

Implements loss_function.

Definition at line 160 of file loss_functions.cc.

{ return "logistic"; }

getUnsafeUpdate()

float logloss::getUnsafeUpdate ( float  prediction, float  label, float  update_scale  )

inlinevirtual

Implements loss_function.

Definition at line 185 of file loss_functions.cc.

References correctedExp.

  {
    float d = correctedExp(label * prediction);
    return label * update_scale / (1 + d);
  }

getUpdate()

float logloss::getUpdate ( float  prediction, float  label, float  update_scale, float  pred_per_update  )

inlinevirtual

Implements loss_function.

Definition at line 169 of file loss_functions.cc.

References correctedExp.

  {
    float w, x;
    float d = correctedExp(label * prediction);
    if (update_scale * pred_per_update < 1e-6)
    {
      /* As with squared loss, for small eta_t we replace the update
       * with its first order Taylor expansion to avoid numerical problems
       */
      return label * update_scale / (1 + d);
    }
    x = update_scale * pred_per_update + label * prediction + d;
    w = wexpmx(x);
    return -(label * w + prediction) / pred_per_update;
  }

second_derivative()

float logloss::second_derivative ( shared_data *  , float  prediction, float  label  )

inlinevirtual

Implements loss_function.

Definition at line 223 of file loss_functions.cc.

References correctedExp.

  {
    float p = 1 / (1 + correctedExp(label * prediction));

    return p * (1 - p);
  }

wexpmx()

float logloss::wexpmx ( float  x )

inline

Definition at line 191 of file loss_functions.cc.

References correctedExp.

  {
    /* This piece of code is approximating W(exp(x))-x.
     * W is the Lambert W function: W(z)*exp(W(z))=z.
     * The absolute error of this approximation is less than 9e-5.
     * Faster/better approximations can be substituted here.
     */
    double w = x >= 1. ? 0.86 * x + 0.01 : correctedExp(0.8 * x - 0.65);  // initial guess
    double r = x >= 1. ? x - log(w) - w : 0.2 * x + 0.65 - w;             // residual
    double t = 1. + w;
    double u = 2. * t * (t + 2. * r / 3.);                          // magic
    return (float)(w * (1. + r / t * (u - r) / (u - 2. * r)) - x);  // more magic
  }

---

The documentation for this class was generated from the following file:

• /mnt/c/w/linux/vowpal_wabbit/vowpalwabbit/loss_functions.cc