org.apache.spark.mllib.optimization
Class LogisticGradient

Object
  org.apache.spark.mllib.optimization.Gradient
      org.apache.spark.mllib.optimization.LogisticGradient

All Implemented Interfaces:

java.io.Serializable

public class LogisticGradient
extends Gradient

:: DeveloperApi :: Compute gradient and loss for a multinomial logistic loss function, as used in multi-class classification (it is also used in binary logistic regression).

In The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, which can be downloaded from http://statweb.stanford.edu/~tibs/ElemStatLearn/ , Eq. (4.17) on page 119 gives the formula of multinomial logistic regression model. A simple calculation shows that

 P(y=0|x, w) = 1 / (1 + \sum_i^{K-1} \exp(x w_i))
 P(y=1|x, w) = exp(x w_1) / (1 + \sum_i^{K-1} \exp(x w_i))
   ...
 P(y=K-1|x, w) = exp(x w_{K-1}) / (1 + \sum_i^{K-1} \exp(x w_i))
 

for K classes multiclass classification problem.

The model weights w = (w_1, w_2, ..., w_{K-1})^T becomes a matrix which has dimension of (K-1) * (N+1) if the intercepts are added. If the intercepts are not added, the dimension will be (K-1) * N.

As a result, the loss of objective function for a single instance of data can be written as

 l(w, x) = -log P(y|x, w) = -\alpha(y) log P(y=0|x, w) - (1-\alpha(y)) log P(y|x, w)
         = log(1 + \sum_i^{K-1}\exp(x w_i)) - (1-\alpha(y)) x w_{y-1}
         = log(1 + \sum_i^{K-1}\exp(margins_i)) - (1-\alpha(y)) margins_{y-1}
 

where \alpha(i) = 1 if i != 0, and \alpha(i) = 0 if i == 0, margins_i = x w_i.

For optimization, we have to calculate the first derivative of the loss function, and a simple calculation shows that

 \frac{\partial l(w, x)}{\partial w_{ij}}
   = (\exp(x w_i) / (1 + \sum_k^{K-1} \exp(x w_k)) - (1-\alpha(y)\delta_{y, i+1})) * x_j
   = multiplier_i * x_j
 

where \delta_{i, j} = 1 if i == j, \delta_{i, j} = 0 if i != j, and multiplier = \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1})

If any of margins is larger than 709.78, the numerical computation of multiplier and loss function will be suffered from arithmetic overflow. This issue occurs when there are outliers in data which are far away from hyperplane, and this will cause the failing of training once infinity / infinity is introduced. Note that this is only a concern when max(margins) > 0.

Fortunately, when max(margins) = maxMargin > 0, the loss function and the multiplier can be easily rewritten into the following equivalent numerically stable formula.

 l(w, x) = log(1 + \sum_i^{K-1}\exp(margins_i)) - (1-\alpha(y)) margins_{y-1}
         = log(\exp(-maxMargin) + \sum_i^{K-1}\exp(margins_i - maxMargin)) + maxMargin
           - (1-\alpha(y)) margins_{y-1}
         = log(1 + sum) + maxMargin - (1-\alpha(y)) margins_{y-1}
 

where sum = \exp(-maxMargin) + \sum_i^{K-1}\exp(margins_i - maxMargin) - 1.

Note that each term, (margins_i - maxMargin) in \exp is smaller than zero; as a result, overflow will not happen with this formula.

For multiplier, similar trick can be applied as the following,

 multiplier = \exp(margins_i) / (1 + \sum_k^{K-1} \exp(margins_i)) - (1-\alpha(y)\delta_{y, i+1})
            = \exp(margins_i - maxMargin) / (1 + sum) - (1-\alpha(y)\delta_{y, i+1})
 

where each term in \exp is also smaller than zero, so overflow is not a concern.

For the detailed mathematical derivation, see the reference at http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297

param: numClasses the number of possible outcomes for k classes classification problem in Multinomial Logistic Regression. By default, it is binary logistic regression so numClasses will be set to 2.

See Also:

Serialized Form

Constructor Summary

LogisticGradient()

LogisticGradient(int numClasses)

Method Summary

scala.Tuple2<Vector,Object>	compute(Vector data, double label, Vector weights) Compute the gradient and loss given the features of a single data point.
double	compute(Vector data, double label, Vector weights, Vector cumGradient) Compute the gradient and loss given the features of a single data point, add the gradient to a provided vector to avoid creating new objects, and return loss.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LogisticGradient

public LogisticGradient(int numClasses)

LogisticGradient

public LogisticGradient()

Method Detail

compute

public scala.Tuple2<Vector,Object> compute(Vector data,
                                           double label,
                                           Vector weights)

Description copied from class: Gradient

Compute the gradient and loss given the features of a single data point.

Overrides:

compute in class Gradient

Parameters:

data - features for one data point

label - label for this data point

weights - weights/coefficients corresponding to features

Returns:

(gradient: Vector, loss: Double)

compute

public double compute(Vector data,
                      double label,
                      Vector weights,
                      Vector cumGradient)

Description copied from class: Gradient

Compute the gradient and loss given the features of a single data point, add the gradient to a provided vector to avoid creating new objects, and return loss.

Specified by:

compute in class Gradient

Parameters:

data - features for one data point

label - label for this data point

weights - weights/coefficients corresponding to features

cumGradient - the computed gradient will be added to this vector

Returns:

loss