Home About Us Contact Blog
 
 
What's New Products Buy Now Downloads Forum  


GeneXproTools Online Guide

Learn how to use the 5 modeling platforms
of GeneXproTools with the Online Guide

   
 
 
Last update: February 19, 2014

 

Custom Fitness Function for Logistic Regression

GeneXproTools allows you to design your own custom fitness functions and then use them to create models.



GeneXproTools gives you access to a wide set of essential and useful parameters you may use to design your fitness functions. Note that most of these parameters, such as the rounding threshold, the cost matrix, the model bounds and the parsimony pressure and variable pressure, are adjustable parameters easily accessible through the Fitness Function Tab in the Settings Panel:

  • aParameters[0] = number of records
  • aParameters[1] = averaged target output
  • aParameters[2] = variance of the target output
  • aParameters[3] = 0/1 rounding threshold
  • aParameters[4] = number of records in the predominant class
  • aParameters[5] = minimum program size
  • aParameters[6] = maximum program size
  • aParameters[7] = number of positive cases
  • aParameters[8] = encoding of negative class
  • aParameters[9] = encoding of positive class
  • aParameters[10] = identifies the dataset: "1" for Training and "0" for Validation
  • aParameters[11] = Cost of True Positives
  • aParameters[12] = Cost of True Negatives
  • aParameters[13] = Cost of False Positives
  • aParameters[14] = Cost of False Negatives
  • aParameters[15] = Lower Bound
  • aParameters[16] = Upper Bound
  • aParameters[17] = Parsimony Pressure Rate
  • aParameters[18] = Variable Pressure Rate

In addition, GeneXproTools also gives you access to useful information about the structure and composition of evolving models that are essential for designing custom fitness functions that favor simpler or more complex solutions:

  • aModelInfo[0] = program size
  • aModelInfo[1] = used variables
  • aModelInfo[2] = number of literals

The code for the custom fitness function must be in JavaScript and can be tested before evolving a model with it. Note that GeneXproTools uses fitness proportionate selection to select the models and, therefore, fitness must increase with performance and only non-negative values are acceptable. Below is the sample code of a simple custom fitness function, based on the classification accuracy. It's an example of a valid custom fitness function for logistic regression problems. Note that in this case maximum fitness equals 1000 both for the training and validation datasets, and you must also feed this information into GeneXproTools through the Custom Fitness Function window so that all the charts in the Run Panel show correctly.


/////////////////////////////////////////////////////////////////////////////
// All the values of the Target output
// are accessible through the array:
// aOutputTarget[0] = 0
// aOutputTarget[1] = 1
// aOutputTarget[2] = 0
// etc.

// All the values of the Model output
// are accessible through the array:
// aOutputModel[0] = 0
// aOutputModel[1] = 1
// aOutputModel[2] = 1
// etc.

// Essential and useful parameters you may use
// to design your fitness function:
// aParameters[0] = number of records
// aParameters[1] = averaged target output
// aParameters[2] = variance of the target output
// aParameters[3] = 0/1 rounding threshold
// aParameters[4] = number of records in the predominant class
// aParameters[5] = minimum program size
// aParameters[6] = maximum program size

// aParameters[7] = number of positive cases
// aParameters[8] = encoding of negative class
// aParameters[9] = encoding of positive class

// aParameters[10] = identifies the dataset: "1" for Training and "0" for Validation

// aParameters[11] = Cost of True Positives
// aParameters[12] = Cost of True Negatives
// aParameters[13] = Cost of False Positives
// aParameters[14] = Cost of False Negatives

// aParameters[15] = Lower Bound
// aParameters[16] = Upper Bound

// aParameters[17] = Parsimony Pressure Rate
// aParameters[18] = Variable Pressure Rate

// Useful information about the evolving models
// you may use to design your fitness function:
// aModelInfo[0] = program size
// aModelInfo[1] = used variables
// aModelInfo[2] = number of literals

// gepFilePath: local variable with the full path to the gep file

// Your custom fitness function must return a value, for example:
// return fitness;

// Below is an example of a simple fitness function, the Accuracy fitness function, 
// for which maximum fitness is equal to 1000:

     // ACCURACY FITNESS FUNCTION
     var nRecords = aParameters[0];
     var roundingThreshold = aParameters[3];
     var fitness = 0.0;
     var hits = 0; 
     var negativeClassEncoding = aParameters[8];
     var positiveClassEncoding = aParameters[9];
     // For the penalty
     var VERY_SMALL_FITNESS = 1.0E-11;
     var trueNegatives = 0;
     var truePositives = 0;
     
     // Fitness evaluation
     for (var nR=0; nR<nRecords; nR++)
     {
          // Convert the value returned by the model into crisp classifications 
          if (aOutputModel[nR] >= roundingThreshold)
          {
               aOutputModel[nR] = positiveClassEncoding;
          }
          else
          {
               aOutputModel[nR] = negativeClassEncoding;
          }
          // Evaluation of the fitness components
          if (aOutputModel[nR] == aOutputTarget[nR])                       
          {
               hits++;
               if(aOutputTarget[nR] == positiveClassEncoding) 
               {
                    truePositives++;
               }
               else
               {
                    trueNegatives++;
               }
          }
     } //for nR
     
     // Fitness evaluation & normalization
     fitness = (hits / nRecords) * 1000.0;
     
     // Penalty
     if ((truePositives == 0) || (trueNegatives == 0))
     {
          fitness = fitness * VERY_SMALL_FITNESS;
     }
     
     return fitness;
/////////////////////////////////////////////////////////////////////////////     


See Also:


Related Tutorials:


Related Videos:



Leave Feedback
 
  Please enter the number below using the combo boxes before sending your feedback.
 3 8 4
   

 

 Time Limited Trial

 Try GeneXproTools for free for 30 days!

 Released February 19, 2014

 Last update: 5.0.3883



New Entries  



Add-ons − GeneXproServer  

   Subscribe to the GEP-List

3 8 4
   
 
 
Home | What's New | Products | Buy Now | Downloads | Quick Tour | Support | Contact Us | About Gepsoft | Sign Up
Forum | Blog | Videos | Tutorials | Server Knowledge Base | Logistic Regression Guide | Terms of Use | Privacy & Cookies
 
 

Copyright (c) 2000-2014 Gepsoft Ltd. All rights reserved.