JarCastingOverview
A tool to make machine learning results by the random forest algorithm into standalone source code.
This tool can generate pure Java source code from RandomForest tree model generated by Weka or RandomForest Learner.
- Stand-alone prediction is possible.
- In other words, it is possible to predict using a random forest without the need for an external library.
It is licensed under MIT.
How to use
from Command Line
1.Download jar file from here
2.Run from command-line
java -jar rfcodegen-1.0.0.jar --package com.example --class RandomForest --main --inFile c:/temp/model.txt --outDir c:/temp
usage: options
-c,--class <className> class name of generated source code
-f,--inFile <treeFile> tree model file path
-l,--language <language> language('java' only)
-m,--main generate main method
-o,--outDir <outDir> output directory for generated source code
-p,--package <packageName> package name of generated source code
from Your code
1.Add dependency into POM.xml or build.gradle
<dependency>
<groupId>org.riversun</groupId>
<artifactId>random-forest-codegen</artifactId>
<version>1.0.0</version>
</dependency>
2.Write your java code
RandomForestCodeGen o = new RandomForestCodeGen.Builder()
.packageName("org.example")//package name
.className("RandomForestExample")//class name
.mainMethod(true)//true:generate main method
.targetLanguage("java")//target language(java only)
.tree(rfTreeModel)//String data of tree model
// .treeFile("model.txt")//tree model file
.build();
o.generateFile(new File("c:/temp"));
What is 'Model file'
Random Forest Model like as follows. You can generate it from Weka or RandomForest Learner.
RandomTree
==========
petallength < 2.45 : Iris-setosa (48/0)
petallength >= 2.45
| petalwidth < 1.65
| | petalwidth < 1.45 : Iris-versicolor (41/0)
| | petalwidth >= 1.45
| | | sepallength < 7.05
| | | | petallength < 5 : Iris-versicolor (12/0)
| | | | petallength >= 5
| | | | | sepallength < 6.15 : Iris-versicolor (1/0)
| | | | | sepallength >= 6.15 : Iris-virginica (2/0)
| | | sepallength >= 7.05 : Iris-virginica (1/0)
| petalwidth >= 1.65 : Iris-virginica (45/0)
Size of the tree : 13
RandomTree
==========
petallength < 2.7 : Iris-setosa (55/0)
petallength >= 2.7
| petallength < 4.85
| | petalwidth < 1.65 : Iris-versicolor (45/0)
| | petalwidth >= 1.65
| | | sepallength < 5.4 : Iris-virginica (1/0)
| | | sepallength >= 5.4 : Iris-versicolor (1/0)
| petallength >= 4.85
| | petalwidth < 1.7
| | | sepalwidth < 2.65 : Iris-virginica (2/0)
| | | sepalwidth >= 2.65
| | | | petallength < 5.45 : Iris-versicolor (4/0)
| | | | petallength >= 5.45 : Iris-virginica (1/0)
| | petalwidth >= 1.7 : Iris-virginica (41/0)
Size of the tree : 15
=== End of trees ===
=== Begin of sample ===
sepallength,sepalwidth,petallength,petalwidth,class
5.1,3.5,1.4,0.2,Iris-setosa
4
=== End of sample ===
How to get "Model file".
You can get the learned Random Forest model from Weka.
Let's learn and get Model file
1.Start Weka
2.Select Preprocess Tab and open ARFF file you want to learn. Generally,ARFF files are in the [WEKA_INSTALL_DIR]/data
3.Select Classify Tab and Choose the Random Foreset algorithm in the tree list. 
4.Click here to open edit menu of Random Forest classifier. 
5.Enable printClassifiers on edit menu. 
6.Press Start to start learning.
7.Learning result comes out when learning is finished.
- 7-1Right click on Result list and 7-2.Select "Save result buffer" on the context menu and save file like model.txt
8.Ok,You can get the Model file model.txt
Example of Generated Source code
The generated source code is shown below. In this way, a prediction engine using a random forest is generated as one class file. (Comments are omitted, indents have been adjusted.)
This is a generated classifier that trained the type of iris.
package org.example;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
public class RandomForestExample {
public static void main(String[] args) throws Exception {
RandomForestExample rf = new RandomForestExample();
// Set attribute values for prediction
rf.sepallength = 5.1;
rf.sepalwidth = 3.5;
rf.petallength = 1.4;
rf.petalwidth = 0.2;
// Perform 'class' prediction
Prediction prediction = rf.runClassification();
System.out.println("prediction=" + prediction);
}
public double sepallength;
public double petalwidth;
public double sepalwidth;
public double petallength;
public String toString() {
StringBuilder b = new StringBuilder();
b.append("MyClass: ");
b.append(MyClass);
b.append(", sepallength: ");
b.append(sepallength);
b.append(", petalwidth: ");
b.append(petalwidth);
b.append(", sepalwidth: ");
b.append(sepalwidth);
b.append(", petallength: ");
b.append(petallength);
return b.toString();
}
protected void runClassifiers(List<Prediction> predictions) {
predictions.add(runTree0());
predictions.add(runTree1());
}
private Prediction runTree0() {
if (petallength < 2.45) {
return new Prediction("Iris-setosa", 48, 0);
}
if (petallength >= 2.45) {
if (petalwidth < 1.65) {
if (petalwidth < 1.45) {
return new Prediction("Iris-versicolor", 41, 0);
}
if (petalwidth >= 1.45) {
if (sepallength >= 7.05) {
return new Prediction("Iris-virginica", 1, 0);
}
if (sepallength < 7.05) {
if (petallength < 5) {
return new Prediction("Iris-versicolor", 12, 0);
}
if (petallength >= 5) {
if (sepallength >= 6.15) {
return new Prediction("Iris-virginica", 2, 0);
}
if (sepallength < 6.15) {
return new Prediction("Iris-versicolor", 1, 0);
}
}
}
}
}
if (petalwidth >= 1.65) {
return new Prediction("Iris-virginica", 45, 0);
}
}
return null;
}
private Prediction runTree1() {
if (petallength < 2.7) {
return new Prediction("Iris-setosa", 55, 0);
}
if (petallength >= 2.7) {
if (petallength >= 4.85) {
if (petalwidth < 1.7) {
if (sepalwidth >= 2.65) {
if (petallength >= 5.45) {
return new Prediction("Iris-virginica", 1, 0);
}
if (petallength < 5.45) {
return new Prediction("Iris-versicolor", 4, 0);
}
}
if (sepalwidth < 2.65) {
return new Prediction("Iris-virginica", 2, 0);
}
}
if (petalwidth >= 1.7) {
return new Prediction("Iris-virginica", 41, 0);
}
}
if (petallength < 4.85) {
if (petalwidth < 1.65) {
return new Prediction("Iris-versicolor", 45, 0);
}
if (petalwidth >= 1.65) {
if (sepallength < 5.4) {
return new Prediction("Iris-virginica", 1, 0);
}
if (sepallength >= 5.4) {
return new Prediction("Iris-versicolor", 1, 0);
}
}
}
}
return null;
}
/**
* This class implements the basic structure of the Random Forests. It contains methods to run the classification.
*
* Modified:
* author Tom Misawa(riversun.org@gmail.com)
* version July 10, 2019
* Copyright: MIT Licence
*
* Original:
* author Martin Pielot
* version June 30, 2015
* Copyright: MIT Licence
*/
public String MyClass;
public final boolean use_votes = true;
public Prediction runClassification() {
List<Prediction> predictions = new ArrayList<Prediction>();
runClassifiers(predictions);
Map<String, Integer> winners = new HashMap<String, Integer>();
Map<String, Integer> votes = new HashMap<String, Integer>();
int totalVotes = 0;
for (Prediction prediction : predictions) {
if (prediction != null) {
inc(winners, prediction.label, 1);
inc(votes, prediction.label, prediction.numPos);
totalVotes += prediction.getNumLeafs();
}
}
String winner = winner(use_votes ? votes : winners);
int numPosVotes = votes.get(winner);
int numNegVotes = totalVotes - numPosVotes;
Prediction result = new Prediction(winner, numPosVotes, numNegVotes);
return result;
}
private String winner(Map<String, Integer> votes) {
String winner = "";
int maxVotes = 0;
for (String label : votes.keySet()) {
int voteCount = votes.get(label);
if (voteCount > maxVotes) {
winner = label;
maxVotes = voteCount;
}
}
return winner;
}
private void inc(Map<String, Integer> votes, String label, int voteCount) {
int count = 0;
if (votes.containsKey(label)) {
count = votes.get(label);
}
votes.put(label, count + voteCount);
}
public static class Prediction {
/** The class label of the prediction, e.g. 'picked' */
public String label;
/** The number of votes FOR this label */
public int numPos;
/** The number of votes for OTHER labels */
public int numNeg;
/** The total number of votes */
public int total;
/** Probability - how certain the classifier is that the prediction is correct */
public double p;
public Prediction(String label, double numPositive, double numNegative) {
this.label = label;
this.numPos = (int) (numPositive + 0.5);
this.numNeg = (int) (numNegative + 0.5);
this.total = this.numPos + this.numNeg;
this.p = getProbability();
}
/** Probability - how certain the classifier is that the prediction is correct */
private double getProbability() {
if (total == 0)
return 0;
int c = 10000;
double p = c * numPos / total;
return p / c;
}
/** The total number of votes */
public int getNumLeafs() {
return total;
}
@Override
public String toString() {
return "result:" + label + ", propability: " + p + ", (positive/negative)=( " + numPos + " / " + numNeg + " )";
}
}
}
And it's resulted in,
prediction=result:Iris-setosa, propability: 1.0, (positive/negative)=( 103 / 0 )
You can get the prediction result. (Well, this is only learning data.:p)