package org.neuroph.samples.germanCreditData;
/**
* Copyright 2013 Neuroph Project http://neuroph.sourceforge.net
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
import java.math.BigDecimal;
import java.math.RoundingMode;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.core.events.LearningEvent;
import org.neuroph.core.events.LearningEventListener;
import org.neuroph.nnet.MultiLayerPerceptron;
import org.neuroph.nnet.learning.BackPropagation;
import org.neuroph.nnet.learning.MomentumBackpropagation;
import org.neuroph.util.data.norm.MaxNormalizer;
import org.neuroph.util.data.norm.Normalizer;
/**
*
* @author Ivan Petrovic
*/
/*
INTRODUCTION TO THE PROBLEM AND DATA SET INFORMATION:
*Data set that will be used in this experiment: Statlog (German Credit Data)
The original data set that will be used in this experiment can be found at link:
https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data-numeric
1. Title: German Credit data
2. Source Information
Professor Dr. Hans Hofmann
Institut f"ur Statistik und "Okonometrie
Universit"at Hamburg
FB Wirtschaftswissenschaften
Von-Melle-Park 5
2000 Hamburg 13
3. Number of Instances: 1000
Two datasets are provided. the original dataset, in the form provided
by Prof. Hofmann, contains categorical/symbolic attributes and
is in the file "german.data".
For algorithms that need numerical attributes, Strathclyde University
produced the file "german.data-numeric". This file has been edited
and several indicator variables added to make it suitable for
algorithms which cannot cope with categorical variables. Several
attributes that are ordered categorical (such as attribute 17) have
been coded as integer. This was the form used by StatLog.
6. Number of Attributes german: 20 (7 numerical, 13 categorical)
Number of Attributes german.numer: 24 (24 numerical)
**In this sample we used german.numer data set
25. and 26. class variables: 0 (absence) or 1 (presence)
1,0 => "good".
0,1 => "bad"
7. Attribute description for german
Attribute 1: (qualitative)
Status of existing checking account
A11 : ... < 0 DM
A12 : 0 <= ... < 200 DM
A13 : ... >= 200 DM /
salary assignments for at least 1 year
A14 : no checking account
Attribute 2: (numerical)
Duration in month
Attribute 3: (qualitative)
Credit history
A30 : no credits taken/
all credits paid back duly
A31 : all credits at this bank paid back duly
A32 : existing credits paid back duly till now
A33 : delay in paying off in the past
A34 : critical account/
other credits existing (not at this bank)
Attribute 4: (qualitative)
Purpose
A40 : car (new)
A41 : car (used)
A42 : furniture/equipment
A43 : radio/television
A44 : domestic appliances
A45 : repairs
A46 : education
A47 : (vacation - does not exist?)
A48 : retraining
A49 : business
A410 : others
Attribute 5: (numerical)
Credit amount
Attibute 6: (qualitative)
Savings account/bonds
A61 : ... < 100 DM
A62 : 100 <= ... < 500 DM
A63 : 500 <= ... < 1000 DM
A64 : .. >= 1000 DM
A65 : unknown/ no savings account
Attribute 7: (qualitative)
Present employment since
A71 : unemployed
A72 : ... < 1 year
A73 : 1 <= ... < 4 years
A74 : 4 <= ... < 7 years
A75 : .. >= 7 years
Attribute 8: (numerical)
Installment rate in percentage of disposable income
Attribute 9: (qualitative)
Personal status and sex
A91 : male : divorced/separated
A92 : female : divorced/separated/married
A93 : male : single
A94 : male : married/widowed
A95 : female : single
Attribute 10: (qualitative)
Other debtors / guarantors
A101 : none
A102 : co-applicant
A103 : guarantor
Attribute 11: (numerical)
Present residence since
Attribute 12: (qualitative)
Property
A121 : real estate
A122 : if not A121 : building society savings agreement/
life insurance
A123 : if not A121/A122 : car or other, not in attribute 6
A124 : unknown / no property
Attribute 13: (numerical)
Age in years
Attribute 14: (qualitative)
Other installment plans
A141 : bank
A142 : stores
A143 : none
Attribute 15: (qualitative)
Housing
A151 : rent
A152 : own
A153 : for free
Attribute 16: (numerical)
Number of existing credits at this bank
Attribute 17: (qualitative)
Job
A171 : unemployed/ unskilled - non-resident
A172 : unskilled - resident
A173 : skilled employee / official
A174 : management/ self-employed/
highly qualified employee/ officer
Attribute 18: (numerical)
Number of people being liable to provide maintenance for
Attribute 19: (qualitative)
Telephone
A191 : none
A192 : yes, registered under the customers name
Attribute 20: (qualitative)
foreign worker
A201 : yes
A202 : no
*The original data set description can be found at link:
https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.doc
*/
public class GermanCreditDataSample implements LearningEventListener {
//Important for evaluating network result
public int[] count = new int[3];
public int[] correct = new int[3];
int unpredicted = 0;
/**
* @param args the command line arguments
*/
public static void main(String[] args) {
(new GermanCreditDataSample()).run();
}
public void run() {
System.out.println("Creating training and test set from file...");
String trainingSetFileName = "data_sets/german credit data.txt";
int inputsCount = 24;
int outputsCount = 2;
//Create data set from file
DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, " ");
dataSet.shuffle();
//Normalizing data set
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(dataSet);
//Creatinig training set (70%) and test set (30%)
DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
DataSet trainingSet = trainingAndTestSet[0];
DataSet testSet = trainingAndTestSet[1];
System.out.println("Creating neural network...");
//Create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 12, 6, outputsCount);
//attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
learningRule.addListener(this);
learningRule.setLearningRate(0.01);
learningRule.setMaxError(0.001);
learningRule.setMaxIterations(10000);
System.out.println("Training network...");
//train the network with training set
neuralNet.learn(trainingSet);
System.out.println("Testing network...\n\n");
testNeuralNetwork(neuralNet, testSet);
System.out.println("Done.");
System.out.println("**************************************************");
}
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
System.out.println("**************************************************");
System.out.println("**********************RESULT**********************");
System.out.println("**************************************************");
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
//Finding network output
double[] networkOutput = neuralNet.getOutput();
int predicted = maxOutput(networkOutput);
//Finding actual output
double[] networkDesiredOutput = testSetRow.getDesiredOutput();
int ideal = maxOutput(networkDesiredOutput);
//Colecting data for network evaluation
keepScore(predicted, ideal);
}
System.out.println("Total cases: " + this.count[2] + ". ");
System.out.println("Correctly predicted cases: " + this.correct[2] + ". ");
System.out.println("Incorrectly predicted cases: " + (this.count[2] - this.correct[2] - unpredicted) + ". ");
System.out.println("Unrecognized cases: " + unpredicted + ". ");
double percentTotal = (double) this.correct[2] * 100 / (double) this.count[2];
System.out.println("Predicted correctly: " + formatDecimalNumber(percentTotal) + "%. ");
double percentM = (double) this.correct[0] * 100.0 / (double) this.count[0];
System.out.println("Prediction for 'Good credit risk' => (Correct/total): "
+ this.correct[0] + "/" + count[0] + "(" + formatDecimalNumber(percentM) + "%). ");
double percentB = (double) this.correct[1] * 100.0 / (double) this.count[1];
System.out.println("Prediction for 'Bad credit risk' => (Correct/total): "
+ this.correct[1] + "/" + count[1] + "(" + formatDecimalNumber(percentB) + "%). ");
}
@Override
public void handleLearningEvent(LearningEvent event) {
BackPropagation bp = (BackPropagation) event.getSource();
if (event.getEventType().equals(LearningEvent.Type.LEARNING_STOPPED)) {
double error = bp.getTotalNetworkError();
System.out.println("Training completed in " + bp.getCurrentIteration() + " iterations, ");
System.out.println("With total error: " + formatDecimalNumber(error));
} else {
System.out.println("Iteration: " + bp.getCurrentIteration() + " | Network error: " + bp.getTotalNetworkError());
}
}
//Metod determines the maximum output. Maximum output is network prediction for one row.
public static int maxOutput(double[] array) {
double max = array[0];
int index = 0;
for (int i = 0; i < array.length; i++) {
if (array[i] > max) {
index = i;
max = array[i];
}
}
//If maximum is less than 0.5, that prediction will not count.
if (max < 0.5) {
return -1;
}
return index;
}
//Colecting data to evaluate network.
public void keepScore(int prediction, int ideal) {
count[ideal]++;
count[2]++;
if (prediction == ideal) {
correct[ideal]++;
correct[2]++;
}
if (prediction == -1) {
unpredicted++;
}
}
//Formating decimal number to have 3 decimal places
public String formatDecimalNumber(double number) {
return new BigDecimal(number).setScale(4, RoundingMode.HALF_UP).toString();
}
}
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