/**
* Read and normalize evaluator datasets
*
* @param schema Schema of the dataset
* @param traindata IDataset with the training data
* @param testdata IDataset with the test data
*/
public void readData(byte[] schema, IDataset traindata, IDataset testdata) {
// Read trainData
try {
unscaledTrainData.read(schema, traindata);
} catch (DatasetException e1) {
e1.printStackTrace();
}
// Read testData
try {
unscaledTestData.read(schema, testdata);
} catch (DatasetException e1) {
e1.printStackTrace();
}
normalizeData();
}
/**
* Configure the execution of the algorithm.
*
* @param jobFilename Name of the KEEL file with properties of the execution
*/
@SuppressWarnings("unchecked")
private static void configureJob(String jobFilename) {
Properties props = new Properties();
try {
InputStream paramsFile = new FileInputStream(jobFilename);
props.load(paramsFile);
paramsFile.close();
} catch (IOException ioe) {
ioe.printStackTrace();
System.exit(0);
}
// Files training and test
String trainFile;
String testFile;
StringTokenizer tokenizer = new StringTokenizer(props.getProperty("inputData"));
tokenizer.nextToken();
trainFile = tokenizer.nextToken();
trainFile = trainFile.substring(1, trainFile.length() - 1);
testFile = tokenizer.nextToken();
testFile = testFile.substring(1, testFile.length() - 1);
// Classification or Regression ??
byte[] schema = null;
try {
schema = readSchema(trainFile);
} catch (IOException e) {
e.printStackTrace();
} catch (DatasetException e) {
e.printStackTrace();
}
// Algorithm auxiliar configuration
XMLConfiguration algConf = new XMLConfiguration();
algConf.setRootElementName("algorithm");
algConf.addProperty("population-size", 1000);
algConf.addProperty("max-of-generations", Integer.parseInt(props.getProperty("Generations")));
algConf.addProperty("creation-ratio", 10.0);
algConf.addProperty("percentage-second-mutator", 10);
algConf.addProperty("max-generations-without-improving-mean", 20);
algConf.addProperty("max-generations-without-improving-best", 20);
algConf.addProperty("fitness-difference", 0.0000001);
algConf.addProperty(
"species[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetIndividualSpecies");
algConf.addProperty(
"species.neural-net-type",
"keel.Algorithms.Neural_Networks.NNEP_Clas.neuralnet.NeuralNetClassifier");
if (props.getProperty("Transfer").equals("Product_Unit")) {
algConf.addProperty(
"species.hidden-layer[@type]",
"keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.ExpLayer");
algConf.addProperty("species.hidden-layer[@biased]", false);
algConf.addProperty("evaluator[@log-input-data]", true);
} else {
algConf.addProperty(
"species.hidden-layer[@type]",
"keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.SigmLayer");
algConf.addProperty("species.hidden-layer[@biased]", true);
}
int neurons = Integer.parseInt(props.getProperty("Hidden_nodes"));
algConf.addProperty(
"species.hidden-layer.minimum-number-of-neurons", (neurons / 3) != 0 ? (neurons / 3) : 1);
algConf.addProperty(
"species.hidden-layer.initial-maximum-number-of-neurons",
(neurons / 2) != 0 ? (neurons / 2) : 1);
algConf.addProperty("species.hidden-layer.maximum-number-of-neurons", neurons);
algConf.addProperty(
"species.hidden-layer.initiator-of-links",
"keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator");
algConf.addProperty(
"species.hidden-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval");
algConf.addProperty("species.hidden-layer.weight-range[@closure]", "closed-closed");
algConf.addProperty("species.hidden-layer.weight-range[@left]", -5.0);
algConf.addProperty("species.hidden-layer.weight-range[@right]", 5.0);
algConf.addProperty(
"species.output-layer[@type]",
"keel.Algorithms.Neural_Networks.NNEP_Common.neuralnet.LinearLayer");
algConf.addProperty("species.output-layer[@biased]", true);
algConf.addProperty(
"species.output-layer.initiator-of-links",
"keel.Algorithms.Neural_Networks.NNEP_Common.initiators.RandomInitiator");
algConf.addProperty(
"species.output-layer.weight-range[@type]", "net.sf.jclec.util.range.Interval");
algConf.addProperty("species.output-layer.weight-range[@closure]", "closed-closed");
algConf.addProperty("species.output-layer.weight-range[@left]", -5.0);
algConf.addProperty("species.output-layer.weight-range[@right]", 5.0);
algConf.addProperty(
"evaluator[@type]",
"keel.Algorithms.Neural_Networks.NNEP_Clas.problem.classification.softmax.SoftmaxClassificationProblemEvaluator");
algConf.addProperty("evaluator[@normalize-data]", true);
algConf.addProperty(
"evaluator.error-function",
"keel.Algorithms.Neural_Networks.NNEP_Clas.problem.errorfunctions.LogisticErrorFunction");
algConf.addProperty("evaluator.input-interval[@closure]", "closed-closed");
if (props.getProperty("Transfer").equals("Product_Unit")) {
algConf.addProperty("evaluator.input-interval[@left]", 1.0);
algConf.addProperty("evaluator.input-interval[@right]", 2.0);
} else {
algConf.addProperty("evaluator.input-interval[@left]", 0.1);
algConf.addProperty("evaluator.input-interval[@right]", 0.9);
}
algConf.addProperty("evaluator.output-interval[@closure]", "closed-closed");
algConf.addProperty("evaluator.output-interval[@left]", 0.0);
algConf.addProperty("evaluator.output-interval[@right]", 1.0);
algConf.addProperty(
"provider[@type]", "keel.Algorithms.Neural_Networks.NNEP_Common.NeuralNetCreator");
algConf.addProperty(
"mutator1[@type]",
"keel.Algorithms.Neural_Networks.NNEP_Common.mutators.structural.StructuralMutator");
algConf.addProperty("mutator1.temperature-exponent[@value]", 1.0);
algConf.addProperty("mutator1.significative-weigth[@value]", 0.0000001);
algConf.addProperty("mutator1.neuron-ranges.added[@min]", 1);
algConf.addProperty("mutator1.neuron-ranges.added[@max]", 2);
algConf.addProperty("mutator1.neuron-ranges.deleted[@min]", 1);
algConf.addProperty("mutator1.neuron-ranges.deleted[@max]", 2);
algConf.addProperty("mutator1.links-ranges[@relative]", true);
algConf.addProperty("mutator1.links-ranges.percentages[@hidden]", 30);
algConf.addProperty("mutator1.links-ranges.percentages[@output]", 5);
algConf.addProperty(
"mutator2[@type]",
"keel.Algorithms.Neural_Networks.NNEP_Common.mutators.parametric.ParametricSRMutator");
algConf.addProperty("mutator2.temperature-exponent[@value]", 0.0);
algConf.addProperty("mutator2.amplitude[@value]", 5.0);
algConf.addProperty("mutator2.fitness-difference[@value]", 0.0000001);
algConf.addProperty("mutator2.initial-alpha-values[@input]", 0.5);
algConf.addProperty("mutator2.initial-alpha-values[@output]", 1.0);
algConf.addProperty(
"rand-gen-factory[@type]",
"keel.Algorithms.Neural_Networks.NNEP_Common.util.random.RanNnepFactory");
algConf.addProperty("rand-gen-factory[@seed]", Integer.parseInt(props.getProperty("seed")));
// Neural Net Algorithm
algorithm = new CCRElitistNeuralNetAlgorithm();
algorithm.configure(algConf);
// Read data
ProblemEvaluator evaluator = (ProblemEvaluator) algorithm.getEvaluator();
evaluator.readData(schema, new KeelDataSet(trainFile), new KeelDataSet(testFile));
((NeuralNetIndividualSpecies) algorithm.getSpecies())
.setNOfInputs(evaluator.getTrainData().getNofinputs());
((NeuralNetIndividualSpecies) algorithm.getSpecies())
.setNOfOutputs(evaluator.getTrainData().getNofoutputs() - 1);
// Read output files
tokenizer = new StringTokenizer(props.getProperty("outputData"));
String trainResultFile = tokenizer.nextToken();
trainResultFile = trainResultFile.substring(1, trainResultFile.length() - 1);
consoleReporter.setTrainResultFile(trainResultFile);
String testResultFile = tokenizer.nextToken();
testResultFile = testResultFile.substring(1, testResultFile.length() - 1);
consoleReporter.setTestResultFile(testResultFile);
String bestModelResultFile = tokenizer.nextToken();
bestModelResultFile = bestModelResultFile.substring(1, bestModelResultFile.length() - 1);
consoleReporter.setBestModelResultFile(bestModelResultFile);
listeners.add(consoleReporter);
}
