private static void analyse(Instances train, Instances datapredict) {
String mpOptions = "-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a";
try {
train.setClassIndex(train.numAttributes() - 1);
train.deleteAttributeAt(0);
int numClasses = train.numClasses();
for (int i = 0; i < numClasses; i++) {
System.out.println("class value [" + i + "]=" + train.classAttribute().value(i) + "");
}
// Instance of NN
MultilayerPerceptron mlp = new MultilayerPerceptron();
mlp.setOptions(weka.core.Utils.splitOptions(mpOptions));
mlp.buildClassifier(train);
datapredict.setClassIndex(datapredict.numAttributes() - 1);
datapredict.deleteAttributeAt(0);
// Instances predicteddata = new Instances(datapredict);
for (int i = 0; i < datapredict.numInstances(); i++) {
Instance newInst = datapredict.instance(i);
double pred = mlp.classifyInstance(newInst);
int predInt = (int) pred; // Math.round(pred);
String predString = train.classAttribute().value(predInt);
System.out.println(
"cliente["
+ i
+ "] pred["
+ pred
+ "] predInt["
+ predInt
+ "] desertor["
+ predString
+ "]");
}
} catch (Exception e) {
e.printStackTrace();
}
}
// Create 70% training data set
public void generateTrainingDataSet() {
trainingDataSet = new Instances(instances);
int size = trainingDataSet.numInstances();
// Remove closing prize "close" attribute
trainingDataSet.deleteAttributeAt(0);
// Randomize data set
trainingDataSet.randomize(trainingDataSet.getRandomNumberGenerator(1));
}
/**
* processes the instances using the HAAR algorithm
*
* @param instances the data to process
* @return the modified data
* @throws Exception in case the processing goes wrong
*/
protected Instances processHAAR(Instances instances) throws Exception {
Instances result;
int i;
int n;
int j;
int clsIdx;
double[] oldVal;
double[] newVal;
int level;
int length;
double[] clsVal;
Attribute clsAtt;
clsIdx = instances.classIndex();
clsVal = null;
clsAtt = null;
if (clsIdx > -1) {
clsVal = instances.attributeToDoubleArray(clsIdx);
clsAtt = (Attribute) instances.classAttribute().copy();
instances.setClassIndex(-1);
instances.deleteAttributeAt(clsIdx);
}
result = new Instances(instances, 0);
level = (int) StrictMath.ceil(StrictMath.log(instances.numAttributes()) / StrictMath.log(2.0));
for (i = 0; i < instances.numInstances(); i++) {
oldVal = instances.instance(i).toDoubleArray();
newVal = new double[oldVal.length];
for (n = level; n > 0; n--) {
length = (int) StrictMath.pow(2, n - 1);
for (j = 0; j < length; j++) {
newVal[j] = (oldVal[j * 2] + oldVal[j * 2 + 1]) / StrictMath.sqrt(2);
newVal[j + length] = (oldVal[j * 2] - oldVal[j * 2 + 1]) / StrictMath.sqrt(2);
}
System.arraycopy(newVal, 0, oldVal, 0, newVal.length);
}
// add new transformed instance
result.add(new DenseInstance(1, newVal));
}
// add class again
if (clsIdx > -1) {
result.insertAttributeAt(clsAtt, clsIdx);
result.setClassIndex(clsIdx);
for (i = 0; i < clsVal.length; i++) result.instance(i).setClassValue(clsVal[i]);
}
return result;
}
public static Double runClassify(String trainFile, String testFile) {
double predictOrder = 0.0;
double trueOrder = 0.0;
try {
String trainWekaFileName = trainFile;
String testWekaFileName = testFile;
Instances train = DataSource.read(trainWekaFileName);
Instances test = DataSource.read(testWekaFileName);
train.setClassIndex(0);
test.setClassIndex(0);
train.deleteAttributeAt(8);
test.deleteAttributeAt(8);
train.deleteAttributeAt(6);
test.deleteAttributeAt(6);
train.deleteAttributeAt(5);
test.deleteAttributeAt(5);
train.deleteAttributeAt(4);
test.deleteAttributeAt(4);
// AdditiveRegression classifier = new AdditiveRegression();
// NaiveBayes classifier = new NaiveBayes();
RandomForest classifier = new RandomForest();
// LibSVM classifier = new LibSVM();
classifier.buildClassifier(train);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(classifier, test);
System.out.println(eval.toSummaryString("\nResults\n\n", true));
// System.out.println(eval.toClassDetailsString());
// System.out.println(eval.toMatrixString());
int k = 892;
for (int i = 0; i < test.numInstances(); i++) {
predictOrder = classifier.classifyInstance(test.instance(i));
trueOrder = test.instance(i).classValue();
System.out.println((k++) + "," + (int) predictOrder);
}
} catch (Exception e) {
e.printStackTrace();
}
return predictOrder;
}
public static void test_NHBS_old() throws Exception {
// load the data
CSVLoader loader = new CSVLoader();
// these must come before the getDataSet()
// loader.setEnclosureCharacters(",\'\"S");
// loader.setNominalAttributes("16,71"); //zip code, drug name
// loader.setStringAttributes("");
// loader.setDateAttributes("0,1");
// loader.setSource(new File("hcv/data/NHBS/IDU2_HCV_model_012913_cleaned_for_weka.csv"));
loader.setSource(new File("/home/sasha/hcv/code/data/IDU2_HCV_model_012913_cleaned.csv"));
Instances nhbs_data = loader.getDataSet();
loader.setMissingValue("NOVALUE");
// loader.setMissingValue("");
nhbs_data.deleteAttributeAt(12); // zip code
nhbs_data.deleteAttributeAt(1); // date - redundant with age
nhbs_data.deleteAttributeAt(0); // date
System.out.println("classifying attribute:");
nhbs_data.setClassIndex(1); // new index 3->2->1
nhbs_data.attribute(1).getMetadata().toString(); // HCVEIARSLT1
// wishlist: perhaps it would be smarter to throw out unclassified instance? they interfere
// with the scoring
nhbs_data.deleteWithMissingClass();
// nhbs_data.setClass(new Attribute("HIVRSLT"));//.setClassIndex(1); //2nd column. all are
// mostly negative
// nhbs_data.setClass(new Attribute("HCVEIARSLT1"));//.setClassIndex(2); //3rd column
// #14, i.e. rds_fem, should be made numeric
System.out.println("NHBS IDU 2009 Dataset");
System.out.println("Summary of input:");
// System.out.printlnnhbs_data.toSummaryString());
System.out.println(" Num of classes: " + nhbs_data.numClasses());
System.out.println(" Num of attributes: " + nhbs_data.numAttributes());
for (int idx = 0; idx < nhbs_data.numAttributes(); ++idx) {
Attribute attr = nhbs_data.attribute(idx);
System.out.println("" + idx + ": " + attr.toString());
System.out.println(" distinct values:" + nhbs_data.numDistinctValues(idx));
// System.out.println("" + attr.enumerateValues());
}
// System.exit(0);
// nhbs_data.deleteAttributeAt(0); //response ID
// nhbs_data.deleteAttributeAt(16); //zip
// Classifier classifier = new NNge(); //best nearest-neighbor classifier: 40.00
// Classifier classifier = new MINND();
// Classifier classifier = new CitationKNN();
// Classifier classifier = new LibSVM(); //requires LibSVM classes. only gets 37.7%
// Classifier classifier = new SMOreg();
// Classifier classifier = new LinearNNSearch();
// LinearRegression: Cannot handle multi-valued nominal class!
// Classifier classifier = new LinearRegression();
Classifier classifier = new RandomForest();
String[] options = {
"-I", "100", "-K", "4"
}; // -I trees, -K features per tree. generally, might want to optimize (or not
// https://cwiki.apache.org/confluence/display/MAHOUT/Random+Forests)
classifier.setOptions(options);
// Classifier classifier = new Logistic();
// KStar classifier = new KStar();
// classifier.setGlobalBlend(20); //the amount of not greedy, in percent
// does poorly
// Classifier classifier = new AdaBoostM1();
// Classifier classifier = new MultiBoostAB();
// Classifier classifier = new Stacking();
// building a C45 tree classifier
// J48 classifier = new J48(); // new instance of tree
// String[] options = new String[1];
// options[0] = "-U"; // unpruned tree
// classifier.setOptions(options); // set the options
// classifier.buildClassifier(nhbs_data); // build classifier
// wishlist: remove infrequent values
// weka.filters.unsupervised.instance.RemoveFrequentValues()
Filter f1 = new RemoveUseless();
f1.setInputFormat(nhbs_data);
nhbs_data = Filter.useFilter(nhbs_data, f1);
// evaluation
Evaluation eval = new Evaluation(nhbs_data);
eval.crossValidateModel(classifier, nhbs_data, 10, new Random(1));
System.out.println(eval.toSummaryString("\nResults\n\n", false));
System.out.println(eval.toClassDetailsString());
// System.out.println(eval.toCumulativeMarginDistributionString());
}
public static void main(String[] args) {
if (args.length < 1) {
System.out.println("usage: C4_5TweetTopicCategorization <root_path>");
System.exit(-1);
}
String rootPath = args[0];
File dataFolder = new File(rootPath + "/data");
String resultFolderPath = rootPath + "/results/C4_5/";
CrisisMailer crisisMailer = CrisisMailer.getCrisisMailer();
Logger logger = Logger.getLogger(C4_5TweetTopicCategorization.class);
PropertyConfigurator.configure(Constants.LOG4J_PROPERTIES_FILE_PATH);
File resultFolder = new File(resultFolderPath);
if (!resultFolder.exists()) resultFolder.mkdir();
CSVLoader csvLoader = new CSVLoader();
try {
for (File dataSetName : dataFolder.listFiles()) {
Instances data = null;
try {
csvLoader.setSource(dataSetName);
csvLoader.setStringAttributes("2");
data = csvLoader.getDataSet();
} catch (IOException ioe) {
logger.error(ioe);
crisisMailer.sendEmailAlert(ioe);
System.exit(-1);
}
data.setClassIndex(data.numAttributes() - 1);
data.deleteWithMissingClass();
Instances vectorizedData = null;
StringToWordVector stringToWordVectorFilter = new StringToWordVector();
try {
stringToWordVectorFilter.setInputFormat(data);
stringToWordVectorFilter.setAttributeIndices("2");
stringToWordVectorFilter.setIDFTransform(true);
stringToWordVectorFilter.setLowerCaseTokens(true);
stringToWordVectorFilter.setOutputWordCounts(false);
stringToWordVectorFilter.setUseStoplist(true);
vectorizedData = Filter.useFilter(data, stringToWordVectorFilter);
vectorizedData.deleteAttributeAt(0);
// System.out.println(vectorizedData);
} catch (Exception exception) {
logger.error(exception);
crisisMailer.sendEmailAlert(exception);
System.exit(-1);
}
J48 j48Classifier = new J48();
/*
FilteredClassifier filteredClassifier = new FilteredClassifier();
filteredClassifier.setFilter(stringToWordVectorFilter);
filteredClassifier.setClassifier(j48Classifier);
*/
try {
Evaluation eval = new Evaluation(vectorizedData);
eval.crossValidateModel(
j48Classifier, vectorizedData, 5, new Random(System.currentTimeMillis()));
FileOutputStream resultOutputStream =
new FileOutputStream(new File(resultFolderPath + dataSetName.getName()));
resultOutputStream.write(eval.toSummaryString("=== Summary ===", false).getBytes());
resultOutputStream.write(eval.toMatrixString().getBytes());
resultOutputStream.write(eval.toClassDetailsString().getBytes());
resultOutputStream.close();
} catch (Exception exception) {
logger.error(exception);
crisisMailer.sendEmailAlert(exception);
System.exit(-1);
}
}
} catch (Exception exception) {
logger.error(exception);
crisisMailer.sendEmailAlert(exception);
System.out.println(-1);
}
}
public void generateDataSet() {
// Read all the instances in the file (ARFF, CSV, XRFF, ...)
try {
source = new DataSource("data\\bne.csv");
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
// Create data set
try {
instances = source.getDataSet();
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
// Reverse the order of instances in the data set to place them in
// chronological order
for (int i = 0; i < (instances.numInstances() / 2); i++) {
instances.swap(i, instances.numInstances() - 1 - i);
}
// Remove "volume", "low price", "high price", "opening price" and
// "data" from data set
instances.deleteAttributeAt(instances.numAttributes() - 1);
instances.deleteAttributeAt(instances.numAttributes() - 2);
instances.deleteAttributeAt(instances.numAttributes() - 2);
instances.deleteAttributeAt(instances.numAttributes() - 2);
instances.deleteAttributeAt(instances.numAttributes() - 2);
// Create list to hold nominal values "purchase", "sale", "retain"
List my_nominal_values = new ArrayList(3);
my_nominal_values.add("purchase");
my_nominal_values.add("sale");
my_nominal_values.add("retain");
// Create nominal attribute "classIndex"
Attribute classIndex = new Attribute("classIndex", my_nominal_values);
// Add "classIndex" as an attribute to each instance
instances.insertAttributeAt(classIndex, instances.numAttributes());
// Set the value of "classIndex" for each instance
for (int i = 0; i < instances.numInstances() - 1; i++) {
if (instances.get(i + 1).value(instances.numAttributes() - 2)
> instances.get(i).value(instances.numAttributes() - 2)) {
instances.get(i).setValue(instances.numAttributes() - 1, "purchase");
} else if (instances.get(i + 1).value(instances.numAttributes() - 2)
< instances.get(i).value(instances.numAttributes() - 2)) {
instances.get(i).setValue(instances.numAttributes() - 1, "sale");
} else if (instances.get(i + 1).value(instances.numAttributes() - 2)
== instances.get(i).value(instances.numAttributes() - 2)) {
instances.get(i).setValue(instances.numAttributes() - 1, "retain");
}
}
// Make the last attribute be the class
instances.setClassIndex(instances.numAttributes() - 1);
// Calculate and insert technical analysis attributes into data set
Strategies strategies = new Strategies();
strategies.applyStrategies();
// Print header and instances
System.out.println("\nDataset:\n");
System.out.println(instances);
System.out.println(instances.numInstances());
}
