/**
* This method builds a naive bayes model
*
* @param sparkContext JavaSparkContext initialized with the application
* @param modelID Model ID
* @param trainingData Training data as a JavaRDD of LabeledPoints
* @param testingData Testing data as a JavaRDD of LabeledPoints
* @param workflow Machine learning workflow
* @param mlModel Deployable machine learning model
* @throws MLModelBuilderException
*/
private ModelSummary buildNaiveBayesModel(
JavaSparkContext sparkContext,
long modelID,
JavaRDD<LabeledPoint> trainingData,
JavaRDD<LabeledPoint> testingData,
Workflow workflow,
MLModel mlModel,
SortedMap<Integer, String> includedFeatures)
throws MLModelBuilderException {
try {
Map<String, String> hyperParameters = workflow.getHyperParameters();
NaiveBayesClassifier naiveBayesClassifier = new NaiveBayesClassifier();
NaiveBayesModel naiveBayesModel =
naiveBayesClassifier.train(
trainingData, Double.parseDouble(hyperParameters.get(MLConstants.LAMBDA)));
// remove from cache
trainingData.unpersist();
// add test data to cache
testingData.cache();
JavaPairRDD<Double, Double> predictionsAndLabels =
naiveBayesClassifier.test(naiveBayesModel, testingData).cache();
ClassClassificationAndRegressionModelSummary classClassificationAndRegressionModelSummary =
SparkModelUtils.getClassClassificationModelSummary(
sparkContext, testingData, predictionsAndLabels);
// remove from cache
testingData.unpersist();
mlModel.setModel(new MLClassificationModel(naiveBayesModel));
classClassificationAndRegressionModelSummary.setFeatures(
includedFeatures.values().toArray(new String[0]));
classClassificationAndRegressionModelSummary.setAlgorithm(
SUPERVISED_ALGORITHM.NAIVE_BAYES.toString());
MulticlassMetrics multiclassMetrics =
getMulticlassMetrics(sparkContext, predictionsAndLabels);
predictionsAndLabels.unpersist();
classClassificationAndRegressionModelSummary.setMulticlassConfusionMatrix(
getMulticlassConfusionMatrix(multiclassMetrics, mlModel));
Double modelAccuracy = getModelAccuracy(multiclassMetrics);
classClassificationAndRegressionModelSummary.setModelAccuracy(modelAccuracy);
classClassificationAndRegressionModelSummary.setDatasetVersion(workflow.getDatasetVersion());
return classClassificationAndRegressionModelSummary;
} catch (Exception e) {
throw new MLModelBuilderException(
"An error occurred while building naive bayes model: " + e.getMessage(), e);
}
}
/**
* Main method reads command-line flags and outputs either the classifications of the test file or
* uses cross-validation to compute a mean accuracy of the classifier.
*
* @param args
* @throws IOException
*/
public static void main(String[] args) throws IOException {
if (args.length < 2) {
System.out.println("usage: java HW3 <trainingFilename> <testFilename>");
}
// Output classifications on test data
File trainingFile = new File(args[0]);
File testFile = new File(args[1]);
Instance[] trainingData = createInstances(trainingFile);
Instance[] testData = createInstances(testFile);
NaiveBayesClassifier nbc = getNewClassifier();
nbc.train(trainingData, vocabularySize(trainingData, testData));
for (Instance i : testData) {
ClassifyResult cr = nbc.classify(i.words);
System.out.println(String.format("%s %s", cr.label, i.label));
System.out.println(String.format("Log probability of spam: %f", cr.log_prob_spam));
System.out.println(String.format("Log probability of ham: %f", cr.log_prob_ham));
}
}
/**
* @param args the command line arguments
* @throws Exception
*/
public static void main(String[] args) throws Exception {
PreProcessor p = new PreProcessor("census-income.data", "census-income-preprocessed.arff");
p.smote();
PreProcessor p_test =
new PreProcessor("census-income.test", "census-income-test-preprocessed.arff");
p_test.run();
BufferedReader traindata =
new BufferedReader(new FileReader("census-income-preprocessed.arff"));
BufferedReader testdata =
new BufferedReader(new FileReader("census-income-test-preprocessed.arff"));
Instances traininstance = new Instances(traindata);
Instances testinstance = new Instances(testdata);
traindata.close();
testdata.close();
traininstance.setClassIndex(traininstance.numAttributes() - 1);
testinstance.setClassIndex(testinstance.numAttributes() - 1);
int numOfAttributes = testinstance.numAttributes();
int numOfInstances = testinstance.numInstances();
NaiveBayesClassifier nb = new NaiveBayesClassifier("census-income-preprocessed.arff");
Classifier cnaive = nb.NBClassify();
DecisionTree dt = new DecisionTree("census-income-preprocessed.arff");
Classifier cls = dt.DTClassify();
AdaBoost ab = new AdaBoost("census-income-preprocessed.arff");
AdaBoostM1 m1 = ab.AdaBoostDTClassify();
BaggingMethod b = new BaggingMethod("census-income-preprocessed.arff");
Bagging bag = b.BaggingDTClassify();
SVM s = new SVM("census-income-preprocessed.arff");
SMO svm = s.SMOClassifier();
knn knnclass = new knn("census-income-preprocessed.arff");
IBk knnc = knnclass.knnclassifier();
Logistic log = new Logistic();
log.buildClassifier(traininstance);
int match = 0;
int error = 0;
int greater = 0;
int less = 0;
for (int i = 0; i < numOfInstances; i++) {
String predicted = "";
greater = 0;
less = 0;
double predictions[] = new double[8];
double pred = cls.classifyInstance(testinstance.instance(i));
predictions[0] = pred;
double abpred = m1.classifyInstance(testinstance.instance(i));
predictions[1] = abpred;
double naivepred = cnaive.classifyInstance(testinstance.instance(i));
predictions[2] = naivepred;
double bagpred = bag.classifyInstance(testinstance.instance(i));
predictions[3] = bagpred;
double smopred = svm.classifyInstance(testinstance.instance(i));
predictions[4] = smopred;
double knnpred = knnc.classifyInstance(testinstance.instance(i));
predictions[5] = knnpred;
for (int j = 0; j < 6; j++) {
if ((testinstance.instance(i).classAttribute().value((int) predictions[j]))
.compareTo(">50K")
== 0) greater++;
else less++;
}
if (greater > less) predicted = ">50K";
else predicted = "<=50K";
if ((testinstance.instance(i).stringValue(numOfAttributes - 1)).compareTo(predicted) == 0)
match++;
else error++;
}
System.out.println("Correctly classified Instances: " + match);
System.out.println("Misclassified Instances: " + error);
double accuracy = (double) match / (double) numOfInstances * 100;
double error_percent = 100 - accuracy;
System.out.println("Accuracy: " + accuracy + "%");
System.out.println("Error: " + error_percent + "%");
}