@Override
protected void _transform(Dataset data, boolean trainingMode) {
// handle non-numeric types, extract columns to dummy variables
Map<Object, Dataset.ColumnType> newColumns = new HashMap<>();
int n = data.size();
Iterator<Map.Entry<Object, Dataset.ColumnType>> it = data.getColumns().entrySet().iterator();
while (it.hasNext()) {
Map.Entry<Object, Dataset.ColumnType> entry = it.next();
Object column = entry.getKey();
Dataset.ColumnType columnType = entry.getValue();
if (columnType == Dataset.ColumnType.CATEGORICAL
|| columnType
== Dataset.ColumnType
.ORDINAL) { // ordinal and categorical are converted into dummyvars
// Remove the old column from the column map
it.remove();
// create dummy variables for all the levels
for (Record r : data) {
if (!r.getX().containsKey(column)) {
continue; // does not contain column
}
Object value = r.getX().get(column);
// remove the column from data
r.getX().remove(column);
List<Object> newColumn = Arrays.<Object>asList(column, value);
// add a new boolean feature with combination of column and value
r.getX().put(newColumn, true);
// add the new column in the list for insertion
newColumns.put(newColumn, Dataset.ColumnType.DUMMYVAR);
}
}
}
// add the new columns in the dataset column map
if (!newColumns.isEmpty()) {
data.getColumns().putAll(newColumns);
}
}
@Override
protected VM validateModel(Dataset validationData) {
predictDataset(validationData);
Set<Object> classesSet = knowledgeBase.getModelParameters().getClasses();
// create new validation metrics object
VM validationMetrics = knowledgeBase.getEmptyValidationMetricsObject();
// short notation
Map<List<Object>, Double> ctMap = validationMetrics.getContingencyTable();
for (Object theClass : classesSet) {
ctMap.put(Arrays.<Object>asList(theClass, SensitivityRates.TP), 0.0); // true possitive
ctMap.put(Arrays.<Object>asList(theClass, SensitivityRates.FP), 0.0); // false possitive
ctMap.put(Arrays.<Object>asList(theClass, SensitivityRates.TN), 0.0); // true negative
ctMap.put(Arrays.<Object>asList(theClass, SensitivityRates.FN), 0.0); // false negative
}
int n = validationData.size();
int c = classesSet.size();
int correctCount = 0;
for (Record r : validationData) {
if (r.getYPredicted().equals(r.getY())) {
++correctCount;
for (Object cl : classesSet) {
if (cl.equals(r.getYPredicted())) {
List<Object> tpk = Arrays.<Object>asList(cl, SensitivityRates.TP);
ctMap.put(tpk, ctMap.get(tpk) + 1.0);
} else {
List<Object> tpk = Arrays.<Object>asList(cl, SensitivityRates.TN);
ctMap.put(tpk, ctMap.get(tpk) + 1.0);
}
}
} else {
for (Object cl : classesSet) {
if (cl.equals(r.getYPredicted())) {
List<Object> tpk = Arrays.<Object>asList(cl, SensitivityRates.FP);
ctMap.put(tpk, ctMap.get(tpk) + 1.0);
} else if (cl.equals(r.getY())) {
List<Object> tpk = Arrays.<Object>asList(cl, SensitivityRates.FN);
ctMap.put(tpk, ctMap.get(tpk) + 1.0);
} else {
List<Object> tpk = Arrays.<Object>asList(cl, SensitivityRates.TN);
ctMap.put(tpk, ctMap.get(tpk) + 1.0);
}
}
}
}
validationMetrics.setAccuracy(correctCount / (double) n);
// Average Precision, Recall and F1:
// http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.104.8244&rep=rep1&type=pdf
for (Object theClass : classesSet) {
double tp = ctMap.get(Arrays.<Object>asList(theClass, SensitivityRates.TP));
double fp = ctMap.get(Arrays.<Object>asList(theClass, SensitivityRates.FP));
double fn = ctMap.get(Arrays.<Object>asList(theClass, SensitivityRates.FN));
double classPrecision = 0.0;
double classRecall = 0.0;
double classF1 = 0.0;
if (tp > 0.0) {
classPrecision = tp / (tp + fp);
classRecall = tp / (tp + fn);
classF1 = 2.0 * classPrecision * classRecall / (classPrecision + classRecall);
} else if (tp == 0.0 && fp == 0.0 && fn == 0.0) {
// if this category did not appear in the dataset then set the metrics to 1
classPrecision = 1.0;
classRecall = 1.0;
classF1 = 1.0;
}
validationMetrics.getMicroPrecision().put(theClass, classPrecision);
validationMetrics.getMicroRecall().put(theClass, classRecall);
validationMetrics.getMicroF1().put(theClass, classF1);
validationMetrics.setMacroPrecision(
validationMetrics.getMacroPrecision() + classPrecision / c);
validationMetrics.setMacroRecall(validationMetrics.getMacroRecall() + classRecall / c);
validationMetrics.setMacroF1(validationMetrics.getMacroF1() + classF1 / c);
}
return validationMetrics;
}
@Override
protected void estimateModelParameters(Dataset trainingData) {
int n = trainingData.size();
int d = trainingData.getColumnSize();
ModelParameters modelParameters = knowledgeBase.getModelParameters();
TrainingParameters trainingParameters = knowledgeBase.getTrainingParameters();
modelParameters.setN(n);
modelParameters.setD(d);
// get model parameters
int k = trainingParameters.getK(); // number of topics
Map<List<Object>, Integer> topicAssignmentOfDocumentWord =
modelParameters.getTopicAssignmentOfDocumentWord();
Map<List<Integer>, Integer> documentTopicCounts = modelParameters.getDocumentTopicCounts();
Map<List<Object>, Integer> topicWordCounts = modelParameters.getTopicWordCounts();
Map<Integer, Integer> documentWordCounts = modelParameters.getDocumentWordCounts();
Map<Integer, Integer> topicCounts = modelParameters.getTopicCounts();
// initialize topic assignments of each word randomly and update the counters
for (Record r : trainingData) {
Integer documentId = r.getId();
documentWordCounts.put(documentId, r.getX().size());
for (Map.Entry<Object, Object> entry : r.getX().entrySet()) {
Object wordPosition = entry.getKey();
Object word = entry.getValue();
// sample a topic
Integer topic = PHPfunctions.mt_rand(0, k - 1);
increase(topicCounts, topic);
topicAssignmentOfDocumentWord.put(Arrays.asList(documentId, wordPosition), topic);
increase(documentTopicCounts, Arrays.asList(documentId, topic));
increase(topicWordCounts, Arrays.asList(topic, word));
}
}
double alpha = trainingParameters.getAlpha();
double beta = trainingParameters.getBeta();
int maxIterations = trainingParameters.getMaxIterations();
int iteration = 0;
while (iteration < maxIterations) {
if (GeneralConfiguration.DEBUG) {
System.out.println("Iteration " + iteration);
}
int changedCounter = 0;
// collapsed gibbs sampler
for (Record r : trainingData) {
Integer documentId = r.getId();
AssociativeArray topicAssignments = new AssociativeArray();
for (int j = 0; j < k; ++j) {
topicAssignments.put(j, 0.0);
}
int totalWords = r.getX().size();
for (Map.Entry<Object, Object> entry : r.getX().entrySet()) {
Object wordPosition = entry.getKey();
Object word = entry.getValue();
// remove the word from the dataset
Integer topic =
topicAssignmentOfDocumentWord.get(Arrays.asList(documentId, wordPosition));
// decrease(documentWordCounts, documentId); //slow
decrease(topicCounts, topic);
decrease(documentTopicCounts, Arrays.asList(documentId, topic));
decrease(topicWordCounts, Arrays.asList(topic, word));
// int numberOfDocumentWords = r.getX().size()-1; //fast - decreased by 1
// compute the posteriors of the topics and sample from it
AssociativeArray topicProbabilities = new AssociativeArray();
for (int j = 0; j < k; ++j) {
double enumerator = 0.0;
Integer njw = topicWordCounts.get(Arrays.asList(j, word));
if (njw != null) {
enumerator = njw + beta;
} else {
enumerator = beta;
}
Integer njd = documentTopicCounts.get(Arrays.asList(documentId, j));
if (njd != null) {
enumerator *= (njd + alpha);
} else {
enumerator *= alpha;
}
double denominator = topicCounts.get((Integer) j) + beta * d;
// denominator *= numberOfDocumentWords+alpha*k; //this is not necessary because it is
// the same for all categories, so it can be omited
topicProbabilities.put(j, enumerator / denominator);
}
// normalize probabilities
Descriptives.normalize(topicProbabilities);
// sample from these probabilieis
Integer newTopic =
(Integer)
SRS.weightedProbabilitySampling(topicProbabilities, 1, true).iterator().next();
topic = newTopic; // new topic assigment
// add back the word in the dataset
topicAssignmentOfDocumentWord.put(Arrays.asList(documentId, wordPosition), topic);
// increase(documentWordCounts, documentId); //slow
increase(topicCounts, topic);
increase(documentTopicCounts, Arrays.asList(documentId, topic));
increase(topicWordCounts, Arrays.asList(topic, word));
topicAssignments.put(
topic, Dataset.toDouble(topicAssignments.get(topic)) + 1.0 / totalWords);
}
Object mainTopic = MapFunctions.selectMaxKeyValue(topicAssignments).getKey();
if (!mainTopic.equals(r.getYPredicted())) {
++changedCounter;
}
r.setYPredicted(mainTopic);
r.setYPredictedProbabilities(topicAssignments);
}
++iteration;
if (GeneralConfiguration.DEBUG) {
System.out.println("Reassigned Records " + changedCounter);
}
if (changedCounter == 0) {
break;
}
}
modelParameters.setTotalIterations(iteration);
}
private ValidationMetrics predictAndValidate(Dataset newData) {
// This method uses similar approach to the training but the most important
// difference is that we do not wish to modify the original training params.
// as a result we need to modify the code to use additional temporary
// counts for the testing data and merge them with the parameters from the
// training data in order to make a decision
ModelParameters modelParameters = knowledgeBase.getModelParameters();
TrainingParameters trainingParameters = knowledgeBase.getTrainingParameters();
// create new validation metrics object
ValidationMetrics validationMetrics = knowledgeBase.getEmptyValidationMetricsObject();
String tmpPrefix = StorageConfiguration.getTmpPrefix();
// get model parameters
int n = modelParameters.getN();
int d = modelParameters.getD();
int k = trainingParameters.getK(); // number of topics
Map<List<Object>, Integer> topicWordCounts = modelParameters.getTopicWordCounts();
Map<Integer, Integer> topicCounts = modelParameters.getTopicCounts();
BigDataStructureFactory.MapType mapType = knowledgeBase.getMemoryConfiguration().getMapType();
int LRUsize = knowledgeBase.getMemoryConfiguration().getLRUsize();
BigDataStructureFactory bdsf = knowledgeBase.getBdsf();
// we create temporary maps for the prediction sets to avoid modifing the maps that we already
// learned
Map<List<Object>, Integer> tmp_topicAssignmentOfDocumentWord =
bdsf.getMap(tmpPrefix + "topicAssignmentOfDocumentWord", mapType, LRUsize);
Map<List<Integer>, Integer> tmp_documentTopicCounts =
bdsf.getMap(tmpPrefix + "documentTopicCounts", mapType, LRUsize);
Map<List<Object>, Integer> tmp_topicWordCounts =
bdsf.getMap(tmpPrefix + "topicWordCounts", mapType, LRUsize);
Map<Integer, Integer> tmp_topicCounts =
bdsf.getMap(tmpPrefix + "topicCounts", mapType, LRUsize);
// initialize topic assignments of each word randomly and update the counters
for (Record r : newData) {
Integer documentId = r.getId();
for (Map.Entry<Object, Object> entry : r.getX().entrySet()) {
Object wordPosition = entry.getKey();
Object word = entry.getValue();
// sample a topic
Integer topic = PHPfunctions.mt_rand(0, k - 1);
increase(tmp_topicCounts, topic);
tmp_topicAssignmentOfDocumentWord.put(Arrays.asList(documentId, wordPosition), topic);
increase(tmp_documentTopicCounts, Arrays.asList(documentId, topic));
increase(tmp_topicWordCounts, Arrays.asList(topic, word));
}
}
double alpha = trainingParameters.getAlpha();
double beta = trainingParameters.getBeta();
int maxIterations = trainingParameters.getMaxIterations();
double perplexity = Double.MAX_VALUE;
for (int iteration = 0; iteration < maxIterations; ++iteration) {
if (GeneralConfiguration.DEBUG) {
System.out.println("Iteration " + iteration);
}
// collapsed gibbs sampler
int changedCounter = 0;
perplexity = 0.0;
double totalDatasetWords = 0.0;
for (Record r : newData) {
Integer documentId = r.getId();
AssociativeArray topicAssignments = new AssociativeArray();
for (int j = 0; j < k; ++j) {
topicAssignments.put(j, 0.0);
}
int totalDocumentWords = r.getX().size();
totalDatasetWords += totalDocumentWords;
for (Map.Entry<Object, Object> entry : r.getX().entrySet()) {
Object wordPosition = entry.getKey();
Object word = entry.getValue();
// remove the word from the dataset
Integer topic =
tmp_topicAssignmentOfDocumentWord.get(Arrays.asList(documentId, wordPosition));
decrease(tmp_topicCounts, topic);
decrease(tmp_documentTopicCounts, Arrays.asList(documentId, topic));
decrease(tmp_topicWordCounts, Arrays.asList(topic, word));
int numberOfDocumentWords = r.getX().size() - 1;
// compute the posteriors of the topics and sample from it
AssociativeArray topicProbabilities = new AssociativeArray();
for (int j = 0; j < k; ++j) {
double enumerator = 0.0;
// get the counts from the current testing data
List<Object> topicWordKey = Arrays.asList(j, word);
Integer njw = tmp_topicWordCounts.get(topicWordKey);
if (njw != null) {
enumerator = njw + beta;
} else {
enumerator = beta;
}
// get also the counts from the training data
Integer njw_original = topicWordCounts.get(topicWordKey);
if (njw_original != null) {
enumerator += njw_original;
}
Integer njd = tmp_documentTopicCounts.get(Arrays.asList(documentId, j));
if (njd != null) {
enumerator *= (njd + alpha);
} else {
enumerator *= alpha;
}
// add the counts from testing data
double denominator = tmp_topicCounts.get((Integer) j) + beta * d - 1;
// and the ones from training data
denominator += topicCounts.get((Integer) j);
denominator *= numberOfDocumentWords + alpha * k;
topicProbabilities.put(j, enumerator / denominator);
}
perplexity += Math.log(Descriptives.sum(topicProbabilities.toFlatDataCollection()));
// normalize probabilities
Descriptives.normalize(topicProbabilities);
// sample from these probabilieis
Integer newTopic =
(Integer)
SRS.weightedProbabilitySampling(topicProbabilities, 1, true).iterator().next();
topic = newTopic; // new topic assignment
// add back the word in the dataset
tmp_topicAssignmentOfDocumentWord.put(Arrays.asList(documentId, wordPosition), topic);
increase(tmp_topicCounts, topic);
increase(tmp_documentTopicCounts, Arrays.asList(documentId, topic));
increase(tmp_topicWordCounts, Arrays.asList(topic, word));
topicAssignments.put(
topic, Dataset.toDouble(topicAssignments.get(topic)) + 1.0 / totalDocumentWords);
}
Object mainTopic = MapFunctions.selectMaxKeyValue(topicAssignments).getKey();
if (!mainTopic.equals(r.getYPredicted())) {
++changedCounter;
}
r.setYPredicted(mainTopic);
r.setYPredictedProbabilities(topicAssignments);
}
perplexity = Math.exp(-perplexity / totalDatasetWords);
if (GeneralConfiguration.DEBUG) {
System.out.println("Reassigned Records " + changedCounter + " - Perplexity: " + perplexity);
}
if (changedCounter == 0) {
break;
}
}
// Drop the temporary Collection
bdsf.dropTable(tmpPrefix + "topicAssignmentOfDocumentWord", tmp_topicAssignmentOfDocumentWord);
bdsf.dropTable(tmpPrefix + "documentTopicCounts", tmp_documentTopicCounts);
bdsf.dropTable(tmpPrefix + "topicWordCounts", tmp_topicWordCounts);
bdsf.dropTable(tmpPrefix + "topicCounts", tmp_topicCounts);
validationMetrics.setPerplexity(perplexity);
return validationMetrics;
}
/**
* Calculates the score of Chisquare test.
*
* @param dataTable
* @return
* @throws IllegalArgumentException
*/
public static AssociativeArray getScore(DataTable2D dataTable) throws IllegalArgumentException {
if (dataTable.isValid() == false) {
throw new IllegalArgumentException();
}
// Estimate marginal scores and sum
Map<Object, Double> XdotJ = new HashMap<>();
Map<Object, Double> XIdot = new HashMap<>();
double Xdotdot = 0.0;
for (Map.Entry<Object, AssociativeArray> entry1 : dataTable.entrySet()) {
Object i = entry1.getKey();
AssociativeArray row = entry1.getValue();
for (Map.Entry<Object, Object> entry2 : row.entrySet()) {
Object j = entry2.getKey();
Object value = entry2.getValue();
double v = Dataset.toDouble(value);
// Summing the columns
if (XdotJ.containsKey(j) == false) {
XdotJ.put(j, v);
} else {
XdotJ.put(j, XdotJ.get(j) + v);
}
// Summing the rows
if (XIdot.containsKey(i) == false) {
XIdot.put(i, v);
} else {
XIdot.put(i, XIdot.get(i) + v);
}
Xdotdot += v;
}
}
int k = XdotJ.size();
int n = XIdot.size();
// Calculating Chisquare score
double ChisquareScore = 0.0;
if (k == 2
&& n
== 2) { // if 2x2 then perform the Yates correction. Make this check outside the loops
// to make it faster
for (Map.Entry<Object, AssociativeArray> entry1 : dataTable.entrySet()) {
Object i = entry1.getKey();
AssociativeArray row = entry1.getValue();
for (Map.Entry<Object, Object> entry2 : row.entrySet()) {
Object j = entry2.getKey();
Object value = entry2.getValue();
double v = Dataset.toDouble(value);
// expected value under null hypothesis
double eij = XIdot.get(i) * XdotJ.get(j) / Xdotdot;
if (eij == 0) {
continue;
}
ChisquareScore += Math.pow((Math.abs(v - eij) - 0.5), 2) / eij;
}
}
} else {
for (Map.Entry<Object, AssociativeArray> entry1 : dataTable.entrySet()) {
Object i = entry1.getKey();
AssociativeArray row = entry1.getValue();
for (Map.Entry<Object, Object> entry2 : row.entrySet()) {
Object j = entry2.getKey();
Object value = entry2.getValue();
double v = Dataset.toDouble(value);
// expected value under null hypothesis
double eij = XIdot.get(i) * XdotJ.get(j) / Xdotdot;
ChisquareScore += Math.pow((v - eij), 2) / eij;
}
}
}
XdotJ = null;
XIdot = null;
AssociativeArray result = new AssociativeArray();
result.put("k", k);
result.put("n", n);
result.put("score", ChisquareScore);
return result;
}
