public Model learn(ExampleSet exampleSet) throws OperatorException {
double value = 0.0;
double[] confidences = null;
int method = getParameterAsInt(PARAMETER_METHOD);
Attribute label = exampleSet.getAttributes().getLabel();
if ((label.isNominal()) && ((method == MEDIAN) || (method == AVERAGE))) {
logWarning(
"Cannot use method '" + METHODS[method] + "' for nominal labels: changing to 'mode'!");
method = MODE;
} else if ((!label.isNominal()) && (method == MODE)) {
logWarning(
"Cannot use method '"
+ METHODS[method]
+ "' for numerical labels: changing to 'average'!");
method = AVERAGE;
}
switch (method) {
case MEDIAN:
double[] labels = new double[exampleSet.size()];
Iterator<Example> r = exampleSet.iterator();
int counter = 0;
while (r.hasNext()) {
Example example = r.next();
labels[counter++] = example.getValue(example.getAttributes().getLabel());
}
java.util.Arrays.sort(labels);
value = labels[exampleSet.size() / 2];
break;
case AVERAGE:
exampleSet.recalculateAttributeStatistics(label);
value = exampleSet.getStatistics(label, Statistics.AVERAGE);
break;
case MODE:
exampleSet.recalculateAttributeStatistics(label);
value = exampleSet.getStatistics(label, Statistics.MODE);
confidences = new double[label.getMapping().size()];
for (int i = 0; i < confidences.length; i++) {
confidences[i] =
exampleSet.getStatistics(label, Statistics.COUNT, label.getMapping().mapIndex(i))
/ exampleSet.size();
}
break;
case CONSTANT:
value = getParameterAsDouble(PARAMETER_CONSTANT);
break;
case ATTRIBUTE:
return new AttributeDefaultModel(
exampleSet, getParameterAsString(PARAMETER_ATTRIBUTE_NAME));
default:
// cannot happen
throw new OperatorException("DefaultLearner: Unknown default method '" + method + "'!");
}
log(
"Default value is '"
+ (label.isNominal() ? label.getMapping().mapIndex((int) value) : value + "")
+ "'.");
return new DefaultModel(exampleSet, value, confidences);
}
private RuleModel createNumericalRuleModel(ExampleSet trainingSet, Attribute attribute) {
RuleModel model = new RuleModel(trainingSet);
// split by best attribute
int oldSize = -1;
while ((trainingSet.size() > 0) && (trainingSet.size() != oldSize)) {
ExampleSet exampleSet = (ExampleSet) trainingSet.clone();
Split bestSplit = splitter.getBestSplit(exampleSet, attribute, null);
double bestSplitValue = bestSplit.getSplitPoint();
if (!Double.isNaN(bestSplitValue)) {
SplittedExampleSet splittedSet =
SplittedExampleSet.splitByAttribute(exampleSet, attribute, bestSplitValue);
Attribute label = splittedSet.getAttributes().getLabel();
splittedSet.selectSingleSubset(0);
SplitCondition condition = new LessEqualsSplitCondition(attribute, bestSplitValue);
splittedSet.recalculateAttributeStatistics(label);
int labelValue = (int) splittedSet.getStatistics(label, Statistics.MODE);
String labelName = label.getMapping().mapIndex(labelValue);
Rule rule = new Rule(labelName, condition);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues())
frequencies[counter++] = (int) splittedSet.getStatistics(label, Statistics.COUNT, value);
rule.setFrequencies(frequencies);
model.addRule(rule);
oldSize = trainingSet.size();
trainingSet = rule.removeCovered(trainingSet);
} else {
break;
}
}
// add default rule if some examples were not yet covered
if (trainingSet.size() > 0) {
Attribute label = trainingSet.getAttributes().getLabel();
trainingSet.recalculateAttributeStatistics(label);
int index = (int) trainingSet.getStatistics(label, Statistics.MODE);
String defaultLabel = label.getMapping().mapIndex(index);
Rule defaultRule = new Rule(defaultLabel);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues())
frequencies[counter++] = (int) (trainingSet.getStatistics(label, Statistics.COUNT, value));
defaultRule.setFrequencies(frequencies);
model.addRule(defaultRule);
}
return model;
}
private double[] getMeanVector(ExampleSet exampleSet) {
exampleSet.recalculateAllAttributeStatistics();
Attributes attributes = exampleSet.getAttributes();
double[] meanVector = new double[attributes.size()];
int i = 0;
for (Attribute attribute : attributes) {
if (Ontology.ATTRIBUTE_VALUE_TYPE.isA(attribute.getValueType(), Ontology.DATE_TIME)) {
meanVector[i] = exampleSet.getStatistics(attribute, Statistics.MINIMUM);
} else if (attribute.isNominal())
meanVector[i] = exampleSet.getStatistics(attribute, Statistics.MODE);
else meanVector[i] = exampleSet.getStatistics(attribute, Statistics.AVERAGE);
i++;
}
return meanVector;
}
private NeuralDataSet getTraining(ExampleSet exampleSet) {
double[][] data = new double[exampleSet.size()][exampleSet.getAttributes().size()];
double[][] labels = new double[exampleSet.size()][1];
int index = 0;
Attribute label = exampleSet.getAttributes().getLabel();
this.attributeMin = new double[exampleSet.getAttributes().size()];
this.attributeMax = new double[attributeMin.length];
exampleSet.recalculateAllAttributeStatistics();
int a = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
this.attributeMin[a] = exampleSet.getStatistics(attribute, Statistics.MINIMUM);
this.attributeMax[a] = exampleSet.getStatistics(attribute, Statistics.MAXIMUM);
a++;
}
this.labelMin = exampleSet.getStatistics(label, Statistics.MINIMUM);
this.labelMax = exampleSet.getStatistics(label, Statistics.MAXIMUM);
for (Example example : exampleSet) {
// attributes
a = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
if (attributeMin[a] != attributeMax[a]) {
data[index][a] =
(example.getValue(attribute) - attributeMin[a]) / (attributeMax[a] - attributeMin[a]);
} else {
data[index][a] = example.getValue(attribute) - attributeMin[a];
}
a++;
}
// label
if (label.isNominal()) {
labels[index][0] = example.getValue(label);
} else {
if (labelMax != labelMin) {
labels[index][0] = (example.getValue(label) - labelMin) / (labelMax - labelMin);
} else {
labels[index][0] = example.getValue(label) - labelMin;
}
}
index++;
}
return new BasicNeuralDataSet(data, labels);
}
public PCAModel(ExampleSet eSet, double[] eigenvalues, double[][] eigenvectors) {
super(eSet);
this.keepAttributes = false;
this.attributeNames = new String[eSet.getAttributes().size()];
this.means = new double[eSet.getAttributes().size()];
int counter = 0;
eSet.recalculateAllAttributeStatistics(); // ensures that the statistics were created
for (Attribute attribute : eSet.getAttributes()) {
attributeNames[counter] = attribute.getName();
means[counter] = eSet.getStatistics(attribute, Statistics.AVERAGE);
counter++;
}
this.eigenVectors = new ArrayList<Eigenvector>(eigenvalues.length);
for (int i = 0; i < eigenvalues.length; i++) {
double[] currentEigenVector = new double[eSet.getAttributes().size()];
for (int j = 0; j < currentEigenVector.length; j++) {
currentEigenVector[j] = eigenvectors[j][i];
}
this.eigenVectors.add(new Eigenvector(currentEigenVector, eigenvalues[i]));
}
// order the eigenvectors by the eigenvalues
Collections.sort(this.eigenVectors);
calculateCumulativeVariance();
}
@Override
public PreprocessingModel createPreprocessingModel(ExampleSet exampleSet)
throws OperatorException {
boolean sortMappings = getParameterAsBoolean(PARAMETER_SORT_MAPPING_ALPHABETICALLY);
Map<String, MappingTranslation> translations = new HashMap<String, MappingTranslation>();
exampleSet.recalculateAllAttributeStatistics();
for (Attribute attribute : exampleSet.getAttributes()) {
MappingTranslation translation =
new MappingTranslation((NominalMapping) attribute.getMapping().clone());
if (attribute.isNominal()) {
for (String value : attribute.getMapping().getValues()) {
double count = exampleSet.getStatistics(attribute, Statistics.COUNT, value);
if (count > 0) {
translation.newMapping.mapString(value);
}
}
if (translation.newMapping.size() < attribute.getMapping().size()) {
if (sortMappings) {
translation.newMapping.sortMappings();
}
translations.put(attribute.getName(), translation);
}
}
}
return new RemoveUnusedNominalValuesModel(exampleSet, translations);
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class);
// only use numeric attributes
Tools.onlyNumericalAttributes(exampleSet, "KernelPCA");
Tools.onlyNonMissingValues(exampleSet, getOperatorClassName(), this);
Attributes attributes = exampleSet.getAttributes();
int numberOfExamples = exampleSet.size();
// calculating means for later zero centering
exampleSet.recalculateAllAttributeStatistics();
double[] means = new double[exampleSet.getAttributes().size()];
int i = 0;
for (Attribute attribute : exampleSet.getAttributes()) {
means[i] = exampleSet.getStatistics(attribute, Statistics.AVERAGE);
i++;
}
// kernel
Kernel kernel = Kernel.createKernel(this);
// copying zero centered exampleValues
ArrayList<double[]> exampleValues = new ArrayList<double[]>(numberOfExamples);
i = 0;
for (Example columnExample : exampleSet) {
double[] columnValues = getAttributeValues(columnExample, attributes, means);
exampleValues.add(columnValues);
i++;
}
// filling kernel matrix
Matrix kernelMatrix = new Matrix(numberOfExamples, numberOfExamples);
for (i = 0; i < numberOfExamples; i++) {
for (int j = 0; j < numberOfExamples; j++) {
kernelMatrix.set(
i, j, kernel.calculateDistance(exampleValues.get(i), exampleValues.get(j)));
}
}
// calculating eigenVectors
EigenvalueDecomposition eig = kernelMatrix.eig();
Model model = new KernelPCAModel(exampleSet, means, eig.getV(), exampleValues, kernel);
if (exampleSetOutput.isConnected()) {
exampleSetOutput.deliver(model.apply(exampleSet));
}
originalOutput.deliver(exampleSet);
modelOutput.deliver(model);
}
@Override
public void updateStatistics(final ExampleSet exampleSet) {
final String days =
WHITESPACE
+ I18N.getMessage(
I18N.getGUIBundle(), "gui.label.attribute_statistics.statistics.days.label");
final String hours =
WHITESPACE
+ I18N.getMessage(
I18N.getGUIBundle(), "gui.label.attribute_statistics.statistics.hours.label");
long minMilliseconds = (long) exampleSet.getStatistics(getAttribute(), Statistics.MINIMUM);
long maxMilliseconds = (long) exampleSet.getStatistics(getAttribute(), Statistics.MAXIMUM);
long difference = maxMilliseconds - minMilliseconds;
String dura = "";
if (getAttribute().getValueType() == Ontology.DATE) {
// days
dura +=
com.rapidminer.tools.Tools.formatIntegerIfPossible(
Math.floor(difference / (H_IN_D * M_IN_H * S_IN_M * MS_IN_S)), 3)
+ days;
} else if (getAttribute().getValueType() == Ontology.TIME) {
// hours
dura +=
com.rapidminer.tools.Tools.formatIntegerIfPossible(
Math.floor(difference / (M_IN_H * S_IN_M * MS_IN_S)), 3)
+ hours;
} else if (getAttribute().getValueType() == Ontology.DATE_TIME) {
// days + hours + minutes + seconds
dura +=
com.rapidminer.tools.Tools.formatIntegerIfPossible(
Math.floor(difference / (H_IN_D * M_IN_H * S_IN_M * MS_IN_S)), 3)
+ SHORT_DAY;
dura += WHITESPACE;
double leftoverMilliSeconds = difference % (H_IN_D * M_IN_H * S_IN_M * MS_IN_S);
dura +=
com.rapidminer.tools.Tools.formatIntegerIfPossible(
Math.floor(leftoverMilliSeconds / (M_IN_H * S_IN_M * MS_IN_S)), 3)
+ SHORT_HOUR;
dura += WHITESPACE;
leftoverMilliSeconds = leftoverMilliSeconds % (M_IN_H * S_IN_M * MS_IN_S);
dura +=
com.rapidminer.tools.Tools.formatIntegerIfPossible(
Math.floor(leftoverMilliSeconds / (S_IN_M * MS_IN_S)), 3)
+ SHORT_MINUTE;
dura += WHITESPACE;
leftoverMilliSeconds = leftoverMilliSeconds % (S_IN_M * MS_IN_S);
dura +=
com.rapidminer.tools.Tools.formatIntegerIfPossible(
Math.floor(leftoverMilliSeconds / MS_IN_S), 3)
+ SHORT_SECOND;
}
String minResult = null;
String maxResult = null;
if (getAttribute().getValueType() == Ontology.DATE) {
minResult = FORMAT_DATE.format(new Date(minMilliseconds));
maxResult = FORMAT_DATE.format(new Date(maxMilliseconds));
} else if (getAttribute().getValueType() == Ontology.TIME) {
minResult = FORMAT_TIME.format(new Date(minMilliseconds));
maxResult = FORMAT_TIME.format(new Date(maxMilliseconds));
} else if (getAttribute().getValueType() == Ontology.DATE_TIME) {
minResult = FORMAT_DATE_TIME.format(new Date(minMilliseconds));
maxResult = FORMAT_DATE_TIME.format(new Date(maxMilliseconds));
}
missing = exampleSet.getStatistics(getAttribute(), Statistics.UNKNOWN);
from = minResult;
until = maxResult;
duration = dura;
fireStatisticsChangedEvent();
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData(ExampleSet.class);
exampleSet.recalculateAllAttributeStatistics();
List<String[]> attributeValueOptions = getParameterList(PARAMETER_ATTRIBUTES);
LinkedHashMap<Attribute, Integer> attributeValueOptionsMap =
new LinkedHashMap<Attribute, Integer>();
int[] valueOptions = new int[attributeValueOptions.size()];
Pattern[] attributeRegexPatterns = new Pattern[attributeValueOptions.size()];
Attribute[] attributes = new Attribute[attributeValueOptions.size()];
Iterator<String[]> iterator = attributeValueOptions.iterator();
int j = 0;
while (iterator.hasNext()) {
String[] pair = iterator.next();
String regex = pair[0];
try {
attributeRegexPatterns[j] = Pattern.compile(regex);
} catch (PatternSyntaxException e) {
throw new UserError(this, 206, regex, e.getMessage());
}
attributes[j] = exampleSet.getAttributes().get(pair[0]);
valueOptions[j] =
((ParameterTypeCategory)
((ParameterTypeList) getParameterType(PARAMETER_ATTRIBUTES)).getValueType())
.getIndex(pair[1]);
j++;
}
for (int i = 0; i < attributeRegexPatterns.length; i++) {
Iterator<Attribute> a = exampleSet.getAttributes().allAttributes();
while (a.hasNext()) {
Attribute attribute = a.next();
Matcher matcher = attributeRegexPatterns[i].matcher(attribute.getName());
if (matcher.matches()) {
attributeValueOptionsMap.put(attribute, valueOptions[i]);
}
}
}
double p = getParameterAsDouble(PARAMETER_P);
boolean filterAttribute = getParameterAsBoolean(PARAMETER_FILTER_ATTRIBUTE);
String iterationMacro = getParameterAsString(PARAMETER_ITERATION_MACRO);
// applying on complete set
if (getParameterAsBoolean(PARAMETER_APPLY_ON_COMPLETE_SET)) {
if (iterationMacro != null) {
getProcess().getMacroHandler().addMacro(iterationMacro, "ALL");
}
innerExampleSetSource.deliver(exampleSet);
getSubprocess(0).execute();
}
// applying on subgroups defined by attributes
for (Entry<Attribute, Integer> attributeEntry : attributeValueOptionsMap.entrySet()) {
Attribute attribute = attributeEntry.getKey();
if (!attribute.isNominal()) {
continue;
}
List<String> values = null;
switch (attributeEntry.getValue()) {
case VALUE_OPTION_ALL:
values = attribute.getMapping().getValues();
break;
case VALUE_OPTION_ABOVE_P:
values = new Vector<String>();
for (String value : attribute.getMapping().getValues()) {
if (exampleSet.getStatistics(attribute, Statistics.COUNT, value) / exampleSet.size()
>= p) {
values.add(value);
}
}
break;
default:
values = attribute.getMapping().getValues();
break;
}
for (String value : values) {
if (exampleSet.getStatistics(attribute, Statistics.COUNT, value) > 0) {
String className = "attribute_value_filter";
String parameter = attribute.getName() + "=" + value;
log("Creating condition '" + className + "' with parameter '" + parameter + "'");
Condition condition = null;
try {
condition = ConditionedExampleSet.createCondition(className, exampleSet, parameter);
} catch (ConditionCreationException e) {
throw new UserError(this, 904, className, e.getMessage());
}
ExampleSet subgroupSet = new ConditionedExampleSet(exampleSet, condition, false);
if (filterAttribute) {
subgroupSet.getAttributes().remove(attribute);
}
if (iterationMacro != null) {
getProcess().getMacroHandler().addMacro(iterationMacro, parameter.replace(' ', '_'));
}
// applying subprocess
innerExampleSetSource.deliver(subgroupSet);
getSubprocess(0).execute();
if (filterAttribute) {
subgroupSet.getAttributes().addRegular(attribute);
}
}
inApplyLoop();
}
}
if (iterationMacro != null) {
getProcess().getMacroHandler().addMacro(iterationMacro, null);
}
}
@Override
public Model learn(ExampleSet exampleSet) throws OperatorException {
Attribute label = exampleSet.getAttributes().getLabel();
RuleModel ruleModel = new RuleModel(exampleSet);
double pureness = getParameterAsDouble(PARAMETER_PURENESS);
TermDetermination termDetermination = new TermDetermination(new AccuracyCriterion(), 0.5d);
ExampleSet trainingSet = (ExampleSet) exampleSet.clone();
for (String labelName : label.getMapping().getValues()) {
trainingSet.recalculateAttributeStatistics(label);
int oldSize = -1;
while (trainingSet.size() > 0
&& trainingSet.size() != oldSize
&& trainingSet.getStatistics(label, Statistics.COUNT, labelName) > 0) {
Rule rule = new Rule(labelName);
ExampleSet oldTrainingSet = (ExampleSet) trainingSet.clone();
// grow rule
int growOldSize = -1;
ExampleSet growSet = (ExampleSet) trainingSet.clone();
while (growSet.size() > 0
&& growSet.size() != growOldSize
&& !rule.isPure(growSet, pureness)
&& growSet.getAttributes().size() > 0) {
SplitCondition term = termDetermination.getBestTerm(growSet, labelName);
if (term == null) {
break;
}
rule.addTerm(term);
Attribute splitAttribute = growSet.getAttributes().get(term.getAttributeName());
growSet.getAttributes().remove(splitAttribute);
growOldSize = growSet.size();
growSet = rule.getCovered(growSet);
}
// add rule if not empty
if (rule.getTerms().size() > 0) {
growSet = rule.getCovered(trainingSet);
growSet.recalculateAttributeStatistics(label);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues()) {
frequencies[counter++] = (int) growSet.getStatistics(label, Statistics.COUNT, value);
}
rule.setFrequencies(frequencies);
ruleModel.addRule(rule);
oldSize = trainingSet.size();
trainingSet = rule.removeCovered(oldTrainingSet);
} else {
break; // no other terms found for this class --> next class
}
trainingSet.recalculateAttributeStatistics(label);
}
checkForStop();
}
// training set not empty? add default rule
if (trainingSet.size() > 0) {
trainingSet.recalculateAttributeStatistics(label);
int index = (int) trainingSet.getStatistics(label, Statistics.MODE);
String defaultLabel = label.getMapping().mapIndex(index);
Rule defaultRule = new Rule(defaultLabel);
int[] frequencies = new int[label.getMapping().size()];
int counter = 0;
for (String value : label.getMapping().getValues()) {
frequencies[counter++] = (int) trainingSet.getStatistics(label, Statistics.COUNT, value);
}
defaultRule.setFrequencies(frequencies);
ruleModel.addRule(defaultRule);
}
return ruleModel;
}
@Override
public PreprocessingModel createPreprocessingModel(ExampleSet exampleSet)
throws OperatorException {
HashMap<Attribute, double[]> ranges = new HashMap<Attribute, double[]>();
// Get and check parametervalues
boolean useSqrt = getParameterAsBoolean(PARAMETER_USE_SQRT_OF_EXAMPLES);
int numberOfBins = 0;
if (!useSqrt) {
// if not automatic sizing of bins, use parametervalue
numberOfBins = getParameterAsInt(PARAMETER_NUMBER_OF_BINS);
if (numberOfBins >= (exampleSet.size() - 1)) {
throw new UserError(
this,
116,
PARAMETER_NUMBER_OF_BINS,
"number of bins must be smaller than number of examples (here: "
+ exampleSet.size()
+ ")");
}
} else {
exampleSet.recalculateAllAttributeStatistics();
}
for (Attribute currentAttribute : exampleSet.getAttributes()) {
if (useSqrt) {
numberOfBins =
(int)
Math.round(
Math.sqrt(
exampleSet.size()
- (int)
exampleSet.getStatistics(currentAttribute, Statistics.UNKNOWN)));
}
double[] attributeRanges = new double[numberOfBins];
ExampleSet sortedSet =
new SortedExampleSet(exampleSet, currentAttribute, SortedExampleSet.INCREASING);
// finding ranges
double examplesPerBin = exampleSet.size() / (double) numberOfBins;
double currentBinSpace = examplesPerBin;
double lastValue = Double.NaN;
int currentBin = 0;
for (Example example : sortedSet) {
double value = example.getValue(currentAttribute);
if (!Double.isNaN(value)) {
// change bin if full and not last
if (currentBinSpace < 1 && currentBin < numberOfBins && value != lastValue) {
if (!Double.isNaN(lastValue)) {
attributeRanges[currentBin] = (lastValue + value) / 2;
currentBin++;
currentBinSpace += examplesPerBin; // adding because same values might
// cause binspace to be negative
if (currentBinSpace < 1) {
throw new UserError(this, 944, currentAttribute.getName());
}
}
}
currentBinSpace--;
lastValue = value;
}
}
attributeRanges[numberOfBins - 1] = Double.POSITIVE_INFINITY;
ranges.put(currentAttribute, attributeRanges);
}
DiscretizationModel model = new DiscretizationModel(exampleSet);
// determine number of digits
int numberOfDigits = -1;
if (getParameterAsBoolean(PARAMETER_AUTOMATIC_NUMBER_OF_DIGITS) == false) {
numberOfDigits = getParameterAsInt(PARAMETER_NUMBER_OF_DIGITS);
}
model.setRanges(ranges, "range", getParameterAsInt(PARAMETER_RANGE_NAME_TYPE), numberOfDigits);
return model;
}
