/*
* RapidMiner
*
* Copyright (C) 2001-2011 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.postprocessing;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.NominalMapping;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeString;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.math.ROCBias;
import com.rapidminer.tools.math.ROCData;
import com.rapidminer.tools.math.ROCDataGenerator;
/**
* This operator finds the best threshold for crisp classifying based on user
* defined costs.
*
* @author Martin Scholz, Ingo Mierswa
*/
public class ThresholdFinder extends Operator {
public static final String PARAMETER_DEFINE_LABELS = "define_labels";
public static final String PARAMETER_FIRST_LABEL = "first_label";
public static final String PARAMETER_SECOND_LABEL = "second_label";
public static final String PARAMETER_MISCLASSIFICATION_COSTS_FIRST = "misclassification_costs_first";
public static final String PARAMETER_MISCLASSIFICATION_COSTS_SECOND = "misclassification_costs_second";
public static final String PARAMETER_SHOW_ROC_PLOT = "show_roc_plot";
public static final String PARAMETER_USE_EXAMPLE_WEIGHTS = "use_example_weights";
private InputPort exampleSetInput = getInputPorts().createPort("example set", ExampleSet.class);
private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
private OutputPort thresholdOutput = getOutputPorts().createPort("threshold");
public ThresholdFinder(OperatorDescription description) {
super(description);
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, Ontology.VALUE_TYPE, Attributes.LABEL_NAME, Attributes.PREDICTION_NAME, Attributes.CONFIDENCE_NAME));
getTransformer().addPassThroughRule(exampleSetInput, exampleSetOutput);
getTransformer().addGenerationRule(thresholdOutput, Threshold.class);
}
@Override
public void doWork() throws OperatorException {
// sanity checks
ExampleSet exampleSet = exampleSetInput.getData();
// checking preconditions
Attribute label = exampleSet.getAttributes().getLabel();
exampleSet.recalculateAttributeStatistics(label);
if (label == null)
throw new UserError(this, 105);
if (!label.isNominal())
throw new UserError(this, 101, label, "threshold finding");
NominalMapping mapping = label.getMapping();
if (mapping.size() != 2)
throw new UserError(this, 118, new Object[] { label, Integer.valueOf(mapping.getValues().size()), Integer.valueOf(2) });
if (exampleSet.getAttributes().getPredictedLabel() == null) {
throw new UserError(this, 107);
}
boolean useExplictLabels = getParameterAsBoolean(PARAMETER_DEFINE_LABELS);
double secondCost = getParameterAsDouble(PARAMETER_MISCLASSIFICATION_COSTS_SECOND);
double firstCost = getParameterAsDouble(PARAMETER_MISCLASSIFICATION_COSTS_FIRST);
if (useExplictLabels) {
String firstLabel = getParameterAsString(PARAMETER_FIRST_LABEL);
String secondLabel = getParameterAsString(PARAMETER_SECOND_LABEL);
if (mapping.getIndex(firstLabel) == -1)
throw new UserError(this, 143, firstLabel, label.getName());
if (mapping.getIndex(secondLabel) == -1)
throw new UserError(this, 143, secondLabel, label.getName());
// if explicit order differs from order in data: internally swap costs.
if (mapping.getIndex(firstLabel) > mapping.getIndex(secondLabel)) {
double temp = firstCost;
firstCost = secondCost;
secondCost = temp;
}
}
// create ROC data
ROCDataGenerator rocDataGenerator = new ROCDataGenerator(firstCost, secondCost);
ROCData rocData = rocDataGenerator.createROCData(exampleSet, getParameterAsBoolean(PARAMETER_USE_EXAMPLE_WEIGHTS), ROCBias.getROCBiasParameter(this));
// create plotter
if (getParameterAsBoolean(PARAMETER_SHOW_ROC_PLOT))
rocDataGenerator.createROCPlotDialog(rocData, true, true);
// create and return output
exampleSetOutput.deliver(exampleSet);
thresholdOutput.deliver(new Threshold(rocDataGenerator.getBestThreshold(), mapping.getNegativeString(), mapping.getPositiveString()));
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> list = super.getParameterTypes();
list.add(new ParameterTypeBoolean(PARAMETER_DEFINE_LABELS, "If checked, you can define explicitly which is the first and the second label.", false));
ParameterTypeString type = new ParameterTypeString(PARAMETER_FIRST_LABEL, "The first label.");
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_DEFINE_LABELS, true, true));
list.add(type);
type = new ParameterTypeString(PARAMETER_SECOND_LABEL, "The second label.");
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_DEFINE_LABELS, true, true));
list.add(type);
list.add(new ParameterTypeDouble(PARAMETER_MISCLASSIFICATION_COSTS_FIRST, "The costs assigned when an example of the first class is classified as one of the second.", 0, Double.POSITIVE_INFINITY, 1, false));
list.add(new ParameterTypeDouble(PARAMETER_MISCLASSIFICATION_COSTS_SECOND, "The costs assigned when an example of the second class is classified as one of the first.", 0, Double.POSITIVE_INFINITY, 1, false));
list.add(new ParameterTypeBoolean(PARAMETER_SHOW_ROC_PLOT, "Display a plot of the ROC curve.", false));
list.add(new ParameterTypeBoolean(PARAMETER_USE_EXAMPLE_WEIGHTS, "Indicates if example weights should be used.", true));
list.add(ROCBias.makeParameterType());
return list;
}
}