/*
* 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.visualization;
import java.util.List;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.ProcessSetupError.Severity;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.performance.PerformanceEvaluator;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.MetaDataInfo;
import com.rapidminer.operator.ports.metadata.SimplePrecondition;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.UndefinedParameterError;
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 creates a ROC chart for the given example set and model. The model
* will be applied on the example set and a ROC chart will be produced afterwards. If
* you are interested in finding an optimal threshold, the operator
* {@link com.rapidminer.operator.postprocessing.ThresholdFinder} should be used. If
* you are interested in the performance criterion Area-Under-Curve (AUC) the usual
* {@link PerformanceEvaluator} can be used. This operator just presents a ROC plot
* for a given model and data set.
*
* Please note that a predicted label of the given example set will be removed during
* the application of this operator.
*
* @author Ingo Mierswa
*
*/
public class ROCChartGenerator extends Operator {
public static final String PARAMETER_USE_EXAMPLE_WEIGHTS = "use_example_weights";
public static final String PARAMETER_USE_MODEL = "use_model";
private InputPort exampleSetInput = getInputPorts().createPort("example set");
private InputPort modelInput = getInputPorts().createPort("model");
private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
private OutputPort modelOutput = getOutputPorts().createPort("model");
public ROCChartGenerator(OperatorDescription description) {
super(description);
exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, Attributes.LABEL_NAME, Ontology.NOMINAL) {
@Override
public void makeAdditionalChecks(ExampleSetMetaData emd) throws UndefinedParameterError {
MetaDataInfo contained = emd.containsSpecialAttribute(Attributes.PREDICTION_NAME);
if (!getParameterAsBoolean(PARAMETER_USE_MODEL) && (contained != MetaDataInfo.YES) ){
if (contained == MetaDataInfo.NO)
createError(Severity.ERROR, "exampleset.needs_prediction");
else
createError(Severity.WARNING, "exampleset.needs_prediction");
}
}
});
modelInput.addPrecondition(new SimplePrecondition(modelInput, new MetaData(Model.class)) {
@Override
protected boolean isMandatory() {
return getParameterAsBoolean(PARAMETER_USE_MODEL);
}
});
getTransformer().addPassThroughRule(exampleSetInput, exampleSetOutput);
getTransformer().addPassThroughRule(modelInput, modelOutput);
}
@Override
public void doWork() throws OperatorException {
ExampleSet exampleSet = exampleSetInput.getData();
if (exampleSet.getAttributes().getLabel() == null) {
throw new UserError(this, 105);
}
if (!exampleSet.getAttributes().getLabel().isNominal()) {
throw new UserError(this, 101, "ROC Charts", exampleSet.getAttributes().getLabel());
}
if (exampleSet.getAttributes().getLabel().getMapping().getValues().size() != 2) {
throw new UserError(this, 114, "ROC Charts", exampleSet.getAttributes().getLabel());
}
if (exampleSet.getAttributes().getPredictedLabel() != null && getParameterAsBoolean(PARAMETER_USE_MODEL)) {
getLogger().warning("Input example already has a predicted label which will be removed.");
PredictionModel.removePredictedLabel(exampleSet);
}
if (exampleSet.getAttributes().getPredictedLabel() == null && !getParameterAsBoolean(PARAMETER_USE_MODEL)) {
throw new UserError(this, 107);
}
Model model = null;
if (getParameterAsBoolean(PARAMETER_USE_MODEL)) {
model = modelInput.getData();
exampleSet = model.apply(exampleSet);
}
if (exampleSet.getAttributes().getPredictedLabel() == null) {
throw new UserError(this, 107);
}
ROCDataGenerator rocDataGenerator = new ROCDataGenerator(1.0d, 1.0d);
ROCData rocPoints = rocDataGenerator.createROCData(exampleSet, getParameterAsBoolean(PARAMETER_USE_EXAMPLE_WEIGHTS),
ROCBias.getROCBiasParameter(this));
rocDataGenerator.createROCPlotDialog(rocPoints);
PredictionModel.removePredictedLabel(exampleSet);
modelOutput.deliver(model);
exampleSetOutput.deliver(exampleSet);
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeBoolean(PARAMETER_USE_EXAMPLE_WEIGHTS, "Indicates if example weights should be used for calculations (use 1 as weights for each example otherwise).", true));
types.add(new ParameterTypeBoolean(PARAMETER_USE_MODEL, "If checked a given model will be applied for generating ROCChart. If not the examples set must have a predicted label.", true));
types.add(ROCBias.makeParameterType());
return types;
}
}