ANOVAMatrixOperator.java example

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rapidminer-vega-master
/*
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 *
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 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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package com.rapidminer.operator.visualization.dependencies;

import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.NonSpecialAttributesExampleSet;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorCreationException;
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.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.operator.ports.metadata.GenerateNewMDRule;
import com.rapidminer.operator.ports.metadata.PassThroughRule;
import com.rapidminer.operator.preprocessing.transformation.GroupedANOVAOperator;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.OperatorService;
import com.rapidminer.tools.math.SignificanceTestResult;


/**
 * <p>This operator calculates the significance of difference for the values for 
 * all numerical attributes depending on the groups defined by all nominal attributes.
 * Please refer to the operator {@link GroupedANOVAOperator} for details of the
 * calculation.</p>
 * 
 * @author Ingo Mierswa
 */
public class ANOVAMatrixOperator extends Operator {

	private InputPort exampleSetInput = getInputPorts().createPort("example set", new ExampleSetMetaData());
	private OutputPort exampleSetOutput = getOutputPorts().createPort("example set");
	private OutputPort anovaOutput = getOutputPorts().createPort("anova");

	public ANOVAMatrixOperator(OperatorDescription description) {
		super(description);

		exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput) {
			@Override
			public void makeAdditionalChecks(ExampleSetMetaData emd) throws UndefinedParameterError {
				int numberOfNominals = 0;
				for (AttributeMetaData amd: emd.getAllAttributes()) {
					if (amd.isNominal())
						numberOfNominals++;
				}
				if (numberOfNominals == 0) {
					createError(Severity.WARNING, "exampleset.must_contain_nominal_attribute");
				}
			}
		});

		getTransformer().addRule(new PassThroughRule(exampleSetInput, exampleSetOutput, false));
		getTransformer().addRule(new GenerateNewMDRule(anovaOutput, ANOVAMatrix.class));
	}

	@Override
	public void doWork() throws OperatorException {
		ExampleSet inputSet = exampleSetInput.getData();
		ExampleSet exampleSet = new NonSpecialAttributesExampleSet(inputSet);

		// determine anova and grouping attributes
		List<String> nominalAttributes = new ArrayList<String>();
		List<String> numericalAttributes = new ArrayList<String>();
		Iterator<Attribute> a = exampleSet.getAttributes().allAttributes();
		while (a.hasNext()) {
			Attribute attribute = a.next();
			if (attribute.isNominal())
				nominalAttributes.add(attribute.getName());
			else if (attribute.isNumerical())
				numericalAttributes.add(attribute.getName());
		}

		// init "inner" operator
		GroupedANOVAOperator groupedAnovaOperator = null;
		try {
			groupedAnovaOperator = OperatorService.createOperator(GroupedANOVAOperator.class);
		} catch (OperatorCreationException e) {
			throw new UserError(this, 109, GroupedANOVAOperator.class.getName());
		}
		double significanceLevel = getParameterAsDouble(GroupedANOVAOperator.PARAMETER_SIGNIFICANCE_LEVEL);
		groupedAnovaOperator.setParameter(GroupedANOVAOperator.PARAMETER_SIGNIFICANCE_LEVEL, significanceLevel + "");
		groupedAnovaOperator.setParameter(GroupedANOVAOperator.PARAMETER_ONLY_DISTINCT, getParameterAsBoolean(GroupedANOVAOperator.PARAMETER_ONLY_DISTINCT) + "");

		// calculate all values
		double[][] probabilities = new double[numericalAttributes.size()][nominalAttributes.size()];
		for (int numericalCounter = 0; numericalCounter < probabilities.length; numericalCounter++) {
			String numericalAttributeName = numericalAttributes.get(numericalCounter);
			for (int nominalCounter = 0; nominalCounter < probabilities[numericalCounter].length; nominalCounter++) {
				String nominalAttributeName = nominalAttributes.get(nominalCounter);
				groupedAnovaOperator.setParameter(GroupedANOVAOperator.PARAMETER_ANOVA_ATTRIBUTE, numericalAttributeName);
				groupedAnovaOperator.setParameter(GroupedANOVAOperator.PARAMETER_GROUP_BY_ATTRIBUTE, nominalAttributeName);
				SignificanceTestResult testResult = groupedAnovaOperator.apply((ExampleSet)exampleSet.clone());
				probabilities[numericalCounter][nominalCounter] = testResult.getProbability();
			}			
		}

		// create and return result
		exampleSetOutput.deliver(exampleSet);
		anovaOutput.deliver(new ANOVAMatrix(probabilities, numericalAttributes, nominalAttributes, significanceLevel));		
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		types.add(new ParameterTypeDouble(GroupedANOVAOperator.PARAMETER_SIGNIFICANCE_LEVEL, "The significance level for the ANOVA calculation.", 0.0d, 1.0d, 0.05d));
		types.add(new ParameterTypeBoolean(GroupedANOVAOperator.PARAMETER_ONLY_DISTINCT, "Indicates if only rows with distinct values for the aggregation attribute should be used for the calculation of the aggregation function.", false));
		return types;
	}
}