/*
* 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.preprocessing.discretization;
import java.util.Collection;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Set;
import java.util.TreeSet;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SortedExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.annotation.ResourceConsumptionEstimator;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.SetRelation;
import com.rapidminer.operator.preprocessing.PreprocessingModel;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
import com.rapidminer.parameter.conditions.EqualTypeCondition;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.OperatorResourceConsumptionHandler;
/**
* This operator discretizes all numeric attributes in the dataset into nominal attributes.
* This discretization is performed by binning examples into bins of same size. The specified number
* of equally sized bins is created and the numerical values are simply sorted into
* those bins, so that all bins contain the same number of examples. Skips all special attributes
* including the label.
*
* @author Sebastian Land
*/
public class AbsoluteDiscretization extends AbstractDiscretizationOperator {
static {
registerDiscretizationOperator(AbsoluteDiscretization.class);
}
/** Indicates the number of used bins. */
public static final String PARAMETER_SIZE_OF_BINS = "size_of_bins";
/** Indicates if long range names should be used. */
public static final String PARAMETER_RANGE_NAME_TYPE = "range_name_type";
public static final String PARAMETER_SORTING_DIRECTION = "sorting_direction";
public static final String PARAMETER_AUTOMATIC_NUMBER_OF_DIGITS = "automatic_number_of_digits";
public static final String PARAMETER_NUMBER_OF_DIGITS = "number_of_digits";
public AbsoluteDiscretization(OperatorDescription description) {
super(description);
}
@Override
protected Collection<AttributeMetaData> modifyAttributeMetaData(ExampleSetMetaData emd, AttributeMetaData amd) throws UndefinedParameterError {
AttributeMetaData newAMD = new AttributeMetaData(amd.getName(), Ontology.NOMINAL, amd.getRole());
Set<String> valueSet = new TreeSet<String>();
newAMD.setValueSet(valueSet, SetRelation.SUPERSET);
if (getParameterAsInt(PARAMETER_RANGE_NAME_TYPE) == DiscretizationModel.RANGE_NAME_SHORT) {
for (int i = 0; i < (int)Math.ceil(((double)emd.getNumberOfExamples().getNumber()) / getParameterAsInt(PARAMETER_SIZE_OF_BINS)); i++) {
valueSet.add("range" + (i + 1));
}
switch (emd.getNumberOfExamples().getRelation()) {
case AT_LEAST:
newAMD.setValueSet(valueSet, SetRelation.SUPERSET);
break;
case AT_MOST:
newAMD.setValueSet(valueSet, SetRelation.SUBSET);
break;
case EQUAL:
newAMD.setValueSet(valueSet, SetRelation.EQUAL);
break;
case UNKNOWN:
newAMD.setValueSet(valueSet, SetRelation.UNKNOWN);
break;
}
}
return Collections.singletonList(newAMD);
}
@Override
public PreprocessingModel createPreprocessingModel(ExampleSet exampleSet) throws OperatorException {
DiscretizationModel model = new DiscretizationModel(exampleSet);
exampleSet.recalculateAllAttributeStatistics();
// calculating number of bins
int sizeOfBins = getParameterAsInt(PARAMETER_SIZE_OF_BINS);
int numberOfBins = exampleSet.size() / sizeOfBins;
int numberOfExamples = exampleSet.size();
// add one bin if a remainder exists
if (numberOfBins * sizeOfBins < numberOfExamples)
numberOfBins++;
HashMap<Attribute, double[]> ranges = new HashMap<Attribute, double[]>();
int sortingDirection = getParameterAsInt(PARAMETER_SORTING_DIRECTION);
for (Attribute attribute : exampleSet.getAttributes()) {
if (attribute.isNumerical()) { // skip nominal and date attributes
ExampleSet sortedSet = new SortedExampleSet(exampleSet, attribute, sortingDirection);
double[] binRange = new double[numberOfBins];
for (int i = 0; i < numberOfBins - 1; i++) {
int offset = (i + 1) * sizeOfBins - 1;
double infimum = sortedSet.getExample(offset).getValue(attribute);
offset++;
double supremum = sortedSet.getExample(offset).getValue(attribute);
// if targets equal values: Search for next different value
while (infimum == supremum && offset < numberOfExamples) {
supremum = sortedSet.getExample(offset).getValue(attribute);
offset++;
}
if (sortingDirection == SortedExampleSet.DECREASING)
binRange[numberOfBins - 2 - i] = (infimum + supremum) / 2d;
else
binRange[i] = (infimum + supremum) / 2d;
}
binRange[numberOfBins - 1] = Double.POSITIVE_INFINITY;
ranges.put(attribute, binRange);
}
}
// 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);
}
@Override
public Class<? extends PreprocessingModel> getPreprocessingModelClass() {
return DiscretizationModel.class;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_SIZE_OF_BINS, "Defines the number of examples which should be used for each bin.", 1, Integer.MAX_VALUE, false);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeCategory(PARAMETER_SORTING_DIRECTION, "Indicates if the values should be sorted in increasing or decreasing order.", SortedExampleSet.SORTING_DIRECTIONS, SortedExampleSet.DECREASING));
types.add(new ParameterTypeCategory(PARAMETER_RANGE_NAME_TYPE, "Indicates if long range names including the limits should be used.", DiscretizationModel.RANGE_NAME_TYPES, DiscretizationModel.RANGE_NAME_LONG));
type = new ParameterTypeBoolean(PARAMETER_AUTOMATIC_NUMBER_OF_DIGITS, "Indicates if the number of digits should be automatically determined for the range names.", true);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_RANGE_NAME_TYPE, DiscretizationModel.RANGE_NAME_TYPES, false, DiscretizationModel.RANGE_NAME_INTERVAL));
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_OF_DIGITS, "The minimum number of digits used for the interval names (-1: determine minimal number automatically).", -1, Integer.MAX_VALUE, -1);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_AUTOMATIC_NUMBER_OF_DIGITS, false, false));
types.add(type);
return types;
}
@Override
public ResourceConsumptionEstimator getResourceConsumptionEstimator() {
return OperatorResourceConsumptionHandler.getResourceConsumptionEstimator(getInputPort(), AbsoluteDiscretization.class, attributeSelector);
}
}