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/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.mahout.classifier.df.builder;

import org.apache.mahout.classifier.df.data.Data;
import org.apache.mahout.classifier.df.data.Dataset;
import org.apache.mahout.classifier.df.data.Instance;
import org.apache.mahout.classifier.df.data.conditions.Condition;
import org.apache.mahout.classifier.df.node.CategoricalNode;
import org.apache.mahout.classifier.df.node.Leaf;
import org.apache.mahout.classifier.df.node.Node;
import org.apache.mahout.classifier.df.node.NumericalNode;
import org.apache.mahout.classifier.df.split.IgSplit;
import org.apache.mahout.classifier.df.split.OptIgSplit;
import org.apache.mahout.classifier.df.split.Split;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.Random;

/**
 * Builds a Decision Tree 
* Based on the algorithm described in the "Decision Trees" tutorials by Andrew W. Moore, available at:
*
* http://www.cs.cmu.edu/~awm/tutorials *

* This class can be used when the criterion variable is the categorical attribute. */ @Deprecated public class DefaultTreeBuilder implements TreeBuilder { private static final Logger log = LoggerFactory.getLogger(DefaultTreeBuilder.class); private static final int[] NO_ATTRIBUTES = new int[0]; /** * indicates which CATEGORICAL attributes have already been selected in the parent nodes */ private boolean[] selected; /** * number of attributes to select randomly at each node */ private int m = 1; /** * IgSplit implementation */ private final IgSplit igSplit; public DefaultTreeBuilder() { igSplit = new OptIgSplit(); } public void setM(int m) { this.m = m; } @Override public Node build(Random rng, Data data) { if (selected == null) { selected = new boolean[data.getDataset().nbAttributes()]; selected[data.getDataset().getLabelId()] = true; // never select the label } if (data.isEmpty()) { return new Leaf(-1); } if (isIdentical(data)) { return new Leaf(data.majorityLabel(rng)); } if (data.identicalLabel()) { return new Leaf(data.getDataset().getLabel(data.get(0))); } int[] attributes = randomAttributes(rng, selected, m); if (attributes == null || attributes.length == 0) { // we tried all the attributes and could not split the data anymore return new Leaf(data.majorityLabel(rng)); } // find the best split Split best = null; for (int attr : attributes) { Split split = igSplit.computeSplit(data, attr); if (best == null || best.getIg() < split.getIg()) { best = split; } } boolean alreadySelected = selected[best.getAttr()]; if (alreadySelected) { // attribute already selected log.warn("attribute {} already selected in a parent node", best.getAttr()); } Node childNode; if (data.getDataset().isNumerical(best.getAttr())) { boolean[] temp = null; Data loSubset = data.subset(Condition.lesser(best.getAttr(), best.getSplit())); Data hiSubset = data.subset(Condition.greaterOrEquals(best.getAttr(), best.getSplit())); if (loSubset.isEmpty() || hiSubset.isEmpty()) { // the selected attribute did not change the data, avoid using it in the child notes selected[best.getAttr()] = true; } else { // the data changed, so we can unselect all previousely selected NUMERICAL attributes temp = selected; selected = cloneCategoricalAttributes(data.getDataset(), selected); } Node loChild = build(rng, loSubset); Node hiChild = build(rng, hiSubset); // restore the selection state of the attributes if (temp != null) { selected = temp; } else { selected[best.getAttr()] = alreadySelected; } childNode = new NumericalNode(best.getAttr(), best.getSplit(), loChild, hiChild); } else { // CATEGORICAL attribute selected[best.getAttr()] = true; double[] values = data.values(best.getAttr()); Node[] children = new Node[values.length]; for (int index = 0; index < values.length; index++) { Data subset = data.subset(Condition.equals(best.getAttr(), values[index])); children[index] = build(rng, subset); } selected[best.getAttr()] = alreadySelected; childNode = new CategoricalNode(best.getAttr(), values, children); } return childNode; } /** * checks if all the vectors have identical attribute values. Ignore selected attributes. * * @return true is all the vectors are identical or the data is empty
* false otherwise */ private boolean isIdentical(Data data) { if (data.isEmpty()) { return true; } Instance instance = data.get(0); for (int attr = 0; attr < selected.length; attr++) { if (selected[attr]) { continue; } for (int index = 1; index < data.size(); index++) { if (data.get(index).get(attr) != instance.get(attr)) { return false; } } } return true; } /** * Make a copy of the selection state of the attributes, unselect all numerical attributes * * @param selected selection state to clone * @return cloned selection state */ private static boolean[] cloneCategoricalAttributes(Dataset dataset, boolean[] selected) { boolean[] cloned = new boolean[selected.length]; for (int i = 0; i < selected.length; i++) { cloned[i] = !dataset.isNumerical(i) && selected[i]; } return cloned; } /** * Randomly selects m attributes to consider for split, excludes IGNORED and LABEL attributes * * @param rng random-numbers generator * @param selected attributes' state (selected or not) * @param m number of attributes to choose * @return list of selected attributes' indices, or null if all attributes have already been selected */ protected static int[] randomAttributes(Random rng, boolean[] selected, int m) { int nbNonSelected = 0; // number of non selected attributes for (boolean sel : selected) { if (!sel) { nbNonSelected++; } } if (nbNonSelected == 0) { log.warn("All attributes are selected !"); return NO_ATTRIBUTES; } int[] result; if (nbNonSelected <= m) { // return all non selected attributes result = new int[nbNonSelected]; int index = 0; for (int attr = 0; attr < selected.length; attr++) { if (!selected[attr]) { result[index++] = attr; } } } else { result = new int[m]; for (int index = 0; index < m; index++) { // randomly choose a "non selected" attribute int rind; do { rind = rng.nextInt(selected.length); } while (selected[rind]); result[index] = rind; selected[rind] = true; // temporarily set the chosen attribute to be selected } // the chosen attributes are not yet selected for (int attr : result) { selected[attr] = false; } } return result; } }




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