org.apache.mahout.classifier.df.node.Node.java Source code

Introduction

Here is the source code for org.apache.mahout.classifier.df.node.Node.java

Source

/**
 * 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.node;

import org.apache.commons.lang.ArrayUtils;
import org.apache.hadoop.io.Writable;
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.math.Arrays;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

/**
 * Represents an abstract node of a decision tree
 */
public abstract class Node implements Writable {

    protected enum Type {
        LEAF, NUMERICAL, CATEGORICAL
    }

    /**
     * predicts the label for the instance
     * 
     * @return -1 if the label cannot be predicted
     */
    public abstract double[] classify(Dataset dataset, Instance instance);

    /**
     * @return the total number of nodes of the tree
     */
    public abstract long nbNodes();

    /**
     * @return the maximum depth of the tree
     */
    public abstract long maxDepth();

    protected abstract Type getType();

    public static Node read(DataInput in) throws IOException {
        Type type = Type.values()[in.readInt()];
        Node node;

        switch (type) {
        case LEAF:
            node = new Leaf();
            break;
        case NUMERICAL:
            node = new NumericalNode();
            break;
        case CATEGORICAL:
            node = new CategoricalNode();
            break;
        default:
            throw new IllegalStateException("This implementation is not currently supported");
        }

        node.readFields(in);

        return node;
    }

    @Override
    public final String toString() {
        return getType() + ":" + getString() + ';';
    }

    protected abstract String getString();

    @Override
    public final void write(DataOutput out) throws IOException {
        out.writeInt(getType().ordinal());
        writeNode(out);
    }

    protected abstract void writeNode(DataOutput out) throws IOException;

    public void computeLeafWeights() {
        // Node is a leaf
        if (this instanceof Leaf) {
            double instanceClassLeaf = ((Leaf) this).getInstanceClassLeaf();
            double instancesLeaf = ((Leaf) this).getInstancesLeaf();
            double leafWeight = instanceClassLeaf / instancesLeaf;
            ((Leaf) this).setLeafWeight(leafWeight);
        } else if (this instanceof CategoricalNode) {
            // For nominal attributes
            for (int i = 0; i < ((CategoricalNode) this).getChilds().length; i++) {
                ((CategoricalNode) this).getChilds()[i].computeLeafWeights();
            }

        } else {
            // For numeric attributes
            ((NumericalNode) this).getLoChild().computeLeafWeights();
            ((NumericalNode) this).getHichild().computeLeafWeights();
        }
    }

    public void computeLeafMeasures(Data data, Instance instance, double[] cWeights) {
        Dataset dataset = data.getDataset();
        // Node is not a leaf
        if (this instanceof CategoricalNode || this instanceof NumericalNode) {
            if (this instanceof CategoricalNode) {
                // For nominal attributes                                                                
                int index = -1;
                double[] values = ((CategoricalNode) this).getValues();
                boolean found = false;
                for (int i = 0; i < values.length && !found; i++) {
                    if (instance.get(((CategoricalNode) this).getAttr()) == values[i]) {
                        found = true;
                        index = i;
                    }
                }
                if (index == -1) {
                    for (int i = 0; i < ((CategoricalNode) this).getChilds().length; i++) {
                        ((CategoricalNode) this).getChilds()[i].computeLeafMeasures(data, instance, cWeights);
                    }
                } else {
                    ((CategoricalNode) this).getChilds()[index].computeLeafMeasures(data, instance, cWeights);
                }
            } else {
                // For numeric attributes               
                if (instance.get(((NumericalNode) this).getAttr()) < ((NumericalNode) this).getSplit()) {
                    ((NumericalNode) this).getLoChild().computeLeafMeasures(data, instance, cWeights);
                } else {
                    ((NumericalNode) this).getHichild().computeLeafMeasures(data, instance, cWeights);
                }
            }
        }
        // Node is a leaf
        if (this instanceof Leaf) {
            ((Leaf) this).setInstancesLeaf(((Leaf) this).getInstancesLeaf() + 1);
            ((Leaf) this).setInstanceClassLeaf(
                    ((Leaf) this).getInstanceClassLeaf() + cWeights[(int) dataset.getLabel(instance)]);
        }
    }

}