zhaozijun109 commented on code in PR #253: URL: https://github.com/apache/flink-ml/pull/253#discussion_r1318012250
########## flink-ml-lib/src/main/java/org/apache/flink/ml/anomalydetection/isolationforest/ITree.java: ########## @@ -0,0 +1,156 @@ +/* + * 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.flink.ml.anomalydetection.isolationforest; + +import org.apache.flink.api.java.tuple.Tuple2; +import org.apache.flink.ml.linalg.DenseVector; + +import java.io.Serializable; +import java.util.Random; + +/** Construct isolation tree. */ +public class ITree implements Serializable { + public final int attributeIndex; + public final double splitAttributeValue; + public ITree leftTree; + public ITree rightTree; + public int currentHeight; + public int leafNodesNum; + + public ITree(int attributeIndex, double splitAttributeValue) { + this.attributeIndex = attributeIndex; + this.splitAttributeValue = splitAttributeValue; + this.leftTree = null; + this.rightTree = null; + this.currentHeight = 0; + this.leafNodesNum = 1; + } + + public static ITree generateIsolationTree( + DenseVector[] samplesData, + int currentHeight, + int limitHeight, + Random randomState, + int[] featureIndices) { + int n = samplesData.length; + ITree isolationTree; + if (samplesData.length == 0) { + return null; + } else if (samplesData.length == 1 || currentHeight >= limitHeight) { + isolationTree = new ITree(0, samplesData[0].get(0)); + isolationTree.currentHeight = currentHeight; + isolationTree.leafNodesNum = samplesData.length; + return isolationTree; + } + boolean flag = true; + for (int i = 1; i < n; i++) { + if (!samplesData[i].equals(samplesData[i - 1])) { + flag = false; + break; + } + } + if (flag) { + isolationTree = new ITree(0, samplesData[0].get(0)); + isolationTree.currentHeight = currentHeight; + isolationTree.leafNodesNum = samplesData.length; + return isolationTree; + } + + Tuple2<Integer, Double> tuple2 = + getRandomFeatureToSplit(samplesData, randomState, featureIndices); + int attributeIndex = tuple2.f0; + double splitAttributeValue = tuple2.f1; + + int leftNodesNum = 0; + int rightNodesNum = 0; + for (DenseVector datum : samplesData) { + if (datum.get(attributeIndex) < splitAttributeValue) { + leftNodesNum++; + } else { + rightNodesNum++; + } + } + + DenseVector[] leftSamples = new DenseVector[leftNodesNum]; + DenseVector[] rightSamples = new DenseVector[rightNodesNum]; + int l = 0, r = 0; + for (DenseVector samplesDatum : samplesData) { + if (samplesDatum.get(attributeIndex) < splitAttributeValue) { + leftSamples[l++] = samplesDatum; + } else { + rightSamples[r++] = samplesDatum; + } + } + + ITree root = new ITree(attributeIndex, splitAttributeValue); + root.currentHeight = currentHeight; + root.leafNodesNum = samplesData.length; + root.leftTree = + generateIsolationTree( + leftSamples, currentHeight + 1, limitHeight, randomState, featureIndices); + root.rightTree = + generateIsolationTree( + rightSamples, currentHeight + 1, limitHeight, randomState, featureIndices); + + return root; + } + + private static Tuple2<Integer, Double> getRandomFeatureToSplit( + DenseVector[] samplesData, Random randomState, int[] featureIndices) { + int attributeIndex = featureIndices[randomState.nextInt(featureIndices.length)]; + + double maxValue = samplesData[0].get(attributeIndex); + double minValue = samplesData[0].get(attributeIndex); + for (int i = 1; i < samplesData.length; i++) { + minValue = Math.min(minValue, samplesData[i].get(attributeIndex)); + maxValue = Math.max(maxValue, samplesData[i].get(attributeIndex)); + } + double splitAttributeValue = (maxValue - minValue) * randomState.nextDouble() + minValue; + + return Tuple2.of(attributeIndex, splitAttributeValue); + } + + public static double calculatePathLength(DenseVector sampleData, ITree isolationTree) + throws Exception { + double pathLength = -1; + ITree tmpITree = isolationTree; + while (tmpITree != null) { + pathLength += 1; + if (tmpITree.leftTree == null + || tmpITree.rightTree == null + || sampleData.get(tmpITree.attributeIndex) == tmpITree.splitAttributeValue) { + break; + } else if (sampleData.get(tmpITree.attributeIndex) < tmpITree.splitAttributeValue) { + tmpITree = tmpITree.leftTree; + } else { + tmpITree = tmpITree.rightTree; + } + } + + assert tmpITree != null; Review Comment: Ok, I will delete it. -- This is an automated message from the Apache Git Service. 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