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* add `isConverged` method to visitors * add new benchmarks for shingled points
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.../benchmark/src/main/java/com/amazon/randomcutforest/RandomCutForestShingledBenchmark.java
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| /* | ||
| * Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"). | ||
| * You may not use this file except in compliance with the License. | ||
| * A copy of the License is located at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * or in the "license" file accompanying this file. This file 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. | ||
| */ | ||
|
|
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| package com.amazon.randomcutforest; | ||
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| import com.amazon.randomcutforest.returntypes.DensityOutput; | ||
| import com.amazon.randomcutforest.returntypes.DiVector; | ||
| import com.amazon.randomcutforest.returntypes.Neighbor; | ||
| import com.amazon.randomcutforest.testutils.ShingledMultiDimDataWithKeys; | ||
| import org.github.jamm.MemoryMeter; | ||
| import org.openjdk.jmh.annotations.Benchmark; | ||
| import org.openjdk.jmh.annotations.Fork; | ||
| import org.openjdk.jmh.annotations.Level; | ||
| import org.openjdk.jmh.annotations.Measurement; | ||
| import org.openjdk.jmh.annotations.OperationsPerInvocation; | ||
| import org.openjdk.jmh.annotations.Param; | ||
| import org.openjdk.jmh.annotations.Scope; | ||
| import org.openjdk.jmh.annotations.Setup; | ||
| import org.openjdk.jmh.annotations.State; | ||
| import org.openjdk.jmh.annotations.Warmup; | ||
| import org.openjdk.jmh.infra.Blackhole; | ||
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|
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| import java.util.List; | ||
| import java.util.Random; | ||
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| @Warmup(iterations = 2) | ||
| @Measurement(iterations = 5) | ||
| @Fork(value = 1) | ||
| @State(Scope.Thread) | ||
| public class RandomCutForestShingledBenchmark { | ||
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| public final static int DATA_SIZE = 50_000; | ||
| public final static int INITIAL_DATA_SIZE = 25_000; | ||
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| @State(Scope.Benchmark) | ||
| public static class BenchmarkState { | ||
| @Param({ "5" }) | ||
| int baseDimensions; | ||
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| @Param({ "8" }) | ||
| int shingleSize; | ||
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| @Param({ "30" }) | ||
| int numberOfTrees; | ||
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| @Param({ "1.0", "0.9", "0.8", "0.7", "0.6", "0.5", "0.4", "0.3", "0.2", "0.1", "0.0" }) | ||
| double boundingBoxCacheFraction; | ||
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| @Param({ "false", "true" }) | ||
| boolean parallel; | ||
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| double[][] data; | ||
| RandomCutForest forest; | ||
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| @Setup(Level.Trial) | ||
| public void setUpData() { | ||
| data = ShingledMultiDimDataWithKeys.getMultiDimData(DATA_SIZE + INITIAL_DATA_SIZE, 50, 100, 5, 17, | ||
| baseDimensions).data; | ||
| } | ||
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| @Setup(Level.Invocation) | ||
| public void setUpForest() { | ||
| forest = RandomCutForest.builder().numberOfTrees(numberOfTrees).dimensions(baseDimensions * shingleSize) | ||
| .internalShinglingEnabled(true).shingleSize(shingleSize).parallelExecutionEnabled(parallel) | ||
| .boundingBoxCacheFraction(boundingBoxCacheFraction).randomSeed(99).build(); | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| forest.update(data[i]); | ||
| } | ||
| } | ||
| } | ||
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| private RandomCutForest forest; | ||
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| @Benchmark | ||
| @OperationsPerInvocation(DATA_SIZE) | ||
| public RandomCutForest updateOnly(BenchmarkState state) { | ||
| double[][] data = state.data; | ||
| forest = state.forest; | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| forest.update(data[i]); | ||
| } | ||
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| return forest; | ||
| } | ||
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| @Benchmark | ||
| @OperationsPerInvocation(DATA_SIZE) | ||
| public RandomCutForest scoreOnly(BenchmarkState state, Blackhole blackhole) { | ||
| double[][] data = state.data; | ||
| forest = state.forest; | ||
| double score = 0.0; | ||
| Random rnd = new Random(0); | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| score += forest.getAnomalyScore(data[i]); | ||
| if (rnd.nextDouble() < 0.01) { | ||
| forest.update(data[i]); // this should execute sparingly | ||
| } | ||
| } | ||
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| blackhole.consume(score); | ||
| return forest; | ||
| } | ||
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| @Benchmark | ||
| @OperationsPerInvocation(DATA_SIZE) | ||
| public RandomCutForest scoreAndUpdate(BenchmarkState state, Blackhole blackhole) { | ||
| double[][] data = state.data; | ||
| forest = state.forest; | ||
| double score = 0.0; | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| score = forest.getAnomalyScore(data[i]); | ||
| forest.update(data[i]); | ||
| } | ||
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| blackhole.consume(score); | ||
| if (!forest.parallelExecutionEnabled) { | ||
| MemoryMeter meter = new MemoryMeter(); | ||
| System.out.println(" forest size " + meter.measureDeep(forest)); | ||
| } | ||
| return forest; | ||
| } | ||
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| @Benchmark | ||
| @OperationsPerInvocation(DATA_SIZE) | ||
| public RandomCutForest attributionAndUpdate(BenchmarkState state, Blackhole blackhole) { | ||
| double[][] data = state.data; | ||
| forest = state.forest; | ||
| DiVector vector = new DiVector(forest.getDimensions()); | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| vector = forest.getAnomalyAttribution(data[i]); | ||
| forest.update(data[i]); | ||
| } | ||
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| blackhole.consume(vector); | ||
| return forest; | ||
| } | ||
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| @Benchmark | ||
| @OperationsPerInvocation(DATA_SIZE) | ||
| public RandomCutForest basicDensityAndUpdate(BenchmarkState state, Blackhole blackhole) { | ||
| double[][] data = state.data; | ||
| forest = state.forest; | ||
| DensityOutput output = new DensityOutput(forest.getDimensions(), forest.getSampleSize()); | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| output = forest.getSimpleDensity(data[i]); | ||
| forest.update(data[i]); | ||
| } | ||
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| blackhole.consume(output); | ||
| return forest; | ||
| } | ||
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| @Benchmark | ||
| @OperationsPerInvocation(DATA_SIZE) | ||
| public RandomCutForest basicNeighborAndUpdate(BenchmarkState state, Blackhole blackhole) { | ||
| double[][] data = state.data; | ||
| forest = state.forest; | ||
| List<Neighbor> output = null; | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| output = forest.getNearNeighborsInSample(data[i]); | ||
| forest.update(data[i]); | ||
| } | ||
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| blackhole.consume(output); | ||
| return forest; | ||
| } | ||
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| @Benchmark | ||
| @OperationsPerInvocation(DATA_SIZE) | ||
| public RandomCutForest basicExtrapolateAndUpdate(BenchmarkState state, Blackhole blackhole) { | ||
| double[][] data = state.data; | ||
| forest = state.forest; | ||
| double[] output = null; | ||
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| for (int i = INITIAL_DATA_SIZE; i < data.length; i++) { | ||
| output = forest.extrapolate(1); | ||
| forest.update(data[i]); | ||
| } | ||
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| blackhole.consume(output); | ||
| return forest; | ||
| } | ||
| } |
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