org.apache.metron.performance.sampler.BiasedSampler.java Source code

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Here is the source code for org.apache.metron.performance.sampler.BiasedSampler.java

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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.metron.performance.sampler;

import com.google.common.base.Splitter;
import com.google.common.collect.Iterables;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.util.AbstractMap;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Random;
import java.util.TreeMap;

public class BiasedSampler implements Sampler {
    TreeMap<Double, Map.Entry<Integer, Integer>> discreteDistribution;

    public BiasedSampler(List<Map.Entry<Integer, Integer>> discreteDistribution, int max) {
        this.discreteDistribution = createDistribution(discreteDistribution, max);
    }

    public static List<Map.Entry<Integer, Integer>> readDistribution(BufferedReader distrFile) throws IOException {
        return readDistribution(distrFile, false);
    }

    public static List<Map.Entry<Integer, Integer>> readDistribution(BufferedReader distrFile, boolean quiet)
            throws IOException {
        List<Map.Entry<Integer, Integer>> ret = new ArrayList<>();
        if (!quiet) {
            System.out.println("Using biased sampler with the following biases:");
        }
        int sumLeft = 0;
        int sumRight = 0;
        for (String line = null; (line = distrFile.readLine()) != null;) {
            if (line.startsWith("#")) {
                continue;
            }
            Iterable<String> it = Splitter.on(",").split(line.trim());
            if (Iterables.size(it) != 2) {
                throw new IllegalArgumentException(
                        line + " should be a comma separated pair of integers, but was not.");
            }
            int left = Integer.parseInt(Iterables.getFirst(it, null));
            int right = Integer.parseInt(Iterables.getLast(it, null));
            if (left <= 0 || left > 100) {
                throw new IllegalArgumentException(
                        line + ": " + (left < 0 ? left : right) + " must a positive integer in (0, 100]");
            }
            if (right <= 0 || right > 100) {
                throw new IllegalArgumentException(line + ": " + right + " must a positive integer in (0, 100]");
            }
            if (!quiet) {
                System.out.println(
                        "\t" + left + "% of templates will comprise roughly " + right + "% of sample output");
            }
            ret.add(new AbstractMap.SimpleEntry<>(left, right));
            sumLeft += left;
            sumRight += right;
        }
        if (sumLeft > 100 || sumRight > 100) {
            throw new IllegalStateException(
                    "Neither columns must sum to beyond 100.  " + "The first column is the % of templates. "
                            + "The second column is the % of the sample that % of template occupies.");
        } else if (sumLeft < 100 && sumRight < 100) {
            int left = 100 - sumLeft;
            int right = 100 - sumRight;
            if (!quiet) {
                System.out.println(
                        "\t" + left + "% of templates will comprise roughly " + right + "% of sample output");
            }
            ret.add(new AbstractMap.SimpleEntry<>(left, right));
        }
        return ret;

    }

    private static TreeMap<Double, Map.Entry<Integer, Integer>> createDistribution(
            List<Map.Entry<Integer, Integer>> discreteDistribution, int max) {
        TreeMap<Double, Map.Entry<Integer, Integer>> ret = new TreeMap<>();
        int from = 0;
        double weight = 0.0d;
        for (Map.Entry<Integer, Integer> kv : discreteDistribution) {
            double pctVals = kv.getKey() / 100.0;
            int to = from + (int) (max * pctVals);
            double pctWeight = kv.getValue() / 100.0;
            ret.put(weight, new AbstractMap.SimpleEntry<>(from, to));
            weight += pctWeight;
            from = to;
        }
        return ret;
    }

    @Override
    public int sample(Random rng, int limit) {
        double weight = rng.nextDouble();
        Map.Entry<Integer, Integer> range = discreteDistribution.floorEntry(weight).getValue();
        return rng.nextInt(range.getValue() - range.getKey()) + range.getKey();
    }
}