java - 使用 java 的 apache spark 中的决策树实现问题

Question

我正在尝试使用 java 和 apache spark 1.0.0 版本为决策树分类器实现简单的演示。我基于 http://spark.apache.org/docs/1.0.0/mllib-decision-tree.html .到目前为止，我已经编写了下面列出的代码。

根据以下代码我得到错误:

org.apache.spark.mllib.tree.impurity.Impurity impurity = new org.apache.spark.mllib.tree.impurity.Entropy();

类型不匹配:无法从熵转换为杂质。这对我来说很奇怪，而类 Entropy 实现了 Impurity 接口(interface):

https://spark.apache.org/docs/1.0.0/api/java/org/apache/spark/mllib/tree/impurity/Entropy.html

我正在寻找问题的答案，为什么我不能完成这项作业？

package decisionTree;

import java.util.regex.Pattern;

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.DecisionTree;
import org.apache.spark.mllib.tree.configuration.Algo;
import org.apache.spark.mllib.tree.configuration.Strategy;
import org.apache.spark.mllib.tree.impurity.Gini;
import org.apache.spark.mllib.tree.impurity.Impurity;

import scala.Enumeration.Value;

public final class DecisionTreeDemo {

    static class ParsePoint implements Function<String, LabeledPoint> {
        private static final Pattern COMMA = Pattern.compile(",");
        private static final Pattern SPACE = Pattern.compile(" ");

        @Override
        public LabeledPoint call(String line) {
            String[] parts = COMMA.split(line);
            double y = Double.parseDouble(parts[0]);
            String[] tok = SPACE.split(parts[1]);
            double[] x = new double[tok.length];
            for (int i = 0; i < tok.length; ++i) {
                x[i] = Double.parseDouble(tok[i]);
            }
            return new LabeledPoint(y, Vectors.dense(x));
        }
    }

    public static void main(String[] args) throws Exception {

        if (args.length < 1) {
            System.err.println("Usage:DecisionTreeDemo <file>");
            System.exit(1);
        }

        JavaSparkContext ctx = new JavaSparkContext("local[4]", "Log Analizer",
                System.getenv("SPARK_HOME"),
                JavaSparkContext.jarOfClass(DecisionTreeDemo.class));

        JavaRDD<String> lines = ctx.textFile(args[0]);
        JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();

        int iterations = 100;

        int maxBins = 2;
        int maxMemory = 512;
        int maxDepth = 1;

        org.apache.spark.mllib.tree.impurity.Impurity impurity = new org.apache.spark.mllib.tree.impurity.Entropy();

        Strategy strategy = new Strategy(Algo.Classification(), impurity, maxDepth,
                maxBins, null, null, maxMemory);

        ctx.stop();
    }
}   

@samthebest 如果我删除杂质变量并更改为以下形式:

Strategy strategy = new Strategy(Algo.Classification(), new org.apache.spark.mllib.tree.impurity.Entropy(), maxDepth, maxBins, null, null, maxMemory);

错误更改为:构造函数 Entropy() 未定义。

[编辑]我发现我认为正确调用方法(https://issues.apache.org/jira/browse/SPARK-2197):

Strategy strategy = new Strategy(Algo.Classification(), new Impurity() {
@Override
public double calculate(double arg0, double arg1, double arg2)
{ return Gini.calculate(arg0, arg1, arg2); }

@Override
public double calculate(double arg0, double arg1)
{ return Gini.calculate(arg0, arg1); }

}, 5, 100, QuantileStrategy.Sort(), null, 256);

不幸的是我遇到了错误:(

Answer 1

Bug 2197 的 Java 解决方案现在可用，通过 this pull request :

Other improvements to Decision Trees for easy-of-use with Java: * impurity classes: Added instance() methods to help with Java interface. * Strategy: Added Java-friendly constructor --> Note: I removed quantileCalculationStrategy from the Java-friendly constructor since (a) it is a special class and (b) there is only 1 option currently. I suspect we will redo the API before the other options are included.

您可以看到一个完整的示例，即使用 Gini 杂质的 intance() 方法解决您的问题 here

Strategy strategy = new Strategy(Algo.Classification(), Gini.instance(), maxDepth, numClasses,maxBins, categoricalFeaturesInfo);
DecisionTreeModel model = DecisionTree$.MODULE$.train(rdd.rdd(), strategy);