machine-learning
Komma igång med Machine Learning med Apache spark MLib

Introduktion

Anmärkningar

Skriv ditt första klassificeringsproblem med Logistic Regression-modellen

Jag använder förmörkelse här, och du måste lägga till under givet beroende till din pom.xml

1.) POM.XML

    <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.predection.classification</groupId>
  <artifactId>logisitcRegression</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>logisitcRegression</name>
  <url>http://maven.apache.org</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>

  <dependencies>
     <!-- Spark -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.1.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_2.10</artifactId>
            <version>2.1.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.1.0</version>
        </dependency>
    </dependencies>
</project>

Vi gör klassificering baserat på land, timmar och vår etikett klickas.

    package com.predection.classification.logisitcRegression;

import org.apache.spark.SparkConf;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.ml.feature.StringIndexer;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import java.util.Arrays;
import java.util.List;
import org.apache.spark.sql.RowFactory;
import static org.apache.spark.sql.types.DataTypes.*;

/**
 * Classification problem using Logistic Regression Model
 *
 */

public class App 
{
    public static void main( String[] args )
    {
        SparkConf sparkConf = new SparkConf().setAppName("JavaLogisticRegressionExample");
        
        // Creating spark session
        SparkSession sparkSession = SparkSession.builder().config(sparkConf).getOrCreate();
        
        StructType schema = createStructType(new StructField[]{
                  createStructField("id", IntegerType, false),
                  createStructField("country", StringType, false),
                  createStructField("hour", IntegerType, false),
                  createStructField("clicked", DoubleType, false)
                });

                List<Row> data = Arrays.asList(
                  RowFactory.create(7, "US", 18, 1.0),
                  RowFactory.create(8, "CA", 12, 0.0),
                  RowFactory.create(9, "NZ", 15, 1.0),
            



 RowFactory.create(10,"FR", 8, 0.0),
                  RowFactory.create(11, "IT", 16, 1.0),
              RowFactory.create(12, "CH", 5, 0.0),
              RowFactory.create(13, "AU", 20, 1.0)
            );

        
Dataset<Row> dataset = sparkSession.createDataFrame(data, schema);        

// Using stringindexer transformer to transform string into index
 dataset = new StringIndexer().setInputCol("country").setOutputCol("countryIndex").fit(dataset).transform(dataset);
 
// creating feature vector using dependent variables countryIndex, hours are features and clicked is label
VectorAssembler assembler = new VectorAssembler()
        .setInputCols(new String[] {"countryIndex", "hour"})
        .setOutputCol("features");

    Dataset<Row> finalDS = assembler.transform(dataset);
    
    // Split the data into training and test sets (30% held out for
    // testing).
        Dataset<Row>[] splits = finalDS.randomSplit(new double[] { 0.7, 0.3 });
        Dataset<Row> trainingData = splits[0];
        Dataset<Row> testData = splits[1];
        trainingData.show();
        testData.show();
        // Building LogisticRegression Model
        LogisticRegression lr = new LogisticRegression().setMaxIter(10).setRegParam(0.3).setElasticNetParam(0.8).setLabelCol("clicked");

        // Fit the model
        LogisticRegressionModel lrModel = lr.fit(trainingData);
        
        // Transform the model, and predict class for test dataset
        Dataset<Row> output = lrModel.transform(testData);
        output.show();
    }
}

3.) För att köra den här applikationen, först utför mvn-clean-package på applikationsprojekt, det skulle skapa burk. 4.) Öppna gnistrotkatalogen och skicka det här jobbet

bin/spark-submit --class com.predection.regression.App --master local[2] ./regression-0.0.1-SNAPSHOT.jar(path to the jar file)

5.) Efter att ha skickat se det bygger träningsdata

6.) testdata på samma sätt

7.) Och här är prediktionsresultatet under förutsägelseskolumnen