specify parameters for Estimators and Transformers using apache spark - Java Big Data

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Description

specify parameters for Estimators and Transformers using apache spark

Demo Code

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package eduonix.spark.ml;

import java.util.List;

import com.google.common.collect.Lists;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.ml.param.ParamMap;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.api.java.JavaSQLContext;
import org.apache.spark.sql.api.java.JavaSchemaRDD;
import org.apache.spark.sql.api.java.Row;

/**
 * A simple example demonstrating ways to specify parameters for Estimators and Transformers.
 * Run with
 * {{{
 * bin/run-example ml.JavaSimpleParamsExample
 * }}}
 */
public class JavaSimpleParamsExample {

    public static void main(String[] args) {
        SparkConf conf = new SparkConf()
                .setAppName("JavaSimpleParamsExample");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        JavaSQLContext jsql = new JavaSQLContext(jsc);

        // Prepare training data.
        // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
        // into SchemaRDDs, where it uses the bean metadata to infer the schema.
        List<LabeledPoint> localTraining = Lists.newArrayList(
                new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
                new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
                new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
                new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
        JavaSchemaRDD training = jsql.applySchema(
                jsc.parallelize(localTraining), LabeledPoint.class);

        // Create a LogisticRegression instance.  This instance is an Estimator.
        LogisticRegression lr = new LogisticRegression();
        // Print out the parameters, documentation, and any default values.
        System.out.println("LogisticRegression parameters:\n"
                + lr.explainParams() + "\n");

        // We may set parameters using setter methods.
        lr.setMaxIter(10).setRegParam(0.01);

        // Learn a LogisticRegression model.  This uses the parameters stored in lr.
        LogisticRegressionModel model1 = lr.fit(training);
        // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
        // we can view the parameters it used during fit().
        // This prints the parameter (name: value) pairs, where names are unique IDs for this
        // LogisticRegression instance.
        System.out.println("Model 1 was fit using parameters: "
                + model1.fittingParamMap());

        // We may alternatively specify parameters using a ParamMap.
        ParamMap paramMap = new ParamMap();
        paramMap.put(lr.maxIter().w(20)); // Specify 1 Param.
        paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
        paramMap.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.

        // One can also combine ParamMaps.
        ParamMap paramMap2 = new ParamMap();
        paramMap2.put(lr.scoreCol().w("probability")); // Change output column name
        ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);

        // Now learn a new model using the paramMapCombined parameters.
        // paramMapCombined overrides all parameters set earlier via lr.set* methods.
        LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
        System.out.println("Model 2 was fit using parameters: "
                + model2.fittingParamMap());

        // Prepare test documents.
        List<LabeledPoint> localTest = Lists.newArrayList(new LabeledPoint(
                1.0, Vectors.dense(-1.0, 1.5, 1.3)), new LabeledPoint(0.0,
                Vectors.dense(3.0, 2.0, -0.1)), new LabeledPoint(1.0,
                Vectors.dense(0.0, 2.2, -1.5)));
        JavaSchemaRDD test = jsql.applySchema(jsc.parallelize(localTest),
                LabeledPoint.class);

        // Make predictions on test documents using the Transformer.transform() method.
        // LogisticRegression.transform will only use the 'features' column.
        // Note that model2.transform() outputs a 'probability' column instead of the usual 'score'
        // column since we renamed the lr.scoreCol parameter previously.
        model2.transform(test).registerAsTable("results");
        JavaSchemaRDD results = jsql
                .sql("SELECT features, label, probability, prediction FROM results");
        for (Row r : results.collect()) {
            System.out.println("(" + r.get(0) + ", " + r.get(1)
                    + ") -> prob=" + r.get(2) + ", prediction=" + r.get(3));
        }
    }
}

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