org.apache.spark.examples.ml.JavaSimpleParamsExample Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of snappy-spark-examples_2.10 Show documentation
SnappyData distributed data store and execution engine
There is a newer version: 1.6.2-6
Show newest version
/*
 * 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.spark.examples.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.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;

/**
 * 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);
    SQLContext jsql = new SQLContext(jsc);

    // Prepare training data.
    // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
    // into DataFrames, where it uses the bean metadata to infer the schema.
    List 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)));
    DataFrame training = jsql.createDataFrame(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.parent().extractParamMap());

    // 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.
    double thresholds[] = {0.45, 0.55};
    paramMap.put(lr.regParam().w(0.1), lr.thresholds().w(thresholds)); // Specify multiple Params.

    // One can also combine ParamMaps.
    ParamMap paramMap2 = new ParamMap();
    paramMap2.put(lr.probabilityCol().w("myProbability")); // 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.parent().extractParamMap());

    // Prepare test documents.
    List 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)));
    DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class);

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

    jsc.stop();
  }
}