TrainValidationSplit user guide and examples.

examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java

@@ -0,0 +1,106 @@
+/*
+ * 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.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.ml.regression.LinearRegression;
+import org.apache.spark.ml.tuning.*;
+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 model selection using TrainValidationSplit.
+ *
+ * The example is based on {@link org.apache.spark.examples.ml.JavaSimpleParamsExample}
+ * using linear regression.
+ *
+ * Run with
+ * {{{
+ * bin/run-example ml.JavaTrainValidationSplitExample
+ * }}}
+ */
+public class JavaTrainValidationSplitExample {
+
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("JavaTrainValidationSplitExample");
+    JavaSparkContext jsc = new JavaSparkContext(conf);
+    SQLContext jsql = new SQLContext(jsc);
+
+    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)));
+
+    DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class);
+
+    LinearRegression lr = new LinearRegression();
+
+    // In this case the estimator is simply the linear regression.
+    // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+    TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEvaluator(new RegressionEvaluator());
+
+    // We use a ParamGridBuilder to construct a grid of parameters to search over.
+    // TrainValidationSplit will try all combinations of values and determine best model using
+    // the evaluator.
+    ParamMap[] paramGrid = new ParamGridBuilder()
+      .addGrid(lr.regParam(), new double[]{0.1, 0.01})
+      .addGrid(lr.fitIntercept())
+      .addGrid(lr.elasticNetParam(), new double[]{0.0, 0.5, 1.0})
+      .addGrid(lr.maxIter(), new int[]{10, 100})
+      .addGrid(lr.tol(), new double[]{1E-5, 1E-6})
+      .build();
+
+    trainValidationSplit.setEstimatorParamMaps(paramGrid);
+
+    // 80% of the data will be used for training and the remaining 20% for validation.
+    trainValidationSplit.setTrainRatio(0.8);
+
+    // Run train validation split, and choose the best set of parameters.
+    TrainValidationSplitModel model = trainValidationSplit.fit(training);
+
+    // Prepare unlabeled test data.
+    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)));
+
+    DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class);
+
+    // Make predictions on test data. model is the model with combination of parameters
+    // that performed best.
+    DataFrame results = model.transform(test);
+    for (Row r: results.select("features", "label", "prediction").collect()) {
+      System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> " + "prediction=" + r.get(2));
+    }
+
+    jsc.stop();
+  }
+}

examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala

@@ -1,5 +1,97 @@
+/*
+ * 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.
+ */
+
+// scalastyle:off println
 package org.apache.spark.examples.ml
 
-class TrainValidationSplitExample {
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.{SparkConf, SparkContext}
+
+/**
+ * A simple example demonstrating model selection using TrainValidationSplit.
+ *
+ * The example is based on [[SimpleParamsExample]] using linear regression.
+ * Run with
+ * {{{
+ * bin/run-example ml.TrainValidationSplitExample
+ * }}}
+ */
+object TrainValidationSplitExample {
+
+  def main(args: Array[String]): Unit = {
+    val conf = new SparkConf().setAppName("TrainValidationSplitExample")
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    val training = sc.parallelize(Seq(
+      LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
+      LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
+      LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
+      LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
+
+    val lr = new LinearRegression()
+
+    // In this case the estimator is simply the linear regression.
+    // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+    val trainValidationSplit = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEvaluator(new RegressionEvaluator)
+
+    // We use a ParamGridBuilder to construct a grid of parameters to search over.
+    // TrainValidationSplit will try all combinations of values and determine best model using
+    // the evaluator.
+    val paramGrid = new ParamGridBuilder()
+      .addGrid(lr.regParam, Array(0.1, 0.01))
+      .addGrid(lr.fitIntercept, Array(true, false))
+      .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
+      .addGrid(lr.maxIter, Array(10, 100))
+      .addGrid(lr.tol, Array(1E-5, 1E-6))
+      .build()
+
+    trainValidationSplit.setEstimatorParamMaps(paramGrid)
+
+    // 80% of the data will be used for training and the remaining 20% for validation.
+    trainValidationSplit.setTrainRatio(0.8)
+
+    // Run train validation split, and choose the best set of parameters.
+    val model = trainValidationSplit.fit(training.toDF())
+
+    // Prepare unlabeled test data.
+    val test = sc.parallelize(Seq(
+      LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+      LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
+      LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))
+
+    // Make predictions on test data. model is the model with combination of parameters
+    // that performed best.
+    model.transform(test.toDF())
+      .select("features", "label", "prediction")
+      .collect()
+      .foreach { case Row(features: Vector, label: Double, prediction: Double) =>
+        println(s"($features, $label) --> prediction=$prediction")
+      }
 
+    sc.stop()
+  }
 }
+// scalastyle:on println