Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8377#discussion_r37943772
--- Diff:
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));
--- End diff --
Use `DataFrame.show()`
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