Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8377#discussion_r37943728
--- Diff: docs/ml-guide.md ---
@@ -801,6 +801,173 @@ jsc.stop();
</div>
+## Example: Model Selection via Train Validation Split
+In addition to `CrossValidator` Spark also offers
+[`TrainValidationSplit`](api/scala/index.html#org.apache.spark.ml.tuning.TrainValidationSplit)
for hyper-parameter tuning.
+It randomly splits the input dataset into train and validation sets based
on ratio passed as parameter
+and use evaluation metric on the validation set to select the best model.
+The use is similar to `CrossValidator`, but simpler and less
computationally expensive.
+
+`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s, and an
+[`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator).
+It begins by splitting the dataset into two parts using *trainRatio*
parameter
+which are used as separate training and test datasets. For example with
`$trainRatio=0.75$` (default),
+`TrainValidationSplit` will generate training and test dataset pair where
75% of the data is used for training and 25% for validation.
+Similarly to `CrossValidator`, `TrainValidationSplit` also iterates
through the set of `ParamMap`s.
+For each combination of parameters, it trains the given `Estimator` and
evaluates it using the given `Evaluator`.
+The `ParamMap` which produces the best evaluation metric is selected as
the best option.
+`TrainValidationSplit` finally fits the `Estimator` using the best
`ParamMap` and the entire dataset.
+
+`TrainValidationSplit` only evaluates each combination of parameters once
as opposed to k times in
+ case of `CrossValidator`. It is therefore less expensive, but will not
produce as reliable results.
+
+<div class="codetabs">
+
+<div data-lang="scala">
+{% highlight scala %}
+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}
+
+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()
+{% endhighlight %}
+</div>
+
+<div data-lang="java">
+{% highlight java %}
+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;
+
+SparkConf conf = new
SparkConf().setAppName("JavaTrainValidationSplitExample");
+JavaSparkContext jsc = new JavaSparkContext(conf);
--- End diff --
Ditto, no need for `jsc` and `jsql`
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