re-org:

1. move SimpleTokenizer to examples
2. move LR to classification, HashingTF and StandardScaler to feature, CV and ParamGridBuilder to tuning
3. define SimpleTransformer

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

@@ -0,0 +1,92 @@
+/*
+ * 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.api.java.JavaSparkContext;
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.LogisticRegression;
+import org.apache.spark.ml.feature.HashingTF;
+import org.apache.spark.sql.api.java.JavaSQLContext;
+import org.apache.spark.sql.api.java.JavaSchemaRDD;
+import org.apache.spark.sql.api.java.Row;
+import org.apache.spark.SparkConf;
+
+/**
+ * A simple text classification pipeline that recognizes "spark" from input text. It uses the Java
+ * bean classes [[LabeledDocument]] and [[Document]], and the tokenizer [[SimpleTokenizer]] defined
+ * in the Scalar counterpart of this example [[SimpleTextClassificationPipeline]]. Run with
+ * <pre>
+ * bin/run-example ml.JavaSimpleTextClassificationPipeline
+ * </pre>
+ */
+public class JavaSimpleTextClassificationPipeline {
+
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("JavaSimpleTextClassificationPipeline");
+    JavaSparkContext jsc = new JavaSparkContext(conf);
+    JavaSQLContext jsql = new JavaSQLContext(jsc);
+
+    // Prepare training documents, which are labeled.
+    List<LabeledDocument> localTraining = Lists.newArrayList(
+      new LabeledDocument(0L, "a b c d e spark", 1.0),
+      new LabeledDocument(1L, "b d", 0.0),
+      new LabeledDocument(2L, "spark f g h", 1.0),
+      new LabeledDocument(3L, "hadoop mapreduce", 0.0));
+    JavaSchemaRDD training =
+      jsql.applySchema(jsc.parallelize(localTraining), LabeledDocument.class);
+
+    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
+    SimpleTokenizer tokenizer = new SimpleTokenizer()
+      .setInputCol("text")
+      .setOutputCol("words");
+    HashingTF hashingTF = new HashingTF()
+      .setNumFeatures(1000)
+      .setInputCol(tokenizer.getOutputCol())
+      .setOutputCol("features");
+    LogisticRegression lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(0.01);
+    Pipeline pipeline = new Pipeline()
+      .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
+
+    // Fit the pipeline to training documents.
+    PipelineModel model = pipeline.fit(training);
+
+    // Prepare test documents, which are unlabeled.
+    List<Document> localTest = Lists.newArrayList(
+      new Document(4L, "spark i j k"),
+      new Document(5L, "l m n"),
+      new Document(6L, "mapreduce spark"),
+      new Document(7L, "apache hadoop"));
+    JavaSchemaRDD test =
+      jsql.applySchema(jsc.parallelize(localTest), Document.class);
+
+    // Make predictions on test documents.
+    model.transform(test).registerAsTable("prediction");
+    JavaSchemaRDD predictions = jsql.sql("SELECT id, text, score, prediction FROM prediction");
+    for (Row r: predictions.collect()) {
+      System.out.println(r);
+    }
+  }
+}

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

@@ -0,0 +1,94 @@
+/*
+ * 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 scala.beans.BeanInfo
+
+import org.apache.spark.ml.classification.LogisticRegression
+import org.apache.spark.ml.feature.HashingTF
+import org.apache.spark.ml.{Pipeline, SimpleTransformer}
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.{SparkConf, SparkContext}
+
+@BeanInfo
+case class LabeledDocument(id: Long, text: String, label: Double)
+
+@BeanInfo
+case class Document(id: Long, text: String)
+
+/**
+ * A tokenizer that converts the input string to lowercase and then splits it by white spaces.
+ */
+class SimpleTokenizer extends SimpleTransformer[String, Seq[String], SimpleTokenizer]
+    with Serializable {
+  override def createTransformFunc: String => Seq[String] = _.toLowerCase.split("\\s")
+}
+
+/**
+ * A simple text classification pipeline that recognizes "spark" from input text. This is to show
+ * how to define a simple tokenizer and then use it as part of a ML pipeline. Run with
+ * {{{
+ * bin/run-example ml.SimpleTextClassificationPipeline
+ * }}}
+ */
+object SimpleTextClassificationPipeline {
+
+  def main(args: Array[String]) {
+    val conf = new SparkConf().setAppName("SimpleTextClassificationPipeline")
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+    import sqlContext._
+
+    // Prepare training documents, which are labeled.
+    val training = sparkContext.parallelize(Seq(
+      LabeledDocument(0L, "a b c d e spark", 1.0),
+      LabeledDocument(1L, "b d", 0.0),
+      LabeledDocument(2L, "spark f g h", 1.0),
+      LabeledDocument(3L, "hadoop mapreduce", 0.0)))
+
+    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
+    val tokenizer = new SimpleTokenizer()
+      .setInputCol("text")
+      .setOutputCol("words")
+    val hashingTF = new HashingTF()
+      .setNumFeatures(1000)
+      .setInputCol(tokenizer.getOutputCol)
+      .setOutputCol("features")
+    val lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(0.01)
+    val pipeline = new Pipeline()
+      .setStages(Array(tokenizer, hashingTF, lr))
+
+    // Fit the pipeline to training documents.
+    val model = pipeline.fit(training)
+
+    // Prepare test documents, which are unlabeled.
+    val test = sparkContext.parallelize(Seq(
+      Document(4L, "spark i j k"),
+      Document(5L, "l m n"),
+      Document(6L, "mapreduce spark"),
+      Document(7L, "apache hadoop")))
+
+    // Make predictions on test documents.
+    model.transform(test)
+      .select('id, 'text, 'score, 'prediction)
+      .collect()
+      .foreach(println)
+  }
+}

mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala

@@ -20,20 +20,12 @@ package org.apache.spark.ml
 import java.util.UUID
 
 /**
- * Something with a unique id.
+ * Object with a unique id.
  */
 trait Identifiable extends Serializable {
 
   /**
    * A unique id for the object.
    */
-  val uid: String = this.getClass.getSimpleName + "-" + Identifiable.randomUid
-}
-
-object Identifiable {
-
-  /**
-   * Returns a random uid, drawn uniformly from 4+ billion candidates.
-   */
-  private def randomUid: String = UUID.randomUUID().toString.take(8)
+  val uid: String = this.getClass.getSimpleName + "-" + UUID.randomUUID().toString.take(8)
 }

mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala

@@ -25,7 +25,7 @@ import org.apache.spark.sql.SchemaRDD
 /**
  * A stage in a pipeline, either an Estimator or an Transformer.
  */
-trait PipelineStage extends Identifiable
+abstract class PipelineStage
 
 /**
  * A simple pipeline, which acts as an estimator.

mllib/src/main/scala/org/apache/spark/ml/Transformer.scala

@@ -18,10 +18,13 @@
 package org.apache.spark.ml
 
 import scala.annotation.varargs
+import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.ml.param.{ParamMap, ParamPair, Params}
+import org.apache.spark.ml.param._
 import org.apache.spark.sql.SchemaRDD
 import org.apache.spark.sql.api.java.JavaSchemaRDD
+import org.apache.spark.sql.catalyst.analysis.Star
+import org.apache.spark.sql.catalyst.dsl._
 
 /**
  * Abstract class for transformers that transform one dataset into another.
@@ -60,3 +63,23 @@ abstract class Transformer extends PipelineStage with Params {
     transform(dataset.schemaRDD, paramMap).toJavaSchemaRDD
   }
 }
+
+/**
+ * Abstract class for transformers that take one input column, apply transformation, and output the
+ * result as a new column.
+ */
+abstract class SimpleTransformer[IN, OUT: TypeTag, SELF <: SimpleTransformer[IN, OUT, SELF]]
+    extends Transformer with HasInputCol with HasOutputCol {
+
+  def setInputCol(value: String): SELF = { set(inputCol, value); this.asInstanceOf[SELF] }
+  def setOutputCol(value: String): SELF = { set(outputCol, value); this.asInstanceOf[SELF] }
+
+  def createTransformFunc: IN => OUT
+
+  override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = {
+    import dataset.sqlContext._
+    val map = this.paramMap ++ paramMap
+    val udf: IN => OUT = this.createTransformFunc
+    dataset.select(Star(None), udf.call(map(inputCol).attr) as map(outputCol))
+  }
+}

mllib/src/main/scala/org/apache/spark/ml/example/LogisticRegression.scala → mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.example
+package org.apache.spark.ml.classification
 
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._

mllib/src/main/scala/org/apache/spark/ml/example/BinaryClassificationEvaluator.scala → mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.example
+package org.apache.spark.ml.evaluation
 
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._

mllib/src/main/scala/org/apache/spark/ml/example/HashingTF.scala → mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala

@@ -1,4 +1,4 @@
-package org.apache.spark.ml.example
+package org.apache.spark.ml.feature
 
 import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.param.{HasInputCol, HasOutputCol, IntParam, ParamMap}

mllib/src/main/scala/org/apache/spark/ml/example/StandardScaler.scala → mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala

@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.example
+package org.apache.spark.ml.feature
 
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._
@@ -31,6 +31,10 @@ import org.apache.spark.sql.catalyst.expressions.Row
  */
 trait StandardScalerParams extends Params with HasInputCol with HasOutputCol
 
+/**
+ * Standardizes features by removing the mean and scaling to unit variance using column summary
+ * statistics on the samples in the training set.
+ */
 class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams {
 
   def setInputCol(value: String): this.type = { set(inputCol, value); this }
@@ -50,6 +54,9 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP
   }
 }
 
+/**
+ * Model fitted by [[StandardScaler]].
+ */
 class StandardScalerModel private[ml] (
     override val parent: StandardScaler,
     override val fittingParamMap: ParamMap,