[SPARK-7557] [ML] [DOC] User guide for spark.ml HashingTF, Tokenizer

Added feature transformer subsection to spark.ml guide, with HashingTF and Tokenizer.  Added JavaHashingTFSuite to test Java examples in new guide.

I've run Scala, Python examples in the Spark/PySpark shells.  I ran the Java examples via the test suite (with small modifications for printing).

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #6093 from jkbradley/hashingtf-guide and squashes the following commits:

d5d213f [Joseph K. Bradley] small fix
dd6e91a [Joseph K. Bradley] fixes from code review of user guide
33c3ff9 [Joseph K. Bradley] small fix
bc6058c [Joseph K. Bradley] fix link
361a174 [Joseph K. Bradley] Added subsection for feature transformers to spark.ml guide, with HashingTF and Tokenizer.  Added JavaHashingTFSuite to test Java examples in new guide

1 files changed, 81 insertions, 0 deletions

diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
new file mode 100644
index 0000000000..23463ab5fe
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
@@ -0,0 +1,81 @@
+/*
+ * 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.ml.feature;
+
+import com.google.common.collect.Lists;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+
+public class JavaHashingTFSuite {
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaHashingTFSuite");
+    jsql = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  @Test
+  public void hashingTF() {
+    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+      RowFactory.create(0, "Hi I heard about Spark"),
+      RowFactory.create(0, "I wish Java could use case classes"),
+      RowFactory.create(1, "Logistic regression models are neat")
+    ));
+    StructType schema = new StructType(new StructField[]{
+      new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
+      new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
+    });
+    DataFrame sentenceDataFrame = jsql.createDataFrame(jrdd, schema);
+
+    Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
+    DataFrame wordsDataFrame = tokenizer.transform(sentenceDataFrame);
+    int numFeatures = 20;
+    HashingTF hashingTF = new HashingTF()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setNumFeatures(numFeatures);
+    DataFrame featurized = hashingTF.transform(wordsDataFrame);
+    for (Row r : featurized.select("features", "words", "label").take(3)) {
+      Vector features = r.getAs(0);
+      Assert.assertEquals(features.size(), numFeatures);
+    }
+  }
+}