[SPARK-6526][ML] Add Normalizer transformer in ML package

See [SPARK-6526](https://issues.apache.org/jira/browse/SPARK-6526).

mengxr Should we add test suite for this transformer? There is no test suite for all feature transformers in ML package now.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #5181 from yinxusen/SPARK-6526 and squashes the following commits:

6faa7bf [Xusen Yin] fix style
8a462da [Xusen Yin] remove duplications
ab35ab0 [Xusen Yin] add test suite
bc8cd0f [Xusen Yin] fix comment
79774c9 [Xusen Yin] add Normalizer transformer in ML package

1 files changed, 109 insertions, 0 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala
new file mode 100644
index 0000000000..a18c335952
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala
@@ -0,0 +1,109 @@
+/*
+ * 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 org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+private case class DataSet(features: Vector)
+
+class NormalizerSuite extends FunSuite with MLlibTestSparkContext {
+
+  @transient var data: Array[Vector] = _
+  @transient var dataFrame: DataFrame = _
+  @transient var normalizer: Normalizer = _
+  @transient var l1Normalized: Array[Vector] = _
+  @transient var l2Normalized: Array[Vector] = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+
+    data = Array(
+      Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
+      Vectors.dense(0.0, 0.0, 0.0),
+      Vectors.dense(0.6, -1.1, -3.0),
+      Vectors.sparse(3, Seq((1, 0.91), (2, 3.2))),
+      Vectors.sparse(3, Seq((0, 5.7), (1, 0.72), (2, 2.7))),
+      Vectors.sparse(3, Seq())
+    )
+    l1Normalized = Array(
+      Vectors.sparse(3, Seq((0, -0.465116279), (1, 0.53488372))),
+      Vectors.dense(0.0, 0.0, 0.0),
+      Vectors.dense(0.12765957, -0.23404255, -0.63829787),
+      Vectors.sparse(3, Seq((1, 0.22141119), (2, 0.7785888))),
+      Vectors.dense(0.625, 0.07894737, 0.29605263),
+      Vectors.sparse(3, Seq())
+    )
+    l2Normalized = Array(
+      Vectors.sparse(3, Seq((0, -0.65617871), (1, 0.75460552))),
+      Vectors.dense(0.0, 0.0, 0.0),
+      Vectors.dense(0.184549876, -0.3383414, -0.922749378),
+      Vectors.sparse(3, Seq((1, 0.27352993), (2, 0.96186349))),
+      Vectors.dense(0.897906166, 0.113419726, 0.42532397),
+      Vectors.sparse(3, Seq())
+    )
+
+    val sqlContext = new SQLContext(sc)
+    dataFrame = sqlContext.createDataFrame(sc.parallelize(data, 2).map(DataSet))
+    normalizer = new Normalizer()
+      .setInputCol("features")
+      .setOutputCol("normalized_features")
+  }
+
+  def collectResult(result: DataFrame): Array[Vector] = {
+    result.select("normalized_features").collect().map {
+      case Row(features: Vector) => features
+    }
+  }
+
+  def assertTypeOfVector(lhs: Array[Vector], rhs: Array[Vector]): Unit = {
+    assert((lhs, rhs).zipped.forall {
+      case (v1: DenseVector, v2: DenseVector) => true
+      case (v1: SparseVector, v2: SparseVector) => true
+      case _ => false
+    }, "The vector type should be preserved after normalization.")
+  }
+
+  def assertValues(lhs: Array[Vector], rhs: Array[Vector]): Unit = {
+    assert((lhs, rhs).zipped.forall { (vector1, vector2) =>
+      vector1 ~== vector2 absTol 1E-5
+    }, "The vector value is not correct after normalization.")
+  }
+
+  test("Normalization with default parameter") {
+    val result = collectResult(normalizer.transform(dataFrame))
+
+    assertTypeOfVector(data, result)
+
+    assertValues(result, l2Normalized)
+  }
+
+  test("Normalization with setter") {
+    normalizer.setP(1)
+
+    val result = collectResult(normalizer.transform(dataFrame))
+
+    assertTypeOfVector(data, result)
+
+    assertValues(result, l1Normalized)
+  }
+}