Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6113#discussion_r30428863
--- Diff: docs/ml-features.md ---
@@ -183,6 +183,86 @@ for words_label in wordsDataFrame.select("words",
"label").take(3):
</div>
</div>
+## PolynomialExpansion
+
+[Polynomial expansion](http://en.wikipedia.org/wiki/Polynomial_expansion)
is the process of expanding your features into a polynomial space, which is
formulated by an n-degree combination of original dimensions. A
[PolynomialExpansion](api/scala/index.html#org.apache.spark.ml.feature.PolynomialExpansion)
class provides this functionality. The example below shows how to expand your
features into a 3-degree polynomial space.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.PolynomialExpansion
+
+val data = Array(
+ Vectors.dense(-2.0, 2.3),
+ Vectors.dense(0.0, 0.0),
+ Vectors.dense(0.6, -1.1)
+)
+val df =
sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
+val polynomialExpansion = new PolynomialExpansion()
+ .setInputCol("features")
+ .setOutputCol("polyFeatures")
+ .setDegree(3)
+val expandedFeatures = polynomialExpansion.transform(df)
+expandedFeatures.select("polyFeatures").take(3).foreach(println)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import scala.Tuple2;
+
+import com.google.common.collect.Lists;
+
+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.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+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.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+JavaSparkContext jsc = ...
+SQLContext jsql = ...
+PolynomialExpansion polyExpansion = new PolynomialExpansion()
+ .setInputCol("features")
+ .setOutputCol("polyFeatures")
+ .setDegree(3);
+JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+ RowFactory.create(Vectors.dense(-2.0, 2.3)),
+ RowFactory.create(Vectors.dense(0.0, 0.0)),
+ RowFactory.create(Vectors.dense(0.6, -1.1))
+));
+StructType schema = new StructType(new StructField[] {
+ new StructField("features", new VectorUDT(), false, Metadata.empty()),
+});
+DataFrame dataset = jsql.createDataFrame(data, schema);
+DataFrame polyDF = polyExpansion.transform(dataset);
+Row[] row = polyDF.select("polyFeatures").take(3);
+for (Row r : row) {
+ System.out.println((((Vector)r.get(0))));
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+{% highlight python %}
+from pyspark.mllib.linalg import Vectors
+from pyspark.ml.feature import PolynomialExpansion
+df = sqlContext.createDataFrame(
+ [(Vectors.dense([-2.0, 2.3]), ), (Vectors.dense([0.0, 0.0]), ),
(Vectors.dense([0.6, -1.1]), )],
--- End diff --
Python style: Please use shorter lines. Here, 1 Vector per line will be
good.
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