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diff --git a/docs/ml-features.md b/docs/ml-features.md index 5df61dd36a..e86f9edc4f 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -268,5 +268,88 @@ for binarized_feature, in binarizedFeatures.collect(): </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 +import org.apache.spark.mllib.linalg.Vectors + +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 polyDF = polynomialExpansion.transform(df) +polyDF.select("polyFeatures").take(3).foreach(println) +{% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> +{% highlight java %} +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 df = jsql.createDataFrame(data, schema); +DataFrame polyDF = polyExpansion.transform(df); +Row[] row = polyDF.select("polyFeatures").take(3); +for (Row r : row) { + System.out.println(r.get(0)); +} +{% endhighlight %} +</div> + +<div data-lang="python" markdown="1"> +{% highlight python %} +from pyspark.ml.feature import PolynomialExpansion +from pyspark.mllib.linalg import Vectors + +df = sqlContext.createDataFrame( + [(Vectors.dense([-2.0, 2.3]), ), + (Vectors.dense([0.0, 0.0]), ), + (Vectors.dense([0.6, -1.1]), )], + ["features"]) +px = PolynomialExpansion(degree=2, inputCol="features", outputCol="polyFeatures") +polyDF = px.transform(df) +for expanded in polyDF.select("polyFeatures").take(3): + print(expanded) +{% endhighlight %} +</div> +</div> + # Feature Selectors |