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diff --git a/docs/ml-features.md b/docs/ml-features.md index efe9b3b8ed..d7851a55fa 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -789,6 +789,92 @@ scaledData = scalerModel.transform(dataFrame) </div> </div> +## Bucketizer + +`Bucketizer` transforms a column of continuous features to a column of feature buckets, where the buckets are specified by users. It takes a parameter: + +* `splits`: Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. Splits should be strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; Otherwise, values outside the splits specified will be treated as errors. Two examples of `splits` are `Array(Double.NegativeInfinity, 0.0, 1.0, Double.PositiveInfinity)` and `Array(0.0, 1.0, 2.0)`. + +Note that if you have no idea of the upper bound and lower bound of the targeted column, you would better add the `Double.NegativeInfinity` and `Double.PositiveInfinity` as the bounds of your splits to prevent a potenial out of Bucketizer bounds exception. + +Note also that the splits that you provided have to be in strictly increasing order, i.e. `s0 < s1 < s2 < ... < sn`. + +More details can be found in the API docs for [Bucketizer](api/scala/index.html#org.apache.spark.ml.feature.Bucketizer). + +The following example demonstrates how to bucketize a column of `Double`s into another index-wised column. + +<div class="codetabs"> +<div data-lang="scala"> +{% highlight scala %} +import org.apache.spark.ml.feature.Bucketizer +import org.apache.spark.sql.DataFrame + +val splits = Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity) + +val data = Array(-0.5, -0.3, 0.0, 0.2) +val dataFrame = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features") + +val bucketizer = new Bucketizer() + .setInputCol("features") + .setOutputCol("bucketedFeatures") + .setSplits(splits) + +// Transform original data into its bucket index. +val bucketedData = bucketizer.transform(dataFrame) +{% endhighlight %} +</div> + +<div data-lang="java"> +{% highlight java %} +import com.google.common.collect.Lists; + +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +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; + +double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY}; + +JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList( + RowFactory.create(-0.5), + RowFactory.create(-0.3), + RowFactory.create(0.0), + RowFactory.create(0.2) +)); +StructType schema = new StructType(new StructField[] { + new StructField("features", DataTypes.DoubleType, false, Metadata.empty()) +}); +DataFrame dataFrame = jsql.createDataFrame(data, schema); + +Bucketizer bucketizer = new Bucketizer() + .setInputCol("features") + .setOutputCol("bucketedFeatures") + .setSplits(splits); + +// Transform original data into its bucket index. +DataFrame bucketedData = bucketizer.transform(dataFrame); +{% endhighlight %} +</div> + +<div data-lang="python"> +{% highlight python %} +from pyspark.ml.feature import Bucketizer + +splits = [-float("inf"), -0.5, 0.0, 0.5, float("inf")] + +data = [(-0.5,), (-0.3,), (0.0,), (0.2,)] +dataFrame = sqlContext.createDataFrame(data, ["features"]) + +bucketizer = Bucketizer(splits=splits, inputCol="features", outputCol="bucketedFeatures") + +# Transform original data into its bucket index. +bucketedData = bucketizer.transform(dataFrame) +{% endhighlight %} +</div> +</div> # Feature Selectors |