Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5980#discussion_r29912970
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
---
@@ -0,0 +1,158 @@
+/*
+ * 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.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.attribute.NominalAttribute
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
+
+/**
+ * :: AlphaComponent ::
+ * `Bucketizer` maps a column of continuous features to a column of
feature buckets.
+ */
+@AlphaComponent
+private[ml] final class Bucketizer(override val parent:
Estimator[Bucketizer])
+ extends Model[Bucketizer] with HasInputCol with HasOutputCol {
+
+ def this() = this(null)
+
+ /**
+ * Parameter for mapping continuous features into buckets. With n
splits, there are n+1 buckets.
+ * A bucket defined by splits x,y holds values in the range [x,y).
+ * @group param
+ */
+ val splits: Param[Array[Double]] = new Param[Array[Double]](this,
"splits",
+ "Split points for mapping continuous features into buckets. With n
splits, there are n+1" +
+ " buckets. A bucket defined by splits x,y holds values in the range
[x,y).",
+ Bucketizer.checkSplits)
+
+ /** @group getParam */
+ def getSplits: Array[Double] = $(splits)
+
+ /** @group setParam */
+ def setSplits(value: Array[Double]): this.type = set(splits, value)
+
+ /** @group Param */
+ val lowerInclusive: BooleanParam = new BooleanParam(this,
"lowerInclusive",
+ "An indicator of the inclusiveness of negative infinite.")
+ setDefault(lowerInclusive -> true)
+
+ /** @group getParam */
+ def getLowerInclusive: Boolean = $(lowerInclusive)
+
+ /** @group setParam */
+ def setLowerInclusive(value: Boolean): this.type = set(lowerInclusive,
value)
+
+ /** @group Param */
+ val upperInclusive: BooleanParam = new BooleanParam(this,
"upperInclusive",
+ "An indicator of the inclusiveness of positive infinite.")
+ setDefault(upperInclusive -> true)
+
+ /** @group getParam */
+ def getUpperInclusive: Boolean = $(upperInclusive)
+
+ /** @group setParam */
+ def setUpperInclusive(value: Boolean): this.type = set(upperInclusive,
value)
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ transformSchema(dataset.schema)
+ val wrappedSplits = Array(Double.MinValue) ++ $(splits) ++
Array(Double.MaxValue)
+ val bucketizer = udf { feature: Double =>
+ Bucketizer
+ .binarySearchForBuckets(wrappedSplits, feature, $(lowerInclusive),
$(upperInclusive)) }
+ val newCol = bucketizer(dataset($(inputCol)))
+ val newField = prepOutputField(dataset.schema)
+ dataset.withColumn($(outputCol), newCol.as($(outputCol),
newField.metadata))
+ }
+
+ private def prepOutputField(schema: StructType): StructField = {
+ val attr = new NominalAttribute(
+ name = Some($(outputCol)),
+ isOrdinal = Some(true),
+ values = Some($(splits).map(_.toString)))
+
+ attr.toStructField()
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
+ require(schema.fields.forall(_.name != $(outputCol)),
+ s"Output column ${$(outputCol)} already exists.")
+ StructType(schema.fields :+ prepOutputField(schema))
+ }
+}
+
+object Bucketizer {
+ /**
+ * The given splits should match 1) its size is larger than zero; 2) it
is ordered in a strictly
+ * increasing way.
+ */
+ private def checkSplits(splits: Array[Double]): Boolean = {
+ if (splits.size == 0) false
+ else if (splits.size == 1) true
+ else {
+ splits.foldLeft((true, Double.MinValue)) { case ((validator,
prevValue), currValue) =>
+ if (validator && prevValue < currValue) {
+ (true, currValue)
+ } else {
+ (false, currValue)
+ }
+ }._1
+ }
+ }
+
+ /**
+ * Binary searching in several buckets to place each data point.
+ */
+ private[feature] def binarySearchForBuckets(
+ splits: Array[Double],
+ feature: Double,
+ lowerInclusive: Boolean,
+ upperInclusive: Boolean): Double = {
+ if ((feature < splits.head && !lowerInclusive) || (feature >
splits.last && !upperInclusive)) {
+ throw new Exception(s"Feature $feature out of bound, check your
features or loose the" +
+ s" lower/upper bound constraint.")
+ }
+ var left = 0
+ var right = splits.length - 2
+ while (left <= right) {
+ val mid = left + (right - left) / 2
+ val split = splits(mid)
+ if ((feature >= split) && (feature < splits(mid + 1))) {
+ return mid
+ } else if (feature < split) {
+ right = mid - 1
+ } else {
+ left = mid + 1
+ }
+ }
+ throw new Exception(s"Failed to find a bucket for feature $feature.")
--- End diff --
This should probably be a RuntimeException. Can it also please say
something like "unexpected error?"
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