Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5980#discussion_r29883753
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
---
@@ -0,0 +1,118 @@
+/*
+ * 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, StructType}
+
+/**
+ * :: AlphaComponent ::
+ * `Bucketizer` maps a column of continuous features to a column of
feature buckets.
+ */
+@AlphaComponent
+final class Bucketizer(override val parent: Estimator[Bucketizer] = null)
+ extends Model[Bucketizer] with HasInputCol with HasOutputCol {
+
+ /**
+ * The given buckets should match 1) its size is larger than zero; 2) it
is ordered in a non-DESC
+ * way.
+ */
+ private def checkBuckets(buckets: Array[Double]): Boolean = {
+ if (buckets.size == 0) false
+ else if (buckets.size == 1) true
+ else {
+ buckets.foldLeft((true, Double.MinValue)) { case ((validator,
prevValue), currValue) =>
+ if (validator & prevValue <= currValue) {
+ (true, currValue)
+ } else {
+ (false, currValue)
+ }
+ }._1
+ }
+ }
+
+ /**
+ * Parameter for mapping continuous features into buckets.
+ * @group param
+ */
+ val buckets: Param[Array[Double]] = new Param[Array[Double]](this,
"buckets",
+ "Split points for mapping continuous features into buckets.",
checkBuckets)
+
+ /** @group getParam */
+ def getBuckets: Array[Double] = $(buckets)
+
+ /** @group setParam */
+ def setBuckets(value: Array[Double]): this.type = set(buckets, value)
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ transformSchema(dataset.schema)
+ val bucketizer = udf { feature: Double =>
binarySearchForBuckets($(buckets), feature) }
+ val outputColName = $(outputCol)
+ val metadata = NominalAttribute.defaultAttr
+
.withName(outputColName).withValues($(buckets).map(_.toString)).toMetadata()
--- End diff --
The values look incorrect since there should be n+1 buckets given n splits.
Also, can you please reuse the code for metadata construction here and in
transformSchema?
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