add bucketizer

mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala

@@ -0,0 +1,100 @@
+/*
+ * 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.Transformer
+import org.apache.spark.ml.attribute.{NominalAttribute, BinaryAttribute}
+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.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, StructType}
+
+/**
+ * :: AlphaComponent ::
+ * Binarize a column of continuous features given a threshold.
+ */
+@AlphaComponent
+final class Bucketizer extends Transformer with HasInputCol with HasOutputCol {
+
+  /**
+   * Param for threshold used to binarize continuous features.
+   * The features greater than the threshold, will be binarized to 1.0.
+   * The features equal to or less than the threshold, will be binarized to 0.0.
+   * @group param
+   */
+  val buckets: Param[Array[Double]] = new Param[Array[Double]](this, "buckets", "")
+
+  /** @group getParam */
+  def getBuckets: Array[Double] = $(buckets)
+
+  /** @group setParam */
+  def setBuckets(value: Array[Double]): this.type = set(buckets, value)
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema)
+    val bucketizer = udf { feature: Double => binarySearchForBins($(buckets), feature) }
+    val outputColName = $(outputCol)
+    val metadata = NominalAttribute.defaultAttr
+      .withName(outputColName).withValues($(buckets).map(_.toString)).toMetadata()
+    dataset.select(col("*"), bucketizer(dataset($(inputCol))).as(outputColName, metadata))
+  }
+
+  /**
+   * Binary searching in several bins to place each data point.
+   */
+  private def binarySearchForBins(splits: Array[Double], feature: Double): Double = {
+    val wrappedSplits = Array(Double.MinValue) ++ splits ++ Array(Double.MaxValue)
+    var left = 0
+    var right = wrappedSplits.length - 2
+    while (left <= right) {
+      val mid = left + (right - left) / 2
+      val split = wrappedSplits(mid)
+      if ((feature > split) && (feature <= wrappedSplits(mid + 1))) {
+        return mid
+      } else if (feature <= split) {
+        right = mid - 1
+      } else {
+        left = mid + 1
+      }
+    }
+    -1
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
+
+    val inputFields = schema.fields
+    val outputColName = $(outputCol)
+
+    require(inputFields.forall(_.name != outputColName),
+      s"Output column $outputColName already exists.")
+
+    val attr = NominalAttribute.defaultAttr.withName(outputColName)
+    val outputFields = inputFields :+ attr.toStructField()
+    StructType(outputFields)
+  }
+}