Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/216#discussion_r10947120
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/discretization/EntropyMinimizationDiscretizer.scala
---
@@ -0,0 +1,402 @@
+/*
+ * 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.mllib.discretization
+
+import scala.collection.mutable.Stack
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.util.InfoTheory
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.mllib.regression.LabeledPoint
+import scala.collection.mutable
+
+
+/**
+ * This class contains methods to discretize continuous values with the
method proposed in
+ * [Fayyad and Irani, Multi-Interval Discretization of Continuous-Valued
Attributes, 1993]
+ */
+class EntropyMinimizationDiscretizer private (
+ @transient var data: RDD[LabeledPoint],
+ @transient var continousFeatures: Seq[Int],
+ var elementsPerPartition: Int = 18000,
+ var maxBins: Int = Int.MaxValue)
+ extends DiscretizerModel {
+
+ private var thresholds = Map.empty[Int, Seq[Double]]
+ private val partitions = { x : Long => math.ceil(x.toDouble /
elementsPerPartition).toInt }
+
+ def this() = this(null, null)
+
+ /**
+ * Sets the RDD[LabeledPoint] to be discretized
+ *
+ * @param data RDD[LabeledPoint] to be discretized
+ */
+ def setData(data: RDD[LabeledPoint]): EntropyMinimizationDiscretizer = {
+ this.data = data
+ this
+ }
+
+ /**
+ * Sets the indexes of the features to be discretized
+ *
+ * @param continuousFeatures Indexes of features to be discretized
+ */
+ def setContinuousFeatures(continuousFeatures: Seq[Int]):
EntropyMinimizationDiscretizer = {
+ this.continousFeatures = continuousFeatures
+ this
+ }
+
+ /**
+ * Sets the maximum number of elements that a partition should have
+ *
+ * @param ratio Maximum number of elements for a partition
+ * @return The Discretizer with the parameter changed
+ */
+ def setElementsPerPartition(ratio: Int): EntropyMinimizationDiscretizer
= {
+ this.elementsPerPartition = ratio
+ this
+ }
+
+ /**
+ * Sets the maximum number of discrete values
+ *
+ * @param maxBins Maximum number of discrete values
+ * @return The Discretizer with the parameter changed
+ */
+ def setMaxBins(maxBins: Int): EntropyMinimizationDiscretizer = {
+ this.maxBins = maxBins
+ this
+ }
+
+ /**
+ * Returns the thresholds used to discretized the given feature
+ *
+ * @param feature The number of the feature to discretize
+ */
+ def getThresholdsForFeature(feature: Int): Seq[Double] = {
+ thresholds.get(feature) match {
+ case Some(a) => a
+ case None =>
+ val featureValues = data.map({
+ case LabeledPoint(label, values) => (values(feature),
label.toString.trim)
+ })
+ val sortedValues = featureValues.sortByKey()
+ val initial_candidates = initialThresholds(sortedValues)
--- End diff --
camelCase for variable names.
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