Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/304#discussion_r11462322
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/preprocessing/OneHotEncoder.scala
---
@@ -0,0 +1,112 @@
+/*
+ * 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.preprocessing
+
+import org.apache.spark.rdd.RDD
+
+import scala.collection.mutable.HashSet
+import scala.collection.mutable.Set
+
+/**
+ * A utility for encoding categorical variables as numeric variables. The
resulting vectors
+ * contain a component for each value that the variable can take. The
component corresponding
+ * to the value that the variable takes is set to 1 and the components
corresponding to all other
+ * categories are set to 0.
+ *
+ * The utility handles input vectors with mixed categorical and numeric
variables by accepting a
+ * list of feature indices that are categorical and only transforming
those.
+ *
+ * An example usage is:
+ *
+ * {{{
+ * val categoricalFields = Array(0, 7, 21)
+ * val categories = OneHotEncoder.categories(rdd, categoricalFields)
+ * val encoded = OneHotEncoder.encode(rdd, categories)
+ * }}}
+ */
+object OneHotEncoder {
+
+ /**
+ * Given a dataset and the set of columns which are categorical
variables, returns a structure
+ * that, for each field, describes the values that are present for in
the dataset. The structure
+ * is meant to be used as input to encode.
+ */
+ def categories(rdd: RDD[Array[Any]], categoricalFields: Seq[Int]):
Array[Map[Any, Int]] = {
+ val categories = rdd.map(categoricals(_,
categoricalFields)).reduce(uniqueCats)
+
+ val catMaps = new Array[Map[Any, Int]](rdd.first().length)
+ for (i <- 0 until categoricalFields.length) {
+ catMaps(categoricalFields(i)) = categories(i).zipWithIndex.toMap
+ }
+
+ catMaps
+ }
+
+ /**
+ * Accepts a vector and set of feature indices that are categorical
variables. Outputs an array
+ * whose size is the number of categorical fields and each element is a
Set of size one
+ * containing a categorical value from the input vec
+ */
+ private def categoricals(tokens: Array[Any], catFields: Seq[Int]):
Array[Set[Any]] = {
+ val categoriesArr = new Array[Set[Any]](catFields.length)
+ for (i <- 0 until catFields.length) {
+ categoriesArr(i) = new HashSet[Any]()
+ categoriesArr(i) += tokens(catFields(i))
+ }
+ categoriesArr
+ }
+
+ private def uniqueCats(a: Array[Set[Any]], b: Array[Set[Any]]):
Array[Set[Any]] = {
+ for (i <- 0 until a.length) {
+ a(i) ++= b(i)
+ }
+ a
+ }
+
+ /**
+ * OneHot encodes the given RDD.
+ */
+ def encode(rdd: RDD[Array[Any]], featureCategories: Array[Map[Any,
Int]]):
+ RDD[Array[Any]] = {
+ var outArrLen = 0
+ for (catMap <- featureCategories) {
+ outArrLen += (if (catMap == null) 1 else catMap.size)
+ }
+ rdd.map(encodeVec(_, featureCategories, outArrLen))
+ }
+
+ private def encodeVec(vec: Array[Any], featureCategories: Array[Map[Any,
Int]],
+ outArrLen: Int): Array[Any] = {
+ var outArrIndex = 0
+ val outVec = new Array[Any](outArrLen)
+ for (i <- 0 until vec.length) {
--- End diff --
Do not use `for` in an inner loop. `while` and `foreach` give better
performance.
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