Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/9524#discussion_r45669204
--- Diff: mllib/src/main/scala/org/apache/spark/ml/tree/codeGenerator.scala
---
@@ -0,0 +1,157 @@
+/*
+ * 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.tree
+
+import org.apache.spark.Logging
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+import org.codehaus.janino.ClassBodyEvaluator
+/**
+ * An object for creating a code generated decision tree model.
+ * NodeToTree is used to convert a node to a series if code gen
+ * if/else statements conditions returning the predicition for a
+ * given vector.
+ * getScorer wraps this and provides a function we can use to get
+ * the prediction.
+ */
+private[spark] object CodeGenerationDecisionTreeModel extends Logging {
+ private val prefix = "mllibCodeGen"
+ private val curId = new java.util.concurrent.atomic.AtomicInteger()
+
+ /**
+ * Compile the Java source code into a Java class, using Janino.
+ * Based on Spark SQL's implementation. This should be moved to a common
class
+ * once we have multiple code generators in ML.
+ *
+ * It will track the time used to compile
+ */
+ protected def compile(code: String, implements: Array[Class[_]]):
Class[_] = {
+ val startTime = System.nanoTime()
+ val evaluator = new ClassBodyEvaluator()
+ val clName = freshName()
+ evaluator.setParentClassLoader(getClass.getClassLoader)
+ evaluator.setImplementedInterfaces(implements)
+ evaluator.setClassName(clName)
+ evaluator.setDefaultImports(Array(
+ "org.apache.spark.mllib.linalg.Vectors",
+ "org.apache.spark.mllib.linalg.Vector"
+ ))
+ evaluator.cook(s"${clName}.java", code)
+ val clazz = evaluator.getClazz()
+ val endTime = System.nanoTime()
+ def timeMs: Double = (endTime - startTime).toDouble / 1000000
+ logDebug(s"Compiled Java code (${code.size} bytes) in $timeMs ms")
+ clazz
+ }
+
+ protected def freshName(): String = {
+ s"$prefix${curId.getAndIncrement}"
+ }
+
+
+ /**
+ * Convert the tree starting at the provided root node into a code
generated
+ * series of if/else statements. If the tree is too large to fit in a
single
+ * in-line method breaks it up into multiple methods.
+ * Returns a string for the current function body and a string of any
additional
+ * functions.
+ */
+ def nodeToTree(root: Node, depth: Int): (String, String) = {
+ // Handle the different types of nodes
+ root match {
+ case node: InternalNode => {
+ // Handle trees that get too large to fit in a single in-line java
method
+ depth match {
+ case 8 => {
+ val newFunctionName = freshName()
+ val newFunction = nodeToFunction(root, newFunctionName)
+ (s"return ${newFunctionName}();", newFunction)
+ }
+ case _ => {
+ val nodeSplit = node.split
+ val (leftSubCode, leftSubFunction) =
nodeToTree(node.leftChild, depth + 1)
+ val (rightSubCode, rightSubFunction) =
nodeToTree(node.rightChild, depth + 1)
+ val subCode = nodeSplit match {
+ case split: CategoricalSplit => {
+ val isLeft = split.isLeft
+ isLeft match {
+ case true => s"""
+ if
(categories.contains(input.apply(${split.featureIndex}))) {
+ ${leftSubCode}
+ } else {
+ ${rightSubCode}
+ }"""
+ case false => s"""
+ if
(categories.contains(input.apply(${split.featureIndex}))) {
+ ${leftSubCode}
+ } else {
+ ${rightSubCode}
+ }"""
+ }
+ }
+ case split: ContinuousSplit => {
+ s"""
+ if (input.apply(${split.featureIndex}) <=
${split.threshold}) {
+ ${leftSubCode}
+ } else {
+ ${rightSubCode}
+ }"""
+ }
+ }
+ (subCode, leftSubFunction + rightSubFunction)
+ }
+ }
+ }
+ case node: LeafNode => (s"return ${node.prediction};", "")
--- End diff --
oh.. my bad. i got ur code.
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