Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/19433#discussion_r150309552
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tree/impl/SplitUtils.scala ---
@@ -0,0 +1,215 @@
+/*
+ * 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.impl
+
+import org.apache.spark.ml.tree.{CategoricalSplit, Split}
+import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
+import org.apache.spark.mllib.tree.model.ImpurityStats
+
+/** Utility methods for choosing splits during local & distributed tree
training. */
+private[impl] object SplitUtils {
+
+ /** Sorts ordered feature categories by label centroid, returning an
ordered list of categories */
+ private def sortByCentroid(
+ binAggregates: DTStatsAggregator,
+ featureIndex: Int,
+ featureIndexIdx: Int): List[Int] = {
+ /* Each bin is one category (feature value).
+ * The bins are ordered based on centroidForCategories, and this
ordering determines which
+ * splits are considered. (With K categories, we consider K - 1
possible splits.)
+ *
+ * centroidForCategories is a list: (category, centroid)
+ */
+ val numCategories = binAggregates.metadata.numBins(featureIndex)
+ val nodeFeatureOffset = binAggregates.getFeatureOffset(featureIndexIdx)
+
+ val centroidForCategories = Range(0, numCategories).map { featureValue
=>
+ val categoryStats =
+ binAggregates.getImpurityCalculator(nodeFeatureOffset,
featureValue)
+ val centroid = ImpurityUtils.getCentroid(binAggregates.metadata,
categoryStats)
+ (featureValue, centroid)
+ }
+ // TODO(smurching): How to handle logging statements like these?
--- End diff --
What's the issue? You should be able to call logDebug if this object
inherits from org.apache.spark.internal.Logging
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