Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5799#discussion_r29405470
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/ml/FrequentItems.scala ---
@@ -0,0 +1,124 @@
+/*
+* 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.sql.ml
+
+
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.types.{StructType, ArrayType, StructField}
+
+import scala.collection.mutable.{Map => MutableMap}
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.{Row, DataFrame, functions}
+
+private[sql] object FrequentItems extends Logging {
+
+ /**
+ * Merge two maps of counts. Subtracts the sum of `otherMap` from
`baseMap`, and fills in
+ * any emptied slots with the most frequent of `otherMap`.
+ * @param baseMap The map containing the global counts
+ * @param otherMap The map containing the counts for that partition
+ * @param maxSize The maximum number of counts to keep in memory
+ */
+ private def mergeCounts[A](
+ baseMap: MutableMap[A, Long],
+ otherMap: MutableMap[A, Long],
+ maxSize: Int): Unit = {
+ val otherSum = otherMap.foldLeft(0L) { case (sum, (k, v)) =>
+ if (!baseMap.contains(k)) sum + v else sum
+ }
+ baseMap.retain((k, v) => v > otherSum)
+ // sort in decreasing order, so that we will add the most frequent
items first
+ val sorted = otherMap.toSeq.sortBy(-_._2)
+ var i = 0
+ val otherSize = sorted.length
+ while (i < otherSize && baseMap.size < maxSize) {
+ val keyVal = sorted(i)
+ baseMap += keyVal._1 -> keyVal._2
+ i += 1
+ }
+ }
+
+
+ /**
+ * Finding frequent items for columns, possibly with false positives.
Using the algorithm
+ * described in `http://www.cs.umd.edu/~samir/498/karp.pdf`.
+ * For Internal use only.
+ *
+ * @param df The input DataFrame
+ * @param cols the names of the columns to search frequent items in
+ * @param support The minimum frequency for an item to be considered
`frequent`
+ * @return A Local DataFrame with the Array of frequent items for each
column.
+ */
+ private[sql] def singlePassFreqItems(
+ df: DataFrame,
+ cols: Array[String],
+ support: Double): DataFrame = {
+ val numCols = cols.length
+ // number of max items to keep counts for
+ val sizeOfMap = math.floor(1 / support).toInt
+ val countMaps = Array.tabulate(numCols)(i => MutableMap.empty[Any,
Long])
+ val originalSchema = df.schema
+ val colInfo = cols.map { name =>
+ val index = originalSchema.fieldIndex(name)
+ val dataType = originalSchema.fields(index)
+ (index, dataType.dataType)
+ }
+ val colIndices = colInfo.map(_._1)
+
+ val freqItems: Array[MutableMap[Any, Long]] =
df.rdd.aggregate(countMaps)(
+ seqOp = (counts, row) => {
+ var i = 0
+ colIndices.foreach { index =>
+ val thisMap = counts(i)
+ val key = row.get(index)
+ if (thisMap.contains(key)) {
+ thisMap(key) += 1
+ } else {
+ if (thisMap.size < sizeOfMap) {
+ thisMap += key -> 1
+ } else {
+ // TODO: Make this more efficient... A flatMap?
+ thisMap.retain((k, v) => v > 1)
+ thisMap.transform((k, v) => v - 1)
+ }
+ }
+ i += 1
+ }
+ counts
+ },
+ combOp = (baseCounts, counts) => {
+ var i = 0
+ while (i < numCols) {
+ mergeCounts(baseCounts(i), counts(i), sizeOfMap)
+ i += 1
+ }
+ baseCounts
+ }
+ )
+ //
+ val justItems = freqItems.map(m => m.keys.toSeq)
+ val resultRow = Row(justItems:_*)
+ // append frequent Items to the column name for easy debugging
+ val outputCols = cols.zip(colInfo).map{ v =>
+ StructField(v._1 + "-freqItems", ArrayType(v._2._2, false))
--- End diff --
`-freqItems` -> `_freqItems`
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