[SPARK-7242][SQL][MLLIB] Frequent items for DataFrames

Finding frequent items with possibly false positives, using the algorithm described in `http://www.cs.umd.edu/~samir/498/karp.pdf`.
public API under:
```
df.stat.freqItems(cols: Array[String], support: Double = 0.001): DataFrame
```

The output is a local DataFrame having the input column names with `-freqItems` appended to it. This is a single pass algorithm that may return false positives, but no false negatives.

cc mengxr rxin

Let's get the implementations in, I can add python API in a follow up PR.

Author: Burak Yavuz <[email protected]>

Closes apache#5799 from brkyvz/freq-items and squashes the following commits:

a6ec82c [Burak Yavuz] addressed comments v?
39b1bba [Burak Yavuz] removed toSeq
0915e23 [Burak Yavuz] addressed comments v2.1
3a5c177 [Burak Yavuz] addressed comments v2.0
482e741 [Burak Yavuz] removed old import
38e784d [Burak Yavuz] addressed comments v1.0
8279d4d [Burak Yavuz] added default value for support
3d82168 [Burak Yavuz] made base implementation

Author: brkyvz

sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala

@@ -330,6 +330,17 @@ class DataFrame private[sql](
    */
   def na: DataFrameNaFunctions = new DataFrameNaFunctions(this)
 
+  /**
+   * Returns a [[DataFrameStatFunctions]] for working statistic functions support.
+   * {{{
+   *   // Finding frequent items in column with name 'a'.
+   *   df.stat.freqItems(Seq("a"))
+   * }}}
+   *
+   * @group dfops
+   */
+  def stat: DataFrameStatFunctions = new DataFrameStatFunctions(this)
+
   /**
    * Cartesian join with another [[DataFrame]].
    *

sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala

@@ -0,0 +1,68 @@
+/*
+* 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
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.sql.execution.stat.FrequentItems
+
+/**
+ * :: Experimental ::
+ * Statistic functions for [[DataFrame]]s.
+ */
+@Experimental
+final class DataFrameStatFunctions private[sql](df: DataFrame) {
+
+  /**
+   * Finding frequent items for columns, possibly with false positives. Using the
+   * frequent element count algorithm described in
+   * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]].
+   * The `support` should be greater than 1e-4.
+   *
+   * @param cols the names of the columns to search frequent items in.
+   * @param support The minimum frequency for an item to be considered `frequent`. Should be greater
+   *                than 1e-4.
+   * @return A Local DataFrame with the Array of frequent items for each column.
+   */
+  def freqItems(cols: Array[String], support: Double): DataFrame = {
+    FrequentItems.singlePassFreqItems(df, cols, support)
+  }
+
+  /**
+   * Runs `freqItems` with a default `support` of 1%.
+   *
+   * @param cols the names of the columns to search frequent items in.
+   * @return A Local DataFrame with the Array of frequent items for each column.
+   */
+  def freqItems(cols: Array[String]): DataFrame = {
+    FrequentItems.singlePassFreqItems(df, cols, 0.01)
+  }
+
+  /**
+   * Python friendly implementation for `freqItems`
+   */
+  def freqItems(cols: List[String], support: Double): DataFrame = {
+    FrequentItems.singlePassFreqItems(df, cols, support)
+  }
+
+  /**
+   * Python friendly implementation for `freqItems` with a default `support` of 1%.
+   */
+  def freqItems(cols: List[String]): DataFrame = {
+    FrequentItems.singlePassFreqItems(df, cols, 0.01)
+  }
+}

sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala

@@ -0,0 +1,121 @@
+/*
+* 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.execution.stat
+
+import scala.collection.mutable.{Map => MutableMap}
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.{Column, DataFrame, Row}
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.types.{ArrayType, StructField, StructType}
+
+private[sql] object FrequentItems extends Logging {
+
+  /** A helper class wrapping `MutableMap[Any, Long]` for simplicity. */
+  private class FreqItemCounter(size: Int) extends Serializable {
+    val baseMap: MutableMap[Any, Long] = MutableMap.empty[Any, Long]
+
+    /**
+     * Add a new example to the counts if it exists, otherwise deduct the count
+     * from existing items.
+     */
+    def add(key: Any, count: Long): this.type = {
+      if (baseMap.contains(key))  {
+        baseMap(key) += count
+      } else {
+        if (baseMap.size < size) {
+          baseMap += key -> count
+        } else {
+          // TODO: Make this more efficient... A flatMap?
+          baseMap.retain((k, v) => v > count)
+          baseMap.transform((k, v) => v - count)
+        }
+      }
+      this
+    }
+
+    /**
+     * Merge two maps of counts.
+     * @param other The map containing the counts for that partition
+     */
+    def merge(other: FreqItemCounter): this.type = {
+      other.baseMap.foreach { case (k, v) =>
+        add(k, v)
+      }
+      this
+    }
+  }
+
+  /**
+   * Finding frequent items for columns, possibly with false positives. Using the 
+   * frequent element count algorithm described in
+   * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]].
+   * The `support` should be greater than 1e-4.
+   * 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`. Should be greater
+   *                than 1e-4.
+   * @return A Local DataFrame with the Array of frequent items for each column.
+   */
+  private[sql] def singlePassFreqItems(
+      df: DataFrame, 
+      cols: Seq[String],
+      support: Double): DataFrame = {
+    require(support >= 1e-4, s"support ($support) must be greater than 1e-4.")
+    val numCols = cols.length
+    // number of max items to keep counts for
+    val sizeOfMap = (1 / support).toInt
+    val countMaps = Seq.tabulate(numCols)(i => new FreqItemCounter(sizeOfMap))
+    val originalSchema = df.schema
+    val colInfo = cols.map { name =>
+      val index = originalSchema.fieldIndex(name)
+      (name, originalSchema.fields(index).dataType)
+    }
+    
+    val freqItems = df.select(cols.map(Column(_)):_*).rdd.aggregate(countMaps)(
+      seqOp = (counts, row) => {
+        var i = 0
+        while (i < numCols) {
+          val thisMap = counts(i)
+          val key = row.get(i)
+          thisMap.add(key, 1L)
+          i += 1
+        }
+        counts
+      },
+      combOp = (baseCounts, counts) => {
+        var i = 0
+        while (i < numCols) {
+          baseCounts(i).merge(counts(i))
+          i += 1
+        }
+        baseCounts
+      }
+    )
+    val justItems = freqItems.map(m => m.baseMap.keys.toSeq)
+    val resultRow = Row(justItems:_*)
+    // append frequent Items to the column name for easy debugging
+    val outputCols = colInfo.map { v =>
+      StructField(v._1 + "_freqItems", ArrayType(v._2, false))
+    }
+    val schema = StructType(outputCols).toAttributes
+    new DataFrame(df.sqlContext, LocalRelation(schema, Seq(resultRow)))
+  }
+}

sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java

@@ -22,10 +22,7 @@
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.sql.DataFrame;
-import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
-import org.apache.spark.sql.TestData$;
+import org.apache.spark.sql.*;
 import org.apache.spark.sql.test.TestSQLContext;
 import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.types.*;
@@ -178,5 +175,12 @@ public void testCreateDataFrameFromJavaBeans() {
       Assert.assertEquals(bean.getD().get(i), d.apply(i));
     }
   }
-
+  
+  @Test
+  public void testFrequentItems() {
+    DataFrame df = context.table("testData2");
+    String[] cols = new String[]{"a"};
+    DataFrame results = df.stat().freqItems(cols, 0.2);
+    Assert.assertTrue(results.collect()[0].getSeq(0).contains(1));
+  }
 }

sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala

@@ -0,0 +1,47 @@
+/*
+ * 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
+
+import org.scalatest.FunSuite
+import org.scalatest.Matchers._
+
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.TestSQLContext.implicits._
+
+class DataFrameStatSuite extends FunSuite  {
+
+  val sqlCtx = TestSQLContext
+
+  test("Frequent Items") {
+    def toLetter(i: Int): String = (i + 96).toChar.toString
+    val rows = Array.tabulate(1000) { i =>
+      if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0)
+    }
+    val df = sqlCtx.sparkContext.parallelize(rows).toDF("numbers", "letters", "negDoubles")
+
+    val results = df.stat.freqItems(Array("numbers", "letters"), 0.1)
+    val items = results.collect().head
+    items.getSeq[Int](0) should contain (1)
+    items.getSeq[String](1) should contain (toLetter(1))
+
+    val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1)
+    val items2 = singleColResults.collect().head
+    items2.getSeq[Double](0) should contain (-1.0)
+
+  }
+}