Mailing-List: contact reviews-help@spark.apache.org; run by ezmlm
Precedence: bulk
From: davies <git@git.apache.org>
To: reviews@spark.apache.org
Reply-To: reviews@spark.apache.org
References: <git-pr-10029-spark@git.apache.org>
In-Reply-To: <git-pr-10029-spark@git.apache.org>
Subject: [GitHub] spark pull request: [SPARK-9297][SQL] Add covar_pop and
 covar_samp
Content-Type: text/plain
Message-Id: <20160113083333.5CE4EE0415@git1-us-west.apache.org>
Date: Wed, 13 Jan 2016 08:33:33 +0000 (UTC)

Github user davies commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10029#discussion_r49562260
  
    --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Covariance.scala ---
    @@ -0,0 +1,203 @@
    +/*
    + * 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.catalyst.expressions.aggregate
    +
    +import org.apache.spark.sql.catalyst.InternalRow
    +import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
    +import org.apache.spark.sql.catalyst.expressions._
    +import org.apache.spark.sql.catalyst.util.TypeUtils
    +import org.apache.spark.sql.types._
    +
    +/**
    + * Compute the covariance between two expressions.
    + * When applied on empty data (i.e., count is zero), it returns NULL.
    + *
    + */
    +abstract class Covariance(left: Expression, right: Expression) extends ImperativeAggregate
    +    with Serializable {
    +  override def children: Seq[Expression] = Seq(left, right)
    +
    +  override def nullable: Boolean = true
    +
    +  override def dataType: DataType = DoubleType
    +
    +  override def inputTypes: Seq[AbstractDataType] = Seq(DoubleType, DoubleType)
    +
    +  override def checkInputDataTypes(): TypeCheckResult = {
    +    if (left.dataType.isInstanceOf[DoubleType] && right.dataType.isInstanceOf[DoubleType]) {
    +      TypeCheckResult.TypeCheckSuccess
    +    } else {
    +      TypeCheckResult.TypeCheckFailure(
    +        s"covariance requires that both arguments are double type, " +
    +          s"not (${left.dataType}, ${right.dataType}).")
    +    }
    +  }
    +
    +  override def aggBufferSchema: StructType = StructType.fromAttributes(aggBufferAttributes)
    +
    +  override def inputAggBufferAttributes: Seq[AttributeReference] = {
    +    aggBufferAttributes.map(_.newInstance())
    +  }
    +
    +  override val aggBufferAttributes: Seq[AttributeReference] = Seq(
    +    AttributeReference("xAvg", DoubleType)(),
    +    AttributeReference("yAvg", DoubleType)(),
    +    AttributeReference("Ck", DoubleType)(),
    +    AttributeReference("count", LongType)())
    +
    +  // Local cache of mutableAggBufferOffset(s) that will be used in update and merge
    +  val xAvgOffset = mutableAggBufferOffset
    +  val yAvgOffset = mutableAggBufferOffset + 1
    +  val CkOffset = mutableAggBufferOffset + 2
    +  val countOffset = mutableAggBufferOffset + 3
    +
    +  // Local cache of inputAggBufferOffset(s) that will be used in update and merge
    +  val inputXAvgOffset = inputAggBufferOffset
    +  val inputYAvgOffset = inputAggBufferOffset + 1
    +  val inputCkOffset = inputAggBufferOffset + 2
    +  val inputCountOffset = inputAggBufferOffset + 3
    +
    +  override def initialize(buffer: MutableRow): Unit = {
    +    buffer.setDouble(xAvgOffset, 0.0)
    +    buffer.setDouble(yAvgOffset, 0.0)
    +    buffer.setDouble(CkOffset, 0.0)
    +    buffer.setLong(countOffset, 0L)
    +  }
    +
    +  override def update(buffer: MutableRow, input: InternalRow): Unit = {
    +    val leftEval = left.eval(input)
    +    val rightEval = right.eval(input)
    +
    +    if (leftEval != null && rightEval != null) {
    +      val x = leftEval.asInstanceOf[Double]
    +      val y = rightEval.asInstanceOf[Double]
    +
    +      var xAvg = buffer.getDouble(xAvgOffset)
    +      var yAvg = buffer.getDouble(yAvgOffset)
    +      var Ck = buffer.getDouble(CkOffset)
    +      var count = buffer.getLong(countOffset)
    +
    +      val deltaX = x - xAvg
    +      val deltaY = y - yAvg
    +      count += 1
    +      xAvg += deltaX / count
    +      yAvg += deltaY / count
    +      Ck += deltaX * (y - yAvg)
    +
    +      buffer.setDouble(xAvgOffset, xAvg)
    +      buffer.setDouble(yAvgOffset, yAvg)
    +      buffer.setDouble(CkOffset, Ck)
    +      buffer.setLong(countOffset, count)
    +    }
    +  }
    +
    +  // Merge counters from other partitions. Formula can be found at:
    +  // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
    +  override def merge(buffer1: MutableRow, buffer2: InternalRow): Unit = {
    +    val count2 = buffer2.getLong(inputCountOffset)
    +
    +    // We only go to merge two buffers if there is at least one record aggregated in buffer2.
    +    // We don't need to check count in buffer1 because if count2 is more than zero, totalCount
    +    // is more than zero too, then we won't get a divide by zero exception.
    +    if (count2 > 0) {
    +      var xAvg = buffer1.getDouble(xAvgOffset)
    +      var yAvg = buffer1.getDouble(yAvgOffset)
    +      var Ck = buffer1.getDouble(CkOffset)
    +      var count = buffer1.getLong(countOffset)
    +
    +      val xAvg2 = buffer2.getDouble(inputXAvgOffset)
    +      val yAvg2 = buffer2.getDouble(inputYAvgOffset)
    +      val Ck2 = buffer2.getDouble(inputCkOffset)
    +
    +      val totalCount = count + count2
    +      val deltaX = xAvg - xAvg2
    +      val deltaY = yAvg - yAvg2
    +      Ck += Ck2 + deltaX * deltaY * count / totalCount * count2
    +      xAvg = (xAvg * count + xAvg2 * count2) / totalCount
    +      yAvg = (yAvg * count + yAvg2 * count2) / totalCount
    +      count = totalCount
    +
    +      buffer1.setDouble(xAvgOffset, xAvg)
    +      buffer1.setDouble(yAvgOffset, yAvg)
    +      buffer1.setDouble(CkOffset, Ck)
    +      buffer1.setLong(countOffset, count)
    +    }
    +  }
    +}
    +
    +case class CovSample(
    +    left: Expression,
    +    right: Expression,
    +    mutableAggBufferOffset: Int = 0,
    +    inputAggBufferOffset: Int = 0)
    +  extends Covariance(left, right) {
    +
    +  override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: Int): ImperativeAggregate =
    +    copy(mutableAggBufferOffset = newMutableAggBufferOffset)
    +
    +  override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int): ImperativeAggregate =
    +    copy(inputAggBufferOffset = newInputAggBufferOffset)
    +
    +  override def eval(buffer: InternalRow): Any = {
    +    val count = buffer.getLong(countOffset)
    +    if (count > 0) {
    +      if (count > 1) {
    +        val Ck = buffer.getDouble(CkOffset)
    +        val cov = Ck / (count - 1)
    +        if (cov.isNaN) {
    +          null
    +        } else {
    +          cov
    +        }
    +      } else {
    +        null
    --- End diff --
    
    Should this be `nan`? cc @mengxr 


---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---

---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscribe@spark.apache.org
For additional commands, e-mail: reviews-help@spark.apache.org