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From viirya <...@git.apache.org>
Subject [GitHub] spark pull request #17419: [SPARK-19634][ML] Multivariate summarizer - dataf...
Date Thu, 30 Mar 2017 04:58:11 GMT
Github user viirya commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17419#discussion_r108840709
  
    --- Diff: mllib/src/test/scala/org/apache/spark/ml/stat/SummarizerSuite.scala ---
    @@ -0,0 +1,338 @@
    +/*
    + * 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.stat
    +
    +import org.scalatest.exceptions.TestFailedException
    +
    +import org.apache.spark.{SparkException, SparkFunSuite}
    +import org.apache.spark.ml.linalg.{Vector, Vectors}
    +import org.apache.spark.ml.stat.SummaryBuilderImpl.Buffer
    +import org.apache.spark.ml.util.TestingUtils._
    +import org.apache.spark.mllib.linalg.{Vector => OldVector, Vectors => OldVectors}
    +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
    +import org.apache.spark.mllib.util.MLlibTestSparkContext
    +import org.apache.spark.sql.{DataFrame, Row}
    +import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema
    +
    +class SummarizerSuite extends SparkFunSuite with MLlibTestSparkContext {
    +
    +  import testImplicits._
    +  import Summarizer._
    +
    +  private case class ExpectedMetrics(
    +      mean: Seq[Double],
    +      variance: Seq[Double],
    +      count: Long,
    +      numNonZeros: Seq[Long],
    +      max: Seq[Double],
    +      min: Seq[Double],
    +      normL2: Seq[Double],
    +      normL1: Seq[Double])
    +
    +  // The input is expected to be either a sparse vector, a dense vector or an array of
doubles
    +  // (which will be converted to a dense vector)
    +  // The expected is the list of all the known metrics.
    +  //
    +  // The tests take an list of input vectors and a list of all the summary values that
    +  // are expected for this input. They currently test against some fixed subset of the
    +  // metrics, but should be made fuzzy in the future.
    +
    +  private def testExample(name: String, input: Seq[Any], exp: ExpectedMetrics): Unit
= {
    +    def inputVec: Seq[Vector] = input.map {
    +      case x: Array[Double @unchecked] => Vectors.dense(x)
    +      case x: Seq[Double @unchecked] => Vectors.dense(x.toArray)
    +      case x: Vector => x
    +      case x => throw new Exception(x.toString)
    +    }
    +
    +    val s = {
    +      val s2 = new MultivariateOnlineSummarizer
    +      inputVec.foreach(v => s2.add(OldVectors.fromML(v)))
    +      s2
    +    }
    +
    +    // Because the Spark context is reset between tests, we cannot hold a reference onto
it.
    +    def wrapped() = {
    +      val df = sc.parallelize(inputVec).map(Tuple1.apply).toDF("features")
    +      val c = df.col("features")
    +      (df, c)
    +    }
    +
    +    registerTest(s"$name - mean only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("mean").summary(c), mean(c)), Seq(Row(exp.mean), s.mean))
    +    }
    +
    +    registerTest(s"$name - mean only (direct)") {
    +      val (df, c) = wrapped()
    +      compare(df.select(mean(c)), Seq(exp.mean))
    +    }
    +
    +    registerTest(s"$name - variance only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("variance").summary(c), variance(c)),
    +        Seq(Row(exp.variance), s.variance))
    +    }
    +
    +    registerTest(s"$name - variance only (direct)") {
    +      val (df, c) = wrapped()
    +      compare(df.select(variance(c)), Seq(s.variance))
    +    }
    +
    +    registerTest(s"$name - count only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("count").summary(c), count(c)),
    +        Seq(Row(exp.count), exp.count))
    +    }
    +
    +    registerTest(s"$name - count only (direct)") {
    +      val (df, c) = wrapped()
    +      compare(df.select(count(c)),
    +        Seq(exp.count))
    +    }
    +
    +    registerTest(s"$name - numNonZeros only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("numNonZeros").summary(c), numNonZeros(c)),
    +        Seq(Row(exp.numNonZeros), exp.numNonZeros))
    +    }
    +
    +    registerTest(s"$name - numNonZeros only (direct)") {
    +      val (df, c) = wrapped()
    +      compare(df.select(numNonZeros(c)),
    +        Seq(exp.numNonZeros))
    +    }
    +
    +    registerTest(s"$name - min only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("min").summary(c), min(c)),
    +        Seq(Row(exp.min), exp.min))
    +    }
    +
    +    registerTest(s"$name - max only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("max").summary(c), max(c)),
    +        Seq(Row(exp.max), exp.max))
    +    }
    +
    +    registerTest(s"$name - normL1 only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("normL1").summary(c), normL1(c)),
    +        Seq(Row(exp.normL1), exp.normL1))
    +    }
    +
    +    registerTest(s"$name - normL2 only") {
    +      val (df, c) = wrapped()
    +      compare(df.select(metrics("normL2").summary(c), normL2(c)),
    +        Seq(Row(exp.normL2), exp.normL2))
    +    }
    +
    +    registerTest(s"$name - all metrics at once") {
    +      val (df, c) = wrapped()
    +      compare(df.select(
    +        metrics("mean", "variance", "count", "numNonZeros").summary(c),
    +        mean(c), variance(c), count(c), numNonZeros(c)),
    +        Seq(Row(exp.mean, exp.variance, exp.count, exp.numNonZeros),
    +          exp.mean, exp.variance, exp.count, exp.numNonZeros))
    +    }
    +  }
    +
    +  private def denseData(input: Seq[Seq[Double]]): DataFrame = {
    +    val data = input.map(_.toArray).map(Vectors.dense).map(Tuple1.apply)
    +    sc.parallelize(data).toDF("features")
    +  }
    +
    +  private def compare(df: DataFrame, exp: Seq[Any]): Unit = {
    +    val coll = df.collect().toSeq
    +    val Seq(row) = coll
    +    val res = row.toSeq
    +    val names = df.schema.fieldNames.zipWithIndex.map { case (n, idx) => s"$n ($idx)"
}
    +    assert(res.size === exp.size, (res.size, exp.size))
    +    for (((x1, x2), name) <- res.zip(exp).zip(names)) {
    +      compareStructures(x1, x2, name)
    +    }
    +  }
    +
    +  // Compares structured content.
    +  private def compareStructures(x1: Any, x2: Any, name: String): Unit = (x1, x2) match
{
    +    case (y1: Seq[Double @unchecked], v1: OldVector) =>
    +      compareStructures(y1, v1.toArray.toSeq, name)
    +    case (d1: Double, d2: Double) =>
    +      assert2(Vectors.dense(d1) ~== Vectors.dense(d2) absTol 1e-4, name)
    +    case (r1: GenericRowWithSchema, r2: Row) =>
    +      assert(r1.size === r2.size, (r1, r2))
    +      for (((fname, x1), x2) <- r1.schema.fieldNames.zip(r1.toSeq).zip(r2.toSeq))
{
    +        compareStructures(x1, x2, s"$name.$fname")
    +      }
    +    case (r1: Row, r2: Row) =>
    +      assert(r1.size === r2.size, (r1, r2))
    +      for ((x1, x2) <- r1.toSeq.zip(r2.toSeq)) { compareStructures(x1, x2, name) }
    +    case (v1: Vector, v2: Vector) =>
    +      assert2(v1 ~== v2 absTol 1e-4, name)
    +    case (l1: Long, l2: Long) => assert(l1 === l2)
    +    case (s1: Seq[_], s2: Seq[_]) =>
    +      assert(s1.size === s2.size, s"$name ${(s1, s2)}")
    +      for (((x1, idx), x2) <- s1.zipWithIndex.zip(s2)) {
    +        compareStructures(x1, x2, s"$name.$idx")
    +      }
    +    case (arr1: Array[_], arr2: Array[_]) =>
    +      assert(arr1.toSeq === arr2.toSeq)
    +    case _ => throw new Exception(s"$name: ${x1.getClass} ${x2.getClass} $x1 $x2")
    +  }
    +
    +  private def assert2(x: => Boolean, hint: String): Unit = {
    +    try {
    +      assert(x, hint)
    +    } catch {
    +      case tfe: TestFailedException =>
    +        throw new TestFailedException(Some(s"Failure with hint $hint"), Some(tfe), 1)
    +    }
    +  }
    +
    +  private def makeBuffer(vecs: Seq[Vector]): Buffer = {
    +    val b = Buffer.allMetrics()
    +    for (v <- vecs) { Buffer.updateInPlace(b, v, 1.0) }
    +    b
    +  }
    +
    +  private def b(x: Array[Double]): Vector = Vectors.dense(x)
    --- End diff --
    
    +1


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