Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17419#discussion_r128429389
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/stat/SummarizerSuite.scala ---
@@ -0,0 +1,406 @@
+/*
+ * 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,
Statistics}
+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)
+
+ private def l(x: Array[Long]): Vector = b(x.map(_.toDouble))
+
+ test("debugging test") {
+ val df = denseData(Nil)
+ val c = df.col("features")
+ val c1 = metrics("mean").summary(c)
+ val res = df.select(c1)
+ intercept[SparkException] {
+ compare(res, Seq.empty)
+ }
+ }
+
+ test("basic error handling") {
+ val df = denseData(Nil)
+ val c = df.col("features")
+ val res = df.select(metrics("mean").summary(c), mean(c))
+ intercept[SparkException] {
+ compare(res, Seq.empty)
+ }
+ }
+
+ test("no element, working metrics") {
+ val df = denseData(Nil)
+ val c = df.col("features")
+ val res = df.select(metrics("count").summary(c), count(c))
+ compare(res, Seq(Row(0L), 0L))
+ }
+
+ {
+ val x = Seq(0.0, 1.0, 2.0)
+ testExample("single element", Seq(x), ExpectedMetrics(
+ mean = x,
+ variance = Seq(0.0, 0.0, 0.0),
+ count = 1,
+ numNonZeros = Seq(0, 1, 1),
+ max = x,
+ min = x,
+ normL1 = x,
+ normL2 = x
+ ))
+ }
+
+ testExample("two elements", Seq(Seq(0.0, 1.0, 2.0), Seq(0.0, -1.0,
-2.0)), ExpectedMetrics(
+ mean = Seq(0.0, 0.0, 0.0),
+ // TODO: I have a doubt about these values, they are not normalized.
+ variance = Seq(0.0, 2.0, 8.0),
+ count = 2,
+ numNonZeros = Seq(0, 2, 2),
+ max = Seq(0.0, 1.0, 2.0),
+ min = Seq(0.0, -1.0, -2.0),
+ normL1 = Seq(0.0, 2.0, 4.0),
+ normL2 = Seq(0.0, math.sqrt(2.0), math.sqrt(2.0) * 2.0)
+ ))
+
+ testExample("dense vector input",
+ Seq(Seq(-1.0, 0.0, 6.0), Seq(3.0, -3.0, 0.0)),
+ ExpectedMetrics(
+ mean = Seq(1.0, -1.5, 3.0),
+ variance = Seq(8.0, 4.5, 18.0),
+ count = 2,
+ numNonZeros = Seq(2, 1, 1),
+ max = Seq(3.0, 0.0, 6.0),
+ min = Seq(-1.0, -3, 0.0),
+ normL1 = Seq(4.0, 3.0, 6.0),
+ normL2 = Seq(math.sqrt(10), 3, 6.0)
+ ))
+
+ test("mixing dense and sparse vector input") {
+ val summarizer = makeBuffer(Seq(
+ Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
+ Vectors.dense(0.0, -1.0, -3.0),
+ Vectors.sparse(3, Seq((1, -5.1))),
+ Vectors.dense(3.8, 0.0, 1.9),
+ Vectors.dense(1.7, -0.6, 0.0),
+ Vectors.sparse(3, Seq((1, 1.9), (2, 0.0)))))
+
+ assert(b(Buffer.mean(summarizer)) ~==
+ Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333)
absTol 1E-5, "mean mismatch")
+
+ assert(b(Buffer.min(summarizer)) ~== Vectors.dense(-2.0, -5.1, -3)
absTol 1E-5, "min " +
+ "mismatch")
+
+ assert(b(Buffer.max(summarizer)) ~== Vectors.dense(3.8, 2.3, 1.9)
absTol 1E-5, "max mismatch")
+
+ assert(l(Buffer.nnz(summarizer)) ~== Vectors.dense(3, 5, 2) absTol
1E-5, "numNonzeros mismatch")
+
+ assert(b(Buffer.variance(summarizer)) ~==
+ Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666)
absTol 1E-5,
+ "variance mismatch")
+
+ assert(Buffer.totalCount(summarizer) === 6)
+ }
+
+
+ test("merging two summarizers") {
+ val summarizer1 = makeBuffer(Seq(
+ Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
+ Vectors.dense(0.0, -1.0, -3.0)))
+
+ val summarizer2 = makeBuffer(Seq(
+ Vectors.sparse(3, Seq((1, -5.1))),
+ Vectors.dense(3.8, 0.0, 1.9),
+ Vectors.dense(1.7, -0.6, 0.0),
+ Vectors.sparse(3, Seq((1, 1.9), (2, 0.0)))))
+
+ val summarizer = Buffer.mergeBuffers(summarizer1, summarizer2)
+
+ assert(b(Buffer.mean(summarizer)) ~==
+ Vectors.dense(0.583333333333, -0.416666666666, -0.183333333333)
absTol 1E-5, "mean mismatch")
+
+ assert(b(Buffer.min(summarizer)) ~== Vectors.dense(-2.0, -5.1, -3)
absTol 1E-5, "min mismatch")
+
+ assert(b(Buffer.max(summarizer)) ~== Vectors.dense(3.8, 2.3, 1.9)
absTol 1E-5, "max mismatch")
+
+ assert(l(Buffer.nnz(summarizer)) ~== Vectors.dense(3, 5, 2) absTol
1E-5, "numNonzeros mismatch")
+
+ assert(b(Buffer.variance(summarizer)) ~==
+ Vectors.dense(3.857666666666, 7.0456666666666, 2.48166666666666)
absTol 1E-5,
+ "variance mismatch")
+
+ assert(Buffer.totalCount(summarizer) === 6)
+ }
+
+ // TODO: this test should not be committed. It is here to isolate some
performance hotspots.
+ test("perf test") {
+ val n = 10000000
+ val rdd1 = sc.parallelize(1 to n).map { idx =>
+ OldVectors.dense(idx.toDouble)
+ }
+ val trieouts = 100
+ rdd1.cache()
+ rdd1.count()
+ val rdd2 = sc.parallelize(1 to n).map { idx =>
+ Vectors.dense(idx.toDouble)
+ }
+ rdd2.cache()
+ rdd2.count()
+ val df = rdd2.map(Tuple1.apply).toDF("features")
+ df.cache()
+ df.count()
+ val x = df.select(
+ metrics("mean", "variance", "count", "numNonZeros", "max", "min",
"normL1",
+ "normL2").summary($"features"))
+ val x1 = df.select(metrics("variance").summary($"features"))
+
+ def print(name: String, l: List[Long]): Unit = {
+ def f(z: Long) = (1e6 * n.toDouble) / z
+ val min = f(l.max)
+ val max = f(l.min)
+ val med = f(l.sorted.drop(l.size / 2).head)
+
+ // scalastyle:off println
+ println(s"$name = [$min ~ $med ~ $max] records / milli")
+ }
+
+
+ var times_df: List[Long] = Nil
+ for (_ <- 1 to trieouts) {
+ System.gc()
+ val t21 = System.nanoTime()
+ x.head()
+ val t22 = System.nanoTime()
+ val dt = t22 - t21
+ times_df ::= dt
+ // scalastyle:off
+ print("Dataframes", times_df)
+ // scalastyle:on
--- End diff --
The print statement should move out from the loop.
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