Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/18798#discussion_r133120087 --- Diff: mllib/src/test/scala/org/apache/spark/ml/stat/SummarizerSuite.scala --- @@ -0,0 +1,619 @@ +/* + * 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.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._ + import SummaryBuilderImpl._ + + 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) + } + } +/* --- End diff -- Remove them if it's not used.
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