modify recommendation assertion.

build.sbt

@@ -8,10 +8,10 @@ import scala.xml.transform.{RewriteRule, RuleTransformer}
 import scala.xml.{Node => XmlNode, NodeSeq => XmlNodeSeq, _}
 
 val condaEnvName = "synapseml"
-val sparkVersion = "3.2.3"
+val sparkVersion = "3.4.1"
 name := "synapseml"
 ThisBuild / organization := "com.microsoft.azure"
-ThisBuild / scalaVersion := "2.12.15"
+ThisBuild / scalaVersion := "2.12.17"
 
 val scalaMajorVersion = 2.12
 
@@ -21,22 +21,23 @@ val excludes = Seq(
 )
 
 val coreDependencies = Seq(
-  "org.apache.spark" %% "spark-core" % sparkVersion % "compile",
+  "org.apache.spark" %% "spark-core" % sparkVersion % "compile" exclude("com.google.protobuf", "protobuf-java"),
   "org.apache.spark" %% "spark-mllib" % sparkVersion % "compile",
-  "org.apache.spark" %% "spark-avro" % sparkVersion % "provided",
+  "org.apache.spark" %% "spark-avro" % sparkVersion % "compile",
   "org.apache.spark" %% "spark-tags" % sparkVersion % "test",
   "org.scalatest" %% "scalatest" % "3.2.14" % "test")
 val extraDependencies = Seq(
+  "commons-lang" % "commons-lang" % "2.6",
   "org.scalactic" %% "scalactic" % "3.2.14",
   "io.spray" %% "spray-json" % "1.3.5",
   "com.jcraft" % "jsch" % "0.1.54",
   "org.apache.httpcomponents.client5" % "httpclient5" % "5.1.3",
   "org.apache.httpcomponents" % "httpmime" % "4.5.13",
-  "com.linkedin.isolation-forest" %% "isolation-forest_3.2.0" % "2.0.8",
+  "com.linkedin.isolation-forest" %% "isolation-forest_3.2.0" % "2.0.8" exclude("com.google.protobuf", "protobuf-java") exclude("org.apache.spark", "spark-mllib_2.12") exclude("org.apache.spark", "spark-core_2.12") exclude("org.apache.spark", "spark-avro_2.12") exclude("org.apache.spark", "spark-sql_2.12"),
   // Although breeze 1.2 is already provided by Spark, this is needed for Azure Synapse Spark 3.2 pools.
   // Otherwise a NoSuchMethodError will be thrown by interpretability code. This problem only happens
   // to Azure Synapse Spark 3.2 pools.
-  "org.scalanlp" %% "breeze" % "1.2"
+  "org.scalanlp" %% "breeze" % "2.1.0"
 ).map(d => d excludeAll (excludes: _*))
 val dependencies = coreDependencies ++ extraDependencies
 

core/src/main/scala/com/microsoft/azure/synapse/ml/codegen/PyCodegen.scala

@@ -68,11 +68,11 @@ object PyCodegen {
       // There's `Already borrowed` error found in transformers 4.16.2 when using tokenizers
       s"""extras_require={"extras": [
          |    "cmake",
-         |    "horovod==0.25.0",
+         |    "horovod==0.28.1",
          |    "pytorch_lightning>=1.5.0,<1.5.10",
-         |    "torch==1.11.0",
-         |    "torchvision>=0.12.0",
-         |    "transformers==4.15.0",
+         |    "torch==1.13.1",
+         |    "torchvision>=0.14.1",
+         |    "transformers==4.32.1",
          |    "petastorm>=0.12.0",
          |    "huggingface-hub>=0.8.1",
          |]},

core/src/main/scala/com/microsoft/azure/synapse/ml/core/env/PackageUtils.scala

@@ -18,7 +18,7 @@ object PackageUtils {
 
   val PackageName = s"synapseml_$ScalaVersionSuffix"
   val PackageMavenCoordinate = s"$PackageGroup:$PackageName:${BuildInfo.version}"
-  private val AvroCoordinate = "org.apache.spark:spark-avro_2.12:3.3.1"
+  private val AvroCoordinate = "org.apache.spark:spark-avro_2.12:3.4.1"
   val PackageRepository: String = SparkMLRepository
 
   // If testing onnx package with snapshots repo, make sure to switch to using

core/src/main/scala/com/microsoft/azure/synapse/ml/exploratory/DistributionBalanceMeasure.scala

@@ -3,7 +3,7 @@
 
 package com.microsoft.azure.synapse.ml.exploratory
 
-import breeze.stats.distributions.ChiSquared
+import breeze.stats.distributions.{ChiSquared, RandBasis}
 import com.microsoft.azure.synapse.ml.codegen.Wrappable
 import com.microsoft.azure.synapse.ml.core.schema.DatasetExtensions
 import com.microsoft.azure.synapse.ml.logging.SynapseMLLogging
@@ -261,6 +261,7 @@ private[exploratory] case class DistributionMetrics(numFeatures: Int,
 
   // Calculates left-tailed p-value from degrees of freedom and chi-squared test statistic
   def chiSquaredPValue: Column = {
+    implicit val rand: RandBasis = RandBasis.mt0
     val degOfFreedom = numFeatures - 1
     val scoreCol = chiSquaredTestStatistic
     val chiSqPValueUdf = udf(

core/src/main/scala/com/microsoft/azure/synapse/ml/io/binary/BinaryFileFormat.scala

@@ -194,7 +194,7 @@ private[ml] class HadoopFileReader(file: PartitionedFile,
 
   private val iterator = {
     val fileSplit = new FileSplit(
-      new Path(new URI(file.filePath)),
+      new Path(new URI(file.filePath.toString())),
       file.start,
       file.length,
       Array.empty)

core/src/main/scala/com/microsoft/azure/synapse/ml/nn/BallTree.scala

@@ -3,7 +3,6 @@
 
 package com.microsoft.azure.synapse.ml.nn
 
-import breeze.linalg.functions.euclideanDistance
 import breeze.linalg.{DenseVector, norm, _}
 import com.microsoft.azure.synapse.ml.core.env.StreamUtilities.using
 

core/src/main/scala/org/apache/spark/ml/recommendation/RecommendationHelper.scala

@@ -199,17 +199,20 @@ object SparkHelpers {
 
   def flatten(ratings: Dataset[_], num: Int, dstOutputColumn: String, srcOutputColumn: String): DataFrame = {
     import ratings.sparkSession.implicits._
-
-    val topKAggregator = new TopByKeyAggregator[Int, Int, Float](num, Ordering.by(_._2))
-    val recs = ratings.as[(Int, Int, Float)].groupByKey(_._1).agg(topKAggregator.toColumn)
-      .toDF("id", "recommendations")
+    import org.apache.spark.sql.functions.{collect_top_k, struct}
 
     val arrayType = ArrayType(
       new StructType()
         .add(dstOutputColumn, IntegerType)
-        .add("rating", FloatType)
+        .add(Constants.RatingCol, FloatType)
     )
-    recs.select(col("id").as(srcOutputColumn), col("recommendations").cast(arrayType))
+
+    ratings.toDF(srcOutputColumn, dstOutputColumn, Constants.RatingCol).groupBy(srcOutputColumn)
+     .agg(collect_top_k(struct(Constants.RatingCol, dstOutputColumn), num, false))
+     .as[(Int, Seq[(Float, Int)])]
+     .map(t => (t._1, t._2.map(p => (p._2, p._1))))
+     .toDF(srcOutputColumn, Constants.Recommendations)
+     .withColumn(Constants.Recommendations, col(Constants.Recommendations).cast(arrayType))
   }
 }
 

core/src/main/scala/org/apache/spark/ml/source/image/PatchedImageFileFormat.scala

@@ -98,7 +98,7 @@ class PatchedImageFileFormat extends ImageFileFormat with Serializable with Logg
         Iterator(emptyUnsafeRow)
       } else {
         val origin = file.filePath
-        val path = new Path(origin)
+        val path = new Path(origin.toString())
         val fs = path.getFileSystem(broadcastedHadoopConf.value.value)
         val stream = fs.open(path)
         val bytes = try {
@@ -107,11 +107,12 @@ class PatchedImageFileFormat extends ImageFileFormat with Serializable with Logg
           IOUtils.close(stream)
         }
 
-        val resultOpt = catchFlakiness(5)(ImageSchema.decode(origin, bytes))  //scalastyle:ignore magic.number
+        val resultOpt = catchFlakiness(5)( //scalastyle:ignore magic.number
+          ImageSchema.decode(origin.toString(), bytes))
         val filteredResult = if (imageSourceOptions.dropInvalid) {
           resultOpt.toIterator
         } else {
-          Iterator(resultOpt.getOrElse(ImageSchema.invalidImageRow(origin)))
+          Iterator(resultOpt.getOrElse(ImageSchema.invalidImageRow(origin.toString())))
         }
 
         if (requiredSchema.isEmpty) {

core/src/test/scala/com/microsoft/azure/synapse/ml/codegen/RTestGen.scala

@@ -101,7 +101,7 @@ object RTestGen {
          |  "spark.sql.shuffle.partitions=10",
          |  "spark.sql.crossJoin.enabled=true")
          |
-         |sc <- spark_connect(master = "local", version = "3.2.4", config = conf)
+         |sc <- spark_connect(master = "local", version = "3.4.1", config = conf)
          |
          |""".stripMargin, StandardOpenOption.CREATE)
 

core/src/test/scala/com/microsoft/azure/synapse/ml/core/test/base/TestBase.scala

@@ -181,6 +181,7 @@ abstract class TestBase extends AnyFunSuite with BeforeAndAfterEachTestData with
   }
 
   protected override def beforeAll(): Unit = {
+    System.setProperty("log4j1.compatibility", "true")
     suiteElapsed = 0
   }
 

core/src/test/scala/com/microsoft/azure/synapse/ml/exploratory/DataBalanceTestBase.scala

@@ -3,7 +3,7 @@
 
 package com.microsoft.azure.synapse.ml.exploratory
 
-import breeze.stats.distributions.ChiSquared
+import breeze.stats.distributions.{ChiSquared, RandBasis}
 import com.microsoft.azure.synapse.ml.core.test.base.TestBase
 import org.apache.spark.sql.functions.{col, count, lit}
 import org.apache.spark.sql.types.DoubleType
@@ -126,6 +126,7 @@ case class DistributionMetricsCalculator(refFeatureProbabilities: Array[Double],
   val totalVariationDistance: Double = 0.5d * absDiffObsRef.sum
   val wassersteinDistance: Double = absDiffObsRef.sum / absDiffObsRef.length
   val chiSquaredTestStatistic: Double = (obsFeatureCounts, refFeatureCounts).zipped.map((a, b) => pow(a - b, 2) / b).sum
+  implicit val rand: RandBasis = RandBasis.mt0
   val chiSquaredPValue: Double = chiSquaredTestStatistic match {
     // limit of CDF as x approaches +inf is 1 (https://en.wikipedia.org/wiki/Cumulative_distribution_function)
     case Double.PositiveInfinity => 1

core/src/test/scala/com/microsoft/azure/synapse/ml/io/split2/DistributedHTTPSuite.scala

@@ -20,14 +20,14 @@ import org.apache.spark.sql.functions.{col, length}
 import org.apache.spark.sql.streaming.{DataStreamReader, DataStreamWriter, StreamingQuery}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{DataFrame, Row}
+import org.json4s.DefaultFormats
+import org.json4s.jackson.Serialization
 
 import java.io.File
 import java.util.UUID
 import java.util.concurrent.{Executors, TimeUnit, TimeoutException}
 import scala.concurrent.duration.{Duration, FiniteDuration}
 import scala.concurrent.{Await, ExecutionContext, ExecutionContextExecutor, Future}
-import scala.util.parsing.json.JSONObject
-
 
 trait HTTPTestUtils extends TestBase with WithFreeUrl {
 
@@ -81,7 +81,8 @@ trait HTTPTestUtils extends TestBase with WithFreeUrl {
 
   def sendJsonRequest(map: Map[String, Any], url: String): String = {
     val post = new HttpPost(url)
-    val params = new StringEntity(JSONObject(map).toString())
+    implicit val defaultFormats: DefaultFormats = DefaultFormats
+    val params = new StringEntity(Serialization.write(map))
     post.addHeader("content-type", "application/json")
     post.setEntity(params)
     val res = RESTHelpers.Client.execute(post)

core/src/test/scala/com/microsoft/azure/synapse/ml/nbtest/DatabricksGPUTests.scala

@@ -11,11 +11,7 @@ import java.io.File
 import scala.collection.mutable.ListBuffer
 
 class DatabricksGPUTests extends DatabricksTestHelper {
-  val horovodInstallationScript: File = FileUtilities.join(
-    BuildInfo.baseDirectory.getParent, "deep-learning",
-    "src", "main", "python", "horovod_installation.sh").getCanonicalFile
-  uploadFileToDBFS(horovodInstallationScript, "/FileStore/horovod-fix-commit/horovod_installation.sh")
-  val clusterId: String = createClusterInPool(GPUClusterName, AdbGpuRuntime, 2, GpuPoolId, GPUInitScripts)
+  val clusterId: String = createClusterInPool(GPUClusterName, AdbGpuRuntime, 2, GpuPoolId, "[]")
   val jobIdsToCancel: ListBuffer[Int] = databricksTestHelper(
     clusterId, GPULibraries, GPUNotebooks)
 

core/src/test/scala/com/microsoft/azure/synapse/ml/nbtest/DatabricksUtilities.scala

@@ -29,10 +29,11 @@ object DatabricksUtilities {
 
   // ADB Info
   val Region = "eastus"
-  val PoolName = "synapseml-build-10.4"
-  val GpuPoolName = "synapseml-build-10.4-gpu"
-  val AdbRuntime = "10.4.x-scala2.12"
-  val AdbGpuRuntime = "10.4.x-gpu-ml-scala2.12"
+  val PoolName = "synapseml-build-13.3"
+  val GpuPoolName = "synapseml-build-13.3-gpu"
+  val AdbRuntime = "13.3.x-scala2.12"
+  // https://learn.microsoft.com/en-us/azure/databricks/release-notes/runtime/11.2
+  val AdbGpuRuntime = "13.3.x-gpu-ml-scala2.12"
   val NumWorkers = 5
   val AutoTerminationMinutes = 15
 
@@ -61,6 +62,7 @@ object DatabricksUtilities {
     "pdf2image",
     "pdfminer.six",
     "pytesseract",
+    "scikit-learn",
     "unstructured"
   )
 
@@ -72,8 +74,11 @@ object DatabricksUtilities {
   // TODO: install synapse.ml.dl wheel package here
   val GPULibraries: String = List(
     Map("maven" -> Map("coordinates" -> PackageMavenCoordinate, "repo" -> PackageRepository)),
-    Map("pypi" -> Map("package" -> "transformers==4.15.0")),
-    Map("pypi" -> Map("package" -> "petastorm==0.12.0"))
+    Map("pypi" -> Map("package" -> "pytorch-lightning==1.5.0")),
+    Map("pypi" -> Map("package" -> "torchvision==0.14.1")),
+    Map("pypi" -> Map("package" -> "transformers==4.32.1")),
+    Map("pypi" -> Map("package" -> "petastorm==0.12.0")),
+    Map("pypi" -> Map("package" -> "protobuf==3.20.3"))
   ).toJson.compactPrint
 
   val GPUInitScripts: String = List(

core/src/test/scala/com/microsoft/azure/synapse/ml/nbtest/SynapseExtension/SynapseExtensionUtilities.scala

@@ -75,7 +75,6 @@ object SynapseExtensionUtilities {
     val store = Secrets.ArtifactStore.capitalize
     val excludes: String = "org.scala-lang:scala-reflect," +
       "org.apache.spark:spark-tags_2.12," +
-      "org.scalactic:scalactic_2.12," +
       "org.scalatest:scalatest_2.12," +
       "org.slf4j:slf4j-api"
 

core/src/test/scala/com/microsoft/azure/synapse/ml/nbtest/SynapseUtilities.scala

@@ -184,7 +184,6 @@ object SynapseUtilities {
     val excludes: String = Seq(
       "org.scala-lang:scala-reflect",
       "org.apache.spark:spark-tags_2.12",
-      "org.scalactic:scalactic_2.12",
       "org.scalatest:scalatest_2.12",
       "org.slf4j:slf4j-api").mkString(",")
     val runName = abfssPath.split('/').last.replace(".py", "")
@@ -255,7 +254,7 @@ object SynapseUtilities {
        |    "nodeSizeFamily": "MemoryOptimized",
        |    "provisioningState": "Succeeded",
        |    "sessionLevelPackagesEnabled": "true",
-       |    "sparkVersion": "3.2"
+       |    "sparkVersion": "3.4"
        |  }
        |}
        |""".stripMargin

core/src/test/scala/com/microsoft/azure/synapse/ml/recommendation/RankingAdapterSpec.scala

@@ -5,6 +5,8 @@ package com.microsoft.azure.synapse.ml.recommendation
 
 import com.microsoft.azure.synapse.ml.core.test.fuzzing.{EstimatorFuzzing, TestObject, TransformerFuzzing}
 import org.apache.spark.ml.util.MLReadable
+import org.apache.spark.sql.DataFrame
+import org.scalactic.Equality
 
 class RankingAdapterSpec extends RankingTestBase with EstimatorFuzzing[RankingAdapter] {
   override def testObjects(): Seq[TestObject[RankingAdapter]] = {
@@ -15,6 +17,19 @@ class RankingAdapterSpec extends RankingTestBase with EstimatorFuzzing[RankingAd
 
   override def modelReader: MLReadable[_] = RankingAdapterModel
 
+  override def assertDFEq(df1: DataFrame, df2: DataFrame)(implicit eq: Equality[DataFrame]): Unit = {
+    def prep(df: DataFrame) = {
+      // sort rows and round decimals before compare two dataframes
+      import org.apache.spark.sql.functions._
+      val roundListDecimals: Seq[Float] => Seq[Float] = _.map { value =>
+        BigDecimal(value.toDouble).setScale(6, BigDecimal.RoundingMode.HALF_UP).toFloat
+      }
+      val castListToIntUDF = udf(roundListDecimals)
+      val sortedDF = df.orderBy(col("prediction"))
+      val updatedDF: DataFrame = sortedDF.withColumn("label", castListToIntUDF(col("label")))
+      updatedDF
+    }
+    super.assertDFEq(prep(df1), prep(df2))(eq)}
 }
 
 class RankingAdapterModelSpec extends RankingTestBase with TransformerFuzzing[RankingAdapterModel] {
@@ -24,4 +39,18 @@ class RankingAdapterModelSpec extends RankingTestBase with TransformerFuzzing[Ra
   }
 
   override def reader: MLReadable[_] = RankingAdapterModel
+
+  override def assertDFEq(df1: DataFrame, df2: DataFrame)(implicit eq: Equality[DataFrame]): Unit = {
+    def prep(df: DataFrame) = {
+      // sort rows and round decimals before compare two dataframes
+      import org.apache.spark.sql.functions._
+      val roundListDecimals: Seq[Float] => Seq[Float] = _.map { value =>
+        BigDecimal(value.toDouble).setScale(6, BigDecimal.RoundingMode.HALF_UP).toFloat
+      }
+      val castListToIntUDF = udf(roundListDecimals)
+      val sortedDF = df.orderBy(col("prediction"))
+      val updatedDF: DataFrame = sortedDF.withColumn("label", castListToIntUDF(col("label")))
+      updatedDF
+    }
+    super.assertDFEq(prep(df1), prep(df2))(eq)}
 }

deep-learning/src/main/python/horovod_installation.sh

@@ -7,8 +7,8 @@ set -eu
 
 # Install prerequisite libraries that horovod depends on
 pip install pytorch-lightning==1.5.0
-pip install torchvision==0.12.0
-pip install transformers==4.15.0
+pip install torchvision==0.14.1
+pip install transformers==4.32.1
 pip install petastorm>=0.12.0
 pip install protobuf==3.20.3
 
@@ -39,12 +39,12 @@ cd horovod
 # git fetch origin refs/tags/v0.25.0:tags/v0.25.0
 # git checkout tags/v0.25.0 -b v0.25.0-branch
 # fix to this commit number until they release a new version
-git checkout ab97fd15bbba3258adcdd12983f36a1cdeacbc94
+git checkout 1d217b59949986d025f6db93c49943fb6b6cc78f
 git checkout -b tmp-branch
 rm -rf build/ dist/
 HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_CUDA_HOME=/usr/local/cuda-11/ HOROVOD_WITH_PYTORCH=1 HOROVOD_WITHOUT_MXNET=1 \
 /databricks/python3/bin/python setup.py bdist_wheel
 
 readlink -f dist/horovod-*.whl
 
-pip install --no-cache-dir dist/horovod-0.25.0-cp38-cp38-linux_x86_64.whl --force-reinstall --no-deps
+pip install --no-cache-dir dist/horovod-0.28.1-cp38-cp38-linux_x86_64.whl --force-reinstall --no-deps

deep-learning/src/main/python/synapse/ml/dl/DeepTextClassifier.py

@@ -11,12 +11,12 @@
 if _TRANSFORMERS_AVAILABLE:
     import transformers
 
-    _TRANSFORMERS_EQUAL_4_15_0 = transformers.__version__ == "4.15.0"
-    if _TRANSFORMERS_EQUAL_4_15_0:
+    _TRANSFORMERS_EQUAL_4_32_1 = transformers.__version__ == "4.32.1"
+    if _TRANSFORMERS_EQUAL_4_32_1:
         from transformers import AutoTokenizer
     else:
         raise RuntimeError(
-            "transformers should be == 4.15.0, found: {}".format(
+            "transformers should be == 4.32.1, found: {}".format(
                 transformers.__version__
             )
         )

deep-learning/src/main/python/synapse/ml/dl/DeepVisionClassifier.py

@@ -19,10 +19,10 @@
 if _HOROVOD_AVAILABLE:
     import horovod
 
-    _HOROVOD_EQUAL_0_25_0 = horovod.__version__ == "0.25.0"
-    if not _HOROVOD_EQUAL_0_25_0:
+    _HOROVOD_EQUAL_0_28_1 = horovod.__version__ == "0.28.1"
+    if not _HOROVOD_EQUAL_0_28_1:
         raise RuntimeError(
-            "horovod should be of version 0.25.0, found: {}".format(horovod.__version__)
+            "horovod should be of version 0.28.1, found: {}".format(horovod.__version__)
         )
 else:
     raise ModuleNotFoundError("module not found: horovod")