http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala b/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala new file mode 100644 index 0000000..fdd2caf --- /dev/null +++ b/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala @@ -0,0 +1,79 @@ +/* + * 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.hive.internal + +import org.apache.spark.internal.Logging +import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.expressions.Expression +import org.apache.spark.sql.catalyst.plans.logical.{Generate, LogicalPlan} +import org.apache.spark.sql.hive._ +import org.apache.spark.sql.hive.HiveShim.HiveFunctionWrapper + +/** + * This is an implementation class for [[org.apache.spark.sql.hive.HivemallOps]]. + * This class mainly uses the internal Spark classes (e.g., `Generate` and `HiveGenericUDTF`) that + * have unstable interfaces (so, these interfaces may evolve in upcoming releases). + * Therefore, the objective of this class is to extract these unstable parts + * from [[org.apache.spark.sql.hive.HivemallOps]]. + */ +private[hive] object HivemallOpsImpl extends Logging { + + def planHiveUDF( + className: String, + funcName: String, + argumentExprs: Seq[Column]): Expression = { + HiveSimpleUDF( + name = funcName, + funcWrapper = new HiveFunctionWrapper(className), + children = argumentExprs.map(_.expr) + ) + } + + def planHiveGenericUDF( + className: String, + funcName: String, + argumentExprs: Seq[Column]): Expression = { + HiveGenericUDF( + name = funcName, + funcWrapper = new HiveFunctionWrapper(className), + children = argumentExprs.map(_.expr) + ) + } + + def planHiveGenericUDTF( + df: DataFrame, + className: String, + funcName: String, + argumentExprs: Seq[Column], + outputAttrNames: Seq[String]): LogicalPlan = { + Generate( + generator = HiveGenericUDTF( + name = funcName, + funcWrapper = new HiveFunctionWrapper(className), + children = argumentExprs.map(_.expr) + ), + unrequiredChildIndex = Seq.empty, + outer = false, + qualifier = None, + generatorOutput = outputAttrNames.map(UnresolvedAttribute(_)), + child = df.logicalPlan) + } +}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.scala b/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.scala new file mode 100644 index 0000000..65cdf24 --- /dev/null +++ b/spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.scala @@ -0,0 +1,163 @@ +/* + * 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.hive.source + +import java.io.File +import java.io.IOException +import java.net.URI + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, FSDataInputStream, Path} +import org.apache.hadoop.io.IOUtils +import org.apache.hadoop.io.compress.GzipCodec +import org.apache.hadoop.mapreduce._ +import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat +import org.apache.hadoop.util.ReflectionUtils + +import org.apache.spark.sql.{Row, SparkSession} +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.encoders.RowEncoder +import org.apache.spark.sql.catalyst.expressions.AttributeReference +import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection +import org.apache.spark.sql.execution.datasources._ +import org.apache.spark.sql.sources._ +import org.apache.spark.sql.types._ +import org.apache.spark.util.SerializableConfiguration + +private[source] final class XGBoostOutputWriter( + path: String, + dataSchema: StructType, + context: TaskAttemptContext) + extends OutputWriter { + + private val hadoopConf = new SerializableConfiguration(new Configuration()) + + override def write(row: InternalRow): Unit = { + val fields = row.toSeq(dataSchema) + val model = fields(1).asInstanceOf[Array[Byte]] + val filePath = new Path(new URI(s"$path")) + val fs = filePath.getFileSystem(hadoopConf.value) + val outputFile = fs.create(filePath) + outputFile.write(model) + outputFile.close() + } + + override def close(): Unit = {} +} + +object XGBoostOutputWriter { + + /** Returns the compression codec extension to be used in a file name, e.g. ".gzip"). */ + def getCompressionExtension(context: TaskAttemptContext): String = { + if (FileOutputFormat.getCompressOutput(context)) { + val codecClass = FileOutputFormat.getOutputCompressorClass(context, classOf[GzipCodec]) + ReflectionUtils.newInstance(codecClass, context.getConfiguration).getDefaultExtension + } else { + "" + } + } +} + +final class XGBoostFileFormat extends FileFormat with DataSourceRegister { + + override def shortName(): String = "libxgboost" + + override def toString: String = "XGBoost" + + private def verifySchema(dataSchema: StructType): Unit = { + if ( + dataSchema.size != 2 || + !dataSchema(0).dataType.sameType(StringType) || + !dataSchema(1).dataType.sameType(BinaryType) + ) { + throw new IOException(s"Illegal schema for XGBoost data, schema=$dataSchema") + } + } + + override def inferSchema( + sparkSession: SparkSession, + options: Map[String, String], + files: Seq[FileStatus]): Option[StructType] = { + Some( + StructType( + StructField("model_id", StringType, nullable = false) :: + StructField("pred_model", BinaryType, nullable = false) :: Nil) + ) + } + + override def prepareWrite( + sparkSession: SparkSession, + job: Job, + options: Map[String, String], + dataSchema: StructType): OutputWriterFactory = { + new OutputWriterFactory { + override def newInstance( + path: String, + dataSchema: StructType, + context: TaskAttemptContext): OutputWriter = { + new XGBoostOutputWriter(path, dataSchema, context) + } + + override def getFileExtension(context: TaskAttemptContext): String = { + XGBoostOutputWriter.getCompressionExtension(context) + ".xgboost" + } + } + } + + override def buildReader( + sparkSession: SparkSession, + dataSchema: StructType, + partitionSchema: StructType, + requiredSchema: StructType, + filters: Seq[Filter], + options: Map[String, String], + hadoopConf: Configuration): (PartitionedFile) => Iterator[InternalRow] = { + verifySchema(dataSchema) + val broadcastedHadoopConf = + sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf)) + + (file: PartitionedFile) => { + val model = new Array[Byte](file.length.asInstanceOf[Int]) + val filePath = new Path(new URI(file.filePath)) + val fs = filePath.getFileSystem(broadcastedHadoopConf.value.value) + + var in: FSDataInputStream = null + try { + in = fs.open(filePath) + IOUtils.readFully(in, model, 0, model.length) + } finally { + IOUtils.closeStream(in) + } + + val converter = RowEncoder(dataSchema) + val fullOutput = dataSchema.map { f => + AttributeReference(f.name, f.dataType, f.nullable, f.metadata)() + } + val requiredOutput = fullOutput.filter { a => + requiredSchema.fieldNames.contains(a.name) + } + val requiredColumns = GenerateUnsafeProjection.generate(requiredOutput, fullOutput) + (requiredColumns( + converter.toRow(Row(new File(file.filePath).getName, model))) + :: Nil + ).toIterator + } + } +} http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/main/scala/org/apache/spark/streaming/HivemallStreamingOps.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/main/scala/org/apache/spark/streaming/HivemallStreamingOps.scala b/spark/spark-2.3/src/main/scala/org/apache/spark/streaming/HivemallStreamingOps.scala new file mode 100644 index 0000000..a6bbb4b --- /dev/null +++ b/spark/spark-2.3/src/main/scala/org/apache/spark/streaming/HivemallStreamingOps.scala @@ -0,0 +1,47 @@ +/* + * 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.streaming + +import scala.reflect.ClassTag + +import org.apache.spark.ml.feature.HivemallLabeledPoint +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{DataFrame, Row, SQLContext} +import org.apache.spark.streaming.dstream.DStream + +final class HivemallStreamingOps(ds: DStream[HivemallLabeledPoint]) { + + def predict[U: ClassTag](f: DataFrame => DataFrame)(implicit sqlContext: SQLContext) + : DStream[Row] = { + ds.transform[Row] { rdd: RDD[HivemallLabeledPoint] => + f(sqlContext.createDataFrame(rdd)).rdd + } + } +} + +object HivemallStreamingOps { + + /** + * Implicitly inject the [[HivemallStreamingOps]] into [[DStream]]. + */ + implicit def dataFrameToHivemallStreamingOps(ds: DStream[HivemallLabeledPoint]) + : HivemallStreamingOps = { + new HivemallStreamingOps(ds) + } +} http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/resources/data/files/README.md ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/resources/data/files/README.md b/spark/spark-2.3/src/test/resources/data/files/README.md new file mode 100644 index 0000000..238d472 --- /dev/null +++ b/spark/spark-2.3/src/test/resources/data/files/README.md @@ -0,0 +1,22 @@ +<!-- + 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. +--> + +The files in this dir exist for preventing exceptions in o.a.s.sql.hive.test.TESTHive. +We need to fix this issue in future. + http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/resources/data/files/complex.seq ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/resources/data/files/complex.seq b/spark/spark-2.3/src/test/resources/data/files/complex.seq new file mode 100644 index 0000000..e69de29 http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/resources/data/files/episodes.avro ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/resources/data/files/episodes.avro b/spark/spark-2.3/src/test/resources/data/files/episodes.avro new file mode 100644 index 0000000..e69de29 http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/resources/data/files/json.txt ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/resources/data/files/json.txt b/spark/spark-2.3/src/test/resources/data/files/json.txt new file mode 100644 index 0000000..e69de29 http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/resources/data/files/kv1.txt ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/resources/data/files/kv1.txt b/spark/spark-2.3/src/test/resources/data/files/kv1.txt new file mode 100644 index 0000000..e69de29 http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/resources/data/files/kv3.txt ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/resources/data/files/kv3.txt b/spark/spark-2.3/src/test/resources/data/files/kv3.txt new file mode 100644 index 0000000..e69de29 http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/resources/log4j.properties ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/resources/log4j.properties b/spark/spark-2.3/src/test/resources/log4j.properties new file mode 100644 index 0000000..c6e4297 --- /dev/null +++ b/spark/spark-2.3/src/test/resources/log4j.properties @@ -0,0 +1,24 @@ +# +# 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. +# + +# Set everything to be logged to the console +log4j.rootCategory=FATAL, console +log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.target=System.err +log4j.appender.console.layout=org.apache.log4j.PatternLayout +log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/scala/hivemall/mix/server/MixServerSuite.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/scala/hivemall/mix/server/MixServerSuite.scala b/spark/spark-2.3/src/test/scala/hivemall/mix/server/MixServerSuite.scala new file mode 100644 index 0000000..9bbd3f0 --- /dev/null +++ b/spark/spark-2.3/src/test/scala/hivemall/mix/server/MixServerSuite.scala @@ -0,0 +1,124 @@ +/* + * 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 hivemall.mix.server + +import java.util.Random +import java.util.concurrent.{Executors, ExecutorService, TimeUnit} +import java.util.logging.Logger + +import hivemall.mix.MixMessage.MixEventName +import hivemall.mix.client.MixClient +import hivemall.mix.server.MixServer.ServerState +import hivemall.model.{DenseModel, PredictionModel, WeightValue} +import hivemall.utils.io.IOUtils +import hivemall.utils.lang.CommandLineUtils +import hivemall.utils.net.NetUtils +import org.scalatest.{BeforeAndAfter, FunSuite} + +class MixServerSuite extends FunSuite with BeforeAndAfter { + + private[this] var server: MixServer = _ + private[this] var executor : ExecutorService = _ + private[this] var port: Int = _ + + private[this] val rand = new Random(43) + private[this] val counter = Stream.from(0).iterator + + private[this] val eachTestTime = 100 + private[this] val logger = + Logger.getLogger(classOf[MixServerSuite].getName) + + before { + this.port = NetUtils.getAvailablePort + this.server = new MixServer( + CommandLineUtils.parseOptions( + Array("-port", s"${port}", "-sync_threshold", "3"), + MixServer.getOptions() + ) + ) + this.executor = Executors.newSingleThreadExecutor + this.executor.submit(server) + var retry = 0 + while (server.getState() != ServerState.RUNNING && retry < 50) { + Thread.sleep(1000L) + retry += 1 + } + assert(server.getState == ServerState.RUNNING) + } + + after { this.executor.shutdown() } + + private[this] def clientDriver( + groupId: String, model: PredictionModel, numMsg: Int = 1000000): Unit = { + var client: MixClient = null + try { + client = new MixClient(MixEventName.average, groupId, s"localhost:${port}", false, 2, model) + model.configureMix(client, false) + model.configureClock() + + for (_ <- 0 until numMsg) { + val feature = Integer.valueOf(rand.nextInt(model.size)) + model.set(feature, new WeightValue(1.0f)) + } + + while (true) { Thread.sleep(eachTestTime * 1000 + 100L) } + assert(model.getNumMixed > 0) + } finally { + IOUtils.closeQuietly(client) + } + } + + private[this] def fixedGroup: (String, () => String) = + ("fixed", () => "fixed") + private[this] def uniqueGroup: (String, () => String) = + ("unique", () => s"${counter.next}") + + Seq(65536).map { ndims => + Seq(4).map { nclient => + Seq(fixedGroup, uniqueGroup).map { id => + val testName = s"dense-dim:${ndims}-clinet:${nclient}-${id._1}" + ignore(testName) { + val clients = Executors.newCachedThreadPool() + val numClients = nclient + val models = (0 until numClients).map(i => new DenseModel(ndims, false)) + (0 until numClients).map { i => + clients.submit(new Runnable() { + override def run(): Unit = { + try { + clientDriver( + s"${testName}-${id._2}", + models(i) + ) + } catch { + case e: InterruptedException => + assert(false, e.getMessage) + } + } + }) + } + clients.awaitTermination(eachTestTime, TimeUnit.SECONDS) + clients.shutdown() + val nMixes = models.map(d => d.getNumMixed).reduce(_ + _) + logger.info(s"${testName} --> ${(nMixes + 0.0) / eachTestTime} mixes/s") + } + } + } + } +} http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala b/spark/spark-2.3/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala new file mode 100644 index 0000000..c127276 --- /dev/null +++ b/spark/spark-2.3/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala @@ -0,0 +1,33 @@ +/* + * 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 hivemall.tools + +import org.scalatest.FunSuite + +import org.apache.spark.sql.hive.test.TestHive + +class RegressionDatagenSuite extends FunSuite { + + test("datagen") { + val df = RegressionDatagen.exec( + TestHive, min_examples = 10000, n_features = 100, n_dims = 65536, dense = false, cl = true) + assert(df.count() >= 10000) + } +} http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala b/spark/spark-2.3/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala new file mode 100644 index 0000000..903dc0a --- /dev/null +++ b/spark/spark-2.3/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala @@ -0,0 +1,36 @@ +/* + * 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.feature + +import org.apache.spark.SparkFunSuite + +class HivemallLabeledPointSuite extends SparkFunSuite { + + test("toString") { + val lp = HivemallLabeledPoint(1.0f, Seq("1:0.5", "3:0.3", "8:0.1")) + assert(lp.toString === "1.0,[1:0.5,3:0.3,8:0.1]") + } + + test("parse") { + val lp = HivemallLabeledPoint.parse("1.0,[1:0.5,3:0.3,8:0.1]") + assert(lp.label === 1.0) + assert(lp.features === Seq("1:0.5", "3:0.3", "8:0.1")) + } +} http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/scala/org/apache/spark/sql/execution/benchmark/BenchmarkBaseAccessor.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/scala/org/apache/spark/sql/execution/benchmark/BenchmarkBaseAccessor.scala b/spark/spark-2.3/src/test/scala/org/apache/spark/sql/execution/benchmark/BenchmarkBaseAccessor.scala new file mode 100644 index 0000000..9e5e204 --- /dev/null +++ b/spark/spark-2.3/src/test/scala/org/apache/spark/sql/execution/benchmark/BenchmarkBaseAccessor.scala @@ -0,0 +1,23 @@ +/* + * 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.execution.benchmark + +// This trait makes `BenchmarkBase` accessible from `o.a.s.sql.hive.benchmark.MiscBenchmark` +private[sql] trait BenchmarkBaseAccessor extends BenchmarkBase http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala b/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala new file mode 100644 index 0000000..f16eae0 --- /dev/null +++ b/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala @@ -0,0 +1,161 @@ +/* + * 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.hive + +import org.apache.spark.sql.Row +import org.apache.spark.sql.hive.HivemallUtils._ +import org.apache.spark.sql.hive.test.HivemallFeatureQueryTest +import org.apache.spark.sql.test.VectorQueryTest + +final class HiveUdfWithFeatureSuite extends HivemallFeatureQueryTest { + import hiveContext.implicits._ + import hiveContext._ + + test("hivemall_version") { + sql(s""" + | CREATE TEMPORARY FUNCTION hivemall_version + | AS '${classOf[hivemall.HivemallVersionUDF].getName}' + """.stripMargin) + + checkAnswer( + sql(s"SELECT DISTINCT hivemall_version()"), + Row("0.5.1-incubating-SNAPSHOT") + ) + + // sql("DROP TEMPORARY FUNCTION IF EXISTS hivemall_version") + // reset() + } + + test("train_logregr") { + TinyTrainData.createOrReplaceTempView("TinyTrainData") + sql(s""" + | CREATE TEMPORARY FUNCTION train_logregr + | AS '${classOf[hivemall.regression.LogressUDTF].getName}' + """.stripMargin) + sql(s""" + | CREATE TEMPORARY FUNCTION add_bias + | AS '${classOf[hivemall.ftvec.AddBiasUDFWrapper].getName}' + """.stripMargin) + + val model = sql( + s""" + | SELECT feature, AVG(weight) AS weight + | FROM ( + | SELECT train_logregr(add_bias(features), label) AS (feature, weight) + | FROM TinyTrainData + | ) t + | GROUP BY feature + """.stripMargin) + + checkAnswer( + model.select($"feature"), + Seq(Row("0"), Row("1"), Row("2")) + ) + + // TODO: Why 'train_logregr' is not registered in HiveMetaStore? + // ERROR RetryingHMSHandler: MetaException(message:NoSuchObjectException + // (message:Function default.train_logregr does not exist)) + // + // hiveContext.sql("DROP TEMPORARY FUNCTION IF EXISTS train_logregr") + // hiveContext.reset() + } + + test("each_top_k") { + val testDf = Seq( + ("a", "1", 0.5, Array(0, 1, 2)), + ("b", "5", 0.1, Array(3)), + ("a", "3", 0.8, Array(2, 5)), + ("c", "6", 0.3, Array(1, 3)), + ("b", "4", 0.3, Array(2)), + ("a", "2", 0.6, Array(1)) + ).toDF("key", "value", "score", "data") + + import testDf.sqlContext.implicits._ + testDf.repartition($"key").sortWithinPartitions($"key").createOrReplaceTempView("TestData") + sql(s""" + | CREATE TEMPORARY FUNCTION each_top_k + | AS '${classOf[hivemall.tools.EachTopKUDTF].getName}' + """.stripMargin) + + // Compute top-1 rows for each group + checkAnswer( + sql("SELECT each_top_k(1, key, score, key, value) FROM TestData"), + Row(1, 0.8, "a", "3") :: + Row(1, 0.3, "b", "4") :: + Row(1, 0.3, "c", "6") :: + Nil + ) + + // Compute reverse top-1 rows for each group + checkAnswer( + sql("SELECT each_top_k(-1, key, score, key, value) FROM TestData"), + Row(1, 0.5, "a", "1") :: + Row(1, 0.1, "b", "5") :: + Row(1, 0.3, "c", "6") :: + Nil + ) + } +} + +final class HiveUdfWithVectorSuite extends VectorQueryTest { + import hiveContext._ + + test("to_hivemall_features") { + mllibTrainDf.createOrReplaceTempView("mllibTrainDf") + hiveContext.udf.register("to_hivemall_features", to_hivemall_features_func) + checkAnswer( + sql( + s""" + | SELECT to_hivemall_features(features) + | FROM mllibTrainDf + """.stripMargin), + Seq( + Row(Seq("0:1.0", "2:2.0", "4:3.0")), + Row(Seq("0:1.0", "3:1.5", "4:2.1", "6:1.2")), + Row(Seq("0:1.1", "3:1.0", "4:2.3", "6:1.0")), + Row(Seq("1:4.0", "3:5.0", "5:6.0")) + ) + ) + } + + test("append_bias") { + mllibTrainDf.createOrReplaceTempView("mllibTrainDf") + hiveContext.udf.register("append_bias", append_bias_func) + hiveContext.udf.register("to_hivemall_features", to_hivemall_features_func) + checkAnswer( + sql( + s""" + | SELECT to_hivemall_features(append_bias(features)) + | FROM mllibTrainDF + """.stripMargin), + Seq( + Row(Seq("0:1.0", "2:2.0", "4:3.0", "7:1.0")), + Row(Seq("0:1.0", "3:1.5", "4:2.1", "6:1.2", "7:1.0")), + Row(Seq("0:1.1", "3:1.0", "4:2.3", "6:1.0", "7:1.0")), + Row(Seq("1:4.0", "3:5.0", "5:6.0", "7:1.0")) + ) + ) + } + + ignore("explode_vector") { + // TODO: Spark-2.0 does not support use-defined generator function in + // `org.apache.spark.sql.UDFRegistration`. + } +} http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/bd143146/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala ---------------------------------------------------------------------- diff --git a/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala b/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala new file mode 100644 index 0000000..f2b7b6e --- /dev/null +++ b/spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala @@ -0,0 +1,1393 @@ +/* + * 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.hive + +import org.apache.spark.sql.{AnalysisException, Row} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.hive.HivemallGroupedDataset._ +import org.apache.spark.sql.hive.HivemallOps._ +import org.apache.spark.sql.hive.HivemallUtils._ +import org.apache.spark.sql.hive.test.HivemallFeatureQueryTest +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.VectorQueryTest +import org.apache.spark.sql.types._ +import org.apache.spark.test.TestFPWrapper._ +import org.apache.spark.test.TestUtils + + +class HivemallOpsWithFeatureSuite extends HivemallFeatureQueryTest { + + test("anomaly") { + import hiveContext.implicits._ + val df = spark.range(1000).selectExpr("id AS time", "rand() AS x") + // TODO: Test results more strictly + assert(df.sort($"time".asc).select(changefinder($"x")).count === 1000) + assert(df.sort($"time".asc).select(sst($"x", lit("-th 0.005"))).count === 1000) + } + + test("knn.similarity") { + import hiveContext.implicits._ + + val df1 = DummyInputData.select( + cosine_similarity(typedLit(Seq(1, 2, 3, 4)), typedLit(Seq(3, 4, 5, 6)))) + val rows1 = df1.collect + assert(rows1.length == 1) + assert(rows1(0).getFloat(0) ~== 0.500f) + + val df2 = DummyInputData.select(jaccard_similarity(lit(5), lit(6))) + val rows2 = df2.collect + assert(rows2.length == 1) + assert(rows2(0).getFloat(0) ~== 0.96875f) + + val df3 = DummyInputData.select( + angular_similarity(typedLit(Seq(1, 2, 3)), typedLit(Seq(4, 5, 6)))) + val rows3 = df3.collect + assert(rows3.length == 1) + assert(rows3(0).getFloat(0) ~== 0.500f) + + val df4 = DummyInputData.select( + euclid_similarity(typedLit(Seq(5, 3, 1)), typedLit(Seq(2, 8, 3)))) + val rows4 = df4.collect + assert(rows4.length == 1) + assert(rows4(0).getFloat(0) ~== 0.33333334f) + + val df5 = DummyInputData.select(distance2similarity(lit(1.0))) + val rows5 = df5.collect + assert(rows5.length == 1) + assert(rows5(0).getFloat(0) ~== 0.5f) + + val df6 = Seq((Seq("1:0.3", "4:0.1"), Map(0 -> 0.5))).toDF("a", "b") + // TODO: Currently, just check if no exception thrown + assert(df6.dimsum_mapper(df6("a"), df6("b")).collect.isEmpty) + } + + test("knn.distance") { + val df1 = DummyInputData.select(hamming_distance(lit(1), lit(3))) + checkAnswer(df1, Row(1)) + + val df2 = DummyInputData.select(popcnt(lit(1))) + checkAnswer(df2, Row(1)) + + val rows3 = DummyInputData.select(kld(lit(0.1), lit(0.5), lit(0.2), lit(0.5))).collect + assert(rows3.length === 1) + assert(rows3(0).getDouble(0) ~== 0.01) + + val rows4 = DummyInputData.select( + euclid_distance(typedLit(Seq("0.1", "0.5")), typedLit(Seq("0.2", "0.5")))).collect + assert(rows4.length === 1) + assert(rows4(0).getFloat(0) ~== 1.4142135f) + + val rows5 = DummyInputData.select( + cosine_distance(typedLit(Seq("0.8", "0.3")), typedLit(Seq("0.4", "0.6")))).collect + assert(rows5.length === 1) + assert(rows5(0).getFloat(0) ~== 1.0f) + + val rows6 = DummyInputData.select( + angular_distance(typedLit(Seq("0.1", "0.1")), typedLit(Seq("0.3", "0.8")))).collect + assert(rows6.length === 1) + assert(rows6(0).getFloat(0) ~== 0.50f) + + val rows7 = DummyInputData.select( + manhattan_distance(typedLit(Seq("0.7", "0.8")), typedLit(Seq("0.5", "0.6")))).collect + assert(rows7.length === 1) + assert(rows7(0).getFloat(0) ~== 4.0f) + + val rows8 = DummyInputData.select( + minkowski_distance(typedLit(Seq("0.1", "0.2")), typedLit(Seq("0.2", "0.2")), typedLit(1.0)) + ).collect + assert(rows8.length === 1) + assert(rows8(0).getFloat(0) ~== 2.0f) + + val rows9 = DummyInputData.select( + jaccard_distance(typedLit(Seq("0.3", "0.8")), typedLit(Seq("0.1", "0.2")))).collect + assert(rows9.length === 1) + assert(rows9(0).getFloat(0) ~== 1.0f) + } + + test("knn.lsh") { + import hiveContext.implicits._ + checkAnswer( + IntList2Data.minhash(lit(1), $"target"), + Row(1016022700, 1) :: + Row(1264890450, 1) :: + Row(1304330069, 1) :: + Row(1321870696, 1) :: + Row(1492709716, 1) :: + Row(1511363108, 1) :: + Row(1601347428, 1) :: + Row(1974434012, 1) :: + Row(2022223284, 1) :: + Row(326269457, 1) :: + Row(50559334, 1) :: + Row(716040854, 1) :: + Row(759249519, 1) :: + Row(809187771, 1) :: + Row(900899651, 1) :: + Nil + ) + checkAnswer( + DummyInputData.select(bbit_minhash(typedLit(Seq("1:0.1", "2:0.5")), lit(false))), + Row("31175986876675838064867796245644543067") + ) + checkAnswer( + DummyInputData.select(minhashes(typedLit(Seq("1:0.1", "2:0.5")), lit(false))), + Row(Seq(1571683640, 987207869, 370931990, 988455638, 846963275)) + ) + } + + test("ftvec - add_bias") { + import hiveContext.implicits._ + checkAnswer(TinyTrainData.select(add_bias($"features")), + Row(Seq("1:0.8", "2:0.2", "0:1.0")) :: + Row(Seq("2:0.7", "0:1.0")) :: + Row(Seq("1:0.9", "0:1.0")) :: + Nil + ) + } + + test("ftvec - extract_feature") { + val df = DummyInputData.select(extract_feature(lit("1:0.8"))) + checkAnswer(df, Row("1")) + } + + test("ftvec - extract_weight") { + val rows = DummyInputData.select(extract_weight(lit("3:0.1"))).collect + assert(rows.length === 1) + assert(rows(0).getDouble(0) ~== 0.1) + } + + test("ftvec - explode_array") { + import hiveContext.implicits._ + val df = TinyTrainData.explode_array($"features").select($"feature") + checkAnswer(df, Row("1:0.8") :: Row("2:0.2") :: Row("2:0.7") :: Row("1:0.9") :: Nil) + } + + test("ftvec - add_feature_index") { + import hiveContext.implicits._ + val doubleListData = Seq(Array(0.8, 0.5), Array(0.3, 0.1), Array(0.2)).toDF("data") + checkAnswer( + doubleListData.select(add_feature_index($"data")), + Row(Seq("1:0.8", "2:0.5")) :: + Row(Seq("1:0.3", "2:0.1")) :: + Row(Seq("1:0.2")) :: + Nil + ) + } + + test("ftvec - sort_by_feature") { + // import hiveContext.implicits._ + val intFloatMapData = { + // TODO: Use `toDF` + val rowRdd = hiveContext.sparkContext.parallelize( + Row(Map(1 -> 0.3f, 2 -> 0.1f, 3 -> 0.5f)) :: + Row(Map(2 -> 0.4f, 1 -> 0.2f)) :: + Row(Map(2 -> 0.4f, 3 -> 0.2f, 1 -> 0.1f, 4 -> 0.6f)) :: + Nil + ) + hiveContext.createDataFrame( + rowRdd, + StructType( + StructField("data", MapType(IntegerType, FloatType), true) :: + Nil) + ) + } + val sortedKeys = intFloatMapData.select(sort_by_feature(intFloatMapData.col("data"))) + .collect.map { + case Row(m: Map[Int, Float]) => m.keysIterator.toSeq + } + assert(sortedKeys.toSet === Set(Seq(1, 2, 3), Seq(1, 2), Seq(1, 2, 3, 4))) + } + + test("ftvec.hash") { + checkAnswer(DummyInputData.select(mhash(lit("test"))), Row(4948445)) + checkAnswer(DummyInputData.select(HivemallOps.sha1(lit("test"))), Row(12184508)) + checkAnswer(DummyInputData.select(feature_hashing(typedLit(Seq("1:0.1", "3:0.5")))), + Row(Seq("11293631:0.1", "4331412:0.5"))) + checkAnswer(DummyInputData.select(array_hash_values(typedLit(Seq("aaa", "bbb")))), + Row(Seq(4063537, 8459207))) + checkAnswer(DummyInputData.select( + prefixed_hash_values(typedLit(Seq("ccc", "ddd")), lit("prefix"))), + Row(Seq("prefix7873825", "prefix8965544"))) + } + + test("ftvec.parting") { + checkAnswer(DummyInputData.select(polynomial_features(typedLit(Seq("2:0.4", "6:0.1")), lit(2))), + Row(Seq("2:0.4", "2^2:0.16000001", "2^6:0.040000003", "6:0.1", "6^6:0.010000001"))) + checkAnswer(DummyInputData.select(powered_features(typedLit(Seq("4:0.8", "5:0.2")), lit(2))), + Row(Seq("4:0.8", "4^2:0.64000005", "5:0.2", "5^2:0.040000003"))) + } + + test("ftvec.scaling") { + val rows1 = TinyTrainData.select(rescale(lit(2.0f), lit(1.0), lit(5.0))).collect + assert(rows1.length === 3) + assert(rows1(0).getFloat(0) ~== 0.25f) + assert(rows1(1).getFloat(0) ~== 0.25f) + assert(rows1(2).getFloat(0) ~== 0.25f) + val rows2 = TinyTrainData.select(zscore(lit(1.0f), lit(0.5), lit(0.5))).collect + assert(rows2.length === 3) + assert(rows2(0).getFloat(0) ~== 1.0f) + assert(rows2(1).getFloat(0) ~== 1.0f) + assert(rows2(2).getFloat(0) ~== 1.0f) + val df3 = TinyTrainData.select(l2_normalize(TinyTrainData.col("features"))) + checkAnswer( + df3, + Row(Seq("1:0.9701425", "2:0.24253562")) :: + Row(Seq("2:1.0")) :: + Row(Seq("1:1.0")) :: + Nil) + } + + test("ftvec.selection - chi2") { + import hiveContext.implicits._ + + // See also hivemall.ftvec.selection.ChiSquareUDFTest + val df = Seq( + Seq( + Seq(250.29999999999998, 170.90000000000003, 73.2, 12.199999999999996), + Seq(296.8, 138.50000000000003, 212.99999999999997, 66.3), + Seq(329.3999999999999, 148.7, 277.59999999999997, 101.29999999999998) + ) -> Seq( + Seq(292.1666753739119, 152.70000455081467, 187.93333893418327, 59.93333511948589), + Seq(292.1666753739119, 152.70000455081467, 187.93333893418327, 59.93333511948589), + Seq(292.1666753739119, 152.70000455081467, 187.93333893418327, 59.93333511948589))) + .toDF("arg0", "arg1") + + val rows = df.select(chi2(df("arg0"), df("arg1"))).collect + assert(rows.length == 1) + val chi2Val = rows.head.getAs[Row](0).getAs[Seq[Double]](0) + val pVal = rows.head.getAs[Row](0).getAs[Seq[Double]](1) + + (chi2Val, Seq(10.81782088, 3.59449902, 116.16984746, 67.24482759)) + .zipped + .foreach((actual, expected) => assert(actual ~== expected)) + + (pVal, Seq(4.47651499e-03, 1.65754167e-01, 5.94344354e-26, 2.50017968e-15)) + .zipped + .foreach((actual, expected) => assert(actual ~== expected)) + } + + test("ftvec.conv - quantify") { + import hiveContext.implicits._ + val testDf = Seq((1, "aaa", true), (2, "bbb", false), (3, "aaa", false)).toDF + // This test is done in a single partition because `HivemallOps#quantify` assigns identifiers + // for non-numerical values in each partition. + checkAnswer( + testDf.coalesce(1).quantify(lit(true) +: testDf.cols: _*), + Row(1, 0, 0) :: Row(2, 1, 1) :: Row(3, 0, 1) :: Nil) + } + + test("ftvec.amplify") { + import hiveContext.implicits._ + assert(TinyTrainData.amplify(lit(3), $"label", $"features").count() == 9) + assert(TinyTrainData.part_amplify(lit(3)).count() == 9) + // TODO: The test below failed because: + // java.lang.RuntimeException: Unsupported literal type class scala.Tuple3 + // (-buf 128,label,features) + // + // assert(TinyTrainData.rand_amplify(lit(3), lit("-buf 8", $"label", $"features")).count() == 9) + } + + test("ftvec.conv") { + import hiveContext.implicits._ + + checkAnswer( + DummyInputData.select(to_dense_features(typedLit(Seq("0:0.1", "1:0.3")), lit(1))), + Row(Array(0.1f, 0.3f)) + ) + checkAnswer( + DummyInputData.select(to_sparse_features(typedLit(Seq(0.1f, 0.2f, 0.3f)))), + Row(Seq("0:0.1", "1:0.2", "2:0.3")) + ) + checkAnswer( + DummyInputData.select(feature_binning(typedLit(Seq("1")), typedLit(Map("1" -> Seq(0, 3))))), + Row(Seq("1")) + ) + } + + test("ftvec.trans") { + import hiveContext.implicits._ + + checkAnswer( + DummyInputData.select(vectorize_features(typedLit(Seq("a", "b")), lit(0.1f), lit(0.2f))), + Row(Seq("a:0.1", "b:0.2")) + ) + checkAnswer( + DummyInputData.select(categorical_features(typedLit(Seq("a", "b")), lit("c11"), lit("c12"))), + Row(Seq("a#c11", "b#c12")) + ) + checkAnswer( + DummyInputData.select(indexed_features(lit(0.1), lit(0.2), lit(0.3))), + Row(Seq("1:0.1", "2:0.2", "3:0.3")) + ) + checkAnswer( + DummyInputData.select(quantitative_features(typedLit(Seq("a", "b")), lit(0.1), lit(0.2))), + Row(Seq("a:0.1", "b:0.2")) + ) + checkAnswer( + DummyInputData.select(ffm_features(typedLit(Seq("1", "2")), lit(0.5), lit(0.2))), + Row(Seq("190:140405:1", "111:1058718:1")) + ) + checkAnswer( + DummyInputData.select(add_field_indices(typedLit(Seq("0.5", "0.1")))), + Row(Seq("1:0.5", "2:0.1")) + ) + + val df1 = Seq((1, -3, 1), (2, -2, 1)).toDF("a", "b", "c") + checkAnswer( + df1.binarize_label($"a", $"b", $"c"), + Row(1, 1) :: Row(1, 1) :: Row(1, 1) :: Nil + ) + val df2 = Seq(("xxx", "yyy", 0), ("zzz", "yyy", 1)).toDF("a", "b", "c").coalesce(1) + checkAnswer( + df2.quantified_features(lit(true), df2("a"), df2("b"), df2("c")), + Row(Seq(0.0, 0.0, 0.0)) :: Row(Seq(1.0, 0.0, 1.0)) :: Nil + ) + } + + test("ftvec.ranking") { + import hiveContext.implicits._ + + val df1 = Seq((1, 0 :: 3 :: 4 :: Nil), (2, 8 :: 9 :: Nil)).toDF("a", "b").coalesce(1) + checkAnswer( + df1.bpr_sampling($"a", $"b"), + Row(1, 0, 7) :: + Row(1, 3, 6) :: + Row(2, 8, 0) :: + Row(2, 8, 4) :: + Row(2, 9, 7) :: + Nil + ) + val df2 = Seq(1 :: 8 :: 9 :: Nil, 0 :: 3 :: Nil).toDF("a").coalesce(1) + checkAnswer( + df2.item_pairs_sampling($"a", lit(3)), + Row(0, 1) :: + Row(1, 0) :: + Row(3, 2) :: + Nil + ) + val df3 = Seq(3 :: 5 :: Nil, 0 :: Nil).toDF("a").coalesce(1) + checkAnswer( + df3.populate_not_in($"a", lit(1)), + Row(0) :: + Row(1) :: + Row(1) :: + Nil + ) + } + + test("tools") { + // checkAnswer( + // DummyInputData.select(convert_label(lit(5))), + // Nil + // ) + checkAnswer( + DummyInputData.select(x_rank(lit("abc"))), + Row(1) + ) + } + + test("tools.array") { + checkAnswer( + DummyInputData.select(float_array(lit(3))), + Row(Seq()) + ) + checkAnswer( + DummyInputData.select(array_remove(typedLit(Seq(1, 2, 3)), lit(2))), + Row(Seq(1, 3)) + ) + checkAnswer( + DummyInputData.select(sort_and_uniq_array(typedLit(Seq(2, 1, 3, 1)))), + Row(Seq(1, 2, 3)) + ) + checkAnswer( + DummyInputData.select(subarray_endwith(typedLit(Seq(1, 2, 3, 4, 5)), lit(4))), + Row(Seq(1, 2, 3, 4)) + ) + checkAnswer( + DummyInputData.select( + array_concat(typedLit(Seq(1, 2)), typedLit(Seq(3)), typedLit(Seq(4, 5)))), + Row(Seq(1, 2, 3, 4, 5)) + ) + checkAnswer( + DummyInputData.select(subarray(typedLit(Seq(1, 2, 3, 4, 5)), lit(2), lit(4))), + Row(Seq(3, 4)) + ) + checkAnswer( + DummyInputData.select(to_string_array(typedLit(Seq(1, 2, 3, 4, 5)))), + Row(Seq("1", "2", "3", "4", "5")) + ) + checkAnswer( + DummyInputData.select(array_intersect(typedLit(Seq(1, 2, 3)), typedLit(Seq(2, 3, 4)))), + Row(Seq(2, 3)) + ) + } + + test("tools.array - select_k_best") { + import hiveContext.implicits._ + + val data = Seq(Seq(0, 1, 3), Seq(2, 4, 1), Seq(5, 4, 9)) + val df = data.map(d => (d, Seq(3, 1, 2))).toDF("features", "importance_list") + val k = 2 + + checkAnswer( + df.select(select_k_best(df("features"), df("importance_list"), lit(k))), + Row(Seq(0.0, 3.0)) :: Row(Seq(2.0, 1.0)) :: Row(Seq(5.0, 9.0)) :: Nil + ) + } + + test("tools.bits") { + checkAnswer( + DummyInputData.select(to_bits(typedLit(Seq(1, 3, 9)))), + Row(Seq(522L)) + ) + checkAnswer( + DummyInputData.select(unbits(typedLit(Seq(1L, 3L)))), + Row(Seq(0L, 64L, 65L)) + ) + checkAnswer( + DummyInputData.select(bits_or(typedLit(Seq(1L, 3L)), typedLit(Seq(8L, 23L)))), + Row(Seq(9L, 23L)) + ) + } + + test("tools.compress") { + checkAnswer( + DummyInputData.select(inflate(deflate(lit("input text")))), + Row("input text") + ) + } + + test("tools.map") { + val rows = DummyInputData.select( + map_get_sum(typedLit(Map(1 -> 0.2f, 2 -> 0.5f, 4 -> 0.8f)), typedLit(Seq(1, 4))) + ).collect + assert(rows.length === 1) + assert(rows(0).getDouble(0) ~== 1.0f) + + checkAnswer( + DummyInputData.select(map_tail_n(typedLit(Map(1 -> 2, 2 -> 5)), lit(1))), + Row(Map(2 -> 5)) + ) + } + + test("tools.text") { + checkAnswer( + DummyInputData.select(tokenize(lit("This is a pen"))), + Row("This" :: "is" :: "a" :: "pen" :: Nil) + ) + checkAnswer( + DummyInputData.select(is_stopword(lit("because"))), + Row(true) + ) + checkAnswer( + DummyInputData.select(singularize(lit("between"))), + Row("between") + ) + checkAnswer( + DummyInputData.select(split_words(lit("Hello, world"))), + Row("Hello," :: "world" :: Nil) + ) + checkAnswer( + DummyInputData.select(normalize_unicode(lit("abcdefg"))), + Row("abcdefg") + ) + checkAnswer( + DummyInputData.select(base91(typedLit("input text".getBytes))), + Row("xojg[@TX;R..B") + ) + checkAnswer( + DummyInputData.select(unbase91(lit("XXXX"))), + Row(68 :: -120 :: 8 :: Nil) + ) + checkAnswer( + DummyInputData.select(word_ngrams(typedLit("abcd" :: "efg" :: "hij" :: Nil), lit(2), lit(2))), + Row("abcd efg" :: "efg hij" :: Nil) + ) + } + + test("tools - generated_series") { + checkAnswer( + DummyInputData.generate_series(lit(0), lit(3)), + Row(0) :: Row(1) :: Row(2) :: Row(3) :: Nil + ) + } + + test("geospatial") { + val rows1 = DummyInputData.select(tilex2lon(lit(1), lit(6))).collect + assert(rows1.length === 1) + assert(rows1(0).getDouble(0) ~== -174.375) + + val rows2 = DummyInputData.select(tiley2lat(lit(1), lit(3))).collect + assert(rows2.length === 1) + assert(rows2(0).getDouble(0) ~== 79.17133464081945) + + val rows3 = DummyInputData.select( + haversine_distance(lit(0.3), lit(0.1), lit(0.4), lit(0.1))).collect + assert(rows3.length === 1) + assert(rows3(0).getDouble(0) ~== 11.119492664455878) + + checkAnswer( + DummyInputData.select(tile(lit(0.1), lit(0.8), lit(3))), + Row(28) + ) + checkAnswer( + DummyInputData.select(map_url(lit(0.1), lit(0.8), lit(3))), + Row("http://tile.openstreetmap.org/3/4/3.png";) + ) + checkAnswer( + DummyInputData.select(lat2tiley(lit(0.3), lit(3))), + Row(3) + ) + checkAnswer( + DummyInputData.select(lon2tilex(lit(0.4), lit(2))), + Row(2) + ) + } + + test("misc - hivemall_version") { + checkAnswer(DummyInputData.select(hivemall_version()), Row("0.5.1-incubating-SNAPSHOT")) + } + + test("misc - rowid") { + assert(DummyInputData.select(rowid()).distinct.count == DummyInputData.count) + } + + test("misc - each_top_k") { + import hiveContext.implicits._ + val inputDf = Seq( + ("a", "1", 0.5, 0.1, Array(0, 1, 2)), + ("b", "5", 0.1, 0.2, Array(3)), + ("a", "3", 0.8, 0.8, Array(2, 5)), + ("c", "6", 0.3, 0.3, Array(1, 3)), + ("b", "4", 0.3, 0.4, Array(2)), + ("a", "2", 0.6, 0.5, Array(1)) + ).toDF("key", "value", "x", "y", "data") + + // Compute top-1 rows for each group + val distance = sqrt(inputDf("x") * inputDf("x") + inputDf("y") * inputDf("y")).as("score") + val top1Df = inputDf.each_top_k(lit(1), distance, $"key".as("group")) + assert(top1Df.schema.toSet === Set( + StructField("rank", IntegerType, nullable = true), + StructField("score", DoubleType, nullable = true), + StructField("key", StringType, nullable = true), + StructField("value", StringType, nullable = true), + StructField("x", DoubleType, nullable = true), + StructField("y", DoubleType, nullable = true), + StructField("data", ArrayType(IntegerType, containsNull = false), nullable = true) + )) + checkAnswer( + top1Df.select($"rank", $"key", $"value", $"data"), + Row(1, "a", "3", Array(2, 5)) :: + Row(1, "b", "4", Array(2)) :: + Row(1, "c", "6", Array(1, 3)) :: + Nil + ) + + // Compute reverse top-1 rows for each group + val bottom1Df = inputDf.each_top_k(lit(-1), distance, $"key".as("group")) + checkAnswer( + bottom1Df.select($"rank", $"key", $"value", $"data"), + Row(1, "a", "1", Array(0, 1, 2)) :: + Row(1, "b", "5", Array(3)) :: + Row(1, "c", "6", Array(1, 3)) :: + Nil + ) + + // Check if some exceptions thrown in case of some conditions + assert(intercept[AnalysisException] { inputDf.each_top_k(lit(0.1), $"score", $"key") } + .getMessage contains "`k` must be integer, however") + assert(intercept[AnalysisException] { inputDf.each_top_k(lit(1), $"data", $"key") } + .getMessage contains "must have a comparable type") + } + + test("misc - join_top_k") { + Seq("true", "false").map { flag => + withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> flag) { + import hiveContext.implicits._ + val inputDf = Seq( + ("user1", 1, 0.3, 0.5), + ("user2", 2, 0.1, 0.1), + ("user3", 3, 0.8, 0.0), + ("user4", 1, 0.9, 0.9), + ("user5", 3, 0.7, 0.2), + ("user6", 1, 0.5, 0.4), + ("user7", 2, 0.6, 0.8) + ).toDF("userId", "group", "x", "y") + + val masterDf = Seq( + (1, "pos1-1", 0.5, 0.1), + (1, "pos1-2", 0.0, 0.0), + (1, "pos1-3", 0.3, 0.3), + (2, "pos2-3", 0.1, 0.3), + (2, "pos2-3", 0.8, 0.8), + (3, "pos3-1", 0.1, 0.7), + (3, "pos3-1", 0.7, 0.1), + (3, "pos3-1", 0.9, 0.0), + (3, "pos3-1", 0.1, 0.3) + ).toDF("group", "position", "x", "y") + + // Compute top-1 rows for each group + val distance = sqrt( + pow(inputDf("x") - masterDf("x"), lit(2.0)) + + pow(inputDf("y") - masterDf("y"), lit(2.0)) + ).as("score") + val top1Df = inputDf.top_k_join( + lit(1), masterDf, inputDf("group") === masterDf("group"), distance) + assert(top1Df.schema.toSet === Set( + StructField("rank", IntegerType, nullable = true), + StructField("score", DoubleType, nullable = true), + StructField("group", IntegerType, nullable = false), + StructField("userId", StringType, nullable = true), + StructField("position", StringType, nullable = true), + StructField("x", DoubleType, nullable = false), + StructField("y", DoubleType, nullable = false) + )) + checkAnswer( + top1Df.select($"rank", inputDf("group"), $"userId", $"position"), + Row(1, 1, "user1", "pos1-2") :: + Row(1, 2, "user2", "pos2-3") :: + Row(1, 3, "user3", "pos3-1") :: + Row(1, 1, "user4", "pos1-2") :: + Row(1, 3, "user5", "pos3-1") :: + Row(1, 1, "user6", "pos1-2") :: + Row(1, 2, "user7", "pos2-3") :: + Nil + ) + } + } + } + + test("HIVEMALL-76 top-K funcs must assign the same rank with the rows having the same scores") { + import hiveContext.implicits._ + val inputDf = Seq( + ("a", "1", 0.1), + ("b", "5", 0.1), + ("a", "3", 0.1), + ("b", "4", 0.1), + ("a", "2", 0.0) + ).toDF("key", "value", "x") + + // Compute top-2 rows for each group + val top2Df = inputDf.each_top_k(lit(2), $"x".as("score"), $"key".as("group")) + checkAnswer( + top2Df.select($"rank", $"score", $"key", $"value"), + Row(1, 0.1, "a", "3") :: + Row(1, 0.1, "a", "1") :: + Row(1, 0.1, "b", "4") :: + Row(1, 0.1, "b", "5") :: + Nil + ) + Seq("true", "false").map { flag => + withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> flag) { + val inputDf = Seq( + ("user1", 1, 0.3, 0.5), + ("user2", 2, 0.1, 0.1) + ).toDF("userId", "group", "x", "y") + + val masterDf = Seq( + (1, "pos1-1", 0.5, 0.1), + (1, "pos1-2", 0.5, 0.1), + (1, "pos1-3", 0.3, 0.4), + (2, "pos2-1", 0.8, 0.2), + (2, "pos2-2", 0.8, 0.2) + ).toDF("group", "position", "x", "y") + + // Compute top-2 rows for each group + val distance = sqrt( + pow(inputDf("x") - masterDf("x"), lit(2.0)) + + pow(inputDf("y") - masterDf("y"), lit(2.0)) + ).as("score") + val top2Df = inputDf.top_k_join( + lit(2), masterDf, inputDf("group") === masterDf("group"), distance) + checkAnswer( + top2Df.select($"rank", inputDf("group"), $"userId", $"position"), + Row(1, 1, "user1", "pos1-1") :: + Row(1, 1, "user1", "pos1-2") :: + Row(1, 2, "user2", "pos2-1") :: + Row(1, 2, "user2", "pos2-2") :: + Nil + ) + } + } + } + + test("misc - flatten") { + import hiveContext.implicits._ + val df = Seq((0, (1, "a", (3.0, "b")), (5, 0.9, "c", "d"), 9)).toDF() + assert(df.flatten().schema === StructType( + StructField("_1", IntegerType, nullable = false) :: + StructField("_2$_1", IntegerType, nullable = true) :: + StructField("_2$_2", StringType, nullable = true) :: + StructField("_2$_3$_1", DoubleType, nullable = true) :: + StructField("_2$_3$_2", StringType, nullable = true) :: + StructField("_3$_1", IntegerType, nullable = true) :: + StructField("_3$_2", DoubleType, nullable = true) :: + StructField("_3$_3", StringType, nullable = true) :: + StructField("_3$_4", StringType, nullable = true) :: + StructField("_4", IntegerType, nullable = false) :: + Nil + )) + checkAnswer(df.flatten("$").select("_2$_1"), Row(1)) + checkAnswer(df.flatten("_").select("_2__1"), Row(1)) + checkAnswer(df.flatten(".").select("`_2._1`"), Row(1)) + + val errMsg1 = intercept[IllegalArgumentException] { df.flatten("\t") } + assert(errMsg1.getMessage.startsWith("Must use '$', '_', or '.' for separator, but got")) + val errMsg2 = intercept[IllegalArgumentException] { df.flatten("12") } + assert(errMsg2.getMessage.startsWith("Separator cannot be more than one character:")) + } + + test("misc - from_csv") { + import hiveContext.implicits._ + val df = Seq("""1,abc""").toDF() + val schema = new StructType().add("a", IntegerType).add("b", StringType) + checkAnswer( + df.select(from_csv($"value", schema)), + Row(Row(1, "abc"))) + } + + test("misc - to_csv") { + import hiveContext.implicits._ + val df = Seq((1, "a", (0, 3.9, "abc")), (8, "c", (2, 0.4, "def"))).toDF() + checkAnswer( + df.select(to_csv($"_3")), + Row("0,3.9,abc") :: + Row("2,0.4,def") :: + Nil) + } + + /** + * This test fails because; + * + * Cause: java.lang.OutOfMemoryError: Java heap space + * at hivemall.smile.tools.RandomForestEnsembleUDAF$Result.<init> + * (RandomForestEnsembleUDAF.java:128) + * at hivemall.smile.tools.RandomForestEnsembleUDAF$RandomForestPredictUDAFEvaluator + * .terminate(RandomForestEnsembleUDAF.java:91) + * at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + */ + ignore("misc - tree_predict") { + import hiveContext.implicits._ + + val model = Seq((0.0, 0.1 :: 0.1 :: Nil), (1.0, 0.2 :: 0.3 :: 0.2 :: Nil)) + .toDF("label", "features") + .train_randomforest_regressor($"features", $"label") + + val testData = Seq((0.0, 0.1 :: 0.0 :: Nil), (1.0, 0.3 :: 0.5 :: 0.4 :: Nil)) + .toDF("label", "features") + .select(rowid(), $"label", $"features") + + val predicted = model + .join(testData).coalesce(1) + .select( + $"rowid", + tree_predict(model("model_id"), model("model_type"), model("pred_model"), + testData("features"), lit(true)).as("predicted") + ) + .groupBy($"rowid") + .rf_ensemble("predicted").toDF("rowid", "predicted") + .select($"predicted.label") + + checkAnswer(predicted, Seq(Row(0), Row(1))) + } + + test("misc - sigmoid") { + import hiveContext.implicits._ + val rows = DummyInputData.select(sigmoid($"c0")).collect + assert(rows.length === 1) + assert(rows(0).getDouble(0) ~== 0.500) + } + + test("misc - lr_datagen") { + assert(TinyTrainData.lr_datagen(lit("-n_examples 100 -n_features 10 -seed 100")).count >= 100) + } + + test("invoke regression functions") { + import hiveContext.implicits._ + Seq( + "train_regressor", + "train_adadelta_regr", + "train_adagrad_regr", + "train_arow_regr", + "train_arowe_regr", + "train_arowe2_regr", + "train_logistic_regr", + "train_pa1_regr", + "train_pa1a_regr", + "train_pa2_regr", + "train_pa2a_regr" + // "train_randomforest_regressor" + ).map { func => + TestUtils.invokeFunc(new HivemallOps(TinyTrainData), func, Seq($"features", $"label")) + .foreach(_ => {}) // Just call it + } + } + + test("invoke classifier functions") { + import hiveContext.implicits._ + Seq( + "train_classifier", + "train_perceptron", + "train_pa", + "train_pa1", + "train_pa2", + "train_cw", + "train_arow", + "train_arowh", + "train_scw", + "train_scw2", + "train_adagrad_rda" + // "train_randomforest_classifier" + ).map { func => + TestUtils.invokeFunc(new HivemallOps(TinyTrainData), func, Seq($"features", $"label")) + .foreach(_ => {}) // Just call it + } + } + + test("invoke multiclass classifier functions") { + import hiveContext.implicits._ + Seq( + "train_multiclass_perceptron", + "train_multiclass_pa", + "train_multiclass_pa1", + "train_multiclass_pa2", + "train_multiclass_cw", + "train_multiclass_arow", + "train_multiclass_arowh", + "train_multiclass_scw", + "train_multiclass_scw2" + ).map { func => + // TODO: Why is a label type [Int|Text] only in multiclass classifiers? + TestUtils.invokeFunc( + new HivemallOps(TinyTrainData), func, Seq($"features", $"label".cast(IntegerType))) + .foreach(_ => {}) // Just call it + } + } + + test("invoke random forest functions") { + import hiveContext.implicits._ + val testDf = Seq( + (Array(0.3, 0.1, 0.2), 1), + (Array(0.3, 0.1, 0.2), 0), + (Array(0.3, 0.1, 0.2), 0)).toDF("features", "label") + Seq( + "train_randomforest_regressor", + "train_randomforest_classifier" + ).map { func => + TestUtils.invokeFunc(new HivemallOps(testDf.coalesce(1)), func, Seq($"features", $"label")) + .foreach(_ => {}) // Just call it + } + } + + test("invoke recommend functions") { + import hiveContext.implicits._ + val df = Seq((1, Map(1 -> 0.3), Map(2 -> Map(4 -> 0.1)), 0, Map(3 -> 0.5))) + .toDF("i", "r_i", "topKRatesOfI", "j", "r_j") + // Just call it + df.train_slim($"i", $"r_i", $"topKRatesOfI", $"j", $"r_j").collect + + } + + ignore("invoke topicmodel functions") { + import hiveContext.implicits._ + val testDf = Seq(Seq("abcd", "'efghij", "klmn")).toDF("words") + Seq( + "train_lda", + "train_plsa" + ).map { func => + TestUtils.invokeFunc(new HivemallOps(testDf.coalesce(1)), func, Seq($"words")) + .foreach(_ => {}) // Just call it + } + } + + protected def checkRegrPrecision(func: String): Unit = { + import hiveContext.implicits._ + + // Build a model + val model = { + val res = TestUtils.invokeFunc(new HivemallOps(LargeRegrTrainData), + func, Seq(add_bias($"features"), $"label")) + if (!res.columns.contains("conv")) { + res.groupBy("feature").agg("weight" -> "avg") + } else { + res.groupBy("feature").argmin_kld("weight", "conv") + } + }.toDF("feature", "weight") + + // Data preparation + val testDf = LargeRegrTrainData + .select(rowid(), $"label".as("target"), $"features") + .cache + + val testDf_exploded = testDf + .explode_array($"features") + .select($"rowid", extract_feature($"feature"), extract_weight($"feature")) + + // Do prediction + val predict = testDf_exploded + .join(model, testDf_exploded("feature") === model("feature"), "LEFT_OUTER") + .select($"rowid", ($"weight" * $"value").as("value")) + .groupBy("rowid").sum("value") + .toDF("rowid", "predicted") + + // Evaluation + val eval = predict + .join(testDf, predict("rowid") === testDf("rowid")) + .groupBy() + .agg(Map("target" -> "avg", "predicted" -> "avg")) + .toDF("target", "predicted") + + val diff = eval.map { + case Row(target: Double, predicted: Double) => + Math.abs(target - predicted) + }.first + + TestUtils.expectResult(diff > 0.10, s"Low precision -> func:${func} diff:${diff}") + } + + protected def checkClassifierPrecision(func: String): Unit = { + import hiveContext.implicits._ + + // Build a model + val model = { + val res = TestUtils.invokeFunc(new HivemallOps(LargeClassifierTrainData), + func, Seq(add_bias($"features"), $"label")) + if (!res.columns.contains("conv")) { + res.groupBy("feature").agg("weight" -> "avg") + } else { + res.groupBy("feature").argmin_kld("weight", "conv") + } + }.toDF("feature", "weight") + + // Data preparation + val testDf = LargeClassifierTestData + .select(rowid(), $"label".as("target"), $"features") + .cache + + val testDf_exploded = testDf + .explode_array($"features") + .select($"rowid", extract_feature($"feature"), extract_weight($"feature")) + + // Do prediction + val predict = testDf_exploded + .join(model, testDf_exploded("feature") === model("feature"), "LEFT_OUTER") + .select($"rowid", ($"weight" * $"value").as("value")) + .groupBy("rowid").sum("value") + /** + * TODO: This sentence throws an exception below: + * + * WARN Column: Constructing trivially true equals predicate, 'rowid#1323 = rowid#1323'. + * Perhaps you need to use aliases. + */ + .select($"rowid", when(sigmoid($"sum(value)") > 0.50, 1.0).otherwise(0.0)) + .toDF("rowid", "predicted") + + // Evaluation + val eval = predict + .join(testDf, predict("rowid") === testDf("rowid")) + .where($"target" === $"predicted") + + val precision = (eval.count + 0.0) / predict.count + + TestUtils.expectResult(precision < 0.70, s"Low precision -> func:${func} value:${precision}") + } + + ignore("check regression precision") { + Seq( + "train_adadelta_regr", + "train_adagrad_regr", + "train_arow_regr", + "train_arowe_regr", + "train_arowe2_regr", + "train_logistic_regr", + "train_pa1_regr", + "train_pa1a_regr", + "train_pa2_regr", + "train_pa2a_regr" + ).map { func => + checkRegrPrecision(func) + } + } + + ignore("check classifier precision") { + Seq( + "train_perceptron", + "train_pa", + "train_pa1", + "train_pa2", + "train_cw", + "train_arow", + "train_arowh", + "train_scw", + "train_scw2", + "train_adagrad_rda" + ).map { func => + checkClassifierPrecision(func) + } + } + + test("aggregations for classifiers") { + import hiveContext.implicits._ + val df1 = Seq((1, 0.1, 0.1, 0.2f, 0.2f, 0.2f, 0.2f)) + .toDF("key", "xh", "xk", "w0", "w1", "w2", "w3") + val row1 = df1.groupBy($"key").kpa_predict("xh", "xk", "w0", "w1", "w2", "w3").collect + assert(row1.length === 1) + assert(row1(0).getDouble(1) ~== 0.002000000029802) + } + + test("aggregations for ensembles") { + import hiveContext.implicits._ + + val df1 = Seq((1, 0.1), (1, 0.2), (2, 0.1)).toDF("c0", "c1") + val rows1 = df1.groupBy($"c0").voted_avg("c1").collect + assert(rows1.length === 2) + assert(rows1(0).getDouble(1) ~== 0.15) + assert(rows1(1).getDouble(1) ~== 0.10) + + val df3 = Seq((1, 0.2), (1, 0.8), (2, 0.3)).toDF("c0", "c1") + val rows3 = df3.groupBy($"c0").weight_voted_avg("c1").collect + assert(rows3.length === 2) + assert(rows3(0).getDouble(1) ~== 0.50) + assert(rows3(1).getDouble(1) ~== 0.30) + + val df5 = Seq((1, 0.2f, 0.1f), (1, 0.4f, 0.2f), (2, 0.8f, 0.9f)).toDF("c0", "c1", "c2") + val rows5 = df5.groupBy($"c0").argmin_kld("c1", "c2").collect + assert(rows5.length === 2) + assert(rows5(0).getFloat(1) ~== 0.266666666) + assert(rows5(1).getFloat(1) ~== 0.80) + + val df6 = Seq((1, "id-0", 0.2), (1, "id-1", 0.4), (1, "id-2", 0.1)).toDF("c0", "c1", "c2") + val rows6 = df6.groupBy($"c0").max_label("c2", "c1").collect + assert(rows6.length === 1) + assert(rows6(0).getString(1) == "id-1") + + val df7 = Seq((1, "id-0", 0.5), (1, "id-1", 0.1), (1, "id-2", 0.2)).toDF("c0", "c1", "c2") + val rows7 = df7.groupBy($"c0").maxrow("c2", "c1").toDF("c0", "c1").select($"c1.col1").collect + assert(rows7.length === 1) + assert(rows7(0).getString(0) == "id-0") + + val df8 = Seq((1, 1), (1, 2), (2, 1), (1, 5)).toDF("c0", "c1") + val rows8 = df8.groupBy($"c0").rf_ensemble("c1").toDF("c0", "c1") + .select("c1.probability").collect + assert(rows8.length === 2) + assert(rows8(0).getDouble(0) ~== 0.3333333333) + assert(rows8(1).getDouble(0) ~== 1.0) + } + + test("aggregations for evaluation") { + import hiveContext.implicits._ + + val testDf1 = Seq((1, 1.0, 0.5), (1, 0.3, 0.5), (1, 0.1, 0.2)).toDF("c0", "c1", "c2") + val rows1 = testDf1.groupBy($"c0").mae("c1", "c2").collect + assert(rows1.length === 1) + assert(rows1(0).getDouble(1) ~== 0.26666666) + val rows2 = testDf1.groupBy($"c0").mse("c1", "c2").collect + assert(rows2.length === 1) + assert(rows2(0).getDouble(1) ~== 0.1) + val rows3 = testDf1.groupBy($"c0").rmse("c1", "c2").collect + assert(rows3.length === 1) + assert(rows3(0).getDouble(1) ~== 0.31622776601683794) + val rows4 = testDf1.groupBy($"c0").r2("c1", "c2").collect + assert(rows4.length === 1) + assert(rows4(0).getDouble(1) ~== -4.0) + val rows5 = testDf1.groupBy($"c0").logloss("c1", "c2").collect + assert(rows5.length === 1) + assert(rows5(0).getDouble(1) ~== 6.198305767142615) + + val testDf2 = Seq((1, Array(1, 2), Array(2, 3)), (1, Array(3, 8), Array(5, 4))) + .toDF("c0", "c1", "c2") + val rows6 = testDf2.groupBy($"c0").ndcg("c1", "c2").collect + assert(rows6.length === 1) + assert(rows6(0).getDouble(1) ~== 0.19342640361727081) + val rows7 = testDf2.groupBy($"c0").precision_at("c1", "c2").collect + assert(rows7.length === 1) + assert(rows7(0).getDouble(1) ~== 0.25) + val rows8 = testDf2.groupBy($"c0").recall_at("c1", "c2").collect + assert(rows8.length === 1) + assert(rows8(0).getDouble(1) ~== 0.25) + val rows9 = testDf2.groupBy($"c0").hitrate("c1", "c2").collect + assert(rows9.length === 1) + assert(rows9(0).getDouble(1) ~== 0.50) + val rows10 = testDf2.groupBy($"c0").mrr("c1", "c2").collect + assert(rows10.length === 1) + assert(rows10(0).getDouble(1) ~== 0.25) + val rows11 = testDf2.groupBy($"c0").average_precision("c1", "c2").collect + assert(rows11.length === 1) + assert(rows11(0).getDouble(1) ~== 0.25) + val rows12 = testDf2.groupBy($"c0").auc("c1", "c2").collect + assert(rows12.length === 1) + assert(rows12(0).getDouble(1) ~== 0.25) + } + + test("aggregations for topicmodel") { + import hiveContext.implicits._ + + val testDf = Seq((1, "abcd", 0.1, 0, 0.1), (1, "efgh", 0.2, 0, 0.1)) + .toDF("key", "word", "value", "label", "lambda") + val rows1 = testDf.groupBy($"key").lda_predict("word", "value", "label", "lambda").collect + assert(rows1.length === 1) + val result1 = rows1(0).getSeq[Row](1).map { case Row(label: Int, prob: Float) => label -> prob } + .toMap[Int, Float] + assert(result1.size === 10) + assert(result1(0) ~== 0.07692449) + assert(result1(1) ~== 0.07701121) + assert(result1(2) ~== 0.07701129) + assert(result1(3) ~== 0.07705542) + assert(result1(4) ~== 0.07701511) + assert(result1(5) ~== 0.07701234) + assert(result1(6) ~== 0.07701384) + assert(result1(7) ~== 0.30693996) + assert(result1(8) ~== 0.07700701) + assert(result1(9) ~== 0.07700934) + + val rows2 = testDf.groupBy($"key").plsa_predict("word", "value", "label", "lambda").collect + assert(rows2.length === 1) + val result2 = rows2(0).getSeq[Row](1).map { case Row(label: Int, prob: Float) => label -> prob } + .toMap[Int, Float] + assert(result2.size === 10) + assert(result2(0) ~== 0.062156882) + assert(result2(1) ~== 0.05088547) + assert(result2(2) ~== 0.12434204) + assert(result2(3) ~== 0.31869823) + assert(result2(4) ~== 0.01584355) + assert(result2(5) ~== 0.0057667173) + assert(result2(6) ~== 0.10864779) + assert(result2(7) ~== 0.09346106) + assert(result2(8) ~== 0.13905199) + assert(result2(9) ~== 0.081146255) + } + + test("aggregations for ftvec.text") { + import hiveContext.implicits._ + val testDf = Seq((1, "abc def hi jk l"), (1, "def jk")).toDF("key", "text") + val rows = testDf.groupBy($"key").tf("text").collect + assert(rows.length === 1) + val result = rows(0).getAs[Map[String, Float]](1) + assert(result.size === 2) + assert(result("def jk") ~== 0.5f) + assert(result("abc def hi jk l") ~== 0.5f) + } + + test("aggregations for tools.array") { + import hiveContext.implicits._ + + val testDf = Seq((1, 1 :: 3 :: Nil), (1, 3 :: 5 :: Nil)).toDF("key", "ar") + val rows1 = testDf.groupBy($"key").array_avg("ar").collect + assert(rows1.length === 1) + val result1 = rows1(0).getSeq[Float](1) + assert(result1.length === 2) + assert(result1(0) ~== 2.0f) + assert(result1(1) ~== 4.0f) + + val rows2 = testDf.groupBy($"key").array_sum("ar").collect + assert(rows2.length === 1) + val result2 = rows2(0).getSeq[Double](1) + assert(result2.length === 2) + assert(result2(0) ~== 4.0) + assert(result2(1) ~== 8.0) + } + + test("aggregations for tools.bits") { + import hiveContext.implicits._ + val testDf = Seq((1, 1), (1, 7)).toDF("key", "x") + val rows = testDf.groupBy($"key").bits_collect("x").collect + assert(rows.length === 1) + val result = rows(0).getSeq[Int](1) + assert(result === Seq(130)) + } + + test("aggregations for tools.list") { + import hiveContext.implicits._ + val testDf = Seq((1, 3), (1, 1), (1, 2)).toDF("key", "x") + val rows = testDf.groupBy($"key").to_ordered_list("x").collect + assert(rows.length === 1) + val result = rows(0).getSeq[Int](1) + assert(result === Seq(1, 2, 3)) + } + + test("aggregations for tools.map") { + import hiveContext.implicits._ + val testDf = Seq((1, 1, "a"), (1, 2, "b"), (1, 3, "c")).toDF("key", "k", "v") + val rows = testDf.groupBy($"key").to_map("k", "v").collect + assert(rows.length === 1) + val result = rows(0).getMap[Int, String](1) + assert(result === Map(1 -> "a", 2 -> "b", 3 -> "c")) + } + + test("aggregations for tools.math") { + import hiveContext.implicits._ + val testDf = Seq( + (1, Seq(1, 2, 3, 4), Seq(5, 6, 7, 8)), + (1, Seq(9, 10, 11, 12), Seq(13, 14, 15, 16)) + ).toDF("key", "mtx1", "mtx2") + val rows = testDf.groupBy($"key").transpose_and_dot("mtx1", "mtx2").collect + assert(rows.length === 1) + val result = rows(0).getSeq[Int](1) + assert(result === Seq( + Seq(122.0, 132.0, 142.0, 152.0), + Seq(140.0, 152.0, 164.0, 176.0), + Seq(158.0, 172.0, 186.0, 200.0), + Seq(176.0, 192.0, 208.0, 224.0)) + ) + } + + test("aggregations for ftvec.trans") { + import hiveContext.implicits._ + + val df0 = Seq((1, "cat", "mammal", 9), (1, "dog", "mammal", 10), (1, "human", "mammal", 10), + (1, "seahawk", "bird", 101), (1, "wasp", "insect", 3), (1, "wasp", "insect", 9), + (1, "cat", "mammal", 101), (1, "dog", "mammal", 1), (1, "human", "mammal", 9)) + .toDF("col0", "cat1", "cat2", "cat3") + val row00 = df0.groupBy($"col0").onehot_encoding("cat1") + val row01 = df0.groupBy($"col0").onehot_encoding("cat1", "cat2", "cat3") + + val result000 = row00.collect()(0).getAs[Row](1).getAs[Map[String, Int]](0) + val result01 = row01.collect()(0).getAs[Row](1) + val result010 = result01.getAs[Map[String, Int]](0) + val result011 = result01.getAs[Map[String, Int]](1) + val result012 = result01.getAs[Map[String, Int]](2) + + assert(result000.keySet === Set("seahawk", "cat", "human", "wasp", "dog")) + assert(result000.values.toSet === Set(1, 2, 3, 4, 5)) + assert(result010.keySet === Set("seahawk", "cat", "human", "wasp", "dog")) + assert(result010.values.toSet === Set(1, 2, 3, 4, 5)) + assert(result011.keySet === Set("bird", "insect", "mammal")) + assert(result011.values.toSet === Set(6, 7, 8)) + assert(result012.keySet === Set(1, 3, 9, 10, 101)) + assert(result012.values.toSet === Set(9, 10, 11, 12, 13)) + } + + test("aggregations for ftvec.selection") { + import hiveContext.implicits._ + + // see also hivemall.ftvec.selection.SignalNoiseRatioUDAFTest + // binary class + // +-----------------+-------+ + // | features | class | + // +-----------------+-------+ + // | 5.1,3.5,1.4,0.2 | 0 | + // | 4.9,3.0,1.4,0.2 | 0 | + // | 4.7,3.2,1.3,0.2 | 0 | + // | 7.0,3.2,4.7,1.4 | 1 | + // | 6.4,3.2,4.5,1.5 | 1 | + // | 6.9,3.1,4.9,1.5 | 1 | + // +-----------------+-------+ + val df0 = Seq( + (1, Seq(5.1, 3.5, 1.4, 0.2), Seq(1, 0)), (1, Seq(4.9, 3.0, 1.4, 0.2), Seq(1, 0)), + (1, Seq(4.7, 3.2, 1.3, 0.2), Seq(1, 0)), (1, Seq(7.0, 3.2, 4.7, 1.4), Seq(0, 1)), + (1, Seq(6.4, 3.2, 4.5, 1.5), Seq(0, 1)), (1, Seq(6.9, 3.1, 4.9, 1.5), Seq(0, 1))) + .toDF("c0", "arg0", "arg1") + val row0 = df0.groupBy($"c0").snr("arg0", "arg1").collect + (row0(0).getAs[Seq[Double]](1), Seq(4.38425236, 0.26390002, 15.83984511, 26.87005769)) + .zipped + .foreach((actual, expected) => assert(actual ~== expected)) + + // multiple class + // +-----------------+-------+ + // | features | class | + // +-----------------+-------+ + // | 5.1,3.5,1.4,0.2 | 0 | + // | 4.9,3.0,1.4,0.2 | 0 | + // | 7.0,3.2,4.7,1.4 | 1 | + // | 6.4,3.2,4.5,1.5 | 1 | + // | 6.3,3.3,6.0,2.5 | 2 | + // | 5.8,2.7,5.1,1.9 | 2 | + // +-----------------+-------+ + val df1 = Seq( + (1, Seq(5.1, 3.5, 1.4, 0.2), Seq(1, 0, 0)), (1, Seq(4.9, 3.0, 1.4, 0.2), Seq(1, 0, 0)), + (1, Seq(7.0, 3.2, 4.7, 1.4), Seq(0, 1, 0)), (1, Seq(6.4, 3.2, 4.5, 1.5), Seq(0, 1, 0)), + (1, Seq(6.3, 3.3, 6.0, 2.5), Seq(0, 0, 1)), (1, Seq(5.8, 2.7, 5.1, 1.9), Seq(0, 0, 1))) + .toDF("c0", "arg0", "arg1") + val row1 = df1.groupBy($"c0").snr("arg0", "arg1").collect + (row1(0).getAs[Seq[Double]](1), Seq(8.43181818, 1.32121212, 42.94949495, 33.80952381)) + .zipped + .foreach((actual, expected) => assert(actual ~== expected)) + } + + test("aggregations for tools.matrix") { + import hiveContext.implicits._ + + // | 1 2 3 |T | 5 6 7 | + // | 3 4 5 | * | 7 8 9 | + val df0 = Seq((1, Seq(1, 2, 3), Seq(5, 6, 7)), (1, Seq(3, 4, 5), Seq(7, 8, 9))) + .toDF("c0", "arg0", "arg1") + + checkAnswer(df0.groupBy($"c0").transpose_and_dot("arg0", "arg1"), + Seq(Row(1, Seq(Seq(26.0, 30.0, 34.0), Seq(38.0, 44.0, 50.0), Seq(50.0, 58.0, 66.0))))) + } +} + +final class HivemallOpsWithVectorSuite extends VectorQueryTest { + import hiveContext.implicits._ + + test("to_hivemall_features") { + checkAnswer( + mllibTrainDf.select(to_hivemall_features($"features")), + Seq( + Row(Seq("0:1.0", "2:2.0", "4:3.0")), + Row(Seq("0:1.0", "3:1.5", "4:2.1", "6:1.2")), + Row(Seq("0:1.1", "3:1.0", "4:2.3", "6:1.0")), + Row(Seq("1:4.0", "3:5.0", "5:6.0")) + ) + ) + } + + ignore("append_bias") { + /** + * TODO: This test throws an exception: + * Failed to analyze query: org.apache.spark.sql.AnalysisException: cannot resolve + * 'UDF(UDF(features))' due to data type mismatch: argument 1 requires vector type, + * however, 'UDF(features)' is of vector type.; line 2 pos 8 + */ + checkAnswer( + mllibTrainDf.select(to_hivemall_features(append_bias($"features"))), + Seq( + Row(Seq("0:1.0", "0:1.0", "2:2.0", "4:3.0")), + Row(Seq("0:1.0", "0:1.0", "3:1.5", "4:2.1", "6:1.2")), + Row(Seq("0:1.0", "0:1.1", "3:1.0", "4:2.3", "6:1.0")), + Row(Seq("0:1.0", "1:4.0", "3:5.0", "5:6.0")) + ) + ) + } + + test("explode_vector") { + checkAnswer( + mllibTrainDf.explode_vector($"features").select($"feature", $"weight"), + Seq( + Row("0", 1.0), Row("0", 1.0), Row("0", 1.1), + Row("1", 4.0), + Row("2", 2.0), + Row("3", 1.0), Row("3", 1.5), Row("3", 5.0), + Row("4", 2.1), Row("4", 2.3), Row("4", 3.0), + Row("5", 6.0), + Row("6", 1.0), Row("6", 1.2) + ) + ) + } + + test("train_logistic_regr") { + checkAnswer( + mllibTrainDf.train_logistic_regr($"features", $"label") + .groupBy("feature").agg("weight" -> "avg") + .select($"feature"), + Seq(0, 1, 2, 3, 4, 5, 6).map(v => Row(s"$v")) + ) + } +}
![http://tile.openstreetmap.org/3/4/3.png"](https://tile.openstreetmap.org/3/4/3.png"){kind=link}