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Posted to commits@hivemall.apache.org by ya...@apache.org on 2017/01/31 14:10:51 UTC
[1/4] incubator-hivemall git commit: Close #35,
#23: [HIVEMALL-31][SPARK] Support Spark-v2.1.0 [Forced Update!]
Repository: incubator-hivemall
Updated Branches:
refs/heads/master d3afb11ba -> c837e51ad (forced update)
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/VectorQueryTest.scala
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diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/VectorQueryTest.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/VectorQueryTest.scala
new file mode 100644
index 0000000..9c23687
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/VectorQueryTest.scala
@@ -0,0 +1,88 @@
+/*
+ * 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.test
+
+import java.io.File
+import java.nio.charset.StandardCharsets
+
+import com.google.common.io.Files
+
+import org.apache.spark.sql.{DataFrame, QueryTest}
+import org.apache.spark.sql.hive.HivemallOps._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.util.Utils
+
+/**
+ * Base class for tests with SparkSQL VectorUDT data.
+ */
+abstract class VectorQueryTest extends QueryTest with SQLTestUtils with TestHiveSingleton {
+
+ private var trainDir: File = _
+ private var testDir: File = _
+
+ // A `libsvm` schema is (Double, ml.linalg.Vector)
+ protected var mllibTrainDf: DataFrame = _
+ protected var mllibTestDf: DataFrame = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ val trainLines =
+ """
+ |1 1:1.0 3:2.0 5:3.0
+ |0 2:4.0 4:5.0 6:6.0
+ |1 1:1.1 4:1.0 5:2.3 7:1.0
+ |1 1:1.0 4:1.5 5:2.1 7:1.2
+ """.stripMargin
+ trainDir = Utils.createTempDir()
+ Files.write(trainLines, new File(trainDir, "train-00000"), StandardCharsets.UTF_8)
+ val testLines =
+ """
+ |1 1:1.3 3:2.1 5:2.8
+ |0 2:3.9 4:5.3 6:8.0
+ """.stripMargin
+ testDir = Utils.createTempDir()
+ Files.write(testLines, new File(testDir, "test-00000"), StandardCharsets.UTF_8)
+
+ mllibTrainDf = spark.read.format("libsvm").load(trainDir.getAbsolutePath)
+ // Must be cached because rowid() is deterministic
+ mllibTestDf = spark.read.format("libsvm").load(testDir.getAbsolutePath)
+ .withColumn("rowid", rowid()).cache
+ }
+
+ override def afterAll(): Unit = {
+ try {
+ Utils.deleteRecursively(trainDir)
+ Utils.deleteRecursively(testDir)
+ } finally {
+ super.afterAll()
+ }
+ }
+
+ protected def withTempModelDir(f: String => Unit): Unit = {
+ var tempDir: File = null
+ try {
+ tempDir = Utils.createTempDir()
+ f(tempDir.getAbsolutePath + "/xgboost_models")
+ } catch {
+ case e: Throwable => fail(s"Unexpected exception detected: ${e}")
+ } finally {
+ Utils.deleteRecursively(tempDir)
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/streaming/HivemallOpsWithFeatureSuite.scala
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diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/streaming/HivemallOpsWithFeatureSuite.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/streaming/HivemallOpsWithFeatureSuite.scala
new file mode 100644
index 0000000..b15c77c
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/streaming/HivemallOpsWithFeatureSuite.scala
@@ -0,0 +1,154 @@
+/*
+ * 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.hive.HivemallOps._
+import org.apache.spark.sql.hive.test.HivemallFeatureQueryTest
+import org.apache.spark.streaming.HivemallStreamingOps._
+import org.apache.spark.streaming.dstream.InputDStream
+import org.apache.spark.streaming.scheduler.StreamInputInfo
+
+/**
+ * This is an input stream just for tests.
+ */
+private[this] class TestInputStream[T: ClassTag](
+ ssc: StreamingContext,
+ input: Seq[Seq[T]],
+ numPartitions: Int) extends InputDStream[T](ssc) {
+
+ override def start() {}
+
+ override def stop() {}
+
+ override def compute(validTime: Time): Option[RDD[T]] = {
+ logInfo("Computing RDD for time " + validTime)
+ val index = ((validTime - zeroTime) / slideDuration - 1).toInt
+ val selectedInput = if (index < input.size) input(index) else Seq[T]()
+
+ // lets us test cases where RDDs are not created
+ if (selectedInput == null) {
+ return None
+ }
+
+ // Report the input data's information to InputInfoTracker for testing
+ val inputInfo = StreamInputInfo(id, selectedInput.length.toLong)
+ ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
+
+ val rdd = ssc.sc.makeRDD(selectedInput, numPartitions)
+ logInfo("Created RDD " + rdd.id + " with " + selectedInput)
+ Some(rdd)
+ }
+}
+
+final class HivemallOpsWithFeatureSuite extends HivemallFeatureQueryTest {
+
+ // This implicit value used in `HivemallStreamingOps`
+ implicit val sqlCtx = hiveContext
+
+ /**
+ * Run a block of code with the given StreamingContext.
+ * This method do not stop a given SparkContext because other tests share the context.
+ */
+ private def withStreamingContext[R](ssc: StreamingContext)(block: StreamingContext => R): Unit = {
+ try {
+ block(ssc)
+ ssc.start()
+ ssc.awaitTerminationOrTimeout(10 * 1000) // 10s wait
+ } finally {
+ try {
+ ssc.stop(stopSparkContext = false)
+ } catch {
+ case e: Exception => logError("Error stopping StreamingContext", e)
+ }
+ }
+ }
+
+ // scalastyle:off line.size.limit
+
+ /**
+ * This test below fails sometimes (too flaky), so we temporarily ignore it.
+ * The stacktrace of this failure is:
+ *
+ * HivemallOpsWithFeatureSuite:
+ * Exception in thread "broadcast-exchange-60" java.lang.OutOfMemoryError: Java heap space
+ * at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57)
+ * at java.nio.ByteBuffer.allocate(ByteBuffer.java:331)
+ * at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$4.apply(TorrentBroadcast.scala:231)
+ * at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$4.apply(TorrentBroadcast.scala:231)
+ * at org.apache.spark.util.io.ChunkedByteBufferOutputStream.allocateNewChunkIfNeeded(ChunkedByteBufferOutputStream.scala:78)
+ * at org.apache.spark.util.io.ChunkedByteBufferOutputStream.write(ChunkedByteBufferOutputStream.scala:65)
+ * at net.jpountz.lz4.LZ4BlockOutputStream.flushBufferedData(LZ4BlockOutputStream.java:205)
+ * at net.jpountz.lz4.LZ4BlockOutputStream.finish(LZ4BlockOutputStream.java:235)
+ * at net.jpountz.lz4.LZ4BlockOutputStream.close(LZ4BlockOutputStream.java:175)
+ * at java.io.ObjectOutputStream$BlockDataOutputStream.close(ObjectOutputStream.java:1827)
+ * at java.io.ObjectOutputStream.close(ObjectOutputStream.java:741)
+ * at org.apache.spark.serializer.JavaSerializationStream.close(JavaSerializer.scala:57)
+ * at org.apache.spark.broadcast.TorrentBroadcast$$anonfun$blockifyObject$1.apply$mcV$sp(TorrentBroadcast.scala:238)
+ * at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1296)
+ * at org.apache.spark.broadcast.TorrentBroadcast$.blockifyObject(TorrentBroadcast.scala:237)
+ * at org.apache.spark.broadcast.TorrentBroadcast.writeBlocks(TorrentBroadcast.scala:107)
+ * at org.apache.spark.broadcast.TorrentBroadcast.<init>(TorrentBroadcast.scala:86)
+ * at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:34)
+ * ...
+ */
+
+ // scalastyle:on line.size.limit
+
+ ignore("streaming") {
+ import sqlCtx.implicits._
+
+ // We assume we build a model in advance
+ val testModel = Seq(
+ ("0", 0.3f), ("1", 0.1f), ("2", 0.6f), ("3", 0.2f)
+ ).toDF("feature", "weight")
+
+ withStreamingContext(new StreamingContext(sqlCtx.sparkContext, Milliseconds(100))) { ssc =>
+ val inputData = Seq(
+ Seq(HivemallLabeledPoint(features = "1:0.6" :: "2:0.1" :: Nil)),
+ Seq(HivemallLabeledPoint(features = "2:0.9" :: Nil)),
+ Seq(HivemallLabeledPoint(features = "1:0.2" :: Nil)),
+ Seq(HivemallLabeledPoint(features = "2:0.1" :: Nil)),
+ Seq(HivemallLabeledPoint(features = "0:0.6" :: "2:0.4" :: Nil))
+ )
+
+ val inputStream = new TestInputStream[HivemallLabeledPoint](ssc, inputData, 1)
+
+ // Apply predictions on input streams
+ val prediction = inputStream.predict { streamDf =>
+ val df = streamDf.select(rowid(), $"features").explode_array($"features")
+ val testDf = df.select(
+ // TODO: `$"feature"` throws AnalysisException, why?
+ $"rowid", extract_feature(df("feature")), extract_weight(df("feature"))
+ )
+ testDf.join(testModel, testDf("feature") === testModel("feature"), "LEFT_OUTER")
+ .select($"rowid", ($"weight" * $"value").as("value"))
+ .groupBy("rowid").sum("value")
+ .toDF("rowid", "value")
+ .select($"rowid", sigmoid($"value"))
+ }
+
+ // Dummy output stream
+ prediction.foreachRDD(_ => {})
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/test/TestUtils.scala
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diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/test/TestUtils.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/test/TestUtils.scala
new file mode 100644
index 0000000..8a2a385
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/test/TestUtils.scala
@@ -0,0 +1,64 @@
+/*
+ * 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.test
+
+import scala.reflect.runtime.{universe => ru}
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.DataFrame
+
+object TestUtils extends Logging {
+
+ // Do benchmark if INFO-log enabled
+ def benchmark(benchName: String)(testFunc: => Unit): Unit = {
+ if (log.isDebugEnabled) {
+ testFunc
+ }
+ }
+
+ def expectResult(res: Boolean, errMsg: String): Unit = if (res) {
+ logWarning(errMsg)
+ }
+
+ def invokeFunc(cls: Any, func: String, args: Any*): DataFrame = try {
+ // Invoke a function with the given name via reflection
+ val im = scala.reflect.runtime.currentMirror.reflect(cls)
+ val mSym = im.symbol.typeSignature.member(ru.newTermName(func)).asMethod
+ im.reflectMethod(mSym).apply(args: _*)
+ .asInstanceOf[DataFrame]
+ } catch {
+ case e: Exception =>
+ assert(false, s"Invoking ${func} failed because: ${e.getMessage}")
+ null // Not executed
+ }
+}
+
+// TODO: Any same function in o.a.spark.*?
+class TestFPWrapper(d: Double) {
+
+ // Check an equality between Double/Float values
+ def ~==(d: Double): Boolean = Math.abs(this.d - d) < 0.001
+}
+
+object TestFPWrapper {
+
+ @inline implicit def toTestFPWrapper(d: Double): TestFPWrapper = {
+ new TestFPWrapper(d)
+ }
+}
[2/4] incubator-hivemall git commit: Close #35,
#23: [HIVEMALL-31][SPARK] Support Spark-v2.1.0
Posted by ya...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala
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diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala
new file mode 100644
index 0000000..81390e0
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HiveUdfSuite.scala
@@ -0,0 +1,160 @@
+/*
+ * 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("0.4.2-rc.2")
+ )
+
+ // 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/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala
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diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala
new file mode 100644
index 0000000..f65b451
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/HivemallOpsSuite.scala
@@ -0,0 +1,784 @@
+/*
+ * 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.test.VectorQueryTest
+import org.apache.spark.sql.types._
+import org.apache.spark.test.TestFPWrapper._
+import org.apache.spark.test.TestUtils
+
+final 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 exactly
+ assert(df.sort($"time".asc).select(sst($"x", lit("-th 0.005"))).count === 1000)
+ }
+
+ test("knn.similarity") {
+ val df1 = DummyInputData.select(cosine_sim(lit2(Seq(1, 2, 3, 4)), lit2(Seq(3, 4, 5, 6))))
+ assert(df1.collect.apply(0).getFloat(0) ~== 0.500f)
+
+ val df2 = DummyInputData.select(jaccard(lit(5), lit(6)))
+ assert(df2.collect.apply(0).getFloat(0) ~== 0.96875f)
+
+ val df3 = DummyInputData.select(angular_similarity(lit2(Seq(1, 2, 3)), lit2(Seq(4, 5, 6))))
+ assert(df3.collect.apply(0).getFloat(0) ~== 0.500f)
+
+ val df4 = DummyInputData.select(euclid_similarity(lit2(Seq(5, 3, 1)), lit2(Seq(2, 8, 3))))
+ assert(df4.collect.apply(0).getFloat(0) ~== 0.33333334f)
+
+ val df5 = DummyInputData.select(distance2similarity(lit(1.0)))
+ assert(df5.collect.apply(0).getFloat(0) ~== 0.5f)
+ }
+
+ test("knn.distance") {
+ val df1 = DummyInputData.select(hamming_distance(lit(1), lit(3)))
+ checkAnswer(df1, Row(1) :: Nil)
+
+ val df2 = DummyInputData.select(popcnt(lit(1)))
+ checkAnswer(df2, Row(1) :: Nil)
+
+ val df3 = DummyInputData.select(kld(lit(0.1), lit(0.5), lit(0.2), lit(0.5)))
+ assert(df3.collect.apply(0).getDouble(0) ~== 0.01)
+
+ val df4 = DummyInputData.select(
+ euclid_distance(lit2(Seq("0.1", "0.5")), lit2(Seq("0.2", "0.5"))))
+ assert(df4.collect.apply(0).getFloat(0) ~== 1.4142135f)
+
+ val df5 = DummyInputData.select(
+ cosine_distance(lit2(Seq("0.8", "0.3")), lit2(Seq("0.4", "0.6"))))
+ assert(df5.collect.apply(0).getFloat(0) ~== 1.0f)
+
+ val df6 = DummyInputData.select(
+ angular_distance(lit2(Seq("0.1", "0.1")), lit2(Seq("0.3", "0.8"))))
+ assert(df6.collect.apply(0).getFloat(0) ~== 0.50f)
+
+ val df7 = DummyInputData.select(
+ manhattan_distance(lit2(Seq("0.7", "0.8")), lit2(Seq("0.5", "0.6"))))
+ assert(df7.collect.apply(0).getFloat(0) ~== 4.0f)
+
+ val df8 = DummyInputData.select(
+ minkowski_distance(lit2(Seq("0.1", "0.2")), lit2(Seq("0.2", "0.2")), lit2(1.0)))
+ assert(df8.collect.apply(0).getFloat(0) ~== 2.0f)
+ }
+
+ test("knn.lsh") {
+ import hiveContext.implicits._
+ assert(IntList2Data.minhash(lit(1), $"target").count() > 0)
+
+ assert(DummyInputData.select(bbit_minhash(lit2(Seq("1:0.1", "2:0.5")), lit(false))).count
+ == DummyInputData.count)
+ assert(DummyInputData.select(minhashes(lit2(Seq("1:0.1", "2:0.5")), lit(false))).count
+ == DummyInputData.count)
+ }
+
+ 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") :: Nil)
+ }
+
+ test("ftvec - extract_weight") {
+ val df = DummyInputData.select(extract_weight(lit("3:0.1")))
+ assert(df.collect.apply(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") {
+ assert(DummyInputData.select(mhash(lit("test"))).count == DummyInputData.count)
+ assert(DummyInputData.select(org.apache.spark.sql.hive.HivemallOps.sha1(lit("test"))).count ==
+ DummyInputData.count)
+ // TODO: The tests below failed because:
+ // org.apache.spark.sql.AnalysisException: List type in java is unsupported because JVM type
+ // erasure makes spark fail to catch a component type in List<>;
+ //
+ // assert(DummyInputData.select(array_hash_values(lit2(Seq("aaa", "bbb")))).count
+ // == DummyInputData.count)
+ // assert(DummyInputData.select(
+ // prefixed_hash_values(lit2(Seq("ccc", "ddd")), lit("prefix"))).count
+ // == DummyInputData.count)
+ }
+
+ test("ftvec.scaling") {
+ val df1 = TinyTrainData.select(rescale(lit(2.0f), lit(1.0), lit(5.0)))
+ assert(df1.collect.apply(0).getFloat(0) === 0.25f)
+ val df2 = TinyTrainData.select(zscore(lit(1.0f), lit(0.5), lit(0.5)))
+ assert(df2.collect.apply(0).getFloat(0) === 1.0f)
+ val df3 = TinyTrainData.select(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 result = df.select(chi2(df("arg0"), df("arg1"))).collect
+ assert(result.length == 1)
+ val chi2Val = result.head.getAs[Row](0).getAs[Seq[Double]](0)
+ val pVal = result.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)
+ }
+
+ ignore("ftvec.conv") {
+ import hiveContext.implicits._
+
+ val df1 = Seq((0.0, "1:0.1" :: "3:0.3" :: Nil), (1, 0, "2:0.2" :: Nil)).toDF("a", "b")
+ checkAnswer(
+ df1.select(to_dense_features(df1("b"), lit(3))),
+ Row(Array(0.1f, 0.0f, 0.3f)) :: Row(Array(0.0f, 0.2f, 0.0f)) :: Nil
+ )
+ val df2 = Seq((0.1, 0.2, 0.3), (0.2, 0.5, 0.4)).toDF("a", "b", "c")
+ checkAnswer(
+ df2.select(to_sparse_features(df2("a"), df2("b"), df2("c"))),
+ Row(Seq("1:0.1", "2:0.2", "3:0.3")) :: Row(Seq("1:0.2", "2:0.5", "3:0.4")) :: Nil
+ )
+ }
+
+ test("ftvec.trans") {
+ import hiveContext.implicits._
+
+ 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((0.1f, 0.2f), (0.5f, 0.3f)).toDF("a", "b")
+ checkAnswer(
+ df2.select(vectorize_features(lit2(Seq("a", "b")), df2("a"), df2("b"))),
+ Row(Seq("a:0.1", "b:0.2")) :: Row(Seq("a:0.5", "b:0.3")) :: Nil
+ )
+
+ val df3 = Seq(("c11", "c12"), ("c21", "c22")).toDF("a", "b")
+ checkAnswer(
+ df3.select(categorical_features(lit2(Seq("a", "b")), df3("a"), df3("b"))),
+ Row(Seq("a#c11", "b#c12")) :: Row(Seq("a#c21", "b#c22")) :: Nil
+ )
+
+ val df4 = Seq((0.1, 0.2, 0.3), (0.2, 0.5, 0.4)).toDF("a", "b", "c")
+ checkAnswer(
+ df4.select(indexed_features(df4("a"), df4("b"), df4("c"))),
+ Row(Seq("1:0.1", "2:0.2", "3:0.3")) :: Row(Seq("1:0.2", "2:0.5", "3:0.4")) :: Nil
+ )
+
+ val df5 = Seq(("xxx", "yyy", 0), ("zzz", "yyy", 1)).toDF("a", "b", "c").coalesce(1)
+ checkAnswer(
+ df5.quantified_features(lit(true), df5("a"), df5("b"), df5("c")),
+ Row(Seq(0.0, 0.0, 0.0)) :: Row(Seq(1.0, 0.0, 1.0)) :: Nil
+ )
+
+ val df6 = Seq((0.1, 0.2), (0.5, 0.3)).toDF("a", "b")
+ checkAnswer(
+ df6.select(quantitative_features(lit2(Seq("a", "b")), df6("a"), df6("b"))),
+ Row(Seq("a:0.1", "b:0.2")) :: Row(Seq("a:0.5", "b:0.3")) :: Nil
+ )
+ }
+
+ test("misc - hivemall_version") {
+ checkAnswer(DummyInputData.select(hivemall_version()), Row("0.4.2-rc.2"))
+ }
+
+ test("misc - rowid") {
+ assert(DummyInputData.select(rowid()).distinct.count == DummyInputData.count)
+ }
+
+ test("misc - each_top_k") {
+ import hiveContext.implicits._
+ 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")
+
+ // Compute top-1 rows for each group
+ checkAnswer(
+ testDf.each_top_k(lit(1), $"key", $"score"),
+ Row(1, "a", "3", 0.8, Array(2, 5)) ::
+ Row(1, "b", "4", 0.3, Array(2)) ::
+ Row(1, "c", "6", 0.3, Array(1, 3)) ::
+ Nil
+ )
+
+ // Compute reverse top-1 rows for each group
+ checkAnswer(
+ testDf.each_top_k(lit(-1), $"key", $"score"),
+ Row(1, "a", "1", 0.5, Array(0, 1, 2)) ::
+ Row(1, "b", "5", 0.1, Array(3)) ::
+ Row(1, "c", "6", 0.3, Array(1, 3)) ::
+ Nil
+ )
+
+ // Check if some exceptions thrown in case of some conditions
+ assert(intercept[AnalysisException] { testDf.each_top_k(lit(0.1), $"key", $"score") }
+ .getMessage contains "`k` must be integer, however")
+ assert(intercept[AnalysisException] { testDf.each_top_k(lit(1), $"key", $"data") }
+ .getMessage contains "must have a comparable type")
+ }
+
+ /**
+ * 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_regr($"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("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("misc - sigmoid") {
+ import hiveContext.implicits._
+ assert(DummyInputData.select(sigmoid($"c0")).collect.apply(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_adadelta",
+ "train_adagrad",
+ "train_arow_regr",
+ "train_arowe_regr",
+ "train_arowe2_regr",
+ "train_logregr",
+ "train_pa1_regr",
+ "train_pa1a_regr",
+ "train_pa2_regr",
+ "train_pa2a_regr"
+ ).map { func =>
+ TestUtils.invokeFunc(new HivemallOps(TinyTrainData), func, Seq($"features", $"label"))
+ .foreach(_ => {}) // Just call it
+ }
+ }
+
+ test("invoke classifier functions") {
+ import hiveContext.implicits._
+ Seq(
+ "train_perceptron",
+ "train_pa",
+ "train_pa1",
+ "train_pa2",
+ "train_cw",
+ "train_arow",
+ "train_arowh",
+ "train_scw",
+ "train_scw2",
+ "train_adagrad_rda"
+ ).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_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_regr",
+ "train_randomforest_classifier"
+ ).map { func =>
+ TestUtils.invokeFunc(new HivemallOps(testDf.coalesce(1)), func, Seq($"features", $"label"))
+ .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",
+ "train_adagrad",
+ "train_arow_regr",
+ "train_arowe_regr",
+ "train_arowe2_regr",
+ "train_logregr",
+ "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("user-defined aggregators for ensembles") {
+ import hiveContext.implicits._
+
+ val df1 = Seq((1, 0.1f), (1, 0.2f), (2, 0.1f)).toDF("c0", "c1")
+ val row1 = df1.groupBy($"c0").voted_avg("c1").collect
+ assert(row1(0).getDouble(1) ~== 0.15)
+ assert(row1(1).getDouble(1) ~== 0.10)
+
+ val df3 = Seq((1, 0.2f), (1, 0.8f), (2, 0.3f)).toDF("c0", "c1")
+ val row3 = df3.groupBy($"c0").weight_voted_avg("c1").collect
+ assert(row3(0).getDouble(1) ~== 0.50)
+ assert(row3(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 row5 = df5.groupBy($"c0").argmin_kld("c1", "c2").collect
+ assert(row5(0).getFloat(1) ~== 0.266666666)
+ assert(row5(1).getFloat(1) ~== 0.80)
+
+ val df6 = Seq((1, "id-0", 0.2f), (1, "id-1", 0.4f), (1, "id-2", 0.1f)).toDF("c0", "c1", "c2")
+ val row6 = df6.groupBy($"c0").max_label("c2", "c1").collect
+ assert(row6(0).getString(1) == "id-1")
+
+ val df7 = Seq((1, "id-0", 0.5f), (1, "id-1", 0.1f), (1, "id-2", 0.2f)).toDF("c0", "c1", "c2")
+ val row7 = df7.groupBy($"c0").maxrow("c2", "c1").toDF("c0", "c1").select($"c1.col1").collect
+ assert(row7(0).getString(0) == "id-0")
+
+ val df8 = Seq((1, 1), (1, 2), (2, 1), (1, 5)).toDF("c0", "c1")
+ val row8 = df8.groupBy($"c0").rf_ensemble("c1").toDF("c0", "c1")
+ .select("c1.probability").collect
+ assert(row8(0).getDouble(0) ~== 0.3333333333)
+ assert(row8(1).getDouble(0) ~== 1.0)
+ }
+
+ test("user-defined aggregators for evaluation") {
+ import hiveContext.implicits._
+
+ val df1 = Seq((1, 1.0f, 0.5f), (1, 0.3f, 0.5f), (1, 0.1f, 0.2f)).toDF("c0", "c1", "c2")
+ val row1 = df1.groupBy($"c0").mae("c1", "c2").collect
+ assert(row1(0).getDouble(1) ~== 0.26666666)
+
+ val df2 = Seq((1, 0.3f, 0.8f), (1, 1.2f, 2.0f), (1, 0.2f, 0.3f)).toDF("c0", "c1", "c2")
+ val row2 = df2.groupBy($"c0").mse("c1", "c2").collect
+ assert(row2(0).getDouble(1) ~== 0.29999999)
+
+ val df3 = Seq((1, 0.3f, 0.8f), (1, 1.2f, 2.0f), (1, 0.2f, 0.3f)).toDF("c0", "c1", "c2")
+ val row3 = df3.groupBy($"c0").rmse("c1", "c2").collect
+ assert(row3(0).getDouble(1) ~== 0.54772253)
+
+ val df4 = Seq((1, Array(1, 2), Array(2, 3)), (1, Array(3, 8), Array(5, 4))).toDF
+ .toDF("c0", "c1", "c2")
+ val row4 = df4.groupBy($"c0").f1score("c1", "c2").collect
+ assert(row4(0).getDouble(1) ~== 0.25)
+ }
+
+ test("user-defined aggregators 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("user-defined aggregators 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("user-defined aggregators 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"))
+ )
+ )
+ }
+
+ test("append_bias") {
+ checkAnswer(
+ mllibTrainDf.select(to_hivemall_features(append_bias($"features"))),
+ 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"))
+ )
+ )
+ }
+
+ 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_logregr") {
+ checkAnswer(
+ mllibTrainDf.train_logregr($"features", $"label")
+ .groupBy("feature").agg("weight" -> "avg")
+ .select($"feature"),
+ Seq(0, 1, 2, 3, 4, 5, 6).map(v => Row(s"$v"))
+ )
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/ModelMixingSuite.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/ModelMixingSuite.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/ModelMixingSuite.scala
new file mode 100644
index 0000000..06a4dc0
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/ModelMixingSuite.scala
@@ -0,0 +1,285 @@
+/*
+ * 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 java.io.{BufferedInputStream, BufferedReader, InputStream, InputStreamReader}
+import java.net.URL
+import java.util.UUID
+import java.util.concurrent.{Executors, ExecutorService}
+
+import hivemall.mix.server.MixServer
+import hivemall.utils.lang.CommandLineUtils
+import hivemall.utils.net.NetUtils
+import org.apache.commons.cli.Options
+import org.apache.commons.compress.compressors.CompressorStreamFactory
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.feature.HivemallLabeledPoint
+import org.apache.spark.sql.{Column, DataFrame, 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.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.test.TestUtils
+
+final class ModelMixingSuite extends SparkFunSuite with BeforeAndAfter {
+
+ // Load A9a training and test data
+ val a9aLineParser = (line: String) => {
+ val elements = line.split(" ")
+ val (label, features) = (elements.head, elements.tail)
+ HivemallLabeledPoint(if (label == "+1") 1.0f else 0.0f, features)
+ }
+
+ lazy val trainA9aData: DataFrame =
+ getDataFromURI(
+ new URL("http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a").openStream,
+ a9aLineParser)
+
+ lazy val testA9aData: DataFrame =
+ getDataFromURI(
+ new URL("http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a.t").openStream,
+ a9aLineParser)
+
+ // Load A9a training and test data
+ val kdd2010aLineParser = (line: String) => {
+ val elements = line.split(" ")
+ val (label, features) = (elements.head, elements.tail)
+ HivemallLabeledPoint(if (label == "1") 1.0f else 0.0f, features)
+ }
+
+ lazy val trainKdd2010aData: DataFrame =
+ getDataFromURI(
+ new CompressorStreamFactory().createCompressorInputStream(
+ new BufferedInputStream(
+ new URL("http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/kdda.bz2")
+ .openStream
+ )
+ ),
+ kdd2010aLineParser,
+ 8)
+
+ lazy val testKdd2010aData: DataFrame =
+ getDataFromURI(
+ new CompressorStreamFactory().createCompressorInputStream(
+ new BufferedInputStream(
+ new URL("http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/kdda.t.bz2")
+ .openStream
+ )
+ ),
+ kdd2010aLineParser,
+ 8)
+
+ // Placeholder for a mix server
+ var mixServExec: ExecutorService = _
+ var assignedPort: Int = _
+
+ private def getDataFromURI(
+ in: InputStream, lineParseFunc: String => HivemallLabeledPoint, numPart: Int = 2)
+ : DataFrame = {
+ val reader = new BufferedReader(new InputStreamReader(in))
+ try {
+ // Cache all data because stream closed soon
+ val lines = FileIterator(reader.readLine()).toSeq
+ val rdd = TestHive.sparkContext.parallelize(lines, numPart).map(lineParseFunc)
+ val df = rdd.toDF.cache
+ df.foreach(_ => {})
+ df
+ } finally {
+ reader.close()
+ }
+ }
+
+ before {
+ assert(mixServExec == null)
+
+ // Launch a MIX server as thread
+ assignedPort = NetUtils.getAvailablePort
+ val method = classOf[MixServer].getDeclaredMethod("getOptions")
+ method.setAccessible(true)
+ val options = method.invoke(null).asInstanceOf[Options]
+ val cl = CommandLineUtils.parseOptions(
+ Array(
+ "-port", Integer.toString(assignedPort),
+ "-sync_threshold", "1"
+ ),
+ options
+ )
+ val server = new MixServer(cl)
+ mixServExec = Executors.newSingleThreadExecutor()
+ mixServExec.submit(server)
+ var retry = 0
+ while (server.getState() != MixServer.ServerState.RUNNING && retry < 32) {
+ Thread.sleep(100L)
+ retry += 1
+ }
+ assert(MixServer.ServerState.RUNNING == server.getState)
+ }
+
+ after {
+ mixServExec.shutdownNow()
+ mixServExec = null
+ }
+
+ TestUtils.benchmark("model mixing test w/ regression") {
+ Seq(
+ "train_adadelta",
+ "train_adagrad",
+ "train_arow_regr",
+ "train_arowe_regr",
+ "train_arowe2_regr",
+ "train_logregr",
+ "train_pa1_regr",
+ "train_pa1a_regr",
+ "train_pa2_regr",
+ "train_pa2a_regr"
+ ).map { func =>
+ // Build a model
+ val model = {
+ val groupId = s"${TestHive.sparkContext.applicationId}-${UUID.randomUUID}"
+ val res = TestUtils.invokeFunc(
+ new HivemallOps(trainA9aData.part_amplify(lit(1))),
+ func,
+ Seq[Column](
+ add_bias($"features"),
+ $"label",
+ lit(s"-mix localhost:${assignedPort} -mix_session ${groupId} -mix_threshold 2 " +
+ "-mix_cancel")
+ )
+ )
+ 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 = testA9aData
+ .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 (target, predicted) = eval.map {
+ case Row(target: Double, predicted: Double) => (target, predicted)
+ }.first
+
+ // scalastyle:off println
+ println(s"func:${func} target:${target} predicted:${predicted} "
+ + s"diff:${Math.abs(target - predicted)}")
+
+ testDf.unpersist()
+ }
+ }
+
+ TestUtils.benchmark("model mixing test w/ binary classification") {
+ Seq(
+ "train_perceptron",
+ "train_pa",
+ "train_pa1",
+ "train_pa2",
+ "train_cw",
+ "train_arow",
+ "train_arowh",
+ "train_scw",
+ "train_scw2",
+ "train_adagrad_rda"
+ ).map { func =>
+ // Build a model
+ val model = {
+ val groupId = s"${TestHive.sparkContext.applicationId}-${UUID.randomUUID}"
+ val res = TestUtils.invokeFunc(
+ new HivemallOps(trainKdd2010aData.part_amplify(lit(1))),
+ func,
+ Seq[Column](
+ add_bias($"features"),
+ $"label",
+ lit(s"-mix localhost:${assignedPort} -mix_session ${groupId} -mix_threshold 2 " +
+ "-mix_cancel")
+ )
+ )
+ 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 = testKdd2010aData
+ .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")
+ .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")
+
+ // scalastyle:off println
+ println(s"func:${func} precision:${(eval.count + 0.0) / predict.count}")
+
+ testDf.unpersist()
+ predict.unpersist()
+ }
+ }
+}
+
+object FileIterator {
+
+ def apply[A](f: => A): Iterator[A] = new Iterator[A] {
+ var opt = Option(f)
+ def hasNext = opt.nonEmpty
+ def next() = {
+ val r = opt.get
+ opt = Option(f)
+ r
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/XGBoostSuite.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/XGBoostSuite.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/XGBoostSuite.scala
new file mode 100644
index 0000000..0d4b894
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/XGBoostSuite.scala
@@ -0,0 +1,150 @@
+/*
+ * 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 java.io.File
+
+import hivemall.xgboost._
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.execution.datasources.DataSource
+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.internal.SQLConf
+import org.apache.spark.sql.test.VectorQueryTest
+import org.apache.spark.sql.types._
+
+final class XGBoostSuite extends VectorQueryTest {
+ import hiveContext.implicits._
+
+ private val defaultOptions = XGBoostOptions()
+ .set("num_round", "10")
+ .set("max_depth", "4")
+
+ private val numModles = 3
+
+ private def countModels(dirPath: String): Int = {
+ new File(dirPath).listFiles().toSeq.count(_.getName.endsWith(".xgboost"))
+ }
+
+ test("resolve libxgboost") {
+ def getProvidingClass(name: String): Class[_] =
+ DataSource(sparkSession = null, className = name).providingClass
+ assert(getProvidingClass("libxgboost") ===
+ classOf[org.apache.spark.sql.hive.source.XGBoostFileFormat])
+ }
+
+ test("check XGBoost options") {
+ assert(s"$defaultOptions" == "-max_depth 4 -num_round 10")
+ val errMsg = intercept[IllegalArgumentException] {
+ defaultOptions.set("unknown", "3")
+ }
+ assert(errMsg.getMessage == "requirement failed: " +
+ "non-existing key detected in XGBoost options: unknown")
+ }
+
+ test("train_xgboost_regr") {
+ withTempModelDir { tempDir =>
+ withSQLConf(SQLConf.CROSS_JOINS_ENABLED.key -> "true") {
+
+ // Save built models in persistent storage
+ mllibTrainDf.repartition(numModles)
+ .train_xgboost_regr($"features", $"label", lit(s"${defaultOptions}"))
+ .write.format("libxgboost").save(tempDir)
+
+ // Check #models generated by XGBoost
+ assert(countModels(tempDir) == numModles)
+
+ // Load the saved models
+ val model = hiveContext.sparkSession.read.format("libxgboost").load(tempDir)
+ val predict = model.join(mllibTestDf)
+ .xgboost_predict($"rowid", $"features", $"model_id", $"pred_model")
+ .groupBy("rowid").avg()
+ .toDF("rowid", "predicted")
+
+ val result = predict.join(mllibTestDf, predict("rowid") === mllibTestDf("rowid"), "INNER")
+ .select(predict("rowid"), $"predicted", $"label")
+
+ result.select(avg(abs($"predicted" - $"label"))).collect.map {
+ case Row(diff: Double) => assert(diff > 0.0)
+ }
+ }
+ }
+ }
+
+ test("train_xgboost_classifier") {
+ withTempModelDir { tempDir =>
+ withSQLConf(SQLConf.CROSS_JOINS_ENABLED.key -> "true") {
+
+ mllibTrainDf.repartition(numModles)
+ .train_xgboost_regr($"features", $"label", lit(s"${defaultOptions}"))
+ .write.format("libxgboost").save(tempDir)
+
+ // Check #models generated by XGBoost
+ assert(countModels(tempDir) == numModles)
+
+ val model = hiveContext.sparkSession.read.format("libxgboost").load(tempDir)
+ val predict = model.join(mllibTestDf)
+ .xgboost_predict($"rowid", $"features", $"model_id", $"pred_model")
+ .groupBy("rowid").avg()
+ .toDF("rowid", "predicted")
+
+ val result = predict.join(mllibTestDf, predict("rowid") === mllibTestDf("rowid"), "INNER")
+ .select(
+ when($"predicted" >= 0.50, 1).otherwise(0),
+ $"label".cast(IntegerType)
+ )
+ .toDF("predicted", "label")
+
+ assert((result.where($"label" === $"predicted").count + 0.0) / result.count > 0.0)
+ }
+ }
+ }
+
+ test("train_xgboost_multiclass_classifier") {
+ withTempModelDir { tempDir =>
+ withSQLConf(SQLConf.CROSS_JOINS_ENABLED.key -> "true") {
+
+ mllibTrainDf.repartition(numModles)
+ .train_xgboost_multiclass_classifier(
+ $"features", $"label", lit(s"${defaultOptions.set("num_class", "2")}"))
+ .write.format("libxgboost").save(tempDir)
+
+ // Check #models generated by XGBoost
+ assert(countModels(tempDir) == numModles)
+
+ val model = hiveContext.sparkSession.read.format("libxgboost").load(tempDir)
+ val predict = model.join(mllibTestDf)
+ .xgboost_multiclass_predict($"rowid", $"features", $"model_id", $"pred_model")
+ .groupBy("rowid").max_label("probability", "label")
+ .toDF("rowid", "predicted")
+
+ val result = predict.join(mllibTestDf, predict("rowid") === mllibTestDf("rowid"), "INNER")
+ .select(
+ predict("rowid"),
+ $"predicted",
+ $"label".cast(IntegerType)
+ )
+
+ assert((result.where($"label" === $"predicted").count + 0.0) / result.count > 0.0)
+ }
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/benchmark/MiscBenchmark.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/benchmark/MiscBenchmark.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/benchmark/MiscBenchmark.scala
new file mode 100644
index 0000000..9b5a1e5
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/benchmark/MiscBenchmark.scala
@@ -0,0 +1,224 @@
+/*
+ * 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.benchmark
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.{Column, DataFrame, Dataset, Row, SparkSession}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{EachTopK, Expression, Literal}
+import org.apache.spark.sql.catalyst.plans.logical.{Generate, LogicalPlan, Project}
+import org.apache.spark.sql.expressions.Window
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.hive.{HiveGenericUDF, HiveGenericUDTF}
+import org.apache.spark.sql.hive.HiveShim.HiveFunctionWrapper
+import org.apache.spark.sql.types._
+import org.apache.spark.test.TestUtils
+import org.apache.spark.util.Benchmark
+
+class TestFuncWrapper(df: DataFrame) {
+
+ def each_top_k(k: Column, group: Column, value: Column, args: Column*)
+ : DataFrame = withTypedPlan {
+ val clusterDf = df.repartition(group).sortWithinPartitions(group)
+ Generate(HiveGenericUDTF(
+ "each_top_k",
+ new HiveFunctionWrapper("hivemall.tools.EachTopKUDTF"),
+ (Seq(k, group, value) ++ args).map(_.expr)),
+ join = false, outer = false, None,
+ (Seq("rank", "key") ++ args.map(_.named.name)).map(UnresolvedAttribute(_)),
+ clusterDf.logicalPlan)
+ }
+
+ def each_top_k_improved(k: Int, group: String, score: String, args: String*)
+ : DataFrame = withTypedPlan {
+ val clusterDf = df.repartition(df(group)).sortWithinPartitions(group)
+ val childrenAttributes = clusterDf.logicalPlan.output
+ val generator = Generate(
+ EachTopK(
+ k,
+ clusterDf.resolve(group),
+ clusterDf.resolve(score),
+ childrenAttributes
+ ),
+ join = false, outer = false, None,
+ (Seq("rank") ++ childrenAttributes.map(_.name)).map(UnresolvedAttribute(_)),
+ clusterDf.logicalPlan)
+ val attributes = generator.generatedSet
+ val projectList = (Seq("rank") ++ args).map(s => attributes.find(_.name == s).get)
+ Project(projectList, generator)
+ }
+
+ /**
+ * A convenient function to wrap a logical plan and produce a DataFrame.
+ */
+ @inline private[this] def withTypedPlan(logicalPlan: => LogicalPlan): DataFrame = {
+ val queryExecution = df.sparkSession.sessionState.executePlan(logicalPlan)
+ val outputSchema = queryExecution.sparkPlan.schema
+ new Dataset[Row](df.sparkSession, queryExecution, RowEncoder(outputSchema))
+ }
+}
+
+object TestFuncWrapper {
+
+ /**
+ * Implicitly inject the [[TestFuncWrapper]] into [[DataFrame]].
+ */
+ implicit def dataFrameToTestFuncWrapper(df: DataFrame): TestFuncWrapper =
+ new TestFuncWrapper(df)
+
+ def sigmoid(exprs: Column*): Column = withExpr {
+ HiveGenericUDF("sigmoid",
+ new HiveFunctionWrapper("hivemall.tools.math.SigmoidGenericUDF"),
+ exprs.map(_.expr))
+ }
+
+ /**
+ * A convenient function to wrap an expression and produce a Column.
+ */
+ @inline private def withExpr(expr: Expression): Column = Column(expr)
+}
+
+class MiscBenchmark extends SparkFunSuite {
+
+ lazy val sparkSession = SparkSession.builder
+ .master("local[1]")
+ .appName("microbenchmark")
+ .config("spark.sql.shuffle.partitions", 1)
+ .config("spark.sql.codegen.wholeStage", true)
+ .getOrCreate()
+
+ val numIters = 3
+
+ private def addBenchmarkCase(name: String, df: DataFrame)(implicit benchmark: Benchmark): Unit = {
+ benchmark.addCase(name, numIters) { _ =>
+ df.queryExecution.executedPlan.execute().foreach(_ => {})
+ }
+ }
+
+ TestUtils.benchmark("closure/exprs/spark-udf/hive-udf") {
+ /**
+ * Java HotSpot(TM) 64-Bit Server VM 1.8.0_31-b13 on Mac OS X 10.10.2
+ * Intel(R) Core(TM) i7-4578U CPU @ 3.00GHz
+ *
+ * sigmoid functions: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
+ * --------------------------------------------------------------------------------
+ * exprs 7708 / 8173 3.4 294.0 1.0X
+ * closure 7722 / 8342 3.4 294.6 1.0X
+ * spark-udf 7963 / 8350 3.3 303.8 1.0X
+ * hive-udf 13977 / 14050 1.9 533.2 0.6X
+ */
+ import sparkSession.sqlContext.implicits._
+ val N = 100L << 18
+ implicit val benchmark = new Benchmark("sigmoid", N)
+ val schema = StructType(
+ StructField("value", DoubleType) :: Nil
+ )
+ val testDf = sparkSession.createDataFrame(
+ sparkSession.range(N).map(_.toDouble).map(Row(_))(RowEncoder(schema)).rdd,
+ schema
+ )
+ testDf.cache.count // Cached
+
+ def sigmoidExprs(expr: Column): Column = {
+ val one: () => Literal = () => Literal.create(1.0, DoubleType)
+ Column(one()) / (Column(one()) + exp(-expr))
+ }
+ addBenchmarkCase(
+ "exprs",
+ testDf.select(sigmoidExprs($"value"))
+ )
+
+ addBenchmarkCase(
+ "closure",
+ testDf.map { d =>
+ Row(1.0 / (1.0 + Math.exp(-d.getDouble(0))))
+ }(RowEncoder(schema))
+ )
+
+ val sigmoidUdf = udf { (d: Double) => 1.0 / (1.0 + Math.exp(-d)) }
+ addBenchmarkCase(
+ "spark-udf",
+ testDf.select(sigmoidUdf($"value"))
+ )
+ addBenchmarkCase(
+ "hive-udf",
+ testDf.select(TestFuncWrapper.sigmoid($"value"))
+ )
+
+ benchmark.run()
+ }
+
+ TestUtils.benchmark("top-k query") {
+ /**
+ * top-k (k=100): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
+ * -------------------------------------------------------------------------------
+ * rank 62748 / 62862 0.4 2393.6 1.0X
+ * each_top_k (hive-udf) 41421 / 41736 0.6 1580.1 1.5X
+ * each_top_k (exprs) 15793 / 16394 1.7 602.5 4.0X
+ */
+ import sparkSession.sqlContext.implicits._
+ import TestFuncWrapper._
+ val N = 100L << 18
+ val topK = 100
+ val numGroup = 3
+ implicit val benchmark = new Benchmark(s"top-k (k=$topK)", N)
+ val schema = StructType(
+ StructField("key", IntegerType) ::
+ StructField("score", DoubleType) ::
+ StructField("value", StringType) ::
+ Nil
+ )
+ val testDf = {
+ val df = sparkSession.createDataFrame(
+ sparkSession.sparkContext.range(0, N).map(_.toInt).map { d =>
+ Row(d % numGroup, scala.util.Random.nextDouble(), s"group-${d % numGroup}")
+ },
+ schema
+ )
+ // Test data are clustered by group keys
+ df.repartition($"key").sortWithinPartitions($"key")
+ }
+ testDf.cache.count // Cached
+
+ addBenchmarkCase(
+ "rank",
+ testDf.withColumn(
+ "rank", rank().over(Window.partitionBy($"key").orderBy($"score".desc))
+ ).where($"rank" <= topK)
+ )
+
+ addBenchmarkCase(
+ "each_top_k (hive-udf)",
+ // TODO: If $"value" given, it throws `AnalysisException`. Why?
+ // testDf.each_top_k(10, $"key", $"score", $"value")
+ // org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to name
+ // on unresolved object, tree: unresolvedalias('value, None)
+ // at org.apache.spark.sql.catalyst.analysis.UnresolvedAlias.name(unresolved.scala:339)
+ testDf.each_top_k(lit(topK), $"key", $"score", testDf("value"))
+ )
+
+ addBenchmarkCase(
+ "each_top_k (exprs)",
+ testDf.each_top_k_improved(topK, "key", "score", "value")
+ )
+
+ benchmark.run()
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/HivemallFeatureQueryTest.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/HivemallFeatureQueryTest.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/HivemallFeatureQueryTest.scala
new file mode 100644
index 0000000..a4733f5
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/HivemallFeatureQueryTest.scala
@@ -0,0 +1,112 @@
+/*
+ * 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.test
+
+import scala.collection.mutable.Seq
+import scala.reflect.runtime.universe.TypeTag
+
+import hivemall.tools.RegressionDatagen
+
+import org.apache.spark.sql.Column
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection}
+import org.apache.spark.sql.catalyst.expressions.Literal
+import org.apache.spark.sql.QueryTest
+
+/**
+ * Base class for tests with Hivemall features.
+ */
+abstract class HivemallFeatureQueryTest extends QueryTest with TestHiveSingleton {
+
+ import hiveContext.implicits._
+
+ /**
+ * TODO: spark-2.0 does not support literals for some types (e.g., Seq[_] and Array[_]).
+ * So, it provides that functionality here.
+ * This helper function will be removed in future releases.
+ */
+ protected def lit2[T : TypeTag](v: T): Column = {
+ val ScalaReflection.Schema(dataType, _) = ScalaReflection.schemaFor[T]
+ val convert = CatalystTypeConverters.createToCatalystConverter(dataType)
+ Column(Literal(convert(v), dataType))
+ }
+
+ protected val DummyInputData = Seq((0, 0)).toDF("c0", "c1")
+
+ protected val IntList2Data =
+ Seq(
+ (8 :: 5 :: Nil, 6 :: 4 :: Nil),
+ (3 :: 1 :: Nil, 3 :: 2 :: Nil),
+ (2 :: Nil, 3 :: Nil)
+ ).toDF("target", "predict")
+
+ protected val Float2Data =
+ Seq(
+ (0.8f, 0.3f), (0.3f, 0.9f), (0.2f, 0.4f)
+ ).toDF("target", "predict")
+
+ protected val TinyTrainData =
+ Seq(
+ (0.0, "1:0.8" :: "2:0.2" :: Nil),
+ (1.0, "2:0.7" :: Nil),
+ (0.0, "1:0.9" :: Nil)
+ ).toDF("label", "features")
+
+ protected val TinyTestData =
+ Seq(
+ (0.0, "1:0.6" :: "2:0.1" :: Nil),
+ (1.0, "2:0.9" :: Nil),
+ (0.0, "1:0.2" :: Nil),
+ (0.0, "2:0.1" :: Nil),
+ (0.0, "0:0.6" :: "2:0.4" :: Nil)
+ ).toDF("label", "features")
+
+ protected val LargeRegrTrainData = RegressionDatagen.exec(
+ hiveContext,
+ n_partitions = 2,
+ min_examples = 100000,
+ seed = 3,
+ prob_one = 0.8f
+ ).cache
+
+ protected val LargeRegrTestData = RegressionDatagen.exec(
+ hiveContext,
+ n_partitions = 2,
+ min_examples = 100,
+ seed = 3,
+ prob_one = 0.5f
+ ).cache
+
+ protected val LargeClassifierTrainData = RegressionDatagen.exec(
+ hiveContext,
+ n_partitions = 2,
+ min_examples = 100000,
+ seed = 5,
+ prob_one = 0.8f,
+ cl = true
+ ).cache
+
+ protected val LargeClassifierTestData = RegressionDatagen.exec(
+ hiveContext,
+ n_partitions = 2,
+ min_examples = 100,
+ seed = 5,
+ prob_one = 0.5f,
+ cl = true
+ ).cache
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala
new file mode 100644
index 0000000..3a88924
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala
@@ -0,0 +1,38 @@
+/*
+ * 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.test
+
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.SparkFunSuite
+
+trait TestHiveSingleton extends SparkFunSuite with BeforeAndAfterAll {
+ protected val spark: SparkSession = TestHive.sparkSession
+ protected val hiveContext: TestHiveContext = TestHive
+
+ protected override def afterAll(): Unit = {
+ try {
+ hiveContext.reset()
+ } finally {
+ super.afterAll()
+ }
+ }
+
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
new file mode 100644
index 0000000..02ea34d
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -0,0 +1,314 @@
+/*
+ * 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.test
+
+import java.nio.charset.StandardCharsets
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, SparkSession, SQLContext, SQLImplicits}
+
+/**
+ * A collection of sample data used in SQL tests.
+ */
+private[sql] trait SQLTestData { self =>
+ protected def spark: SparkSession
+
+ // Helper object to import SQL implicits without a concrete SQLContext
+ private object internalImplicits extends SQLImplicits {
+ protected override def _sqlContext: SQLContext = self.spark.sqlContext
+ }
+
+ import internalImplicits._
+ import SQLTestData._
+
+ // Note: all test data should be lazy because the SQLContext is not set up yet.
+
+ protected lazy val emptyTestData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ Seq.empty[Int].map(i => TestData(i, i.toString))).toDF()
+ df.createOrReplaceTempView("emptyTestData")
+ df
+ }
+
+ protected lazy val testData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ (1 to 100).map(i => TestData(i, i.toString))).toDF()
+ df.createOrReplaceTempView("testData")
+ df
+ }
+
+ protected lazy val testData2: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ TestData2(1, 1) ::
+ TestData2(1, 2) ::
+ TestData2(2, 1) ::
+ TestData2(2, 2) ::
+ TestData2(3, 1) ::
+ TestData2(3, 2) :: Nil, 2).toDF()
+ df.createOrReplaceTempView("testData2")
+ df
+ }
+
+ protected lazy val testData3: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ TestData3(1, None) ::
+ TestData3(2, Some(2)) :: Nil).toDF()
+ df.createOrReplaceTempView("testData3")
+ df
+ }
+
+ protected lazy val negativeData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ (1 to 100).map(i => TestData(-i, (-i).toString))).toDF()
+ df.createOrReplaceTempView("negativeData")
+ df
+ }
+
+ protected lazy val largeAndSmallInts: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ LargeAndSmallInts(2147483644, 1) ::
+ LargeAndSmallInts(1, 2) ::
+ LargeAndSmallInts(2147483645, 1) ::
+ LargeAndSmallInts(2, 2) ::
+ LargeAndSmallInts(2147483646, 1) ::
+ LargeAndSmallInts(3, 2) :: Nil).toDF()
+ df.createOrReplaceTempView("largeAndSmallInts")
+ df
+ }
+
+ protected lazy val decimalData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ DecimalData(1, 1) ::
+ DecimalData(1, 2) ::
+ DecimalData(2, 1) ::
+ DecimalData(2, 2) ::
+ DecimalData(3, 1) ::
+ DecimalData(3, 2) :: Nil).toDF()
+ df.createOrReplaceTempView("decimalData")
+ df
+ }
+
+ protected lazy val binaryData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ BinaryData("12".getBytes(StandardCharsets.UTF_8), 1) ::
+ BinaryData("22".getBytes(StandardCharsets.UTF_8), 5) ::
+ BinaryData("122".getBytes(StandardCharsets.UTF_8), 3) ::
+ BinaryData("121".getBytes(StandardCharsets.UTF_8), 2) ::
+ BinaryData("123".getBytes(StandardCharsets.UTF_8), 4) :: Nil).toDF()
+ df.createOrReplaceTempView("binaryData")
+ df
+ }
+
+ protected lazy val upperCaseData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ UpperCaseData(1, "A") ::
+ UpperCaseData(2, "B") ::
+ UpperCaseData(3, "C") ::
+ UpperCaseData(4, "D") ::
+ UpperCaseData(5, "E") ::
+ UpperCaseData(6, "F") :: Nil).toDF()
+ df.createOrReplaceTempView("upperCaseData")
+ df
+ }
+
+ protected lazy val lowerCaseData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ LowerCaseData(1, "a") ::
+ LowerCaseData(2, "b") ::
+ LowerCaseData(3, "c") ::
+ LowerCaseData(4, "d") :: Nil).toDF()
+ df.createOrReplaceTempView("lowerCaseData")
+ df
+ }
+
+ protected lazy val arrayData: RDD[ArrayData] = {
+ val rdd = spark.sparkContext.parallelize(
+ ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) ::
+ ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil)
+ rdd.toDF().createOrReplaceTempView("arrayData")
+ rdd
+ }
+
+ protected lazy val mapData: RDD[MapData] = {
+ val rdd = spark.sparkContext.parallelize(
+ MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
+ MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
+ MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
+ MapData(Map(1 -> "a4", 2 -> "b4")) ::
+ MapData(Map(1 -> "a5")) :: Nil)
+ rdd.toDF().createOrReplaceTempView("mapData")
+ rdd
+ }
+
+ protected lazy val repeatedData: RDD[StringData] = {
+ val rdd = spark.sparkContext.parallelize(List.fill(2)(StringData("test")))
+ rdd.toDF().createOrReplaceTempView("repeatedData")
+ rdd
+ }
+
+ protected lazy val nullableRepeatedData: RDD[StringData] = {
+ val rdd = spark.sparkContext.parallelize(
+ List.fill(2)(StringData(null)) ++
+ List.fill(2)(StringData("test")))
+ rdd.toDF().createOrReplaceTempView("nullableRepeatedData")
+ rdd
+ }
+
+ protected lazy val nullInts: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ NullInts(1) ::
+ NullInts(2) ::
+ NullInts(3) ::
+ NullInts(null) :: Nil).toDF()
+ df.createOrReplaceTempView("nullInts")
+ df
+ }
+
+ protected lazy val allNulls: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ NullInts(null) ::
+ NullInts(null) ::
+ NullInts(null) ::
+ NullInts(null) :: Nil).toDF()
+ df.createOrReplaceTempView("allNulls")
+ df
+ }
+
+ protected lazy val nullStrings: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ NullStrings(1, "abc") ::
+ NullStrings(2, "ABC") ::
+ NullStrings(3, null) :: Nil).toDF()
+ df.createOrReplaceTempView("nullStrings")
+ df
+ }
+
+ protected lazy val tableName: DataFrame = {
+ val df = spark.sparkContext.parallelize(TableName("test") :: Nil).toDF()
+ df.createOrReplaceTempView("tableName")
+ df
+ }
+
+ protected lazy val unparsedStrings: RDD[String] = {
+ spark.sparkContext.parallelize(
+ "1, A1, true, null" ::
+ "2, B2, false, null" ::
+ "3, C3, true, null" ::
+ "4, D4, true, 2147483644" :: Nil)
+ }
+
+ // An RDD with 4 elements and 8 partitions
+ protected lazy val withEmptyParts: RDD[IntField] = {
+ val rdd = spark.sparkContext.parallelize((1 to 4).map(IntField), 8)
+ rdd.toDF().createOrReplaceTempView("withEmptyParts")
+ rdd
+ }
+
+ protected lazy val person: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ Person(0, "mike", 30) ::
+ Person(1, "jim", 20) :: Nil).toDF()
+ df.createOrReplaceTempView("person")
+ df
+ }
+
+ protected lazy val salary: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ Salary(0, 2000.0) ::
+ Salary(1, 1000.0) :: Nil).toDF()
+ df.createOrReplaceTempView("salary")
+ df
+ }
+
+ protected lazy val complexData: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true) ::
+ ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false) ::
+ Nil).toDF()
+ df.createOrReplaceTempView("complexData")
+ df
+ }
+
+ protected lazy val courseSales: DataFrame = {
+ val df = spark.sparkContext.parallelize(
+ CourseSales("dotNET", 2012, 10000) ::
+ CourseSales("Java", 2012, 20000) ::
+ CourseSales("dotNET", 2012, 5000) ::
+ CourseSales("dotNET", 2013, 48000) ::
+ CourseSales("Java", 2013, 30000) :: Nil).toDF()
+ df.createOrReplaceTempView("courseSales")
+ df
+ }
+
+ /**
+ * Initialize all test data such that all temp tables are properly registered.
+ */
+ def loadTestData(): Unit = {
+ assert(spark != null, "attempted to initialize test data before SparkSession.")
+ emptyTestData
+ testData
+ testData2
+ testData3
+ negativeData
+ largeAndSmallInts
+ decimalData
+ binaryData
+ upperCaseData
+ lowerCaseData
+ arrayData
+ mapData
+ repeatedData
+ nullableRepeatedData
+ nullInts
+ allNulls
+ nullStrings
+ tableName
+ unparsedStrings
+ withEmptyParts
+ person
+ salary
+ complexData
+ courseSales
+ }
+}
+
+/**
+ * Case classes used in test data.
+ */
+private[sql] object SQLTestData {
+ case class TestData(key: Int, value: String)
+ case class TestData2(a: Int, b: Int)
+ case class TestData3(a: Int, b: Option[Int])
+ case class LargeAndSmallInts(a: Int, b: Int)
+ case class DecimalData(a: BigDecimal, b: BigDecimal)
+ case class BinaryData(a: Array[Byte], b: Int)
+ case class UpperCaseData(N: Int, L: String)
+ case class LowerCaseData(n: Int, l: String)
+ case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]])
+ case class MapData(data: scala.collection.Map[Int, String])
+ case class StringData(s: String)
+ case class IntField(i: Int)
+ case class NullInts(a: Integer)
+ case class NullStrings(n: Int, s: String)
+ case class TableName(tableName: String)
+ case class Person(id: Int, name: String, age: Int)
+ case class Salary(personId: Int, salary: Double)
+ case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean)
+ case class CourseSales(course: String, year: Int, earnings: Double)
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
new file mode 100644
index 0000000..b926a01
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -0,0 +1,335 @@
+/*
+ * 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.test
+
+import java.io.File
+import java.util.UUID
+
+import scala.language.implicitConversions
+import scala.util.Try
+import scala.util.control.NonFatal
+
+import org.apache.hadoop.conf.Configuration
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.NoSuchTableException
+import org.apache.spark.sql.catalyst.catalog.SessionCatalog.DEFAULT_DATABASE
+import org.apache.spark.sql.catalyst.FunctionIdentifier
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.execution.FilterExec
+import org.apache.spark.util.{UninterruptibleThread, Utils}
+
+/**
+ * Helper trait that should be extended by all SQL test suites.
+ *
+ * This allows subclasses to plugin a custom [[SQLContext]]. It comes with test data
+ * prepared in advance as well as all implicit conversions used extensively by dataframes.
+ * To use implicit methods, import `testImplicits._` instead of through the [[SQLContext]].
+ *
+ * Subclasses should *not* create [[SQLContext]]s in the test suite constructor, which is
+ * prone to leaving multiple overlapping [[org.apache.spark.SparkContext]]s in the same JVM.
+ */
+private[sql] trait SQLTestUtils
+ extends SparkFunSuite
+ with BeforeAndAfterAll
+ with SQLTestData { self =>
+
+ protected def sparkContext = spark.sparkContext
+
+ // Whether to materialize all test data before the first test is run
+ private var loadTestDataBeforeTests = false
+
+ // Shorthand for running a query using our SQLContext
+ protected lazy val sql = spark.sql _
+
+ /**
+ * A helper object for importing SQL implicits.
+ *
+ * Note that the alternative of importing `spark.implicits._` is not possible here.
+ * This is because we create the [[SQLContext]] immediately before the first test is run,
+ * but the implicits import is needed in the constructor.
+ */
+ protected object testImplicits extends SQLImplicits {
+ protected override def _sqlContext: SQLContext = self.spark.sqlContext
+ }
+
+ /**
+ * Materialize the test data immediately after the [[SQLContext]] is set up.
+ * This is necessary if the data is accessed by name but not through direct reference.
+ */
+ protected def setupTestData(): Unit = {
+ loadTestDataBeforeTests = true
+ }
+
+ protected override def beforeAll(): Unit = {
+ super.beforeAll()
+ if (loadTestDataBeforeTests) {
+ loadTestData()
+ }
+ }
+
+ /**
+ * Sets all SQL configurations specified in `pairs`, calls `f`, and then restore all SQL
+ * configurations.
+ *
+ * @todo Probably this method should be moved to a more general place
+ */
+ protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
+ val (keys, values) = pairs.unzip
+ val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption)
+ (keys, values).zipped.foreach(spark.conf.set)
+ try f finally {
+ keys.zip(currentValues).foreach {
+ case (key, Some(value)) => spark.conf.set(key, value)
+ case (key, None) => spark.conf.unset(key)
+ }
+ }
+ }
+
+ /**
+ * Generates a temporary path without creating the actual file/directory, then pass it to `f`. If
+ * a file/directory is created there by `f`, it will be delete after `f` returns.
+ *
+ * @todo Probably this method should be moved to a more general place
+ */
+ protected def withTempPath(f: File => Unit): Unit = {
+ val path = Utils.createTempDir()
+ path.delete()
+ try f(path) finally Utils.deleteRecursively(path)
+ }
+
+ /**
+ * Creates a temporary directory, which is then passed to `f` and will be deleted after `f`
+ * returns.
+ *
+ * @todo Probably this method should be moved to a more general place
+ */
+ protected def withTempDir(f: File => Unit): Unit = {
+ val dir = Utils.createTempDir().getCanonicalFile
+ try f(dir) finally Utils.deleteRecursively(dir)
+ }
+
+ /**
+ * Drops functions after calling `f`. A function is represented by (functionName, isTemporary).
+ */
+ protected def withUserDefinedFunction(functions: (String, Boolean)*)(f: => Unit): Unit = {
+ try {
+ f
+ } catch {
+ case cause: Throwable => throw cause
+ } finally {
+ // If the test failed part way, we don't want to mask the failure by failing to remove
+ // temp tables that never got created.
+ functions.foreach { case (functionName, isTemporary) =>
+ val withTemporary = if (isTemporary) "TEMPORARY" else ""
+ spark.sql(s"DROP $withTemporary FUNCTION IF EXISTS $functionName")
+ assert(
+ !spark.sessionState.catalog.functionExists(FunctionIdentifier(functionName)),
+ s"Function $functionName should have been dropped. But, it still exists.")
+ }
+ }
+ }
+
+ /**
+ * Drops temporary table `tableName` after calling `f`.
+ */
+ protected def withTempView(tableNames: String*)(f: => Unit): Unit = {
+ try f finally {
+ // If the test failed part way, we don't want to mask the failure by failing to remove
+ // temp tables that never got created.
+ try tableNames.foreach(spark.catalog.dropTempView) catch {
+ case _: NoSuchTableException =>
+ }
+ }
+ }
+
+ /**
+ * Drops table `tableName` after calling `f`.
+ */
+ protected def withTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally {
+ tableNames.foreach { name =>
+ spark.sql(s"DROP TABLE IF EXISTS $name")
+ }
+ }
+ }
+
+ /**
+ * Drops view `viewName` after calling `f`.
+ */
+ protected def withView(viewNames: String*)(f: => Unit): Unit = {
+ try f finally {
+ viewNames.foreach { name =>
+ spark.sql(s"DROP VIEW IF EXISTS $name")
+ }
+ }
+ }
+
+ /**
+ * Creates a temporary database and switches current database to it before executing `f`. This
+ * database is dropped after `f` returns.
+ *
+ * Note that this method doesn't switch current database before executing `f`.
+ */
+ protected def withTempDatabase(f: String => Unit): Unit = {
+ val dbName = s"db_${UUID.randomUUID().toString.replace('-', '_')}"
+
+ try {
+ spark.sql(s"CREATE DATABASE $dbName")
+ } catch { case cause: Throwable =>
+ fail("Failed to create temporary database", cause)
+ }
+
+ try f(dbName) finally {
+ if (spark.catalog.currentDatabase == dbName) {
+ spark.sql(s"USE ${DEFAULT_DATABASE}")
+ }
+ spark.sql(s"DROP DATABASE $dbName CASCADE")
+ }
+ }
+
+ /**
+ * Activates database `db` before executing `f`, then switches back to `default` database after
+ * `f` returns.
+ */
+ protected def activateDatabase(db: String)(f: => Unit): Unit = {
+ spark.sessionState.catalog.setCurrentDatabase(db)
+ try f finally spark.sessionState.catalog.setCurrentDatabase("default")
+ }
+
+ /**
+ * Strip Spark-side filtering in order to check if a datasource filters rows correctly.
+ */
+ protected def stripSparkFilter(df: DataFrame): DataFrame = {
+ val schema = df.schema
+ val withoutFilters = df.queryExecution.sparkPlan.transform {
+ case FilterExec(_, child) => child
+ }
+
+ spark.internalCreateDataFrame(withoutFilters.execute(), schema)
+ }
+
+ /**
+ * Turn a logical plan into a [[DataFrame]]. This should be removed once we have an easier
+ * way to construct [[DataFrame]] directly out of local data without relying on implicits.
+ */
+ protected implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
+ Dataset.ofRows(spark, plan)
+ }
+
+ /**
+ * Disable stdout and stderr when running the test. To not output the logs to the console,
+ * ConsoleAppender's `follow` should be set to `true` so that it will honors reassignments of
+ * System.out or System.err. Otherwise, ConsoleAppender will still output to the console even if
+ * we change System.out and System.err.
+ */
+ protected def testQuietly(name: String)(f: => Unit): Unit = {
+ test(name) {
+ quietly {
+ f
+ }
+ }
+ }
+
+ /** Run a test on a separate [[UninterruptibleThread]]. */
+ protected def testWithUninterruptibleThread(name: String, quietly: Boolean = false)
+ (body: => Unit): Unit = {
+ val timeoutMillis = 10000
+ @transient var ex: Throwable = null
+
+ def runOnThread(): Unit = {
+ val thread = new UninterruptibleThread(s"Testing thread for test $name") {
+ override def run(): Unit = {
+ try {
+ body
+ } catch {
+ case NonFatal(e) =>
+ ex = e
+ }
+ }
+ }
+ thread.setDaemon(true)
+ thread.start()
+ thread.join(timeoutMillis)
+ if (thread.isAlive) {
+ thread.interrupt()
+ // If this interrupt does not work, then this thread is most likely running something that
+ // is not interruptible. There is not much point to wait for the thread to termniate, and
+ // we rather let the JVM terminate the thread on exit.
+ fail(
+ s"Test '$name' running on o.a.s.util.UninterruptibleThread timed out after" +
+ s" $timeoutMillis ms")
+ } else if (ex != null) {
+ throw ex
+ }
+ }
+
+ if (quietly) {
+ testQuietly(name) { runOnThread() }
+ } else {
+ test(name) { runOnThread() }
+ }
+ }
+}
+
+private[sql] object SQLTestUtils {
+
+ def compareAnswers(
+ sparkAnswer: Seq[Row],
+ expectedAnswer: Seq[Row],
+ sort: Boolean): Option[String] = {
+ def prepareAnswer(answer: Seq[Row]): Seq[Row] = {
+ // Converts data to types that we can do equality comparison using Scala collections.
+ // For BigDecimal type, the Scala type has a better definition of equality test (similar to
+ // Java's java.math.BigDecimal.compareTo).
+ // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for
+ // equality test.
+ // This function is copied from Catalyst's QueryTest
+ val converted: Seq[Row] = answer.map { s =>
+ Row.fromSeq(s.toSeq.map {
+ case d: java.math.BigDecimal => BigDecimal(d)
+ case b: Array[Byte] => b.toSeq
+ case o => o
+ })
+ }
+ if (sort) {
+ converted.sortBy(_.toString())
+ } else {
+ converted
+ }
+ }
+ if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) {
+ val errorMessage =
+ s"""
+ | == Results ==
+ | ${sideBySide(
+ s"== Expected Answer - ${expectedAnswer.size} ==" +:
+ prepareAnswer(expectedAnswer).map(_.toString()),
+ s"== Actual Answer - ${sparkAnswer.size} ==" +:
+ prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")}
+ """.stripMargin
+ Some(errorMessage)
+ } else {
+ None
+ }
+ }
+}
[3/4] incubator-hivemall git commit: Close #35,
#23: [HIVEMALL-31][SPARK] Support Spark-v2.1.0
Posted by ya...@apache.org.
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallOps.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallOps.scala b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallOps.scala
new file mode 100644
index 0000000..28653a5
--- /dev/null
+++ b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallOps.scala
@@ -0,0 +1,1368 @@
+/*
+ * 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 java.util.UUID
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.feature.HivemallFeature
+import org.apache.spark.ml.linalg.{DenseVector, SparseVector, VectorUDT}
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.{EachTopK, Expression, Literal, NamedExpression, UserDefinedGenerator}
+import org.apache.spark.sql.catalyst.plans.logical.{Generate, LogicalPlan, Project}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * Hivemall wrapper and some utility functions for DataFrame.
+ *
+ * @groupname regression
+ * @groupname classifier
+ * @groupname classifier.multiclass
+ * @groupname xgboost
+ * @groupname anomaly
+ * @groupname knn.similarity
+ * @groupname knn.distance
+ * @groupname knn.lsh
+ * @groupname ftvec
+ * @groupname ftvec.amplify
+ * @groupname ftvec.hashing
+ * @groupname ftvec.scaling
+ * @groupname ftvec.conv
+ * @groupname ftvec.trans
+ * @groupname misc
+ */
+final class HivemallOps(df: DataFrame) extends Logging {
+ import internal.HivemallOpsImpl._
+
+ private[this] val _sparkSession = df.sparkSession
+ private[this] val _analyzer = _sparkSession.sessionState.analyzer
+
+ /**
+ * @see [[hivemall.regression.AdaDeltaUDTF]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_adadelta(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.AdaDeltaUDTF",
+ "train_adadelta",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.AdaGradUDTF]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_adagrad(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.AdaGradUDTF",
+ "train_adagrad",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.AROWRegressionUDTF]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_arow_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.AROWRegressionUDTF",
+ "train_arow_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.AROWRegressionUDTF.AROWe]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_arowe_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.AROWRegressionUDTF$AROWe",
+ "train_arowe_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.AROWRegressionUDTF.AROWe2]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_arowe2_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.AROWRegressionUDTF$AROWe2",
+ "train_arowe2_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.LogressUDTF]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_logregr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.LogressUDTF",
+ "train_logregr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.PassiveAggressiveRegressionUDTF]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_pa1_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.PassiveAggressiveRegressionUDTF",
+ "train_pa1_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.PassiveAggressiveRegressionUDTF.PA1a]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_pa1a_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.PassiveAggressiveRegressionUDTF$PA1a",
+ "train_pa1a_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.PassiveAggressiveRegressionUDTF.PA2]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_pa2_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.PassiveAggressiveRegressionUDTF$PA2",
+ "train_pa2_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.regression.PassiveAggressiveRegressionUDTF.PA2a]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_pa2a_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.regression.PassiveAggressiveRegressionUDTF$PA2a",
+ "train_pa2a_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.smile.regression.RandomForestRegressionUDTF]]
+ * @group regression
+ */
+ @scala.annotation.varargs
+ def train_randomforest_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.smile.regression.RandomForestRegressionUDTF",
+ "train_randomforest_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("model_id", "model_type", "pred_model", "var_importance", "oob_errors", "oob_tests")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.PerceptronUDTF]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_perceptron(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.PerceptronUDTF",
+ "train_perceptron",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.PassiveAggressiveUDTF]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_pa(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.PassiveAggressiveUDTF",
+ "train_pa",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.PassiveAggressiveUDTF.PA1]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_pa1(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.PassiveAggressiveUDTF$PA1",
+ "train_pa1",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.PassiveAggressiveUDTF.PA2]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_pa2(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.PassiveAggressiveUDTF$PA2",
+ "train_pa2",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.ConfidenceWeightedUDTF]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_cw(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.ConfidenceWeightedUDTF",
+ "train_cw",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.AROWClassifierUDTF]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_arow(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.AROWClassifierUDTF",
+ "train_arow",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.AROWClassifierUDTF.AROWh]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_arowh(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.AROWClassifierUDTF$AROWh",
+ "train_arowh",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.SoftConfideceWeightedUDTF.SCW1]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_scw(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.SoftConfideceWeightedUDTF$SCW1",
+ "train_scw",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.SoftConfideceWeightedUDTF.SCW1]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_scw2(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.SoftConfideceWeightedUDTF$SCW2",
+ "train_scw2",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.AdaGradRDAUDTF]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_adagrad_rda(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.AdaGradRDAUDTF",
+ "train_adagrad_rda",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.smile.classification.RandomForestClassifierUDTF]]
+ * @group classifier
+ */
+ @scala.annotation.varargs
+ def train_randomforest_classifier(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.smile.classification.RandomForestClassifierUDTF",
+ "train_randomforest_classifier",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("model_id", "model_type", "pred_model", "var_importance", "oob_errors", "oob_tests")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassPerceptronUDTF]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_perceptron(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassPerceptronUDTF",
+ "train_multiclass_perceptron",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_pa(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF",
+ "train_multiclass_pa",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF.PA1]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_pa1(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF$PA1",
+ "train_multiclass_pa1",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF.PA2]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_pa2(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassPassiveAggressiveUDTF$PA2",
+ "train_multiclass_pa2",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassConfidenceWeightedUDTF]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_cw(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassConfidenceWeightedUDTF",
+ "train_multiclass_cw",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassAROWClassifierUDTF]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_arow(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassAROWClassifierUDTF",
+ "train_multiclass_arow",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassSoftConfidenceWeightedUDTF.SCW1]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_scw(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassSoftConfidenceWeightedUDTF$SCW1",
+ "train_multiclass_scw",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * @see [[hivemall.classifier.multiclass.MulticlassSoftConfidenceWeightedUDTF.SCW2]]
+ * @group classifier.multiclass
+ */
+ @scala.annotation.varargs
+ def train_multiclass_scw2(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.classifier.multiclass.MulticlassSoftConfidenceWeightedUDTF$SCW2",
+ "train_multiclass_scw2",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "feature", "weight", "conv")
+ )
+ }
+
+ /**
+ * :: Experimental ::
+ * @see [[hivemall.xgboost.regression.XGBoostRegressionUDTF]]
+ * @group xgboost
+ */
+ @Experimental
+ @scala.annotation.varargs
+ def train_xgboost_regr(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.xgboost.regression.XGBoostRegressionUDTFWrapper",
+ "train_xgboost_regr",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("model_id", "pred_model")
+ )
+ }
+
+ /**
+ * :: Experimental ::
+ * @see [[hivemall.xgboost.classification.XGBoostBinaryClassifierUDTF]]
+ * @group xgboost
+ */
+ @Experimental
+ @scala.annotation.varargs
+ def train_xgboost_classifier(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.xgboost.classification.XGBoostBinaryClassifierUDTFWrapper",
+ "train_xgboost_classifier",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("model_id", "pred_model")
+ )
+ }
+
+ /**
+ * :: Experimental ::
+ * @see [[hivemall.xgboost.classification.XGBoostMulticlassClassifierUDTF]]
+ * @group xgboost
+ */
+ @Experimental
+ @scala.annotation.varargs
+ def train_xgboost_multiclass_classifier(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.xgboost.classification.XGBoostMulticlassClassifierUDTFWrapper",
+ "train_xgboost_multiclass_classifier",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("model_id", "pred_model")
+ )
+ }
+
+ /**
+ * :: Experimental ::
+ * @see [[hivemall.xgboost.tools.XGBoostPredictUDTF]]
+ * @group xgboost
+ */
+ @Experimental
+ @scala.annotation.varargs
+ def xgboost_predict(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.xgboost.tools.XGBoostPredictUDTF",
+ "xgboost_predict",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("rowid", "predicted")
+ )
+ }
+
+ /**
+ * :: Experimental ::
+ * @see [[hivemall.xgboost.tools.XGBoostMulticlassPredictUDTF]]
+ * @group xgboost
+ */
+ @Experimental
+ @scala.annotation.varargs
+ def xgboost_multiclass_predict(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.xgboost.tools.XGBoostMulticlassPredictUDTF",
+ "xgboost_multiclass_predict",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("rowid", "label", "probability")
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.lsh.MinHashUDTF]]
+ * @group knn.lsh
+ */
+ @scala.annotation.varargs
+ def minhash(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.knn.lsh.MinHashUDTF",
+ "minhash",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("clusterid", "item")
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.amplify.AmplifierUDTF]]
+ * @group ftvec.amplify
+ */
+ @scala.annotation.varargs
+ def amplify(exprs: Column*): DataFrame = withTypedPlan {
+ val outputAttr = exprs.drop(1).map {
+ case Column(expr: NamedExpression) => UnresolvedAttribute(expr.name)
+ case Column(expr: Expression) => UnresolvedAttribute(expr.simpleString)
+ }
+ planHiveGenericUDTF(
+ df,
+ "hivemall.ftvec.amplify.AmplifierUDTF",
+ "amplify",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("clusterid", "item")
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.amplify.RandomAmplifierUDTF]]
+ * @group ftvec.amplify
+ */
+ @scala.annotation.varargs
+ def rand_amplify(exprs: Column*): DataFrame = withTypedPlan {
+ throw new UnsupportedOperationException("`rand_amplify` not supported yet")
+ }
+
+ /**
+ * Amplifies and shuffle data inside partitions.
+ * @group ftvec.amplify
+ */
+ def part_amplify(xtimes: Column): DataFrame = {
+ val xtimesInt = xtimes.expr match {
+ case Literal(v: Any, IntegerType) => v.asInstanceOf[Int]
+ case e => throw new AnalysisException("`xtimes` must be integer, however " + e)
+ }
+ val rdd = df.rdd.mapPartitions({ iter =>
+ val elems = iter.flatMap{ row =>
+ Seq.fill[Row](xtimesInt)(row)
+ }
+ // Need to check how this shuffling affects results
+ scala.util.Random.shuffle(elems)
+ }, true)
+ df.sqlContext.createDataFrame(rdd, df.schema)
+ }
+
+ /**
+ * Quantifies input columns.
+ * @see [[hivemall.ftvec.conv.QuantifyColumnsUDTF]]
+ * @group ftvec.conv
+ */
+ @scala.annotation.varargs
+ def quantify(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.ftvec.conv.QuantifyColumnsUDTF",
+ "quantify",
+ setMixServs(toHivemallFeatures(exprs)),
+ (0 until exprs.size - 1).map(i => s"c$i")
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.trans.BinarizeLabelUDTF]]
+ * @group ftvec.trans
+ */
+ @scala.annotation.varargs
+ def binarize_label(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.ftvec.trans.BinarizeLabelUDTF",
+ "binarize_label",
+ setMixServs(toHivemallFeatures(exprs)),
+ (0 until exprs.size - 1).map(i => s"c$i")
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.trans.QuantifiedFeaturesUDTF]]
+ * @group ftvec.trans
+ */
+ @scala.annotation.varargs
+ def quantified_features(exprs: Column*): DataFrame = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.ftvec.trans.QuantifiedFeaturesUDTF",
+ "quantified_features",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("features")
+ )
+ }
+
+ /**
+ * Splits Seq[String] into pieces.
+ * @group ftvec
+ */
+ def explode_array(expr: Column): DataFrame = {
+ df.explode(expr) { case Row(v: Seq[_]) =>
+ // Type erasure removes the component type in Seq
+ v.map(s => HivemallFeature(s.asInstanceOf[String]))
+ }
+ }
+
+ /**
+ * Splits [[Vector]] into pieces.
+ * @group ftvec
+ */
+ def explode_vector(expr: Column): DataFrame = {
+ val elementSchema = StructType(
+ StructField("feature", StringType) :: StructField("weight", DoubleType) :: Nil)
+ val explodeFunc: Row => TraversableOnce[InternalRow] = (row: Row) => {
+ row.get(0) match {
+ case dv: DenseVector =>
+ dv.values.zipWithIndex.map {
+ case (value, index) =>
+ InternalRow(UTF8String.fromString(s"$index"), value)
+ }
+ case sv: SparseVector =>
+ sv.values.zip(sv.indices).map {
+ case (value, index) =>
+ InternalRow(UTF8String.fromString(s"$index"), value)
+ }
+ }
+ }
+ withTypedPlan {
+ Generate(
+ UserDefinedGenerator(elementSchema, explodeFunc, expr.expr :: Nil),
+ join = true, outer = false, None,
+ generatorOutput = Nil,
+ df.logicalPlan)
+ }
+ }
+
+ /**
+ * Returns `top-k` records for each `group`.
+ * @group misc
+ */
+ def each_top_k(k: Column, group: Column, score: Column): DataFrame = withTypedPlan {
+ val kInt = k.expr match {
+ case Literal(v: Any, IntegerType) => v.asInstanceOf[Int]
+ case e => throw new AnalysisException("`k` must be integer, however " + e)
+ }
+ val clusterDf = df.repartition(group).sortWithinPartitions(group)
+ val child = clusterDf.logicalPlan
+ val logicalPlan = Project(group.named +: score.named +: child.output, child)
+ _analyzer.execute(logicalPlan) match {
+ case Project(group :: score :: origCols, c) =>
+ Generate(
+ EachTopK(kInt, group, score, c.output),
+ join = false, outer = false, None,
+ (Seq("rank") ++ origCols.map(_.name)).map(UnresolvedAttribute(_)),
+ clusterDf.logicalPlan
+ )
+ }
+ }
+
+ /**
+ * @see [[hivemall.dataset.LogisticRegressionDataGeneratorUDTF]]
+ * @group misc
+ */
+ @scala.annotation.varargs
+ def lr_datagen(exprs: Column*): Dataset[Row] = withTypedPlan {
+ planHiveGenericUDTF(
+ df,
+ "hivemall.dataset.LogisticRegressionDataGeneratorUDTFWrapper",
+ "lr_datagen",
+ setMixServs(toHivemallFeatures(exprs)),
+ Seq("label", "features")
+ )
+ }
+
+ /**
+ * Returns all the columns as Seq[Column] in this [[DataFrame]].
+ */
+ private[sql] def cols: Seq[Column] = {
+ df.schema.fields.map(col => df.col(col.name)).toSeq
+ }
+
+ /**
+ * :: Experimental ::
+ * If a parameter '-mix' does not exist in a 3rd argument,
+ * set it from an environmental variable
+ * 'HIVEMALL_MIX_SERVERS'.
+ *
+ * TODO: This could work if '--deploy-mode' has 'client';
+ * otherwise, we need to set HIVEMALL_MIX_SERVERS
+ * in all possible spark workers.
+ */
+ @Experimental
+ private[this] def setMixServs(exprs: Seq[Column]): Seq[Column] = {
+ val mixes = System.getenv("HIVEMALL_MIX_SERVERS")
+ if (mixes != null && !mixes.isEmpty()) {
+ val groupId = df.sqlContext.sparkContext.applicationId + "-" + UUID.randomUUID
+ logInfo(s"set '${mixes}' as default mix servers (session: ${groupId})")
+ exprs.size match {
+ case 2 => exprs :+ Column(
+ Literal.create(s"-mix ${mixes} -mix_session ${groupId}", StringType))
+ /** TODO: Add codes in the case where exprs.size == 3. */
+ case _ => exprs
+ }
+ } else {
+ exprs
+ }
+ }
+
+ /**
+ * If the input is a [[Vector]], transform it into Hivemall features.
+ */
+ @inline private[this] def toHivemallFeatures(exprs: Seq[Column]): Seq[Column] = {
+ df.select(exprs: _*).queryExecution.analyzed.schema.zip(exprs).map {
+ case (StructField(_, _: VectorUDT, _, _), c) => HivemallUtils.to_hivemall_features(c)
+ case (_, c) => c
+ }
+ }
+
+ /**
+ * A convenient function to wrap a logical plan and produce a DataFrame.
+ */
+ @inline private[this] def withTypedPlan(logicalPlan: => LogicalPlan): DataFrame = {
+ val queryExecution = df.sparkSession.sessionState.executePlan(logicalPlan)
+ val outputSchema = queryExecution.sparkPlan.schema
+ new Dataset[Row](df.sparkSession, queryExecution, RowEncoder(outputSchema))
+ }
+}
+
+object HivemallOps {
+ import internal.HivemallOpsImpl._
+
+ /**
+ * Implicitly inject the [[HivemallOps]] into [[DataFrame]].
+ */
+ implicit def dataFrameToHivemallOps(df: DataFrame): HivemallOps =
+ new HivemallOps(df)
+
+ /**
+ * @see [[hivemall.HivemallVersionUDF]]
+ * @group misc
+ */
+ def hivemall_version(): Column = withExpr {
+ planHiveUDF(
+ "hivemall.HivemallVersionUDF",
+ "hivemall_version",
+ Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.anomaly.SingularSpectrumTransformUDF]]
+ * @group anomaly
+ */
+ @scala.annotation.varargs
+ def sst(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.anomaly.SingularSpectrumTransformUDF",
+ "sst",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.similarity.CosineSimilarityUDF]]
+ * @group knn.similarity
+ */
+ @scala.annotation.varargs
+ def cosine_sim(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.similarity.CosineSimilarityUDF",
+ "cosine_sim",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.similarity.JaccardIndexUDF]]
+ * @group knn.similarity
+ */
+ @scala.annotation.varargs
+ def jaccard(exprs: Column*): Column = withExpr {
+ planHiveUDF(
+ "hivemall.knn.similarity.JaccardIndexUDF",
+ "jaccard",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.similarity.AngularSimilarityUDF]]
+ * @group knn.similarity
+ */
+ @scala.annotation.varargs
+ def angular_similarity(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.similarity.AngularSimilarityUDF",
+ "angular_similarity",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.similarity.EuclidSimilarity]]
+ * @group knn.similarity
+ */
+ @scala.annotation.varargs
+ def euclid_similarity(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.similarity.EuclidSimilarity",
+ "euclid_similarity",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.similarity.Distance2SimilarityUDF]]
+ * @group knn.similarity
+ */
+ @scala.annotation.varargs
+ def distance2similarity(exprs: Column*): Column = withExpr {
+ // TODO: Need a wrapper class because of using unsupported types
+ planHiveGenericUDF(
+ "hivemall.knn.similarity.Distance2SimilarityUDF",
+ "distance2similarity",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.HammingDistanceUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def hamming_distance(exprs: Column*): Column = withExpr {
+ planHiveUDF(
+ "hivemall.knn.distance.HammingDistanceUDF",
+ "hamming_distance",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.PopcountUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def popcnt(exprs: Column*): Column = withExpr {
+ planHiveUDF(
+ "hivemall.knn.distance.PopcountUDF",
+ "popcnt",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.KLDivergenceUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def kld(exprs: Column*): Column = withExpr {
+ planHiveUDF(
+ "hivemall.knn.distance.KLDivergenceUDF",
+ "kld",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.EuclidDistanceUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def euclid_distance(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.distance.EuclidDistanceUDF",
+ "euclid_distance",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.CosineDistanceUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def cosine_distance(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.distance.CosineDistanceUDF",
+ "cosine_distance",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.AngularDistanceUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def angular_distance(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.distance.AngularDistanceUDF",
+ "angular_distance",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.ManhattanDistanceUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def manhattan_distance(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.distance.ManhattanDistanceUDF",
+ "manhattan_distance",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.distance.MinkowskiDistanceUDF]]
+ * @group knn.distance
+ */
+ @scala.annotation.varargs
+ def minkowski_distance (exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.distance.MinkowskiDistanceUDF",
+ "minkowski_distance",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.lsh.bBitMinHashUDF]]
+ * @group knn.lsh
+ */
+ @scala.annotation.varargs
+ def bbit_minhash(exprs: Column*): Column = withExpr {
+ planHiveUDF(
+ "hivemall.knn.lsh.bBitMinHashUDF",
+ "bbit_minhash",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.knn.lsh.MinHashesUDFWrapper]]
+ * @group knn.lsh
+ */
+ @scala.annotation.varargs
+ def minhashes(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.knn.lsh.MinHashesUDFWrapper",
+ "minhashes",
+ exprs
+ )
+ }
+
+ /**
+ * Returns new features with `1.0` (bias) appended to the input features.
+ * @see [[hivemall.ftvec.AddBiasUDFWrapper]]
+ * @group ftvec
+ */
+ def add_bias(expr: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.AddBiasUDFWrapper",
+ "add_bias",
+ expr :: Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.ExtractFeatureUDFWrapper]]
+ * @group ftvec
+ *
+ * TODO: This throws java.lang.ClassCastException because
+ * HiveInspectors.toInspector has a bug in spark.
+ * Need to fix it later.
+ */
+ def extract_feature(expr: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.ExtractFeatureUDFWrapper",
+ "extract_feature",
+ expr :: Nil
+ )
+ }.as("feature")
+
+ /**
+ * @see [[hivemall.ftvec.ExtractWeightUDFWrapper]]
+ * @group ftvec
+ *
+ * TODO: This throws java.lang.ClassCastException because
+ * HiveInspectors.toInspector has a bug in spark.
+ * Need to fix it later.
+ */
+ def extract_weight(expr: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.ExtractWeightUDFWrapper",
+ "extract_weight",
+ expr :: Nil
+ )
+ }.as("value")
+
+ /**
+ * @see [[hivemall.ftvec.AddFeatureIndexUDFWrapper]]
+ * @group ftvec
+ */
+ def add_feature_index(expr: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.AddFeatureIndexUDFWrapper",
+ "add_feature_index",
+ expr :: Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.SortByFeatureUDFWrapper]]
+ * @group ftvec
+ */
+ def sort_by_feature(expr: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.SortByFeatureUDFWrapper",
+ "sort_by_feature",
+ expr :: Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.hashing.MurmurHash3UDF]]
+ * @group ftvec.hashing
+ */
+ def mhash(expr: Column): Column = withExpr {
+ planHiveUDF(
+ "hivemall.ftvec.hashing.MurmurHash3UDF",
+ "mhash",
+ expr :: Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.hashing.Sha1UDF]]
+ * @group ftvec.hashing
+ */
+ def sha1(expr: Column): Column = withExpr {
+ planHiveUDF(
+ "hivemall.ftvec.hashing.Sha1UDF",
+ "sha1",
+ expr :: Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.hashing.ArrayHashValuesUDF]]
+ * @group ftvec.hashing
+ */
+ @scala.annotation.varargs
+ def array_hash_values(exprs: Column*): Column = withExpr {
+ // TODO: Need a wrapper class because of using unsupported types
+ planHiveUDF(
+ "hivemall.ftvec.hashing.ArrayHashValuesUDF",
+ "array_hash_values",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.hashing.ArrayPrefixedHashValuesUDF]]
+ * @group ftvec.hashing
+ */
+ @scala.annotation.varargs
+ def prefixed_hash_values(exprs: Column*): Column = withExpr {
+ // TODO: Need a wrapper class because of using unsupported types
+ planHiveUDF(
+ "hivemall.ftvec.hashing.ArrayPrefixedHashValuesUDF",
+ "prefixed_hash_values",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.scaling.RescaleUDF]]
+ * @group ftvec.scaling
+ */
+ def rescale(value: Column, max: Column, min: Column): Column = withExpr {
+ planHiveUDF(
+ "hivemall.ftvec.scaling.RescaleUDF",
+ "rescale",
+ value.cast(FloatType) :: max :: min :: Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.scaling.ZScoreUDF]]
+ * @group ftvec.scaling
+ */
+ @scala.annotation.varargs
+ def zscore(exprs: Column*): Column = withExpr {
+ planHiveUDF(
+ "hivemall.ftvec.scaling.ZScoreUDF",
+ "zscore",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.scaling.L2NormalizationUDFWrapper]]
+ * @group ftvec.scaling
+ */
+ def normalize(expr: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.scaling.L2NormalizationUDFWrapper",
+ "normalize",
+ expr :: Nil
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.selection.ChiSquareUDF]]
+ * @group ftvec.selection
+ */
+ def chi2(observed: Column, expected: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.selection.ChiSquareUDF",
+ "chi2",
+ Seq(observed, expected)
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.conv.ToDenseFeaturesUDF]]
+ * @group ftvec.conv
+ */
+ @scala.annotation.varargs
+ def to_dense_features(exprs: Column*): Column = withExpr {
+ // TODO: Need a wrapper class because of using unsupported types
+ planHiveGenericUDF(
+ "hivemall.ftvec.conv.ToDenseFeaturesUDF",
+ "to_dense_features",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.conv.ToSparseFeaturesUDF]]
+ * @group ftvec.conv
+ */
+ @scala.annotation.varargs
+ def to_sparse_features(exprs: Column*): Column = withExpr {
+ // TODO: Need a wrapper class because of using unsupported types
+ planHiveGenericUDF(
+ "hivemall.ftvec.conv.ToSparseFeaturesUDF",
+ "to_sparse_features",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.trans.VectorizeFeaturesUDF]]
+ * @group ftvec.trans
+ */
+ @scala.annotation.varargs
+ def vectorize_features(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.trans.VectorizeFeaturesUDF",
+ "vectorize_features",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.trans.CategoricalFeaturesUDF]]
+ * @group ftvec.trans
+ */
+ @scala.annotation.varargs
+ def categorical_features(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.trans.CategoricalFeaturesUDF",
+ "categorical_features",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.trans.IndexedFeatures]]
+ * @group ftvec.trans
+ */
+ @scala.annotation.varargs
+ def indexed_features(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.trans.IndexedFeatures",
+ "indexed_features",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.ftvec.trans.QuantitativeFeaturesUDF]]
+ * @group ftvec.trans
+ */
+ @scala.annotation.varargs
+ def quantitative_features(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.ftvec.trans.QuantitativeFeaturesUDF",
+ "quantitative_features",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.smile.tools.TreePredictUDF]]
+ * @group misc
+ */
+ @scala.annotation.varargs
+ def tree_predict(exprs: Column*): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.smile.tools.TreePredictUDF",
+ "tree_predict",
+ exprs
+ )
+ }
+
+ /**
+ * @see [[hivemall.tools.array.SelectKBestUDF]]
+ * @group tools.array
+ */
+ def select_k_best(X: Column, importanceList: Column, k: Column): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.tools.array.SelectKBestUDF",
+ "select_k_best",
+ Seq(X, importanceList, k)
+ )
+ }
+
+ /**
+ * @see [[hivemall.tools.math.SigmoidGenericUDF]]
+ * @group misc
+ */
+ def sigmoid(expr: Column): Column = {
+ val one: () => Literal = () => Literal.create(1.0, DoubleType)
+ Column(one()) / (Column(one()) + exp(-expr))
+ }
+
+ /**
+ * @see [[hivemall.tools.mapred.RowIdUDFWrapper]]
+ * @group misc
+ */
+ def rowid(): Column = withExpr {
+ planHiveGenericUDF(
+ "hivemall.tools.mapred.RowIdUDFWrapper",
+ "rowid",
+ Nil
+ )
+ }.as("rowid")
+
+ /**
+ * A convenient function to wrap an expression and produce a Column.
+ */
+ @inline private def withExpr(expr: Expression): Column = Column(expr)
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallUtils.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallUtils.scala b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallUtils.scala
new file mode 100644
index 0000000..056d6d6
--- /dev/null
+++ b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallUtils.scala
@@ -0,0 +1,145 @@
+/*
+ * 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.ml.linalg.{BLAS, DenseVector, SparseVector, Vector, Vectors}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.expressions.UserDefinedFunction
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+
+object HivemallUtils {
+
+ // # of maximum dimensions for feature vectors
+ private[this] val maxDims = 100000000
+
+ /**
+ * Check whether the given schema contains a column of the required data type.
+ * @param colName column name
+ * @param dataType required column data type
+ */
+ private[this] def checkColumnType(schema: StructType, colName: String, dataType: DataType)
+ : Unit = {
+ val actualDataType = schema(colName).dataType
+ require(actualDataType.equals(dataType),
+ s"Column $colName must be of type $dataType but was actually $actualDataType.")
+ }
+
+ def to_vector_func(dense: Boolean, dims: Int): Seq[String] => Vector = {
+ if (dense) {
+ // Dense features
+ i: Seq[String] => {
+ val features = new Array[Double](dims)
+ i.map { ft =>
+ val s = ft.split(":").ensuring(_.size == 2)
+ features(s(0).toInt) = s(1).toDouble
+ }
+ Vectors.dense(features)
+ }
+ } else {
+ // Sparse features
+ i: Seq[String] => {
+ val features = i.map { ft =>
+ // val s = ft.split(":").ensuring(_.size == 2)
+ val s = ft.split(":")
+ (s(0).toInt, s(1).toDouble)
+ }
+ Vectors.sparse(dims, features)
+ }
+ }
+ }
+
+ def to_hivemall_features_func(): Vector => Array[String] = {
+ case dv: DenseVector =>
+ dv.values.zipWithIndex.map {
+ case (value, index) => s"$index:$value"
+ }
+ case sv: SparseVector =>
+ sv.values.zip(sv.indices).map {
+ case (value, index) => s"$index:$value"
+ }
+ case v =>
+ throw new IllegalArgumentException(s"Do not support vector type ${v.getClass}")
+ }
+
+ def append_bias_func(): Vector => Vector = {
+ case dv: DenseVector =>
+ val inputValues = dv.values
+ val inputLength = inputValues.length
+ val outputValues = Array.ofDim[Double](inputLength + 1)
+ System.arraycopy(inputValues, 0, outputValues, 0, inputLength)
+ outputValues(inputLength) = 1.0
+ Vectors.dense(outputValues)
+ case sv: SparseVector =>
+ val inputValues = sv.values
+ val inputIndices = sv.indices
+ val inputValuesLength = inputValues.length
+ val dim = sv.size
+ val outputValues = Array.ofDim[Double](inputValuesLength + 1)
+ val outputIndices = Array.ofDim[Int](inputValuesLength + 1)
+ System.arraycopy(inputValues, 0, outputValues, 0, inputValuesLength)
+ System.arraycopy(inputIndices, 0, outputIndices, 0, inputValuesLength)
+ outputValues(inputValuesLength) = 1.0
+ outputIndices(inputValuesLength) = dim
+ Vectors.sparse(dim + 1, outputIndices, outputValues)
+ case v =>
+ throw new IllegalArgumentException(s"Do not support vector type ${v.getClass}")
+ }
+
+ /**
+ * Transforms Hivemall features into a [[Vector]].
+ */
+ def to_vector(dense: Boolean = false, dims: Int = maxDims): UserDefinedFunction = {
+ udf(to_vector_func(dense, dims))
+ }
+
+ /**
+ * Transforms a [[Vector]] into Hivemall features.
+ */
+ def to_hivemall_features: UserDefinedFunction = udf(to_hivemall_features_func)
+
+ /**
+ * Returns a new [[Vector]] with `1.0` (bias) appended to the input [[Vector]].
+ * @group ftvec
+ */
+ def append_bias: UserDefinedFunction = udf(append_bias_func)
+
+ /**
+ * Builds a [[Vector]]-based model from a table of Hivemall models
+ */
+ def vectorized_model(df: DataFrame, dense: Boolean = false, dims: Int = maxDims)
+ : UserDefinedFunction = {
+ checkColumnType(df.schema, "feature", StringType)
+ checkColumnType(df.schema, "weight", DoubleType)
+
+ import df.sqlContext.implicits._
+ val intercept = df
+ .where($"feature" === "0")
+ .select($"weight")
+ .map { case Row(weight: Double) => weight}
+ .reduce(_ + _)
+ val weights = to_vector_func(dense, dims)(
+ df.select($"feature", $"weight")
+ .where($"feature" !== "0")
+ .map { case Row(label: String, feature: Double) => s"${label}:$feature"}
+ .collect.toSeq)
+
+ udf((input: Vector) => BLAS.dot(input, weights) + intercept)
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala
new file mode 100644
index 0000000..ab5c5fb
--- /dev/null
+++ b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/internal/HivemallOpsImpl.scala
@@ -0,0 +1,78 @@
+/*
+ * 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)
+ ),
+ join = false,
+ outer = false,
+ qualifier = None,
+ generatorOutput = outputAttrNames.map(UnresolvedAttribute(_)),
+ child = df.logicalPlan)
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.scala b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.scala
new file mode 100644
index 0000000..9f2cb64
--- /dev/null
+++ b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/source/XGBoostFileFormat.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.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: Row): Unit = {
+ val model = row.get(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/c837e51a/spark/spark-2.1/src/test/resources/data/files/README.md
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/resources/data/files/README.md b/spark/spark-2.1/src/test/resources/data/files/README.md
new file mode 100644
index 0000000..0fd0299
--- /dev/null
+++ b/spark/spark-2.1/src/test/resources/data/files/README.md
@@ -0,0 +1,3 @@
+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/c837e51a/spark/spark-2.1/src/test/resources/data/files/complex.seq
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/resources/data/files/complex.seq b/spark/spark-2.1/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/c837e51a/spark/spark-2.1/src/test/resources/data/files/episodes.avro
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/resources/data/files/episodes.avro b/spark/spark-2.1/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/c837e51a/spark/spark-2.1/src/test/resources/data/files/json.txt
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/resources/data/files/json.txt b/spark/spark-2.1/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/c837e51a/spark/spark-2.1/src/test/resources/data/files/kv1.txt
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/resources/data/files/kv1.txt b/spark/spark-2.1/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/c837e51a/spark/spark-2.1/src/test/resources/data/files/kv3.txt
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/resources/data/files/kv3.txt b/spark/spark-2.1/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/c837e51a/spark/spark-2.1/src/test/resources/log4j.properties
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/resources/log4j.properties b/spark/spark-2.1/src/test/resources/log4j.properties
new file mode 100644
index 0000000..1db11f0
--- /dev/null
+++ b/spark/spark-2.1/src/test/resources/log4j.properties
@@ -0,0 +1,7 @@
+# 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/c837e51a/spark/spark-2.1/src/test/scala/hivemall/mix/server/MixServerSuite.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/hivemall/mix/server/MixServerSuite.scala b/spark/spark-2.1/src/test/scala/hivemall/mix/server/MixServerSuite.scala
new file mode 100644
index 0000000..dbb818b
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/hivemall/mix/server/MixServerSuite.scala
@@ -0,0 +1,123 @@
+/*
+ * 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/c837e51a/spark/spark-2.1/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala b/spark/spark-2.1/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala
new file mode 100644
index 0000000..8c06837
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/hivemall/tools/RegressionDatagenSuite.scala
@@ -0,0 +1,32 @@
+/*
+ * 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/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/SparkFunSuite.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/SparkFunSuite.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/SparkFunSuite.scala
new file mode 100644
index 0000000..0b101c8
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/SparkFunSuite.scala
@@ -0,0 +1,50 @@
+/*
+ * 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
+
+// scalastyle:off
+import org.scalatest.{FunSuite, Outcome}
+
+import org.apache.spark.internal.Logging
+
+/**
+ * Base abstract class for all unit tests in Spark for handling common functionality.
+ */
+private[spark] abstract class SparkFunSuite extends FunSuite with Logging {
+// scalastyle:on
+
+ /**
+ * Log the suite name and the test name before and after each test.
+ *
+ * Subclasses should never override this method. If they wish to run
+ * custom code before and after each test, they should mix in the
+ * {{org.scalatest.BeforeAndAfter}} trait instead.
+ */
+ final protected override def withFixture(test: NoArgTest): Outcome = {
+ val testName = test.text
+ val suiteName = this.getClass.getName
+ val shortSuiteName = suiteName.replaceAll("org.apache.spark", "o.a.s")
+ try {
+ logInfo(s"\n\n===== TEST OUTPUT FOR $shortSuiteName: '$testName' =====\n")
+ test()
+ } finally {
+ logInfo(s"\n\n===== FINISHED $shortSuiteName: '$testName' =====\n")
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala
new file mode 100644
index 0000000..f57983f
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/ml/feature/HivemallLabeledPointSuite.scala
@@ -0,0 +1,35 @@
+/*
+ * 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/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/QueryTest.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/QueryTest.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/QueryTest.scala
new file mode 100644
index 0000000..14c8f1b
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -0,0 +1,359 @@
+/*
+ * 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
+
+import java.util.{ArrayDeque, Locale, TimeZone}
+
+import scala.collection.JavaConverters._
+import scala.util.control.NonFatal
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.ImperativeAggregate
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.aggregate.TypedAggregateExpression
+import org.apache.spark.sql.execution.columnar.InMemoryRelation
+import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.execution.streaming.MemoryPlan
+import org.apache.spark.sql.types.{Metadata, ObjectType}
+
+
+abstract class QueryTest extends PlanTest {
+
+ protected def spark: SparkSession
+
+ // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
+ TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+ // Add Locale setting
+ Locale.setDefault(Locale.US)
+
+ /**
+ * Runs the plan and makes sure the answer contains all of the keywords.
+ */
+ def checkKeywordsExist(df: DataFrame, keywords: String*): Unit = {
+ val outputs = df.collect().map(_.mkString).mkString
+ for (key <- keywords) {
+ assert(outputs.contains(key), s"Failed for $df ($key doesn't exist in result)")
+ }
+ }
+
+ /**
+ * Runs the plan and makes sure the answer does NOT contain any of the keywords.
+ */
+ def checkKeywordsNotExist(df: DataFrame, keywords: String*): Unit = {
+ val outputs = df.collect().map(_.mkString).mkString
+ for (key <- keywords) {
+ assert(!outputs.contains(key), s"Failed for $df ($key existed in the result)")
+ }
+ }
+
+ /**
+ * Evaluates a dataset to make sure that the result of calling collect matches the given
+ * expected answer.
+ */
+ protected def checkDataset[T](
+ ds: => Dataset[T],
+ expectedAnswer: T*): Unit = {
+ val result = getResult(ds)
+
+ if (!compare(result.toSeq, expectedAnswer)) {
+ fail(
+ s"""
+ |Decoded objects do not match expected objects:
+ |expected: $expectedAnswer
+ |actual: ${result.toSeq}
+ |${ds.exprEnc.deserializer.treeString}
+ """.stripMargin)
+ }
+ }
+
+ /**
+ * Evaluates a dataset to make sure that the result of calling collect matches the given
+ * expected answer, after sort.
+ */
+ protected def checkDatasetUnorderly[T : Ordering](
+ ds: => Dataset[T],
+ expectedAnswer: T*): Unit = {
+ val result = getResult(ds)
+
+ if (!compare(result.toSeq.sorted, expectedAnswer.sorted)) {
+ fail(
+ s"""
+ |Decoded objects do not match expected objects:
+ |expected: $expectedAnswer
+ |actual: ${result.toSeq}
+ |${ds.exprEnc.deserializer.treeString}
+ """.stripMargin)
+ }
+ }
+
+ private def getResult[T](ds: => Dataset[T]): Array[T] = {
+ val analyzedDS = try ds catch {
+ case ae: AnalysisException =>
+ if (ae.plan.isDefined) {
+ fail(
+ s"""
+ |Failed to analyze query: $ae
+ |${ae.plan.get}
+ |
+ |${stackTraceToString(ae)}
+ """.stripMargin)
+ } else {
+ throw ae
+ }
+ }
+ assertEmptyMissingInput(analyzedDS)
+
+ try ds.collect() catch {
+ case e: Exception =>
+ fail(
+ s"""
+ |Exception collecting dataset as objects
+ |${ds.exprEnc}
+ |${ds.exprEnc.deserializer.treeString}
+ |${ds.queryExecution}
+ """.stripMargin, e)
+ }
+ }
+
+ private def compare(obj1: Any, obj2: Any): Boolean = (obj1, obj2) match {
+ case (null, null) => true
+ case (null, _) => false
+ case (_, null) => false
+ case (a: Array[_], b: Array[_]) =>
+ a.length == b.length && a.zip(b).forall { case (l, r) => compare(l, r)}
+ case (a: Iterable[_], b: Iterable[_]) =>
+ a.size == b.size && a.zip(b).forall { case (l, r) => compare(l, r)}
+ case (a, b) => a == b
+ }
+
+ /**
+ * Runs the plan and makes sure the answer matches the expected result.
+ *
+ * @param df the [[DataFrame]] to be executed
+ * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+ */
+ protected def checkAnswer(df: => DataFrame, expectedAnswer: Seq[Row]): Unit = {
+ val analyzedDF = try df catch {
+ case ae: AnalysisException =>
+ if (ae.plan.isDefined) {
+ fail(
+ s"""
+ |Failed to analyze query: $ae
+ |${ae.plan.get}
+ |
+ |${stackTraceToString(ae)}
+ |""".stripMargin)
+ } else {
+ throw ae
+ }
+ }
+
+ assertEmptyMissingInput(analyzedDF)
+
+ QueryTest.checkAnswer(analyzedDF, expectedAnswer) match {
+ case Some(errorMessage) => fail(errorMessage)
+ case None =>
+ }
+ }
+
+ protected def checkAnswer(df: => DataFrame, expectedAnswer: Row): Unit = {
+ checkAnswer(df, Seq(expectedAnswer))
+ }
+
+ protected def checkAnswer(df: => DataFrame, expectedAnswer: DataFrame): Unit = {
+ checkAnswer(df, expectedAnswer.collect())
+ }
+
+ /**
+ * Runs the plan and makes sure the answer is within absTol of the expected result.
+ *
+ * @param dataFrame the [[DataFrame]] to be executed
+ * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+ * @param absTol the absolute tolerance between actual and expected answers.
+ */
+ protected def checkAggregatesWithTol(dataFrame: DataFrame,
+ expectedAnswer: Seq[Row],
+ absTol: Double): Unit = {
+ // TODO: catch exceptions in data frame execution
+ val actualAnswer = dataFrame.collect()
+ require(actualAnswer.length == expectedAnswer.length,
+ s"actual num rows ${actualAnswer.length} != expected num of rows ${expectedAnswer.length}")
+
+ actualAnswer.zip(expectedAnswer).foreach {
+ case (actualRow, expectedRow) =>
+ QueryTest.checkAggregatesWithTol(actualRow, expectedRow, absTol)
+ }
+ }
+
+ protected def checkAggregatesWithTol(dataFrame: DataFrame,
+ expectedAnswer: Row,
+ absTol: Double): Unit = {
+ checkAggregatesWithTol(dataFrame, Seq(expectedAnswer), absTol)
+ }
+
+ /**
+ * Asserts that a given [[Dataset]] will be executed using the given number of cached results.
+ */
+ def assertCached(query: Dataset[_], numCachedTables: Int = 1): Unit = {
+ val planWithCaching = query.queryExecution.withCachedData
+ val cachedData = planWithCaching collect {
+ case cached: InMemoryRelation => cached
+ }
+
+ assert(
+ cachedData.size == numCachedTables,
+ s"Expected query to contain $numCachedTables, but it actually had ${cachedData.size}\n" +
+ planWithCaching)
+ }
+
+ /**
+ * Asserts that a given [[Dataset]] does not have missing inputs in all the analyzed plans.
+ */
+ def assertEmptyMissingInput(query: Dataset[_]): Unit = {
+ assert(query.queryExecution.analyzed.missingInput.isEmpty,
+ s"The analyzed logical plan has missing inputs:\n${query.queryExecution.analyzed}")
+ assert(query.queryExecution.optimizedPlan.missingInput.isEmpty,
+ s"The optimized logical plan has missing inputs:\n${query.queryExecution.optimizedPlan}")
+ assert(query.queryExecution.executedPlan.missingInput.isEmpty,
+ s"The physical plan has missing inputs:\n${query.queryExecution.executedPlan}")
+ }
+}
+
+object QueryTest {
+ /**
+ * Runs the plan and makes sure the answer matches the expected result.
+ * If there was exception during the execution or the contents of the DataFrame does not
+ * match the expected result, an error message will be returned. Otherwise, a [[None]] will
+ * be returned.
+ *
+ * @param df the [[DataFrame]] to be executed
+ * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+ * @param checkToRDD whether to verify deserialization to an RDD. This runs the query twice.
+ */
+ def checkAnswer(
+ df: DataFrame,
+ expectedAnswer: Seq[Row],
+ checkToRDD: Boolean = true): Option[String] = {
+ val isSorted = df.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty
+ if (checkToRDD) {
+ df.rdd.count() // Also attempt to deserialize as an RDD [SPARK-15791]
+ }
+
+ val sparkAnswer = try df.collect().toSeq catch {
+ case e: Exception =>
+ val errorMessage =
+ s"""
+ |Exception thrown while executing query:
+ |${df.queryExecution}
+ |== Exception ==
+ |$e
+ |${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
+ """.stripMargin
+ return Some(errorMessage)
+ }
+
+ sameRows(expectedAnswer, sparkAnswer, isSorted).map { results =>
+ s"""
+ |Results do not match for query:
+ |Timezone: ${TimeZone.getDefault}
+ |Timezone Env: ${sys.env.getOrElse("TZ", "")}
+ |
+ |${df.queryExecution}
+ |== Results ==
+ |$results
+ """.stripMargin
+ }
+ }
+
+
+ def prepareAnswer(answer: Seq[Row], isSorted: Boolean): Seq[Row] = {
+ // Converts data to types that we can do equality comparison using Scala collections.
+ // For BigDecimal type, the Scala type has a better definition of equality test (similar to
+ // Java's java.math.BigDecimal.compareTo).
+ // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for
+ // equality test.
+ val converted: Seq[Row] = answer.map(prepareRow)
+ if (!isSorted) converted.sortBy(_.toString()) else converted
+ }
+
+ // We need to call prepareRow recursively to handle schemas with struct types.
+ def prepareRow(row: Row): Row = {
+ Row.fromSeq(row.toSeq.map {
+ case null => null
+ case d: java.math.BigDecimal => BigDecimal(d)
+ // Convert array to Seq for easy equality check.
+ case b: Array[_] => b.toSeq
+ case r: Row => prepareRow(r)
+ case o => o
+ })
+ }
+
+ def sameRows(
+ expectedAnswer: Seq[Row],
+ sparkAnswer: Seq[Row],
+ isSorted: Boolean = false): Option[String] = {
+ if (prepareAnswer(expectedAnswer, isSorted) != prepareAnswer(sparkAnswer, isSorted)) {
+ val errorMessage =
+ s"""
+ |== Results ==
+ |${sideBySide(
+ s"== Correct Answer - ${expectedAnswer.size} ==" +:
+ prepareAnswer(expectedAnswer, isSorted).map(_.toString()),
+ s"== Spark Answer - ${sparkAnswer.size} ==" +:
+ prepareAnswer(sparkAnswer, isSorted).map(_.toString())).mkString("\n")}
+ """.stripMargin
+ return Some(errorMessage)
+ }
+ None
+ }
+
+ /**
+ * Runs the plan and makes sure the answer is within absTol of the expected result.
+ *
+ * @param actualAnswer the actual result in a [[Row]].
+ * @param expectedAnswer the expected result in a[[Row]].
+ * @param absTol the absolute tolerance between actual and expected answers.
+ */
+ protected def checkAggregatesWithTol(actualAnswer: Row, expectedAnswer: Row, absTol: Double) = {
+ require(actualAnswer.length == expectedAnswer.length,
+ s"actual answer length ${actualAnswer.length} != " +
+ s"expected answer length ${expectedAnswer.length}")
+
+ // TODO: support other numeric types besides Double
+ // TODO: support struct types?
+ actualAnswer.toSeq.zip(expectedAnswer.toSeq).foreach {
+ case (actual: Double, expected: Double) =>
+ assert(math.abs(actual - expected) < absTol,
+ s"actual answer $actual not within $absTol of correct answer $expected")
+ case (actual, expected) =>
+ assert(actual == expected, s"$actual did not equal $expected")
+ }
+ }
+
+ def checkAnswer(df: DataFrame, expectedAnswer: java.util.List[Row]): String = {
+ checkAnswer(df, expectedAnswer.asScala) match {
+ case Some(errorMessage) => errorMessage
+ case None => null
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
new file mode 100644
index 0000000..8672bf2
--- /dev/null
+++ b/spark/spark-2.1/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
@@ -0,0 +1,108 @@
+/*
+ * 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.catalyst.plans
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.util._
+
+/**
+ * Provides helper methods for comparing plans.
+ */
+abstract class PlanTest extends SparkFunSuite with PredicateHelper {
+ /**
+ * Since attribute references are given globally unique ids during analysis,
+ * we must normalize them to check if two different queries are identical.
+ */
+ protected def normalizeExprIds(plan: LogicalPlan) = {
+ plan transformAllExpressions {
+ case s: ScalarSubquery =>
+ s.copy(exprId = ExprId(0))
+ case e: Exists =>
+ e.copy(exprId = ExprId(0))
+ case l: ListQuery =>
+ l.copy(exprId = ExprId(0))
+ case p: PredicateSubquery =>
+ p.copy(exprId = ExprId(0))
+ case a: AttributeReference =>
+ AttributeReference(a.name, a.dataType, a.nullable)(exprId = ExprId(0))
+ case a: Alias =>
+ Alias(a.child, a.name)(exprId = ExprId(0))
+ case ae: AggregateExpression =>
+ ae.copy(resultId = ExprId(0))
+ }
+ }
+
+ /**
+ * Normalizes plans:
+ * - Filter the filter conditions that appear in a plan. For instance,
+ * ((expr 1 && expr 2) && expr 3), (expr 1 && expr 2 && expr 3), (expr 3 && (expr 1 && expr 2)
+ * etc., will all now be equivalent.
+ * - Sample the seed will replaced by 0L.
+ * - Join conditions will be resorted by hashCode.
+ */
+ private def normalizePlan(plan: LogicalPlan): LogicalPlan = {
+ plan transform {
+ case filter @ Filter(condition: Expression, child: LogicalPlan) =>
+ Filter(splitConjunctivePredicates(condition).map(rewriteEqual(_)).sortBy(_.hashCode())
+ .reduce(And), child)
+ case sample: Sample =>
+ sample.copy(seed = 0L)(true)
+ case join @ Join(left, right, joinType, condition) if condition.isDefined =>
+ val newCondition =
+ splitConjunctivePredicates(condition.get).map(rewriteEqual(_)).sortBy(_.hashCode())
+ .reduce(And)
+ Join(left, right, joinType, Some(newCondition))
+ }
+ }
+
+ /**
+ * Rewrite [[EqualTo]] and [[EqualNullSafe]] operator to keep order. The following cases will be
+ * equivalent:
+ * 1. (a = b), (b = a);
+ * 2. (a <=> b), (b <=> a).
+ */
+ private def rewriteEqual(condition: Expression): Expression = condition match {
+ case eq @ EqualTo(l: Expression, r: Expression) =>
+ Seq(l, r).sortBy(_.hashCode()).reduce(EqualTo)
+ case eq @ EqualNullSafe(l: Expression, r: Expression) =>
+ Seq(l, r).sortBy(_.hashCode()).reduce(EqualNullSafe)
+ case _ => condition // Don't reorder.
+ }
+
+ /** Fails the test if the two plans do not match */
+ protected def comparePlans(plan1: LogicalPlan, plan2: LogicalPlan) {
+ val normalized1 = normalizePlan(normalizeExprIds(plan1))
+ val normalized2 = normalizePlan(normalizeExprIds(plan2))
+ if (normalized1 != normalized2) {
+ fail(
+ s"""
+ |== FAIL: Plans do not match ===
+ |${sideBySide(normalized1.treeString, normalized2.treeString).mkString("\n")}
+ """.stripMargin)
+ }
+ }
+
+ /** Fails the test if the two expressions do not match */
+ protected def compareExpressions(e1: Expression, e2: Expression): Unit = {
+ comparePlans(Filter(e1, OneRowRelation), Filter(e2, OneRowRelation))
+ }
+}
[4/4] incubator-hivemall git commit: Close #35,
#23: [HIVEMALL-31][SPARK] Support Spark-v2.1.0
Posted by ya...@apache.org.
Close #35, #23: [HIVEMALL-31][SPARK] Support Spark-v2.1.0
Project: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/commit/c837e51a
Tree: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/tree/c837e51a
Diff: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/diff/c837e51a
Branch: refs/heads/master
Commit: c837e51adcf840f478537d8bb7c9ca810b3df200
Parents: bfd129c
Author: Takeshi YAMAMURO <li...@gmail.com>
Authored: Tue Jan 31 23:04:40 2017 +0900
Committer: Takeshi YAMAMURO <li...@gmail.com>
Committed: Tue Jan 31 23:10:33 2017 +0900
----------------------------------------------------------------------
.travis.yml | 5 +-
NOTICE | 12 +-
pom.xml | 11 +
spark/spark-2.1/bin/mvn-zinc | 99 ++
spark/spark-2.1/extra-src/README | 1 +
.../org/apache/spark/sql/hive/HiveShim.scala | 279 ++++
spark/spark-2.1/pom.xml | 269 ++++
.../java/hivemall/xgboost/XGBoostOptions.scala | 58 +
.../XGBoostBinaryClassifierUDTFWrapper.java | 47 +
.../XGBoostMulticlassClassifierUDTFWrapper.java | 47 +
.../XGBoostRegressionUDTFWrapper.java | 47 +
....apache.spark.sql.sources.DataSourceRegister | 1 +
.../src/main/resources/log4j.properties | 12 +
.../hivemall/tools/RegressionDatagen.scala | 66 +
.../sql/catalyst/expressions/EachTopK.scala | 109 ++
.../spark/sql/hive/HivemallGroupedDataset.scala | 303 ++++
.../org/apache/spark/sql/hive/HivemallOps.scala | 1368 ++++++++++++++++++
.../apache/spark/sql/hive/HivemallUtils.scala | 145 ++
.../sql/hive/internal/HivemallOpsImpl.scala | 78 +
.../sql/hive/source/XGBoostFileFormat.scala | 161 +++
.../src/test/resources/data/files/README.md | 3 +
.../src/test/resources/data/files/complex.seq | 0
.../src/test/resources/data/files/episodes.avro | 0
.../src/test/resources/data/files/json.txt | 0
.../src/test/resources/data/files/kv1.txt | 0
.../src/test/resources/data/files/kv3.txt | 0
.../src/test/resources/log4j.properties | 7 +
.../hivemall/mix/server/MixServerSuite.scala | 123 ++
.../hivemall/tools/RegressionDatagenSuite.scala | 32 +
.../scala/org/apache/spark/SparkFunSuite.scala | 50 +
.../ml/feature/HivemallLabeledPointSuite.scala | 35 +
.../scala/org/apache/spark/sql/QueryTest.scala | 359 +++++
.../spark/sql/catalyst/plans/PlanTest.scala | 108 ++
.../apache/spark/sql/hive/HiveUdfSuite.scala | 160 ++
.../spark/sql/hive/HivemallOpsSuite.scala | 784 ++++++++++
.../spark/sql/hive/ModelMixingSuite.scala | 285 ++++
.../apache/spark/sql/hive/XGBoostSuite.scala | 150 ++
.../sql/hive/benchmark/MiscBenchmark.scala | 224 +++
.../hive/test/HivemallFeatureQueryTest.scala | 112 ++
.../spark/sql/hive/test/TestHiveSingleton.scala | 38 +
.../org/apache/spark/sql/test/SQLTestData.scala | 314 ++++
.../apache/spark/sql/test/SQLTestUtils.scala | 335 +++++
.../apache/spark/sql/test/VectorQueryTest.scala | 88 ++
.../streaming/HivemallOpsWithFeatureSuite.scala | 154 ++
.../scala/org/apache/spark/test/TestUtils.scala | 64 +
45 files changed, 6538 insertions(+), 5 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/.travis.yml
----------------------------------------------------------------------
diff --git a/.travis.yml b/.travis.yml
index ffa529c..a4cb7ea 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -34,8 +34,9 @@ notifications:
email: false
script:
- - mvn -q scalastyle:check test -Pspark-2.0
- # test the spark-1.6 module only in this second run
+ - mvn -q scalastyle:check test -Pspark-2.1
+ # test the spark-1.6/2.0 modules only in the following runs
+ - mvn -q scalastyle:check clean -Pspark-2.0 -pl spark/spark-2.0 -am test -Dtest=none
- mvn -q scalastyle:check clean -Pspark-1.6 -pl spark/spark-1.6 -am test -Dtest=none
after_success:
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/NOTICE
----------------------------------------------------------------------
diff --git a/NOTICE b/NOTICE
index 013774b..699b055 100644
--- a/NOTICE
+++ b/NOTICE
@@ -38,7 +38,7 @@ Copyright notifications which have been relocated from ASF projects
o hivemall/core/src/main/java/hivemall/utils/math/MathUtils.java#erfInv()
- Copyright (C) 2003-2016 The Apache Software Foundation.
+ Copyright (C) 2003-2016 The Apache Software Foundation.
http://commons.apache.org/proper/commons-math/
Licensed under the Apache License, Version 2.0
@@ -55,8 +55,14 @@ o hivemall/spark/spark-1.6/extra-src/hive/src/main/scala/org/apache/spark/sql/hi
hivemall/spark/spark-2.0/src/test/scala/org/apache/spark/sql/QueryTest.scala
hivemall/spark/spark-2.0/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
hivemall/spark/spark-2.0/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala
-
- Copyright (C) 2014-2016 The Apache Software Foundation.
+ hivemall/spark/spark-2.1/extra-src/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+ hivemall/spark/spark-2.1/src/test/scala/org/apache/spark/sql/QueryTest.scala
+ hivemall/spark/spark-2.1/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
+ hivemall/spark/spark-2.1/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala
+ hivemall/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+ hivemall/spark/spark-2.1/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
+
+ Copyright (C) 2014-2017 The Apache Software Foundation.
http://spark.apache.org/
Licensed under the Apache License, Version 2.0
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/pom.xml
----------------------------------------------------------------------
diff --git a/pom.xml b/pom.xml
index 715e8d4..17ee4ee 100644
--- a/pom.xml
+++ b/pom.xml
@@ -235,6 +235,17 @@
<profiles>
<profile>
+ <id>spark-2.1</id>
+ <modules>
+ <module>spark/spark-2.1</module>
+ <module>spark/spark-common</module>
+ </modules>
+ <properties>
+ <spark.version>2.1.0</spark.version>
+ <spark.binary.version>2.1</spark.binary.version>
+ </properties>
+ </profile>
+ <profile>
<id>spark-2.0</id>
<modules>
<module>spark/spark-2.0</module>
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/bin/mvn-zinc
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/bin/mvn-zinc b/spark/spark-2.1/bin/mvn-zinc
new file mode 100755
index 0000000..759b0a5
--- /dev/null
+++ b/spark/spark-2.1/bin/mvn-zinc
@@ -0,0 +1,99 @@
+#!/usr/bin/env bash
+
+#
+# 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.
+#
+
+# Copyed from commit 48682f6bf663e54cb63b7e95a4520d34b6fa890b in Apache Spark
+
+# Determine the current working directory
+_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+# Preserve the calling directory
+_CALLING_DIR="$(pwd)"
+# Options used during compilation
+_COMPILE_JVM_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
+
+# Installs any application tarball given a URL, the expected tarball name,
+# and, optionally, a checkable binary path to determine if the binary has
+# already been installed
+## Arg1 - URL
+## Arg2 - Tarball Name
+## Arg3 - Checkable Binary
+install_app() {
+ local remote_tarball="$1/$2"
+ local local_tarball="${_DIR}/$2"
+ local binary="${_DIR}/$3"
+ local curl_opts="--progress-bar -L"
+ local wget_opts="--progress=bar:force ${wget_opts}"
+
+ if [ -z "$3" -o ! -f "$binary" ]; then
+ # check if we already have the tarball
+ # check if we have curl installed
+ # download application
+ [ ! -f "${local_tarball}" ] && [ $(command -v curl) ] && \
+ echo "exec: curl ${curl_opts} ${remote_tarball}" 1>&2 && \
+ curl ${curl_opts} "${remote_tarball}" > "${local_tarball}"
+ # if the file still doesn't exist, lets try `wget` and cross our fingers
+ [ ! -f "${local_tarball}" ] && [ $(command -v wget) ] && \
+ echo "exec: wget ${wget_opts} ${remote_tarball}" 1>&2 && \
+ wget ${wget_opts} -O "${local_tarball}" "${remote_tarball}"
+ # if both were unsuccessful, exit
+ [ ! -f "${local_tarball}" ] && \
+ echo -n "ERROR: Cannot download $2 with cURL or wget; " && \
+ echo "please install manually and try again." && \
+ exit 2
+ cd "${_DIR}" && tar -xzf "$2"
+ rm -rf "$local_tarball"
+ fi
+}
+
+# Install zinc under the bin/ folder
+install_zinc() {
+ local zinc_path="zinc-0.3.9/bin/zinc"
+ [ ! -f "${_DIR}/${zinc_path}" ] && ZINC_INSTALL_FLAG=1
+ install_app \
+ "http://downloads.typesafe.com/zinc/0.3.9" \
+ "zinc-0.3.9.tgz" \
+ "${zinc_path}"
+ ZINC_BIN="${_DIR}/${zinc_path}"
+}
+
+# Setup healthy defaults for the Zinc port if none were provided from
+# the environment
+ZINC_PORT=${ZINC_PORT:-"3030"}
+
+# Install Zinc for the bin/
+install_zinc
+
+# Reset the current working directory
+cd "${_CALLING_DIR}"
+
+# Now that zinc is ensured to be installed, check its status and, if its
+# not running or just installed, start it
+if [ ! -f "${ZINC_BIN}" ]; then
+ exit -1
+fi
+if [ -n "${ZINC_INSTALL_FLAG}" -o -z "`"${ZINC_BIN}" -status -port ${ZINC_PORT}`" ]; then
+ export ZINC_OPTS=${ZINC_OPTS:-"$_COMPILE_JVM_OPTS"}
+ "${ZINC_BIN}" -shutdown -port ${ZINC_PORT}
+ "${ZINC_BIN}" -start -port ${ZINC_PORT} &>/dev/null
+fi
+
+# Set any `mvn` options if not already present
+export MAVEN_OPTS=${MAVEN_OPTS:-"$_COMPILE_JVM_OPTS"}
+
+# Last, call the `mvn` command as usual
+mvn -DzincPort=${ZINC_PORT} "$@"
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/extra-src/README
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/extra-src/README b/spark/spark-2.1/extra-src/README
new file mode 100644
index 0000000..8b5d0cd
--- /dev/null
+++ b/spark/spark-2.1/extra-src/README
@@ -0,0 +1 @@
+Copyed from spark master [commit 908e37bcc10132bb2aa7f80ae694a9df6e40f31a]
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/extra-src/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/extra-src/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/spark/spark-2.1/extra-src/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
new file mode 100644
index 0000000..9e98948
--- /dev/null
+++ b/spark/spark-2.1/extra-src/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -0,0 +1,279 @@
+/*
+ * 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 java.io.{InputStream, OutputStream}
+import java.rmi.server.UID
+
+import scala.collection.JavaConverters._
+import scala.language.implicitConversions
+import scala.reflect.ClassTag
+
+import com.google.common.base.Objects
+import org.apache.avro.Schema
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.hive.ql.exec.{UDF, Utilities}
+import org.apache.hadoop.hive.ql.plan.{FileSinkDesc, TableDesc}
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFMacro
+import org.apache.hadoop.hive.serde2.ColumnProjectionUtils
+import org.apache.hadoop.hive.serde2.avro.{AvroGenericRecordWritable, AvroSerdeUtils}
+import org.apache.hadoop.hive.serde2.objectinspector.primitive.HiveDecimalObjectInspector
+import org.apache.hadoop.io.Writable
+import org.apache.hive.com.esotericsoftware.kryo.Kryo
+import org.apache.hive.com.esotericsoftware.kryo.io.{Input, Output}
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.types.Decimal
+import org.apache.spark.util.Utils
+
+private[hive] object HiveShim {
+ // Precision and scale to pass for unlimited decimals; these are the same as the precision and
+ // scale Hive 0.13 infers for BigDecimals from sources that don't specify them (e.g. UDFs)
+ val UNLIMITED_DECIMAL_PRECISION = 38
+ val UNLIMITED_DECIMAL_SCALE = 18
+ val HIVE_GENERIC_UDF_MACRO_CLS = "org.apache.hadoop.hive.ql.udf.generic.GenericUDFMacro"
+
+ /*
+ * This function in hive-0.13 become private, but we have to do this to walkaround hive bug
+ */
+ private def appendReadColumnNames(conf: Configuration, cols: Seq[String]) {
+ val old: String = conf.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, "")
+ val result: StringBuilder = new StringBuilder(old)
+ var first: Boolean = old.isEmpty
+
+ for (col <- cols) {
+ if (first) {
+ first = false
+ } else {
+ result.append(',')
+ }
+ result.append(col)
+ }
+ conf.set(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, result.toString)
+ }
+
+ /*
+ * Cannot use ColumnProjectionUtils.appendReadColumns directly, if ids is null
+ */
+ def appendReadColumns(conf: Configuration, ids: Seq[Integer], names: Seq[String]) {
+ if (ids != null) {
+ ColumnProjectionUtils.appendReadColumns(conf, ids.asJava)
+ }
+ if (names != null) {
+ appendReadColumnNames(conf, names)
+ }
+ }
+
+ /*
+ * Bug introduced in hive-0.13. AvroGenericRecordWritable has a member recordReaderID that
+ * is needed to initialize before serialization.
+ */
+ def prepareWritable(w: Writable, serDeProps: Seq[(String, String)]): Writable = {
+ w match {
+ case w: AvroGenericRecordWritable =>
+ w.setRecordReaderID(new UID())
+ // In Hive 1.1, the record's schema may need to be initialized manually or a NPE will
+ // be thrown.
+ if (w.getFileSchema() == null) {
+ serDeProps
+ .find(_._1 == AvroSerdeUtils.AvroTableProperties.SCHEMA_LITERAL.getPropName())
+ .foreach { kv =>
+ w.setFileSchema(new Schema.Parser().parse(kv._2))
+ }
+ }
+ case _ =>
+ }
+ w
+ }
+
+ def toCatalystDecimal(hdoi: HiveDecimalObjectInspector, data: Any): Decimal = {
+ if (hdoi.preferWritable()) {
+ Decimal(hdoi.getPrimitiveWritableObject(data).getHiveDecimal().bigDecimalValue,
+ hdoi.precision(), hdoi.scale())
+ } else {
+ Decimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue(), hdoi.precision(), hdoi.scale())
+ }
+ }
+
+ /**
+ * This class provides the UDF creation and also the UDF instance serialization and
+ * de-serialization cross process boundary.
+ *
+ * Detail discussion can be found at https://github.com/apache/spark/pull/3640
+ *
+ * @param functionClassName UDF class name
+ * @param instance optional UDF instance which contains additional information (for macro)
+ */
+ private[hive] case class HiveFunctionWrapper(var functionClassName: String,
+ private var instance: AnyRef = null) extends java.io.Externalizable {
+
+ // for Serialization
+ def this() = this(null)
+
+ override def hashCode(): Int = {
+ if (functionClassName == HIVE_GENERIC_UDF_MACRO_CLS) {
+ Objects.hashCode(functionClassName, instance.asInstanceOf[GenericUDFMacro].getBody())
+ } else {
+ functionClassName.hashCode()
+ }
+ }
+
+ override def equals(other: Any): Boolean = other match {
+ case a: HiveFunctionWrapper if functionClassName == a.functionClassName =>
+ // In case of udf macro, check to make sure they point to the same underlying UDF
+ if (functionClassName == HIVE_GENERIC_UDF_MACRO_CLS) {
+ a.instance.asInstanceOf[GenericUDFMacro].getBody() ==
+ instance.asInstanceOf[GenericUDFMacro].getBody()
+ } else {
+ true
+ }
+ case _ => false
+ }
+
+ @transient
+ def deserializeObjectByKryo[T: ClassTag](
+ kryo: Kryo,
+ in: InputStream,
+ clazz: Class[_]): T = {
+ val inp = new Input(in)
+ val t: T = kryo.readObject(inp, clazz).asInstanceOf[T]
+ inp.close()
+ t
+ }
+
+ @transient
+ def serializeObjectByKryo(
+ kryo: Kryo,
+ plan: Object,
+ out: OutputStream) {
+ val output: Output = new Output(out)
+ kryo.writeObject(output, plan)
+ output.close()
+ }
+
+ def deserializePlan[UDFType](is: java.io.InputStream, clazz: Class[_]): UDFType = {
+ deserializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), is, clazz)
+ .asInstanceOf[UDFType]
+ }
+
+ def serializePlan(function: AnyRef, out: java.io.OutputStream): Unit = {
+ serializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), function, out)
+ }
+
+ def writeExternal(out: java.io.ObjectOutput) {
+ // output the function name
+ out.writeUTF(functionClassName)
+
+ // Write a flag if instance is null or not
+ out.writeBoolean(instance != null)
+ if (instance != null) {
+ // Some of the UDF are serializable, but some others are not
+ // Hive Utilities can handle both cases
+ val baos = new java.io.ByteArrayOutputStream()
+ serializePlan(instance, baos)
+ val functionInBytes = baos.toByteArray
+
+ // output the function bytes
+ out.writeInt(functionInBytes.length)
+ out.write(functionInBytes, 0, functionInBytes.length)
+ }
+ }
+
+ def readExternal(in: java.io.ObjectInput) {
+ // read the function name
+ functionClassName = in.readUTF()
+
+ if (in.readBoolean()) {
+ // if the instance is not null
+ // read the function in bytes
+ val functionInBytesLength = in.readInt()
+ val functionInBytes = new Array[Byte](functionInBytesLength)
+ in.readFully(functionInBytes)
+
+ // deserialize the function object via Hive Utilities
+ instance = deserializePlan[AnyRef](new java.io.ByteArrayInputStream(functionInBytes),
+ Utils.getContextOrSparkClassLoader.loadClass(functionClassName))
+ }
+ }
+
+ def createFunction[UDFType <: AnyRef](): UDFType = {
+ if (instance != null) {
+ instance.asInstanceOf[UDFType]
+ } else {
+ val func = Utils.getContextOrSparkClassLoader
+ .loadClass(functionClassName).newInstance.asInstanceOf[UDFType]
+ if (!func.isInstanceOf[UDF]) {
+ // We cache the function if it's no the Simple UDF,
+ // as we always have to create new instance for Simple UDF
+ instance = func
+ }
+ func
+ }
+ }
+ }
+
+ /*
+ * Bug introduced in hive-0.13. FileSinkDesc is serializable, but its member path is not.
+ * Fix it through wrapper.
+ */
+ implicit def wrapperToFileSinkDesc(w: ShimFileSinkDesc): FileSinkDesc = {
+ val f = new FileSinkDesc(new Path(w.dir), w.tableInfo, w.compressed)
+ f.setCompressCodec(w.compressCodec)
+ f.setCompressType(w.compressType)
+ f.setTableInfo(w.tableInfo)
+ f.setDestTableId(w.destTableId)
+ f
+ }
+
+ /*
+ * Bug introduced in hive-0.13. FileSinkDesc is serializable, but its member path is not.
+ * Fix it through wrapper.
+ */
+ private[hive] class ShimFileSinkDesc(
+ var dir: String,
+ var tableInfo: TableDesc,
+ var compressed: Boolean)
+ extends Serializable with Logging {
+ var compressCodec: String = _
+ var compressType: String = _
+ var destTableId: Int = _
+
+ def setCompressed(compressed: Boolean) {
+ this.compressed = compressed
+ }
+
+ def getDirName(): String = dir
+
+ def setDestTableId(destTableId: Int) {
+ this.destTableId = destTableId
+ }
+
+ def setTableInfo(tableInfo: TableDesc) {
+ this.tableInfo = tableInfo
+ }
+
+ def setCompressCodec(intermediateCompressorCodec: String) {
+ compressCodec = intermediateCompressorCodec
+ }
+
+ def setCompressType(intermediateCompressType: String) {
+ compressType = intermediateCompressType
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/pom.xml
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/pom.xml b/spark/spark-2.1/pom.xml
new file mode 100644
index 0000000..a0f380f
--- /dev/null
+++ b/spark/spark-2.1/pom.xml
@@ -0,0 +1,269 @@
+<!--
+ 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.
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+ xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+ <modelVersion>4.0.0</modelVersion>
+
+ <parent>
+ <groupId>io.github.myui</groupId>
+ <artifactId>hivemall</artifactId>
+ <version>0.4.2-rc.2</version>
+ <relativePath>../../pom.xml</relativePath>
+ </parent>
+
+ <artifactId>hivemall-spark</artifactId>
+ <name>Hivemall on Spark 2.1</name>
+ <packaging>jar</packaging>
+
+ <properties>
+ <PermGen>64m</PermGen>
+ <MaxPermGen>512m</MaxPermGen>
+ <CodeCacheSize>512m</CodeCacheSize>
+ <main.basedir>${project.parent.basedir}</main.basedir>
+ </properties>
+
+ <dependencies>
+ <!-- hivemall dependencies -->
+ <dependency>
+ <groupId>io.github.myui</groupId>
+ <artifactId>hivemall-core</artifactId>
+ <version>${project.version}</version>
+ <scope>compile</scope>
+ </dependency>
+ <dependency>
+ <groupId>io.github.myui</groupId>
+ <artifactId>hivemall-xgboost</artifactId>
+ <version>${project.version}</version>
+ <scope>compile</scope>
+ </dependency>
+ <dependency>
+ <groupId>io.github.myui</groupId>
+ <artifactId>hivemall-spark-common</artifactId>
+ <version>${project.version}</version>
+ <scope>compile</scope>
+ </dependency>
+
+ <!-- third-party dependencies -->
+ <dependency>
+ <groupId>org.scala-lang</groupId>
+ <artifactId>scala-library</artifactId>
+ <version>${scala.version}</version>
+ <scope>compile</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-compress</artifactId>
+ <version>1.8</version>
+ <scope>compile</scope>
+ </dependency>
+
+ <!-- other provided dependencies -->
+ <dependency>
+ <groupId>org.apache.spark</groupId>
+ <artifactId>spark-core_${scala.binary.version}</artifactId>
+ <version>${spark.version}</version>
+ <scope>provided</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.spark</groupId>
+ <artifactId>spark-sql_${scala.binary.version}</artifactId>
+ <version>${spark.version}</version>
+ <scope>provided</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.spark</groupId>
+ <artifactId>spark-hive_${scala.binary.version}</artifactId>
+ <version>${spark.version}</version>
+ <scope>provided</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.spark</groupId>
+ <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+ <version>${spark.version}</version>
+ <scope>provided</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.spark</groupId>
+ <artifactId>spark-mllib_${scala.binary.version}</artifactId>
+ <version>${spark.version}</version>
+ <scope>provided</scope>
+ </dependency>
+
+ <!-- test dependencies -->
+ <dependency>
+ <groupId>io.github.myui</groupId>
+ <artifactId>hivemall-mixserv</artifactId>
+ <version>${project.version}</version>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.xerial</groupId>
+ <artifactId>xerial-core</artifactId>
+ <version>3.2.3</version>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.scalatest</groupId>
+ <artifactId>scalatest_${scala.binary.version}</artifactId>
+ <version>2.2.4</version>
+ <scope>test</scope>
+ </dependency>
+ </dependencies>
+
+ <build>
+ <directory>target</directory>
+ <outputDirectory>target/classes</outputDirectory>
+ <finalName>${project.artifactId}-${spark.binary.version}_${scala.binary.version}-${project.version}</finalName>
+ <testOutputDirectory>target/test-classes</testOutputDirectory>
+ <plugins>
+ <!-- For incremental compilation -->
+ <plugin>
+ <groupId>net.alchim31.maven</groupId>
+ <artifactId>scala-maven-plugin</artifactId>
+ <version>3.2.2</version>
+ <executions>
+ <execution>
+ <id>scala-compile-first</id>
+ <phase>process-resources</phase>
+ <goals>
+ <goal>compile</goal>
+ </goals>
+ </execution>
+ <execution>
+ <id>scala-test-compile-first</id>
+ <phase>process-test-resources</phase>
+ <goals>
+ <goal>testCompile</goal>
+ </goals>
+ </execution>
+ </executions>
+ <configuration>
+ <scalaVersion>${scala.version}</scalaVersion>
+ <recompileMode>incremental</recompileMode>
+ <useZincServer>true</useZincServer>
+ <args>
+ <arg>-unchecked</arg>
+ <arg>-deprecation</arg>
+ <!-- TODO: To enable this option, we need to fix many wornings -->
+ <!-- <arg>-feature</arg> -->
+ </args>
+ <jvmArgs>
+ <jvmArg>-Xms1024m</jvmArg>
+ <jvmArg>-Xmx1024m</jvmArg>
+ <jvmArg>-XX:PermSize=${PermGen}</jvmArg>
+ <jvmArg>-XX:MaxPermSize=${MaxPermGen}</jvmArg>
+ <jvmArg>-XX:ReservedCodeCacheSize=${CodeCacheSize}</jvmArg>
+ </jvmArgs>
+ </configuration>
+ </plugin>
+ <!-- hivemall-spark_xx-xx.jar -->
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-jar-plugin</artifactId>
+ <version>2.5</version>
+ <configuration>
+ <finalName>${project.artifactId}-${spark.binary.version}_${scala.binary.version}-${project.version}</finalName>
+ <outputDirectory>${project.parent.build.directory}</outputDirectory>
+ </configuration>
+ </plugin>
+ <!-- hivemall-spark_xx-xx-with-dependencies.jar including minimum dependencies -->
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-shade-plugin</artifactId>
+ <version>2.3</version>
+ <executions>
+ <execution>
+ <id>jar-with-dependencies</id>
+ <phase>package</phase>
+ <goals>
+ <goal>shade</goal>
+ </goals>
+ <configuration>
+ <finalName>${project.artifactId}-${spark.binary.version}_${scala.binary.version}-${project.version}-with-dependencies</finalName>
+ <outputDirectory>${project.parent.build.directory}</outputDirectory>
+ <minimizeJar>false</minimizeJar>
+ <createDependencyReducedPom>false</createDependencyReducedPom>
+ <artifactSet>
+ <includes>
+ <include>io.github.myui:hivemall-core</include>
+ <include>io.github.myui:hivemall-xgboost</include>
+ <include>io.github.myui:hivemall-spark-common</include>
+ <include>com.github.haifengl:smile-core</include>
+ <include>com.github.haifengl:smile-math</include>
+ <include>com.github.haifengl:smile-data</include>
+ <include>ml.dmlc:xgboost4j</include>
+ <include>com.esotericsoftware.kryo:kryo</include>
+ </includes>
+ </artifactSet>
+ </configuration>
+ </execution>
+ </executions>
+ </plugin>
+ <!-- disable surefire because there is no java test -->
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-surefire-plugin</artifactId>
+ <version>2.7</version>
+ <configuration>
+ <skipTests>true</skipTests>
+ </configuration>
+ </plugin>
+ <!-- then, enable scalatest -->
+ <plugin>
+ <groupId>org.scalatest</groupId>
+ <artifactId>scalatest-maven-plugin</artifactId>
+ <version>1.0</version>
+ <configuration>
+ <reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory>
+ <junitxml>.</junitxml>
+ <filereports>SparkTestSuite.txt</filereports>
+ <argLine>-ea -Xmx2g -XX:MaxPermSize=${MaxPermGen} -XX:ReservedCodeCacheSize=${CodeCacheSize}</argLine>
+ <stderr/>
+ <environmentVariables>
+ <SPARK_PREPEND_CLASSES>1</SPARK_PREPEND_CLASSES>
+ <SPARK_SCALA_VERSION>${scala.binary.version}</SPARK_SCALA_VERSION>
+ <SPARK_TESTING>1</SPARK_TESTING>
+ <JAVA_HOME>${env.JAVA_HOME}</JAVA_HOME>
+ </environmentVariables>
+ <systemProperties>
+ <log4j.configuration>file:src/test/resources/log4j.properties</log4j.configuration>
+ <derby.system.durability>test</derby.system.durability>
+ <java.awt.headless>true</java.awt.headless>
+ <java.io.tmpdir>${project.build.directory}/tmp</java.io.tmpdir>
+ <spark.testing>1</spark.testing>
+ <spark.ui.enabled>false</spark.ui.enabled>
+ <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
+ <spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
+ <!-- Needed by sql/hive tests. -->
+ <test.src.tables>__not_used__</test.src.tables>
+ </systemProperties>
+ <tagsToExclude>${test.exclude.tags}</tagsToExclude>
+ </configuration>
+ <executions>
+ <execution>
+ <id>test</id>
+ <goals>
+ <goal>test</goal>
+ </goals>
+ </execution>
+ </executions>
+ </plugin>
+ </plugins>
+ </build>
+</project>
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/java/hivemall/xgboost/XGBoostOptions.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/java/hivemall/xgboost/XGBoostOptions.scala b/spark/spark-2.1/src/main/java/hivemall/xgboost/XGBoostOptions.scala
new file mode 100644
index 0000000..48a773b
--- /dev/null
+++ b/spark/spark-2.1/src/main/java/hivemall/xgboost/XGBoostOptions.scala
@@ -0,0 +1,58 @@
+/*
+ * 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.xgboost
+
+import scala.collection.mutable
+
+import org.apache.commons.cli.Options
+import org.apache.spark.annotation.AlphaComponent
+
+/**
+ * :: AlphaComponent ::
+ * An utility class to generate a sequence of options used in XGBoost.
+ */
+@AlphaComponent
+case class XGBoostOptions() {
+ private val params: mutable.Map[String, String] = mutable.Map.empty
+ private val options: Options = {
+ new XGBoostUDTF() {
+ def options(): Options = super.getOptions()
+ }.options()
+ }
+
+ private def isValidKey(key: String): Boolean = {
+ // TODO: Is there another way to handle all the XGBoost options?
+ options.hasOption(key) || key == "num_class"
+ }
+
+ def set(key: String, value: String): XGBoostOptions = {
+ require(isValidKey(key), s"non-existing key detected in XGBoost options: ${key}")
+ params.put(key, value)
+ this
+ }
+
+ def help(): Unit = {
+ import scala.collection.JavaConversions._
+ options.getOptions.map { case option => println(option) }
+ }
+
+ override def toString(): String = {
+ params.map { case (key, value) => s"-$key $value" }.mkString(" ")
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostBinaryClassifierUDTFWrapper.java
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostBinaryClassifierUDTFWrapper.java b/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostBinaryClassifierUDTFWrapper.java
new file mode 100644
index 0000000..310d15e
--- /dev/null
+++ b/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostBinaryClassifierUDTFWrapper.java
@@ -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 hivemall.xgboost.classification;
+
+import java.util.UUID;
+
+import org.apache.hadoop.hive.ql.exec.Description;
+
+/** An alternative implementation of [[hivemall.xgboost.classification.XGBoostBinaryClassifierUDTF]]. */
+@Description(
+ name = "train_xgboost_classifier",
+ value = "_FUNC_(string[] features, double target [, string options]) - Returns a relation consisting of <string model_id, array<byte> pred_model>"
+)
+public class XGBoostBinaryClassifierUDTFWrapper extends XGBoostBinaryClassifierUDTF {
+ private long sequence;
+ private long taskId;
+
+ public XGBoostBinaryClassifierUDTFWrapper() {
+ this.sequence = 0L;
+ this.taskId = Thread.currentThread().getId();
+ }
+
+ @Override
+ protected String generateUniqueModelId() {
+ sequence++;
+ /**
+ * TODO: Check if it is unique over all tasks in executors of Spark.
+ */
+ return "xgbmodel-" + taskId + "-" + UUID.randomUUID() + "-" + sequence;
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostMulticlassClassifierUDTFWrapper.java
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostMulticlassClassifierUDTFWrapper.java b/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostMulticlassClassifierUDTFWrapper.java
new file mode 100644
index 0000000..81e6fe8
--- /dev/null
+++ b/spark/spark-2.1/src/main/java/hivemall/xgboost/classification/XGBoostMulticlassClassifierUDTFWrapper.java
@@ -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 hivemall.xgboost.classification;
+
+import java.util.UUID;
+
+import org.apache.hadoop.hive.ql.exec.Description;
+
+/** An alternative implementation of [[hivemall.xgboost.classification.XGBoostMulticlassClassifierUDTFWrapper]]. */
+@Description(
+ name = "train_multiclass_xgboost_classifier",
+ value = "_FUNC_(string[] features, double target [, string options]) - Returns a relation consisting of <string model_id, array<byte> pred_model>"
+)
+public class XGBoostMulticlassClassifierUDTFWrapper extends XGBoostMulticlassClassifierUDTF {
+ private long sequence;
+ private long taskId;
+
+ public XGBoostMulticlassClassifierUDTFWrapper() {
+ this.sequence = 0L;
+ this.taskId = Thread.currentThread().getId();
+ }
+
+ @Override
+ protected String generateUniqueModelId() {
+ sequence++;
+ /**
+ * TODO: Check if it is unique over all tasks in executors of Spark.
+ */
+ return "xgbmodel-" + taskId + "-" + UUID.randomUUID() + "-" + sequence;
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/java/hivemall/xgboost/regression/XGBoostRegressionUDTFWrapper.java
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/java/hivemall/xgboost/regression/XGBoostRegressionUDTFWrapper.java b/spark/spark-2.1/src/main/java/hivemall/xgboost/regression/XGBoostRegressionUDTFWrapper.java
new file mode 100644
index 0000000..b72e045
--- /dev/null
+++ b/spark/spark-2.1/src/main/java/hivemall/xgboost/regression/XGBoostRegressionUDTFWrapper.java
@@ -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 hivemall.xgboost.regression;
+
+import java.util.UUID;
+
+import org.apache.hadoop.hive.ql.exec.Description;
+
+/** An alternative implementation of [[hivemall.xgboost.regression.XGBoostRegressionUDTF]]. */
+@Description(
+ name = "train_xgboost_regr",
+ value = "_FUNC_(string[] features, double target [, string options]) - Returns a relation consisting of <string model_id, array<byte> pred_model>"
+)
+public class XGBoostRegressionUDTFWrapper extends XGBoostRegressionUDTF {
+ private long sequence;
+ private long taskId;
+
+ public XGBoostRegressionUDTFWrapper() {
+ this.sequence = 0L;
+ this.taskId = Thread.currentThread().getId();
+ }
+
+ @Override
+ protected String generateUniqueModelId() {
+ sequence++;
+ /**
+ * TODO: Check if it is unique over all tasks in executors of Spark.
+ */
+ return "xgbmodel-" + taskId + "-" + UUID.randomUUID() + "-" + sequence;
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
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diff --git a/spark/spark-2.1/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/spark/spark-2.1/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000..b49e20a
--- /dev/null
+++ b/spark/spark-2.1/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1 @@
+org.apache.spark.sql.hive.source.XGBoostFileFormat
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/resources/log4j.properties
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/resources/log4j.properties b/spark/spark-2.1/src/main/resources/log4j.properties
new file mode 100644
index 0000000..72bf5b6
--- /dev/null
+++ b/spark/spark-2.1/src/main/resources/log4j.properties
@@ -0,0 +1,12 @@
+# Set everything to be logged to the console
+log4j.rootCategory=INFO, 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
+
+# Settings to quiet third party logs that are too verbose
+log4j.logger.org.eclipse.jetty=INFO
+log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
+log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
+log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/scala/hivemall/tools/RegressionDatagen.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/scala/hivemall/tools/RegressionDatagen.scala b/spark/spark-2.1/src/main/scala/hivemall/tools/RegressionDatagen.scala
new file mode 100644
index 0000000..72a5c83
--- /dev/null
+++ b/spark/spark-2.1/src/main/scala/hivemall/tools/RegressionDatagen.scala
@@ -0,0 +1,66 @@
+/*
+ * 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.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.hive.HivemallOps._
+import org.apache.spark.sql.types._
+
+object RegressionDatagen {
+
+ /**
+ * Generate data for regression/classification.
+ * See [[hivemall.dataset.LogisticRegressionDataGeneratorUDTF]]
+ * for the details of arguments below.
+ */
+ def exec(sc: SQLContext,
+ n_partitions: Int = 2,
+ min_examples: Int = 1000,
+ n_features: Int = 10,
+ n_dims: Int = 200,
+ seed: Int = 43,
+ dense: Boolean = false,
+ prob_one: Float = 0.6f,
+ sort: Boolean = false,
+ cl: Boolean = false): DataFrame = {
+
+ require(n_partitions > 0, "Non-negative #n_partitions required.")
+ require(min_examples > 0, "Non-negative #min_examples required.")
+ require(n_features > 0, "Non-negative #n_features required.")
+ require(n_dims > 0, "Non-negative #n_dims required.")
+
+ // Calculate #examples to generate in each partition
+ val n_examples = (min_examples + n_partitions - 1) / n_partitions
+
+ val df = sc.createDataFrame(
+ sc.sparkContext.parallelize((0 until n_partitions).map(Row(_)), n_partitions),
+ StructType(
+ StructField("data", IntegerType, true) ::
+ Nil)
+ )
+ import sc.implicits._
+ df.lr_datagen(
+ lit(s"-n_examples $n_examples -n_features $n_features -n_dims $n_dims -prob_one $prob_one"
+ + (if (dense) " -dense" else "")
+ + (if (sort) " -sort" else "")
+ + (if (cl) " -cl" else ""))
+ ).select($"label".cast(DoubleType).as("label"), $"features")
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/catalyst/expressions/EachTopK.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/catalyst/expressions/EachTopK.scala b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/catalyst/expressions/EachTopK.scala
new file mode 100644
index 0000000..491363d
--- /dev/null
+++ b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/catalyst/expressions/EachTopK.scala
@@ -0,0 +1,109 @@
+/*
+ * 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.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.catalyst.util.TypeUtils
+import org.apache.spark.sql.types._
+import org.apache.spark.util.BoundedPriorityQueue
+
+case class EachTopK(
+ k: Int,
+ groupingExpression: Expression,
+ scoreExpression: Expression,
+ children: Seq[Attribute]) extends Generator with CodegenFallback {
+ type QueueType = (AnyRef, InternalRow)
+
+ require(k != 0, "`k` must not have 0")
+
+ private[this] lazy val scoreType = scoreExpression.dataType
+ private[this] lazy val scoreOrdering = {
+ val ordering = TypeUtils.getInterpretedOrdering(scoreType)
+ .asInstanceOf[Ordering[AnyRef]]
+ if (k > 0) {
+ ordering
+ } else {
+ ordering.reverse
+ }
+ }
+ private[this] lazy val reverseScoreOrdering = scoreOrdering.reverse
+
+ private[this] val queue: BoundedPriorityQueue[QueueType] = {
+ new BoundedPriorityQueue(Math.abs(k))(new Ordering[QueueType] {
+ override def compare(x: QueueType, y: QueueType): Int =
+ scoreOrdering.compare(x._1, y._1)
+ })
+ }
+
+ lazy private[this] val groupingProjection: UnsafeProjection =
+ UnsafeProjection.create(groupingExpression :: Nil, children)
+
+ lazy private[this] val scoreProjection: UnsafeProjection =
+ UnsafeProjection.create(scoreExpression :: Nil, children)
+
+ // The grouping key of the current partition
+ private[this] var currentGroupingKey: UnsafeRow = _
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ if (!TypeCollection.Ordered.acceptsType(scoreExpression.dataType)) {
+ TypeCheckResult.TypeCheckFailure(
+ s"$scoreExpression must have a comparable type")
+ } else {
+ TypeCheckResult.TypeCheckSuccess
+ }
+ }
+
+ override def elementSchema: StructType =
+ StructType(
+ Seq(StructField("rank", IntegerType)) ++
+ children.map(d => StructField(d.prettyName, d.dataType))
+ )
+
+ override def eval(input: InternalRow): TraversableOnce[InternalRow] = {
+ val groupingKey = groupingProjection(input)
+ val ret = if (currentGroupingKey != groupingKey) {
+ val part = queue.iterator.toSeq.sortBy(_._1)(reverseScoreOrdering)
+ .zipWithIndex.map { case ((_, row), index) =>
+ new JoinedRow(InternalRow(1 + index), row)
+ }
+ currentGroupingKey = groupingKey.copy()
+ queue.clear()
+ part
+ } else {
+ Iterator.empty
+ }
+ queue += Tuple2(scoreProjection(input).get(0, scoreType), input.copy())
+ ret
+ }
+
+ override def terminate(): TraversableOnce[InternalRow] = {
+ if (queue.size > 0) {
+ val part = queue.iterator.toSeq.sortBy(_._1)(reverseScoreOrdering)
+ .zipWithIndex.map { case ((_, row), index) =>
+ new JoinedRow(InternalRow(1 + index), row)
+ }
+ queue.clear()
+ part
+ } else {
+ Iterator.empty
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/c837e51a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallGroupedDataset.scala
----------------------------------------------------------------------
diff --git a/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallGroupedDataset.scala b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallGroupedDataset.scala
new file mode 100644
index 0000000..bdeff98
--- /dev/null
+++ b/spark/spark-2.1/src/main/scala/org/apache/spark/sql/hive/HivemallGroupedDataset.scala
@@ -0,0 +1,303 @@
+/*
+ * 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
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.Dataset
+import org.apache.spark.sql.RelationalGroupedDataset
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAlias
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical.Aggregate
+import org.apache.spark.sql.catalyst.plans.logical.Pivot
+import org.apache.spark.sql.hive.HiveShim.HiveFunctionWrapper
+import org.apache.spark.sql.types._
+
+/**
+ * Groups the [[DataFrame]] using the specified columns, so we can run aggregation on them.
+ *
+ * @groupname ensemble
+ * @groupname ftvec.trans
+ * @groupname evaluation
+ */
+final class HivemallGroupedDataset(groupBy: RelationalGroupedDataset) {
+
+ /**
+ * @see hivemall.ensemble.bagging.VotedAvgUDAF
+ * @group ensemble
+ */
+ def voted_avg(weight: String): DataFrame = {
+ // checkType(weight, NumericType)
+ val udaf = HiveUDAFFunction(
+ "voted_avg",
+ new HiveFunctionWrapper("hivemall.ensemble.bagging.WeightVotedAvgUDAF"),
+ Seq(weight).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.ensemble.bagging.WeightVotedAvgUDAF
+ * @group ensemble
+ */
+ def weight_voted_avg(weight: String): DataFrame = {
+ // checkType(weight, NumericType)
+ val udaf = HiveUDAFFunction(
+ "weight_voted_avg",
+ new HiveFunctionWrapper("hivemall.ensemble.bagging.WeightVotedAvgUDAF"),
+ Seq(weight).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.ensemble.ArgminKLDistanceUDAF
+ * @group ensemble
+ */
+ def argmin_kld(weight: String, conv: String): DataFrame = {
+ // checkType(weight, NumericType)
+ // checkType(conv, NumericType)
+ val udaf = HiveUDAFFunction(
+ "argmin_kld",
+ new HiveFunctionWrapper("hivemall.ensemble.ArgminKLDistanceUDAF"),
+ Seq(weight, conv).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.ensemble.MaxValueLabelUDAF"
+ * @group ensemble
+ */
+ def max_label(score: String, label: String): DataFrame = {
+ // checkType(score, NumericType)
+ checkType(label, StringType)
+ val udaf = HiveUDAFFunction(
+ "max_label",
+ new HiveFunctionWrapper("hivemall.ensemble.MaxValueLabelUDAF"),
+ Seq(score, label).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.ensemble.MaxRowUDAF
+ * @group ensemble
+ */
+ def maxrow(score: String, label: String): DataFrame = {
+ // checkType(score, NumericType)
+ checkType(label, StringType)
+ val udaf = HiveUDAFFunction(
+ "maxrow",
+ new HiveFunctionWrapper("hivemall.ensemble.MaxRowUDAF"),
+ Seq(score, label).map(df.col(_).expr),
+ isUDAFBridgeRequired = false)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.smile.tools.RandomForestEnsembleUDAF
+ * @group ensemble
+ */
+ def rf_ensemble(predict: String): DataFrame = {
+ // checkType(predict, NumericType)
+ val udaf = HiveUDAFFunction(
+ "rf_ensemble",
+ new HiveFunctionWrapper("hivemall.smile.tools.RandomForestEnsembleUDAF"),
+ Seq(predict).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.tools.matrix.TransposeAndDotUDAF
+ */
+ def transpose_and_dot(X: String, Y: String): DataFrame = {
+ val udaf = HiveUDAFFunction(
+ "transpose_and_dot",
+ new HiveFunctionWrapper("hivemall.tools.matrix.TransposeAndDotUDAF"),
+ Seq(X, Y).map(df.col(_).expr),
+ isUDAFBridgeRequired = false)
+ .toAggregateExpression()
+ toDF(Seq(Alias(udaf, udaf.prettyName)()))
+ }
+
+ /**
+ * @see hivemall.ftvec.trans.OnehotEncodingUDAF
+ * @group ftvec.trans
+ */
+ def onehot_encoding(cols: String*): DataFrame = {
+ val udaf = HiveUDAFFunction(
+ "onehot_encoding",
+ new HiveFunctionWrapper("hivemall.ftvec.trans.OnehotEncodingUDAF"),
+ cols.map(df.col(_).expr),
+ isUDAFBridgeRequired = false)
+ .toAggregateExpression()
+ toDF(Seq(Alias(udaf, udaf.prettyName)()))
+ }
+
+ /**
+ * @see hivemall.ftvec.selection.SignalNoiseRatioUDAF
+ */
+ def snr(X: String, Y: String): DataFrame = {
+ val udaf = HiveUDAFFunction(
+ "snr",
+ new HiveFunctionWrapper("hivemall.ftvec.selection.SignalNoiseRatioUDAF"),
+ Seq(X, Y).map(df.col(_).expr),
+ isUDAFBridgeRequired = false)
+ .toAggregateExpression()
+ toDF(Seq(Alias(udaf, udaf.prettyName)()))
+ }
+
+ /**
+ * @see hivemall.evaluation.MeanAbsoluteErrorUDAF
+ * @group evaluation
+ */
+ def mae(predict: String, target: String): DataFrame = {
+ checkType(predict, FloatType)
+ checkType(target, FloatType)
+ val udaf = HiveUDAFFunction(
+ "mae",
+ new HiveFunctionWrapper("hivemall.evaluation.MeanAbsoluteErrorUDAF"),
+ Seq(predict, target).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.evaluation.MeanSquareErrorUDAF
+ * @group evaluation
+ */
+ def mse(predict: String, target: String): DataFrame = {
+ checkType(predict, FloatType)
+ checkType(target, FloatType)
+ val udaf = HiveUDAFFunction(
+ "mse",
+ new HiveFunctionWrapper("hivemall.evaluation.MeanSquaredErrorUDAF"),
+ Seq(predict, target).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.evaluation.RootMeanSquareErrorUDAF
+ * @group evaluation
+ */
+ def rmse(predict: String, target: String): DataFrame = {
+ checkType(predict, FloatType)
+ checkType(target, FloatType)
+ val udaf = HiveUDAFFunction(
+ "rmse",
+ new HiveFunctionWrapper("hivemall.evaluation.RootMeanSquaredErrorUDAF"),
+ Seq(predict, target).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * @see hivemall.evaluation.FMeasureUDAF
+ * @group evaluation
+ */
+ def f1score(predict: String, target: String): DataFrame = {
+ // checkType(target, ArrayType(IntegerType))
+ // checkType(predict, ArrayType(IntegerType))
+ val udaf = HiveUDAFFunction(
+ "f1score",
+ new HiveFunctionWrapper("hivemall.evaluation.FMeasureUDAF"),
+ Seq(predict, target).map(df.col(_).expr),
+ isUDAFBridgeRequired = true)
+ .toAggregateExpression()
+ toDF((Alias(udaf, udaf.prettyName)() :: Nil).toSeq)
+ }
+
+ /**
+ * [[RelationalGroupedDataset]] has the three values as private fields, so, to inject Hivemall
+ * aggregate functions, we fetch them via Java Reflections.
+ */
+ private val df = getPrivateField[DataFrame]("org$apache$spark$sql$RelationalGroupedDataset$$df")
+ private val groupingExprs = getPrivateField[Seq[Expression]]("groupingExprs")
+ private val groupType = getPrivateField[RelationalGroupedDataset.GroupType]("groupType")
+
+ private def getPrivateField[T](name: String): T = {
+ val field = groupBy.getClass.getDeclaredField(name)
+ field.setAccessible(true)
+ field.get(groupBy).asInstanceOf[T]
+ }
+
+ private def toDF(aggExprs: Seq[Expression]): DataFrame = {
+ val aggregates = if (df.sqlContext.conf.dataFrameRetainGroupColumns) {
+ groupingExprs ++ aggExprs
+ } else {
+ aggExprs
+ }
+
+ val aliasedAgg = aggregates.map(alias)
+
+ groupType match {
+ case RelationalGroupedDataset.GroupByType =>
+ Dataset.ofRows(
+ df.sparkSession, Aggregate(groupingExprs, aliasedAgg, df.logicalPlan))
+ case RelationalGroupedDataset.RollupType =>
+ Dataset.ofRows(
+ df.sparkSession, Aggregate(Seq(Rollup(groupingExprs)), aliasedAgg, df.logicalPlan))
+ case RelationalGroupedDataset.CubeType =>
+ Dataset.ofRows(
+ df.sparkSession, Aggregate(Seq(Cube(groupingExprs)), aliasedAgg, df.logicalPlan))
+ case RelationalGroupedDataset.PivotType(pivotCol, values) =>
+ val aliasedGrps = groupingExprs.map(alias)
+ Dataset.ofRows(
+ df.sparkSession, Pivot(aliasedGrps, pivotCol, values, aggExprs, df.logicalPlan))
+ }
+ }
+
+ private def alias(expr: Expression): NamedExpression = expr match {
+ case u: UnresolvedAttribute => UnresolvedAlias(u)
+ case expr: NamedExpression => expr
+ case expr: Expression => Alias(expr, expr.prettyName)()
+ }
+
+ private def checkType(colName: String, expected: DataType) = {
+ val dataType = df.resolve(colName).dataType
+ if (dataType != expected) {
+ throw new AnalysisException(
+ s""""$colName" must be $expected, however it is $dataType""")
+ }
+ }
+}
+
+object HivemallGroupedDataset {
+
+ /**
+ * Implicitly inject the [[HivemallGroupedDataset]] into [[RelationalGroupedDataset]].
+ */
+ implicit def relationalGroupedDatasetToHivemallOne(
+ groupBy: RelationalGroupedDataset): HivemallGroupedDataset = {
+ new HivemallGroupedDataset(groupBy)
+ }
+}