| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| */ |
| package org.apache.spark.sql.hive |
| |
| import org.apache.spark.sql.{AnalysisException, Row} |
| import org.apache.spark.sql.functions._ |
| import org.apache.spark.sql.hive.HivemallGroupedDataset._ |
| import org.apache.spark.sql.hive.HivemallOps._ |
| import org.apache.spark.sql.hive.HivemallUtils._ |
| import org.apache.spark.sql.hive.test.HivemallFeatureQueryTest |
| import org.apache.spark.sql.internal.SQLConf |
| import org.apache.spark.sql.test.VectorQueryTest |
| import org.apache.spark.sql.types._ |
| import org.apache.spark.test.TestFPWrapper._ |
| import org.apache.spark.test.TestUtils |
| |
| 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 strictly |
| assert(df.sort($"time".asc).select(changefinder($"x")).count === 1000) |
| assert(df.sort($"time".asc).select(sst($"x", lit("-th 0.005"))).count === 1000) |
| } |
| |
| test("knn.similarity") { |
| 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("0.5.2-incubating")) |
| } |
| |
| test("misc - rowid") { |
| assert(DummyInputData.select(rowid()).distinct.count == DummyInputData.count) |
| } |
| |
| test("misc - each_top_k") { |
| import hiveContext.implicits._ |
| val inputDf = Seq( |
| ("a", "1", 0.5, 0.1, Array(0, 1, 2)), |
| ("b", "5", 0.1, 0.2, Array(3)), |
| ("a", "3", 0.8, 0.8, Array(2, 5)), |
| ("c", "6", 0.3, 0.3, Array(1, 3)), |
| ("b", "4", 0.3, 0.4, Array(2)), |
| ("a", "2", 0.6, 0.5, Array(1)) |
| ).toDF("key", "value", "x", "y", "data") |
| |
| // Compute top-1 rows for each group |
| val distance = sqrt(inputDf("x") * inputDf("x") + inputDf("y") * inputDf("y")).as("score") |
| val top1Df = inputDf.each_top_k(lit(1), distance, $"key".as("group")) |
| assert(top1Df.schema.toSet === Set( |
| StructField("rank", IntegerType, nullable = true), |
| StructField("score", DoubleType, nullable = true), |
| StructField("key", StringType, nullable = true), |
| StructField("value", StringType, nullable = true), |
| StructField("x", DoubleType, nullable = true), |
| StructField("y", DoubleType, nullable = true), |
| StructField("data", ArrayType(IntegerType, containsNull = false), nullable = true) |
| )) |
| checkAnswer( |
| top1Df.select($"rank", $"key", $"value", $"data"), |
| Row(1, "a", "3", Array(2, 5)) :: |
| Row(1, "b", "4", Array(2)) :: |
| Row(1, "c", "6", Array(1, 3)) :: |
| Nil |
| ) |
| |
| // Compute reverse top-1 rows for each group |
| val bottom1Df = inputDf.each_top_k(lit(-1), distance, $"key".as("group")) |
| checkAnswer( |
| bottom1Df.select($"rank", $"key", $"value", $"data"), |
| Row(1, "a", "1", Array(0, 1, 2)) :: |
| Row(1, "b", "5", Array(3)) :: |
| Row(1, "c", "6", Array(1, 3)) :: |
| Nil |
| ) |
| |
| // Check if some exceptions thrown in case of some conditions |
| assert(intercept[AnalysisException] { inputDf.each_top_k(lit(0.1), $"score", $"key") } |
| .getMessage contains "`k` must be integer, however") |
| assert(intercept[AnalysisException] { inputDf.each_top_k(lit(1), $"data", $"key") } |
| .getMessage contains "must have a comparable type") |
| } |
| |
| test("misc - join_top_k") { |
| Seq("true", "false").map { flag => |
| withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> flag) { |
| import hiveContext.implicits._ |
| val inputDf = Seq( |
| ("user1", 1, 0.3, 0.5), |
| ("user2", 2, 0.1, 0.1), |
| ("user3", 3, 0.8, 0.0), |
| ("user4", 1, 0.9, 0.9), |
| ("user5", 3, 0.7, 0.2), |
| ("user6", 1, 0.5, 0.4), |
| ("user7", 2, 0.6, 0.8) |
| ).toDF("userId", "group", "x", "y") |
| |
| val masterDf = Seq( |
| (1, "pos1-1", 0.5, 0.1), |
| (1, "pos1-2", 0.0, 0.0), |
| (1, "pos1-3", 0.3, 0.3), |
| (2, "pos2-3", 0.1, 0.3), |
| (2, "pos2-3", 0.8, 0.8), |
| (3, "pos3-1", 0.1, 0.7), |
| (3, "pos3-1", 0.7, 0.1), |
| (3, "pos3-1", 0.9, 0.0), |
| (3, "pos3-1", 0.1, 0.3) |
| ).toDF("group", "position", "x", "y") |
| |
| // Compute top-1 rows for each group |
| val distance = sqrt( |
| pow(inputDf("x") - masterDf("x"), lit(2.0)) + |
| pow(inputDf("y") - masterDf("y"), lit(2.0)) |
| ).as("score") |
| val top1Df = inputDf.top_k_join( |
| lit(1), masterDf, inputDf("group") === masterDf("group"), distance) |
| assert(top1Df.schema.toSet === Set( |
| StructField("rank", IntegerType, nullable = true), |
| StructField("score", DoubleType, nullable = true), |
| StructField("group", IntegerType, nullable = false), |
| StructField("userId", StringType, nullable = true), |
| StructField("position", StringType, nullable = true), |
| StructField("x", DoubleType, nullable = false), |
| StructField("y", DoubleType, nullable = false) |
| )) |
| checkAnswer( |
| top1Df.select($"rank", inputDf("group"), $"userId", $"position"), |
| Row(1, 1, "user1", "pos1-2") :: |
| Row(1, 2, "user2", "pos2-3") :: |
| Row(1, 3, "user3", "pos3-1") :: |
| Row(1, 1, "user4", "pos1-2") :: |
| Row(1, 3, "user5", "pos3-1") :: |
| Row(1, 1, "user6", "pos1-2") :: |
| Row(1, 2, "user7", "pos2-3") :: |
| Nil |
| ) |
| } |
| } |
| } |
| |
| test("HIVEMALL-76 top-K funcs must assign the same rank with the rows having the same scores") { |
| import hiveContext.implicits._ |
| val inputDf = Seq( |
| ("a", "1", 0.1), |
| ("b", "5", 0.1), |
| ("a", "3", 0.1), |
| ("b", "4", 0.1), |
| ("a", "2", 0.0) |
| ).toDF("key", "value", "x") |
| |
| // Compute top-2 rows for each group |
| val top2Df = inputDf.each_top_k(lit(2), $"x".as("score"), $"key".as("group")) |
| checkAnswer( |
| top2Df.select($"rank", $"score", $"key", $"value"), |
| Row(1, 0.1, "a", "3") :: |
| Row(1, 0.1, "a", "1") :: |
| Row(1, 0.1, "b", "4") :: |
| Row(1, 0.1, "b", "5") :: |
| Nil |
| ) |
| Seq("true", "false").map { flag => |
| withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> flag) { |
| val inputDf = Seq( |
| ("user1", 1, 0.3, 0.5), |
| ("user2", 2, 0.1, 0.1) |
| ).toDF("userId", "group", "x", "y") |
| |
| val masterDf = Seq( |
| (1, "pos1-1", 0.5, 0.1), |
| (1, "pos1-2", 0.5, 0.1), |
| (1, "pos1-3", 0.3, 0.4), |
| (2, "pos2-1", 0.8, 0.2), |
| (2, "pos2-2", 0.8, 0.2) |
| ).toDF("group", "position", "x", "y") |
| |
| // Compute top-2 rows for each group |
| val distance = sqrt( |
| pow(inputDf("x") - masterDf("x"), lit(2.0)) + |
| pow(inputDf("y") - masterDf("y"), lit(2.0)) |
| ).as("score") |
| val top2Df = inputDf.top_k_join( |
| lit(2), masterDf, inputDf("group") === masterDf("group"), distance) |
| checkAnswer( |
| top2Df.select($"rank", inputDf("group"), $"userId", $"position"), |
| Row(1, 1, "user1", "pos1-1") :: |
| Row(1, 1, "user1", "pos1-2") :: |
| Row(1, 2, "user2", "pos2-1") :: |
| Row(1, 2, "user2", "pos2-2") :: |
| Nil |
| ) |
| } |
| } |
| } |
| |
| test("misc - flatten") { |
| import hiveContext.implicits._ |
| val df = Seq((0, (1, "a", (3.0, "b")), (5, 0.9, "c", "d"), 9)).toDF() |
| assert(df.flatten().schema === StructType( |
| StructField("_1", IntegerType, nullable = false) :: |
| StructField("_2$_1", IntegerType, nullable = true) :: |
| StructField("_2$_2", StringType, nullable = true) :: |
| StructField("_2$_3$_1", DoubleType, nullable = true) :: |
| StructField("_2$_3$_2", StringType, nullable = true) :: |
| StructField("_3$_1", IntegerType, nullable = true) :: |
| StructField("_3$_2", DoubleType, nullable = true) :: |
| StructField("_3$_3", StringType, nullable = true) :: |
| StructField("_3$_4", StringType, nullable = true) :: |
| StructField("_4", IntegerType, nullable = false) :: |
| Nil |
| )) |
| checkAnswer(df.flatten("$").select("_2$_1"), Row(1)) |
| checkAnswer(df.flatten("_").select("_2__1"), Row(1)) |
| checkAnswer(df.flatten(".").select("`_2._1`"), Row(1)) |
| |
| val errMsg1 = intercept[IllegalArgumentException] { df.flatten("\t") } |
| assert(errMsg1.getMessage.startsWith("Must use '$', '_', or '.' for separator, but got")) |
| val errMsg2 = intercept[IllegalArgumentException] { df.flatten("12") } |
| assert(errMsg2.getMessage.startsWith("Separator cannot be more than one character:")) |
| } |
| |
| test("misc - from_csv") { |
| import hiveContext.implicits._ |
| val df = Seq("""1,abc""").toDF() |
| val schema = new StructType().add("a", IntegerType).add("b", StringType) |
| checkAnswer( |
| df.select(from_csv($"value", schema)), |
| Row(Row(1, "abc")) :: Nil) |
| } |
| |
| test("misc - to_csv") { |
| import hiveContext.implicits._ |
| val df = Seq((1, "a", (0, 3.9, "abc")), (8, "c", (2, 0.4, "def"))).toDF() |
| checkAnswer( |
| df.select(to_csv($"_3")), |
| Row("0,3.9,abc") :: |
| Row("2,0.4,def") :: |
| Nil) |
| } |
| |
| /** |
| * This test fails because; |
| * |
| * Cause: java.lang.OutOfMemoryError: Java heap space |
| * at hivemall.smile.tools.RandomForestEnsembleUDAF$Result.<init> |
| * (RandomForestEnsembleUDAF.java:128) |
| * at hivemall.smile.tools.RandomForestEnsembleUDAF$RandomForestPredictUDAFEvaluator |
| * .terminate(RandomForestEnsembleUDAF.java:91) |
| * at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) |
| */ |
| ignore("misc - tree_predict") { |
| import hiveContext.implicits._ |
| |
| val model = Seq((0.0, 0.1 :: 0.1 :: Nil), (1.0, 0.2 :: 0.3 :: 0.2 :: Nil)) |
| .toDF("label", "features") |
| .train_randomforest_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_regressor", |
| "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", $"label")) |
| .foreach(_ => {}) // Just call it |
| } |
| } |
| |
| test("invoke classifier functions") { |
| import hiveContext.implicits._ |
| Seq( |
| "train_classifier", |
| "train_perceptron", |
| "train_pa", |
| "train_pa1", |
| "train_pa2", |
| "train_cw", |
| "train_arow", |
| "train_arowh", |
| "train_scw", |
| "train_scw2", |
| "train_adagrad_rda" |
| ).map { func => |
| TestUtils.invokeFunc(new HivemallOps(TinyTrainData), func, Seq($"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:${precision}") |
| } |
| |
| ignore("check regression precision") { |
| Seq( |
| "train_regressor", |
| "train_adadelta", |
| "train_adagrad", |
| "train_arow_regr", |
| "train_arowe_regr", |
| "train_arowe2_regr", |
| "train_logregr", |
| "train_pa1_regr", |
| "train_pa1a_regr", |
| "train_pa2_regr", |
| "train_pa2a_regr" |
| ).map { func => |
| checkRegrPrecision(func) |
| } |
| } |
| |
| ignore("check classifier precision") { |
| Seq( |
| "train_classifier", |
| "train_perceptron", |
| "train_pa", |
| "train_pa1", |
| "train_pa2", |
| "train_cw", |
| "train_arow", |
| "train_arowh", |
| "train_scw", |
| "train_scw2", |
| "train_adagrad_rda" |
| ).map { func => |
| checkClassifierPrecision(func) |
| } |
| } |
| |
| test("user-defined aggregators for ensembles") { |
| import hiveContext.implicits._ |
| |
| val df1 = Seq((1, 0.1f), (1, 0.2f), (2, 0.1f)).toDF("c0", "c1") |
| val row1 = df1.groupBy($"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")) |
| ) |
| ) |
| } |
| |
| ignore("append_bias") { |
| /** |
| * TODO: This test throws an exception: |
| * Failed to analyze query: org.apache.spark.sql.AnalysisException: cannot resolve |
| * 'UDF(UDF(features))' due to data type mismatch: argument 1 requires vector type, |
| * however, 'UDF(features)' is of vector type.; line 2 pos 8 |
| */ |
| checkAnswer( |
| mllibTrainDf.select(to_hivemall_features(append_bias($"features"))), |
| Seq( |
| Row(Seq("0:1.0", "0:1.0", "2:2.0", "4:3.0")), |
| Row(Seq("0:1.0", "0:1.0", "3:1.5", "4:2.1", "6:1.2")), |
| Row(Seq("0:1.0", "0:1.1", "3:1.0", "4:2.3", "6:1.0")), |
| Row(Seq("0:1.0", "1:4.0", "3:5.0", "5:6.0")) |
| ) |
| ) |
| } |
| |
| test("explode_vector") { |
| checkAnswer( |
| mllibTrainDf.explode_vector($"features").select($"feature", $"weight"), |
| Seq( |
| Row("0", 1.0), Row("0", 1.0), Row("0", 1.1), |
| Row("1", 4.0), |
| Row("2", 2.0), |
| Row("3", 1.0), Row("3", 1.5), Row("3", 5.0), |
| Row("4", 2.1), Row("4", 2.3), Row("4", 3.0), |
| Row("5", 6.0), |
| Row("6", 1.0), Row("6", 1.2) |
| ) |
| ) |
| } |
| |
| test("train_logregr") { |
| checkAnswer( |
| mllibTrainDf.train_logregr($"features", $"label") |
| .groupBy("feature").agg("weight" -> "avg") |
| .select($"feature"), |
| Seq(0, 1, 2, 3, 4, 5, 6).map(v => Row(s"$v")) |
| ) |
| } |
| } |
