zhengruifeng commented on code in PR #48347: URL: https://github.com/apache/spark/pull/48347#discussion_r1818332491
########## mllib/src/test/scala/org/apache/spark/ml/feature/TargetEncoderSuite.scala: ########## @@ -0,0 +1,538 @@ +/* + * 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 scala.collection.immutable.HashMap + +import org.apache.spark.{SparkException, SparkRuntimeException} +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.Row +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + +class TargetEncoderSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ + + @transient var data_binary: Seq[Row] = _ + @transient var data_continuous: Seq[Row] = _ + @transient var schema: StructType = _ + + override def beforeAll(): Unit = { + super.beforeAll() + + // scalastyle:off + data_binary = Seq( + Row(0.toShort, 3, 5.0, 0.0, 1.0/3, 0.0, 1.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9)), + Row(1.toShort, 4, 5.0, 1.0, 2.0/3, 1.0, 1.0/3, (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9)), + Row(2.toShort, 3, 5.0, 0.0, 1.0/3, 0.0, 1.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9)), + Row(0.toShort, 4, 6.0, 1.0, 1.0/3, 1.0, 2.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9)), + Row(1.toShort, 3, 6.0, 0.0, 2.0/3, 0.0, 2.0/3, (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9)), + Row(2.toShort, 4, 6.0, 1.0, 1.0/3, 1.0, 2.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9)), + Row(0.toShort, 3, 7.0, 0.0, 1.0/3, 0.0, 0.0, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (1-1.0/2)*(4.0/9)), + Row(1.toShort, 4, 8.0, 1.0, 2.0/3, 1.0, 1.0, (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (1.0/2) +(1-1.0/2)*(4.0/9)), + Row(2.toShort, 3, 9.0, 0.0, 1.0/3, 0.0, 0.0, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (1-1.0/2)*(4.0/9))) + + data_continuous = Seq( + Row(0.toShort, 3, 5.0, 10.0, 40.0, 50.0, 20.0, 42.5, 50.0, 27.5), + Row(1.toShort, 4, 5.0, 20.0, 50.0, 50.0, 20.0, 50.0, 50.0, 27.5), + Row(2.toShort, 3, 5.0, 30.0, 60.0, 50.0, 20.0, 57.5, 50.0, 27.5), + Row(0.toShort, 4, 6.0, 40.0, 40.0, 50.0, 50.0, 42.5, 50.0, 50.0), + Row(1.toShort, 3, 6.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0), + Row(2.toShort, 4, 6.0, 60.0, 60.0, 50.0, 50.0, 57.5, 50.0, 50.0), + Row(0.toShort, 3, 7.0, 70.0, 40.0, 50.0, 70.0, 42.5, 50.0, 60.0), + Row(1.toShort, 4, 8.0, 80.0, 50.0, 50.0, 80.0, 50.0, 50.0, 65.0), + Row(2.toShort, 3, 9.0, 90.0, 60.0, 50.0, 90.0, 57.5, 50.0, 70.0)) + // scalastyle:on + + schema = StructType(Array( + StructField("input1", ShortType, nullable = true), + StructField("input2", IntegerType, nullable = true), + StructField("input3", DoubleType, nullable = true), + StructField("label", DoubleType), + StructField("expected1", DoubleType), + StructField("expected2", DoubleType), + StructField("expected3", DoubleType), + StructField("smoothing1", DoubleType), + StructField("smoothing2", DoubleType), + StructField("smoothing3", DoubleType))) + } + + test("params") { + ParamsSuite.checkParams(new TargetEncoder) + } + + test("TargetEncoder - binary target") { + + val df = spark.createDataFrame(sc.parallelize(data_binary), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_BINARY) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val expected_stats = Array( + Map(Some(0.0) -> (3.0, 1.0), Some(1.0) -> (3.0, 2.0), Some(2.0) -> (3.0, 1.0), + Some(-1.0) -> (9.0, 4.0)), + Map(Some(3.0) -> (5.0, 0.0), Some(4.0) -> (4.0, 4.0), Some(-1.0) -> (9.0, 4.0)), + HashMap(Some(5.0) -> (3.0, 1.0), Some(6.0) -> (3.0, 2.0), Some(7.0) -> (1.0, 0.0), + Some(8.0) -> (1.0, 1.0), Some(9.0) -> (1.0, 0.0), Some(-1.0) -> (9.0, 4.0))) + + model.stats.zip(expected_stats).foreach{ + case (actual, expected) => assert(actual.equals(expected)) + } + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "expected1", "expected2", "expected3"), + model, + "output1", "expected1", + "output2", "expected2", + "output3", "expected3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + val model_smooth = model.setSmoothing(1.0) + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "smoothing1", "smoothing2", "smoothing3"), + model_smooth, + "output1", "smoothing1", + "output2", "smoothing2", + "output3", "smoothing3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + + } + + test("TargetEncoder - continuous target") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val expected_stats = Array( + Map(Some(0.0) -> (3.0, 40.0), Some(1.0) -> (3.0, 50.0), Some(2.0) -> (3.0, 60.0), + Some(-1.0) -> (9.0, 50.0)), + Map(Some(3.0) -> (5.0, 50.0), Some(4.0) -> (4.0, 50.0), Some(-1.0) -> (9.0, 50.0)), + HashMap(Some(5.0) -> (3.0, 20.0), Some(6.0) -> (3.0, 50.0), Some(7.0) -> (1.0, 70.0), + Some(8.0) -> (1.0, 80.0), Some(9.0) -> (1.0, 90.0), Some(-1.0) -> (9.0, 50.0))) + + model.stats.zip(expected_stats).foreach{ + case (actual, expected) => assert(actual.equals(expected)) + } + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "expected1", "expected2", "expected3"), + model, + "output1", "expected1", + "output2", "expected2", + "output3", "expected3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + val model_smooth = model.setSmoothing(1.0) + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "smoothing1", "smoothing2", "smoothing3"), + model_smooth, + "output1", "smoothing1", + "output2", "smoothing2", + "output3", "smoothing3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + } + + test("TargetEncoder - unseen value - keep") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setHandleInvalid(TargetEncoder.KEEP_INVALID) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val data_unseen = Row(0.toShort, 3, 10.0, 0.0, 40.0, 50.0, 50.0, 0.0, 0.0, 0.0) + + val df_unseen = spark + .createDataFrame(sc.parallelize(data_continuous :+ data_unseen), schema) + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df_unseen.select("input1", "input2", "input3", + "expected1", "expected2", "expected3"), + model, + "output1", "expected1", + "output2", "expected2", + "output3", "expected3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + } + + test("TargetEncoder - unseen value - error") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setHandleInvalid(TargetEncoder.ERROR_INVALID) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val data_unseen = Row(0.toShort, 3, 10.0, 0.0, 4.0/9, 4.0/9, 4.0/9, 0.0, 0.0, 0.0) + + val df_unseen = spark + .createDataFrame(sc.parallelize(data_continuous :+ data_unseen), schema) + + val ex = intercept[SparkRuntimeException] { + val out = model.transform(df_unseen) + out.show(false) + } + + assert(ex.isInstanceOf[SparkRuntimeException]) + assert(ex.getMessage.contains("Unseen value 10.0 in feature input3")) + + } + + test("TargetEncoder - missing feature") { + + val df = spark + .createDataFrame(sc.parallelize(data_binary), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setInputCols(Array("input1", "input2", "input3")) + .setTargetType(TargetEncoder.TARGET_BINARY) + .setOutputCols(Array("output1", "output2", "output3")) + + val ex = intercept[SparkException] { + val model = encoder.fit(df.drop("input3")) + print(model.stats) + } + + assert(ex.isInstanceOf[SparkException]) + assert(ex.getMessage.contains("No column named input3 found on dataset")) + } + + test("TargetEncoder - wrong data type") { + + val wrong_schema = new StructType( + schema.map{ + field: StructField => if (field.name != "input3") field + else StructField(field.name, StringType, field.nullable, field.metadata) + }.toArray) + + val df = spark + .createDataFrame(sc.parallelize(data_binary), wrong_schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setInputCols(Array("input1", "input2", "input3")) + .setTargetType(TargetEncoder.TARGET_BINARY) + .setOutputCols(Array("output1", "output2", "output3")) + + val ex = intercept[SparkException] { + val model = encoder.fit(df) + print(model.stats) + } + + assert(ex.isInstanceOf[SparkException]) + assert(ex.getMessage.contains("Data type for column input3 is StringType")) + } + + test("TargetEncoder - seen null category") { + + val data_null = Row(2.toShort, 3, null, 90.0, 60.0, 50.0, 90.0, 57.5, 50.0, 70.0) + + val df_null = spark + .createDataFrame(sc.parallelize(data_continuous.dropRight(1) :+ data_null), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df_null) + + val expected_stats = Array( + Map(Some(0.0) -> (3.0, 40.0), Some(1.0) -> (3.0, 50.0), Some(2.0) -> (3.0, 60.0), + Some(-1.0) -> (9.0, 50.0)), + Map(Some(3.0) -> (5.0, 50.0), Some(4.0) -> (4.0, 50.0), Some(-1.0) -> (9.0, 50.0)), + HashMap(Some(5.0) -> (3.0, 20.0), Some(6.0) -> (3.0, 50.0), Some(7.0) -> (1.0, 70.0), + Some(8.0) -> (1.0, 80.0), None -> (1.0, 90.0), Some(-1.0) -> (9.0, 50.0))) + + model.stats.zip(expected_stats).foreach{ + case (actual, expected) => assert(actual.equals(expected)) + } + + val output = model.transform(df_null) + + assert_true( + output("output1") === output("expected1") && + output("output2") === output("expected2") && + output("output3") === output("expected3")) + + } + + test("TargetEncoder - unseen null category") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setHandleInvalid(TargetEncoder.KEEP_INVALID) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val data_null = Row(null, null, null, 90.0, 50.0, 50.0, 50.0, 57.5, 50.0, 70.0) + + val df_null = spark + .createDataFrame(sc.parallelize(data_continuous :+ data_null), schema) + + val model = encoder.fit(df) + + val output = model.transform(df_null) + + assert_true( + output("output1") === output("expected1") && + output("output2") === output("expected2") && + output("output3") === output("expected3")) + + } + + test("TargetEncoder - non-indexed categories") { + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_BINARY) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val data_noindex = Row(0.toShort, 3, 5.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) + + val df_noindex = spark + .createDataFrame(sc.parallelize(data_binary :+ data_noindex), schema) + + val ex = intercept[SparkException] { + val model = encoder.fit(df_noindex) + print(model.stats) + } + + assert(ex.isInstanceOf[SparkException]) + assert(ex.getMessage.contains( + "Values from column input3 must be indices, but got 5.1")) + + } + + test("TargetEncoder - null label") { Review Comment: how do it handle `NaN`? treat as a normal value or invalid value? ########## mllib/src/test/scala/org/apache/spark/ml/feature/TargetEncoderSuite.scala: ########## @@ -0,0 +1,538 @@ +/* + * 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 scala.collection.immutable.HashMap + +import org.apache.spark.{SparkException, SparkRuntimeException} +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTest} +import org.apache.spark.sql.Row +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + +class TargetEncoderSuite extends MLTest with DefaultReadWriteTest { + + import testImplicits._ + + @transient var data_binary: Seq[Row] = _ + @transient var data_continuous: Seq[Row] = _ + @transient var schema: StructType = _ + + override def beforeAll(): Unit = { + super.beforeAll() + + // scalastyle:off + data_binary = Seq( + Row(0.toShort, 3, 5.0, 0.0, 1.0/3, 0.0, 1.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9)), + Row(1.toShort, 4, 5.0, 1.0, 2.0/3, 1.0, 1.0/3, (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9)), + Row(2.toShort, 3, 5.0, 0.0, 1.0/3, 0.0, 1.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9)), + Row(0.toShort, 4, 6.0, 1.0, 1.0/3, 1.0, 2.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9)), + Row(1.toShort, 3, 6.0, 0.0, 2.0/3, 0.0, 2.0/3, (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9)), + Row(2.toShort, 4, 6.0, 1.0, 1.0/3, 1.0, 2.0/3, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9)), + Row(0.toShort, 3, 7.0, 0.0, 1.0/3, 0.0, 0.0, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (1-1.0/2)*(4.0/9)), + Row(1.toShort, 4, 8.0, 1.0, 2.0/3, 1.0, 1.0, (3.0/4)*(2.0/3)+(1-3.0/4)*(4.0/9), (4.0/5)*1+(1-4.0/5)*(4.0/9), (1.0/2) +(1-1.0/2)*(4.0/9)), + Row(2.toShort, 3, 9.0, 0.0, 1.0/3, 0.0, 0.0, (3.0/4)*(1.0/3)+(1-3.0/4)*(4.0/9), (1-5.0/6)*(4.0/9), (1-1.0/2)*(4.0/9))) + + data_continuous = Seq( + Row(0.toShort, 3, 5.0, 10.0, 40.0, 50.0, 20.0, 42.5, 50.0, 27.5), + Row(1.toShort, 4, 5.0, 20.0, 50.0, 50.0, 20.0, 50.0, 50.0, 27.5), + Row(2.toShort, 3, 5.0, 30.0, 60.0, 50.0, 20.0, 57.5, 50.0, 27.5), + Row(0.toShort, 4, 6.0, 40.0, 40.0, 50.0, 50.0, 42.5, 50.0, 50.0), + Row(1.toShort, 3, 6.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0), + Row(2.toShort, 4, 6.0, 60.0, 60.0, 50.0, 50.0, 57.5, 50.0, 50.0), + Row(0.toShort, 3, 7.0, 70.0, 40.0, 50.0, 70.0, 42.5, 50.0, 60.0), + Row(1.toShort, 4, 8.0, 80.0, 50.0, 50.0, 80.0, 50.0, 50.0, 65.0), + Row(2.toShort, 3, 9.0, 90.0, 60.0, 50.0, 90.0, 57.5, 50.0, 70.0)) + // scalastyle:on + + schema = StructType(Array( + StructField("input1", ShortType, nullable = true), + StructField("input2", IntegerType, nullable = true), + StructField("input3", DoubleType, nullable = true), + StructField("label", DoubleType), + StructField("expected1", DoubleType), + StructField("expected2", DoubleType), + StructField("expected3", DoubleType), + StructField("smoothing1", DoubleType), + StructField("smoothing2", DoubleType), + StructField("smoothing3", DoubleType))) + } + + test("params") { + ParamsSuite.checkParams(new TargetEncoder) + } + + test("TargetEncoder - binary target") { + + val df = spark.createDataFrame(sc.parallelize(data_binary), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_BINARY) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val expected_stats = Array( + Map(Some(0.0) -> (3.0, 1.0), Some(1.0) -> (3.0, 2.0), Some(2.0) -> (3.0, 1.0), + Some(-1.0) -> (9.0, 4.0)), + Map(Some(3.0) -> (5.0, 0.0), Some(4.0) -> (4.0, 4.0), Some(-1.0) -> (9.0, 4.0)), + HashMap(Some(5.0) -> (3.0, 1.0), Some(6.0) -> (3.0, 2.0), Some(7.0) -> (1.0, 0.0), + Some(8.0) -> (1.0, 1.0), Some(9.0) -> (1.0, 0.0), Some(-1.0) -> (9.0, 4.0))) + + model.stats.zip(expected_stats).foreach{ + case (actual, expected) => assert(actual.equals(expected)) + } + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "expected1", "expected2", "expected3"), + model, + "output1", "expected1", + "output2", "expected2", + "output3", "expected3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + val model_smooth = model.setSmoothing(1.0) + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "smoothing1", "smoothing2", "smoothing3"), + model_smooth, + "output1", "smoothing1", + "output2", "smoothing2", + "output3", "smoothing3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + + } + + test("TargetEncoder - continuous target") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val expected_stats = Array( + Map(Some(0.0) -> (3.0, 40.0), Some(1.0) -> (3.0, 50.0), Some(2.0) -> (3.0, 60.0), + Some(-1.0) -> (9.0, 50.0)), + Map(Some(3.0) -> (5.0, 50.0), Some(4.0) -> (4.0, 50.0), Some(-1.0) -> (9.0, 50.0)), + HashMap(Some(5.0) -> (3.0, 20.0), Some(6.0) -> (3.0, 50.0), Some(7.0) -> (1.0, 70.0), + Some(8.0) -> (1.0, 80.0), Some(9.0) -> (1.0, 90.0), Some(-1.0) -> (9.0, 50.0))) + + model.stats.zip(expected_stats).foreach{ + case (actual, expected) => assert(actual.equals(expected)) + } + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "expected1", "expected2", "expected3"), + model, + "output1", "expected1", + "output2", "expected2", + "output3", "expected3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + val model_smooth = model.setSmoothing(1.0) + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df.select("input1", "input2", "input3", + "smoothing1", "smoothing2", "smoothing3"), + model_smooth, + "output1", "smoothing1", + "output2", "smoothing2", + "output3", "smoothing3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + + } + + test("TargetEncoder - unseen value - keep") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setHandleInvalid(TargetEncoder.KEEP_INVALID) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val data_unseen = Row(0.toShort, 3, 10.0, 0.0, 40.0, 50.0, 50.0, 0.0, 0.0, 0.0) + + val df_unseen = spark + .createDataFrame(sc.parallelize(data_continuous :+ data_unseen), schema) + + testTransformer[(Double, Double, Double, Double, Double, Double)]( + df_unseen.select("input1", "input2", "input3", + "expected1", "expected2", "expected3"), + model, + "output1", "expected1", + "output2", "expected2", + "output3", "expected3") { + case Row(output1: Double, expected1: Double, + output2: Double, expected2: Double, + output3: Double, expected3: Double) => + assert(output1 === expected1) + assert(output2 === expected2) + assert(output3 === expected3) + } + } + + test("TargetEncoder - unseen value - error") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setHandleInvalid(TargetEncoder.ERROR_INVALID) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df) + + val data_unseen = Row(0.toShort, 3, 10.0, 0.0, 4.0/9, 4.0/9, 4.0/9, 0.0, 0.0, 0.0) + + val df_unseen = spark + .createDataFrame(sc.parallelize(data_continuous :+ data_unseen), schema) + + val ex = intercept[SparkRuntimeException] { + val out = model.transform(df_unseen) + out.show(false) + } + + assert(ex.isInstanceOf[SparkRuntimeException]) + assert(ex.getMessage.contains("Unseen value 10.0 in feature input3")) + + } + + test("TargetEncoder - missing feature") { + + val df = spark + .createDataFrame(sc.parallelize(data_binary), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setInputCols(Array("input1", "input2", "input3")) + .setTargetType(TargetEncoder.TARGET_BINARY) + .setOutputCols(Array("output1", "output2", "output3")) + + val ex = intercept[SparkException] { + val model = encoder.fit(df.drop("input3")) + print(model.stats) + } + + assert(ex.isInstanceOf[SparkException]) + assert(ex.getMessage.contains("No column named input3 found on dataset")) + } + + test("TargetEncoder - wrong data type") { + + val wrong_schema = new StructType( + schema.map{ + field: StructField => if (field.name != "input3") field + else StructField(field.name, StringType, field.nullable, field.metadata) + }.toArray) + + val df = spark + .createDataFrame(sc.parallelize(data_binary), wrong_schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setInputCols(Array("input1", "input2", "input3")) + .setTargetType(TargetEncoder.TARGET_BINARY) + .setOutputCols(Array("output1", "output2", "output3")) + + val ex = intercept[SparkException] { + val model = encoder.fit(df) + print(model.stats) + } + + assert(ex.isInstanceOf[SparkException]) + assert(ex.getMessage.contains("Data type for column input3 is StringType")) + } + + test("TargetEncoder - seen null category") { + + val data_null = Row(2.toShort, 3, null, 90.0, 60.0, 50.0, 90.0, 57.5, 50.0, 70.0) + + val df_null = spark + .createDataFrame(sc.parallelize(data_continuous.dropRight(1) :+ data_null), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val model = encoder.fit(df_null) + + val expected_stats = Array( + Map(Some(0.0) -> (3.0, 40.0), Some(1.0) -> (3.0, 50.0), Some(2.0) -> (3.0, 60.0), + Some(-1.0) -> (9.0, 50.0)), + Map(Some(3.0) -> (5.0, 50.0), Some(4.0) -> (4.0, 50.0), Some(-1.0) -> (9.0, 50.0)), + HashMap(Some(5.0) -> (3.0, 20.0), Some(6.0) -> (3.0, 50.0), Some(7.0) -> (1.0, 70.0), + Some(8.0) -> (1.0, 80.0), None -> (1.0, 90.0), Some(-1.0) -> (9.0, 50.0))) + + model.stats.zip(expected_stats).foreach{ + case (actual, expected) => assert(actual.equals(expected)) + } + + val output = model.transform(df_null) + + assert_true( + output("output1") === output("expected1") && + output("output2") === output("expected2") && + output("output3") === output("expected3")) + + } + + test("TargetEncoder - unseen null category") { + + val df = spark + .createDataFrame(sc.parallelize(data_continuous), schema) + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_CONTINUOUS) + .setHandleInvalid(TargetEncoder.KEEP_INVALID) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val data_null = Row(null, null, null, 90.0, 50.0, 50.0, 50.0, 57.5, 50.0, 70.0) + + val df_null = spark + .createDataFrame(sc.parallelize(data_continuous :+ data_null), schema) + + val model = encoder.fit(df) + + val output = model.transform(df_null) + + assert_true( + output("output1") === output("expected1") && + output("output2") === output("expected2") && + output("output3") === output("expected3")) + + } + + test("TargetEncoder - non-indexed categories") { + + val encoder = new TargetEncoder() + .setLabelCol("label") + .setTargetType(TargetEncoder.TARGET_BINARY) + .setInputCols(Array("input1", "input2", "input3")) + .setOutputCols(Array("output1", "output2", "output3")) + + val data_noindex = Row(0.toShort, 3, 5.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) + + val df_noindex = spark + .createDataFrame(sc.parallelize(data_binary :+ data_noindex), schema) + + val ex = intercept[SparkException] { + val model = encoder.fit(df_noindex) + print(model.stats) + } + + assert(ex.isInstanceOf[SparkException]) + assert(ex.getMessage.contains( + "Values from column input3 must be indices, but got 5.1")) + + } + + test("TargetEncoder - null label") { Review Comment: how does it handle `NaN`? treat as a normal value or invalid value? -- This is an automated message from the Apache Git Service. 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