diff --git a/api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/AlgorithmConfigurations.scala b/api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/AlgorithmConfigurations.scala index e4c4477198..044fbe0941 100644 --- a/api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/AlgorithmConfigurations.scala +++ b/api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/AlgorithmConfigurations.scala @@ -29,16 +29,18 @@ import hex.schemas.DRFModelV3.DRFModelOutputV3 import hex.schemas.DeepLearningModelV3.DeepLearningModelOutputV3 import hex.schemas.DeepLearningV3.{DeepLearningParametersV3 => DLParamsV3} import hex.schemas.ExtendedIsolationForestModelV3.ExtendedIsolationForestModelOutputV3 +import hex.schemas.ExtendedIsolationForestV3.{ExtendedIsolationForestParametersV3 => ExtIFParamsV3} import hex.schemas.GAMModelV3.GAMModelOutputV3 import hex.schemas.GBMModelV3.GBMModelOutputV3 import hex.schemas.GLMModelV3.GLMModelOutputV3 import hex.schemas.IsolationForestModelV3.IsolationForestModelOutputV3 import hex.schemas.IsolationForestV3.{IsolationForestParametersV3 => IFParamsV3} -import hex.schemas.ExtendedIsolationForestV3.{ExtendedIsolationForestParametersV3 => ExtIFParamsV3} import hex.schemas.KMeansModelV3.KMeansModelOutputV3 import hex.schemas.KMeansV3.{KMeansParametersV3 => KMeansParamsV3} import hex.schemas.RuleFitModelV3.RuleFitModelOutputV3 import hex.schemas.RuleFitV3.RuleFitParametersV3 +import hex.schemas.UpliftDRFModelV3.UpliftDRFModelOutputV3 +import hex.schemas.UpliftDRFV3.{UpliftDRFParametersV3 => UpliftParams} import hex.schemas.XGBoostModelV3.XGBoostModelOutputV3 import hex.schemas.XGBoostV3.{XGBoostParametersV3 => XGBParamsV3} import hex.schemas.{DRFV3, GAMV3, GBMV3, GLMV3} @@ -46,6 +48,7 @@ import hex.tree.drf.DRFModel.DRFParameters import hex.tree.gbm.GBMModel.GBMParameters import hex.tree.isofor.IsolationForestModel.{IsolationForestParameters => IFParameters} import hex.tree.isoforextended.ExtendedIsolationForestModel.{ExtendedIsolationForestParameters => ExtIFParams} +import hex.tree.uplift.UpliftDRFModel.UpliftDRFParameters import hex.tree.xgboost.XGBoostModel.XGBoostParameters import java.util @@ -97,6 +100,10 @@ class AlgorithmConfigurations extends MultipleAlgorithmsConfiguration { val gamFields = Seq(ignoredCols, betaConstraints, gamCols) val gbmFields = Seq(monotonicity, calibrationDataFrame, ignoredCols) val drfFields = Seq(calibrationDataFrame, ignoredCols) + val upliftDrfFields = Seq( + ExplicitField("treatment_column", "HasTreatmentCol", "treatment"), + ExplicitField("response_column", "HasLabelCol", "label"), + ignoredCols) val kmeansFields = Seq(userPoints, ignoredCols) val coxPHFields = Seq(ignoredCols, interactionPairs) val ifFields = Seq(ignoredCols, calibrationDataFrame, validationLabelCol) @@ -118,6 +125,7 @@ class AlgorithmConfigurations extends MultipleAlgorithmsConfiguration { ("H2OXGBoostParams", classOf[XGBParamsV3], classOf[XGBoostParameters], xgboostFields, noDeprecation), ("H2OGBMParams", classOf[GBMV3.GBMParametersV3], classOf[GBMParameters], gbmFields, noDeprecation), ("H2ODRFParams", classOf[DRFV3.DRFParametersV3], classOf[DRFParameters], drfFields, noDeprecation), + ("H2OUpliftDRFParams", classOf[UpliftParams], classOf[UpliftDRFParameters], upliftDrfFields, noDeprecation), ("H2OGLMParams", classOf[GLMV3.GLMParametersV3], classOf[GLMParameters], glmFields, noDeprecation), ("H2OGAMParams", classOf[GAMV3.GAMParametersV3], classOf[GAMParameters], gamFields, noDeprecation), ("H2ODeepLearningParams", classOf[DLParamsV3], classOf[DeepLearningParameters], dlFields, noDeprecation), @@ -158,6 +166,12 @@ class AlgorithmConfigurations extends MultipleAlgorithmsConfiguration { ("H2OXGBoost", classOf[XGBoostParameters], treeSupervised, Seq(withDistribution), None), ("H2OGBM", classOf[GBMParameters], treeSupervised, Seq(withDistribution), None), ("H2ODRF", classOf[DRFParameters], treeSupervised, Seq(withDistribution), None), + ( + "H2OUpliftDRF", + classOf[UpliftDRFParameters], + treeUnsupervised, + Seq(withDistribution, "H2OUpliftDRFExtras"), + None), ("H2OGLM", classOf[GLMParameters], cvSupervised, Seq(withFamily), Some("H2OGLMMetrics")), ("H2OGAM", classOf[GAMParameters], cvSupervised, Seq(withFamily), None), ("H2ODeepLearning", classOf[DeepLearningParameters], cvSupervised, Seq(withDistribution), None), @@ -202,6 +216,7 @@ class AlgorithmConfigurations extends MultipleAlgorithmsConfiguration { ("H2OXGBoostModelOutputs", classOf[XGBoostModelOutputV3]), ("H2OGBMModelOutputs", classOf[GBMModelOutputV3]), ("H2ODRFModelOutputs", classOf[DRFModelOutputV3]), + ("H2OUpliftDRFModelOutputs", classOf[UpliftDRFModelOutputV3]), ("H2OGLMModelOutputs", classOf[GLMModelOutputV3]), ("H2OGAMModelOutputs", classOf[GAMModelOutputV3]), ("H2ODeepLearningModelOutputs", classOf[DeepLearningModelOutputV3]), diff --git a/booklet/src/sections/calling_h2o_algos.tex b/booklet/src/sections/calling_h2o_algos.tex index 2446c810d5..c937346d5b 100644 --- a/booklet/src/sections/calling_h2o_algos.tex +++ b/booklet/src/sections/calling_h2o_algos.tex @@ -19,6 +19,7 @@ \begin{itemize} \item DeepLearning \item DRF + \item UpliftDRF \item GBM \item XGBoost \item AutoML diff --git a/doc/src/site/sphinx/deployment/load_mojo.rst b/doc/src/site/sphinx/deployment/load_mojo.rst index 025a08c6fa..2d76da6f37 100644 --- a/doc/src/site/sphinx/deployment/load_mojo.rst +++ b/doc/src/site/sphinx/deployment/load_mojo.rst @@ -282,6 +282,7 @@ The list of specific MOJO models: - ``H2OKMeansMOJOModel`` - ``H2OIsolationForestMOJOModel`` - ``H2OExtendedIsolationForestMOJOModel`` +- ``H2OUpliftDRFMOJOModel`` - ``H2OCoxPHMOJOModel`` - ``H2OTargetEncoderMOJOModel`` - ``H2OAutoEncoderMOJOModel`` diff --git a/doc/src/site/sphinx/ml/ml.rst b/doc/src/site/sphinx/ml/ml.rst index 9df93ffb99..7478e0b035 100644 --- a/doc/src/site/sphinx/ml/ml.rst +++ b/doc/src/site/sphinx/ml/ml.rst @@ -15,6 +15,7 @@ Machine Learning sw_xgboost sw_isolation_forest sw_extended_isolation_forest + sw_uplift_drf sw_coxph sw_deep_learning sw_rule_fit diff --git a/doc/src/site/sphinx/ml/sw_uplift_drf.rst b/doc/src/site/sphinx/ml/sw_uplift_drf.rst new file mode 100644 index 0000000000..6833a0ce48 --- /dev/null +++ b/doc/src/site/sphinx/ml/sw_uplift_drf.rst @@ -0,0 +1,144 @@ +.. _uplift_drf: + +Train Distributed Uplift Random Forest (Uplift DRF) Model in Sparkling Water +---------------------------------------------------------------------------- + +Introduction +~~~~~~~~~~~~ +Distributed Uplift Random Forest (Uplift DRF) is a classification tool for modeling uplift - the incremental impact of a treatment. Only binomial classification (distribution="bernoulli") is currently supported. +Uplift DRF can be applied in fields where we operate with two groups of subjects. First group, let’s call it treatment, receive some kind of treatment (e.g. marketing campaign, medicine,…), and a second group, let’s call it control, is separated from the treatment. We also gather information about their response, whether they bought a product, recover from disease, or similar. Then, Uplift DRF trains so-called uplift trees. +For more comprehensive description see `H2O-3 Distributed Uplift Random Forest (Uplift DRF) documentation `__. + +Example +~~~~~~~ + +The following section describes how to train the Distributed Uplift Random Forest (Uplift DRF) model in Sparkling Water in Scala & Python following the same example as H2O-3 documentation mentioned above. See also :ref:`parameters_H2OUpliftDRF` +and :ref:`model_details_H2OUpliftDRFMOJOModel`. + +.. content-tabs:: + + .. tab-container:: Scala + :title: Scala + + First, let's start Sparkling Shell as + + .. code:: shell + + ./bin/sparkling-shell + + Start H2O cluster inside the Spark environment + + .. code:: scala + + import ai.h2o.sparkling._ + import java.net.URI + val hc = H2OContext.getOrCreate() + + Parse the data using H2O and convert them to Spark Frame + + .. code:: scala + + import org.apache.spark.SparkFiles + val datasetUrl = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv" + spark.sparkContext.addFile(datasetUrl) //for example purposes, on a real cluster it's better to load directly from distributed storage + val sparkDF = spark.read.option("header", "true").option("inferSchema", "true").csv(SparkFiles.get("prostate.csv")) + val Array(trainingDF, testingDF) = sparkDF.randomSplit(Array(0.8, 0.2)) + + Train the model. You can configure all the available Distributed Uplift Random Forest (Uplift DRF) arguments using provided setters. + + .. code:: scala + + import ai.h2o.sparkling.ml.algos.H2OUpliftDRF + + val predictorColumns = Array("f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8") + val responseColumn = "conversion" + val treatmentColumn = "treatment" + + val algo = new H2OUpliftDRF() + .setNtrees(10) + .setMaxDepth(5) + .setTreatmentCol(treatmentColumn) + .setUpliftMetric("KL") + .setMinRows(10) + .setSeed(1234) + .setAuucType("qini") + .setLabelCol(responseColumn) + .setFeaturesCols(predictorColumns :+ treatmentColumn :+ responseColumn) + + val model = algo.fit(trainingDF) + + Run Predictions + + .. code:: scala + + model.transform(testingDF).show(truncate = false) + + View model summary containing info about trained trees etc. + + .. code:: scala + + model.getModelSummary() + + You can also get other model details by calling methods listed in :ref:`model_details_H2OUpliftDRFMOJOModel`. + + + .. tab-container:: Python + :title: Python + + First, let's start PySparkling Shell as + + .. code:: shell + + ./bin/pysparkling + + Start H2O cluster inside the Spark environment + + .. code:: python + + from pysparkling import * + hc = H2OContext.getOrCreate() + + Parse the data using H2O and convert them to Spark Frame + + .. code:: python + + import h2o + frame = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/uplift/criteo_uplift_13k.csv") + sparkDF = hc.asSparkFrame(frame) + [trainingDF, testingDF] = sparkDF.randomSplit([0.8, 0.2]) + + Train the model. You can configure all the available UpliftDRF arguments using provided setters or constructor parameters. + + .. code:: python + + from pysparkling.ml import H2OUpliftDRF + + treatmentColumn = "treatment" + responseColumn = "conversion" + predictors = ["f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", treatmentColumn, responseColumn] + + algo = H2OUpliftDRF(featuresCols=predictors, + ntrees = 10, + maxDepth = 5, + treatmentCol = treatmentColumn, + upliftMetric = "KL", + minRows = 10, + seed = 1234, + auucType = "qini", + labelCol = responseColumn) + + model = algo.fit(trainingDF) + + Run Predictions + + .. code:: python + + model.transform(testingDF).show(truncate = False) + + View model summary containing info about trained trees etc. + + .. code:: python + + model.getModelSummary() + + You can also get other model details by calling methods listed in :ref:`model_details_H2OUpliftDRFMOJOModel`. diff --git a/examples/smalldata/uplift/criteo_uplift_1k.csv 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+14.468217122391696,10.059654474774549,8.835155571218818,2.5875434914387134,10.280525225748212,4.115453421277861,-6.359690430716956,4.833814577796811,3.85339550790835,13.190055934673358,5.300374864042156,-0.16867922100056124,"1","0",0,0 +24.33007787188116,10.059654474774549,8.214382844395335,4.679881620097284,10.280525225748212,4.115453421277861,-3.2821094810628875,4.833814577796811,3.971857989350875,13.190055934673358,5.300374864042156,-0.16867922100056124,"1","0",0,0 diff --git a/ml/src/main/scala/ai/h2o/sparkling/ml/algos/H2OStackedEnsemble.scala b/ml/src/main/scala/ai/h2o/sparkling/ml/algos/H2OStackedEnsemble.scala index 12fbe82cbd..2e4e88281d 100644 --- a/ml/src/main/scala/ai/h2o/sparkling/ml/algos/H2OStackedEnsemble.scala +++ b/ml/src/main/scala/ai/h2o/sparkling/ml/algos/H2OStackedEnsemble.scala @@ -112,9 +112,6 @@ class H2OStackedEnsemble(override val uid: String) override protected def paramTag: ClassTag[StackedEnsembleParameters] = scala.reflect.classTag[StackedEnsembleParameters] - override private[sparkling] def getInputCols(): Array[String] = getFeaturesCols() - - override private[sparkling] def setInputCols(value: Array[String]): this.type = setFeaturesCols(value) } object H2OStackedEnsemble extends H2OParamsReadable[H2OStackedEnsemble] diff --git a/ml/src/main/scala/ai/h2o/sparkling/ml/algos/H2OUpliftDRFExtras.scala b/ml/src/main/scala/ai/h2o/sparkling/ml/algos/H2OUpliftDRFExtras.scala new file mode 100644 index 0000000000..2e5a76e51a --- /dev/null +++ b/ml/src/main/scala/ai/h2o/sparkling/ml/algos/H2OUpliftDRFExtras.scala @@ -0,0 +1,29 @@ +/* + * 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 ai.h2o.sparkling.ml.algos + +import ai.h2o.sparkling.ml.params.{H2OAlgorithmCommonParams, HasLabelCol, HasTreatmentCol} +import hex.tree.uplift.UpliftDRFModel.UpliftDRFParameters + +trait H2OUpliftDRFExtras + extends H2OAlgorithm[UpliftDRFParameters] + with HasTreatmentCol + with HasLabelCol + with H2OAlgorithmCommonParams { + override def getInputCols(): Array[String] = getFeaturesCols() :+ getTreatmentCol() :+ getLabelCol() +} diff --git a/ml/src/main/scala/ai/h2o/sparkling/ml/params/HasLabelCol.scala b/ml/src/main/scala/ai/h2o/sparkling/ml/params/HasLabelCol.scala new file mode 100644 index 0000000000..9ba5a6f7df --- /dev/null +++ b/ml/src/main/scala/ai/h2o/sparkling/ml/params/HasLabelCol.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 ai.h2o.sparkling.ml.params + +import ai.h2o.sparkling.H2OFrame +import org.apache.spark.expose.Logging + +trait HasLabelCol extends ParameterConstructorMethods with Logging { + protected val labelCol = stringParam(name = "labelCol", doc = """Response variable column.""") + + def getLabelCol(): String = $(labelCol) + + def setLabelCol(value: String): this.type = { + set(labelCol, value) + } + + private[sparkling] def getLabelColParam(trainingFrame: H2OFrame): Map[String, Any] = { + Map("response_column" -> getLabelCol()) + } + + setDefault(labelCol -> "label") + +} diff --git a/ml/src/main/scala/ai/h2o/sparkling/ml/params/HasTreatmentCol.scala b/ml/src/main/scala/ai/h2o/sparkling/ml/params/HasTreatmentCol.scala new file mode 100644 index 0000000000..21edbe96f8 --- /dev/null +++ b/ml/src/main/scala/ai/h2o/sparkling/ml/params/HasTreatmentCol.scala @@ -0,0 +1,40 @@ +/* + * 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 ai.h2o.sparkling.ml.params + +import ai.h2o.sparkling.H2OFrame +import org.apache.spark.expose.Logging +trait HasTreatmentCol extends ParameterConstructorMethods with Logging { + protected val treatmentCol = stringParam( + name = "treatmentCol", + doc = + """Define the column which will be used for computing uplift gain to select best split for a tree. The column has to divide the dataset into treatment (value 1) and control (value 0) groups.""") + + def getTreatmentCol(): String = $(treatmentCol) + + def setTreatmentCol(value: String): this.type = { + set(treatmentCol, value) + } + + private[sparkling] def getTreatmentColParam(trainingFrame: H2OFrame): Map[String, Any] = { + Map("treatment_column" -> getTreatmentCol()) + } + + setDefault(treatmentCol -> "treatment") + +} diff --git a/ml/src/test/scala/ai/h2o/sparkling/ml/algos/H2OUpliftDRFTestSuite.scala b/ml/src/test/scala/ai/h2o/sparkling/ml/algos/H2OUpliftDRFTestSuite.scala new file mode 100644 index 0000000000..f18ab17205 --- /dev/null +++ b/ml/src/test/scala/ai/h2o/sparkling/ml/algos/H2OUpliftDRFTestSuite.scala @@ -0,0 +1,90 @@ +/* + * 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 ai.h2o.sparkling.ml.algos + +import ai.h2o.sparkling.{SharedH2OTestContext, TestUtils} +import org.apache.spark.ml.{Pipeline, PipelineModel} +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.functions.col +import org.junit.runner.RunWith +import org.scalatest.junit.JUnitRunner +import org.scalatest.{FunSuite, Matchers} + +@RunWith(classOf[JUnitRunner]) +class H2OUpliftDRFTestSuite extends FunSuite with Matchers with SharedH2OTestContext { + + override def createSparkSession(): SparkSession = sparkSession("local[*]") + + private val predictorColumns = Array("f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8") + private val responseColumn = "conversion" + private val treatmentColumn = "treatment" + + private lazy val dataset = spark.read + .option("header", "true") + .option("inferSchema", "true") + .csv(TestUtils.locate("smalldata/uplift/criteo_uplift_1k.csv")) + .withColumn(treatmentColumn, col(treatmentColumn).cast("string")) + .withColumn(responseColumn, col(responseColumn).cast("string")) + + private lazy val Array(trainingDataset, testingDataset) = dataset.randomSplit(Array(0.8, 0.2), 1234) + + private lazy val algo = new H2OUpliftDRF() + .setNtrees(10) + .setMaxDepth(5) + .setTreatmentCol(treatmentColumn) + .setUpliftMetric("KL") + .setMinRows(10) + .setSeed(1234) + .setAuucType("qini") + .setLabelCol(responseColumn) + .setFeaturesCols(predictorColumns) + + test("H2OUpliftDRF happy path") { + val model = algo.fit(trainingDataset) + val result = model.transform(testingDataset) + result.columns should contain theSameElementsAs predictorColumns ++ Seq( + responseColumn, + treatmentColumn, + "f0", + "f9", + "f10", + "f11", + "visit", + "exposure", + "detailed_prediction", + "prediction") + result.count() shouldEqual 205 + result.select("prediction").collect().head.getSeq[Double](0) shouldBe Seq( + -0.04689146913588047, + 0.04408255908638239, + 0.09097402822226286) + } + + test("H2OUpliftDRF Pipeline serialization and deserialization") { + val pipeline = new Pipeline().setStages(Array(algo)) + pipeline.write.overwrite().save("ml/build/UpliftDRF_pipeline") + val loadedPipeline = Pipeline.load("ml/build/UpliftDRF_pipeline") + val model = loadedPipeline.fit(trainingDataset) + + model.write.overwrite().save("ml/build/UpliftDRF_pipeline_model") + val loadedModel = PipelineModel.load("ml/build/UpliftDRF_pipeline_model") + + loadedModel.transform(testingDataset).count() + } + +} diff --git a/ml/src/test/scala/ai/h2o/sparkling/ml/models/MOJOParameterTestSuite.scala b/ml/src/test/scala/ai/h2o/sparkling/ml/models/MOJOParameterTestSuite.scala index 813ee61635..99ebca4b7d 100644 --- a/ml/src/test/scala/ai/h2o/sparkling/ml/models/MOJOParameterTestSuite.scala +++ b/ml/src/test/scala/ai/h2o/sparkling/ml/models/MOJOParameterTestSuite.scala @@ -62,6 +62,22 @@ class MOJOParameterTestSuite extends FunSuite with SharedH2OTestContext with Mat compareParameterValues(algorithm, mojo) } + test("Test MOJO parameters on UpliftDRF") { + val algorithm = new H2OUpliftDRF() + .setLabelCol("CAPSULE") + .setSeed(1) + .setAuucType("lift") + .setAuucNbins(5) + .setUpliftMetric("ChiSquared") + .setTreatmentCol("CAPSULE") + .setCheckConstantResponse(true) + .setDistribution("bernoulii") + .setCategoricalEncoding("auto") + val mojo = algorithm.fit(dataset) + + compareParameterValues(algorithm, mojo) + } + test("Test MOJO parameters on XGBoost") { val algorithm = new H2OXGBoost() .setLabelCol("CAPSULE") diff --git a/py-scoring/src/ai/h2o/sparkling/ml/__init__.py b/py-scoring/src/ai/h2o/sparkling/ml/__init__.py index 66247a7147..a596e639b3 100644 --- a/py-scoring/src/ai/h2o/sparkling/ml/__init__.py +++ b/py-scoring/src/ai/h2o/sparkling/ml/__init__.py @@ -17,6 +17,6 @@ from ai.h2o.sparkling.ml.models import H2OSupervisedMOJOModel, H2OTreeBasedSupervisedMOJOModel, H2OUnsupervisedMOJOModel, H2OTreeBasedUnsupervisedMOJOModel, H2OBinaryModel from ai.h2o.sparkling.ml.models import H2OKMeansMOJOModel, H2OGLMMOJOModel, H2OGAMMOJOModel, H2OGBMMOJOModel, H2OXGBoostMOJOModel -from ai.h2o.sparkling.ml.models import H2ODeepLearningMOJOModel, H2ODRFMOJOModel, H2OIsolationForestMOJOModel, H2OPCAMOJOModel, H2OGLRMMOJOModel +from ai.h2o.sparkling.ml.models import H2ODeepLearningMOJOModel, H2ODRFMOJOModel, H2OUpliftDRFMOJOModel, H2OIsolationForestMOJOModel, H2OPCAMOJOModel, H2OGLRMMOJOModel from ai.h2o.sparkling.ml.models import H2OMOJOModel, H2OAlgorithmMOJOModel, H2OFeatureMOJOModel, H2OMOJOPipelineModel, H2OMOJOSettings from ai.h2o.sparkling.ml.models import H2OCoxPHMOJOModel, H2ORuleFitMOJOModel, H2OWord2VecMOJOModel, H2OExtendedIsolationForestMOJOModel diff --git a/py-scoring/src/ai/h2o/sparkling/ml/models/__init__.py b/py-scoring/src/ai/h2o/sparkling/ml/models/__init__.py index 22ba4bb7ab..fb74d4e69d 100644 --- a/py-scoring/src/ai/h2o/sparkling/ml/models/__init__.py +++ b/py-scoring/src/ai/h2o/sparkling/ml/models/__init__.py @@ -21,6 +21,7 @@ from ai.h2o.sparkling.ml.models.H2OXGBoostMOJOModel import H2OXGBoostMOJOModel from ai.h2o.sparkling.ml.models.H2OGBMMOJOModel import H2OGBMMOJOModel from ai.h2o.sparkling.ml.models.H2ODRFMOJOModel import H2ODRFMOJOModel +from ai.h2o.sparkling.ml.models.H2OUpliftDRFMOJOModel import H2OUpliftDRFMOJOModel from ai.h2o.sparkling.ml.models.H2OGLMMOJOModel import H2OGLMMOJOModel from ai.h2o.sparkling.ml.models.H2OGAMMOJOModel import H2OGAMMOJOModel from ai.h2o.sparkling.ml.models.H2OPCAMOJOModel import H2OPCAMOJOModel diff --git a/py-scoring/src/ai/h2o/sparkling/ml/params/HasLabelCol.py b/py-scoring/src/ai/h2o/sparkling/ml/params/HasLabelCol.py new file mode 100644 index 0000000000..b85e49bc0a --- /dev/null +++ b/py-scoring/src/ai/h2o/sparkling/ml/params/HasLabelCol.py @@ -0,0 +1,33 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from ai.h2o.sparkling.ml.params.H2OTypeConverters import H2OTypeConverters +from pyspark.ml.param import * + + +class HasLabelCol(Params): + labelCol = Param( + Params._dummy(), + "labelCol", + "Response variable column.", + H2OTypeConverters.toNullableString()) + + def getLabelCol(self): + return self.getOrDefault(self.labelCol) + + def setLabelCol(self, value): + return self._set(labelCol=value) diff --git a/py-scoring/src/ai/h2o/sparkling/ml/params/HasTreatmentCol.py b/py-scoring/src/ai/h2o/sparkling/ml/params/HasTreatmentCol.py new file mode 100644 index 0000000000..dd6d54edad --- /dev/null +++ b/py-scoring/src/ai/h2o/sparkling/ml/params/HasTreatmentCol.py @@ -0,0 +1,34 @@ +# +# 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. +# + +from ai.h2o.sparkling.ml.params.H2OTypeConverters import H2OTypeConverters +from pyspark.ml.param import * + + +class HasTreatmentCol(Params): + treatmentCol = Param( + Params._dummy(), + "treatmentCol", + "Define the column which will be used for computing uplift gain to select best split for a tree." + "The column has to divide the dataset into treatment (value 1) and control (value 0) groups.", + H2OTypeConverters.toNullableString()) + + def getTreatmentCol(self): + return self.getOrDefault(self.treatmentCol) + + def setTreatmentCol(self, value): + return self._set(treatmentCol=value) diff --git a/py-scoring/src/pysparkling/ml/__init__.py b/py-scoring/src/pysparkling/ml/__init__.py index f4d2f558f7..d4b76d54cc 100644 --- a/py-scoring/src/pysparkling/ml/__init__.py +++ b/py-scoring/src/pysparkling/ml/__init__.py @@ -20,7 +20,7 @@ __all__ = ["H2OMOJOModel", "H2OSupervisedMOJOModel", "H2OTreeBasedSupervisedMOJOModel", "H2OUnsupervisedMOJOModel", "H2OTreeBasedUnsupervisedMOJOModel", "H2OMOJOPipelineModel", "H2OMOJOSettings", "H2OBinaryModel", "H2OKMeansMOJOModel", "H2OGLMMOJOModel", "H2OGAMMOJOModel", "H2OGBMMOJOModel", "H2OXGBoostMOJOModel", - "H2ODeepLearningMOJOModel", "H2ODRFMOJOModel", "H2OIsolationForestMOJOModel", + "H2ODeepLearningMOJOModel", "H2ODRFMOJOModel", "H2OUpliftDRFMOJOModel", "H2OIsolationForestMOJOModel", "H2OExtendedIsolationForestMOJOModel", "H2OPCAMOJOModel", "H2OGLRMMOJOModel", "H2OCoxPHMOJOModel", "H2ORuleFitMOJOModel", "H2OWord2VecMOJOModel"] diff --git a/py-scoring/src/pysparkling/ml/models/__init__.py b/py-scoring/src/pysparkling/ml/models/__init__.py index 16bea00d58..08f6566bc7 100644 --- a/py-scoring/src/pysparkling/ml/models/__init__.py +++ b/py-scoring/src/pysparkling/ml/models/__init__.py @@ -23,7 +23,7 @@ from ai.h2o.sparkling.ml.models import H2ODeepLearningMOJOModel, H2OAutoEncoderMOJOModel, H2ODRFMOJOModel from ai.h2o.sparkling.ml.models import H2OCoxPHMOJOModel, H2OGLRMMOJOModel, H2ORuleFitMOJOModel from ai.h2o.sparkling.ml.models import H2OXGBoostMOJOModel, H2OIsolationForestMOJOModel, H2OPCAMOJOModel -from ai.h2o.sparkling.ml.models import H2OWord2VecMOJOModel, H2OStackedEnsembleMOJOModel +from ai.h2o.sparkling.ml.models import H2OWord2VecMOJOModel, H2OStackedEnsembleMOJOModel, H2OUpliftDRFMOJOModel __all__ = ["H2OMOJOSettings", "H2OMOJOPipelineModel", "H2OMOJOModel", "H2OAlgorithmMOJOModel", "H2OFeatureMOJOModel", "H2OSupervisedMOJOModel", "H2OTreeBasedSupervisedMOJOModel", "H2OUnsupervisedMOJOModel", @@ -31,4 +31,4 @@ "H2OGBMMOJOModel", "H2OXGBoostMOJOModel", "H2ODeepLearningMOJOModel", "H2OAutoEncoderMOJOModel", "H2ODRFMOJOModel", "H2OIsolationForestMOJOModel", "H2OCoxPHMOJOModel", "H2OBinaryModel", "H2OPCAMOJOModel", "H2OGLRMMOJOModel", "H2ORuleFitMOJOModel", "H2OWord2VecMOJOModel", "H2OStackedEnsembleMOJOModel", - "H2OExtendedIsolationForestMOJOModel"] + "H2OExtendedIsolationForestMOJOModel", "H2OUpliftDRFMOJOModel"] diff --git a/py/src/ai/h2o/sparkling/ml/__init__.py b/py/src/ai/h2o/sparkling/ml/__init__.py index dbac500a71..e033e23478 100644 --- a/py/src/ai/h2o/sparkling/ml/__init__.py +++ b/py/src/ai/h2o/sparkling/ml/__init__.py @@ -16,7 +16,7 @@ # from ai.h2o.sparkling.ml.algos import H2OKMeans, H2OAutoML, H2OGridSearch, H2OGLM, H2OGAM, H2OGBM, H2OXGBoost -from ai.h2o.sparkling.ml.algos import H2ODeepLearning, H2ODRF, H2OIsolationForest, H2OCoxPH, H2ORuleFit, H2OStackedEnsemble, H2OExtendedIsolationForest +from ai.h2o.sparkling.ml.algos import H2ODeepLearning, H2ODRF, H2OUpliftDRF, H2OIsolationForest, H2OCoxPH, H2ORuleFit, H2OStackedEnsemble, H2OExtendedIsolationForest from ai.h2o.sparkling.ml.algos.classification import H2OAutoMLClassifier, H2OGLMClassifier, H2OGAMClassifier, H2OGBMClassifier from ai.h2o.sparkling.ml.algos.classification import H2OXGBoostClassifier, H2ODeepLearningClassifier, H2ODRFClassifier, H2ORuleFitClassifier from ai.h2o.sparkling.ml.algos.regression import H2OAutoMLRegressor, H2OGLMRegressor, H2OGAMRegressor, H2OGBMRegressor @@ -26,4 +26,4 @@ from ai.h2o.sparkling.ml.models import H2OKMeansMOJOModel, H2OGLMMOJOModel, H2OGAMMOJOModel, H2OGBMMOJOModel, H2OXGBoostMOJOModel from ai.h2o.sparkling.ml.models import H2ODeepLearningMOJOModel, H2OWord2VecMOJOModel, H2OAutoEncoderMOJOModel, H2ODRFMOJOModel, H2OPCAMOJOModel, H2OGLRMMOJOModel from ai.h2o.sparkling.ml.models import H2OIsolationForestMOJOModel, H2OCoxPHMOJOModel, H2ORuleFitMOJOModel, H2OExtendedIsolationForestMOJOModel, H2OStackedEnsembleMOJOModel -from ai.h2o.sparkling.ml.models import H2OMOJOModel, H2OAlgorithmMOJOModel, H2OFeatureMOJOModel, H2OMOJOPipelineModel, H2OMOJOSettings +from ai.h2o.sparkling.ml.models import H2OMOJOModel, H2OAlgorithmMOJOModel, H2OFeatureMOJOModel, H2OMOJOPipelineModel, H2OMOJOSettings, H2OUpliftDRFMOJOModel diff --git a/py/src/ai/h2o/sparkling/ml/algos/H2OUpliftDRFExtras.py b/py/src/ai/h2o/sparkling/ml/algos/H2OUpliftDRFExtras.py new file mode 100644 index 0000000000..6263dc1986 --- /dev/null +++ b/py/src/ai/h2o/sparkling/ml/algos/H2OUpliftDRFExtras.py @@ -0,0 +1,20 @@ +# +# 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. +# + + +class H2OUpliftDRFExtras: + pass diff --git a/py/src/ai/h2o/sparkling/ml/algos/__init__.py b/py/src/ai/h2o/sparkling/ml/algos/__init__.py index 8e43e36f94..f54a36614d 100644 --- a/py/src/ai/h2o/sparkling/ml/algos/__init__.py +++ b/py/src/ai/h2o/sparkling/ml/algos/__init__.py @@ -24,6 +24,7 @@ from ai.h2o.sparkling.ml.algos.H2OGridSearch import H2OGridSearch from ai.h2o.sparkling.ml.algos.H2OAutoML import H2OAutoML from ai.h2o.sparkling.ml.algos.H2ODRF import H2ODRF +from ai.h2o.sparkling.ml.algos.H2OUpliftDRF import H2OUpliftDRF from ai.h2o.sparkling.ml.algos.H2OCoxPH import H2OCoxPH from ai.h2o.sparkling.ml.algos.H2OIsolationForest import H2OIsolationForest from ai.h2o.sparkling.ml.algos.H2OExtendedIsolationForest import H2OExtendedIsolationForest diff --git a/py/src/pysparkling/ml/__init__.py b/py/src/pysparkling/ml/__init__.py index b08eb61eb1..10b2a2eb43 100644 --- a/py/src/pysparkling/ml/__init__.py +++ b/py/src/pysparkling/ml/__init__.py @@ -24,15 +24,16 @@ "H2OMOJOModel", "H2OAlgorithmMOJOModel", "H2OFeatureMOJOModel", "H2OSupervisedMOJOModel", "H2OTreeBasedSupervisedMOJOModel", "H2OUnsupervisedMOJOModel", "H2OCoxPHMOJOModel", "H2OTreeBasedUnsupervisedMOJOModel", "H2OMOJOPipelineModel", "H2OGridSearch", "H2OMOJOSettings", "H2OKMeans", - "H2OTargetEncoder", "H2ODRF", "H2OAutoMLClassifier", "H2OGLMClassifier", "H2OGAMClassifier", + "H2OTargetEncoder", "H2ODRF", "H2OUpliftDRF", "H2OAutoMLClassifier", "H2OGLMClassifier", "H2OGAMClassifier", "H2OGBMClassifier", "H2OXGBoostClassifier", "H2ODeepLearningClassifier", "H2ODRFClassifier", "H2OAutoMLRegressor", "H2OGLMRegressor", "H2OGBMRegressor", "H2OGAMRegressor", "H2OXGBoostRegressor", "H2ODeepLearningRegressor", "H2ODRFRegressor", "H2OBinaryModel", "H2OIsolationForest", "H2OKMeansMOJOModel", "H2OGLMMOJOModel", "H2OGAMMOJOModel", "H2OGBMMOJOModel", "H2OXGBoostMOJOModel", "H2ODeepLearningMOJOModel", - "H2ODRFMOJOModel", "H2OIsolationForestMOJOModel", "H2OWord2Vec", "H2OWord2VecMOJOModel", "H2OAutoEncoder", - "H2OAutoEncoderMOJOModel", "H2OPCA", "H2OPCAMOJOModel", "H2OGLRM", "H2OGLRMMOJOModel", "H2ORuleFit", - "H2ORuleFitClassifier", "H2ORuleFitRegressor", "H2ORuleFitMOJOModel", "H2OStackedEnsemble", - "H2OStackedEnsembleMOJOModel", "H2OExtendedIsolationForest", "H2OExtendedIsolationForestMOJOModel"] + "H2ODRFMOJOModel", "H2OUpliftDRFMOJOModel", "H2OIsolationForestMOJOModel", "H2OWord2Vec", + "H2OWord2VecMOJOModel", "H2OAutoEncoder", "H2OAutoEncoderMOJOModel", "H2OPCA", "H2OPCAMOJOModel", "H2OGLRM", + "H2OGLRMMOJOModel", "H2ORuleFit", "H2ORuleFitClassifier", "H2ORuleFitRegressor", "H2ORuleFitMOJOModel", + "H2OStackedEnsemble", "H2OStackedEnsembleMOJOModel", "H2OExtendedIsolationForest", + "H2OExtendedIsolationForestMOJOModel"] from pysparkling.initializer import Initializer diff --git a/py/src/pysparkling/ml/algos/__init__.py b/py/src/pysparkling/ml/algos/__init__.py index fe11a550ec..3f867395a9 100644 --- a/py/src/pysparkling/ml/algos/__init__.py +++ b/py/src/pysparkling/ml/algos/__init__.py @@ -16,8 +16,9 @@ # from ai.h2o.sparkling.ml.algos import H2OKMeans, H2OAutoML, H2OGridSearch, H2OGLM, H2OGBM, H2OXGBoost, H2ODeepLearning -from ai.h2o.sparkling.ml.algos import H2ODRF, H2OGAM, H2OIsolationForest, H2OCoxPH, H2ORuleFit +from ai.h2o.sparkling.ml.algos import H2ODRF, H2OUpliftDRF, H2OGAM, H2OIsolationForest, H2OCoxPH, H2ORuleFit from ai.h2o.sparkling.ml.algos import H2OStackedEnsemble, H2OExtendedIsolationForest __all__ = ["H2OAutoML", "H2OGridSearch", "H2OGLM", "H2OGAM", "H2OGBM", "H2OXGBoost", "H2ODeepLearning", "H2OKMeans", - "H2ODRF", "H2OIsolationForest", "H2OCoxPH", "H2ORuleFit", "H2OStackedEnsemble", "H2OExtendedIsolationForest"] + "H2ODRF", "H2OUpliftDRF", "H2OIsolationForest", "H2OCoxPH", "H2ORuleFit", "H2OStackedEnsemble", + "H2OExtendedIsolationForest"] diff --git a/py/src/pysparkling/ml/models/__init__.py b/py/src/pysparkling/ml/models/__init__.py index ce1dc02f62..1ab625419b 100644 --- a/py/src/pysparkling/ml/models/__init__.py +++ b/py/src/pysparkling/ml/models/__init__.py @@ -20,7 +20,7 @@ from ai.h2o.sparkling.ml.models import H2OSupervisedMOJOModel, H2OTreeBasedSupervisedMOJOModel, H2OUnsupervisedMOJOModel from ai.h2o.sparkling.ml.models import H2OTreeBasedUnsupervisedMOJOModel from ai.h2o.sparkling.ml.models import H2OKMeansMOJOModel, H2OGLMMOJOModel, H2OGAMMOJOModel, H2OGBMMOJOModel -from ai.h2o.sparkling.ml.models import H2ODeepLearningMOJOModel, H2OAutoEncoderMOJOModel, H2OPCAMOJOModel, H2ODRFMOJOModel +from ai.h2o.sparkling.ml.models import H2ODeepLearningMOJOModel, H2OAutoEncoderMOJOModel, H2OPCAMOJOModel, H2ODRFMOJOModel, H2OUpliftDRFMOJOModel from ai.h2o.sparkling.ml.models import H2OXGBoostMOJOModel, H2OIsolationForestMOJOModel, H2OCoxPHMOJOModel, H2ORuleFitMOJOModel from ai.h2o.sparkling.ml.models import H2OStackedEnsembleMOJOModel, H2OWord2VecMOJOModel, H2OExtendedIsolationForestMOJOModel @@ -28,6 +28,6 @@ "H2OSupervisedMOJOModel", "H2OTreeBasedSupervisedMOJOModel", "H2OUnsupervisedMOJOModel", "H2OTreeBasedUnsupervisedMOJOModel", "H2OKMeansMOJOModel", "H2OGLMMOJOModel", "H2OGAMMOJOModel", "H2OGBMMOJOModel", "H2OXGBoostMOJOModel", "H2ODeepLearningMOJOModel", "H2OAutoEncoderMOJOModel", - "H2ODRFMOJOModel", "H2OIsolationForestMOJOModel", "H2OBinaryModel", "H2OCoxPHMOJOModel", "H2OPCAMOJOModel", + "H2ODRFMOJOModel", "H2OUpliftDRFMOJOModel", "H2OIsolationForestMOJOModel", "H2OBinaryModel", "H2OCoxPHMOJOModel", "H2OPCAMOJOModel", "H2ORuleFitMOJOModel", "H2OWord2VecMOJOModel", "H2OStackedEnsembleMOJOModel", "H2OExtendedIsolationForestMOJOModel"] diff --git a/py/tests/unit/with_runtime_sparkling/conftest.py b/py/tests/unit/with_runtime_sparkling/conftest.py index 2b8c0799d2..23240f82f3 100644 --- a/py/tests/unit/with_runtime_sparkling/conftest.py +++ b/py/tests/unit/with_runtime_sparkling/conftest.py @@ -64,6 +64,9 @@ def irisDatasetPath(): def airlinesDatasetPath(): return "file://" + os.path.abspath("../examples/smalldata/airlines/allyears2k_headers.csv") +@pytest.fixture(scope="module") +def criteoDatasetPath(): + return "file://" + os.path.abspath("../examples/smalldata/uplift/criteo_uplift_1k.csv") @pytest.fixture(scope="module") def carsDatasetPath(): @@ -89,6 +92,16 @@ def prostateDataset(spark, prostateDatasetPath): def airlinesDataset(spark, airlinesDatasetPath): return spark.read.csv(airlinesDatasetPath, header=True, inferSchema=True) +@pytest.fixture(scope="module") +def criteoDataset(spark, criteoDatasetPath): + dataset = spark.read.csv(criteoDatasetPath, header=True, inferSchema=True) + treatmentColumn = "treatment" + responseColumn = "conversion" + return (dataset + .withColumn(treatmentColumn, dataset[treatmentColumn].cast("string")) + .withColumn(responseColumn, dataset[responseColumn].cast("string"))) + + @pytest.fixture(scope="module") def semiconductorDataset(spark, semiconductorDatasetPath): return spark.read.csv(semiconductorDatasetPath, header=True, inferSchema=True) diff --git a/py/tests/unit/with_runtime_sparkling/test_mojo_parameters.py b/py/tests/unit/with_runtime_sparkling/test_mojo_parameters.py index de4133f910..0167c73cd2 100644 --- a/py/tests/unit/with_runtime_sparkling/test_mojo_parameters.py +++ b/py/tests/unit/with_runtime_sparkling/test_mojo_parameters.py @@ -16,7 +16,7 @@ from pysparkling.ml import H2OGBM, H2ODRF, H2OXGBoost, H2OGLM, H2OGAM, H2OCoxPH from pysparkling.ml import H2ODeepLearning, H2OKMeans, H2OIsolationForest, H2OExtendedIsolationForest -from pysparkling.ml import H2OAutoEncoder, H2OPCA, H2OGLRM, H2ORuleFit, H2OWord2Vec +from pysparkling.ml import H2OAutoEncoder, H2OPCA, H2OGLRM, H2ORuleFit, H2OWord2Vec, H2OUpliftDRF def testGBMParameters(prostateDataset): features = ['AGE', 'RACE', 'DPROS', 'DCAPS', 'PSA'] @@ -85,6 +85,19 @@ def testExtendedIsolationForestParameters(prostateDataset): model = algorithm.fit(prostateDataset) compareParameterValues(algorithm, model) +def testUpliftDRFParameters(criteoDataset): + features = ['f1', 'f2', 'f3' , 'f4', 'f5', 'f6', 'f7', 'f8'] + algorithm = H2OUpliftDRF(featuresCols=features, + ntrees=10, + maxDepth=5, + treatmentCol="treatment", + upliftMetric="KL", + minRows=10, + seed=1234, + auucType="qini", + labelCol="conversion") + model = algorithm.fit(criteoDataset) + compareParameterValues(algorithm, model) def testCoxPHParameters(heartDataset): features = ['age', 'year', 'surgery', 'transplant', 'start', 'stop'] @@ -146,6 +159,7 @@ def isMethodRelevant(method): for method in methods: modelValue = getattr(model, method)() algorithmValue = getattr(algorithm, method)() + print("model: " + str(modelValue) + " algo: " + str(algorithmValue)) assert(valuesAreEqual(algorithmValue, modelValue)) diff --git a/py/tests/unit/with_runtime_sparkling/test_uplift_drf.py b/py/tests/unit/with_runtime_sparkling/test_uplift_drf.py new file mode 100644 index 0000000000..f0774d4fb2 --- /dev/null +++ b/py/tests/unit/with_runtime_sparkling/test_uplift_drf.py @@ -0,0 +1,80 @@ +# 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. +# + +import os +from pyspark.ml import Pipeline, PipelineModel +from pyspark.mllib.linalg import * +from pyspark.mllib.linalg import * +from pyspark.sql.types import * +from pyspark.sql.types import * +from pyspark.sql.functions import * +from pysparkling.ml import H2OUpliftDRF +from tests import unit_test_utils + +from tests.unit.with_runtime_sparkling.algo_test_utils import * + + +def testParamsPassedByConstructor(): + assertParamsViaConstructor("H2OUpliftDRF") + + +def testParamsPassedBySetters(): + assertParamsViaSetters("H2OUpliftDRF") + + +def testPipelineSerialization(criteoDataset): + features = ['f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8'] + algo = H2OUpliftDRF(featuresCols=features, + ntrees=10, + maxDepth=5, + treatmentCol="treatment", + upliftMetric="KL", + minRows=10, + seed=1234, + auucType="qini", + labelCol="conversion") + + pipeline = Pipeline(stages=[algo]) + pipeline.write().overwrite().save("file://" + os.path.abspath("build/uplift_drf_pipeline")) + loadedPipeline = Pipeline.load("file://" + os.path.abspath("build/uplift_drf_pipeline")) + model = loadedPipeline.fit(criteoDataset) + expected = model.transform(criteoDataset) + + model.write().overwrite().save("file://" + os.path.abspath("build/uplift_drf_pipeline_model")) + loadedModel = PipelineModel.load("file://" + os.path.abspath("build/uplift_drf_pipeline_model")) + result = loadedModel.transform(criteoDataset) + + unit_test_utils.assert_data_frames_are_identical(expected, result) + + +def testUpliftDRFModelGiveDifferentPredictionsOnDifferentRecords(criteoDataset): + [trainingDataset, testingDataset] = criteoDataset.randomSplit([0.9, 0.1], 42) + features = ['f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8'] + algo = H2OUpliftDRF(featuresCols=features, + ntrees=10, + maxDepth=5, + treatmentCol="treatment", + upliftMetric="KL", + minRows=10, + seed=1234, + auucType="qini", + labelCol="conversion") + model = algo.fit(trainingDataset) + + result = model.transform(testingDataset) + predictions = result.select("prediction").take(2) + + assert(predictions[0][0] != predictions[1][0]) diff --git a/scoring/src/main/scala/ai/h2o/sparkling/ml/internals/H2OModelCategory.scala b/scoring/src/main/scala/ai/h2o/sparkling/ml/internals/H2OModelCategory.scala index b2dc7baf75..3ea117f36d 100644 --- a/scoring/src/main/scala/ai/h2o/sparkling/ml/internals/H2OModelCategory.scala +++ b/scoring/src/main/scala/ai/h2o/sparkling/ml/internals/H2OModelCategory.scala @@ -22,7 +22,7 @@ package ai.h2o.sparkling.ml.internals */ private[sparkling] object H2OModelCategory extends Enumeration { val Unknown, Binomial, Multinomial, Ordinal, Regression, HGLMRegression, Clustering, AutoEncoder, TargetEncoder, - DimReduction, WordEmbedding, CoxPH, AnomalyDetection = Value + DimReduction, WordEmbedding, CoxPH, AnomalyDetection, BinomialUplift = Value def fromString(modelCategory: String): Value = { values diff --git a/scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPrediction.scala b/scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPrediction.scala index 31c906b3e5..8e411d5046 100644 --- a/scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPrediction.scala +++ b/scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPrediction.scala @@ -31,7 +31,8 @@ trait H2OMOJOPrediction with H2OMOJOPredictionClustering with H2OMOJOPredictionBinomial with H2OMOJOPredictionCoxPH - with H2OMOJOPredictionOrdinal { + with H2OMOJOPredictionOrdinal + with H2OMOJOPredictionUpliftBinomial { self: H2OAlgorithmMOJOModel => def extractPredictionColContent(): Column = { @@ -46,6 +47,7 @@ trait H2OMOJOPrediction case ModelCategory.AnomalyDetection => extractAnomalyPredictionColContent() case ModelCategory.Ordinal => extractOrdinalPredictionColContent() case ModelCategory.CoxPH => extractCoxPHPredictionColContent() + case ModelCategory.BinomialUplift => extractUpliftBinomialPredictionColContent() case _ => throw new RuntimeException("Unknown model category " + mojoModel.getModelCategory) } } @@ -65,6 +67,7 @@ trait H2OMOJOPrediction case ModelCategory.AnomalyDetection => getAnomalyPredictionUDF(schema, uid, mojoFileName, configInitializers) case ModelCategory.Ordinal => getOrdinalPredictionUDF(schema, uid, mojoFileName, configInitializers) case ModelCategory.CoxPH => getCoxPHPredictionUDF(schema, uid, mojoFileName, configInitializers) + case ModelCategory.BinomialUplift => getUpliftBinomialPredictionUDF(schema, uid, mojoFileName, configInitializers) case _ => throw new RuntimeException("Unknown model category " + mojoModel.getModelCategory) } } @@ -81,6 +84,7 @@ trait H2OMOJOPrediction case ModelCategory.AnomalyDetection => getAnomalyPredictionColSchema() case ModelCategory.Ordinal => getOrdinalPredictionColSchema() case ModelCategory.CoxPH => getCoxPHPredictionColSchema() + case ModelCategory.BinomialUplift => getUpliftBinomialPredictionColSchema() case _ => throw new RuntimeException("Unknown model category " + mojoModel.getModelCategory) } } @@ -101,6 +105,7 @@ trait H2OMOJOPrediction case ModelCategory.AnomalyDetection => getAnomalyPredictionSchema() case ModelCategory.Ordinal => getOrdinalPredictionSchema() case ModelCategory.CoxPH => getCoxPHPredictionSchema() + case ModelCategory.BinomialUplift => getUpliftBinomialPredictionSchema() case _ => throw new RuntimeException("Unknown model category " + mojoModel.getModelCategory) } } diff --git a/scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPredictionUpliftBinomial.scala b/scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPredictionUpliftBinomial.scala new file mode 100644 index 0000000000..c760432dc4 --- /dev/null +++ b/scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPredictionUpliftBinomial.scala @@ -0,0 +1,65 @@ +/* + * 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 ai.h2o.sparkling.ml.models + +import ai.h2o.sparkling.sql.functions.udf +import hex.genmodel.easy.EasyPredictModelWrapper +import org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema +import org.apache.spark.sql.expressions.UserDefinedFunction +import org.apache.spark.sql.functions.col +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{Column, Row} + +import scala.collection.mutable + +trait H2OMOJOPredictionUpliftBinomial { + self: H2OAlgorithmMOJOModel => + + private val predictionColType = ArrayType(DoubleType, containsNull = false) + private val predictionColNullable = true + + def getUpliftBinomialPredictionUDF( + schema: StructType, + modelUID: String, + mojoFileName: String, + configInitializers: Seq[(EasyPredictModelWrapper.Config) => EasyPredictModelWrapper.Config]) + : UserDefinedFunction = { + val function = (r: Row) => { + val model = H2OMOJOModel.loadEasyPredictModelWrapper(modelUID, mojoFileName, configInitializers) + val pred = model.predictUpliftBinomial(RowConverter.toH2ORowData(r)) + val resultBuilder = mutable.ArrayBuffer[Any]() + resultBuilder += pred.predictions + new GenericRowWithSchema(resultBuilder.toArray, schema) + } + udf(function, schema) + } + + def getUpliftBinomialPredictionColSchema(): Seq[StructField] = { + Seq(StructField(getPredictionCol(), predictionColType, nullable = predictionColNullable)) + } + + def getUpliftBinomialPredictionSchema(): StructType = { + val valueField = StructField("value", predictionColType, nullable = false) + + StructType(valueField :: Nil) + } + + def extractUpliftBinomialPredictionColContent(): Column = { + col(s"${getDetailedPredictionCol()}.value") + } +}