forked from apache/spark
-
Notifications
You must be signed in to change notification settings - Fork 6
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[SPARK-1406] added pmml export for LinearRegressionModel,
RidgeRegressionModel and LassoModel
- Loading branch information
1 parent
e29dfb9
commit 78515ec
Showing
5 changed files
with
226 additions
and
2 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
94 changes: 94 additions & 0 deletions
94
.../src/main/scala/org/apache/spark/mllib/export/pmml/GeneralizedLinearPMMLModelExport.scala
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,94 @@ | ||
| /* | ||
| * 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.mllib.export.pmml | ||
|
|
||
| import org.dmg.pmml.Array.Type | ||
| import org.dmg.pmml.Cluster | ||
| import org.dmg.pmml.ClusteringField | ||
| import org.dmg.pmml.ClusteringModel | ||
| import org.dmg.pmml.ClusteringModel.ModelClass | ||
| import org.dmg.pmml.CompareFunctionType | ||
| import org.dmg.pmml.ComparisonMeasure | ||
| import org.dmg.pmml.ComparisonMeasure.Kind | ||
| import org.dmg.pmml.DataDictionary | ||
| import org.dmg.pmml.DataField | ||
| import org.dmg.pmml.DataType | ||
| import org.dmg.pmml.FieldName | ||
| import org.dmg.pmml.FieldUsageType | ||
| import org.dmg.pmml.MiningField | ||
| import org.dmg.pmml.MiningFunctionType | ||
| import org.dmg.pmml.MiningSchema | ||
| import org.dmg.pmml.OpType | ||
| import org.dmg.pmml.SquaredEuclidean | ||
| import org.apache.spark.mllib.clustering.KMeansModel | ||
| import org.apache.spark.mllib.regression.LinearRegressionModel | ||
| import org.apache.spark.mllib.regression.GeneralizedLinearModel | ||
| import org.dmg.pmml.RegressionModel | ||
| import org.dmg.pmml.RegressionTable | ||
| import org.dmg.pmml.NumericPredictor | ||
|
|
||
| /** | ||
| * PMML Model Export for GeneralizedLinear abstract class | ||
| */ | ||
| private[mllib] class GeneralizedLinearPMMLModelExport( | ||
| model : GeneralizedLinearModel, | ||
| description : String) | ||
| extends PMMLModelExport{ | ||
|
|
||
| /** | ||
| * Export the input GeneralizedLinearModel model to PMML format | ||
| */ | ||
| populateGeneralizedLinearPMML(model) | ||
|
|
||
| private def populateGeneralizedLinearPMML(model : GeneralizedLinearModel): Unit = { | ||
|
|
||
| pmml.getHeader().setDescription(description) | ||
|
|
||
| if(model.weights.size > 0){ | ||
|
|
||
| val fields = new Array[FieldName](model.weights.size) | ||
|
|
||
| val dataDictionary = new DataDictionary() | ||
|
|
||
| val miningSchema = new MiningSchema() | ||
|
|
||
| val regressionTable = new RegressionTable(model.intercept) | ||
|
|
||
| val regressionModel = new RegressionModel(miningSchema,MiningFunctionType.REGRESSION) | ||
| .withModelName(description).withRegressionTables(regressionTable) | ||
|
|
||
| for ( i <- 0 until model.weights.size) { | ||
| fields(i) = FieldName.create("field_" + i) | ||
| dataDictionary | ||
| .withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE)) | ||
| miningSchema | ||
| .withMiningFields(new MiningField(fields(i)) | ||
| .withUsageType(FieldUsageType.ACTIVE)) | ||
| regressionTable.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i))) | ||
| } | ||
|
|
||
| dataDictionary.withNumberOfFields((dataDictionary.getDataFields()).size()) | ||
|
|
||
| pmml.setDataDictionary(dataDictionary) | ||
| pmml.withModels(regressionModel) | ||
|
|
||
| } | ||
|
|
||
| } | ||
|
|
||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
87 changes: 87 additions & 0 deletions
87
...test/scala/org/apache/spark/mllib/export/pmml/GeneralizedLinearPMMLModelExportSuite.scala
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,87 @@ | ||
| /* | ||
| * 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.mllib.export.pmml | ||
|
|
||
| import org.apache.spark.annotation.DeveloperApi | ||
| import org.apache.spark.mllib.export.ModelExportFactory | ||
| import org.apache.spark.mllib.export.ModelExportType | ||
| import org.apache.spark.mllib.regression.LassoModel | ||
| import org.apache.spark.mllib.regression.LinearRegressionModel | ||
| import org.apache.spark.mllib.regression.RidgeRegressionModel | ||
| import org.apache.spark.mllib.util.LinearDataGenerator | ||
| import org.scalatest.FunSuite | ||
| import org.dmg.pmml.RegressionModel | ||
|
|
||
| class GeneralizedLinearPMMLModelExportSuite extends FunSuite{ | ||
|
|
||
| test("GeneralizedLinearPMMLModelExport generate PMML format") { | ||
|
|
||
| //arrange models to test | ||
| val linearInput = LinearDataGenerator.generateLinearInput( | ||
| 3.0, Array(10.0, 10.0), 1, 17) | ||
| val linearRegressionModel = new LinearRegressionModel(linearInput(0).features, linearInput(0).label); | ||
| val ridgeRegressionModel = new RidgeRegressionModel(linearInput(0).features, linearInput(0).label); | ||
| val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label); | ||
|
|
||
| //act by exporting the model to the PMML format | ||
| val linearModelExport = ModelExportFactory.createModelExport(linearRegressionModel, ModelExportType.PMML) | ||
| //assert that the PMML format is as expected | ||
| assert(linearModelExport.isInstanceOf[PMMLModelExport]) | ||
| var pmml = linearModelExport.asInstanceOf[PMMLModelExport].getPmml() | ||
| assert(pmml.getHeader().getDescription() === "linear regression") | ||
| //check that the number of fields match the weights size | ||
| assert(pmml.getDataDictionary().getNumberOfFields() === linearRegressionModel.weights.size) | ||
| //this verify that there is a model attached to the pmml object and the model is a regression one | ||
| //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights | ||
| assert(pmml.getModels().get(0).asInstanceOf[RegressionModel] | ||
| .getRegressionTables().get(0).getNumericPredictors().size() === linearRegressionModel.weights.size) | ||
|
|
||
| //act | ||
| val ridgeModelExport = ModelExportFactory.createModelExport(ridgeRegressionModel, ModelExportType.PMML) | ||
| //assert that the PMML format is as expected | ||
| assert(ridgeModelExport.isInstanceOf[PMMLModelExport]) | ||
| pmml = ridgeModelExport.asInstanceOf[PMMLModelExport].getPmml() | ||
| assert(pmml.getHeader().getDescription() === "ridge regression") | ||
| //check that the number of fields match the weights size | ||
| assert(pmml.getDataDictionary().getNumberOfFields() === ridgeRegressionModel.weights.size) | ||
| //this verify that there is a model attached to the pmml object and the model is a regression one | ||
| //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights | ||
| assert(pmml.getModels().get(0).asInstanceOf[RegressionModel] | ||
| .getRegressionTables().get(0).getNumericPredictors().size() === ridgeRegressionModel.weights.size) | ||
|
|
||
| //act | ||
| val lassoModelExport = ModelExportFactory.createModelExport(lassoModel, ModelExportType.PMML) | ||
| //assert that the PMML format is as expected | ||
| assert(lassoModelExport.isInstanceOf[PMMLModelExport]) | ||
| pmml = lassoModelExport.asInstanceOf[PMMLModelExport].getPmml() | ||
| assert(pmml.getHeader().getDescription() === "lasso regression") | ||
| //check that the number of fields match the weights size | ||
| assert(pmml.getDataDictionary().getNumberOfFields() === lassoModel.weights.size) | ||
| //this verify that there is a model attached to the pmml object and the model is a regression one | ||
| //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights | ||
| assert(pmml.getModels().get(0).asInstanceOf[RegressionModel] | ||
| .getRegressionTables().get(0).getNumericPredictors().size() === lassoModel.weights.size) | ||
|
|
||
| //manual checking | ||
| //ModelExporter.toPMML(linearRegressionModel,"/tmp/linearregression.xml") | ||
| //ModelExporter.toPMML(ridgeRegressionModel,"/tmp/ridgeregression.xml") | ||
| //ModelExporter.toPMML(lassoModel,"/tmp/lassoregression.xml") | ||
|
|
||
| } | ||
|
|
||
| } |