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101 changes: 101 additions & 0 deletions
101
...src/main/scala/org/apache/spark/mllib/export/pmml/LogisticRegressionPMMLModelExport.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.mllib.export.pmml | ||
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| 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.NumericPredictor | ||
| import org.dmg.pmml.OpType | ||
| import org.dmg.pmml.RegressionModel | ||
| import org.dmg.pmml.RegressionTable | ||
| import org.apache.spark.mllib.classification.LogisticRegressionModel | ||
| import org.dmg.pmml.RegressionNormalizationMethodType | ||
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| /** | ||
| * PMML Model Export for LogisticRegressionModel class | ||
| */ | ||
| private[mllib] class LogisticRegressionPMMLModelExport( | ||
| model : LogisticRegressionModel, | ||
| description : String) | ||
| extends PMMLModelExport{ | ||
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| /** | ||
| * Export the input LogisticRegressionModel model to PMML format | ||
| */ | ||
| populateLogisticRegressionPMML(model) | ||
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| private def populateLogisticRegressionPMML(model : LogisticRegressionModel): Unit = { | ||
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| pmml.getHeader().setDescription(description) | ||
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| if(model.weights.size > 0){ | ||
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| val fields = new Array[FieldName](model.weights.size) | ||
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| val dataDictionary = new DataDictionary() | ||
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| val miningSchema = new MiningSchema() | ||
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| val regressionTableYES = new RegressionTable(model.intercept) | ||
| .withTargetCategory("YES") | ||
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| val regressionTableNO = new RegressionTable(0.0) | ||
| .withTargetCategory("NO") | ||
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| val regressionModel = new RegressionModel(miningSchema,MiningFunctionType.CLASSIFICATION) | ||
| .withModelName(description) | ||
| .withNormalizationMethod(RegressionNormalizationMethodType.LOGIT) | ||
| .withRegressionTables(regressionTableYES, regressionTableNO) | ||
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| 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)) | ||
| regressionTableYES | ||
| .withNumericPredictors(new NumericPredictor(fields(i), model.weights(i))) | ||
| } | ||
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| // add target field | ||
| val targetField = FieldName.create("target"); | ||
| dataDictionary | ||
| .withDataFields( | ||
| new DataField(targetField, OpType.CONTINUOUS, DataType.DOUBLE) | ||
| ) | ||
| miningSchema | ||
| .withMiningFields(new MiningField(targetField) | ||
| .withUsageType(FieldUsageType.TARGET)) | ||
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| dataDictionary.withNumberOfFields((dataDictionary.getDataFields()).size()) | ||
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| pmml.setDataDictionary(dataDictionary) | ||
| pmml.withModels(regressionModel) | ||
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| } | ||
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| } | ||
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| } |
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62 changes: 62 additions & 0 deletions
62
...est/scala/org/apache/spark/mllib/export/pmml/LogisticRegressionPMMLModelExportSuite.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,62 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
|
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| package org.apache.spark.mllib.export.pmml | ||
|
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| import org.dmg.pmml.RegressionModel | ||
| import org.scalatest.FunSuite | ||
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| import org.apache.spark.mllib.classification.LogisticRegressionModel | ||
| import org.apache.spark.mllib.export.ModelExportFactory | ||
| import org.apache.spark.mllib.export.ModelExportType | ||
| import org.apache.spark.mllib.util.LinearDataGenerator | ||
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| class LogisticRegressionPMMLModelExportSuite extends FunSuite{ | ||
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| test("LogisticRegressionPMMLModelExport generate PMML format") { | ||
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| //arrange models to test | ||
| val linearInput = LinearDataGenerator.generateLinearInput( | ||
| 3.0, Array(10.0, 10.0), 1, 17) | ||
| val logisticRegressionModel = new LogisticRegressionModel(linearInput(0).features, linearInput(0).label); | ||
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| //act by exporting the model to the PMML format | ||
| val logisticModelExport = ModelExportFactory.createModelExport(logisticRegressionModel, ModelExportType.PMML) | ||
| //assert that the PMML format is as expected | ||
| assert(logisticModelExport.isInstanceOf[PMMLModelExport]) | ||
| var pmml = logisticModelExport.asInstanceOf[PMMLModelExport].getPmml() | ||
| assert(pmml.getHeader().getDescription() === "logistic regression: if predicted value > 0.5, the outcome is positive, or negative otherwise") | ||
| //check that the number of fields match the weights size | ||
| assert(pmml.getDataDictionary().getNumberOfFields() === logisticRegressionModel.weights.size + 1) | ||
| //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 (for target category YES) with the same number of predictors of the model weights | ||
| assert(pmml.getModels().get(0).asInstanceOf[RegressionModel] | ||
| .getRegressionTables().get(0).getTargetCategory() === "YES") | ||
| assert(pmml.getModels().get(0).asInstanceOf[RegressionModel] | ||
| .getRegressionTables().get(0).getNumericPredictors().size() === logisticRegressionModel.weights.size) | ||
| //verify if there is a second table with target category NO and no predictors | ||
| assert(pmml.getModels().get(0).asInstanceOf[RegressionModel] | ||
| .getRegressionTables().get(1).getTargetCategory() === "NO") | ||
| assert(pmml.getModels().get(0).asInstanceOf[RegressionModel] | ||
| .getRegressionTables().get(1).getNumericPredictors().size() === 0) | ||
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| //manual checking | ||
| //ModelExporter.toPMML(logisticRegressionModel,"/tmp/logisticregression.xml") | ||
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| } | ||
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| } |