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[SPARK-13010][ML][SPARKR] Implement a simple wrapper of AFTSurvivalRe…
…gression in SparkR ## What changes were proposed in this pull request? This PR continues the work in #11447, we implemented the wrapper of ```AFTSurvivalRegression``` named ```survreg``` in SparkR. ## How was this patch tested? Test against output from R package survival's survreg. cc mengxr felixcheung Close #11447 Author: Yanbo Liang <[email protected]> Closes #11932 from yanboliang/spark-13010-new.
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mllib/src/main/scala/org/apache/spark/ml/r/AFTSurvivalRegressionWrapper.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.ml.r | ||
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| import org.apache.spark.SparkException | ||
| import org.apache.spark.ml.{Pipeline, PipelineModel} | ||
| import org.apache.spark.ml.attribute.AttributeGroup | ||
| import org.apache.spark.ml.feature.RFormula | ||
| import org.apache.spark.ml.regression.{AFTSurvivalRegression, AFTSurvivalRegressionModel} | ||
| import org.apache.spark.sql.DataFrame | ||
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| private[r] class AFTSurvivalRegressionWrapper private ( | ||
| pipeline: PipelineModel, | ||
| features: Array[String]) { | ||
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| private val aftModel: AFTSurvivalRegressionModel = | ||
| pipeline.stages(1).asInstanceOf[AFTSurvivalRegressionModel] | ||
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| lazy val rCoefficients: Array[Double] = if (aftModel.getFitIntercept) { | ||
| Array(aftModel.intercept) ++ aftModel.coefficients.toArray ++ Array(math.log(aftModel.scale)) | ||
| } else { | ||
| aftModel.coefficients.toArray ++ Array(math.log(aftModel.scale)) | ||
| } | ||
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| lazy val rFeatures: Array[String] = if (aftModel.getFitIntercept) { | ||
| Array("(Intercept)") ++ features ++ Array("Log(scale)") | ||
| } else { | ||
| features ++ Array("Log(scale)") | ||
| } | ||
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| def transform(dataset: DataFrame): DataFrame = { | ||
| pipeline.transform(dataset) | ||
| } | ||
| } | ||
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| private[r] object AFTSurvivalRegressionWrapper { | ||
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| private def formulaRewrite(formula: String): (String, String) = { | ||
| var rewritedFormula: String = null | ||
| var censorCol: String = null | ||
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| val regex = """Surv\(([^,]+), ([^,]+)\) ~ (.+)""".r | ||
| try { | ||
| val regex(label, censor, features) = formula | ||
| // TODO: Support dot operator. | ||
| if (features.contains(".")) { | ||
| throw new UnsupportedOperationException( | ||
| "Terms of survreg formula can not support dot operator.") | ||
| } | ||
| rewritedFormula = label.trim + "~" + features.trim | ||
| censorCol = censor.trim | ||
| } catch { | ||
| case e: MatchError => | ||
| throw new SparkException(s"Could not parse formula: $formula") | ||
| } | ||
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| (rewritedFormula, censorCol) | ||
| } | ||
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| def fit(formula: String, data: DataFrame): AFTSurvivalRegressionWrapper = { | ||
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| val (rewritedFormula, censorCol) = formulaRewrite(formula) | ||
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| val rFormula = new RFormula().setFormula(rewritedFormula) | ||
| val rFormulaModel = rFormula.fit(data) | ||
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| // get feature names from output schema | ||
| val schema = rFormulaModel.transform(data).schema | ||
| val featureAttrs = AttributeGroup.fromStructField(schema(rFormula.getFeaturesCol)) | ||
| .attributes.get | ||
| val features = featureAttrs.map(_.name.get) | ||
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| val aft = new AFTSurvivalRegression() | ||
| .setCensorCol(censorCol) | ||
| .setFitIntercept(rFormula.hasIntercept) | ||
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| val pipeline = new Pipeline() | ||
| .setStages(Array(rFormulaModel, aft)) | ||
| .fit(data) | ||
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| new AFTSurvivalRegressionWrapper(pipeline, features) | ||
| } | ||
| } |