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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.
*/
package org.apache.spark.ml.r
import org.apache.hadoop.fs.Path
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
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.ml.util._
import org.apache.spark.sql.{DataFrame, Dataset}
private[r] class AFTSurvivalRegressionWrapper private (
val pipeline: PipelineModel,
val features: Array[String]) extends MLWritable {
private val aftModel: AFTSurvivalRegressionModel =
pipeline.stages(1).asInstanceOf[AFTSurvivalRegressionModel]
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))
}
lazy val rFeatures: Array[String] = if (aftModel.getFitIntercept) {
Array("(Intercept)") ++ features ++ Array("Log(scale)")
} else {
features ++ Array("Log(scale)")
}
def transform(dataset: Dataset[_]): DataFrame = {
pipeline.transform(dataset).drop(aftModel.getFeaturesCol)
}
override def write: MLWriter =
new AFTSurvivalRegressionWrapper.AFTSurvivalRegressionWrapperWriter(this)
}
private[r] object AFTSurvivalRegressionWrapper extends MLReadable[AFTSurvivalRegressionWrapper] {
private def formulaRewrite(formula: String): (String, String) = {
var rewritedFormula: String = null
var censorCol: String = null
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")
}
(rewritedFormula, censorCol)
}
def fit(
formula: String,
data: DataFrame,
aggregationDepth: Int): AFTSurvivalRegressionWrapper = {
val (rewritedFormula, censorCol) = formulaRewrite(formula)
val rFormula = new RFormula().setFormula(rewritedFormula)
RWrapperUtils.checkDataColumns(rFormula, data)
val rFormulaModel = rFormula.fit(data)
// 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)
val aft = new AFTSurvivalRegression()
.setCensorCol(censorCol)
.setFitIntercept(rFormula.hasIntercept)
.setFeaturesCol(rFormula.getFeaturesCol)
.setAggregationDepth(aggregationDepth)
val pipeline = new Pipeline()
.setStages(Array(rFormulaModel, aft))
.fit(data)
new AFTSurvivalRegressionWrapper(pipeline, features)
}
override def read: MLReader[AFTSurvivalRegressionWrapper] = new AFTSurvivalRegressionWrapperReader
override def load(path: String): AFTSurvivalRegressionWrapper = super.load(path)
class AFTSurvivalRegressionWrapperWriter(instance: AFTSurvivalRegressionWrapper)
extends MLWriter {
override protected def saveImpl(path: String): Unit = {
val rMetadataPath = new Path(path, "rMetadata").toString
val pipelinePath = new Path(path, "pipeline").toString
val rMetadata = ("class" -> instance.getClass.getName) ~
("features" -> instance.features.toSeq)
val rMetadataJson: String = compact(render(rMetadata))
sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)
instance.pipeline.save(pipelinePath)
}
}
class AFTSurvivalRegressionWrapperReader extends MLReader[AFTSurvivalRegressionWrapper] {
override def load(path: String): AFTSurvivalRegressionWrapper = {
implicit val format = DefaultFormats
val rMetadataPath = new Path(path, "rMetadata").toString
val pipelinePath = new Path(path, "pipeline").toString
val rMetadataStr = sc.textFile(rMetadataPath, 1).first()
val rMetadata = parse(rMetadataStr)
val features = (rMetadata \ "features").extract[Array[String]]
val pipeline = PipelineModel.load(pipelinePath)
new AFTSurvivalRegressionWrapper(pipeline, features)
}
}
}
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