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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.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.attribute.AttributeGroup
import org.apache.spark.ml.feature.RFormula
import org.apache.spark.ml.regression._
import org.apache.spark.sql._
private[r] class GeneralizedLinearRegressionWrapper private (
pipeline: PipelineModel,
val features: Array[String]) {
private val glm: GeneralizedLinearRegressionModel =
pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel]
lazy val rCoefficients: Array[Double] = if (glm.getFitIntercept) {
Array(glm.intercept) ++ glm.coefficients.toArray
} else {
glm.coefficients.toArray
}
lazy val rFeatures: Array[String] = if (glm.getFitIntercept) {
Array("(Intercept)") ++ features
} else {
features
}
def transform(dataset: DataFrame): DataFrame = {
pipeline.transform(dataset).drop(glm.getFeaturesCol)
}
}
private[r] object GeneralizedLinearRegressionWrapper {
def fit(
formula: String,
data: DataFrame,
family: String,
link: String,
epsilon: Double,
maxit: Int): GeneralizedLinearRegressionWrapper = {
val rFormula = new RFormula()
.setFormula(formula)
val rFormulaModel = rFormula.fit(data)
// get labels and 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)
// assemble and fit the pipeline
val glm = new GeneralizedLinearRegression()
.setFamily(family)
.setLink(link)
.setFitIntercept(rFormula.hasIntercept)
.setTol(epsilon)
.setMaxIter(maxit)
val pipeline = new Pipeline()
.setStages(Array(rFormulaModel, glm))
.fit(data)
new GeneralizedLinearRegressionWrapper(pipeline, features)
}
}
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