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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.api.r
import org.apache.spark.ml.attribute._
import org.apache.spark.ml.feature.RFormula
import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel}
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.sql.DataFrame
private[r] object SparkRWrappers {
def fitRModelFormula(
value: String,
df: DataFrame,
family: String,
lambda: Double,
alpha: Double,
standardize: Boolean,
solver: String): PipelineModel = {
val formula = new RFormula().setFormula(value)
val estimator = family match {
case "gaussian" => new LinearRegression()
.setRegParam(lambda)
.setElasticNetParam(alpha)
.setFitIntercept(formula.hasIntercept)
.setStandardization(standardize)
.setSolver(solver)
case "binomial" => new LogisticRegression()
.setRegParam(lambda)
.setElasticNetParam(alpha)
.setFitIntercept(formula.hasIntercept)
.setStandardization(standardize)
}
val pipeline = new Pipeline().setStages(Array(formula, estimator))
pipeline.fit(df)
}
def getModelCoefficients(model: PipelineModel): Array[Double] = {
model.stages.last match {
case m: LinearRegressionModel => {
val coefficientStandardErrorsR = Array(m.summary.coefficientStandardErrors.last) ++
m.summary.coefficientStandardErrors.dropRight(1)
val tValuesR = Array(m.summary.tValues.last) ++ m.summary.tValues.dropRight(1)
val pValuesR = Array(m.summary.pValues.last) ++ m.summary.pValues.dropRight(1)
if (m.getFitIntercept) {
Array(m.intercept) ++ m.coefficients.toArray ++ coefficientStandardErrorsR ++
tValuesR ++ pValuesR
} else {
m.coefficients.toArray ++ coefficientStandardErrorsR ++ tValuesR ++ pValuesR
}
}
case m: LogisticRegressionModel => {
if (m.getFitIntercept) {
Array(m.intercept) ++ m.coefficients.toArray
} else {
m.coefficients.toArray
}
}
}
}
def getModelDevianceResiduals(model: PipelineModel): Array[Double] = {
model.stages.last match {
case m: LinearRegressionModel =>
m.summary.devianceResiduals
case m: LogisticRegressionModel =>
throw new UnsupportedOperationException(
"No deviance residuals available for LogisticRegressionModel")
}
}
def getModelFeatures(model: PipelineModel): Array[String] = {
model.stages.last match {
case m: LinearRegressionModel =>
val attrs = AttributeGroup.fromStructField(
m.summary.predictions.schema(m.summary.featuresCol))
if (m.getFitIntercept) {
Array("(Intercept)") ++ attrs.attributes.get.map(_.name.get)
} else {
attrs.attributes.get.map(_.name.get)
}
case m: LogisticRegressionModel =>
val attrs = AttributeGroup.fromStructField(
m.summary.predictions.schema(m.summary.featuresCol))
if (m.getFitIntercept) {
Array("(Intercept)") ++ attrs.attributes.get.map(_.name.get)
} else {
attrs.attributes.get.map(_.name.get)
}
}
}
def getModelName(model: PipelineModel): String = {
model.stages.last match {
case m: LinearRegressionModel =>
"LinearRegressionModel"
case m: LogisticRegressionModel =>
"LogisticRegressionModel"
}
}
}
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