org.apache.sysml.api.ml.LinearRegression.scala Maven / Gradle / Ivy
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Declarative Machine Learning
/*
* 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.sysml.api.ml
import org.apache.spark.rdd.RDD
import java.io.File
import org.apache.spark.SparkContext
import org.apache.spark.ml.{ Model, Estimator }
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.types.StructType
import org.apache.spark.ml.param.{ Params, Param, ParamMap, DoubleParam }
import org.apache.sysml.runtime.matrix.MatrixCharacteristics
import org.apache.sysml.runtime.matrix.data.MatrixBlock
import org.apache.sysml.runtime.DMLRuntimeException
import org.apache.sysml.runtime.instructions.spark.utils.{ RDDConverterUtilsExt => RDDConverterUtils }
import org.apache.sysml.api.mlcontext._
import org.apache.sysml.api.mlcontext.ScriptFactory._
object LinearRegression {
final val scriptPathCG = "scripts" + File.separator + "algorithms" + File.separator + "LinearRegCG.dml"
final val scriptPathDS = "scripts" + File.separator + "algorithms" + File.separator + "LinearRegDS.dml"
}
// algorithm = "direct-solve", "conjugate-gradient"
class LinearRegression(override val uid: String, val sc: SparkContext, val solver:String="direct-solve")
extends Estimator[LinearRegressionModel] with HasIcpt
with HasRegParam with HasTol with HasMaxOuterIter with BaseSystemMLRegressor {
def setIcpt(value: Int) = set(icpt, value)
def setMaxIter(value: Int) = set(maxOuterIter, value)
def setRegParam(value: Double) = set(regParam, value)
def setTol(value: Double) = set(tol, value)
override def copy(extra: ParamMap): Estimator[LinearRegressionModel] = {
val that = new LinearRegression(uid, sc, solver)
copyValues(that, extra)
}
def getTrainingScript(isSingleNode:Boolean):(Script, String, String) = {
val script = dml(ScriptsUtils.getDMLScript(
if(solver.compareTo("direct-solve") == 0) LinearRegression.scriptPathDS
else if(solver.compareTo("newton-cg") == 0) LinearRegression.scriptPathCG
else throw new DMLRuntimeException("The algorithm should be direct-solve or newton-cg")))
.in("$X", " ")
.in("$Y", " ")
.in("$B", " ")
.in("$Log", " ")
.in("$fmt", "binary")
.in("$icpt", toDouble(getIcpt))
.in("$reg", toDouble(getRegParam))
.in("$tol", toDouble(getTol))
.in("$maxi", toDouble(getMaxOuterIte))
.out("beta_out")
(script, "X", "y")
}
def fit(X_mb: MatrixBlock, y_mb: MatrixBlock): LinearRegressionModel =
new LinearRegressionModel("lr")(baseFit(X_mb, y_mb, sc), sc)
def fit(df: ScriptsUtils.SparkDataType): LinearRegressionModel =
new LinearRegressionModel("lr")(baseFit(df, sc), sc)
}
class LinearRegressionModel(override val uid: String)(val mloutput: MLResults, val sc: SparkContext) extends Model[LinearRegressionModel] with HasIcpt
with HasRegParam with HasTol with HasMaxOuterIter with BaseSystemMLRegressorModel {
override def copy(extra: ParamMap): LinearRegressionModel = {
val that = new LinearRegressionModel(uid)(mloutput, sc)
copyValues(that, extra)
}
def getPredictionScript(mloutput: MLResults, isSingleNode:Boolean): (Script, String) =
PredictionUtils.getGLMPredictionScript(mloutput.getBinaryBlockMatrix("beta_out"), isSingleNode)
def transform(df: ScriptsUtils.SparkDataType): DataFrame = baseTransform(df, mloutput, sc, "means")
def transform(X: MatrixBlock): MatrixBlock = baseTransform(X, mloutput, sc, "means")
}