org.apache.sysml.api.ml.BaseSystemMLClassifier.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.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._
import org.apache.spark.sql._
trait HasLaplace extends Params {
final val laplace: Param[Double] = new Param[Double](this, "laplace", "Laplace smoothing specified by the user to avoid creation of 0 probabilities.")
setDefault(laplace, 1.0)
final def getLaplace: Double = $(laplace)
}
trait HasIcpt extends Params {
final val icpt: Param[Int] = new Param[Int](this, "icpt", "Intercept presence, shifting and rescaling X columns")
setDefault(icpt, 0)
final def getIcpt: Int = $(icpt)
}
trait HasMaxOuterIter extends Params {
final val maxOuterIter: Param[Int] = new Param[Int](this, "maxOuterIter", "max. number of outer (Newton) iterations")
setDefault(maxOuterIter, 100)
final def getMaxOuterIte: Int = $(maxOuterIter)
}
trait HasMaxInnerIter extends Params {
final val maxInnerIter: Param[Int] = new Param[Int](this, "maxInnerIter", "max. number of inner (conjugate gradient) iterations, 0 = no max")
setDefault(maxInnerIter, 0)
final def getMaxInnerIter: Int = $(maxInnerIter)
}
trait HasTol extends Params {
final val tol: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms")
setDefault(tol, 0.000001)
final def getTol: Double = $(tol)
}
trait HasRegParam extends Params {
final val regParam: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms")
setDefault(regParam, 0.000001)
final def getRegParam: Double = $(regParam)
}
trait BaseSystemMLEstimator {
def transformSchema(schema: StructType): StructType = schema
// Returns the script and variables for X and y
def getTrainingScript(isSingleNode:Boolean):(Script, String, String)
def toDouble(i:Int): java.lang.Double = {
double2Double(i.toDouble)
}
def toDouble(d:Double): java.lang.Double = {
double2Double(d)
}
}
trait BaseSystemMLEstimatorModel {
def toDouble(i:Int): java.lang.Double = {
double2Double(i.toDouble)
}
def toDouble(d:Double): java.lang.Double = {
double2Double(d)
}
def transformSchema(schema: StructType): StructType = schema
// Returns the script and variable for X
def getPredictionScript(mloutput: MLResults, isSingleNode:Boolean): (Script, String)
}
trait BaseSystemMLClassifier extends BaseSystemMLEstimator {
def fit(X_mb: MatrixBlock, y_mb: MatrixBlock, sc: SparkContext): (MLResults, java.util.HashMap[Int, String]) = {
val isSingleNode = true
val ml = new MLContext(sc)
val revLabelMapping = new java.util.HashMap[Int, String]
PredictionUtils.fillLabelMapping(y_mb, revLabelMapping)
y_mb.recomputeNonZeros();
val ret = getTrainingScript(isSingleNode)
val script = ret._1.in(ret._2, X_mb).in(ret._3, y_mb)
(ml.execute(script), revLabelMapping)
}
def fit(df: ScriptsUtils.SparkDataType, sc: SparkContext): (MLResults, java.util.HashMap[Int, String]) = {
val isSingleNode = false
val ml = new MLContext(df.rdd.sparkContext)
val mcXin = new MatrixCharacteristics()
val Xin = RDDConverterUtils.dataFrameToBinaryBlock(sc, df.asInstanceOf[DataFrame].select("features"), mcXin, false, true)
val revLabelMapping = new java.util.HashMap[Int, String]
val yin = PredictionUtils.fillLabelMapping(df, revLabelMapping)
val ret = getTrainingScript(isSingleNode)
val Xbin = new BinaryBlockMatrix(Xin, mcXin)
val script = ret._1.in(ret._2, Xbin).in(ret._3, yin)
(ml.execute(script), revLabelMapping)
}
}
trait BaseSystemMLClassifierModel extends BaseSystemMLEstimatorModel {
def transform(X: MatrixBlock, mloutput: MLResults, labelMapping: java.util.HashMap[Int, String], sc: SparkContext, probVar:String): MatrixBlock = {
val isSingleNode = true
val ml = new MLContext(sc)
val script = getPredictionScript(mloutput, isSingleNode)
val modelPredict = ml.execute(script._1.in(script._2, X))
val ret = PredictionUtils.computePredictedClassLabelsFromProbability(modelPredict, isSingleNode, sc, probVar)
.getBinaryBlockMatrix("Prediction").getMatrixBlock
if(ret.getNumColumns != 1) {
throw new RuntimeException("Expected predicted label to be a column vector")
}
PredictionUtils.updateLabels(isSingleNode, null, ret, null, labelMapping)
return ret
}
def transform(df: ScriptsUtils.SparkDataType, mloutput: MLResults, labelMapping: java.util.HashMap[Int, String], sc: SparkContext,
probVar:String, outputProb:Boolean=true): DataFrame = {
val isSingleNode = false
val ml = new MLContext(sc)
val mcXin = new MatrixCharacteristics()
val Xin = RDDConverterUtils.dataFrameToBinaryBlock(df.rdd.sparkContext, df.asInstanceOf[DataFrame].select("features"), mcXin, false, true)
val script = getPredictionScript(mloutput, isSingleNode)
val Xin_bin = new BinaryBlockMatrix(Xin, mcXin)
val modelPredict = ml.execute(script._1.in(script._2, Xin_bin))
val predLabelOut = PredictionUtils.computePredictedClassLabelsFromProbability(modelPredict, isSingleNode, sc, probVar)
val predictedDF = PredictionUtils.updateLabels(isSingleNode, predLabelOut.getDataFrame("Prediction"), null, "C1", labelMapping).select(RDDConverterUtils.DF_ID_COLUMN, "prediction")
if(outputProb) {
val prob = modelPredict.getDataFrame(probVar, true).withColumnRenamed("C1", "probability").select(RDDConverterUtils.DF_ID_COLUMN, "probability")
val dataset = RDDConverterUtilsExt.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, RDDConverterUtils.DF_ID_COLUMN)
return PredictionUtils.joinUsingID(dataset, PredictionUtils.joinUsingID(prob, predictedDF))
}
else {
val dataset = RDDConverterUtilsExt.addIDToDataFrame(df.asInstanceOf[DataFrame], df.sqlContext, RDDConverterUtils.DF_ID_COLUMN)
return PredictionUtils.joinUsingID(dataset, predictedDF)
}
}
}