org.apache.sysml.api.ml.LogisticRegression.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 LogisticRegression {
final val scriptPath = "scripts" + File.separator + "algorithms" + File.separator + "MultiLogReg.dml"
}
/**
* Logistic Regression Scala API
*/
class LogisticRegression(override val uid: String, val sc: SparkContext) extends Estimator[LogisticRegressionModel] with HasIcpt
with HasRegParam with HasTol with HasMaxOuterIter with HasMaxInnerIter with BaseSystemMLClassifier {
def setIcpt(value: Int) = set(icpt, value)
def setMaxOuterIter(value: Int) = set(maxOuterIter, value)
def setMaxInnerIter(value: Int) = set(maxInnerIter, value)
def setRegParam(value: Double) = set(regParam, value)
def setTol(value: Double) = set(tol, value)
override def copy(extra: ParamMap): LogisticRegression = {
val that = new LogisticRegression(uid, sc)
copyValues(that, extra)
}
// Note: will update the y_mb as this will be called by Python mllearn
def fit(X_mb: MatrixBlock, y_mb: MatrixBlock): LogisticRegressionModel = {
val ret = baseFit(X_mb, y_mb, sc)
new LogisticRegressionModel("log")(ret, sc)
}
def fit(df: ScriptsUtils.SparkDataType): LogisticRegressionModel = {
val ret = baseFit(df, sc)
new LogisticRegressionModel("log")(ret, sc)
}
def getTrainingScript(isSingleNode:Boolean):(Script, String, String) = {
val script = dml(ScriptsUtils.getDMLScript(LogisticRegression.scriptPath))
.in("$X", " ")
.in("$Y", " ")
.in("$B", " ")
.in("$icpt", toDouble(getIcpt))
.in("$reg", toDouble(getRegParam))
.in("$tol", toDouble(getTol))
.in("$moi", toDouble(getMaxOuterIte))
.in("$mii", toDouble(getMaxInnerIter))
.out("B_out")
(script, "X", "Y_vec")
}
}
object LogisticRegressionModel {
final val scriptPath = "scripts" + File.separator + "algorithms" + File.separator + "GLM-predict.dml"
}
/**
* Logistic Regression Scala API
*/
class LogisticRegressionModel(override val uid: String)(
val mloutput: MLResults, val sc: SparkContext)
extends Model[LogisticRegressionModel] with HasIcpt
with HasRegParam with HasTol with HasMaxOuterIter with HasMaxInnerIter with BaseSystemMLClassifierModel {
override def copy(extra: ParamMap): LogisticRegressionModel = {
val that = new LogisticRegressionModel(uid)(mloutput, sc)
copyValues(that, extra)
}
var outputRawPredictions = true
def setOutputRawPredictions(outRawPred:Boolean): Unit = { outputRawPredictions = outRawPred }
def getPredictionScript(mloutput: MLResults, isSingleNode:Boolean): (Script, String) =
PredictionUtils.getGLMPredictionScript(mloutput.getBinaryBlockMatrix("B_out"), isSingleNode, 3)
def transform(X: MatrixBlock): MatrixBlock = baseTransform(X, mloutput, sc, "means")
def transform(df: ScriptsUtils.SparkDataType): DataFrame = baseTransform(df, mloutput, sc, "means")
}
/**
* Example code for Logistic Regression
*/
object LogisticRegressionExample {
import org.apache.spark.{ SparkConf, SparkContext }
import org.apache.spark.sql.types._
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
def main(args: Array[String]) = {
val sparkConf: SparkConf = new SparkConf();
val sc: SparkContext = new SparkContext("local", "TestLocal", sparkConf);
val sqlContext = new org.apache.spark.sql.SQLContext(sc);
import sqlContext.implicits._
val training = sc.parallelize(Seq(
LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0)),
LabeledPoint(1.0, Vectors.dense(1.0, 0.4, 2.1)),
LabeledPoint(2.0, Vectors.dense(1.2, 0.0, 3.5)),
LabeledPoint(1.0, Vectors.dense(1.0, 0.5, 2.2)),
LabeledPoint(2.0, Vectors.dense(1.6, 0.8, 3.6)),
LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 2.3))))
val lr = new LogisticRegression("log", sc)
val lrmodel = lr.fit(training.toDF)
// lrmodel.mloutput.getDF(sqlContext, "B_out").show()
val testing = sc.parallelize(Seq(
LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0)),
LabeledPoint(1.0, Vectors.dense(1.0, 0.4, 2.1)),
LabeledPoint(2.0, Vectors.dense(1.2, 0.0, 3.5)),
LabeledPoint(1.0, Vectors.dense(1.0, 0.5, 2.2)),
LabeledPoint(2.0, Vectors.dense(1.6, 0.8, 3.6)),
LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 2.3))))
lrmodel.transform(testing.toDF).show
}
}