org.apache.sysml.api.ml.SVM.scala Maven / Gradle / Ivy

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Declarative Machine Learning
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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.sysml.api.ml

import org.apache.spark.rdd.RDD
import java.io.File
import org.apache.spark.SparkContext
import org.apache.spark.ml.{ Estimator, Model }
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.types.StructType
import org.apache.spark.ml.param.{ DoubleParam, Param, ParamMap, Params }
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 SVM {
  final val scriptPathBinary     = "scripts" + File.separator + "algorithms" + File.separator + "l2-svm.dml"
  final val scriptPathMulticlass = "scripts" + File.separator + "algorithms" + File.separator + "m-svm.dml"
}

class SVM(override val uid: String, val sc: SparkContext, val isMultiClass: Boolean = false)
    extends Estimator[SVMModel]
    with HasIcpt
    with HasRegParam
    with HasTol
    with HasMaxOuterIter
    with BaseSystemMLClassifier {

  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[SVMModel] = {
    val that = new SVM(uid, sc, isMultiClass)
    copyValues(that, extra)
  }

  def getTrainingScript(isSingleNode: Boolean): (Script, String, String) = {
    val script = dml(ScriptsUtils.getDMLScript(if (isMultiClass) SVM.scriptPathMulticlass else SVM.scriptPathBinary))
      .in("$X", " ")
      .in("$Y", " ")
      .in("$model", " ")
      .in("$Log", " ")
      .in("$icpt", toDouble(getIcpt))
      .in("$reg", toDouble(getRegParam))
      .in("$tol", toDouble(getTol))
      .in("$maxiter", toDouble(getMaxOuterIte))
      .out("w")
    (script, "X", "Y")
  }
  
  def fit(X_file: String, y_file: String): SVMModel = {
    mloutput = baseFit(X_file, y_file, sc)
    new SVMModel(this, isMultiClass)
  }

  // Note: will update the y_mb as this will be called by Python mllearn
  def fit(X_mb: MatrixBlock, y_mb: MatrixBlock): SVMModel = {
    mloutput = baseFit(X_mb, y_mb, sc)
    new SVMModel(this, isMultiClass)
  }

  def fit(df: ScriptsUtils.SparkDataType): SVMModel = {
    mloutput = baseFit(df, sc)
    new SVMModel(this, isMultiClass)
  }

}

object SVMModel {
  final val predictionScriptPathBinary     = "scripts" + File.separator + "algorithms" + File.separator + "l2-svm-predict.dml"
  final val predictionScriptPathMulticlass = "scripts" + File.separator + "algorithms" + File.separator + "m-svm-predict.dml"
}

class SVMModel(override val uid: String)(estimator: SVM, val sc: SparkContext, val isMultiClass: Boolean) extends Model[SVMModel] with BaseSystemMLClassifierModel {
  override def copy(extra: ParamMap): SVMModel = {
    val that = new SVMModel(uid)(estimator, sc, isMultiClass)
    copyValues(that, extra)
  }

  def this(estimator: SVM, isMultiClass: Boolean) = {
    this("model")(estimator, estimator.sc, isMultiClass)
  }

  def baseEstimator(): BaseSystemMLEstimator = estimator
  def modelVariables(): List[String]         = List[String]("w")

  def getPredictionScript(isSingleNode: Boolean): (Script, String) = {
    val script = dml(ScriptsUtils.getDMLScript(if (isMultiClass) SVMModel.predictionScriptPathMulticlass else SVMModel.predictionScriptPathBinary))
      .in("$X", " ")
      .in("$model", " ")
      .in("$scoring_only", "TRUE")
      .out("scores")

    val w    = estimator.mloutput.getMatrix("w")
    val wVar = if (isMultiClass) "W" else "w"

    val ret = if (isSingleNode) {
      script.in(wVar, w.toMatrixBlock, w.getMatrixMetadata)
    } else {
      script.in(wVar, w)
    }
    (ret, "X")
  }

  def transform(X: MatrixBlock): MatrixBlock               = baseTransform(X, sc, "scores")
  def transform_probability(X: MatrixBlock): MatrixBlock   = baseTransformProbability(X, sc, "scores")
  def transform(X: String): String                         = baseTransform(X, sc, "scores")
  def transform_probability(X: String): String             = baseTransformProbability(X, sc, "scores")
  def transform(df: ScriptsUtils.SparkDataType): DataFrame = baseTransform(df, sc, "scores")
}