/*
 * 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.wayang.apps.sgd

import org.apache.wayang.apps.util.{ExperimentDescriptor, Parameters, ProfileDBHelper}
import org.apache.wayang.apps.util.ProfileDBHelper
import org.apache.wayang.core.api.Configuration

/**
  * Companion for the [[SGDImpl]] class.
  */
object SGD extends ExperimentDescriptor {

  override def version = "1.0"

  def main(args: Array[String]): Unit = {
    // Parse args.
    if (args.isEmpty) {
      println(s"Usage: scala <main class> ${Parameters.experimentHelp} <plugin(,plugin)*> " +
        s"<aggregation (regular|preaggregation)> <dataset URL> <dataset size> <#features> <max iterations> <accuracy> <sample size>")
      sys.exit(1)
    }

    implicit val experiment = Parameters.createExperiment(args(0), this)
    implicit val configuration = new Configuration
    val plugins = Parameters.loadPlugins(args(1))
    experiment.getSubject.addConfiguration("plugins", args(1))
    val aggregationType = args(2)
    experiment.getSubject.addConfiguration("aggregationType", aggregationType)
    val datasetUrl = args(3)
    experiment.getSubject.addConfiguration("input", datasetUrl)
    val datasetSize = args(4).toInt
    experiment.getSubject.addConfiguration("inputSize", datasetSize)
    val numFeatures = args(5).toInt
    experiment.getSubject.addConfiguration("features", numFeatures)
    val maxIterations = args(6).toInt
    experiment.getSubject.addConfiguration("maxIterations", maxIterations)
    val accuracy = args(7).toDouble
    experiment.getSubject.addConfiguration("accuracy", accuracy)
    val sampleSize = args(8).toInt
    experiment.getSubject.addConfiguration("sampleSize", sampleSize)

    var weights: Array[Double] = null
    aggregationType match {
      case "regular" =>
        // Initialize the SGD algorithm.
        val sgd = new SGDImpl(configuration, plugins.toArray)
        // Run the SGD.
        weights = sgd(datasetUrl, datasetSize, numFeatures, maxIterations, accuracy, sampleSize, experiment)
      case "preaggregation" =>
        // Initialize the SGD algorithm.
        val sgd = new SGDImprovedImpl(configuration, plugins.toArray)
        // Run the SGD.
        weights = sgd(datasetUrl, datasetSize, numFeatures, maxIterations, accuracy, sampleSize, experiment)
      case other => sys.error("Unknown aggregation type: " + other)
    }

    // Store experiment data.
    ProfileDBHelper.store(experiment, configuration)

    // Print the result.
    if (weights != null) println(s"Determined weights: ${weights.map(w => f"$w%,.5f").mkString(", ")}")
  }

}