/*
 * 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.spark.mllib.tree.impurity

import org.apache.spark.annotation.{DeveloperApi, Experimental}

/**
 * :: Experimental ::
 * Trait for calculating information gain.
 * This trait is used for
 *  (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
 *  (b) calculating impurity values from sufficient statistics.
 */
@Experimental
trait Impurity extends Serializable {

  /**
   * :: DeveloperApi ::
   * information calculation for multiclass classification
   * @param counts Array[Double] with counts for each label
   * @param totalCount sum of counts for all labels
   * @return information value, or 0 if totalCount = 0
   */
  @DeveloperApi
  def calculate(counts: Array[Double], totalCount: Double): Double

  /**
   * :: DeveloperApi ::
   * information calculation for regression
   * @param count number of instances
   * @param sum sum of labels
   * @param sumSquares summation of squares of the labels
   * @return information value, or 0 if count = 0
   */
  @DeveloperApi
  def calculate(count: Double, sum: Double, sumSquares: Double): Double
}

/**
 * Interface for updating views of a vector of sufficient statistics,
 * in order to compute impurity from a sample.
 * Note: Instances of this class do not hold the data; they operate on views of the data.
 * @param statsSize  Length of the vector of sufficient statistics for one bin.
 */
private[tree] abstract class ImpurityAggregator(val statsSize: Int) extends Serializable {

  /**
   * Merge the stats from one bin into another.
   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
   * @param offset    Start index of stats for (node, feature, bin) which is modified by the merge.
   * @param otherOffset  Start index of stats for (node, feature, other bin) which is not modified.
   */
  def merge(allStats: Array[Double], offset: Int, otherOffset: Int): Unit = {
    var i = 0
    while (i < statsSize) {
      allStats(offset + i) += allStats(otherOffset + i)
      i += 1
    }
  }

  /**
   * Update stats for one (node, feature, bin) with the given label.
   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
   * @param offset    Start index of stats for this (node, feature, bin).
   */
  def update(allStats: Array[Double], offset: Int, label: Double, instanceWeight: Double): Unit

  /**
   * Get an [[ImpurityCalculator]] for a (node, feature, bin).
   * @param allStats  Flat stats array, with stats for this (node, feature, bin) contiguous.
   * @param offset    Start index of stats for this (node, feature, bin).
   */
  def getCalculator(allStats: Array[Double], offset: Int): ImpurityCalculator

}

/**
 * Stores statistics for one (node, feature, bin) for calculating impurity.
 * Unlike [[ImpurityAggregator]], this class stores its own data and is for a specific
 * (node, feature, bin).
 * @param stats  Array of sufficient statistics for a (node, feature, bin).
 */
private[tree] abstract class ImpurityCalculator(val stats: Array[Double]) {

  /**
   * Make a deep copy of this [[ImpurityCalculator]].
   */
  def copy: ImpurityCalculator

  /**
   * Calculate the impurity from the stored sufficient statistics.
   */
  def calculate(): Double

  /**
   * Add the stats from another calculator into this one, modifying and returning this calculator.
   */
  def add(other: ImpurityCalculator): ImpurityCalculator = {
    require(stats.size == other.stats.size,
      s"Two ImpurityCalculator instances cannot be added with different counts sizes." +
        s"  Sizes are ${stats.size} and ${other.stats.size}.")
    var i = 0
    while (i < other.stats.size) {
      stats(i) += other.stats(i)
      i += 1
    }
    this
  }

  /**
   * Subtract the stats from another calculator from this one, modifying and returning this
   * calculator.
   */
  def subtract(other: ImpurityCalculator): ImpurityCalculator = {
    require(stats.size == other.stats.size,
      s"Two ImpurityCalculator instances cannot be subtracted with different counts sizes." +
      s"  Sizes are ${stats.size} and ${other.stats.size}.")
    var i = 0
    while (i < other.stats.size) {
      stats(i) -= other.stats(i)
      i += 1
    }
    this
  }

  /**
   * Number of data points accounted for in the sufficient statistics.
   */
  def count: Long

  /**
   * Prediction which should be made based on the sufficient statistics.
   */
  def predict: Double

  /**
   * Probability of the label given by [[predict]], or -1 if no probability is available.
   */
  def prob(label: Double): Double = -1

  /**
   * Return the index of the largest array element.
   * Fails if the array is empty.
   */
  protected def indexOfLargestArrayElement(array: Array[Double]): Int = {
    val result = array.foldLeft(-1, Double.MinValue, 0) {
      case ((maxIndex, maxValue, currentIndex), currentValue) =>
        if (currentValue > maxValue) {
          (currentIndex, currentValue, currentIndex + 1)
        } else {
          (maxIndex, maxValue, currentIndex + 1)
        }
    }
    if (result._1 < 0) {
      throw new RuntimeException("ImpurityCalculator internal error:" +
        " indexOfLargestArrayElement failed")
    }
    result._1
  }

}
