/*!
 * Copyright 2015-2020 by Contributors
 * \file random.h
 * \brief Utility related to random.
 * \author Tianqi Chen
 */
#ifndef XGBOOST_COMMON_RANDOM_H_
#define XGBOOST_COMMON_RANDOM_H_

#include <xgboost/logging.h>

#include <algorithm>
#include <functional>
#include <limits>
#include <map>
#include <memory>
#include <numeric>
#include <random>
#include <utility>
#include <vector>

#include "../collective/communicator-inl.h"
#include "common.h"
#include "xgboost/host_device_vector.h"

namespace xgboost {
namespace common {
/*!
 * \brief Define mt19937 as default type Random Engine.
 */
using RandomEngine = std::mt19937;

#if XGBOOST_CUSTOMIZE_GLOBAL_PRNG
/*!
 * \brief An customized random engine, used to be plugged in PRNG from other systems.
 *  The implementation of this library is not provided by xgboost core library.
 *  Instead the other library can implement this class, which will be used as GlobalRandomEngine
 *  If XGBOOST_RANDOM_CUSTOMIZE = 1, by default this is switched off.
 */
class CustomGlobalRandomEngine {
 public:
  /*! \brief The result type */
  using result_type = uint32_t;
  /*! \brief The minimum of random numbers generated */
  inline static constexpr result_type min() {
    return 0;
  }
  /*! \brief The maximum random numbers generated */
  inline static constexpr result_type max() {
    return std::numeric_limits<result_type>::max();
  }
  /*!
   * \brief seed function, to be implemented
   * \param val The value of the seed.
   */
  void seed(result_type val);
  /*!
   * \return next random number.
   */
  result_type operator()();
};

/*!
 * \brief global random engine
 */
typedef CustomGlobalRandomEngine GlobalRandomEngine;

#else
/*!
 * \brief global random engine
 */
using GlobalRandomEngine = RandomEngine;
#endif  // XGBOOST_CUSTOMIZE_GLOBAL_PRNG

/*!
 * \brief global singleton of a random engine.
 *  This random engine is thread-local and
 *  only visible to current thread.
 */
GlobalRandomEngine& GlobalRandom(); // NOLINT(*)

/*
 * Original paper:
 * Weighted Random Sampling (2005; Efraimidis, Spirakis)
 *
 * Blog:
 * https://timvieira.github.io/blog/post/2019/09/16/algorithms-for-sampling-without-replacement/
*/
template <typename T>
std::vector<T> WeightedSamplingWithoutReplacement(
    std::vector<T> const &array, std::vector<float> const &weights, size_t n) {
  // ES sampling.
  CHECK_EQ(array.size(), weights.size());
  std::vector<float> keys(weights.size());
  std::uniform_real_distribution<float> dist;
  auto& rng = GlobalRandom();
  for (size_t i = 0; i < array.size(); ++i) {
    auto w = std::max(weights.at(i), kRtEps);
    auto u = dist(rng);
    auto k = std::log(u) / w;
    keys[i] = k;
  }
  auto ind = ArgSort<size_t>(Span<float>{keys}, std::greater<>{});
  ind.resize(n);

  std::vector<T> results(ind.size());
  for (size_t k = 0; k < ind.size(); ++k) {
    auto idx = ind[k];
    results[k] = array[idx];
  }
  return results;
}

/**
 * \class ColumnSampler
 *
 * \brief Handles selection of columns due to colsample_bytree, colsample_bylevel and
 * colsample_bynode parameters. Should be initialised before tree construction and to
 * reset when tree construction is completed.
 */
class ColumnSampler {
  std::shared_ptr<HostDeviceVector<bst_feature_t>> feature_set_tree_;
  std::map<int, std::shared_ptr<HostDeviceVector<bst_feature_t>>> feature_set_level_;
  std::vector<float> feature_weights_;
  float colsample_bylevel_{1.0f};
  float colsample_bytree_{1.0f};
  float colsample_bynode_{1.0f};
  GlobalRandomEngine rng_;

 public:
  std::shared_ptr<HostDeviceVector<bst_feature_t>> ColSample(
      std::shared_ptr<HostDeviceVector<bst_feature_t>> p_features, float colsample);
  /**
   * \brief Column sampler constructor.
   * \note This constructor manually sets the rng seed
   */
  explicit ColumnSampler(uint32_t seed) {
    rng_.seed(seed);
  }

  /**
  * \brief Column sampler constructor.
  * \note This constructor synchronizes the RNG seed across processes.
  */
  ColumnSampler() {
    uint32_t seed = common::GlobalRandom()();
    collective::Broadcast(&seed, sizeof(seed), 0);
    rng_.seed(seed);
  }

  /**
   * \brief Initialise this object before use.
   *
   * \param num_col
   * \param colsample_bynode
   * \param colsample_bylevel
   * \param colsample_bytree
   * \param skip_index_0      (Optional) True to skip index 0.
   */
  void Init(int64_t num_col, std::vector<float> feature_weights, float colsample_bynode,
            float colsample_bylevel, float colsample_bytree) {
    feature_weights_ = std::move(feature_weights);
    colsample_bylevel_ = colsample_bylevel;
    colsample_bytree_ = colsample_bytree;
    colsample_bynode_ = colsample_bynode;

    if (feature_set_tree_ == nullptr) {
      feature_set_tree_ = std::make_shared<HostDeviceVector<bst_feature_t>>();
    }
    Reset();

    feature_set_tree_->Resize(num_col);
    std::iota(feature_set_tree_->HostVector().begin(), feature_set_tree_->HostVector().end(), 0);

    feature_set_tree_ = ColSample(feature_set_tree_, colsample_bytree_);
  }

  /**
   * \brief Resets this object.
   */
  void Reset() {
    feature_set_tree_->Resize(0);
    feature_set_level_.clear();
  }

  /**
   * \brief Samples a feature set.
   *
   * \param depth The tree depth of the node at which to sample.
   * \return The sampled feature set.
   * \note If colsample_bynode_ < 1.0, this method creates a new feature set each time it
   * is called. Therefore, it should be called only once per node.
   * \note With distributed xgboost, this function must be called exactly once for the
   * construction of each tree node, and must be called the same number of times in each
   * process and with the same parameters to return the same feature set across processes.
   */
  std::shared_ptr<HostDeviceVector<bst_feature_t>> GetFeatureSet(int depth) {
    if (colsample_bylevel_ == 1.0f && colsample_bynode_ == 1.0f) {
      return feature_set_tree_;
    }

    if (feature_set_level_.count(depth) == 0) {
      // Level sampling, level does not yet exist so generate it
      feature_set_level_[depth] = ColSample(feature_set_tree_, colsample_bylevel_);
    }
    if (colsample_bynode_ == 1.0f) {
      // Level sampling
      return feature_set_level_[depth];
    }
    // Need to sample for the node individually
    return ColSample(feature_set_level_[depth], colsample_bynode_);
  }
};

}  // namespace common
}  // namespace xgboost
#endif  // XGBOOST_COMMON_RANDOM_H_