svdplusplus_method.hpp

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#ifndef MLPACK_METHODS_CF_DECOMPOSITION_POLICIES_SVDPLUSPLUS_METHOD_HPP
#define MLPACK_METHODS_CF_DECOMPOSITION_POLICIES_SVDPLUSPLUS_METHOD_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/methods/svdplusplus/svdplusplus.hpp>

namespace mlpack {
namespace cf {

class SVDPlusPlusPolicy
{
 public:
  SVDPlusPlusPolicy(const size_t maxIterations = 10,
                    const double alpha = 0.001,
                    const double lambda = 0.1) :
      maxIterations(maxIterations),
      alpha(alpha),
      lambda(lambda)
  {
    /* Nothing to do here */
  }

  void Apply(const arma::mat& data,
             const arma::sp_mat& /* cleanedData */,
             const size_t rank,
             const size_t maxIterations,
             const double /* minResidue */,
             const bool /* mit */)
  {
    svd::SVDPlusPlus<> svdpp(maxIterations, alpha, lambda);

    // Save implicit data in the form of sparse matrix.
    arma::mat implicitDenseData = data.submat(0, 0, 1, data.n_cols - 1);
    svdpp.CleanData(implicitDenseData, implicitData, data);

    // Perform decomposition using the svdplusplus algorithm.
    svdpp.Apply(data, implicitDenseData, rank, w, h, p, q, y);
  }

  double GetRating(const size_t user, const size_t item) const
  {
    // Iterate through each item which the user interacted with to calculate
    // user vector.
    arma::vec userVec(h.n_rows, arma::fill::zeros);
    arma::sp_mat::const_iterator it = implicitData.begin_col(user);
    arma::sp_mat::const_iterator it_end = implicitData.end_col(user);
    size_t implicitCount = 0;
    for (; it != it_end; ++it)
    {
      userVec += y.col(it.row());
      implicitCount += 1;
    }
    if (implicitCount != 0)
      userVec /= std::sqrt(implicitCount);
    userVec += h.col(user);

    double rating =
        arma::as_scalar(w.row(item) * userVec) + p(item) + q(user);
    return rating;
  }

  void GetRatingOfUser(const size_t user, arma::vec& rating) const
  {
    // Iterate through each item which the user interacted with to calculate
    // user vector.
    arma::vec userVec(h.n_rows, arma::fill::zeros);
    arma::sp_mat::const_iterator it = implicitData.begin_col(user);
    arma::sp_mat::const_iterator it_end = implicitData.end_col(user);
    size_t implicitCount = 0;
    for (; it != it_end; ++it)
    {
      userVec += y.col(it.row());
      implicitCount += 1;
    }
    if (implicitCount != 0)
      userVec /= std::sqrt(implicitCount);
    userVec += h.col(user);

    rating = w * userVec + p + q(user);
  }

  template<typename NeighborSearchPolicy>
  void GetNeighborhood(const arma::Col<size_t>& users,
                       const size_t numUsersForSimilarity,
                       arma::Mat<size_t>& neighborhood,
                       arma::mat& similarities) const
  {
    // User latent vectors (matrix H) are used for neighbor search.
    // Temporarily store feature vector of queried users.
    arma::mat query(h.n_rows, users.n_elem);
    // Select feature vectors of queried users.
    for (size_t i = 0; i < users.n_elem; ++i)
      query.col(i) = h.col(users(i));

    NeighborSearchPolicy neighborSearch(h);
    neighborSearch.Search(
        query, numUsersForSimilarity, neighborhood, similarities);
  }

  const arma::mat& W() const { return w; }
  const arma::mat& H() const { return h; }
  const arma::vec& Q() const { return q; }
  const arma::vec& P() const { return p; }
  const arma::mat& Y() const { return y; }
  const arma::sp_mat& ImplicitData() const { return implicitData; }

  size_t MaxIterations() const { return maxIterations; }
  size_t& MaxIterations() { return maxIterations; }

  double Alpha() const { return alpha; }
  double& Alpha() { return alpha; }

  double Lambda() const { return lambda; }
  double& Lambda() { return lambda; }

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */)
  {
    ar(CEREAL_NVP(maxIterations));
    ar(CEREAL_NVP(alpha));
    ar(CEREAL_NVP(lambda));
    ar(CEREAL_NVP(w));
    ar(CEREAL_NVP(h));
    ar(CEREAL_NVP(p));
    ar(CEREAL_NVP(q));
    ar(CEREAL_NVP(y));
    ar(CEREAL_NVP(implicitData));
  }

 private:
  size_t maxIterations;
  double alpha;
  double lambda;
  arma::mat w;
  arma::mat h;
  arma::vec p;
  arma::vec q;
  arma::mat y;
  arma::sp_mat implicitData;
};

} // namespace cf
} // namespace mlpack

#endif