mlapiDecomposition.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/decomposition.R
\docType{data}
\name{mlapiDecomposition}
\alias{mlapiDecomposition}
\alias{mlapiDecomposition}
\alias{mlapiDecompositionOnline}
\title{Base abstract class for all decompositions}
\format{\code{R6Class} object.}
\usage{
mlapiDecomposition

mlapiDecompositionOnline
}
\description{
Base class for all \bold{decompositions} which are methods which can decompose matrix into
2 low-dimensional matrices \code{x = f(A, B)}.
(Think of this Latent Dirichlet Allocation, Non-negative Matrix Factorization, etc).
It iherits from \link{mlapiTransformation} and additionally requires to implement \code{components} member.

Base class for all \bold{decompositions} which are methods which can decompose matrix into
2 low-dimensional matrices \code{x = f(A, B)} \bold{incrementally}.
It iherits from \link{mlapiDecomposition} and additionally requires
to implement \code{partial_fit} method which can learn \code{components} incrementally.
}
\section{Fields}{

\describe{
\item{\code{components}}{features embeddings. So if matrix is decomposed in a form \code{x = f(A, B)} where
X = n\*m, A = n\*k, B = k\*m them \code{B = components}}

\item{\code{components}}{features embeddings. So if matrix is decomposed in a form \code{x = f(A, B)} where
X = n\*m, A = n\*k, B = k\*m them \code{B = components}}
}}

\section{Methods}{

\describe{
  \item{\code{$fit_transform(x, y = NULL, ...)}}{}
  \item{\code{$transform(x, ...)}}{Performs transformation of the new data (after model was trained)}
}


\describe{
  \item{\code{$fit_transform(x, y = NULL, ...)}}{}
  \item{\code{$partial_fit(x, y = NULL, ...)}}{}
  \item{\code{$transform(x, ...)}}{Performs transformation of the new data (after model was trained)}
}
}

\section{Arguments}{

\describe{
 \item{x}{A matrix like object, should \bold{inherit from \code{Matrix} or \code{matrix}}.
 Allowed classes should be defined in child classes.}
 \item{y}{\code{NULL}. Optional taget variable. Usually this should be \code{NULL}.
 There few cases when it could be used.}
 \item{...}{additional parameters \bold{with default values}}
}


\describe{
 \item{x}{A matrix like object, should \bold{inherit from \code{Matrix} or \code{matrix}}.
 Allowed classes should be defined in child classes.}
 \item{y}{\code{NULL}. Optional taget variable. Usually this should be \code{NULL}.
 There few cases when it could be used.}
 \item{...}{additional parameters \bold{with default values}}
}
}

\examples{
TruncatedSVD = R6::R6Class(
  classname = "TruncatedSVD",
  inherit = mlapi::mlapiDecomposition,
  public = list(
    initialize = function(rank = 10) {
      private$rank = rank
      super$set_internal_matrix_formats(dense = "matrix", sparse = NULL)
    },
    fit_transform = function(x, ...) {
      x = super$check_convert_input(x)
      private$n_features = ncol(x)
      svd_fit = svd(x, nu = private$rank, nv = private$rank, ...)
      sing_values = svd_fit$d[seq_len(private$rank)]
      result = svd_fit$u \%*\% diag(x = sqrt(sing_values))
      private$components_ = t(svd_fit$v \%*\% diag(x = sqrt(sing_values)))
      rm(svd_fit)
      rownames(result) = rownames(x)
      colnames(private$components_) = colnames(x)
      private$fitted = TRUE
      invisible(result)
    },
    transform = function(x, ...) {
      if (private$fitted) {
        stopifnot(ncol(x) == ncol(private$components_))
        lhs = tcrossprod(private$components_)
        rhs = as.matrix(tcrossprod(private$components_, x))
        t(solve(lhs, rhs))
      }
      else
        stop("Fit the model first woth model$fit_transform()!")
    }
  ),
  private = list(
    rank = NULL,
    n_features = NULL,
    fitted = NULL
  )
)
set.seed(1)
model = TruncatedSVD$new(2)
x = matrix(sample(100 * 10, replace = TRUE), ncol = 10)
x_trunc = model$fit_transform(x)
dim(x_trunc)

x_trunc_2 = model$transform(x)
sum(x_trunc_2 - x_trunc)

#' check pipe-compatible S3 interface
x_trunc_2_s3 = transform(x, model)
identical(x_trunc_2, x_trunc_2_s3)
}
\keyword{datasets}