
#######################################################
## Functions to prepare a MOFA object for training ##
#######################################################

#' @title Prepare a MOFA for training
#' @name prepare_mofa
#' @description Function to prepare a \code{\link{MOFA}} object for training. 
#' It requires defining data, model and training options.
#' @param object an untrained \code{\link{MOFA}}
#' @param data_options list of data_options (see \code{\link{get_default_data_options}} details). 
#' If NULL, default options are used.
#' @param model_options list of model options (see \code{\link{get_default_model_options}} for details). 
#' If NULL, default options are used.
#' @param training_options list of training options (see \code{\link{get_default_training_options}} for details). 
#' If NULL, default options are used.
#' @param stochastic_options list of options for stochastic variational inference (see \code{\link{get_default_stochastic_options}} for details). 
#' If NULL, default options are used.
#' @param mefisto_options list of options for mefisto (see \code{\link{get_default_mefisto_options}} for details). 
#' If NULL, default options are used.
#' @return Returns an untrained \code{\link{MOFA}} with specified options filled in the corresponding slots
#' @details This function is called after creating a \code{\link{MOFA}} object (using  \code{\link{create_mofa}}) 
#' and before starting the training (using \code{\link{run_mofa}}). Here, we can specify different options for
#' the data (data_options), the model (model_options) and the training (training_options, stochastic_options). Take a look at the
#' individual default options for an overview using the get_default_XXX_options functions above.
#' @export
#' @examples
#' # Using an existing simulated data with two groups and two views
#' file <- system.file("extdata", "test_data.RData", package = "MOFA2")
#' 
#' # Load data dt (in data.frame format)
#' load(file) 
#' 
#' # Create the MOFA object
#' MOFAmodel <- create_mofa(dt)
#' 
#' # Prepare MOFA object using default options
#' MOFAmodel <- prepare_mofa(MOFAmodel)
#' 
#' # Prepare MOFA object changing some of the default model options values
#' model_opts <- get_default_model_options(MOFAmodel)
#' model_opts$num_factors <- 10
#' MOFAmodel <- prepare_mofa(MOFAmodel, model_options = model_opts)
prepare_mofa <- function(object, data_options = NULL, model_options = NULL, 
                         training_options = NULL, stochastic_options = NULL, mefisto_options = NULL) {
  
  # Sanity checks
  if (!is(object, "MOFA")) stop("'object' has to be an instance of MOFA")
  if (any(object@dimensions$N<10) & !(.hasSlot(object, "covariates") && length(object@covariates)>=1)) warning("Some group(s) have less than 10 samples, MOFA will have little power to learn meaningful factors for these group(s)...")
  if (any(object@dimensions$D<15)) warning("Some view(s) have less than 15 features, MOFA will have little power to to learn meaningful factors for these view(s)....")
  if (any(object@dimensions$D>1e4)) warning("Some view(s) have a lot of features, it is recommended to perform a more stringent feature selection before creating the MOFA object....")
  if (length(object@samples_metadata)>0) { 
    stopifnot(c("sample","group") %in% colnames(object@samples_metadata))
  } else {
    stop("object@samples_metadata not found") 
  }
  if (length(object@features_metadata)>0) { 
    stopifnot(c("feature","view") %in% colnames(object@features_metadata))
  } else {
    stop("object@features_metadata not found") 
  }
  if (object@dimensions$G>1) {
    message("\n# Multi-group mode requested.")
    message("\nThis is an advanced option, if this is the first time that you are running MOFA, we suggest that you try do some exploration first without specifying groups. Two important remarks:")
    message("\n - The aim of the multi-group framework is to identify the sources of variability *within* the groups. If your aim is to find a factor that 'separates' the groups, you DO NOT want to use the multi-group framework. Please see the FAQ on the MOFA2 webpage.") 
    message("\n - It is important to account for the group effect before selecting highly variable features (HVFs). We suggest that either you calculate HVFs per group and then take the union, or regress out the group effect before HVF selection")
  }

  # Get data options
  message("Checking data options...")
  if (is.null(data_options)) {
    message("No data options specified, using default...")
    object@data_options <- get_default_data_options(object)
  } else {
    if (!is(data_options,"list") || !setequal(names(data_options), names(get_default_data_options(object)) ))
      stop("data_options are incorrectly specified, please read the documentation in get_default_data_options")
    object@data_options <- data_options
  }
  # if (any(nchar(unlist(samples_names(object)))>50))
  #   warning("Due to string size limitations in the HDF5 format, sample names will be trimmed to less than 50 characters")
  
  # Get training options
  if (is.null(training_options)) {
    message("No training options specified, using default...")
    object@training_options <- get_default_training_options(object)
  } else {
    message("Checking training options...")
    if (!is(training_options,"list") || !setequal(names(training_options), names(get_default_training_options(object)) ))
      stop("training_options are incorrectly specified, please read the documentation in get_default_training_options")
    object@training_options <- training_options
    
    if (object@training_options$maxiter<=100)
      warning("Maximum number of iterations is very small\n")
    if (object@training_options$startELBO<1) object@training_options$startELBO <- 1
    if (object@training_options$freqELBO<1) object@training_options$freqELBO <- 1
    if (!object@training_options$convergence_mode %in% c("fast","medium","slow")) 
      stop("Convergence mode has to be either 'fast', 'medium', or 'slow'")
  }
  
  # Get stochastic options
  if (is.null(stochastic_options)) {
    object@stochastic_options <- list()
  } else {
    if (isFALSE(object@training_options[["stochastic"]]))
      stop("stochastic_options have been provided but training_opts$stochastic is FALSE. If you want to use stochastic inference you have to set training_opts$stochastic = TRUE")
    # object@training_options$stochastic <- TRUE
  }
  
  if (object@training_options$stochastic) {
    message("Stochastic inference activated. Note that this is only recommended if you have a very large sample size (>1e4) and access to a GPU")
    
    if (is.null(stochastic_options)) {
      message("No stochastic options specified, using default...")
      object@stochastic_options <- get_default_stochastic_options(object)
    } else {
      object@training_options$stochastic <- TRUE
      message("Checking stochastic inference options...")
      if (!is(stochastic_options,"list") || !setequal(names(stochastic_options), names(get_default_stochastic_options(object)) ))
        stop("stochastic_options are incorrectly specified, please read the documentation in get_default_stochastic_options")
      
      if (!stochastic_options$batch_size %in% c(0.05,0.10,0.15,0.20,0.25,0.50))
        stop("Batch size has to be one of the following numeric values: 0.05, 0.10, 0.15, 0.20, 0.25, 0.50")
      if (stochastic_options$batch_size==1)
        warning("A batch size equal to 1 is equivalent to non-stochastic inference.")
      if (stochastic_options$learning_rate<=0 || stochastic_options$learning_rate>1)
        stop("The learning rate has to be a value between 0 and 1")
      if (stochastic_options$forgetting_rate<=0 || stochastic_options$forgetting_rate>1)
        stop("The forgetting rate has to be a value between 0 and 1")
      
      if (sum(object@dimensions$N)<1e4) warning("Stochastic inference is only recommended when you have a lot of samples (at least N>10,000))\n")
      
      object@stochastic_options <- stochastic_options
    }
  }
  
  # Get model options
  if (is.null(model_options)) {
    message("No model options specified, using default...")
    object@model_options <- get_default_model_options(object)
  } else {
    message("Checking model options...")
    if (!is(model_options,"list") || !setequal(names(model_options), names(get_default_model_options(object)) ))
      stop("model_options are incorrectly specified, please read the documentation in get_default_model_options")
    object@model_options <- model_options
  }
  if (object@model_options$num_factors > 50) warning("The number of factors is very large, training will be slow...")
  # if (!object@model_options$ard_weights) warning("model_options$ard_weights should always be set to TRUE")
  if (sum(object@dimensions$N) < 4 * object@model_options$num_factors) {
    warning(sprintf("The total number of samples is very small for learning %s factors.  
    Try to reduce the number of factors to obtain meaningful results. It should not exceed ~%s.",
                    object@model_options$num_factors, floor(min(object@dimensions$N/4))))
  }
  
  # Get mefisto covariates options
  if (.hasSlot(object, "covariates") && length(object@covariates)>=1) {
    if (is.null(mefisto_options)) {
        message("Covariates provided but no mefisto options specified, using default...")
        object@mefisto_options <- get_default_mefisto_options(object)
    } else {
      message("Checking inference options for mefisto covariates...")
      # message("mefisto covariates have been provided as prior information.")
      if (!is(mefisto_options,"list") || !setequal(names(mefisto_options), names(get_default_mefisto_options(object)) ))
        stop("mefisto_options are incorrectly specified, please read the documentation in get_default_mefisto_options")
      
      if (isTRUE(mefisto_options$sparseGP)) {
        if (object@dimensions[["N"]] < 1000) warning("Warning: sparseGPs should only be used when having a large sample size (>1e3)")
        if (isTRUE(mefisto_options$warping)) stop("Warping is not implemented in conjunction with sparseGPs")
      }
      
      # Check warping options
      if (isTRUE(mefisto_options$warping)) {
        stopifnot(object@dimensions[['G']] > 1) # check that multi-group is TRUE
        
        if (!is.null(mefisto_options$warping_ref)) {
          stopifnot(length(mefisto_options$warping_ref)==1)
          stopifnot(is.character(mefisto_options$warping_ref))
          stopifnot(mefisto_options$warping_ref %in% groups_names(object))
        }
        
        if (!is.null(mefisto_options$warping_groups)) {
          # check that warping groups are a partition of groups
          groups_ok <- sapply(unique(object@samples_metadata$group), function(g) {
            length(unique(mefisto_options$warping_groups[object@samples_metadata$group == g])) == 1
          })
          if (!all(groups_ok)) stop("Warping group assignment needs to be unique within each indiviudal group.")
        }
      }
      
      # Disable spike-slab on the factors
      if(isTRUE(model_options$spikeslab_factors)) {
        print("Spike-and-Slab sparsity prior on the factors is not available when using MEFISTO, setting to False")
        model_options$spikeslab_factors <- FALSE
      }
      
      # Disable stochastic inference
      if (isTRUE(model_options$stochastic)) {
        print("Stochastic inference is not available when using MEFISTO, setting to False")
        model_options$stochastic <- FALSE
        object@stochastic_options <- list()
      }
      
      # TO-DO: CHECKS ON MODEL_GROUPS
      
      object@mefisto_options <- mefisto_options
    }
    
  } else {
    object@mefisto_options <- list()
  }
  
  # Center the data
  # message("Centering the features (per group, this is a mandatory requirement)...")
  # for (m in views_names(object)) {
  #   if (model_options$likelihoods[[m]] == "gaussian") {
  #     for (g in groups_names(object)) {
  #       object@data[[m]][[g]] <- scale(object@data[[m]][[g]], center=T, scale=F)
  #     }
  #   }
  # }
  
  # Transform sparse matrices into dense ones
  # See https://github.com/rstudio/reticulate/issues/72
  for (m in views_names(object)) {
    for (g in groups_names(object)) {
      if (is(object@data[[m]][[g]], "dgCMatrix") || is(object@data[[m]][[g]], "dgTMatrix"))
        object@data[[m]][[g]] <- as(object@data[[m]][[g]], "matrix")
    }
  }
  
  return(object)
}



#' @title Get default training options
#' @name get_default_training_options
#' @description Function to obtain the default training options.
#' @param object an untrained \code{\link{MOFA}}
#' @details This function provides a default set of training options that can be modified and passed to the \code{\link{MOFA}} object
#' in the \code{\link{prepare_mofa}} step (see example), i.e. after creating a \code{\link{MOFA}} object
#'  (using \code{\link{create_mofa}}) and before starting the training (using \code{\link{run_mofa}})
#' The training options are the following: \cr
#' \itemize{
#'  \item{\strong{maxiter}: numeric value indicating the maximum number of iterations. 
#'  Default is 1000. Convergence is assessed using the ELBO statistic.}
#'  \item{\strong{drop_factor_threshold}: numeric indicating the threshold on fraction of variance explained to consider a factor inactive and drop it from the model.
#'  For example, a value of 0.01 implies that factors explaining less than 1\% of variance (in each view) will be dropped. Default is -1 (no dropping of factors)}
#'  \item{\strong{convergence_mode}: character indicating the convergence criteria, either "fast", "medium" or "slow", corresponding to 0.0005\%, 0.00005\% or 0.000005\% deltaELBO change. }
#'  \item{\strong{verbose}: logical indicating whether to generate a verbose output.}
#'  \item{\strong{startELBO}: integer indicating the first iteration to compute the ELBO (default is 1). }
#'  \item{\strong{freqELBO}: integer indicating the first iteration to compute the ELBO (default is 1). }
#'  \item{\strong{stochastic}: logical indicating whether to use stochastic variational inference (only required for very big data sets, default is \code{FALSE}).}
#'  \item{\strong{gpu_mode}: logical indicating whether to use GPUs (see details).}
#'  \item{\strong{gpu_device}: integer indicating which GPU to use.}
#'  \item{\strong{seed}: numeric indicating the seed for reproducibility (default is 42).}
#' }
#' @return Returns a list with default training options
#' @importFrom utils modifyList
#' @export
#' @examples
#' # Using an existing simulated data with two groups and two views
#' file <- system.file("extdata", "test_data.RData", package = "MOFA2")
#' 
#' # Load data dt (in data.frame format)
#' load(file) 
#' 
#' # Create the MOFA object
#' MOFAmodel <- create_mofa(dt)
#' 
#' # Load default training options
#' train_opts <- get_default_training_options(MOFAmodel)
#' 
#' # Edit some of the training options
#' train_opts$convergence_mode <- "medium"
#' train_opts$startELBO <- 100
#' train_opts$seed <- 42
#' 
#' # Prepare the MOFA object
#' MOFAmodel <- prepare_mofa(MOFAmodel, training_options = train_opts)
get_default_training_options <- function(object) {
  
  # Get default train options
  training_options <- list(
    maxiter = 1000,                # (numeric) Maximum number of iterations
    convergence_mode = 'fast',     # (string) Convergence mode based on change in the ELBO ("slow","medium","fast")
    drop_factor_threshold = -1,    # (numeric) Threshold on fraction of variance explained to drop a factor
    verbose = FALSE,               # (logical) Verbosity
    startELBO = 1,                 # (numeric) First iteration to compute the ELBO
    freqELBO = 5,                  # (numeric) Frequency of ELBO calculation
    stochastic = FALSE,            # (logical) Do stochastic variational inference?
    gpu_mode = FALSE,              # (logical) Use GPU?
    gpu_device = NULL,             # (integer) Which GPU to use?
    seed = 42,                     # (numeric) random seed
    outfile = NULL,                # (string)  Output file name
    weight_views = FALSE,          # (logical) Weight the ELBO based on the number of features per view?
    save_interrupted = FALSE       # (logical) Save partially trained model when training is interrupted?
  )
  
  # if training_options already exist, replace the default values but keep the additional ones
  if (length(object@training_options)>0)
    training_options <- modifyList(training_options, object@training_options)
  
  return(training_options)
}


#' @title Get default data options
#' @name get_default_data_options
#' @description Function to obtain the default data options.
#' @param object an untrained \code{\link{MOFA}} object
#' @details This function provides a default set of data options that can be modified and passed to the \code{\link{MOFA}} object
#' in the \code{\link{prepare_mofa}} step (see example), i.e. after creating a \code{\link{MOFA}} object
#'  (using \code{\link{create_mofa}}) and before starting the training (using \code{\link{run_mofa}})
#' The data options are the following: \cr
#' \itemize{
#'  \item{\strong{scale_views}: logical indicating whether to scale views to have the same unit variance. 
#'  As long as the scale differences between the views is not too high, this is not required. Default is FALSE.}
#'  \item{\strong{scale_groups}: logical indicating whether to scale groups to have the same unit variance. 
#'  As long as the scale differences between the groups is not too high, this is not required. Default is FALSE.}
#'  \item{\strong{use_float32}: logical indicating whether use float32 instead of float64 arrays to increase speed and memory usage. Default is FALSE.}
#'  }
#' @return Returns a list with the default data options.
#' @importFrom utils modifyList
#' @export
#' @examples
#' # Using an existing simulated data with two groups and two views
#' file <- system.file("extdata", "test_data.RData", package = "MOFA2")
#' 
#' # Load data dt (in data.frame format)
#' load(file) 
#' 
#' # Create the MOFA object
#' MOFAmodel <- create_mofa(dt)
#' 
#' # Load default data options
#' data_opts <- get_default_data_options(MOFAmodel)
#' 
#' # Edit some of the data options
#' data_opts$scale_views <- TRUE
#' 
#' # Prepare the MOFA object
#' MOFAmodel <- prepare_mofa(MOFAmodel, data_options = data_opts)
get_default_data_options <- function(object) {
  
  # Define default data options
  data_options <- list(
    scale_views = FALSE,     # (logical) Scale views to unit variance?
    scale_groups = FALSE,    # (logical) Scale groups to unit variance?
    center_groups = TRUE,   # (logical) Center groups?
    use_float32 = TRUE       # (logical) Use float32 instead of float64 arrays to increase speed and memory usage
  )
  
  # Activate float32 arrays for large sample sizes  
  if (sum(object@dimensions$N)>1e5) {
    message("A lot of samples detected, using float32 arrays instead of float64 arrays to increase speed and memory usage. 
    You can modify this using the `data_options` argument of the `prepare_mofa` function.")
    data_options$use_float32 <- TRUE
  }
  
  # if data_options already exists, replace the default values but keep the additional ones
  if (length(object@data_options)>0)
    data_options <- modifyList(data_options, object@data_options)
  
  return(data_options)
}

#' @title Get default model options
#' @name get_default_model_options
#' @description Function to obtain the default model options.
#' @param object an untrained \code{\link{MOFA}} object
#' @details This function provides a default set of model options that can be modified and passed to the \code{\link{MOFA}} object
#' in the \code{\link{prepare_mofa}} step (see example), i.e. after creating a \code{\link{MOFA}} object
#'  (using \code{\link{create_mofa}}) and before starting the training (using \code{\link{run_mofa}})
#' The model options are the following: \cr
#' \itemize{
#'  \item{\strong{likelihoods}: character vector with data likelihoods per view: 
#'  'gaussian' for continuous data (Default for all views), 'bernoulli' for binary data and 'poisson' for count data.}
#'  \item{\strong{num_factors}: numeric value indicating the (initial) number of factors. Default is 15.}
#'  \item{\strong{spikeslab_factors}: logical indicating whether to use spike and slab sparsity on the factors (Default is FALSE)}
#'  \item{\strong{spikeslab_weights}: logical indicating whether to use spike and slab sparsity on the weights (Default is TRUE)}
#'  \item{\strong{ard_factors}: logical indicating whether to use ARD sparsity on the factors (Default is TRUE only if using multiple groups)}
#'  \item{\strong{ard_weights}: logical indicating whether to use ARD sparsity on the weights (Default is TRUE)}
#'  }
#' @return Returns a list with the default model options.
#' @importFrom utils modifyList
#' @export
#' @examples
#' # Using an existing simulated data with two groups and two views
#' file <- system.file("extdata", "test_data.RData", package = "MOFA2")
#' 
#' # Load data dt (in data.frame format)
#' load(file) 
#' 
#' # Create the MOFA object
#' MOFAmodel <- create_mofa(dt)
#' 
#' # Load default model options
#' model_opts <- get_default_model_options(MOFAmodel)
#' 
#' # Edit some of the model options
#' model_opts$num_factors <- 10
#' model_opts$spikeslab_weights <- FALSE
#' 
#' # Prepare the MOFA object
#' MOFAmodel <- prepare_mofa(MOFAmodel, model_options = model_opts)
get_default_model_options <- function(object) {
  
  # Sanity checks
  if (!is(object, "MOFA")) stop("'object' has to be an instance of MOFA")
  if (!.hasSlot(object,"dimensions") | length(object@dimensions) == 0) 
    stop("dimensions of object need to be defined before getting the model options")
  if (.hasSlot(object,"data")) {
    if (length(object@data)==0) stop("data slot is empty")
  } else {
    stop("data slot not found")
  }
  
  # Guess likelihoods from the data
  # likelihoods <- .infer_likelihoods(object)
  likelihoods <- rep(x="gaussian", times=object@dimensions$M)
  names(likelihoods) <- views_names(object)
  
  # Define default model options
  model_options <- list(
    likelihoods = likelihoods,   # (character vector) likelihood per view [gaussian/bernoulli/poisson]
    num_factors = 10,            # (numeric) initial number of latent factors
    spikeslab_factors = FALSE,   # (logical) Spike and Slab sparsity on the factors
    spikeslab_weights = FALSE,    # (logical) Spike and Slab sparsity on the weights
    ard_factors = FALSE,         # (logical) Group-wise ARD sparsity on the factors
    ard_weights = TRUE           # (logical) View-wise ARD sparsity on the weights
  )
  
  # (Heuristic) set the number of factors depending on the sample size
  N <- sum(object@dimensions$N)
  if (N<=25) {
    model_options$num_factors <- 5
  } else if (N>25 & N<=1e3) {
    model_options$num_factors <- 15
  } else if (N>1e3 & N<=1e4) {
    model_options$num_factors <- 20
  } else if (N>1e4) {
    model_options$num_factors <- 25
  }
  
  # Group-wise ARD sparsity on the factors only if there are multiple groups
  if (object@dimensions$G>1)
    model_options$ard_factors <- TRUE
  
  # if model_options already exist, replace the default values but keep the additional ones
  if (length(object@model_options)>0)
    model_options <- modifyList(model_options, object@model_options)
  
  return(model_options)
}




#' @title Get default stochastic options
#' @name get_default_stochastic_options
#' @description Function to obtain the default options for stochastic variational inference.
#' @param object an untrained \code{\link{MOFA}}
#' @details This function provides a default set of stochastic inference options that can be modified and passed to the \code{\link{MOFA}} object
#' in the \code{\link{prepare_mofa}} step), i.e. after creating a \code{\link{MOFA}} object
#'  (using \code{\link{create_mofa}}) and before starting the training (using \code{\link{run_mofa}})
#' These options are only relevant when activating stochastic inference in training_options (see example).
#' The stochastic inference options are the following: \cr
#' \itemize{
#'  \item{\strong{batch_size}: numeric value indicating the batch size (as a fraction)}. 
#'  Default is 0.5 (half of the data set).
#'  \item{\strong{learning_rate}: numeric value indicating the learning rate. }
#'  Default is 1.0
#'  \item{\strong{forgetting_rate}: numeric indicating the forgetting rate.}
#'  Default is 0.5
#'  \item{\strong{start_stochastic}: integer indicating the first iteration to start stochastic inference}
#'  Default is 1
#'  }
#' @return Returns a list with default options
#' @importFrom utils modifyList
#' @export
#' @examples
#' # Using an existing simulated data with two groups and two views
#' file <- system.file("extdata", "test_data.RData", package = "MOFA2")
#' 
#' # Load data dt (in data.frame format)
#' load(file) 
#' 
#' # Create the MOFA object
#' MOFAmodel <- create_mofa(dt)
#' 
#' # activate stochastic inference in training options
#' train_opts <- get_default_training_options(MOFAmodel)
#' train_opts$stochastic <- TRUE
#' 
#' # Load default stochastic options
#' stochastic_opts <- get_default_stochastic_options(MOFAmodel)
#' 
#' # Edit some of the stochastic options
#' stochastic_opts$learning_rate <- 0.75
#' stochastic_opts$batch_size <- 0.25
#' 
#' # Prepare the MOFA object
#' MOFAmodel <- prepare_mofa(MOFAmodel, 
#'   training_options = train_opts,
#'   stochastic_options = stochastic_opts
#' )
#' 
get_default_stochastic_options <- function(object) {
  
  # Get default stochastic options
  stochastic_options <- list(
    batch_size = 0.5,        # Batch size (as a fraction)
    learning_rate = 1.0,     # Starting learning rate
    forgetting_rate = 0.5,   # Forgetting rate
    start_stochastic = 1     # First iteration to start stochastic inference
  )
  
  # if stochastic_options already exist, replace the default values but keep the additional ones
  if (length(object@stochastic_options)>0)
    stochastic_options <- modifyList(stochastic_options, object@stochastic_options)
  
  return(stochastic_options)
}

