callback.R

CB_ENV <- R6Class(
  "lgb.cb_env",
  cloneable = FALSE,
  public = list(
    model = NULL,
    iteration = NULL,
    begin_iteration = NULL,
    end_iteration = NULL,
    eval_list = list(),
    eval_err_list = list(),
    best_iter = -1,
    best_score = -1,
    met_early_stop = FALSE
  )
)

cb.reset.parameters <- function(new_params) {
  
  # Check for parameter list
  if (!is.list(new_params)) {
    stop(sQuote("new_params"), " must be a list")
  }
  
  # Deparse parameter list
  pnames  <- gsub("\\.", "_", names(new_params))
  nrounds <- NULL

  # Run some checks in the beginning
  init <- function(env) {
    
    # Store boosting rounds
    nrounds <<- env$end_iteration - env$begin_iteration + 1
    
    # Check for model environment
    if (is.null(env$model)) { stop("Env should have a ", sQuote("model")) }
    
    # Some parameters are not allowed to be changed,
    # since changing them would simply wreck some chaos
    not_allowed <- c("num_class", "metric", "boosting_type")
    if (any(pnames %in% not_allowed)) {
      stop("Parameters ", paste0(pnames[pnames %in% not_allowed], collapse = ", "), " cannot be changed during boosting")
    }
    
    # Check parameter names
    for (n in pnames) {
      
      # Set name
      p <- new_params[[n]]
      
      # Check if function for parameter
      if (is.function(p)) {
        
        # Check if requires at least two arguments
        if (length(formals(p)) != 2) {
          stop("Parameter ", sQuote(n), " is a function but not of two arguments")
        }
        
        # Check if numeric or character
      } else if (is.numeric(p) || is.character(p)) {
        
        # Check if length is matching
        if (length(p) != nrounds) {
          stop("Length of ", sQuote(n), " has to be equal to length of ", sQuote("nrounds"))
        }
        
      } else {
        
        stop("Parameter ", sQuote(n), " is not a function or a vector")
        
      }
      
    }
    
  }

  callback <- function(env) {
    
    # Check if rounds is null
    if (is.null(nrounds)) {
      init(env)
    }
    
    # Store iteration
    i <- env$iteration - env$begin_iteration
    
    # Apply list on parameters
    pars <- lapply(new_params, function(p) {
      if (is.function(p)) {
        return(p(i, nrounds))
      }
      p[i]
    })
    
    # To-do check pars
    if (!is.null(env$model)) {
      env$model$reset_parameter(pars)
    }
    
  }
  
  attr(callback, "call") <- match.call()
  attr(callback, "is_pre_iteration") <- TRUE
  attr(callback, "name") <- "cb.reset.parameters"
  callback
}

# Format the evaluation metric string
format.eval.string <- function(eval_res, eval_err = NULL) {
  
  # Check for empty evaluation string
  if (is.null(eval_res) || length(eval_res) == 0) {
    stop("no evaluation results")
  }
  
  # Check for empty evaluation error
  if (!is.null(eval_err)) {
    sprintf("%s\'s %s:%g+%g", eval_res$data_name, eval_res$name, eval_res$value, eval_err)
  } else {
    sprintf("%s\'s %s:%g", eval_res$data_name, eval_res$name, eval_res$value)
  }
  
}

merge.eval.string <- function(env) {
  
  # Check length of evaluation list
  if (length(env$eval_list) <= 0) {
    return("")
  }
  
  # Get evaluation
  msg <- list(sprintf("[%d]:", env$iteration))
  
  # Set if evaluation error
  is_eval_err <- FALSE
  
  # Check evaluation error list length
  if (length(env$eval_err_list) > 0) {
    is_eval_err <- TRUE
  }
  
  # Loop through evaluation list
  for (j in seq_along(env$eval_list)) {
    
    # Store evaluation error
    eval_err <- NULL
    if (is_eval_err) {
      eval_err <- env$eval_err_list[[j]]
    }
    
    # Set error message
    msg <- c(msg, format.eval.string(env$eval_list[[j]], eval_err))
    
  }
  
  # Return tabulated separated message
  paste0(msg, collapse = "\t")
  
}

cb.print.evaluation <- function(period = 1) {
  
  # Create callback
  callback <- function(env) {
    
    # Check if period is at least 1 or more
    if (period > 0) {
      
      # Store iteration
      i <- env$iteration
      
      # Check if iteration matches moduo
      if ((i - 1) %% period == 0 | i == env$begin_iteration | i == env$end_iteration ) {
        
        # Merge evaluation string
        msg <- merge.eval.string(env)
        
        # Check if message is existing
        if (nchar(msg) > 0) {
          cat(merge.eval.string(env), "\n")
        }
        
      }
      
    }
    
  }
  
  # Store attributes
  attr(callback, "call") <- match.call()
  attr(callback, "name") <- "cb.print.evaluation"
  
  # Return callback
  callback
  
}

cb.record.evaluation <- function() {
  
  # Create callback
  callback <- function(env) {
    
    # Return empty if empty evaluation list
    if (length(env$eval_list) <= 0) {
      return()
    }
    
    # Set if evaluation error
    is_eval_err <- FALSE
    
    # Check evaluation error list length
    if (length(env$eval_err_list) > 0) {
      is_eval_err <- TRUE
    }
    
    # Check length of recorded evaluation
    if (length(env$model$record_evals) == 0) {
      
      # Loop through each evaluation list element
      for (j in seq_along(env$eval_list)) {
        
        # Store names
        data_name <- env$eval_list[[j]]$data_name
        name <- env$eval_list[[j]]$name
        env$model$record_evals$start_iter <- env$begin_iteration
        
        # Check if evaluation record exists
        if (is.null(env$model$record_evals[[data_name]])) {
          env$model$record_evals[[data_name]] <- list()
        }
        
        # Create dummy lists
        env$model$record_evals[[data_name]][[name]] <- list()
        env$model$record_evals[[data_name]][[name]]$eval <- list()
        env$model$record_evals[[data_name]][[name]]$eval_err <- list()
        
      }
      
    }
    
    # Loop through each evaluation list element
    for (j in seq_along(env$eval_list)) {
      
      # Get evaluation data
      eval_res <- env$eval_list[[j]]
      eval_err <- NULL
      if (is_eval_err) {
        eval_err <- env$eval_err_list[[j]]
      }
      
      # Store names
      data_name <- eval_res$data_name
      name <- eval_res$name
      
      # Store evaluation data
      env$model$record_evals[[data_name]][[name]]$eval <- c(env$model$record_evals[[data_name]][[name]]$eval, eval_res$value)
      env$model$record_evals[[data_name]][[name]]$eval_err <- c(env$model$record_evals[[data_name]][[name]]$eval_err, eval_err)
      
    }
    
  }
  
  # Store attributes
  attr(callback, "call") <- match.call()
  attr(callback, "name") <- "cb.record.evaluation"
  
  # Return callback
  callback
  
}

cb.early.stop <- function(stopping_rounds, verbose = TRUE) {
  
  # Initialize variables
  factor_to_bigger_better <- NULL
  best_iter <- NULL
  best_score <- NULL
  best_msg <- NULL
  eval_len <- NULL
  
  # Initalization function
  init <- function(env) {
    
    # Store evaluation length
    eval_len <<- length(env$eval_list)
    
    # Early stopping cannot work without metrics
    if (eval_len == 0) {
      stop("For early stopping, valids must have at least one element")
    }
    
    # Check if verbose or not
    if (isTRUE(verbose)) {
      cat("Will train until there is no improvement in ", stopping_rounds, " rounds.\n\n", sep = "")
    }
    
    # Maximization or minimization task
    factor_to_bigger_better <<- rep(1.0, eval_len)
    best_iter <<- rep(-1, eval_len)
    best_score <<- rep(-Inf, eval_len)
    best_msg <<- list()
    
    # Loop through evaluation elements
    for (i in seq_len(eval_len)) {
      
      # Prepend message
      best_msg <<- c(best_msg, "")
      
      # Check if maximization or minimization
      if (!env$eval_list[[i]]$higher_better) {
        factor_to_bigger_better[i] <<- -1.0
      }
      
    }
    
  }
  
  # Create callback
  callback <- function(env, finalize = FALSE) {
    
    # Check for empty evaluation
    if (is.null(eval_len)) {
      init(env)
    }
    
    # Store iteration
    cur_iter <- env$iteration
    
    # Loop through evaluation
    for (i in seq_len(eval_len)) {
      
      # Store score
      score <- env$eval_list[[i]]$value * factor_to_bigger_better[i]
      
      # Check if score is better
      if (score > best_score[i]) {
        
        # Store new scores
        best_score[i] <<- score
        best_iter[i] <<- cur_iter
        
        # Prepare to print if verbose
        if (verbose) {
          best_msg[[i]] <<- as.character(merge.eval.string(env))
        }
        
      } else {
        
        # Check if early stopping is required
        if (cur_iter - best_iter[i] >= stopping_rounds) {
          
          # Check if model is not null
          if (!is.null(env$model)) {
            env$model$best_score <- best_score[i]
            env$model$best_iter <- best_iter[i]
          }
          
          # Print message if verbose
          if (isTRUE(verbose)) {
            
            cat("Early stopping, best iteration is:", "\n")
            cat(best_msg[[i]], "\n")
            
          }
          
          # Store best iteration and stop
          env$best_iter <- best_iter[i]
          env$met_early_stop <- TRUE
          
        }
        
      }
      
    }
    
  }
  
  # Set attributes
  attr(callback, "call") <- match.call()
  attr(callback, "name") <- "cb.early.stop"
  
  # Return callback
  callback
  
}

# Extract callback names from the list of callbacks
callback.names <- function(cb_list) { unlist(lapply(cb_list, attr, "name")) }

add.cb <- function(cb_list, cb) {
  
  # Combine two elements
  cb_list <- c(cb_list, cb)
  
  # Set names of elements
  names(cb_list) <- callback.names(cb_list)
  
  # Check for existence
  if ("cb.early.stop" %in% names(cb_list)) {
    
    # Concatenate existing elements
    cb_list <- c(cb_list, cb_list["cb.early.stop"])
    
    # Remove only the first one
    cb_list["cb.early.stop"] <- NULL
    
  }
  
  # Return element
  cb_list
  
}

categorize.callbacks <- function(cb_list) {
  
  # Check for pre-iteration or post-iteration
  list(
    pre_iter = Filter(function(x) {
        pre <- attr(x, "is_pre_iteration")
        !is.null(pre) && pre
      }, cb_list),
    post_iter = Filter(function(x) {
        pre <- attr(x, "is_pre_iteration")
        is.null(pre) || !pre
      }, cb_list)
  )
  
}