rtemis: Machine Learning and Visualization

# cartLinBoostTV
# ::rtemis::
# 2019 E.D. Gennatas www.lambdamd.org

#' CART and linear model boosting \[R\]
#'
#' Perform regression by iteratively boosting a CART and a GLM base learner
#'
#' If `learning.rate` is set to 0, a nullmod will be created
#' 
#' @inheritParams boost
#' @param weights.p Float (0, 1]: Percent of weights to set to 1, the rest will be set 
#' to `weights.0`. Default = 1
#' @param weights.0 Float (0, 1): Set weights of excluded cases to this number. 
#' Default = 0, which is equivalent to excluding them, in which case, these cases can 
#' act as a validation set
#' @param learning.rate Float (0, 1] Learning rate for the additive steps
#' @param init Float: Initial value for prediction.
#' @param seed Integer: Set seed to allow reproducibility when `weights.p` is not 1
#' @param max.iter Integer: Maximum number of iterations (additive steps) to perform.
#' @param trace Integer: If > 0, print diagnostic info to console
#' @param base.verbose Logical: `verbose` argument passed to learner
#' @param print.error.plot String or Integer: "final" plots a training and validation 
#' (if available) error curve at the end of training. If integer, plot training and 
#' validation error curve every this many iterations during training. "none" for no 
#' plot.
#' @param print.base.plot Logical: Passed to `print.plot` argument of base learner, 
#' i.e. if TRUE, print error plot for each base learner.
#' 
#' @keywords internal
#' @noRd
#' @author E.D. Gennatas


cartLinBoostTV <- function(x, y = NULL,
                           x.valid = NULL, y.valid = NULL,
                           x.test = NULL, y.test = NULL,
                           resid = NULL,
                           base.mod = c("cart", "glm"),
                           boost.obj = NULL,
                           mod.params = list(),
                           cart.params = list(maxdepth = 1),
                           glm.params = list(lambda = .1),
                           weights.p = 1,
                           weights.0 = 0,
                           weights = NULL,
                           learning.rate = .1,
                           max.iter = 10,
                           init = NULL,
                           seed = NULL,
                           x.name = NULL,
                           y.name = NULL,
                           question = NULL,
                           base.verbose = FALSE,
                           verbose = TRUE,
                           trace = 0,
                           print.progress.every = 5,
                           print.error.plot = "final",
                           prefix = NULL,
                           plot.theme = rtTheme,
                           plot.fitted = NULL,
                           plot.predicted = NULL,
                           print.plot = FALSE,
                           print.base.plot = FALSE,
                           plot.type = "l",
                           outdir = NULL) {

  # Intro ----
  if (missing(x)) {
    print(args(boost))
    return(invisible(9))
  }
  if (!is.null(outdir)) outdir <- normalizePath(outdir, mustWork = FALSE)
  logFile <- if (!is.null(outdir)) {
    paste0(
      outdir, "/", sys.calls()[[1]][[1]], ".",
      format(Sys.time(), "%Y%m%d.%H%M%S"), ".log"
    )
  } else {
    NULL
  }
  start.time <- intro(verbose = verbose, logFile = logFile)
  mod.name <- "CARTLINBOOSTTV"

  # Arguments ----
  if (is.null(x.name)) x.name <- getName(x, "x")
  if (is.null(y.name)) y.name <- getName(y, "y")
  if (!verbose) print.plot <- FALSE
  verbose <- verbose | !is.null(logFile)
  if (!is.null(outdir)) outdir <- paste0(normalizePath(outdir, mustWork = FALSE), "/")

  # Define learners
  learner <- list(cart = "cartLite", glm = "glmLite")
  if (base.mod[1] == "glm") learner <- rev(learner)
  learner.params <- list(cart = cart.params, glm = glm.params)
  if (base.mod[1] == "glm") learner.params <- rev(learner.params)

  # Data ----
  dt <- prepare_data(x, y, x.test, y.test,
                    x.valid = x.valid, y.valid = y.valid,
                    verbose = verbose)
  x <- dt$x
  y <- dt$y
  x.test <- dt$x.test
  y.test <- dt$y.test
  x.valid <- dt$x.valid
  y.valid <- dt$y.valid
  xnames <- dt$xnames
  type <- dt$type
  checkType(type, "Regression", "CARTLINBOOST")
  # .weights <- if (is.null(weights) & ifw) dt$weights else weights
  # x0 <- if (upsample|downsample) dt$x0 else x
  # y0 <- if (upsample|downsample) dt$y0 else y
  if (verbose) dataSummary(x, y, x.test, y.test, type)
  if (print.plot) {
    if (is.null(plot.fitted)) plot.fitted <- if (is.null(y.test)) TRUE else FALSE
    if (is.null(plot.predicted)) plot.predicted <- if (!is.null(y.test)) TRUE else FALSE
  } else {
    plot.fitted <- plot.predicted <- FALSE
  }
  if (is.null(init)) init <- mean(y)
  train.ncases <- length(y)
  if (is.null(weights)) weights <- rep(1, train.ncases)
  if (!is.null(x.valid)) {
    valid.ncases <- length(y.valid)
    trainval.ncases <- train.ncases + valid.ncases
    valid.index <- (train.ncases + 1):trainval.ncases
    weights1 <- c(weights, rep(0, valid.ncases))
    x1 <- rbind(x, x.valid)
    y1 <- c(y, y.valid)
  } else {
    x1 <- x
    y1 <- y
    weights1 <- weights
  }

  # Boost ----
  learner.name <- paste0(toupper(base.mod), "lite")
  learner.short <- paste0(paste0(toupper(base.mod), collapse = "-"), "seq")

  if (verbose) parameterSummary(cart.params,
                                glm.params,
                                init,
                                max.iter,
                                learning.rate,
                                weights.p,
                                weights.0)
  if (trace > 0) msg2("Initial MSE =", mse(y, init))

  # '- New series ----
  # init learning.rate vector
  if (is.null(boost.obj)) {
    mods <- list()
    Fval <- penult.fitted <- init
    .learning.rate <- numeric()

    error <- vector("numeric")
    error[[1]] <- mse(y, Fval) # will be overwritten, needed for while statement

    if (!is.null(x.valid)) {
      error.valid <- vector("numeric")
      Fvalid <- init
    } else {
      error.valid <- predicted.valid <- Fvalid <- NULL
    }
    i <- 1
    if (verbose) msg2("[ Boosting ", learner.name, "... ]", sep = "")
  } else {
    .learning.rate <- boost.obj$mod$learning.rate
    # '- Expand series ----
    mods <- boost.obj$mod$mods
    Fval <- penult.fitted <- boost.obj$mod$fitted_tv # CHECK
    error <- boost.obj$mod$error
    if (!is.null(x.valid)) {
      error.valid <- boost.obj$mod$error.valid
      Fvalid <- boost.obj$mod$predicted.valid
    } else {
      error.valid <- predicted.valid <- Fvalid <- NULL
    }
    max.iter <- max.iter + length(mods)
    i <- length(mods) + 1
    if (trace > 0) msg2("i =", i)
    if (verbose) msg2("[ Expanding boosted ", learner.name, "... ]", sep = "")
  } # / Expand

  if (is.null(resid)) resid <- y - Fval
  resid1 <- if (!is.null(x.valid)) c(resid, rep(0, valid.ncases)) else resid

  # Print error during training
  if (max.iter == 1 && is.null(boost.obj)) {
    print.progress.index <- FALSE
    print.error.plot <- "none"
  } else if (print.progress.every < max.iter) {
    print.progress.index <- seq(print.progress.every, max.iter, print.progress.every)
  } else {
    print.progress.index <- max.iter
  }

  # Print error plot
  if (max.iter > 1 && is.numeric(print.error.plot)) {
    if (print.error.plot < max.iter) {
      print.error.plot.index <- seq(print.error.plot, max.iter, print.error.plot)
    } else {
      print.error.plot.index <- max.iter
    }
    print.error.plot <- "iter"
  }

  # '- Iterate learner ----
  if (!is.null(seed)) set.seed(seed)
  while (i <= max.iter) {
    .learning.rate[i] <- learning.rate
    if (trace > 0) msg2("learning.rate is", .learning.rate[i])
    if (trace > 0) msg2("i =", i)
    if (weights.p < 1) {
      holdout.index <- sample(train.ncases, (1 - weights.p) * train.ncases)
      weights1[holdout.index] <- weights.0
    }

    mod.args <- list(x = x1, y = resid1,
                     weights = weights1,
                     save.fitted = TRUE)

    # '- Train base learner ----
    if (.learning.rate[i] != 0) {
      mods[[i]] <- do.call(learner[[(i + 1) %% 2 + 1]], args = c(mod.args,
                                                                 learner.params[[(i + 1) %% 2 + 1]]))
    } else {
      mods[[i]] <- list(fitted = Fval)
      class(mods[[i]]) <- c("nullmod", "list")
    }

    names(mods)[i] <- paste0(learner.name[[(i + 1) %% 2 + 1]], ".", i)

    fitted <- mods[[i]]$fitted

    Fval <- Fval + .learning.rate[i] * fitted
    if (i == max.iter - 1) penult.fitted <- Fval # CHECK: limit to train.ncases?
    resid1 <- y1 - Fval
    error[[i]] <- mse(y, Fval[seq(train.ncases)])
    if (!is.null(x.valid)) {
      predicted.valid <- fitted[valid.index]
      Fvalid <- Fval[valid.index]
      error.valid[[i]] <- mse(y.valid, Fvalid)
      if (verbose && i %in% print.progress.index) if (verbose) msg2("Iteration #", i, ": Training MSE = ",
                                                                   ddSci(error[[i]]),
                                                                   "; Validation MSE = ", ddSci(error.valid[[i]]),
                                                                   sep = "")
    } else {
      if (verbose && i %in% print.progress.index) {
        msg2("Iteration #", i, ": Training MSE = ", ddSci(error[[i]]), sep = "")
      }
    }
    if (print.error.plot == "iter" && i %in% print.error.plot.index) {
      if (is.null(x.valid)) {
        mplot3_xy(seq(error), error, type = plot.type,
                  xlab = "Iteration", ylab = "MSE",
                  x.axis.at = seq(error),
                  main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
                  theme = plot.theme)
      } else {
        mplot3_xy(seq(error), list(training = error, validation = error.valid), type = plot.type,
                  xlab = "Iteration", ylab = "MSE", group.adj = .95,
                  x.axis.at = seq(error),
                  main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
                  theme = plot.theme)
      }

    }
    i <- i + 1
  }
  if (verbose && i > max.iter) msg2("Reached max iterations")

  if (print.error.plot == "final") {
    if (is.null(x.valid)) {
      mplot3_xy(seq(error), error, type = plot.type,
                xlab = "Iteration", ylab = "MSE",
                x.axis.at = seq(error),
                main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
                theme = plot.theme)
    } else {
      mplot3_xy(seq(error), list(Training = error,
                                 Validation = error.valid), type = plot.type,
                xlab = "Iteration", ylab = "MSE", group.adj = .95,
                x.axis.at = seq(error),
                main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
                theme = plot.theme)
    }
  }

  # '- boost object ----
  obj <- list(mod.name = mod.name,
              learning.rate = .learning.rate,
              init = init,
              penult.fitted = penult.fitted,
              train.ncases = train.ncases,
              fitted_tv = Fval,
              last.fitted_tv = fitted,
              predicted.valid = Fvalid,
              error = error,
              error.valid = error.valid,
              mods = mods)
  class(obj) <- c("cartLinBoostTV", "list")

  # Fitted ----
  error.train <- mod_error(y, obj$fitted_tv[seq(train.ncases)])
  if (verbose) errorSummary(error.train)

  # Predicted ----
  predicted <- error.test <- NULL
  if (!is.null(x.test)) {
    predicted <- predict(obj, x.test)
    if (!is.null(y.test)) {
      error.test <- mod_error(y.test, predicted)
      if (verbose) errorSummary(error.test)
    }
  }

  # Outro ----
  parameters <- list(mod = learner.short,
                     cart.params = cart.params,
                     glm.params = glm.params,
                     init = init,
                     n.its = length(error),
                     learning.rate = learning.rate,
                     max.iter = max.iter,
                     weights.p = weights.p,
                     weights.0 = weights.0,
                     weights = weights)
  extra <- list(error.valid = error.valid)
  rt <- rtModSet(mod = obj,
                 mod.name = mod.name,
                 type = type,
                 parameters = parameters,
                 call = NULL,
                 y.train = y,
                 y.test = y.test,
                 x.name = x.name,
                 y.name = y.name,
                 xnames = xnames,
                 fitted = obj$fitted_tv[seq(train.ncases)],
                 se.fit = NULL,
                 error.train = error.train,
                 predicted = predicted,
                 se.prediction = NULL,
                 error.test = error.test,
                 varimp = NULL,
                 question = question,
                 extra = extra)

  rtMod.out(rt,
            print.plot,
            plot.fitted,
            plot.predicted,
            y.test,
            mod.name,
            outdir,
            save.mod = FALSE,
            verbose,
            plot.theme)

  outro(start.time, verbose = verbose, sinkOff = ifelse(is.null(logFile), FALSE, TRUE))
  rt

} # rtemis::cartLinBoostTV


#' Print method for `cartLinBoostTV` object
#'
#' @method print cartLinBoostTV
#' @param x `cartLinBoostTV` object
#' @param ... Not used
#' 
#' @author E.D. Gennatas
#' 
#' @keywords internal
#' @noRd

print.cartLinBoostTV <- function(x, ...) {

  mod.name <- "CARTLINlite"
  n.iter <- length(x$mods)
  cat("\n  A boosted", mod.name, "model with", n.iter, "iterations\n")
  cat("  and a learning rate of", x$learning.rate[1], "\n\n")

} # rtemis::print.cartLinBoostTV

#' Predict method for `cartLinBoostTV` object
#'
#' @param object `cartLinBoostTV` object
#' @param newdata Set of predictors
#' @param n.feat Integer: N of features to use. Default = NCOL(newdata)
#' @param n.iter Integer: N of iterations to predict from. Default = (all available)
#' @param as.matrix Logical: If TRUE, return predictions from each iterations. Default = FALSE
#' @param verbose Logical: If TRUE, print messages to console. Default = FALSE
#' @param n.cores Integer: Number of cores to use. Default = `rtCores`
#' 
#' @method predict cartLinBoostTV
#' @author E.D. Gennatas
#' @keywords internal
#' @noRd

predict.cartLinBoostTV <- function(object,
                                   newdata = NULL,
                                   n.feat = NCOL(newdata),
                                   n.iter = NULL,
                                   as.matrix = FALSE,
                                   verbose = FALSE,
                                   n.cores = rtCores, ...) {

  if (inherits(object, "rtMod") && inherits(object$mod, "cartLinBoostTV")) {
    object <- object$mod
    if (verbose) msg2("Found rtemis cartLinBoostTV object")
  } else if (inherits(object, "cartLinBoostTV")) {
    if (verbose) msg2("Found cartLinBoostTV object")
  } else {
    stop("Please provide cartLinBoostTV object")
  }

  if (is.null(newdata)) return(object$fitted_tv[seq(object$train.ncases)])

  if (!is.null(newdata)) {
    if (!is.data.frame(newdata)) {
      .colnames <- if (!is.null(colnames(newdata))) colnames(newdata) else paste0("V", seq_len(NCOL(newdata)))
      newdata <- as.data.frame(newdata)
      colnames(newdata) <- .colnames
      newdata <- newdata[, seq(n.feat), drop = FALSE]
    }
  }

  if (is.null(n.iter)) n.iter <- length(object$mods)

  if (!as.matrix) {
    predicted <- rowSums(cbind(rep(object$init, NROW(newdata)),
                               pbapply::pbsapply(seq(n.iter), \(i)
                                 predict(object$mods[[i]], newdata) * object$learning.rate[i],
                                 cl = n.cores)))
  } else {
    predicted.n <- pbapply::pbsapply(seq(n.iter), \(i)
      predict(object$mods[[i]], newdata) * object$learning.rate[i],
      cl = n.cores)

    predicted <- matrix(nrow = NROW(newdata), ncol = n.iter)
    predicted[, 1] <- object$init + predicted.n[, 1]
    for (i in seq(n.iter)[-1]) {
      predicted[, i] <- predicted[, i - 1] + predicted.n[, i]
    }
  }

  predicted

} # rtemis::predict.cartLinBoostTV


#' Expand boosting series
#'
#' Expand a `cartLinBoostTV` object by adding more iterations
#'
#' @inheritParams boost
#' @param object `cartLinBoostTV` object
#' 
#' @author E.D. Gennatas
#' @keywords internal
#' @noRd

expand.cartLinBoostTV <- function(object,
                                  x, y = NULL,
                                  x.valid = NULL, y.valid = NULL,
                                  x.test = NULL, y.test = NULL,
                                  resid = NULL,
                                  cart.params = NULL,
                                  glm.params = NULL,
                                  mod.params = NULL,
                                  max.iter = 10,
                                  learning.rate = NULL,
                                  # case.p = 1,
                                  weights.p = 1,
                                  weights.0 = 0,
                                  seed = NULL,
                                  prefix = NULL,
                                  verbose = TRUE,
                                  trace = 0,
                                  print.error.plot = "final",
                                  print.plot = FALSE) {

  if (is.null(y)) y <- object$y.train
  if (is.null(cart.params)) cart.params <- object$parameters$cart.params
  if (is.null(glm.params)) glm.params <- object$parameters$glm.params
  if (is.null(learning.rate)) learning.rate <- object$parameters$learning.rate

  cartLinBoostTV(x = x, y = y,
                 x.valid = x.valid, y.valid = y.valid,
                 x.test = x.test, y.test = y.test,
                 resid = resid,
                 boost.obj = object,
                 cart.params = cart.params,
                 glm.params = glm.params,
                 weights.p = weights.p,
                 weights.0 = weights.0,
                 learning.rate = learning.rate,
                 max.iter = max.iter,
                 seed = seed,
                 prefix = prefix,
                 verbose = verbose,
                 trace = trace,
                 print.error.plot = print.error.plot,
                 print.plot = print.plot)

} # rtemis::expand.cartLinBoostTV


#' Place model in `cartLinBoostTV` structure
#'
#' @inheritParams as.boost
#' @param object rtMod model
#' @param learning.rate Float: Learning rate for new boost object.
#' @param init Float: Initial value for new boost object.
#' @param apply.lr Logical: Only considered is `x = NULL`. If TRUE, new boost object's fitted values will
#' be object$fitted * learning.rate, otherwise object$fitted
#' @author E.D. Gennatas
#' 
#' @keywords internal
#' @noRd
# TODO: add x = NULL, if not NULL calculate fitted values

as.cartLinBoostTV <- function(object,
                              x,
                              y = NULL,
                              x.valid = NULL,
                              y.valid = NULL,
                              learning.rate = 1,
                              init = 0,
                              apply.lr = TRUE) {

  mods <- list(CARTlin.1 = object)
  fitted <- init + predict(object, x)
  if (apply.lr) fitted <- fitted * learning.rate
  error <- if (!is.null(y)) mse(y, fitted) else NULL

  if (!is.null(x.valid)) {
    predicted.valid <- init + predict(object, x.valid)
    if (apply.lr) predicted.valid <- predicted.valid * learning.rate
    if (!is.null(y.valid)) {
      error.valid <- mse(y.valid, predicted.valid)
    } else {
      error.valid <- NULL
    }
  } else {
    predicted.valid <- error.valid <- NULL
  }

  obj <- list(mod.name = "CARTLINBOOSTTV",
              learning.rate = learning.rate,
              init = init,
              penult.fitted = NULL,
              train.ncases = NROW(x),
              fitted_tv = c(fitted, predicted.valid),
              last.fitted_tv = c(fitted, predicted.valid),
              predicted.valid = predicted.valid,
              error = error,
              error.valid = error.valid,
              mods = mods)
  class(obj) <- c("cartLinBoostTV", "list")

  # Outro ----
  parameters <- list(mod = object$mod.name,
                     cart.params = object$parameters$cart.params,
                     glm.params = object$parameters$glm.params,
                     init = init,
                     n.its = 1,
                     learning.rate = learning.rate,
                     max.iter = 1)
  extra <- list(error.valid = NULL)
  rt <- rtModSet(rtclass = "rtMod",
                 mod = obj,
                 mod.name = "CARTLINBOOSTTV",
                 type = "Regression",
                 parameters = parameters,
                 call = NULL,
                 y.train = object$y,
                 y.test = object$y.test,
                 x.name = object$x.name,
                 y.name = object$y.name,
                 xnames = object$xnames,
                 fitted = fitted,
                 se.fit = NULL,
                 error.train = object$error.train,
                 predicted = object$predicted,
                 se.prediction = NULL,
                 error.test = object$error.test,
                 varimp = NULL,
                 question = object$question,
                 extra = extra)
  rt

} # rtemis::as.cartLinBoostTV


#' \pkg{rtemis} internals: Update `cartLinBoostTV` object's fitted values
#'
#' Calculate new fitted values for a `cartLinBoostTV` object.
#' Advanced use only: run with new `x` or after updating learning.rate in object
#'
#' @method update cartLinBoostTV
#' @param object `cartLinBoostTV` object
#' @param x Data frame: Features
#' @param last.step.only Logical: If TRUE, `x` must be provided and only the last meta model will be updated
#' using this `x`
#' 
#' @author E.D. Gennatas
#' @return Nothing; updates `object` in-place
#' @keywords internal
#' @noRd

update.cartLinBoostTV <- function(object,
                                  x = NULL,
                                  x.valid = NULL,
                                  trace = 0,
                                  last.step.only = FALSE,
                                  n.cores = rtCores, ...) {

  if (trace > 0) fitted.orig <- object$fitted_tv
  # Create n.iter x n.cases fitted values; one row per iteration
  if (is.null(x)) {
    # fitted <- t(vapply(object$mod$mods, function(i) i$fitted, vector("numeric", length(object$fitted_tv))))
    fitted <- t(sapply(object$mod$mods, function(i) i$fitted))
  } else {
    if (!last.step.only) {
      fitted <- t(as.data.frame(pbapply::pblapply(object$mod$mods, function(i) predict(i, x), cl = n.cores)))
    } else {
      u <- length(object$mod$mods)
      object$mod$mods[[u]]$fitted_tv <- predict(object$mod$mods[[u]], x)
      fitted <- t(vapply(object$mod$mods, function(i) i$fitted, vector("numeric", length(object$fitted))))
    }
  }

  # TODO: finish x.valid
  # if (is.null(x.valid)) {
  #   predicted.valid
  # }

  # Multiply each row by its corresponding learning.rate, and sum all n.case-length vectors to get fitted value
  object$mod$fitted_tv <- object$mod$init + colSums(fitted * object$mod$learning.rate)
  object$fitted <- object$mod$fitted_tv[seq(object$mod$train.ncases)]
  object$error.train <- mod_error(object$y.train, object$mod$fitted_tv[seq(object$mod$train.ncases)])
  if (trace > 0) {
    mse.orig <- mse(object$y.train, fitted.orig)
    mse.new <- mse(object$y.train, fitted)
    msg20("old MSE = ", mse.orig, "; new MSE = ", mse.new)
    # if (mse.new > mse.orig) warning("Whatever you did, it didn't really help:\nnew MSE is higher than original")
  }

} # rtemis::update.cartLinBoostTV