## ----results = "asis", echo = FALSE-------------------------------------------

# output format should be of the form
#> output
#> output
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

# initialize: load library, make everything deterministic
library("mlrCPO")
set.seed(123)

# get the path of the parent document
# path = names(knitr::opts_knit$get("encoding"))[1]
base = knitr::opts_knit$get("output.dir")
file = sys.frame(min(grep("^knitr::knit$|^knit$", sapply(sys.calls(), function(x) as.character(x)[1]))))$input
file = basename(file)
path = file.path(base, file)
rpath = gsub("\\.[^.]*$", ".R", path)

# strip whitespace from lines in tangle (R file) output for lintr
knitr::knit_hooks$set(document = function(x) {
  if (file_test("-f", rpath)) {
    lines = readLines(rpath)
    lines = gsub(" *(\n|$)", "\\1", lines)
    cat(lines, file = rpath, sep = "\n", append = FALSE)
  }
  x
})

#############################
# do the trans-vignette ToC #
#############################
fullfile = file

allfiles = list.files(path = base, pattern = ".*\\.Rmd$")
stopifnot(file %in% allfiles)

# collect information (title, url, main / compact) for each file in vignette dir
fileinfolist = list()
for (cf in allfiles) {
  ismain = TRUE
  if (grepl("^z_", cf)) {
    infoslot = gsub("^z_", "", cf)
    infoslot = gsub("_terse\\.Rmd$", "", infoslot)
    subslot = "compact"
  } else {
    infoslot = gsub("^a_", "", cf)
    infoslot = gsub("\\.Rmd$", "", infoslot)
    subslot = "main"
  }

  content = scan(paste(base, cf, sep = "/"), what = "character", quiet = TRUE)
  pos = min(c(which(content == "title:"), Inf))
  if (is.infinite(pos)) {
    stop(sprintf("parsing error: %s", cf))
  }
  infolist = list(title = content[pos + 1], url = cf, iscurrent = cf == file)

  applist = list(infolist)
  names(applist) = subslot
  fileinfolist[[infoslot]] = c(fileinfolist[[infoslot]], applist)
}

# helper function that creates a link for all files except the current one
linkify = function(info, title) {
  if (info$iscurrent) {
    title
  } else {
    sprintf("[%s](%s)", title, gsub("\\.Rmd$", ".html", info$url))
  }
}

# output ToC
for (idx in seq_along(fileinfolist)) {
  content = fileinfolist[[sort(names(fileinfolist))[idx]]]
  if (!is.null(content$compact)) {
    if (paste(sub("[0-9]\\. ", "", content$main$title), "(No Output)") != sub("^z ", "", content$compact$title)) {
      stop(sprintf("File %s and its compact version %s have incompatible titles\nThe compact version must be paste(main_title, \"(No Output)\"). Is: '%s', expected: '%s'",
        content$main$url, content$compact$url, content$compact$title, paste(content$main$title, "(No Output)")))
    }
    line = sprintf("%s (%s)", linkify(content$main, content$main$title), linkify(content$compact, "compact version"))
  } else {
    line = linkify(content$main, content$main$title)
  }
  cat(sprintf("%s. %s\n", idx, line))
  if (content$main$iscurrent || content$compact$iscurrent) {
    fullfile = content$main$url
  }
}

fullpath = file.path(base, fullfile)

#############################
# Optional Document TOC     #
#############################

# print everything up to level `print.level`.
# level is the number of '#' prefixes. The lowest level is usually 2.
printToc = function(print.level = 3) {
  owncontent = readLines(fullpath)
  tripletic = grepl("^```", owncontent)
  owncontent = owncontent[cumsum(tripletic) %% 2 == 0]  # exclude ```-delimited code
  headlines = grep("^#+ +", owncontent, value = TRUE)
  headlevels = nchar(gsub(" .*", "", headlines))
  headlines = gsub("^[#]+ +", "", headlines)

  links = gsub("[^-a-z. ]", "", tolower(headlines))
  links = gsub(" +", "-", links)
  links = gsub("-$", "", links)

  if (!sum(headlevels <= print.level)) {
    return(invisible(NULL))
  }

  cat("<h", headlevels[1], ">Table of Contents</h", headlevels[1], ">\n<div id=\"TOC\">\n", sep = "")

  lastlevel = headlevels[1] - 1
  for (idx in seq_along(headlines)) {
    line = headlines[idx]
    level = headlevels[idx]
    link = links[idx]
    if (level > print.level) {
      next
    }
    if (level < headlevels[1]) {
      stop("First headline level must be the lowest one used, but '", line, "' is lower.")
    }
    lvldiff = level - lastlevel
    if (lvldiff > 1) {
      stop("Cannot jump headline levels. Error on: ", line)
    }
    if (lvldiff > 0) {
      # higher level -> open a <ul>
      cat("<ul>")
    } else {
      cat("</li>\n")
    }
    if (lvldiff < 0) {
      # lower level -> close a few <ul>
      for (l in seq_len(-lvldiff)) {
        cat("</ul></li>")
      }
    }
    cat("<li><a href=\"#", link, "\">", line, "</a>", sep = "")
    lastlevel = level
  }
  # if the last level is greater than the first level, close a few <ul>
  lvldiff = lastlevel - headlevels[1]

  cat("</li></ul>\n</div>\n")
}



#############################
# Some output settings      #
#############################

options(width = 80)


replaceprint = function(ofunc) {
  force(ofunc)
  function(x, ...) {
    cu = capture.output({ret = ofunc(x, ...)})
    cu = grep("time: [-+e0-9.]{1,6}", cu, value = TRUE, invert = TRUE)
    cat(paste(cu, collapse = "\n"))
    if (!grepl("\n$", tail(cu, 1))) {
      cat("\n")
    }
    ret
  }
}

for (pfunc in grep("print\\.", ls(asNamespace("mlr")), value = TRUE)) {
  ofunc = get(pfunc, asNamespace("mlr"))
  assign(pfunc, replaceprint(ofunc))
}



## ----eval = TRUE, echo = FALSE, results = 'asis'------------------------------
printToc(4)

## -----------------------------------------------------------------------------
# !cpoPca()

## -----------------------------------------------------------------------------
# xmpSample = makeCPORetrafoless("exsample",  # nolint
#   pSS(fraction: numeric[0, 1]),
#   dataformat = "df.all",
#   cpo.trafo = function(data, target, fraction) {
#     newsize = round(nrow(data) * fraction)
#     row.indices = sample(nrow(data), newsize)
#     data[row.indices, ]
#   })
#
# cpo = xmpSample(0.01)

## -----------------------------------------------------------------------------
# iris %>>% cpo

## -----------------------------------------------------------------------------
# xmpSampleHeadless = makeCPORetrafoless("exsample",  # nolint
#   pSS(fraction: numeric[0, 1]),
#   dataformat = "df.all",
#   cpo.trafo = {
#     newsize = round(nrow(data) * fraction)
#     row.indices = sample(nrow(data), newsize)
#     data[row.indices, ]
#   })

## -----------------------------------------------------------------------------
# xmpFilterVar = makeCPO("exemplvar",  # nolint
#   pSS(n.col: integer[0, ]),
#   dataformat = "numeric",
#   cpo.train = function(data, target, n.col) {
#     cat("*** cpo.train ***\n")
#     sapply(data, var, na.rm = TRUE)
#   },
#   cpo.retrafo = function(data, control, n.col) {
#     cat("*** cpo.retrafo ***\n")
#     cat("Control:\n")
#     print(control)
#     cat("\n")
#     greatest = order(-control)  # columns, ordered greatest to smallest var
#     data[greatest[seq_len(n.col)]]
#   })
#
# cpo = xmpFilterVar(2)

## -----------------------------------------------------------------------------
# (trafd = head(iris) %>>% cpo)

## -----------------------------------------------------------------------------
# head(iris %>>% cpo)

## -----------------------------------------------------------------------------
# head(iris %>>% retrafo(trafd))

## -----------------------------------------------------------------------------
# getCPOTrainedState(retrafo(trafd))

## -----------------------------------------------------------------------------
# xmpFilterVarFunc = makeCPO("exemplvar.func",  # nolint
#   pSS(n.col: integer[0, ]),
#   dataformat = "numeric",
#   cpo.retrafo = NULL,
#   cpo.train = function(data, target, n.col) {
#     cat("*** cpo.train ***\n")
#     ctrl = sapply(data, var, na.rm = TRUE)
#     function(x) {  # the data is given to the only present parameter: 'x'
#       cat("*** cpo.retrafo ***\n")
#       cat("Control:\n")
#       print(ctrl)
#       cat("\ndata:\n")
#       print(data)  # 'data' is deleted: NULL
#       cat("target:\n")
#       print(target)  # 'target' is deleted: NULL
#       greatest = order(-ctrl)  # columns, ordered greatest to smallest var
#       x[greatest[seq_len(n.col)]]
#     }
#   })
#
# cpo = xmpFilterVarFunc(2)

## -----------------------------------------------------------------------------
# (trafd = head(iris) %>>% cpo)

## -----------------------------------------------------------------------------
# getCPOTrainedState(retrafo(trafd))

## -----------------------------------------------------------------------------
# xmpAsNum = makeCPO("asnum",  # nolint
#   cpo.train = NULL,
#   cpo.retrafo = function(data) {
#     data.frame(lapply(data, as.numeric))
#   })
#
# cpo = xmpAsNum()

## -----------------------------------------------------------------------------
# (trafd = head(iris) %>>% cpo)

## -----------------------------------------------------------------------------
# getCPOTrainedState(retrafo(trafd))

## -----------------------------------------------------------------------------
# xmpPca = makeCPOExtendedTrafo("simple.pca",  # nolint
#   pSS(n.col: integer[0, ]),
#   dataformat = "numeric",
#   cpo.trafo = function(data, target, n.col) {
#     cat("*** cpo.trafo ***\n")
#     pcr = prcomp(as.matrix(data), center = FALSE, scale. = FALSE, rank = n.col)
#     # save the rotation matrix as 'control' variable
#     control = pcr$rotation
#     pcr$x
#   },
#   cpo.retrafo = function(data, control, n.col) {
#     cat("*** cpo.retrafo ***\n")
#     # rotate the data by the rotation matrix
#     as.matrix(data) %*% control
#   })
#
# cpo = xmpPca(2)

## -----------------------------------------------------------------------------
# (trafd = head(iris) %>>% cpo)

## -----------------------------------------------------------------------------
# tail(iris) %>>% retrafo(trafd)

## -----------------------------------------------------------------------------
# getCPOTrainedState(retrafo(trafd))

## -----------------------------------------------------------------------------
# xmpPcaFunc = makeCPOExtendedTrafo("simple.pca.func",  # nolint
#   pSS(n.col: integer[0, ]),
#   dataformat = "numeric",
#   cpo.retrafo = NULL,
#   cpo.trafo = function(data, target, n.col) {
#     cat("*** cpo.trafo ***\n")
#     pcr = prcomp(as.matrix(data), center = FALSE, scale. = FALSE, rank = n.col)
#     # save the rotation matrix as 'control' variable
#     cpo.retrafo = function(data) {
#       cat("*** cpo.retrafo ***\n")
#       # rotate the data by the rotation matrix
#       as.matrix(data) %*% pcr$rotation
#     }
#     pcr$x
#   })
#
# cpo = xmpPcaFunc(2)

## -----------------------------------------------------------------------------
# (trafd = head(iris) %>>% cpo)

## -----------------------------------------------------------------------------
# getCPOTrainedState(retrafo(trafd))$pcr$x

## ----eval = FALSE-------------------------------------------------------------
# c(response = "response", se = "prob")

## -----------------------------------------------------------------------------
# xmpMetaLearn = makeCPOTargetOp("xmp.meta",  # nolint
#   pSS(lrn: untyped),
#   dataformat = "task",
#   properties.target = c("classif", "twoclass"),
#   predict.type.map = c(response = "response", prob = "prob"),
#   cpo.train = function(data, target, lrn) {
#     cat("*** cpo.train ***\n")
#     lrn = setPredictType(lrn, "prob")
#     train(lrn, data)
#   },
#   cpo.retrafo = function(data, target, control, lrn) {
#     cat("*** cpo.retrafo ***\n")
#     prediction = predict(control, target)
#     tname = getTaskTargetNames(target)
#     tdata = getTaskData(target)
#     tdata[[tname]] = factor(prediction$data$response == prediction$data$truth)
#     makeClassifTask(getTaskId(target), tdata, tname, positive = "TRUE",
#       fixup.data = "no", check.data = FALSE)
#   },
#   cpo.train.invert = function(data, control, lrn) {
#     cat("*** cpo.train.invert ***\n")
#     predict(control, newdata = data)$data
#   },
#   cpo.invert = function(target, control.invert, predict.type, lrn) {
#     cat("*** cpo.invert ***\n")
#     if (predict.type == "prob") {
#       outmat = as.matrix(control.invert[grep("^prob\\.", names(control.invert))])
#       revmat = outmat[, c(2, 1)]
#       outmat * target[, "prob.TRUE", drop = TRUE] +
#         revmat * target[, "prob.FALSE", drop = TRUE]
#     } else {
#       stopifnot(levels(target) == c("FALSE", "TRUE"))
#       numeric.prediction = as.numeric(control.invert$response)
#       numeric.res = ifelse(target == "TRUE",
#         numeric.prediction,
#         3 - numeric.prediction)
#       factor(levels(control.invert$response)[numeric.res],
#         levels(control.invert$response))
#     }
#   })
#
# cpo = xmpMetaLearn(makeLearner("classif.logreg"))

## -----------------------------------------------------------------------------
# set.seed(12)
# split = makeResampleInstance(hout, pid.task)
# train.task = subsetTask(pid.task, split$train.inds[[1]])
# test.task = subsetTask(pid.task, split$predict.inds[[1]])

## -----------------------------------------------------------------------------
# trafd = train.task %>>% cpo
# attributes(trafd)

## -----------------------------------------------------------------------------
# head(getTaskData(trafd))

## -----------------------------------------------------------------------------
# model = train(makeLearner("classif.logreg", predict.type = "prob"), train.task)
# head(predict(model, train.task)$data[c("truth", "response")])

## -----------------------------------------------------------------------------
# retr = test.task %>>% retrafo(trafd)
# attributes(retr)

## -----------------------------------------------------------------------------
# retr.df = getTaskData(test.task, target.extra = TRUE)$data %>>% retrafo(trafd)
# names(attributes(retr.df))

## -----------------------------------------------------------------------------
# ext.model = train("classif.svm", trafd)
# ext.pred = predict(ext.model, retr)
# newpred = invert(inverter(retr), ext.pred)
# performance(newpred)

## -----------------------------------------------------------------------------
# cpo.learner = cpo %>>% makeLearner("classif.svm")
# cpo.model = train(cpo.learner, train.task)

## -----------------------------------------------------------------------------
# lrnpred = predict(cpo.model, test.task)
# performance(lrnpred)

## -----------------------------------------------------------------------------
#
# xmpMetaLearn = makeCPOTargetOp("xmp.meta.fnc",  # nolint
#   pSS(lrn: untyped),
#   dataformat = "task",
#   properties.target = c("classif", "twoclass"),
#   predict.type.map = c(response = "response", prob = "prob"),
#   # set the cpo.* parameters not needed to NULL:
#   cpo.retrafo = NULL, cpo.train.invert = NULL, cpo.invert = NULL,
#   cpo.train = function(data, target, lrn) {
#     cat("*** cpo.train ***\n")
#     lrn = setPredictType(lrn, "prob")
#     model = train(lrn, data)
#     cpo.retrafo = function(data, target) {
#       cat("*** cpo.retrafo ***\n")
#       prediction = predict(model, target)
#       tname = getTaskTargetNames(target)
#       tdata = getTaskData(target)
#       tdata[[tname]] = factor(prediction$data$response == prediction$data$truth)
#       makeClassifTask(getTaskId(target), tdata, tname, positive = "TRUE",
#         fixup.data = "no", check.data = FALSE)
#     }
#     cpo.train.invert = function(data) {
#       cat("*** cpo.train.invert ***\n")
#       prediction = predict(model, newdata = data)$data
#       function(target, predict.type) {  # this is returned as cpo.invert
#         cat("*** cpo.invert ***\n")
#         if (predict.type == "prob") {
#           outmat = as.matrix(prediction[grep("^prob\\.", names(prediction))])
#           revmat = outmat[, c(2, 1)]
#           outmat * target[, "prob.TRUE", drop = TRUE] +
#             revmat * target[, "prob.FALSE", drop = TRUE]
#         } else {
#           stopifnot(levels(target) == c("FALSE", "TRUE"))
#           numeric.prediction = as.numeric(prediction$response)
#           numeric.res = ifelse(target == "TRUE",
#             numeric.prediction,
#             3 - numeric.prediction)
#           factor(levels(prediction$response)[numeric.res],
#             levels(prediction$response))
#         }
#       }
#     }
#   })

## -----------------------------------------------------------------------------
# xmpRegCenter = makeCPOTargetOp("xmp.center",  # nolint
#   constant.invert = TRUE,
#   cpo.train.invert = NULL,  # necessary for constant.invert = TRUE
#   dataformat = "df.feature",
#   properties.target = "regr",
#   cpo.train = function(data, target) {
#     # control value is just the mean of the target column
#     mean(target[[1]])
#   },
#   cpo.retrafo = function(data, target, control) {
#     # subtract mean from target column in retrafo
#     target[[1]] = target[[1]] - control
#     target
#   },
#   cpo.invert = function(target, predict.type, control.invert) {
#     target + control.invert
#   })
#
# cpo = xmpRegCenter()

## -----------------------------------------------------------------------------
# train.task = subsetTask(bh.task, 150:155)
# getTaskTargets(train.task)

## -----------------------------------------------------------------------------
# predict.task = subsetTask(bh.task, 156:160)
# getTaskTargets(predict.task)

## -----------------------------------------------------------------------------
# trafd = train.task %>>% cpo
# getTaskTargets(trafd)

## -----------------------------------------------------------------------------
# getTaskTargets(predict.task)

## -----------------------------------------------------------------------------
# retr = retrafo(trafd)
# predict.traf = predict.task %>>% retr
# getTaskTargets(predict.traf)

## ----warnings = FALSE---------------------------------------------------------
# model = train("regr.lm", trafd)
# pred = predict(model, predict.traf)
# pred

## -----------------------------------------------------------------------------
# invert(inverter(predict.traf), pred)

## ----warnings = FALSE---------------------------------------------------------
# model = train("regr.lm", train.task)
# predict(model, predict.task)

## -----------------------------------------------------------------------------
# getCPOTrainedCapability(retr)

## -----------------------------------------------------------------------------
# invert(retr, pred)

## -----------------------------------------------------------------------------
# xmpLogRegr = makeCPOTargetOp("log.regr",  # nolint
#   constant.invert = TRUE,
#   properties.target = "regr",
#   cpo.train = NULL, cpo.train.invert = NULL,
#   cpo.retrafo = function(data, target) {
#     target[[1]] = log(target[[1]])
#     target
#   },
#   cpo.invert = function(target, predict.type) {
#     exp(target)
#   })
#
# cpo = xmpLogRegr()

## -----------------------------------------------------------------------------
# trafd = train.task %>>% cpo
# getTaskTargets(trafd)

## -----------------------------------------------------------------------------
# retr = retrafo(trafd)
# predict.traf = predict.task %>>% retr
# getTaskTargets(predict.traf)

## ----warnings = FALSE---------------------------------------------------------
# model = train("regr.lm", trafd)
# pred = predict(model, predict.traf)
# pred

## -----------------------------------------------------------------------------
# invert(inverter(predict.traf), pred)

## -----------------------------------------------------------------------------
# invert(retr, pred)

## -----------------------------------------------------------------------------
# xmpSynCPO = makeCPOExtendedTargetOp("syn.cpo",  # nolint
#   properties.target = "regr",
#   cpo.trafo = function(data, target) {
#     cat("*** cpo.trafo ***\n")
#     target[[1]] = target[[1]] + 1
#     control = "control created in cpo.trafo"
#     control.invert = "control.invert created in cpo.trafo"
#     target
#   },
#   cpo.retrafo = function(data, target, control) {
#     cat("*** cpo.retrafo ***", "control is:", deparse(control), sep = "\n")
#     control.invert = "control.invert created in cpo.retrafo"
#     if (!is.null(target)) {
#       cat("target is non-NULL, performing transformation\n")
#       target[[1]] = target[[1]] - 1
#       return(target)
#     } else {
#       cat("target is NULL, no transformation (but control.invert was created)\n")
#       return(NULL)  # is ignored.
#     }
#   },
#   cpo.invert = function(target, control.invert, predict.type) {
#     cat("*** invert ***", "control.invert is:", deparse(control.invert),
#       sep = "\n")
#     target
#   })
#
# cpo = xmpSynCPO()

## -----------------------------------------------------------------------------
# trafd = train.task %>>% cpo
# getTaskTargets(trafd)

## -----------------------------------------------------------------------------
# retrafd = train.task %>>% retrafo(trafd)

## -----------------------------------------------------------------------------
# getTaskTargets(retrafd)

## -----------------------------------------------------------------------------
# retrafd = getTaskData(train.task, target.extra = TRUE)$data %>>% retrafo(trafd)

## -----------------------------------------------------------------------------
# inv = invert(inverter(trafd), 1:6)

## -----------------------------------------------------------------------------
# inv = invert(inverter(retrafd), 1:6)

## ----echo = FALSE-------------------------------------------------------------
# oscipen = options("scipen")
# options(scipen = 10)

## -----------------------------------------------------------------------------
# learners = list(
#     logreg = makeLearner("classif.logreg"),
#     svm = makeLearner("classif.svm"),
#     cpo = xmpMetaLearn(makeLearner("classif.logreg")) %>>%
#       makeLearner("classif.svm")
# )
#
# # suppress output of '*** cpo.train ***' etc.
# configureMlr(show.info = FALSE, show.learner.output = FALSE)
#
# perfs = sapply(learners, function(lrn) {
#   unname(replicate(20, resample(lrn, pid.task, cv10)$aggr))
# })
#
# # reset mlr settings
# configureMlr()
#
# boxplot(perfs)

## -----------------------------------------------------------------------------
# pvals = c(
#     logreg = t.test(perfs[, "logreg"], perfs[, "cpo"], "greater")$p.value,
#     svm = t.test(perfs[, "svm"], perfs[, "cpo"], "greater")$p.value
# )
#
# round(p.adjust(pvals), 3)

## ----echo = FALSE-------------------------------------------------------------
# options(scipen = oscipen$scipen)

## ----results = "asis", echo = FALSE-------------------------------------------
cat(knitr::knit_child("a_4_custom_CPOs.Rmd", options = list(eval = FALSE)), sep = "\n")

