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Merge pull request #846 from mlr-org/cli
feat: add cli package for class printer
2 parents 7fcdcf0 + 74b57b4 commit fc6e74d

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‎DESCRIPTION‎

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@@ -62,6 +62,7 @@ Depends:
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Imports:
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backports,
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checkmate,
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cli,
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data.table,
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digest,
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lgr,
@@ -101,6 +102,7 @@ Suggests:
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htmlwidgets,
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ranger,
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themis
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Remotes: mlr-org/mlr3misc
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ByteCompile: true
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Encoding: UTF-8
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Config/testthat/edition: 3

‎NAMESPACE‎

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@@ -229,6 +229,7 @@ if (getRversion() >= "4.3.0") S3method(chooseOpsMethod,CnfAtom)
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if (getRversion() >= "4.3.0") S3method(chooseOpsMethod,CnfClause)
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if (getRversion() >= "4.3.0") S3method(chooseOpsMethod,CnfFormula)
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import(checkmate)
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import(cli)
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import(data.table)
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import(mlr3)
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import(mlr3misc)

‎R/Graph.R‎

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@@ -414,15 +414,25 @@ Graph = R6Class("Graph",
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scc = self$edges[, list(sccssors = paste(unique(dst_id), collapse = ",")), by = list(ID = src_id)]
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lines = scc[prd[lines, on = "ID"], on = "ID"][, c("ID", "State", "sccssors", "prdcssors")]
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lines[is.na(lines)] = ""
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catf("Graph with %s PipeOps:", nrow(lines))
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cat_cli(cli_h1("Graph with {nrow(lines)} PipeOps:"))
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## limit column width ##
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outwidth = getOption("width") %??% 80 # output width we want (default 80)
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colwidths = map_int(lines, function(x) max(nchar(x), na.rm = TRUE)) # original width of columns
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collimit = calculate_collimit(colwidths, outwidth)
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opts = options(datatable.prettyprint.char = collimit)
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on.exit(options(opts), add = TRUE)
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print(lines, row.names = FALSE)
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is_sequential = all(table(self$edges$src_id) <= 1) && all(table(self$edges$dst_id) <= 1)
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if(is_sequential) {
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ppunit = paste0(self$ids(), collapse = " -> ")
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pp = paste0(c("<INPUT>", ppunit, "<OUTPUT>"), collapse = " -> ")
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} else {
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pp = "non-sequential"
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}
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cat_cli(cli_h3("Pipeline: {.strong {pp}}"))
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} else {
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cat("Empty Graph.\n")
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}

‎R/GraphLearner.R‎

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@@ -312,6 +312,18 @@ GraphLearner = R6Class("GraphLearner", inherit = Learner,
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},
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plot = function(html = FALSE, horizontal = FALSE, ...) {
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private$.graph$plot(html = html, horizontal = horizontal, ...)
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},
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print = function() {
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super$print(self)
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is_sequential = all(table(self$edges$src_id) <= 1) && all(table(self$edges$dst_id) <= 1)
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if(is_sequential) {
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ppunit = paste0(self$ids(), collapse = " -> ")
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pp = paste0(c("<INPUT>", ppunit, "<OUTPUT>"), collapse = " -> ")
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} else {
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pp = "non-sequential"
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}
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cat_cli(cli_h3("Pipeline: {.strong {pp}}"))
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}
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),
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active = list(

‎R/PipeOp.R‎

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Original file line numberDiff line numberDiff line change
@@ -278,18 +278,23 @@ PipeOp = R6Class("PipeOp",
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print = function(...) {
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type_table_printout = function(table) {
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strings = do.call(sprintf, cbind(fmt = "%s`[%s,%s]", table[, c("name", "train", "predict")]))
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strings = strwrap(paste(strings, collapse = ", "), indent = 2, exdent = 2)
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if (length(strings) > 6) {
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strings = c(strings[1:5], sprintf(" [... (%s lines omitted)]", length(strings) - 5))
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print(head(table, 5L), row.names = FALSE, print.keys = FALSE)
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if (nrow(table) > 5L) {
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catf("[...] (%i rows omitted)", nrow(table) - 5L)
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}
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gsub("`", " ", paste(strings, collapse = "\n"))
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}
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catf("PipeOp: <%s> (%strained)", self$id, if (self$is_trained) "" else "not ")
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catf("values: <%s>", as_short_string(self$param_set$values))
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catf("Input channels <name [train type, predict type]>:\n%s", type_table_printout(self$input))
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catf("Output channels <name [train type, predict type]>:\n%s", type_table_printout(self$output))
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msg_h = if (self$is_trained) "" else "not "
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cat_cli({
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cli_h1("PipeOp {.cls {self$id}}: {msg_h}trained")
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cli_text("Values: {as_short_string(self$param_set$values)}")
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cli_h3("{.strong Input channels:}")
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})
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type_table_printout(self$input)
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cat_cli({
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cli_h3("{.strong Output channels:}")
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})
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type_table_printout(self$output)
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},
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train = function(input) {

‎R/multiplicity.R‎

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@@ -56,7 +56,7 @@ print.Multiplicity = function(x, ...) {
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if (!length(x)) {
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cat("Empty Multiplicity.\n")
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} else {
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cat("Multiplicity:\n")
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cli_h2("Multiplicity:")
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print(unclass(x), ...)
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}
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}

‎R/zzz.R‎

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@@ -1,5 +1,6 @@
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#' @import data.table
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#' @import checkmate
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#' @import cli
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#' @import mlr3
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#' @import paradox
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#' @import mlr3misc

‎inst/testthat/helper_functions.R‎

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@@ -97,7 +97,7 @@ expect_pipeop = function(po, check_ps_default_values = TRUE) {
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expect_class(po$param_set, "ParamSet", label = label)
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expect_list(po$param_set$values, names = "unique", label = label)
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expect_flag(po$is_trained, label = label)
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expect_output(print(po), "PipeOp:", label = label)
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expect_output(print(po), "PipeOp", label = label)
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expect_character(po$packages, any.missing = FALSE, unique = TRUE, label = label)
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expect_function(po$train, nargs = 1)
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expect_function(po$predict, nargs = 1)

‎tests/testthat/_snaps/PipeOp.md‎

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# PipeOp printer
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Code
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print(PipeOpNOP$new())
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Output
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-- PipeOp <nop>: not trained ---------------------------------------------------
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Values: list()
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-- Input channels:
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name train predict
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<char> <char> <char>
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input * *
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-- Output channels:
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name train predict
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<char> <char> <char>
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output * *
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---
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Code
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print(PipeOpDebugMulti$new(3, 4))
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Output
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-- PipeOp <debug.multi>: not trained -------------------------------------------
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Values: list()
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-- Input channels:
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name train predict
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<char> <char> <char>
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input_1 * *
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input_2 * *
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input_3 * *
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-- Output channels:
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name train predict
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<char> <char> <char>
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output_1 * *
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output_2 * *
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output_3 * *
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output_4 * *
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---
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Code
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print(PipeOpDebugMulti$new(100, 0))
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Output
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-- PipeOp <debug.multi>: not trained -------------------------------------------
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Values: list()
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-- Input channels:
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name train predict
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<char> <char> <char>
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input_1 * *
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input_2 * *
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input_3 * *
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input_4 * *
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input_5 * *
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[...] (95 rows omitted)
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-- Output channels:
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name train predict
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<char> <char> <char>
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output_ * *
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---
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Code
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print(PipeOpBranch$new(c("odin", "dva", "tri")))
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Output
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-- PipeOp <branch>: not trained ------------------------------------------------
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Values: selection=odin
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-- Input channels:
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name train predict
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<char> <char> <char>
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input * *
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-- Output channels:
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name train predict
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<char> <char> <char>
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odin * *
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dva * *
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tri * *
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---
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Code
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print(PipeOpLearner$new(mlr_learners$get("classif.debug")))
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Output
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-- PipeOp <classif.debug>: not trained -----------------------------------------
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Values: list()
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-- Input channels:
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name train predict
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<char> <char> <char>
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input TaskClassif TaskClassif
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-- Output channels:
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name train predict
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<char> <char> <char>
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output NULL PredictionClassif
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‎tests/testthat/test_Graph.R‎

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@@ -20,15 +20,15 @@ test_that("linear graph", {
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expect_graph(g)
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expect_output(print(g), "Graph with 2 PipeOps.*subsample.*UNTRAINED.*pca.*UNTRAINED")
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expect_output(print(g), "Graph with 2 PipeOps:")
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expect_output(print(g), ".*subsample.*UNTRAINED.*pca.*UNTRAINED")
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inputs = mlr_tasks$get("iris")
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x = g$train(inputs)
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expect_task(x[[1]])
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expect_output(print(g), "Graph with 2 PipeOps.*subsample.*list.*pca.*prcomp")
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expect_output(print(g), "Graph with 2 PipeOps")
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expect_output(print(g), ".*subsample.*list.*pca.*prcomp")
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out = g$predict(inputs)
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expect_task(x[[1]])

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