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dependencies with new syntax
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@mb706
mb706 committed Jan 13, 2024
1 parent 5717a02 commit 7b5afd8
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Showing 5 changed files with 25 additions and 34 deletions.
19 changes: 9 additions & 10 deletions R/PipeOpNMF.R
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Expand Up @@ -125,10 +125,8 @@ PipeOpNMF = R6Class("PipeOpNMF",
track = p_lgl(default = FALSE, tags = c("train", "nmf.options")),
verbose = p_uty(default = FALSE, tags = c("train", "nmf.options")),
pbackend = p_uty(tags = c("train", "nmf")), # .pbackend
callback = p_uty(tags = c("train", "nmf")) # .callback
callback = p_uty(tags = c("train", "nmf"), depends = keep.all == TRUE) # .callback
)
ps$add_dep("keep.all", on = "nrun", cond = CondLarger$new(1))
ps$add_dep("callback", on = "keep.all", cond = CondEqual$new(TRUE))
ps$values = list(rank = 2L, method = "brunet", parallel = FALSE, parallel.required = FALSE)
super$initialize(id, param_set = ps, param_vals = param_vals, feature_types = c("numeric", "integer"), packages = c("MASS", "NMF"))
}
Expand Down Expand Up @@ -179,10 +177,11 @@ PipeOpNMF = R6Class("PipeOpNMF",

mlr_pipeops$add("nmf", PipeOpNMF)

CondLarger = R6Class("CondLarger", inherit = Condition,
public = list(
initialize = function(rhs) super$initialize("larger", rhs),
test = function(x) !is.na(x) & x > self$rhs,
as_string = function(lhs_chr = "x") sprintf("%s > %s", lhs_chr, as.character(self$rhs))
)
)
# this is just a really bad idea
## CondLarger = R6Class("CondLarger", inherit = Condition,
## public = list(
## initialize = function(rhs) super$initialize("larger", rhs),
## test = function(x) !is.na(x) & x > self$rhs,
## as_string = function(lhs_chr = "x") sprintf("%s > %s", lhs_chr, as.character(self$rhs))
## )
## )
33 changes: 13 additions & 20 deletions R/PipeOpTextVectorizer.R
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Expand Up @@ -187,7 +187,8 @@ PipeOpTextVectorizer = R6Class("PipeOpTextVectorizer",
skip = p_uty(default = 0, tags = c("train", "predict", "ngrams"), custom_check = curry(check_integerish, min.len = 1, lower = 0, any.missing = FALSE)),

sparsity = p_dbl(lower = 0, upper = 1, default = NULL,
tags = c("train", "dfm_trim"), special_vals = list(NULL)),
tags = c("train", "dfm_trim"), special_vals = list(NULL),
depends = return_type == "bow"),
termfreq_type = p_fct(default = "count", tags = c("train", "dfm_trim"),
levels = c("count", "prop", "rank", "quantile")),
min_termfreq = p_dbl(lower = 0, default = NULL,
Expand All @@ -197,29 +198,21 @@ PipeOpTextVectorizer = R6Class("PipeOpTextVectorizer",

scheme_df = p_fct(default = "count", tags = c("train", "docfreq"),
levels = c("count", "inverse", "inversemax", "inverseprob", "unary")),
smoothing_df = p_dbl(lower = 0, default = 0, tags = c("train", "docfreq")),
k_df = p_dbl(lower = 0, tags = c("train", "docfreq")),
threshold_df = p_dbl(lower = 0, default = 0, tags = c("train", "docfreq")),
base_df = p_dbl(lower = 0, default = 10, tags = c("train", "docfreq")),
smoothing_df = p_dbl(lower = 0, default = 0, tags = c("train", "docfreq"), depends = scheme_df %in% c("inverse", "inversemax", "inverseprob")),
k_df = p_dbl(lower = 0, tags = c("train", "docfreq"), depends = scheme_df %in% c("inverse", "inversemax", "inverseprob")),
threshold_df = p_dbl(lower = 0, default = 0, tags = c("train", "docfreq"), depends = scheme_df == "count"),
base_df = p_dbl(lower = 0, default = 10, tags = c("train", "docfreq"),
depends = scheme_df %in% c("inverse", "inversemax", "inverseprob")),

scheme_tf = p_fct(default = "count", tags = c("train", "predict", "dfm_weight"),
scheme_tf = p_fct(default = "count", tags = c("train", "predict", "dfm_weight", depends = return_type == "bow"),
levels = c("count", "prop", "propmax", "logcount", "boolean", "augmented", "logave")),
k_tf = p_dbl(lower = 0, upper = 1, tags = c("train", "predict", "dfm_weight")),
base_tf = p_dbl(lower = 0, default = 10, tags = c("train", "predict", "dfm_weight")),
k_tf = p_dbl(lower = 0, upper = 1, tags = c("train", "predict", "dfm_weight"), depends = scheme_tf == "augmented"),
base_tf = p_dbl(lower = 0, default = 10, tags = c("train", "predict", "dfm_weight"), depends = scheme_tf %in% c("logcount", "logave")),

return_type = p_fct(levels = c("bow", "integer_sequence", "factor_sequence"), tags = c("train", "predict")),
sequence_length = p_int(default = 0, lower = 0, upper = Inf, tags = c("train", "predict", "integer_sequence"))
)$
add_dep("base_df", "scheme_df", CondAnyOf$new(c("inverse", "inversemax", "inverseprob")))$
add_dep("smoothing_df", "scheme_df", CondAnyOf$new(c("inverse", "inversemax", "inverseprob")))$
add_dep("k_df", "scheme_df", CondAnyOf$new(c("inverse", "inversemax", "inverseprob")))$
add_dep("base_df", "scheme_df", CondAnyOf$new(c("inverse", "inversemax", "inverseprob")))$
add_dep("threshold_df", "scheme_df", CondEqual$new("count"))$
add_dep("k_tf", "scheme_tf", CondEqual$new("augmented"))$
add_dep("base_tf", "scheme_tf", CondAnyOf$new(c("logcount", "logave")))$
add_dep("scheme_tf", "return_type", CondEqual$new("bow"))$
add_dep("sparsity", "return_type", CondEqual$new("bow"))$
add_dep("sequence_length", "return_type", CondAnyOf$new(c("integer_sequence", "factor_sequence")))
sequence_length = p_int(default = 0, lower = 0, upper = Inf, tags = c("train", "predict", "integer_sequence"),
depends = return_type %in% c("integer_sequence", "factor_sequence"))
)

ps$values = list(stopwords_language = "smart", extra_stopwords = character(0), n = 1, scheme_df = "unary", return_type = "bow")
super$initialize(id = id, param_set = ps, param_vals = param_vals, packages = c("quanteda", "stopwords"), feature_types = "character")
Expand Down
2 changes: 1 addition & 1 deletion R/PipeOpTuneThreshold.R
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Expand Up @@ -120,7 +120,7 @@ PipeOpTuneThreshold = R6Class("PipeOpTuneThreshold",
ps = private$.make_param_set(pred)
measure = self$param_set$values$measure
if (is.character(measure)) measure = msr(measure) else measure
codomain = do.call(ps, structure(list(p_dbl(tags = ifelse(measure$minimize, "minimize", "maximize"))), names = measure$id))
codomain = do.call(paradox::ps, structure(list(p_dbl(tags = ifelse(measure$minimize, "minimize", "maximize"))), names = measure$id))

objfun = bbotk::ObjectiveRFun$new(
fun = function(xs) private$.objfun(xs, pred = pred, measure = measure),
Expand Down
3 changes: 1 addition & 2 deletions R/PipeOpVtreat.R
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Expand Up @@ -136,7 +136,7 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
smFactor = p_dbl(lower = 0, upper = Inf, default = 0, tags = c("train", "regression", "classification", "multinomial")),
rareCount = p_int(lower = 0L, upper = Inf, default = 0, tags = c("train", "regression", "classification", "multinomial")),
rareSig = p_dbl(lower = 0, upper = 1, special_vals = list(NULL), tags = c("train", "regression", "classification", "multinomial")), # default NULL for regression, classification, 1 for multinomial
collarProb = p_dbl(lower = 0, upper = 1, default = 0, tags = c("train", "regression", "classification", "multinomial")),
collarProb = p_dbl(lower = 0, upper = 1, default = 0, tags = c("train", "regression", "classification", "multinomial"), depends = doCollar == TRUE),
doCollar = p_lgl(default = FALSE, tags = c("train", "regression", "classification", "multinomial")),
codeRestriction = p_uty(default = NULL, custom_check = function(x) checkmate::check_character(x, any.missing = FALSE, null.ok = TRUE),
tags = c("train", "regression", "classification", "multinomial")),
Expand All @@ -160,7 +160,6 @@ PipeOpVtreat = R6Class("PipeOpVtreat",
imputation_map = p_uty(default = NULL, custom_check = function(x) checkmate::check_list(x, null.ok = TRUE), tags = c("train", "predict"))
# NOTE: parallelCluster missing intentionally and will be set to NULL
)
ps$add_dep("collarProb", on = "doCollar", cond = CondEqual$new(TRUE))
ps$values = list(recommended = TRUE, cols_to_copy = selector_none())
super$initialize(id, param_set = ps, param_vals = param_vals, packages = "vtreat", tags = c("encode", "missings"))
}
Expand Down
2 changes: 1 addition & 1 deletion tests/testthat/helper_test_pipeops.R
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Expand Up @@ -45,7 +45,7 @@ PipeOpDebugMulti = R6Class("PipeOpDebugMulti",
p = ps(par = p_int(lower = 0, upper = 10, default = 0, tags = c("train", "predict")))
self$nin = length(inputs)
self$nout = length(outputs)
super$initialize(id, p),
super$initialize(id, param_set = p,
input = data.table(name = inputs, train = "*", predict = "*"),
output = data.table(name = outputs, train = "*", predict = "*"))
}),
Expand Down

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