R/SelectorMaybe.R
dict_selectors_maybe.RdSelector that wraps two other Selectors given during construction and uses both for selection proportionally.
Each of the resulting n_select individuals is chosen either from $selector, or from $selector_not.
This makes it possible to implement selection methods such as random interleaving, where only a fraction of p
individuals were selected by a criterion, while the others are taken randomly.
To perform selection, n_selector_in rows of values are given to $selector, and the remaining
nrow(values) - n_selector_in rows are given to $selector_not. Both selectors are used to generate
a subset of selected individuals: $selector generates n_selector_out individuals, and $selector_not generates
n_select - n_selector_out individuals.
n_selector_in is either set to
round(nrow(values) * p_in) when proportion_in is "exact", or to rbinom(1, nrow(values), p_in) when proportion_in is "random".
n_selector_out is set to round(n_select * p_out) when proportion_out is "exact", or to rbinom(1, n_select, p_out) when proportion_out is "random".
When odds_correction is TRUE, then p_out is adjusted depending on the used n_selector_in value before being applied. Let odds(p) = p/(1-p).
Then the effective p_out is set such that odds(effective p_out) = odds(p_out) * n_selector_in / (nrow(values) - n_selector_in) / odds(p_in).
This corrects for the discrepancy between the chosen p_in and the effective proportion of n_selector_in / nrow(values) caused either by rounding
errors or when proportion_in is "random".
When p_in is exactly 1 or exactly 0, and p_out is not equal to p_in, then an error is given.
If nrow(values) is 1, then this individuum is returned and $selector / $selector_not are not called.
If try_unique is TRUE, then n_selector_out is set to at most n_selector_in and at least n_select - nrow(values) + n_selector_in,
and an error is generated when nrow(values) is less than n_select.
If try_unique is FALSE and odds_correction is TRUE and n_selector_in is either 0 or nrow(values), then $p_out is set to either 0 or 1, respectively.
If try_unique is FALSE and odds_correction is FALSE and n_selector_in is either 0 or nrow(values), and n_selector_out is not equal
to 0 or n_select, respectively, then
n_selector_in is increased / decreased by 1 to give $selector_not / $selector at least one individuum to choose from. While this behaviour
may seem pathological, it is to ensure continuity with sampled values of n_selector_in that are close to 0 or n_select.
If n_selector_out is n_select or 0, or if n_selector_in is nrows(values) - 1 or 1,
then only $selector / $selector_not is executed, respectively; possibly with a subset
of values if n_selector_in differs from nrow(values) / 0.
This operator has the configuration parameters of the Selectors that it wraps: The configuration parameters of the operator given to the selector construction argument
are prefixed with "maybe.", the configuration parameters of the operator given to the selector_not construction argument are prefixed with "maybe_not.".
Additional configuration parameters:
p_in :: numeric(1)
Probability per individual (when random_choise is TRUE), or fraction of individuals (when random_choice is FALSE),
that are given to $selector instead of $selector_not. This may be overriden when try_unique is TRUE, in which
case at least as many rows are given to $selector and $selector_not as they are generating output values respectively.
When this is exactly 1 or exactly 0, then p_out must be equal to p_in.
Must be set by the user.
p_out :: numeric(1)
Probability per output value (when random_choise is TRUE), or fraction of output values (when random_choice is FALSE),
that are generated by $selector instead of $selector_not. When this values is not given, it defaults to p_in.
shuffle_input :: logical(1)
Whether to distribute input values randomly to $selector / $selector_not. If FALSE, then the first part of values
is given to $selector. This only randomizes which lines of values are given to $selector / $selector_not, but it
does not necessarily reorder the lines of values given to each. In particular, if p_out is 0 or 1, then no shuffling takes place. Initialized to TRUE.
proportion_in :: character(1)
When set to "random", sample the number of individuals given to $selector according to rbinom(1, nrow(values), p_in).
When set to "exact", give $selector round(nrow(values) * p_in) individuals. Initialized to "exact".
proportion_out :: character(1)
When set to "random", sample the number of individuals generated by $selector according to rbinom(1, n_select, p_out).
When set to "exact", have $selector generate round(n_select * p_out) individuals.
odds_correction :: logical(1)
When set, the effectively used value of p_out is set to
1 / (1 + ((nrow(values) - n_selector_in) * p_in * (1 - p_out)) / (n_selector_in * p_out * (1 - p_in))), see the Algorithm section.
Initialized to FALSE.
try_unique :: logical(1)
Whether to give at least as many rows of values to each of $selector and $selector_not as they are generating output
values. This should be set to TRUE whenever SelectorMaybe is used to select unique values, and can be set to
FALSE when selecting values multiple times is acceptable. When this is TRUE, then having n_select > nrow(values)
generates an error. Initialized to TRUE.
Supported Param classes are the set intersection of supported classes of selector and selector_not.
This Filtor can be created with the short access form ftr()
(ftrs() to get a list), or through the the dictionary
dict_filtors in the following way:
# preferred:
ftr("maybe", <selector> [, <selector_not>])
ftrs("maybe", <selector> [, <selector_not>]) # takes vector IDs, returns list of Filtors
# long form:
dict_filtors$get("maybe", <selector> [, <selector_not>])Other selectors:
SelectorScalar,
Selector,
dict_selectors_best,
dict_selectors_null,
dict_selectors_proxy,
dict_selectors_random,
dict_selectors_sequential,
dict_selectors_tournament
Other selector wrappers:
dict_selectors_proxy,
dict_selectors_sequential
miesmuschel::MiesOperator -> miesmuschel::Selector -> SelectorMaybe
selector(Selector)Selector being wrapped. This operator gets run with probability / proportion p_in and generates
output with probability / proportion p_out (configuration parameters).
selector_not(Selector)
Alternative Selector being wrapped. This operator gets run with probability / proportion 1 - p_in
and generates output with probability / proportion 1 - p_out (configuration parameters).
new()Initialize the SelectorMaybe object.
SelectorMaybe$new(selector, selector_not = SelectorRandom$new())selector(Selector)Selector to wrap. This operator gets run with probability / fraction p_in (Configuration parameter).
The constructed object gets a clone of this argument.
The $selector field will reflect this value.
selector_not(Selector)
Another Selector to wrap. This operator runs when selector is not chosen. By
default, this is SelectorRandom, i.e. selecting randomly.
The constructed object gets a clone of this argument.
The $selector_not field will reflect this value.
prime()See MiesOperator method. Primes both this operator, as well as the wrapped operators
given to selector and selector_not during construction.
param_set(ParamSet)
Passed to MiesOperator$prime().
invisible self.