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Create Transition-Predictor Relationship Table

Source: R/trans_preds_t.R
trans_preds_t.Rd

Creates a trans_preds_t table based on the relationships between transitions and predictors. This function establishes which predictors are useful for modelling each transition type.

Usage

as_trans_preds_t(x)

# S3 method for class 'trans_preds_t'
print(x, nrow = 10, ...)

set_full_trans_preds(self, overwrite = FALSE)

get_pred_filter_score(
  self,
  filter,
  cluster = NULL,
  ordered_pred_data = FALSE,
  ...
)

Arguments

x

A list or data.frame coercible to a trans_preds_t object. If missing, an empty table will be created.

nrow

see data.table::print.data.table

...

Additional arguments passed to flt if filter is a character string

self

An evoland_db instance with populated trans_meta_t and pred_meta_t tables

overwrite

Bool, should a potentially existing table be overwritten?

filter

An mlr3filters::Filter object or a character string specifying the filter method, retrieved via mlr3filters::flt. Note that your filter must be compatible with the feature data types; compare your pred_meta_t table to https://mlr3filters.mlr-org.com for filter compatibility.

cluster

An optional cluster object, see run_parallel_evoland

ordered_pred_data

Bool, should the predictor data be ordered? Needed for fully deterministic behavior

Value

A data.table of class "trans_preds_t" with columns:

  • id_run: Foreign key to runs_t

  • id_pred: Foreign key to pred_meta_t

  • id_trans: Foreign key to trans_meta_t

Methods (by generic)

  • print(trans_preds_t): Print a trans_preds_t object, passing params to data.table print

Functions

  • set_full_trans_preds(): Set an initial full set of transition / predictor relations

  • get_pred_filter_score(): Get a filter score for all transition-predictor relationships based on mlr3filters. Returns trans_preds_t with an additional column named after the filter$id. The filter score can be used for feature selection: simply subset according to the score and overwrite trans_preds_t in the database using db$trans_preds_t <- trans_preds_t[score > threshold] or similar.