Calculate Transition Potential — trans_pot

Estimate transition potential at id_period_post. Based on the LULC at id_period_anterior

Usage

as_trans_pot_t(x)

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

predict_trans_pot(
  self,
  id_period_post,
  select_score,
  select_maximize,
  force = FALSE
)

Arguments

x: A list or data.frame coercible to a data.table
nrow: see data.table::print.data.table
...: passed to data.table::print.data.table
self: an evoland_db instance
id_period_post: scalar integerish, passed to pred_data_wide_v()
select_score: character scalar, name of score/measure to identify best fitting model
select_maximize: logical scalar, whether to maximize or minimize select_score
force: logical, Force prediction even if a prediction is found

Value

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

id_trans: Foreign key to trans_meta_t()
id_period_post: Foreign key to periods_t()
id_coord: Foreign key to coords_t()
value: Map of model (hyper) parameters

predict_trans_pot(): called for side effect; commit trans_pot_t to database

Methods (by generic)

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

Functions

predict_trans_pot(): For each viable transition in current id_run, predict the raw transition potential for a given period and store it in trans_pot_t in the database. Raw potentials are per-transition MLR3 model probabilities; they are not yet allocation-ready (not column-scaled to target rates, not row-closed to max probability of 1). Use adjusted_trans_pot_v() to obtain allocation-ready values. Set options(evoland.use_prefetch_predict=TRUE) to prefetch all predictors; this causes higher memory pressure but only needs to go to disk once. The learners have parallel_predict enabled, see mlr3::Learner: the prediction task is automatically chunked out to any future workers available.