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Create Transition Models Table

Source: R/trans_models_t.R
trans_models_t.Rd

Creates a trans_models_t table for storing transition model metadata and serialized model objects. This function creates an empty table with proper structure for storing fitted models via the mlr3 interface.

Usage

as_trans_models_t(x)

fit_partial_models(
  self,
  learner,
  measures,
  sample_frac = 0.7,
  seed = NULL,
  cluster = NULL
)

fit_full_models(
  self,
  learner = NULL,
  select_score = NULL,
  select_maximize = TRUE,
  cluster = NULL
)

# S3 method for class 'trans_models_t'
print(x, ...)

get_crossval_plots(self, id_run = NULL, id_trans = NULL)

Arguments

x

A list or data.frame coercible to a data.table

self

evoland_db instance

learner

An mlr3 Learner or AutoTuner object for direct-learner mode that supports twoclass classification. Its predict_type is coerced to "prob" if not already set. Must be NULL when select_score is provided.

measures

Either a character vector of mlr3 measure IDs (e.g. c("classif.auc", "classif.acc")) or a list of instantiated mlr3 Measure objects (e.g. list(mlr3::msr("classif.auc"))). Character IDs are converted via mlr3::msrs() internally. Results are written to crossval_score.

sample_frac

Numeric between 0 and 1 indicating the fraction of data to use for training the partial models. The rest is used for testing and calculating goodness-of-fit metrics. Default is 0.7 (70% training, 30% testing).

seed

Optional integer seed for reproducible subsampling.

cluster

An optional cluster object created by parallel::makeCluster() or mirai::make_cluster().

select_score

Character string; mlr3 measure ID (e.g. "classif.auc") used to rank partial models in score-select mode. Must be NULL when learner is provided.

select_maximize

Logical; if TRUE (default) the model with the highest select_score is selected; if FALSE, the lowest. Only used in score-select mode.

...

ignored

id_run

Optional integer; filter by run ID.

id_trans

Optional integer; filter by transition ID.

Value

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

  • id_run: Foreign key to runs_t

  • id_trans: Foreign key to trans_meta_t

  • learner_id: mlr3 twoclass LearnerClassif key, e.g. "classif.ranger"

  • learner_params: MAP of atomic scalar learner hyperparameters for querying; complete hyperparameters are captured by learner_spec

  • learner_spec: BLOB of serialized untrained mlr3 Learner; for AutoTuners, this is the optimal inner learner after tuning

  • crossval_score: MAP of cross-validation performance scores (from prediction$score(measures))

  • crossval_predictions: BLOB of serialized mlr3 PredictionClassif on the held-out test split

  • learner_full: BLOB of serialized trained mlr3 Learner fitted on the full dataset, used for extrapolation

Methods (by generic)

  • print(trans_models_t): Print a trans_models_t object as yaml-style list; additional arguments silently ignored

Functions

  • fit_partial_models(): Fit partial (cross-validation) models for each viable transition; returns a trans_models_t object with one row per viable transition, containing the learner identity, serialized spec, cross-validation scores (crossval_score), and serialized held-out predictions (crossval_predictions).

  • fit_full_models(): Fit full models (trained on the complete dataset) for each viable transition and return a trans_models_t object with learner_full populated. Two mutually exclusive modes are supported:

    • Direct-learner mode (learner provided, select_score omitted): a fresh clone of learner is trained on the full data for each transition. crossval_score and crossval_predictions will be NULL in the result. Does not require a prior call to fit_partial_models().

    • Score-select mode (select_score provided, learner omitted): selects the best partial model per transition by select_score, reconstructs its learner from learner_spec, and retrains on the full data. Requires fit_partial_models() to have been run first.

  • get_crossval_plots(): Deserialize cross-validation predictions and return plots via mlr3viz::autoplot(). Requires the mlr3viz package.