Inspect the frozen warm-start writing feature schema
Source:R/warm_start_feature_schema.R
warm_start_feature_schema.RdWarm-start prediction uses text features to predict relative writing quality before collecting pairwise comparisons. This function lists the frozen predictor definitions; it does not extract features or require Python.
Value
A tibble with one row per feature in fixed predictor order. Columns:
schema,position,feature: version, integer order, and canonical name.source_package,source_version,component,upstream_field: audited upstream mapping.family,type,unit,interpretation: meaning of the feature.configuration,definition,requirements,missing_behavior: fixed calculation settings, formula, capabilities, and undefined values.source_url: upstream implementation reference.
Details
Version 1 describes English writing using TextDescriptives and a supplementary textstat readability measure. The schema records definitions, not evidence of predictive validity for any particular writing population.
upstream_entropy_per_token divides TextDescriptives' entropy by the number
of all spaCy tokens, including punctuation and whitespace tokens. This is an
average of upstream probability-weighted contributions, not normalized
document Shannon entropy or language-model cross-entropy. Its denominator
differs from the filtered n_tokens feature. Zero tokens give a missing value.
Definitions, settings, membership, and ordering are frozen. Changes require a new schema identifier rather than silently modifying version 1. Optional Python software is needed only for extraction, not schema inspection or prediction from precomputed features.
See also
extract_warm_start_features(), fit_warm_start_model()
Other adaptive warm start:
ensemble_warm_start_models(),
extract_warm_start_features(),
fit_warm_start_model(),
make_warm_start_cv_plan(),
make_warm_start_prior(),
pairwiseLLM_warm_model,
predict.pairwiseLLM_warm_ensemble(),
predict.pairwiseLLM_warm_model(),
prepare_warm_start_model(),
register_warm_start_model(),
save_warm_start_model(),
summary.pairwiseLLM_warm_ensemble(),
summary.pairwiseLLM_warm_predictions(),
warm_start_coefficients(),
warm_start_python_status()
Examples
features <- warm_start_feature_schema()
features[c("position", "feature", "family")]
#> # A tibble: 20 × 3
#> position feature family
#> <int> <chr> <chr>
#> 1 1 n_tokens length_productivity
#> 2 2 proportion_unique_tokens lexical_diversity
#> 3 3 token_length_mean lexical_surface_complexity
#> 4 4 token_length_std lexical_surface_complexity
#> 5 5 sentence_length_mean sentence_syntactic_complexity
#> 6 6 sentence_length_std sentence_syntactic_complexity
#> 7 7 pos_prop_noun pos_composition
#> 8 8 pos_prop_verb pos_composition
#> 9 9 pos_prop_adj pos_composition
#> 10 10 pos_prop_adv pos_composition
#> 11 11 pos_prop_pron pos_composition
#> 12 12 pos_prop_adp pos_composition
#> 13 13 pos_prop_cconj pos_composition
#> 14 14 pos_prop_sconj pos_composition
#> 15 15 dependency_distance_mean dependency_characteristics
#> 16 16 dependency_distance_std dependency_characteristics
#> 17 17 prop_adjacent_dependency_relation_mean dependency_characteristics
#> 18 18 upstream_entropy_per_token information_theory
#> 19 19 first_order_coherence semantic_coherence
#> 20 20 dale_chall_readability_score readability