Calibrate matches end-to-end (features -> filter -> apply)

High-level Q5 verb. Builds features via match_features(), fits a Filter_Model via fit_filter(), and applies it via apply_filter() to return a Calibrated_Matches object enriched with tp_prob / predicted_tp. Dispatches on the strategy class.

Usage

calibrate_matches(matches, strategy, ...)

Arguments

matches

Match output table (data.table / tibble / data.frame / DuckDB lazy tbl).

strategy

The search_strategy() or embedding_strategy() used to produce matches.

...

Method-specific arguments. Required: labels (manually labelled rows produced by import_labels()), base, and id. Optional: target, target_id (forwarded to match_features()), model, class_weighted, na_fill, threshold, plus all match_features() tuning knobs (top_n, include_string_sim, include_block_stats, method).

Value

A Calibrated_Matches object.

Examples

strat <- search_strategy(
  workshop   ~ normalize_text() + word_tokens(min_nchar = 3),
  proprietor ~ normalize_text() + word_tokens(min_nchar = 2),
  block_by  = c("postcode_area", "trade"),
  threshold = 0.30
)
matches <- search_candidates(
  workshop_listings, workshop_register,
  base_id = "listing_id", target_id = "reg_no", strategy = strat
)
# One call: build features, fit the filter, apply it. Uses the shipped
# labelled pairs, which line up with this exact search.
calibrate_matches(matches, strat, labels = match_labels_example,
                  base = workshop_listings, id = "listing_id",
                  target = workshop_register, target_id = "reg_no")
#> 
#> ── Calibrated_Matches ──────────────────────────────────────────────────────────
#> <joinery::Calibrated_Matches>
#> threshold : 0.7237 (method: youden_j)
#> n_rows : 1930
#> predicted_tp == 1: 1232
#> predicted_tp == 0: 698
#> tp_prob quantiles: 0.000 / 0.016 / 0.972 / 0.998 / 1.000