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Score Record Pairs with a Trained Model

Source: R/il_score_pairs.R
il_score_pairs.Rd

Scores every pair formed by one record from records_l and one record from records_r, without blocking. This is useful for scoring a single known pair, checking a handful of candidate matches, or clerical review. For blocked scoring of new records against a model's data, use il_find_matches().

Usage

il_score_pairs(model, records_l, records_r, con = NULL)

Arguments

model

A trained il_model object.

records_l, records_r

Data frames of records to compare. Every record in records_l is compared with every record in records_r. A unique_id column is added when missing.

con

A DBI connection object from DBI::dbConnect(). Defaults to the model's connection, or a temporary DuckDB connection when the model has none.

Value

An il_compared tibble with one row per pair, containing unique_id_l and unique_id_r (ids within records_l and records_r), match_weight, total_match_weight, match_probability, the comparison levels, and the compared fields.

Details

Term-frequency adjustments use the model's own term-frequency tables, which come from its full data, rather than frequencies within the few records being scored. A model with term-frequency comparisons therefore needs its data attached, via il_model() or il_attach(), or its tables registered with il_register_tf().

Examples

con <- DBI::dbConnect(duckdb::duckdb())
#> duckdb keeps downloaded extensions and secrets in a temporary directory:
#> ℹ /tmp/RtmpAun1I8/duckdb
#> This is removed when the R session ends.
#> • Extensions are re-downloaded each session.
#> • Secrets are lost.
#> ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
#> ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
#> ℹ See ?duckdb_storage for details and alternatives.
spec <- il_spec() |>
  il_compare(first_name, cl_jaro_winkler(0.9, 0.7)) |>
  il_compare(surname, cl_jaro_winkler(0.9, 0.7)) |>
  il_compare(dob, cl_exact()) |>
  il_block_on(surname)
model <- il_model(fake_1000, spec = spec, con = con) |>
  il_estimate_u() |>
  il_estimate_em(block_on(dob))
#> EM trained: first_name and surname | skipped (blocked on): dob

il_score_pairs(
  model,
  data.frame(first_name = 'Jon', surname = 'Smith', dob = '1990-01-15'),
  data.frame(
    first_name = c('John', 'Jane'),
    surname = c('Smith', 'Smyth'),
    dob = c('1990-01-15', '1985-06-02')
  )
)
#> # A tibble: 2 × 14
#>   unique_id_l unique_id_r gamma_first_name gamma_surname gamma_dob match_weight
#> *       <int>       <int>            <int>         <int>     <int>        <dbl>
#> 1           1           2                1             1         0         3.13
#> 2           1           1                2             2         1        20.8 
#> # ℹ 8 more variables: total_match_weight <dbl>, match_probability <dbl>,
#> #   first_name_l <chr>, surname_l <chr>, dob_l <chr>, first_name_r <chr>,
#> #   surname_r <chr>, dob_r <chr>
DBI::dbDisconnect(con, shutdown = TRUE)