Compute Precision-Recall Curve Data

Returns a tidy tibble of precision and recall values at each match-probability threshold. Requires labeled pairs. Designed for use with ggplot2::geom_line().

Usage

il_precision_recall(model, labels = NULL, labels_col = NULL)

Arguments

model

A trained il_model object.

labels

A data frame of labeled pairs with a logical or integer match indicator. Required unless labels_col is provided.

labels_col

Optional string naming a column in the original data containing ground-truth cluster/entity IDs.

Value

A tibble::tibble() with columns threshold, precision, and recall.

Examples

df <- data.frame(
  unique_id = 1:20,
  first_name = c(
    'John', 'Jon', 'Jane', 'Jane', 'Bob',
    'Bobby', 'Alice', 'Alicia', 'Tom', 'Thomas',
    'John', 'Jon', 'Jane', 'Janet', 'Bob',
    'Robert', 'Alice', 'Alison', 'Tom', 'Tomas'
  ),
  surname = c(
    'Smith', 'Smith', 'Doe', 'Doe', 'Jones',
    'Jones', 'Brown', 'Brown', 'White', 'White',
    'Smith', 'Smyth', 'Doe', 'Doe', 'Jones',
    'Jones', 'Brown', 'Browne', 'White', 'White'
  ),
  dob = c(
    '1990-01-01', '1990-01-01', '1985-06-15', '1985-06-15',
    '2000-12-01', '2000-12-01', '1975-03-22', '1975-03-22',
    '1988-07-04', '1988-07-04', '1990-01-01', '1990-01-02',
    '1985-06-15', '1985-06-16', '2000-12-01', '2000-12-02',
    '1975-03-22', '1975-03-23', '1988-07-04', '1988-07-05'
  ),
  city = c(
    'London', 'London', 'Paris', 'Paris', 'Berlin',
    'Berlin', 'Rome', 'Rome', 'Madrid', 'Madrid',
    'London', 'London', 'Paris', 'Paris', 'Berlin',
    'Berlin', 'Rome', 'Rome', 'Madrid', 'Madrid'
  ),
  email = c(
    'john@example.com', 'jon@example.com', 'jane@example.com',
    'jane@example.com', 'bob@example.com', 'bobby@example.com',
    'alice@example.com', 'alicia@example.com', 'tom@example.com',
    'thomas@example.com', 'john@example.com', 'jon@example.com',
    'jane@example.com', 'janet@example.com', 'bob@example.com',
    'robert@example.com', 'alice@example.com', 'alison@example.com',
    'tom@example.com', 'tomas@example.com'
  )
)
con <- DBI::dbConnect(duckdb::duckdb())
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) |>
  il_block_on(first_name)
model <- il_model(df, spec = spec, con = con)
model <- il_estimate_u(model)
model <- il_estimate_em(model, block_on(surname))
#> EM trained: first_name and dob | skipped (blocked on): surname
labels <- data.frame(
  unique_id_l = c(1L, 1L),
  unique_id_r = c(11L, 2L),
  is_match = c(1L, 0L)
)

il_precision_recall(model, labels = labels)
#> # A tibble: 3 × 3
#>   threshold precision recall
#>       <dbl>     <dbl>  <dbl>
#> 1     0           0.5      1
#> 2     0.998       0.5      1
#> 3     1           1        0
DBI::dbDisconnect(con, shutdown = TRUE)