Computes a full suite of classification metrics at a range of match-probability thresholds. Requires labeled pairs.
Arguments
- model
A trained
il_modelobject.- labels
A data frame of labeled pairs with a logical or integer match indicator. Required unless
labels_colis provided.- labels_col
Optional string naming a column in the original data containing ground-truth cluster/entity IDs. When provided, pairwise labels are derived automatically via
labels_from_column().
Value
A tibble::tibble() with one row per threshold, containing columns
threshold, tp, fp, fn, tn, fn_blocking_miss,
precision, recall, f1, f2, f0_5, specificity, npv,
accuracy, p4, and phi.
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_accuracy(model, labels = labels)
#> # A tibble: 3 × 16
#> threshold tp fp fn tn fn_blocking_miss precision recall f1
#> <dbl> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 0 1 1 0 0 0 0.5 1 0.667
#> 2 0.998 1 1 0 0 0 0.5 1 0.667
#> 3 1 0 0 1 1 0 1 0 0
#> # ℹ 7 more variables: f2 <dbl>, f0_5 <dbl>, specificity <dbl>, npv <dbl>,
#> # accuracy <dbl>, p4 <dbl>, phi <dbl>
DBI::dbDisconnect(con, shutdown = TRUE)