uk_address_matcher 1.0.0.dev19

A package for matching UK addresses using a pretrained Splink model

High performance UK addresses matcher (geocoder)

Extremely fast address matching using a pre-trained Splink model.

Full time taken: 11.05 seconds
to match 176,640 messy addresses to 273,832 canonical addresses
at a rate of 15,008 addresses per second

(On Macbook M4 Max)

Installation

At the moment this uses a branch of Splink only available on Github.

pip install --pre uk_address_matcher

Usage

High performance address matching using a pre-trained Splink model.

Assuming you have two duckdb dataframes in this format:

unique_id	address_concat	postcode
1	123 Fake Street, Faketown	FA1 2KE
2	456 Other Road, Otherville	NO1 3WY
...	...	...

Basic Matching

Match them with:

import duckdb

from uk_address_matcher import (
    clean_data_using_precomputed_rel_tok_freq,
    get_linker,
    best_matches_with_distinguishability,
    improve_predictions_using_distinguishing_tokens,
)

p_ch = "./example_data/companies_house_addresess_postcode_overlap.parquet"
p_fhrs = "./example_data/fhrs_addresses_sample.parquet"

con = duckdb.connect(database=":memory:")

df_ch = con.read_parquet(p_ch).order("postcode")
df_fhrs = con.read_parquet(p_fhrs).order("postcode")

df_ch_clean = clean_data_using_precomputed_rel_tok_freq(df_ch, con=con)
df_fhrs_clean = clean_data_using_precomputed_rel_tok_freq(df_fhrs, con=con)

linker = get_linker(
    df_addresses_to_match=df_fhrs_clean,
    df_addresses_to_search_within=df_ch_clean,
    con=con,
    include_full_postcode_block=True,
    additional_columns_to_retain=["original_address_concat"],
)

# First pass - standard probabilistic linkage
df_predict = linker.inference.predict(
    threshold_match_weight=-50, experimental_optimisation=True
)
df_predict_ddb = df_predict.as_duckdbpyrelation()

# Second pass - improve predictions using distinguishing tokens

df_predict_improved = improve_predictions_using_distinguishing_tokens(
    df_predict=df_predict_ddb,
    con=con,
    match_weight_threshold=-20,
)

# Find best matches within group and compute distinguishability

best_matches = best_matches_with_distinguishability(
    df_predict=df_predict_improved,
    df_addresses_to_match=df_fhrs,
    con=con,
)

best_matches

Two-Pass Matching Approach

The package uses a two-pass approach to achieve high accuracy matching:

1. First Pass: A standard probabilistic linkage model using Splink generates candidate matches for each input address.

2. Second Pass: Within each candidate group, the model analyzes distinguishing tokens to refine matches:

   • Identifies tokens that uniquely distinguish addresses within a candidate group
   • Detects "punishment tokens" (tokens in the messy address that don't match the current candidate but do match other candidates)
   • Uses this contextual information to improve match scores

This approach is particularly effective when matching to a canonical (deduplicated) address list, as it can identify subtle differences between very similar addresses.

Refer to the example, which has detailed comments, for how to match your data.

See an example of comparing two addresses to get a sense of what it does/how it scores

Run an interactive example in your browser:

Match 5,000 FHRS records to 21,952 companies house records in < 10 seconds.

Investigate and understand how the model works

Development

The scripts and tests will run better if you create .vscode/settings.json with the following:

{
    "jupyter.notebookFileRoot": "${workspaceFolder}",
    "python.analysis.extraPaths": [
        "${workspaceFolder}"
    ],
    "python.testing.pytestEnabled": true,
    "python.testing.unittestEnabled": false,
    "python.testing.pytestArgs": [
        "-v",
        "--capture=tee-sys"
    ]
}