Deduplicate 50k rows historical persons

Linking a dataset of real historical persons¶

In this example, we deduplicate a more realistic dataset. The data is based on historical persons scraped from wikidata. Duplicate records are introduced with a variety of errors introduced.

from splink.datasets import splink_datasets
from splink.duckdb.linker import DuckDBLinker
import altair as alt

import pandas as pd 
pd.options.display.max_rows = 1000
df = splink_datasets.historical_50k

from splink.duckdb.blocking_rule_library import block_on

# Simple settings dictionary will be used for exploratory analysis
settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        block_on(["first_name", "surname"]),
        block_on(["surname", "dob"]),
        block_on(["first_name", "dob"]),
        block_on(["postcode_fake", "first_name"]),
    ],
}
linker = DuckDBLinker(df, settings)

linker.profile_columns(
    ["first_name", "postcode_fake", "substr(dob, 1,4)"], top_n=10, bottom_n=5
)

linker.cumulative_num_comparisons_from_blocking_rules_chart()

import splink.duckdb.comparison_template_library as ctl
import splink.duckdb.comparison_library as cl

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        block_on(["first_name", "surname"]),
        block_on(["surname", "dob"]),
        block_on(["first_name", "dob"]),
        block_on(["postcode_fake", "first_name"]),
    ],
    "comparisons": [
        ctl.name_comparison("first_name", term_frequency_adjustments=True),
        ctl.name_comparison("surname", term_frequency_adjustments=True),
        ctl.date_comparison("dob", cast_strings_to_date=True, invalid_dates_as_null=True),
        ctl.postcode_comparison("postcode_fake"),
        cl.exact_match("birth_place", term_frequency_adjustments=True),
        cl.exact_match("occupation",  term_frequency_adjustments=True),
    ],
    "retain_matching_columns": True,
    "retain_intermediate_calculation_columns": True,
    "max_iterations": 10,
    "em_convergence": 0.01
}

linker = DuckDBLinker(df, settings)

linker.estimate_probability_two_random_records_match(
    [
        "l.first_name = r.first_name and l.surname = r.surname and l.dob = r.dob",
        "substr(l.first_name,1,2) = substr(r.first_name,1,2) and l.surname = r.surname and substr(l.postcode_fake,1,2) = substr(r.postcode_fake,1,2)",
        "l.dob = r.dob and l.postcode_fake = r.postcode_fake",
    ],
    recall=0.6,
)

Probability two random records match is estimated to be  0.000136.
This means that amongst all possible pairwise record comparisons, one in 7,362.31 are expected to match.  With 1,279,041,753 total possible comparisons, we expect a total of around 173,728.33 matching pairs

linker.estimate_u_using_random_sampling(max_pairs=5e6)

----- Estimating u probabilities using random sampling -----



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Estimated u probabilities using random sampling

Your model is not yet fully trained. Missing estimates for:
    - first_name (no m values are trained).
    - surname (no m values are trained).
    - dob (no m values are trained).
    - postcode_fake (no m values are trained).
    - birth_place (no m values are trained).
    - occupation (no m values are trained).

training_blocking_rule = block_on(["first_name", "surname"])
training_session_names = linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)

----- Starting EM training session -----

Estimating the m probabilities of the model by blocking on:
(l."first_name" = r."first_name") AND (l."surname" = r."surname")

Parameter estimates will be made for the following comparison(s):
    - dob
    - postcode_fake
    - birth_place
    - occupation

Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: 
    - first_name
    - surname

Iteration 1: Largest change in params was -0.52 in probability_two_random_records_match
Iteration 2: Largest change in params was -0.0446 in the m_probability of birth_place, level `All other comparisons`
Iteration 3: Largest change in params was -0.0185 in the m_probability of birth_place, level `All other comparisons`
Iteration 4: Largest change in params was 0.00717 in the m_probability of birth_place, level `Exact match`

EM converged after 4 iterations

Your model is not yet fully trained. Missing estimates for:
    - first_name (no m values are trained).
    - surname (no m values are trained).

training_blocking_rule = block_on("dob")
training_session_dob = linker.estimate_parameters_using_expectation_maximisation(training_blocking_rule)

----- Starting EM training session -----

Estimating the m probabilities of the model by blocking on:
l."dob" = r."dob"

Parameter estimates will be made for the following comparison(s):
    - first_name
    - surname
    - postcode_fake
    - birth_place
    - occupation

Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: 
    - dob



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Iteration 1: Largest change in params was -0.362 in the m_probability of first_name, level `Exact match first_name`
Iteration 2: Largest change in params was 0.0426 in the m_probability of first_name, level `All other comparisons`
Iteration 3: Largest change in params was 0.00794 in the m_probability of surname, level `All other comparisons`

EM converged after 3 iterations

Your model is fully trained. All comparisons have at least one estimate for their m and u values

The final match weights can be viewed in the match weights chart:

linker.match_weights_chart()

linker.unlinkables_chart()

df_predict = linker.predict()
df_e = df_predict.as_pandas_dataframe(limit=5)
df_e

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	match_weight	match_probability	unique_id_l	unique_id_r	first_name_l	first_name_r	gamma_first_name	tf_first_name_l	tf_first_name_r	bf_first_name	...	bf_birth_place	bf_tf_adj_birth_place	occupation_l	occupation_r	gamma_occupation	tf_occupation_l	tf_occupation_r	bf_occupation	bf_tf_adj_occupation
0	12.731320	0.999853	Q2296770-1	Q2296770-14	thomas	thomas	4	0.028667	0.028667	43.19637	...	1.000000	1.0	politician	politician	1	0.088932	0.088932	25.927656	0.388868
1	21.634289	1.000000	Q2296770-3	Q2296770-7	tom	tom	4	0.012948	0.012948	43.19637	...	0.159229	1.0	politician	NaN	-1	0.088932	NaN	1.000000	1.000000
2	9.131145	0.998220	Q2296770-10	Q2296770-14	thomas	thomas	4	0.028667	0.028667	43.19637	...	1.000000	1.0	politician	politician	1	0.088932	0.088932	25.927656	0.388868
3	13.443772	0.999910	Q2296770-13	Q2296770-7	tom	tom	4	0.012948	0.012948	43.19637	...	0.159229	1.0	politician	NaN	-1	0.088932	NaN	1.000000	1.000000
4	35.771776	1.000000	Q1443188-1	Q1443188-3	frank	frank	4	0.006335	0.006335	43.19637	...	0.159229	1.0	liturgist	liturgist	1	0.000237	0.000237	25.927656	145.760504

5 rows × 41 columns

You can also view rows in this dataset as a waterfall chart as follows:

from splink.charts import waterfall_chart
records_to_plot = df_e.to_dict(orient="records")
linker.waterfall_chart(records_to_plot, filter_nulls=False)

clusters = linker.cluster_pairwise_predictions_at_threshold(df_predict, threshold_match_probability=0.95)

Completed iteration 1, root rows count 671
Completed iteration 2, root rows count 147
Completed iteration 3, root rows count 43
Completed iteration 4, root rows count 12
Completed iteration 5, root rows count 3
Completed iteration 6, root rows count 2
Completed iteration 7, root rows count 1
Completed iteration 8, root rows count 0

linker.cluster_studio_dashboard(df_predict, clusters, "dashboards/50k_cluster.html", sampling_method='by_cluster_size', overwrite=True)

from IPython.display import IFrame

IFrame(
    src="./dashboards/50k_cluster.html", width="100%", height=1200
)

linker.roc_chart_from_labels_column("cluster",match_weight_round_to_nearest=0.02)

FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))

records = linker.prediction_errors_from_labels_column(
    "cluster",
    threshold=0.999,
    include_false_negatives=False,
    include_false_positives=True,
).as_record_dict()
linker.waterfall_chart(records)

# Some of the false negatives will be because they weren't detected by the blocking rules
records = linker.prediction_errors_from_labels_column(
    "cluster",
    threshold=0.5,
    include_false_negatives=True,
    include_false_positives=False,
).as_record_dict(limit=50)

linker.waterfall_chart(records)