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Deduplicate 50k rows historial 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.duckdb.duckdb_linker import DuckDBLinker
import altair as alt
alt.renderers.enable('mimetype')

import pandas as pd 
pd.options.display.max_rows = 1000
df = pd.read_parquet("./data/historical_figures_with_errors_50k.parquet")
# Simple settings dictionary will be used for exploratory analysis
settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        "l.first_name = r.first_name and l.surname = r.surname",
        "l.surname = r.surname and l.dob = r.dob",
        "l.first_name = r.first_name and l.dob = r.dob",
        "l.postcode_fake = r.postcode_fake and l.first_name = r.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.duckdb_comparison_library as cl

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        "l.first_name = r.first_name and l.surname = r.surname",
        "l.surname = r.surname and l.dob = r.dob",
        "l.first_name = r.first_name and l.dob = r.dob",
        "l.postcode_fake = r.postcode_fake and l.first_name = r.first_name",
    ],
    "comparisons": [
        cl.jaro_winkler_at_thresholds("first_name", [0.9, 0.7], term_frequency_adjustments=True),
        cl.jaro_winkler_at_thresholds("surname", [0.9, 0.7], term_frequency_adjustments=True),
        cl.levenshtein_at_thresholds("dob", [1,2], term_frequency_adjustments=True),
        cl.levenshtein_at_thresholds("postcode_fake", 2,term_frequency_adjustments=True),
        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, connection=":temporary:")
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(target_rows=5e6)
----- Estimating u probabilities using random sampling -----

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).

blocking_rule = "l.first_name = r.first_name and l.surname = r.surname"
training_session_names = linker.estimate_parameters_using_expectation_maximisation(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 -0.524 in probability_two_random_records_match
Iteration 2: Largest change in params was -0.0339 in probability_two_random_records_match
Iteration 3: Largest change in params was 0.0119 in the m_probability of birth_place, level `Exact match`
Iteration 4: Largest change in params was 0.00374 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).

blocking_rule = "l.dob = r.dob"
training_session_dob = linker.estimate_parameters_using_expectation_maximisation(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

Iteration 1: Largest change in params was -0.362 in the m_probability of first_name, level `Exact match`
Iteration 2: Largest change in params was -0.0302 in the m_probability of first_name, level `Exact match`
Iteration 3: Largest change in params was 0.00453 in the m_probability of first_name, 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
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 match_key
0 18.131419 0.999997 Q2296770-1 Q2296770-14 thomas thomas 3 0.028667 0.028667 44.380695 ... 1.000000 1.000000 politician politician 1 0.088932 0.088932 22.786409 0.443341 0
1 40.311237 1.000000 Q1443188-1 Q1443188-3 frank frank 3 0.006335 0.006335 44.380695 ... 0.154553 1.000000 liturgist liturgist 1 0.000237 0.000237 22.786409 166.178966 0
2 40.311237 1.000000 Q1443188-2 Q1443188-3 frank frank 3 0.006335 0.006335 44.380695 ... 0.154553 1.000000 liturgist liturgist 1 0.000237 0.000237 22.786409 166.178966 0
3 41.258395 1.000000 Q90404618-1 Q90404618-3 emlie emlie 3 0.000099 0.000099 44.380695 ... 178.565257 4.701184 playwright playwright 1 0.002728 0.002728 22.786409 14.450345 0
4 41.258395 1.000000 Q90404618-2 Q90404618-3 emlie emlie 3 0.000099 0.000099 44.380695 ... 178.565257 4.701184 playwright playwright 1 0.002728 0.002728 22.786409 14.450345 0

5 rows × 47 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 638
Completed iteration 2, root rows count 122
Completed iteration 3, root rows count 35
Completed iteration 4, root rows count 6
Completed iteration 5, root rows count 0

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

from IPython.display import IFrame

IFrame(
    src="./50k_cluster.html", width="100%", height=1200
)  
linker.roc_chart_from_labels_column("cluster",match_weight_round_to_nearest=0.02)
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)