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parameter_estimate_comparisons_chart

At a glance

Useful for: Looking at the m and u value estimates across multiple Splink model training sessions.

API Documentation: parameter_estimate_comparisons_chart()

What is needed to generate the chart? A trained Splink model.

Worked Example

from splink.duckdb.linker import DuckDBLinker
import splink.duckdb.comparison_library as cl
import splink.duckdb.comparison_template_library as ctl
from splink.duckdb.blocking_rule_library import block_on
from splink.datasets import splink_datasets
import logging, sys
logging.disable(sys.maxsize)

df = splink_datasets.fake_1000

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        block_on("first_name"),
        block_on("surname"),
    ],
    "comparisons": [
        ctl.name_comparison("first_name"),
        ctl.name_comparison("surname"),
        ctl.date_comparison("dob", cast_strings_to_date=True),
        cl.exact_match("city", term_frequency_adjustments=True),
        ctl.email_comparison("email", include_username_fuzzy_level=False),
    ],
}

linker = DuckDBLinker(df, settings)
linker.estimate_u_using_random_sampling(max_pairs=1e6)

blocking_rule_for_training = block_on(["first_name", "surname"])

linker.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)

blocking_rule_for_training = block_on("dob")
linker.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)

blocking_rule_for_training = block_on("email")
linker.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)


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

What the chart shows

How to interpret the chart

Actions to take as a result of the chart