Work in Progress
This page is currently under construction.
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'))
