comparison_viewer_dashboard

At a glance

API Documentation: comparison_viewer_dashboard()

Worked Example

import splink.comparison_library as cl
import splink.comparison_template_library as ctl
from splink import DuckDBAPI, Linker, SettingsCreator, block_on, splink_datasets

df = splink_datasets.fake_1000

settings = SettingsCreator(
    link_type="dedupe_only",
    comparisons=[
        cl.JaroWinklerAtThresholds("first_name", [0.9, 0.7]),
        cl.JaroAtThresholds("surname", [0.9, 0.7]),
        ctl.DateComparison(
            "dob",
            input_is_string=True,
            datetime_metrics=["year", "month"],
            datetime_thresholds=[1, 1],
        ),
        cl.ExactMatch("city").configure(term_frequency_adjustments=True),
        ctl.EmailComparison("email"),
    ],
    blocking_rules_to_generate_predictions=[
        block_on("substr(first_name,1,1)"),
        block_on("substr(surname, 1,1)"),
    ],
    retain_intermediate_calculation_columns=True,
    retain_matching_columns=True,
)

linker = Linker(df, settings, DuckDBAPI())
linker.training.estimate_u_using_random_sampling(max_pairs=1e6)

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

linker.training.estimate_parameters_using_expectation_maximisation(
    blocking_rule_for_training
)

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

df_predictions = linker.inference.predict(threshold_match_probability=0.2)

linker.visualisations.comparison_viewer_dashboard(
    df_predictions, "img/scv.html", overwrite=True
)

# You can view the scv.html file in your browser, or inline in a notebook as follows
from IPython.display import IFrame
IFrame(
    src="./img/scv.html", width="100%", height=1200
)

What the chart shows

See the following video: An introduction to the Splink Comparison Viewer dashboard