cluster_studio_dashboard

At a glance

API Documentation: cluster_studio_dashboard()

Worked Example

```python import splink.comparison_library as cl 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]), cl.DateOfBirthComparison( "dob", input_is_string=True, datetime_metrics=["year", "month"], datetime_thresholds=[1, 1], ), cl.ExactMatch("city").configure(term_frequency_adjustments=True), cl.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) df_clusters = linker.clustering.cluster_pairwise_predictions_at_threshold( df_predictions, threshold_match_probability=0.5 )

linker.visualisations.cluster_studio_dashboard( df_predictions, df_clusters, "img/cluster_studio.html", sampling_method="by_cluster_size", 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/cluster_studio.html", width="100%", height=1200)

```

What the chart shows

See here for a video explanation of the chart.