Divisi - Interactive Subgroup Discovery

Divisi is a tool to find interpretable patterns in large datasets that can be expressed as tabular features (for example, transactions, survey responses, electronic health records, or text documents). It runs faster than existing rule-based subgroup discovery algorithms and has an interactive interface to help you probe and curate subgroups of interest. Check out the paper (CHI 2025) to learn more.

Quickstart

Optionally create a virtual environment with Python >3.7. Install the package:

pip install divisi-toolkit

Install Jupyter Notebook or Jupyter Lab if not already installed. Then start a Jupyter server. The example_data/demo.ipynb notebook shows how to start the interactive widget or use the subgroup discovery algorithm programmatically.

Usage

To run Divisi, you first need to create a preprocessed, discretized version of your dataset. The easiest way is to take a Pandas dataframe and run the discretize_data command:

import divisi

discrete_df = divisi.discretize_data(
    df, 
    custom_cols={
        # Specify custom discretization strategies here
        'Age': divisi.bin_values(quantiles=5),
        # ...
    }, 
    remove_cols=[
        # Specify columns to remove from subgroup discovery
        'Label'
        # ...
    ])

If you have a text dataset, you can also use the discretize_token_sets method. (TODO provide example of text encoding)

Then, to use the Divisi interface in a notebook, simply create a DivisiWidget instance:

w = divisi.DivisiWidget(
    discrete_df, 
    # provide a path to store interface state so you can pick up where you left off
    state_path="divisi_state",
    # metrics to display for each subgroup (must be numpy arrays)
    metrics={
        "Label": y,
        "Error": is_error
    })
w

By default, ranking functions will be created based on the metrics you provide. You can also provide ranking functions using the ranking_functions keyword argument to the DivisiWidget constructor. The following ranking functions are available in divisi.ranking:

• OutcomeRate(y: ndarray, inverse: bool = false): Prioritizes subgroups with a higher rate of the given binary outcome y within the subgroup. If inverse is True, prioritizes subgroups with a lower rate.
• OutcomeShare(y: ndarray): Prioritizes subgroups that capture more of the positive instances of the binary outcome y. Helps to measure coverage of the subgroup.
• InteractionEffect(y: ndarray): Prioritizes subgroups for which all rule features contribute highly to the rate of the given binary outcome.
• MeanDifference(y: ndarray): Prioritizes subgroups which have a mean of the given continuous metric y substantially different from the average.
• Entropy(y: ndarray, inverse: bool = false): Prioritizes subgroups with a lower (or, if inverse is True, higher) entropy for the given integer-valued metric y inside the subgroup than outside.
• SubgroupSize(ideal_fraction: number, spread: number): Scores subgroups by their size according to a Gaussian curve with a mean of ideal_fraction and a standard deviation of spread.
• SimpleRule(): Prioritizes subgroups defined by rules with fewer features.

Programmatic Usage

To generate subgroups using pure Python without the interface, initialize an instance of SamplingSubgroupSearch with the discretized data object, ranking functions, and any search parameters, then run the sampler:

finder = divisi.sampling.SamplingSubgroupSearch(
    discrete_df,
    {
        "High True Labels": divisi.ranking.OutcomeRate(y),
        "High Errors": divisi.ranking.OutcomeRate(is_error),
        "Simple Rule": divisi.ranking.SimpleRule()
    },
    # additional sampling options
    min_items_fraction=0.05
    # ...
)

results, _ = finder.sample(50)

After running the sampler, you can re-rank the results based on the provided ranking functions without rerunning the search:

for rule in results.rank({"High True Labels": 1.0, "Simple Rule": 0.25}):
    # rule.feature gets the predicate, rule.score_values contains the scores for each ranking function
    print(rule)
    # make a boolean mask over the dataframe corresponding to the rule
    mask = discrete_df.mask_for_rule(rule)

Citation

Please use the following citation if using Divisi in your projects:

@inproceedings{sivaraman2025divisi,
	title = {{Divisi: Interactive Search and Visualization for Scalable Exploratory Subgroup Analysis}},
	author = {Sivaraman, Venkatesh and Li, Zexuan and Perer, Adam},
	year = {2025},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    doi = {10.1145/3706598.3713103},
    booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems},
    numpages = {17},
    location = {Yokohama, Japan},
    series = {CHI '25}
}

If you have a cool use case for Divisi, tell us about it!

Running in Development Mode

To develop the frontend, make sure you have an up-to-date version of NodeJS in your terminal, then run:

cd client
npm install
vite

The vite command starts a live hot-reload server for the frontend. Then, when you initialize the DivisiWidget, pass the dev=True keyword argument to use the live server. (Make sure that you don't have anything else running on port 5173 while you do this.)