pycatdap 0.4.0

Python implementation of CATDAP (CATegorical Data Analysis Program)

pycatdap

AIC-based EDA and ML error analysis library for categorical data.

pycatdap is a Python implementation of CATDAP (CATegorical Data Analysis Program), developed by Sakamoto & Katsura (1980) at the Institute of Statistical Mathematics. It extends the classic CATDAP toolkit with modern exploratory data analysis (EDA) and machine learning error analysis workflows.

📖 Documentation: https://nbx-liz.github.io/pycatdap/

Why pycatdap?

Unlike general profilers (ydata-profiling, Skrub) or slice discovery tools (DivExplorer, pysubgroup), pycatdap uses AIC as its core relevance measure. This gives it four unique advantages:

Feature	Most tools	pycatdap
Variable relevance	Cramér's V, mutual info	AIC — explicit info-vs-complexity trade-off
Continuous binning	Equal-width or quantile	AIC-optimal binning
Subset discovery	Feature importance ranking	CATDAP-02 combinatorial search
Model coupling	Tied to specific frameworks	Model-agnostic (works with y_true, y_pred from anywhere)

Installation

# Core
pip install pycatdap

# With visualization (matplotlib)
pip install "pycatdap[plot]"

# With interactive Plotly figures + HTML reports
pip install "pycatdap[plotly]"

Supported: Python 3.10 / 3.11 / 3.12 / 3.13

Quickstart

Classic CATDAP

import pycatdap

df = pycatdap.datasets.load_health_data()

# CATDAP-01: pairwise AIC analysis
result = pycatdap.catdap1(df, response_names=["symptoms"])
print(result.aic_order["symptoms"])  # variables ranked by ΔAIC

# CATDAP-02: best explanatory subset
result2 = pycatdap.catdap2(
    df,
    pool=[2, 2, 2, 0, 0, 0, 0, 2],
    response_name="symptoms",
    accuracy=[0., 0., 0., 1., 1., 1., 0.1, 0.],
)
for s in result2.subsets[:3]:
    print(f"AIC={s.aic:.2f}, vars={s.variables}")

EDA (planned, v0.3+)

# Coming in v0.5
profile = pycatdap.profile(df, response="symptoms")
profile.to_html("report.html")
profile.show()  # Jupyter inline

ML error analysis (planned, v0.8+)

# Coming in v0.8
result = pycatdap.error_analysis(
    df=test_df,
    y_true=y_test,
    y_pred=model.predict(X_test),
)
result.show()                       # Jupyter
result.to_html("errors.html")       # standalone report
result.top_slices                   # natural-language cohort descriptions

Status & Roadmap

Version	Theme
v0.2.0 ✅	Core CATDAP-01/02 (released)
v0.3.0 — v0.6.0	EDA workflow (Plotly backend, profile, target analysis)
v0.7.0 — v0.11.0	ML error analysis (slice discovery, calibration, drift)
v0.12.0	LizyStudio integration
v1.0.0	API stabilization

Full roadmap: PLAN.md · Meta Issue #11

Development

git clone https://github.com/nbx-liz/pycatdap.git
cd pycatdap
uv sync --all-groups
uv run pytest                                  # tests (excluding slow R cross-validation)
uv run pytest -m slow                          # slow tests (requires R + catdap package)
uv run python -m mkdocs serve                  # local docs preview
make ci                                        # ruff + mypy + pytest + build

Contributing guidelines: CONTRIBUTING.md

Project structure

Document	Purpose
BLUEPRINT.md	Canonical specification (Japanese)
HISTORY.md	Proposal-to-decision log (Japanese)
PLAN.md	Development roadmap (Japanese)
CHANGELOG.md	Release history
docs/	Published documentation site

Citation

If you use pycatdap in research, please cite the original CATDAP work:

@article{sakamoto1980categorical,
  title={Categorical Data Analysis by AIC},
  author={Sakamoto, Yosiyuki and Katsura, Koichi},
  journal={Mathematical Sciences},
  year={1980}
}

License

MIT