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Getting started

The documentation site is built with MkDocs Material and reads the local Python modules directly from the repository root.

Install docs dependencies

python -m pip install -r requirements-docs.txt

Preview the site

mkdocs serve

Build a static version

mkdocs build

What you will find

  • A conceptual guide for the main noise models handled by the library.
  • A dedicated page for TabPFN-based filtering and local explainability.
  • An evaluation guide for 5-fold experiments, persisted noise masks, and continuous noise scores.
  • An API reference generated directly from the public docstrings.

Minimal examples

Generate label noise and keep its true mask:

from noisers.funcs import urlf

y_noisy, noise_mask = urlf(y, noise_level=0.2, random_state=42, return_mask=True)

Run a filter in detection mode:

from filters import ENNFilter

filt = ENNFilter(action="detect")
filt.fit_resample(X, y_noisy)
report = filt.get_detection_report()

Combine multiple filters with FEF:

from filters import ENNFilter, CVCFFilter, FilterEnsembleFilter

fef = FilterEnsembleFilter(
    base_filters=[("ENN", ENNFilter()), ("CVCF", CVCFFilter())],
    strategy="majority",
)
X_clean, y_clean = fef.fit_resample(X, y_noisy)

Tip

Run MkDocs from the repository root so the local filters and cleaners packages can be imported without any extra packaging step.