Ports the "hard" histogram variant of HistNetQ (from
https://github.com/pglez84/histnetq) into quapy/method/_histnet.py, dropping
that repo's quantificationlib-backed bag generators in favor of QuaPy's own
sampling protocols (UPP by default). Implemented as a BaseQuantifier,
alongside QuaNet, since it trains end-to-end on samples of known prevalence
rather than following the classify-then-aggregate pattern.
- HistNetQ.fit(X, y): resamples training/validation bags from a
LabelledCollection via a configurable protocol (UPP by default; fresh
random bags each training epoch, a fixed reproducible sequence for
validation).
- HistNetQ.fit_from_samples(protocol, val_protocol=None, mix_bags=False):
trains directly from a protocol that already yields bags (e.g. LeQua's
SamplesFromDir), with an optional mixer to synthesize extra
intermediate-prevalence bags from the given ones.
- Aliased in meta.py (torch-optional, mirroring the existing QuaNet guard)
and registered in META_METHODS.
- Adds test_histnetq covering both entry points on binary and multiclass
synthetic data.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Alejandro Moreo Fernandez
24719ed0af