Installation

pip install maldideepkit

maldiamrkit is a core dependency and is installed automatically - MaldiDeepKit duck-types on the maldiamrkit.MaldiSet data model and reuses maldiamrkit.alignment.Warping for leak-safe spectral warping.

MaldiDeepKit requires Python 3.10 - 3.13 and pulls in PyTorch (≥ 2.0), scikit-learn, einops, numpy, pandas, scipy, and matplotlib as its core runtime dependencies.

Optional extras

The uncertainty-quantification estimators in maldideepkit.uncertainty work out of the box for Monte Carlo Dropout and split conformal prediction. The Laplace approximation estimator additionally requires the optional laplace-torch dependency, available via the uncertainty extra:

pip install "maldideepkit[uncertainty]"

GPU vs. CPU

Every classifier runs on CPU, which is what the project’s continuous integration tests against. All four architectures benefit significantly from CUDA when training on ~6000-bin spectra; point a classifier at a GPU by passing device="cuda" at construction, or leave the default device="auto" to pick the best available.

Development Installation

git clone https://github.com/EttoreRocchi/MaldiDeepKit.git
cd MaldiDeepKit
pip install -e ".[dev]"
pre-commit install

See Contributing for coding conventions, testing, and PR guidelines.

Documentation Build

To build the documentation locally:

pip install -e ".[docs]"
make docs           # builds HTML into docs/build/html
make docs-serve     # same, then serves on http://localhost:8080

The rendered site lands in docs/build/html/index.html.