Installation

Prerequisites

Before installing Ageas, please ensure your system meets the following requirements:

  • Python: Version 3.11 or newer is required.

  • Hardware: A CUDA-capable GPU is recommended for the neural-network classifiers (NN_Classifier, Mixer_Classifier) and for SHAP-based explanation of XGBoost models. The classical classifiers run comfortably on CPU.

Install from Source

The latest development version can be installed directly from GitHub:

gh repo clone MaftyLab/Ageas
cd Ageas
pip install .

This command will automatically resolve and install all required core dependencies (PyTorch, PyTorch Lightning, XGBoost, scikit-learn, imbalanced-learn, AnnData, SHAP, Captum, and others).

Verifying the Installation

A quick sanity check uses the synthetic data helper bundled with the ageas.tool module:

import ageas
from ageas.tool import make_fake_adata, Multimodal_Corpus

adata = make_fake_adata(n_cells=200, n_genes=50, n_class=3)
corpus = Multimodal_Corpus(adata=adata, label_key='celltype')
print(corpus.label_dict)

If the corpus prints a {0: 0, 1: 1, 2: 2}-style label dictionary, the package and its core dependencies are correctly installed.