Metadata-Version: 2.4
Name: ml4fmri
Version: 0.2.0
Summary: Machine learning models for fMRI classification
Author: Pavel Popov
License: MIT
Project-URL: Homepage, https://github.com/neuroneural/meanMLP/tree/ml4fmri
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: torch>=2.1
Requires-Dist: numpy>=1.24
Requires-Dist: matplotlib>=3.7
Requires-Dist: scikit-learn>=1.3
Requires-Dist: pandas>=1.5
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: ipykernel; extra == "dev"

# ml4fmri
This repo contains implementation of several __deep learning models__ for fMRI data analysis, gathered and packaged together for the ease of experimentation. Originally based on the codebase behind the NeuroImage paper ["A simple but tough-to-beat baseline for fMRI time-series classification"](https://doi.org/10.1016/j.neuroimage.2024.120909).

TODO: Explain how to use, add tutorials. Add polyssifier-like functionality.

# Use example
```pip install ml4fmri```

```
# in python, get fMRI time series data in shape (samples, time, n_features)
# and labels in shape (samples) (binary or multiclass)
from ml4fmri import cvbench # runs CV experiments with implemented models on the given data
report = cvbench(data, labels, models='all', n_folds=5)
report.plot_scores()
```
