Metadata-Version: 2.4
Name: ltfmselector
Version: 0.1.1
Summary: Locally-Tailored Feature and Model Selector with Deep Q-Learning
Author-email: RenZhen95 <j-liaw@hotmail.com>
License-File: LICENSE
Requires-Python: >=3.12
Requires-Dist: numpy>=2.2.4
Requires-Dist: openpyxl>=3.1.5
Requires-Dist: pandas>=2.2.3
Requires-Dist: scikit-learn>=1.6.1
Requires-Dist: torch>=2.6.0
Description-Content-Type: text/markdown

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# LTFMSelector
Locally-Tailored Feature and Model Selector with Deep Q-Learning

## Installation
```
pip install ltfmselector
```

## Basic usage
```python
from ltfmselector import LTFMSelector

# Initialize an agent to learn to selects features and models, specifically tailored to each example
AgentSelector = LTFMSelector(<#episodes>, pType=<'classification', 'regression'>)

# Fit
AgentSelector(<X: pd.DataFrame>, <y: pd.Series>)

# Predict
y_pred, doc = AgentSelector.predict(<X_test: pd.DataFrame>)
```

For more examples check out the [examples](https://github.com/RenZhen95/ltfmselector/tree/master/examples).

## Citing LTFMSelector
This library is implemented based on the work presented in this abstract:

J.C. Liaw, C.Z. Chaing, D. Raab, M. Siebler, H. Hefter, D. Zietz, M. Jäger, A. Kecskeméthy, F. Geu Flores. Interdisciplinary Gait Assessment with Patient-Specific Feature and Model Selection via Reinforcement Learning. 11. IFToMM D-A-CH Konferenz 2025, 20./21. Februar 2025, FH Kärnten, Villach. [HTML](https://doi.org/10.17185/duepublico/82941)
