Metadata-Version: 2.4
Name: ltfmselector
Version: 0.1.7
Summary: Locally-Tailored Feature and Model Selector with Deep Q-Learning
Project-URL: GitHub, https://github.com/RenZhen95/ltfmselector/
Author-email: RenZhen95 <j-liaw@hotmail.com>
License: MIT License
        
        Copyright (c) 2025 Jin Cheng Liaw
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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License-File: LICENSE
Requires-Python: >=3.12
Requires-Dist: gymnasium>=1.1.1
Requires-Dist: matplotlib>=3.10.1
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: seaborn>=0.13.2
Requires-Dist: torch>=2.6.0
Description-Content-Type: text/markdown

﻿<h1>
  <picture>
    <img alt="LTFMSelectorLogo" src="icons/icon.png" width="550px">
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</h1>

# 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)
