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# AMR prediction with LGBMClassifier models
This repository contains a Python script for predicting antimicrobial resistance (AMR) using the LGBMClassifier model. The script reads input datasets from a directory, applies feature extraction techniques to obtain k-mer features, trains and tests the models using cross-validation, and outputs the results in text files.
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
### Prerequisites
This script requires the following Python libraries:
pandas
scikit-learn
numpy
tqdm
lightgbm
hyperopt
joblib
bayesian-optimization
skopt
### Installing
Clone the repository to your local machine and install the required libraries:
```bash
$ git clone https://github.com/username/repo.git
$ cd repo
$ pip install -r requirements.txt
```
### Usage
To use the script, execute the following command:
css
Copy code
```bash
$ python main.py
```
## Code Structure
The main script consists of several sections:
1 Import necessary libraries
2 Set seed for reproducibility
3 Define function to get list of models to evaluate
4 Load list of selected samples
5 Call function to get list of models
6 Initialize KFold cross-validation
7 Iterate over values of k to read the corresponding k-mer feature dataset
8 Iterate over the models list
9 Write results to text file
## Data Description
The input datasets are CSV files containing bacterial genomic sequences and their corresponding resistance profiles for selected antibiotics. The script reads these files from a directory and applies k-mer feature extraction techniques to obtain numerical feature vectors.
## Models
The script uses two models for AMR prediction: Random Forest and LGBMClassifier.
## Output
The script outputs the results of each model to a text file in the specified output directory. The results include accuracy, precision, recall, F1 score, and area under the ROC curve.
## Authors
Gabriel Sousa - gabrieltxs
## License
This project is licensed under the MIT License - see the LICENSE.md file for details.
[](https://choosealicense.com/licenses/mit/)
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