| # 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/) | |