Metadata-Version: 2.1
Name: PySRAG
Version: 0.1.2
Summary: This Python package provides tools for analyzing and processing data related to Severe Acute Respiratory Syndrome (SARS) and other respiratory viruses. It includes functions for data preprocessing, feature engineering, and training Gradient Boosting Models (GBMs) for binary or multiclass classification.
Home-page: https://github.com/joao-1988/PySRAG
Author: João Flávio Andrade Silva
Author-email: joaoflavio1988@gmail.com
License: UNKNOWN
Description: # PySRAG
        
        This Python package provides tools for analyzing and processing data related to Severe Acute Respiratory Syndrome (SARS) and other respiratory viruses. It includes functions for data preprocessing, feature engineering, and training Gradient Boosting Models (GBMs) for binary or multiclass classification.
        
        ## Getting Started
        
        These instructions will help you get started with using the PySRAG package.
        
        ### Prerequisites
        
        Before you begin, ensure you have met the following requirements:
        
        - Python 3.10.12 installed
        - Required Python packages (you can install them using `pip`):
          - `pandas==1.5.3`
          - `numpy==1.23.5`
          - `joblib==1.3.2`
          - `scikit-learn==1.2.2`
          - `lightgbm==4.0.0`
        
        <!---
        ### Installation
        
        You can install the PySRAG package using `pip`:
        
        ```bash
        pip install PySRAG
        ```
        --->
        
        ### Usage
        
        Here's an example of how to use the SRAG package:
        
        ```python
        from PySRAG.PySRAG import SRAG, GBMTrainer
        
        # from https://opendatasus.saude.gov.br/dataset/srag-2021-a-2023
        filepath = 'https://s3.sa-east-1.amazonaws.com/ckan.saude.gov.br/SRAG/2023/INFLUD23-16-10-2023.csv' 
        
        # Initialize the SRAG class
        srag = SRAG(filepath)
        
        # Generate training data
        X, y = srag.generate_training_data(lag=None, objective='multiclass')
        
        # Train a Gradient Boosting Model
        trainer = GBMTrainer(objective='multiclass', eval_metric='multi_logloss')
        trainer.fit(X, y)
        
        # Get Prevalences
        trainer.model.predict_proba(X)
        array([[0.36010109, 0.00913779, 0.01018454, 0.0413374 , 0.57923918],
               [0.26766377, 0.16900332, 0.13882407, 0.10029527, 0.32421357],
               [0.01113844, 0.0879723 , 0.00920112, 0.87940126, 0.01228688],
               ...,
               [0.02176705, 0.03438226, 0.01555221, 0.11300813, 0.81529035],
               [0.02176705, 0.03438226, 0.01555221, 0.11300813, 0.81529035],
               [0.08954213, 0.17430267, 0.041657  , 0.66829007, 0.02620812]])
        ```
        
        <!---
        For more detailed information and examples, please refer to the package documentation.
        
        ## Documentation
        
        You can find the full documentation for the SRAG package in the [docs](docs/) directory.
        
        ## Contributing
        
        If you would like to contribute to this project, please follow these steps:
        
        1. Fork the repository.
        2. Create a new branch for your feature or bug fix: `git checkout -b feature/your-feature-name`
        3. Commit your changes: `git commit -m "Add new feature"`
        4. Push to your branch: `git push origin feature/your-feature-name`
        5. Create a pull request.
        
        ## License
        
        This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
        
        ## Acknowledgments
        
        - Special thanks to the contributors and maintainers of the SRAG Analysis package.
        
        Happy coding!
        -->
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Python Software Foundation License
Description-Content-Type: text/markdown
