Metadata-Version: 2.1
Name: PySRAG
Version: 0.1.9
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: MIT
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 installed
        - Required Python packages (you can install them using `pip`):
          - `pandas==1.5.3`
          - `numpy==1.23.5`
          - `scikit-learn==1.2.2`
          - `lightgbm==4.0.0`
        
        ### Installation
        
        You can install the PySRAG package using `pip`:
        
        ```python
        pip install PySRAG
        ```
        
        ### Usage
        
        Here's an example of how to use the SRAG package:
        
        ```python
        from pysrag.data import SRAG
        from pysrag.model import GBMTrainer
        
        # from https://opendatasus.saude.gov.br/dataset/srag-2021-a-2024
        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!
        -->
        
        ## Web Application
        
        The PySRAG package includes a web application that allows users to interactively explore data related to Severe Acute Respiratory Syndrome (SARS) in Brazil. This web-based interface provides a practical way for users to visualize data without needing deep technical knowledge of Python or the underlying code.
        
        ### Accessing the Web Application
        
        To access the web application, visit:
        
        [PySRAG Web App](https://joaoflavio.shinyapps.io/Virus_Monitor/)
        
        This link will take you to a hosted version of our application, equipped with preloaded data and features for easy exploration.
         
        [![](webapp_PySRAG.png)](https://joaoflavio.shinyapps.io/Virus_Monitor/)
        
        ### Features
        
        The web application offers the following features:
        
        - **Data Visualization**: Interactive graphs display processed data, giving insights into the distribution of respiratory viruses.
        - **Data Filtering**: Users can apply filters based on city and patient age to narrow down the data and focus on specific demographics or regions.
        
        ### How to Use
        
        1. **Navigate to the Dashboard**: Start on the dashboard, which provides an overview of the visualizations.
        2. **Apply Filters**: Use the filtering options to select specific cities or age ranges to view customized data visualizations.
        4. **Explore Visualizations**: Interact with the visual data representations to gain deeper insights into the trends and patterns.
        
        ### Support
        
        If you encounter any issues while using the web application or have suggestions for improvements, please submit an issue on our [GitHub page](https://github.com/joao-1988/PySRAG/issues).
        
        This web application is designed to make the data analysis capabilities of the PySRAG package accessible to both technical and non-technical users, enhancing understanding and facilitating research on respiratory viruses.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
