PySRAG 0.1.3

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.

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:

pip install PySRAG

Usage

Here's an example of how to use the SRAG package:

from pysrag.data import SRAG
from pysrag.model import 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]])