Usage Examples
==============

This section provides detailed examples of how to use the LimeSoDa package for various soil mapping tasks.

Basic Usage
-----------

First, let's import the necessary modules and load a dataset:

.. code-block:: python

   import numpy as np
   import pandas as pd
   from sklearn.linear_model import LinearRegression
   from sklearn.metrics import r2_score, mean_squared_error
   from LimeSoDa import load_dataset
   from LimeSoDa.utils import split_dataset

   def basic_usage():
       """Basic usage example with 10-fold CV"""
       # Set random seed
       np.random.seed(2025)
       
       # Load dataset
       BB_250 = load_dataset('BB.250')

       # Perform 10-fold CV
       y_true_all = []
       y_pred_all = []
       
       for fold in range(1, 11):
           X_train, X_test, y_train, y_test = split_dataset(BB_250, fold=fold, targets='SOC_target')
           
           model = LinearRegression()
           model.fit(X_train, y_train)
           y_pred = model.predict(X_test)
           
           y_true_all.extend(y_test.values)
           y_pred_all.extend(y_pred)

       # Calculate overall performance
       y_true_all = np.array(y_true_all)
       y_pred_all = np.array(y_pred_all)
       mean_r2 = r2_score(y_true_all, y_pred_all)
       mean_rmse = np.sqrt(mean_squared_error(y_true_all, y_pred_all))
       std_r2 = 0  
       std_rmse = 0

       return mean_r2, std_r2, mean_rmse, std_rmse

   if __name__ == "__main__":
       mean_r2, std_r2, mean_rmse, std_rmse = basic_usage()
       print("\nSOC prediction (10-fold CV):")
       print(f"Mean R-squared: {mean_r2:.7f} ± {std_r2:.7f}") # Mean R-squared: 0.7507837 ± 0.0000000
       print(f"Mean RMSE: {mean_rmse:.7f} ± {std_rmse:.7f}") # Mean RMSE: 0.2448791 ± 0.0000000