# Copyright 2026, Niquel Mendoza | Leo.
# https://www.mql5.com/es/users/nique_372
# trainer_regression.py

#+------------------------------------------------------------------+
#| Imports                                                          |
#+------------------------------------------------------------------+
from final_trainer import CPipelineTraining
from final_trainer import SimpleLogger
from final_trainer import CMqlComunication
import argparse
import json 
from pathlib import Path 
import sys



# nota
# cuando se pasa a .exe hacerlo con todas las dependecias de los otros folders
#pyinstaller --onefile AiDataGenByLeo\Py\run_training.py
#$env:PYTHONPATH = "C:\Users\leoxd\AppData\Roaming\MetaQuotes\Terminal\D0E8209F77C8CF37AD8BF550E51FF075\MQL5\Shared Projects;C:\Users\leoxd\AppData\Roaming\MetaQuotes\Terminal\D0E8209F77C8CF37AD8BF550E51FF075\MQL5\Shared Projects\AiDataGenByLeo\Py"
#+------------------------------------------------------------------+
#| Funcion principal de ejecucion                                   |
#+------------------------------------------------------------------+
def main():
    parser = argparse.ArgumentParser() 
    
    # argrumentos requeridos
    parser.add_argument('--config', required=True)
    parser.add_argument("--results_json", required=True)
    parser.add_argument("--lock_bin",  required=True)
    parser.add_argument('--log_file', required=False, default=None)  # opcional
    
    # args
    args = parser.parse_args() 
    
    # logs
    if args.log_file:
        log_file = open(args.log_file, "w", encoding="utf-8")
        sys.stdout = log_file
        sys.stderr = log_file
    
    # Clase comunicador
    config_comunicador : dict = {
        "path_json" : args.results_json,
        "path_bin" : args.lock_bin
    }
    
    comunicador : CMqlComunication = CMqlComunication(config_comunicador)

    # Cargamos la configuracion
    cfg = json.loads(Path(args.config).read_text(encoding="utf-16"))

    # Creamos el pipeline de entreminto
    ejecutor : CPipelineTraining = CPipelineTraining(
        cfg['general'],
        cfg['regresion'],
        cfg['clasificacion'],
        SimpleLogger.CLoggerBase.LOG_ALL,
        comunicador
    )
    
    # Ejecutamos el entreamiento
    if not ejecutor.Execute():         
        print("Fallo al ejecutar el pipeline")
        
 
#+------------------------------------------------------------------+
#| Main                                                             |
#+------------------------------------------------------------------+
if __name__ == "__main__":        
    main()