mctuning/myoptuna_newPar_nomlu_nbarsEvents_newBarFlags.py at main · stracka/mctuning · GitHub

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#!/usr/bin/env python
# coding: utf-8

"""
Optimization with various strategies:
For illustration, currently limited to tree ensemble classifiers

- Optuna: tree-structured Parzen estimator (TPE). Not limited to sklearn

"""


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import json
import subprocess

import logging
FORMAT = '%(asctime)-15s- %(levelname)s - %(name)s -%(message)s'
logging.basicConfig(format=FORMAT, level=logging.INFO)
logger = logging.getLogger(__name__)


# optuna
import optuna

# gpyopt
#import GPyOpt
#from GPyOpt.methods import BayesianOptimization


def objective(trial):

    mydict = {
        #"mlu_scale":[0.,1.,False],
        "top_gain":[0.1,1.5,False],
        "top_spread":[0.001,0.3,True],
        "qE_top":[0.001,0.5,True],
        "kE_top":[0.001,0.5,True],
        "bot_gain":[0.1,1.5,False],
        "bot_spread":[0.001,0.3,True],
        "qE_bot":[0.001,0.5,True],
        "kE_bot":[0.001,0.5,True],
        "gain_c":[0.001,2.5,True],
        "lambda":[0.7,2,False],
        "c_Ej":[0.5e8,2e8,False],
        "t_smearing_top":[300e-12,1e-9,True],
        "t_smearing_bot":[300e-12,1e-9,True]
        # "t_offset_a":[-5e-9,5e-9,False],
        # "t_walk_b_top":[0,5e-9,False],
        # "t_walk_b_bot":[0,5e-9,False]
    }

    params=''
    for key in mydict:
        val = trial.suggest_float(key,mydict[key][0],mydict[key][1],log=mydict[key][2]) #log se è true
        params += str(val) + ','
    params=params[:-1]

    command = "root -l -b -q macro_newPar_nomlu_nbarsEvents_newBarFlags.C+\(-1,"+ params +",104\) | grep double | awk '{print $2}' "

    print(command)
    out = subprocess.run(command,shell=True,capture_output=True)
    x = float(out.stdout.decode())

    return x


def optuna_mc(n_trials=100, timeout=600): #quando fermare ottimizzazione
    """
    https://arxiv.org/pdf/1907.10902.pdf
    https://optuna.org/
    """

    SEED = 4005

    logger.info("OPTUNA")

    print("hello")


    study = optuna.create_study(
        direction="minimize",
        sampler=optuna.samplers.TPESampler(seed=SEED),
        pruner=optuna.pruners.MedianPruner(n_warmup_steps=10),
    )
    study.optimize(objective, n_trials=n_trials, timeout=timeout)

    # fig = optuna.visualization.plot_intermediate_values(study) #non funzia
    # fig.show()

    #######################################################

    print("\n\nThis is the end!!!!!\n\n")

    logger.info(study.best_trial)
    logger.info(study.best_value)
    logger.info(study.best_params)

    bestpardict = study.best_params
    bestpar = study.best_params.values()
    bestpar_str = str(bestpar)[13:-2].replace(" ","")

    print("\n")
    command = "root -l -b -q macro_newPar_nomlu_nbarsEvents_newBarFlags.C+\(-1,"+ bestpar_str +",104\) | grep double | awk '{print $2}' "
    print(command)
    subprocess.run(command,shell=True,capture_output=True)
    print("\n")

    file = open("../results/Tuning_ParametriComuni/TuningResults_NewPar/newPar_nomlu_nbarsEvents-1_run104_BestPars.txt", "w")
    for key in bestpardict:
        val = bestpardict[key]
        file.write(key+"    "+f"{val}\n")
    file.close()

    return study.best_trial


#https://www.blopig.com/blog/wp-content/uploads/2019/10/GPyOpt-Tutorial1.html

#def obj_func(x):


#   return(out)