import os
from os.path import join
import pandas as pd
import quapy as qp
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

os.chdir('/home/moreo/QuaPy/LeQua2024/util_scripts')
print(os.getcwd())

qp.environ['SAMPLE_SIZE']=250

true_prevs_path = '../TruePrevalences/T4.test_prevalences/T4/public/test_prevalences.txt'
domain_prevs_path = '../T4_domain_prevalence/test_domain_prevalences.txt'
folder = '../Results_CODALAB_2024/extracted/TASK_4'

def load_result_file(path):
    df = pd.read_csv(path, index_col=0)
    id = df.index.to_numpy()
    prevs = df.values
    return id, prevs

method_files = [
    #'ACC.csv',
    #'CC.csv',
    #'DistMatching-y.csv',
    #'KDEy.csv',
    #'PACC.csv',
    'PCC.csv',
    #'SLD.csv',
    #'TeamCUFE.csv',
    #'TeamGMNet.csv',
    'tobiaslotz.csv'
]

method_names_nice={
    'DistMatching-y': 'DM',
    'TeamGMNet': 'UniOviedo(Team1)',
    'tobiaslotz': 'Lamarr'
}

desired_order=[
    'Lamarr',
    'SLD',
    'DM',
    'KDEy',
    'UniOviedo(Team1)'
]
desired_order=[
    'PCC', 'Lamarr'
]

# load the true values (sentiment prevalence, domain prevalence)
true_id, true_prevs = load_result_file(true_prevs_path)
dom_id, dom_prevs = load_result_file(domain_prevs_path)
assert (true_id == dom_id).all(), 'unmatched files'

# define the loss for evaluation
error_name = 'RAE'
error_log = False

if error_name == 'RAE':
    err_function_ = qp.error.rae
elif error_name == 'AE':
    err_function_ = qp.error.ae
else:
    raise ValueError()

if error_log:
    error_name = f'log({error_name})'
    err_function = lambda x,y: np.log(err_function_(x,y))
else:
    err_function = err_function_

# load the participant and baseline results
errors = {}
for method_file in method_files:
    method_name = method_file.replace('.csv', '')
    id, method_prevs = load_result_file(join(folder, method_file))
    print(method_file)
    assert (true_id == id).all(), f'unmatched files for {method_file}'
    method_error = err_function(true_prevs, method_prevs)
    method_name = method_names_nice.get(method_name, method_name)
    errors[method_name] = method_error

dom_A_prevs = dom_prevs[:,0]

n_bins = 5
bins = np.linspace(dom_A_prevs.min(), dom_A_prevs.max(), n_bins + 1)

# Crear un DataFrame para los datos
df = pd.DataFrame({'dom_A_prevs': dom_A_prevs})
for method, err in errors.items():
    df[method] = err

# Asignar cada valor de dom_A_prevs a un bin
df['bin'] = pd.cut(df['dom_A_prevs'], bins=bins, labels=False, include_lowest=True)

# Convertir el DataFrame a formato largo
df_long = df.melt(id_vars=['dom_A_prevs', 'bin'], value_vars=errors.keys(), var_name='Método', value_name='Error')

# Crear etiquetas de los bins para el eje X
bin_labels = [f"[{bins[i]:.3f}-{bins[i + 1]:.3f}" + (']' if i == n_bins-1 else ')') for i in range(n_bins)]
df_long['bin_label'] = df_long['bin'].map(dict(enumerate(bin_labels)))

# Crear el gráfico de boxplot en Seaborn
plt.figure(figsize=(14, 8))
sns.boxplot(x='bin', y='Error', hue='Método', data=df_long, palette='Set2', showfliers=False, hue_order=desired_order)

# Configurar etiquetas del eje X con los rangos de los bins
plt.xticks(ticks=range(n_bins), labels=bin_labels, rotation=0)
plt.xlabel("Prevalence of Books")
plt.ylabel(error_name)
#plt.title("Boxplots de Errores por Método dentro de Bins de dom_A_prevs")
plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
plt.tight_layout()
plt.grid(True, which='both', linestyle='--', linewidth=0.5)
#plt.show()
plt.savefig(f'./t4_{error_name}_pcc.png')