improving plots for overview paper
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@ -2,31 +2,31 @@ import os
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from os.path import join
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import pandas as pd
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from scripts.data import load_vector_documents
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from quapy.data.base import LabelledCollection
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import sys
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')))
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), './')))
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# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')))
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# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))
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# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), './')))
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#from LeQua2024.scripts import constants
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#from LeQua2024._lequa2024 import fetch_lequa2024
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import quapy as qp
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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# import seaborn as sns
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from pathlib import Path
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import glob
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from scripts.constants import SAMPLE_SIZE
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os.chdir('/home/moreo/QuaPy/LeQua2024')
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print(os.getcwd())
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# os.chdir('/home/moreo/QuaPy/LeQua2024')
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# print(os.getcwd())
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TASK=2
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qp.environ['SAMPLE_SIZE']=SAMPLE_SIZE[f'T{TASK}']
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qp.environ['SAMPLE_SIZE']=250
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TASK=1
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true_prevs_path = f'./TruePrevalences/T{TASK}.test_prevalences/T{TASK}/public/test_prevalences.txt'
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folder = F'./Results_CODALAB_2024/extracted/TASK_{TASK}'
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true_prevs_path = f'../TruePrevalences/T{TASK}.test_prevalences/T{TASK}/public/test_prevalences.txt'
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folder = F'../Results_CODALAB_2024/extracted/TASK_{TASK}'
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def load_result_file(path):
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df = pd.read_csv(path, index_col=0)
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@ -85,30 +85,12 @@ else:
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err_function = err_function_
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def load_vector_documents(path):
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"""
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Loads vectorized documents. In case the sample is unlabelled,
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the labels returned are None
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:param path: path to the data sample containing the raw documents
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:return: a tuple with the documents (np.ndarray of shape `(n,256)`) and the labels (a np.ndarray of shape `(n,)` if
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the sample is labelled, or None if the sample is unlabelled), with `n` the number of instances in the sample
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(250 for T1 and T4, 1000 for T2, and 200 for T3)
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"""
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D = pd.read_csv(path).to_numpy(dtype=float)
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labelled = D.shape[1] == 257
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if labelled:
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X, y = D[:,1:], D[:,0].astype(int).flatten()
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else:
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X, y = D, None
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return X, y
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#train_prevalence = fetch_lequa2024(task=f'T{TASK}', data_home='./data')
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train = LabelledCollection.load(f'/home/moreo/QuaPy/LeQua2024/data/lequa2024/T{TASK}/public/training_data.txt', loader_func=load_vector_documents)
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train = LabelledCollection.load(f'../data/lequa2024/T{TASK}/public/training_data.txt', loader_func=load_vector_documents)
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train_prev = train.prevalence()
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#train_prev = np.tile(train_prev, (len(true_id),1))
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from quapy.plot import error_by_drift
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from quapy.plot import error_by_drift, binary_diagonal
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# load the participant and baseline results
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method_names, estim_prevs = [], []
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@ -123,46 +105,17 @@ for method_file in method_files:
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estim_prevs.append(method_prevs)
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true_prevs = [true_prevs]*len(method_names)
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savepath = f'./t{TASK}_diagonal.png'
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if TASK==1:
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binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,
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train_prev=train.prevalence(), savepath=savepath, method_order=None)
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tr_prevs =[train.prevalence()]*len(method_names)
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error_by_drift(method_names,
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true_prevs,
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estim_prevs,
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tr_prevs,
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error_name='mrae', show_std=True,
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show_density=True, vlines=True, savepath=f'./util_scripts/t{TASK}_{error_name}_pcc.png')
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sys.exit()
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shift=qp.error.ae(train_prev, true_prevs)
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n_bins = 5
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bins = np.linspace(shift.min(), shift.max(), n_bins + 1)
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# Crear un DataFrame para los datos
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df = pd.DataFrame({'dom_A_prevs': shift})
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for method, err in errors.items():
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df[method] = err
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# Asignar cada valor de dom_A_prevs a un bin
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df['bin'] = pd.cut(df['dom_A_prevs'], bins=bins, labels=False, include_lowest=True)
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# Convertir el DataFrame a formato largo
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df_long = df.melt(id_vars=['dom_A_prevs', 'bin'], value_vars=errors.keys(), var_name='Método', value_name='Error')
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# Crear etiquetas de los bins para el eje X
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bin_labels = [f"[{bins[i]:.3f}-{bins[i + 1]:.3f}" + (']' if i == n_bins-1 else ')') for i in range(n_bins)]
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df_long['bin_label'] = df_long['bin'].map(dict(enumerate(bin_labels)))
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# Crear el gráfico de boxplot en Seaborn
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plt.figure(figsize=(14, 8))
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sns.boxplot(x='bin', y='Error', hue='Método', data=df_long, palette='Set2', showfliers=False)
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# Configurar etiquetas del eje X con los rangos de los bins
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plt.xticks(ticks=range(n_bins), labels=bin_labels, rotation=0)
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plt.xlabel("Amount of PPS between the training prevalence and the test prevalences, in terms of AE ")
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plt.ylabel(error_name)
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#plt.title("Boxplots de Errores por Método dentro de Bins de dom_A_prevs")
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plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
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plt.tight_layout()
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plt.grid(True, which='both', linestyle='--', linewidth=0.5)
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#plt.show()
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plt.savefig(f'./util_scripts/t{TASK}_{error_name}_pcc.png')
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savepath = f'./t{TASK}_{error_name}_pps.png'
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error_by_drift(method_names,
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true_prevs,
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estim_prevs,
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tr_prevs, title=None,
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error_name='rae', show_std=True, n_bins=1000,
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show_density=True, vlines=[tr_prevs[0][1]], savepath=savepath)
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