diff --git a/LeQua2024/T4_domain_prevalence/dev_domain_prevalences.txt b/LeQua2024/T4_domain_prevalence/dev_domain_prevalences.txt
new file mode 100644
index 0000000..9e01017
--- /dev/null
+++ b/LeQua2024/T4_domain_prevalence/dev_domain_prevalences.txt
@@ -0,0 +1,1001 @@
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diff --git a/LeQua2024/T4_domain_prevalence/test_domain_prevalences.txt b/LeQua2024/T4_domain_prevalence/test_domain_prevalences.txt
new file mode 100644
index 0000000..873d56f
--- /dev/null
+++ b/LeQua2024/T4_domain_prevalence/test_domain_prevalences.txt
@@ -0,0 +1,5001 @@
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diff --git a/LeQua2024/util_scripts/covariate_shift_plot.py b/LeQua2024/util_scripts/covariate_shift_plot.py
new file mode 100644
index 0000000..275947e
--- /dev/null
+++ b/LeQua2024/util_scripts/covariate_shift_plot.py
@@ -0,0 +1,120 @@
+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')
diff --git a/LeQua2024/util_scripts/prior_shift_plot.py b/LeQua2024/util_scripts/prior_shift_plot.py
new file mode 100644
index 0000000..d3e3c8c
--- /dev/null
+++ b/LeQua2024/util_scripts/prior_shift_plot.py
@@ -0,0 +1,168 @@
+import os
+from os.path import join
+import pandas as pd
+
+from quapy.data.base import LabelledCollection
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')))
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), './')))
+#from LeQua2024.scripts import constants
+#from LeQua2024._lequa2024 import fetch_lequa2024
+import quapy as qp
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pathlib import Path
+import glob
+
+
+os.chdir('/home/moreo/QuaPy/LeQua2024')
+print(os.getcwd())
+
+
+qp.environ['SAMPLE_SIZE']=250
+
+TASK=1
+
+true_prevs_path = f'./TruePrevalences/T{TASK}.test_prevalences/T{TASK}/public/test_prevalences.txt'
+folder = F'./Results_CODALAB_2024/extracted/TASK_{TASK}'
+
+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 = glob.glob(f"{folder}/*.csv")
+
+
+method_names_nice={
+    'DistMatching-y': 'DM',
+    'TeamGMNet': 'UniOviedo(Team1)',
+    'tobiaslotz': 'Lamarr'
+}
+
+exclude_methods=[
+    'TeamCUFE',
+    'hustav',
+    'PCC',
+    'CC'
+]
+
+
+# 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)
+
+
+# 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_
+
+
+def load_vector_documents(path):
+    """
+    Loads vectorized documents. In case the sample is unlabelled,
+    the labels returned are None
+
+    :param path: path to the data sample containing the raw documents
+    :return: a tuple with the documents (np.ndarray of shape `(n,256)`) and the labels (a np.ndarray of shape `(n,)` if
+        the sample is labelled, or None if the sample is unlabelled), with `n` the number of instances in the sample
+        (250 for T1 and T4, 1000 for T2, and 200 for T3)
+    """
+    D = pd.read_csv(path).to_numpy(dtype=float)
+    labelled = D.shape[1] == 257
+    if labelled:
+        X, y = D[:,1:], D[:,0].astype(int).flatten()
+    else:
+        X, y = D, None
+    return X, y
+
+#train_prevalence = fetch_lequa2024(task=f'T{TASK}', data_home='./data')
+train = LabelledCollection.load(f'/home/moreo/QuaPy/LeQua2024/data/lequa2024/T{TASK}/public/training_data.txt', loader_func=load_vector_documents)
+train_prev = train.prevalence()
+#train_prev = np.tile(train_prev, (len(true_id),1))
+
+from quapy.plot import error_by_drift
+
+# load the participant and baseline results
+method_names, estim_prevs = [], []
+for method_file in method_files:
+    method_name = Path(method_file).name.replace('.csv', '')
+    if method_name in exclude_methods:
+        continue
+    id, method_prevs = load_result_file(join(folder, method_name+'.csv'))
+    assert (true_id == id).all(), f'unmatched files for {method_file}'
+    method_name = method_names_nice.get(method_name, method_name)
+    method_names.append(method_name)
+    estim_prevs.append(method_prevs)
+
+true_prevs = [true_prevs]*len(method_names)
+tr_prevs =[train.prevalence()]*len(method_names)
+error_by_drift(method_names, 
+               true_prevs, 
+               estim_prevs, 
+               tr_prevs, 
+               error_name='mrae', show_std=True,
+               show_density=True, vlines=True, savepath=f'./util_scripts/t{TASK}_{error_name}_pcc.png')
+sys.exit()
+
+shift=qp.error.ae(train_prev, true_prevs)
+
+n_bins = 5
+bins = np.linspace(shift.min(), shift.max(), n_bins + 1)
+
+# Crear un DataFrame para los datos
+df = pd.DataFrame({'dom_A_prevs': shift})
+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)
+
+# Configurar etiquetas del eje X con los rangos de los bins
+plt.xticks(ticks=range(n_bins), labels=bin_labels, rotation=0)
+plt.xlabel("Amount of PPS between the training prevalence and the test prevalences, in terms of AE ")
+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'./util_scripts/t{TASK}_{error_name}_pcc.png')