diff --git a/.gitignore b/.gitignore
index 84f1054e..bac538bb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -58,6 +58,7 @@ qolmat/.converge
 examples/*.ipynb
 examples/figures/*
 examples/data/*
+!examples/data/imp_pred/
 examples/local
 
 
@@ -66,3 +67,4 @@ examples/local
 
 # Logs
 nohup.txt
+examples/mlruns/*
diff --git a/environment.ci.yml b/environment.ci.yml
index 86949837..5488e6f2 100644
--- a/environment.ci.yml
+++ b/environment.ci.yml
@@ -6,6 +6,7 @@ dependencies:
     - codecov
     - flake8
     - matplotlib
+    - plotly
     - mypy
     - numpy
     - numpydoc
diff --git a/environment.dev.yml b/environment.dev.yml
index 9c14ff64..093ae7bb 100644
--- a/environment.dev.yml
+++ b/environment.dev.yml
@@ -34,4 +34,8 @@ dependencies:
           - pytest-cov==4.0.0
           - pytest-mock==3.10.0
           - sphinx_markdown_tables==0.0.17
+          - plotly
+          - xgboost
+          - torch
+          - datasets
           - -e .
diff --git a/examples/benchmark_impu_predicter.md b/examples/benchmark_impu_predicter.md
new file mode 100644
index 00000000..da519578
--- /dev/null
+++ b/examples/benchmark_impu_predicter.md
@@ -0,0 +1,1374 @@
+---
+jupyter:
+  jupytext:
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.14.4
+  kernelspec:
+    display_name: qolmat-_zMstDTT
+    language: python
+    name: python3
+---
+
+```python
+%reload_ext autoreload
+%autoreload 2
+
+import sys
+sys.path.append('/home/ec2-user/qolmat/')
+
+# import warnings
+# warnings.filterwarnings('error')
+import pandas as pd
+import numpy as np
+import pickle
+from scipy import stats
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+import scikit_posthocs as sp
+
+from datasets import load_dataset
+
+import qolmat.benchmark.imputer_predictor as imppred
+```
+
+# Load data
+
+```python
+# # from urllib import request
+# # import zipfile
+
+# # data_url = "https://archive.ics.uci.edu/static/public/20/census+income.zip"
+# data_path = "data/census+income"
+# # request.urlretrieve(data_url, data_path + ".zip")
+# # with zipfile.ZipFile(data_path + ".zip", "r") as zip_ref:
+# #     zip_ref.extractall(data_path)
+
+# data_types = {'age': 'int32', 'workclass': 'string', 'fnlwgt': 'float32', 'education': 'string', 'education-num': 'int32', 'marital-status': 'string', 'occupation': 'string', 'relationship': 'string', 'race': 'string', 'sex': 'string', 'capital-gain': 'float32', 'capital-loss': 'float32', 'hours-per-week': 'int32', 'native-country': 'string', 'income': 'string'}
+# df_data = pd.read_csv(data_path+"/adult.data", header=None, names=data_types.keys(), dtype=data_types)
+
+# columns_categorical = df_data.dtypes[(df_data.dtypes=='string')].index.to_list()
+# columns_numerical = df_data.dtypes[(df_data.dtypes=='float32') | (df_data.dtypes=='int32')].index.to_list()
+
+# print(f'df shape: {df_data.shape}, cols cat: {len(columns_categorical)}, cols num: {len(columns_numerical)}')
+```
+
+```python
+# data_path = "data/conductors.csv"
+# df_data = pd.read_csv(data_path)
+
+# columns_categorical = df_data.dtypes[(df_data.dtypes=='int64')].index.to_list()
+# columns_numerical = df_data.dtypes[(df_data.dtypes=='float64')].index.to_list()
+
+# print(f'df shape: {df_data.shape}, cols cat: {len(columns_categorical)}, cols num: {len(columns_numerical)}')
+```
+
+```python
+# from datasets import load_dataset
+
+# dataset = load_dataset("inria-soda/tabular-benchmark", data_files="reg_num/wine_quality.csv")
+# df_data = dataset["train"].to_pandas()
+# column_target = df_data.columns.to_list()[-1]
+# columns_numerical = df_data.select_dtypes(include='number').columns.tolist()
+# columns_categorical = df_data.select_dtypes(include='object').columns.tolist()
+
+# print(f'df shape: {df_data.shape}, cols cat: {len(columns_categorical)}, cols num: {len(columns_numerical)}')
+```
+
+# Experiment
+
+```python
+# from sklearn.compose import ColumnTransformer
+# from sklearn import preprocessing
+
+# from qolmat.benchmark import missing_patterns
+# from qolmat.imputations import imputers, imputers_pytorch
+# from qolmat.imputations.diffusions import ddpms
+
+# from sklearn.linear_model import Ridge, RidgeClassifier
+# from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
+# from xgboost import XGBClassifier, XGBRegressor
+# from lightgbm import LGBMRegressor, LGBMClassifier
+
+# # Hole generators
+# hole_generators = [
+#     # None,
+#     # missing_patterns.MCAR(ratio_masked=0.1),
+#     missing_patterns.MCAR(ratio_masked=0.3),
+#     # missing_patterns.MCAR(ratio_masked=0.5),
+#     # missing_patterns.MCAR(ratio_masked=0.7),
+# ]
+
+# imputation_pipelines = [None,
+#                         # {"imputer": imputers.ImputerMean()},
+#                         {"imputer": imputers.ImputerEM(max_iter_em=2, method='mle')},
+#                         ]
+
+# # Prediction pipelines
+# transformers = []
+# columns_numerical_ = [col for col in columns_numerical if col != column_target]
+# if len(columns_numerical_) != 0:
+#     transformers.append(("num", preprocessing.StandardScaler(), columns_numerical_))
+# columns_categorical_ = [col for col in columns_categorical if col != column_target]
+# if len(columns_categorical) != 0:
+#     transformers.append(("cat", preprocessing.OrdinalEncoder(), columns_categorical_))
+# transformer_prediction_x = ColumnTransformer(transformers=transformers)
+
+# target_prediction_pipeline_pairs = {}
+
+# if column_target in columns_numerical:
+#     transformer_prediction_y = ColumnTransformer(
+#         transformers=[
+#             ("y_num", preprocessing.StandardScaler(), [column_target]),
+#         ]
+#     )
+#     target_prediction_pipeline_pairs[column_target] = [
+#         {
+#             "transformer_x": transformer_prediction_x,
+#             "transformer_y": transformer_prediction_y,
+#             "predictor": Ridge(),
+#             "handle_nan": False,
+#         },
+#     ]
+
+# benchmark = imppred.BenchmarkImputationPrediction(
+#     n_masks=1,
+#     n_folds=2,
+#     imputation_metrics=["mae", "KL_columnwise"],
+#     prediction_metrics=["mae"],
+# )
+
+# results = benchmark.compare(
+#     df_data=df_data.iloc[:1000],
+#     columns_numerical=columns_numerical,
+#     columns_categorical=columns_categorical,
+#     file_path=f"data/benchmark_prediction.pkl",
+#     hole_generators=hole_generators,
+#     imputation_pipelines=imputation_pipelines,
+#     target_prediction_pipeline_pairs=target_prediction_pipeline_pairs,
+# )
+
+# results = pd.read_pickle('data/benchmark_prediction.pkl')
+# results_agg = imppred.get_benchmark_aggregate(results, cols_groupby=['hole_generator', 'ratio_masked', 'imputer', 'predictor'])
+# results_agg
+```
+
+## Computational time
+
+```python
+# from qolmat.imputations import imputers, imputers_pytorch
+# from qolmat.imputations.diffusions import ddpms
+# from qolmat.benchmark import missing_patterns
+# from xgboost import XGBRegressor
+# import time
+
+# imputers = [
+#     imputers.ImputerMedian(),
+#     imputers.ImputerShuffle(),
+#     imputers.ImputerMICE(estimator=XGBRegressor(tree_method="hist", n_jobs=1), max_iter=100),
+#     imputers.ImputerKNN(),
+#     imputers.ImputerRPCA(max_iterations=100),
+#     imputers.ImputerEM(max_iter_em=100, method="mle"),
+#     imputers_pytorch.ImputerDiffusion(model=ddpms.TabDDPM(num_sampling=50), batch_size=1000, epochs=100)
+#     ]
+
+# benchmark_duration_rows = []
+# num_cols = 5
+# for num_rows in [100, 150]:
+#     df_sub_data = df_data.iloc[:num_rows, :num_cols]
+#     hole_generator = missing_patterns.MCAR(ratio_masked=0.1)
+#     df_sub_mask = hole_generator.split(df_sub_data)[0]
+#     df_sub_data[df_sub_mask] = np.nan
+
+#     for imputer in imputers:
+#         start_time = time.time()
+#         imputer = imputer.fit(df_sub_data)
+#         duration_imputation_fit = time.time() - start_time
+
+#         start_time = time.time()
+#         df_imputed = imputer.transform(df_sub_data)
+#         duration_imputation_transform = time.time() - start_time
+
+#         benchmark_duration_rows.append({
+#             'imputer': imputer.__class__.__name__,
+#             'n_columns': df_sub_data.shape[1],
+#             'size_data': df_sub_data.shape[0],
+#             'duration_imputation_fit': duration_imputation_fit,
+#             'duration_imputation_transform': duration_imputation_transform,
+#         })
+
+# benchmark_duration_cols = []
+# num_rows = 100
+# for num_cols in [5, 6]:
+#     df_sub_data = df_data.iloc[:num_rows, :num_cols]
+#     hole_generator = missing_patterns.MCAR(ratio_masked=0.1)
+#     df_sub_mask = hole_generator.split(df_sub_data)[0]
+#     df_sub_data[df_sub_mask] = np.nan
+
+#     for imputer in imputers:
+#         start_time = time.time()
+#         imputer = imputer.fit(df_sub_data)
+#         duration_imputation_fit = time.time() - start_time
+
+#         start_time = time.time()
+#         df_imputed = imputer.transform(df_sub_data)
+#         duration_imputation_transform = time.time() - start_time
+
+#         benchmark_duration_cols.append({
+#             'imputer': imputer.__class__.__name__,
+#             'n_columns': df_sub_data.shape[1],
+#             'size_data': df_sub_data.shape[0],
+#             'duration_imputation_fit': duration_imputation_fit,
+#             'duration_imputation_transform': duration_imputation_transform,
+#         })
+
+# df_benchmark_rows = pd.DataFrame(benchmark_duration_rows)
+# with open('data/imp_pred/benchmark_time_rows.pkl', "wb") as handle:
+#     pickle.dump(df_benchmark_rows, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+# df_benchmark_cols = pd.DataFrame(benchmark_duration_cols)
+# with open('data/imp_pred/benchmark_time_cols.pkl', "wb") as handle:
+#     pickle.dump(df_benchmark_cols, handle, protocol=pickle.HIGHEST_PROTOCOL)
+```
+
+# Checking state of experiments
+
+```python
+# results = pd.read_pickle('data/imp_pred/benchmark_houses.pkl')
+# results = pd.read_pickle('data/imp_pred/benchmark_elevators.pkl')
+# results = pd.read_pickle('data/imp_pred/benchmark_MiamiHousing2016.pkl')
+# results = pd.read_pickle('data/imp_pred/benchmark_Brazilian_houses.pkl')
+# results = pd.read_pickle('data/imp_pred/benchmark_sulfur.pkl')
+# results = pd.read_pickle('data/imp_pred/benchmark_wine_quality.pkl')
+```
+
+```python
+# visualize_mlflow(results, exp_name='census_income')
+```
+
+```python
+# results_agg = imppred.get_benchmark_aggregate(results, cols_groupby=['hole_generator', 'ratio_masked', 'imputer', 'predictor'])
+# display(results_agg)
+```
+
+```python
+# selected_columns=['n_fold', 'hole_generator', 'ratio_masked', 'imputer', 'predictor', 'prediction_score_nan_mae', 'duration_imputation_fit']
+# fig = imppred.visualize_plotly(results, selected_columns=selected_columns)
+# fig.update_layout(height=300, width=1000)
+# fig
+```
+
+# Export
+
+```python
+# results_1 = pd.read_pickle('data/imp_pred/benchmark_sulfur.pkl')
+# results_1['dataset'] = 'sulfur'
+# results_2 = pd.read_pickle('data/imp_pred/benchmark_wine_quality.pkl')
+# results_2['dataset'] = 'wine_quality'
+# results_3 = pd.read_pickle('data/imp_pred/benchmark_MiamiHousing2016.pkl')
+# results_3['dataset'] = 'MiamiHousing2016'
+# results_4 = pd.read_pickle('data/imp_pred/benchmark_elevators.pkl')
+# results_4['dataset'] = 'elevators'
+# results_5 = pd.read_pickle('data/imp_pred/benchmark_houses.pkl')
+# results_5['dataset'] = 'houses'
+# results_6 = pd.read_pickle('data/imp_pred/benchmark_Brazilian_houses.pkl')
+# results_6['dataset'] = 'Brazilian_houses'
+# results_7 = pd.read_pickle('data/imp_pred/benchmark_Bike_Sharing_Demand.pkl')
+# results_7['dataset'] = 'Bike_Sharing_Demand'
+# results_8 = pd.read_pickle('data/imp_pred/benchmark_diamonds.pkl')
+# results_8['dataset'] = 'diamonds'
+# results_9 = pd.read_pickle('data/imp_pred/benchmark_medical_charges.pkl')
+# results_9['dataset'] = 'medical_charges'
+
+# results = pd.concat([results_1, results_2, results_3,
+#                      results_4, results_5, results_6,
+#                      results_7, results_8, results_9,]).reset_index(drop=True)
+# with open('data/imp_pred/benchmark_all.pkl', "wb") as handle:
+#     pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+# # results = pd.read_pickle('data/imp_pred/benchmark_all.pkl')
+
+# results_agg = imppred.get_benchmark_aggregate(results, cols_groupby=['dataset', 'hole_generator', 'ratio_masked', 'imputer', 'predictor'])
+
+# results_agg.reset_index(inplace=True)
+# results_agg.columns = ['_'.join(col).replace('__', '') for col in results_agg.columns.values]
+# results_agg.to_csv('data/imp_pred/benchmark_all.csv', index=False)
+```
+
+```python
+# results_1 = pd.read_pickle('data/imp_pred/benchmark_sulfur_new.pkl')
+# results_1['dataset'] = 'sulfur'
+# results_2 = pd.read_pickle('data/imp_pred/benchmark_wine_quality_new.pkl')
+# results_2['dataset'] = 'wine_quality'
+# results_3 = pd.read_pickle('data/imp_pred/benchmark_MiamiHousing2016_new.pkl')
+# results_3['dataset'] = 'MiamiHousing2016'
+# results_4 = pd.read_pickle('data/imp_pred/benchmark_elevators_new.pkl')
+# results_4['dataset'] = 'elevators'
+# results_5 = pd.read_pickle('data/imp_pred/benchmark_houses_new.pkl')
+# results_5['dataset'] = 'houses'
+# results_6 = pd.read_pickle('data/imp_pred/benchmark_Brazilian_houses_new.pkl')
+# results_6['dataset'] = 'Brazilian_houses'
+# results_7 = pd.read_pickle('data/imp_pred/benchmark_Bike_Sharing_Demand_new.pkl')
+# results_7['dataset'] = 'Bike_Sharing_Demand'
+# results_8 = pd.read_pickle('data/imp_pred/benchmark_diamonds_new.pkl')
+# results_8['dataset'] = 'diamonds'
+# results_9 = pd.read_pickle('data/imp_pred/benchmark_medical_charges_new.pkl')
+# results_9['dataset'] = 'medical_charges'
+
+# results = pd.concat([results_1, results_2, results_3,
+#                      results_4, results_5, results_6,
+#                      results_7, results_8, results_9]).reset_index(drop=True)
+# with open('data/imp_pred/benchmark_all_new.pkl', "wb") as handle:
+#     pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+# results_agg = imppred.get_benchmark_aggregate(results, cols_groupby=['dataset', 'hole_generator', 'ratio_masked', 'imputer', 'predictor'])
+
+# results_agg.reset_index(inplace=True)
+# results_agg.columns = ['_'.join(col).replace('__', '') for col in results_agg.columns.values]
+# results_agg.to_csv('data/imp_pred/benchmark_all_new.csv', index=False)
+```
+
+# Benchmark
+
+```python
+# results = pd.read_pickle('data/imp_pred/benchmark_all_new.pkl')
+# results_plot = results.copy()
+
+results_plot = pd.read_pickle('data/imp_pred/benchmark_plot.pkl')
+```
+
+```python
+# results_agg = imppred.get_benchmark_aggregate(results, cols_groupby=['dataset', 'hole_generator', 'ratio_masked', 'imputer', 'predictor'], keep_values=True)
+# display(results_agg)
+```
+
+```python
+num_dataset = len(results_plot['dataset'].unique())
+num_predictor = len(results_plot['predictor'].unique())
+num_imputer = len(results_plot['imputer'].unique()) - 1
+num_fold = len(results_plot['n_fold'].unique())
+# We remove the case [hole_generator=None, ratio_masked=0, n_mask=nan]
+num_mask = len(results_plot['n_mask'].unique()) - 1
+num_ratio_masked = len(results_plot['ratio_masked'].unique()) - 1
+num_trial = num_fold * num_mask
+
+print(f"datasets: {results_plot['dataset'].unique()}")
+print(f"predictor: {results_plot['predictor'].unique()}")
+print(f"imputer: {results_plot['imputer'].unique()}")
+```
+
+```python
+dict_type_set = {"test_set": "test sets", "train_set": "train sets"}
+dict_metric = {"wmape": "WMAPE", "dist_corr_pattern": "Corr. distance"}
+```
+
+```python
+results_plot[['dataset', 'hole_generator', 'ratio_masked', 'imputer', 'predictor']]
+```
+
+## The Friedman test on performance differences
+
+Friedman test tests the null hypothesis that performance scores of different imputers in the same trial and configuration have the same distribution.
+E.g., we have N sets of performance scores for N imputers. Each set has a size of M trials/configurations.
+
+https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.friedmanchisquare.html
+
+
+### Prediction performance
+
+
+For each ratio of nans, for each predictors and for all configurations, the prediction performance scores between **different imputers** are statistically different.
+
+```python
+# metric = "mae"
+metric = "wmape"
+# type_set = 'nan'
+type_set = 'notnan'
+
+imppred.statistic_test(results_plot[results_plot['imputer']!='None'], col_evaluated=f'prediction_score_{type_set}_{metric}', cols_grouped=['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask', 'predictor', 'imputer'], cols_displayed=['ratio_masked', 'predictor'], func=stats.friedmanchisquare)
+```
+
+For each ratio of nans, for each imputers and for all configurations, the prediction performance scores between **different predictors** are statistically different.
+
+```python
+# metric = "mae"
+metric = "wmape"
+# type_set = 'nan'
+type_set = 'notnan'
+
+imppred.statistic_test(results_plot[results_plot['imputer']!='None'], col_evaluated=f'prediction_score_{type_set}_{metric}', cols_grouped=['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask', 'imputer', 'predictor'], cols_displayed=['ratio_masked', 'imputer'], func=stats.friedmanchisquare)
+```
+
+For each ratio of nans, for each imputers and for all configurations, the prediction performance scores between **different pairs imputer-predictor** are statistically different.
+
+```python
+# metric = "mae"
+metric = "wmape"
+
+type_set = 'nan'
+# type_set = 'notnan'
+
+# results_plot['imputer_predictor'] = results_plot['imputer'] + '_' + results_plot['predictor']
+imppred.statistic_test(results_plot[results_plot['imputer']!='None'], col_evaluated=f'prediction_score_{type_set}_{metric}', cols_grouped=['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask', 'imputer_predictor'], cols_displayed=['ratio_masked'], func=stats.friedmanchisquare)
+
+```
+
+The null hypothesis is rejected with p-values way below the 0.05 level for all the ratios. This indicates that at least one algorithm has significantly different performances from one other.
+
+
+### Imputation performance
+
+```python
+# metric = "mae"
+metric = "wmape"
+
+# evaluated_set = 'train_set'
+evaluated_set = 'test_set'
+
+imppred.statistic_test(results_plot[results_plot['imputer']!='None'], col_evaluated=f'imputation_score_{metric}_{evaluated_set}', cols_grouped=['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask', 'imputer'], cols_displayed=['ratio_masked'], func=stats.friedmanchisquare)
+```
+
+## Performance gain of predictors trained on imputed data vs complete data
+
+- Gain = Score(Prediction_Data_complete) - Score(Imputation + Prediction_Data_complet)
+- Gain = Score(Prediction_Data_complete) - Score(Imputation + Prediction_Data_incomplet)
+
+```python
+# metric = 'wmape'
+
+# num_runs = results_plot.groupby(['hole_generator', 'ratio_masked', 'imputer', 'predictor']).count().max().max()
+# print(f"num_runs = {num_runs} runs for each {num_dataset} datasets * {num_fold} folds * {num_mask} masks = {num_dataset * num_fold * num_mask}")
+
+# for type_set in ['notnan', 'nan']:
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_relative_percentage_gain_data_complete'] = results_plot.apply(lambda x: imppred.get_relative_score(x, results_plot, col=f'prediction_score_{type_set}_{metric}', method='relative_percentage_gain', is_ref_hole_generator_none=True), axis=1)
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_data_complete'] = results_plot.apply(lambda x: imppred.get_relative_score(x, results_plot, col=f'prediction_score_{type_set}_{metric}', method='gain', is_ref_hole_generator_none=True), axis=1)
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_count_data_complete'] = results_plot.apply(lambda x: 1 if x[f'prediction_score_{type_set}_{metric}_gain_data_complete'] > 0 else 0, axis=1)
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_ratio_data_complete'] = results_plot[f'prediction_score_{type_set}_{metric}_gain_count_data_complete']/num_runs
+```
+
+### Ratio of runs
+
+```python
+metric = 'wmape_gain_ratio_data_complete'
+
+type_set = "test_set_not_nan"
+# type_set = "test_set_with_nan"
+
+# model = 'HistGradientBoostingRegressor'
+# model = 'XGBRegressor'
+model = 'Ridge'
+
+fig = imppred.plot_bar(
+    results_plot[(results_plot['predictor'].isin([model]))
+                 & ~(results_plot['imputer'].isin(['None']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['hole_generator', 'ratio_masked', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    agg_func=pd.DataFrame.sum)
+
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset} runs = {num_trial} trials x {num_dataset} datasets) where a gain of prediction performance <br>is found for {model}. Evaluation based on WMAPE computed on imputed test sets.<br>Baseline: the predictor is trained on a complete train set.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset} runs = {num_trial} trials x {num_dataset} datasets) where a gain of prediction performance <br>is found for {model}. Evaluation based on WMAPE computed on complete test sets.<br>Baseline: the predictor is trained on a complete train set.")
+fig.update_xaxes(title="Types and Ratios of missing values")
+fig.update_yaxes(title="Ratio of runs")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+### Gain
+
+```python
+# metric = "mae_relative_percentage_gain"
+# metric = "wmape_gain"
+metric = "wmape_relative_percentage_gain_data_complete"
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+# model = 'HistGradientBoostingRegressor'
+# model = 'XGBRegressor'
+model = 'Ridge'
+
+fig = imppred.plot_bar(
+    results_plot[(results_plot['predictor'].isin([model]))
+                 & ~(results_plot['imputer'].isin(['None']))
+                #  & (results_plot['dataset'].isin(['Brazilian_houses', 'MiamiHousing2016', 'medical_charges']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['dataset', 'ratio_masked', 'imputer'],
+    add_annotation=False,
+    add_confidence_interval=True,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean,
+    #yaxes_type='log'
+    )
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials, for {model}.<br>Evaluation based on WMAPE computed on imputed test sets.<br>Baseline: the predictor is trained on a complete train set.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials, for {model}.<br>Evaluation based on WMAPE computed on complete test sets.<br>Baseline: the predictor is trained on a complete train set.")
+
+
+fig.update_xaxes(title="Datasets and Ratios of missing values")
+fig.update_yaxes(title="(WMAPE(P) - WMAPE(I+P))/WMAPE(P)")
+fig.update_layout(height=400, width=2000)
+fig
+```
+
+## Prediction performance of predictors supporting missing values vs using imputation
+
+- Gain = Score(Prediction) - Score(Imputation + Prediction)
+
+```python
+# metric = 'wmape'
+
+# num_runs = results_plot.groupby(['hole_generator', 'ratio_masked', 'imputer', 'predictor']).count().max().max()
+# print(f"num_runs = {num_runs} runs for each {num_dataset} datasets * {num_fold} folds * {num_mask - 1} masks = {num_dataset * num_fold * num_mask}")
+
+# for type_set in ['notnan', 'nan']:
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_relative_percentage_gain'] = results_plot.apply(lambda x: imppred.get_relative_score(x, results_plot, col=f'prediction_score_{type_set}_{metric}', method='relative_percentage_gain'), axis=1)
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain'] = results_plot.apply(lambda x: imppred.get_relative_score(x, results_plot, col=f'prediction_score_{type_set}_{metric}', method='gain'), axis=1)
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_count'] = results_plot.apply(lambda x: 1 if x[f'prediction_score_{type_set}_{metric}_gain'] > 0 else 0, axis=1)
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_ratio'] = results_plot[f'prediction_score_{type_set}_{metric}_gain_count']/num_runs
+```
+
+### Ratio of runs
+
+```python
+metric = 'wmape_gain_ratio'
+
+type_set = "test_set_not_nan"
+# type_set = "test_set_with_nan"
+
+# model = 'HistGradientBoostingRegressor'
+model = 'XGBRegressor'
+
+fig = imppred.plot_bar(
+    results_plot[(results_plot['predictor'].isin([model]))
+                 & ~(results_plot['imputer'].isin(['None']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['hole_generator', 'ratio_masked', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    agg_func=pd.DataFrame.sum)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset} runs = {num_trial} trials x {num_dataset} datasets) where a gain of prediction performance <br>is found for {model}. Evaluation based on WMAPE computed on imputed test sets.<br>Baseline: the predictor is trained on an incomplete train set and evaluated on an incomplete test set.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset} runs = {num_trial} trials x {num_dataset} datasets) where a gain of prediction performance <br>is found for {model}. Evaluation based on WMAPE computed on complete test sets.<br>Baseline: the predictor is trained on an incomplete train set and evaluated on an incomplete test set.")
+
+fig.update_xaxes(title="Types and Ratios of missing values")
+fig.update_yaxes(title="Ratio of runs")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+### Gain
+
+```python
+# metric = "mae_relative_percentage_gain"
+# metric = "wmape_gain"
+metric = "wmape_relative_percentage_gain"
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+# model = 'HistGradientBoostingRegressor'
+model = 'XGBRegressor'
+
+fig = imppred.plot_bar(
+    results_plot[(results_plot['predictor'].isin([model]))
+                 & ~(results_plot['imputer'].isin(['None']))
+                 & (results_plot['dataset'].isin(['MiamiHousing2016', 'elevators', 'medical_charges']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['dataset', 'ratio_masked', 'imputer'],
+    add_annotation=False,
+    add_confidence_interval=True,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials, for {model}.<br>Evaluation based on WMAPE computed on imputed test sets.<br>Baseline: the predictor is trained on an incomplete train set and evaluated on an incomplete test set.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials, for {model}.<br>Evaluation based on WMAPE computed on complete test sets.<br>Baseline: the predictor is trained on an incomplete train set.")
+fig.update_xaxes(title="Datasets and Ratios of missing values")
+fig.update_yaxes(title="(WMAPE(P) - WMAPE(I+P))/WMAPE(P)")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+#### The Wilcoxon signed-rank test on gains
+
+```python
+metric = 'wmape_gain'
+
+type_set = 'nan'
+# type_set = 'notnan'
+
+results_plot_ = results_plot[~(results_plot['imputer'].isin(['None'])) & (results_plot['predictor'].isin(['HistGradientBoostingRegressor','XGBRegressor']))].copy()
+groupby_cols = ['ratio_masked', 'predictor', 'imputer']
+num_runs = results_plot_.groupby(groupby_cols).count()[f'prediction_score_{type_set}_{metric}'].max()
+print(f'For a combinaison of {groupby_cols}, there are {num_runs} gains')
+wilcoxon_test = pd.DataFrame(results_plot_.groupby(groupby_cols).apply(lambda x: stats.wilcoxon(x[f'prediction_score_{type_set}_{metric}'], alternative='greater').statistic).rename('wilcoxon_test_statistic'))
+wilcoxon_test['wilcoxon_test_pvalue'] = pd.DataFrame(results_plot_.groupby(groupby_cols).apply(lambda x: stats.wilcoxon(x[f'prediction_score_{type_set}_{metric}'], alternative='greater').pvalue))
+
+wilcoxon_test['size_set'] = num_runs
+wilcoxon_test[wilcoxon_test['wilcoxon_test_pvalue'] < 0.05]
+# results_plot_wilcoxon_test
+```
+
+If a p-value < 5%, the null hypothesis that the median is negative can be rejected at a confidence level of 5% in favor of the alternative that the median is greater than zero.
+
+
+## Performance gain for prediction: Imputation conditional vs Imputation constant
+
+
+- Imputation conditional: KNN, MICE, RPCA, Diffusion
+- Baseline - Imputation constant: Median, Shuffle*
+
+```python
+# metric = 'wmape'
+
+# # ref_imputer='ImputerMedian'
+# ref_imputer='ImputerShuffle'
+
+# num_runs_all_predictors = results_plot.groupby(['hole_generator', 'ratio_masked', 'imputer']).count().max().max()
+# print(f"num_runs = {num_runs} runs for each {num_dataset} datasets * {num_fold} folds * {num_mask} masks * {num_predictor} predictors = {num_dataset * num_fold * num_mask * num_predictor}")
+
+# num_runs_each_predictor = results_plot.groupby(['hole_generator', 'ratio_masked', 'imputer', 'predictor']).count().max().max()
+# print(f"num_runs = {num_runs} runs for each {num_dataset} datasets * {num_fold} folds * {num_mask} masks = {num_dataset * num_fold * num_mask}")
+
+# for type_set in ['notnan', 'nan']:
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_relative_percentage_gain_{ref_imputer}'] = results_plot.apply(lambda x: imppred.get_relative_score(x, results_plot, col=f'prediction_score_{type_set}_{metric}', method='relative_percentage_gain', ref_imputer=ref_imputer), axis=1)
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_{ref_imputer}'] = results_plot.apply(lambda x: imppred.get_relative_score(x, results_plot, col=f'prediction_score_{type_set}_{metric}', method='gain', ref_imputer=ref_imputer), axis=1)
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_count_{ref_imputer}'] = results_plot.apply(lambda x: 1 if x[f'prediction_score_{type_set}_{metric}_gain_{ref_imputer}'] > 0 else 0, axis=1)
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_ratio_{ref_imputer}_all'] = results_plot[f'prediction_score_{type_set}_{metric}_gain_count_{ref_imputer}']/num_runs_all_predictors
+
+#     results_plot[f'prediction_score_{type_set}_{metric}_gain_ratio_{ref_imputer}_each'] = results_plot[f'prediction_score_{type_set}_{metric}_gain_count_{ref_imputer}']/num_runs_each_predictor
+```
+
+### Ratio of runs
+
+
+Graph for all predictors
+
+```python
+ref_imputer='ImputerMedian'
+# ref_imputer='ImputerShuffle'
+
+metric = f'wmape_gain_ratio_{ref_imputer}_all'
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['hole_generator', 'ratio_masked', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    agg_func=pd.DataFrame.sum)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset * num_predictor} runs = {num_trial} trials x {num_dataset} datasets x {num_predictor} predictors) where a prediction performance of<br>a cond. imp. method is better than {ref_imputer}.<br>Evaluation based on WMAPE computed on imputed test sets.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset * num_predictor} runs = {num_trial} trials x {num_dataset} datasets x {num_predictor} predictors) where a prediction performance of<br>a cond. imp. method is better than {ref_imputer}.<br>Evaluation based on WMAPE computed on complete test sets.")
+
+fig.update_xaxes(title="Types and Ratios of missing values")
+fig.update_yaxes(title="Ratio of runs")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+Graph for each predictor
+
+```python
+ref_imputer='ImputerMedian'
+# ref_imputer='ImputerShuffle'
+
+metric = f'wmape_gain_ratio_{ref_imputer}_each'
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+# model = 'HistGradientBoostingRegressor'
+# model = 'XGBRegressor'
+model = 'Ridge'
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                 & (results_plot['predictor'].isin([model]))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['hole_generator', 'ratio_masked', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    agg_func=pd.DataFrame.sum)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset} runs = {num_trial} trials x {num_dataset} datasets) where a prediction performance of a cond. imp.<br>method is better than {ref_imputer}, for {model}.<br>Evaluation based on WMAPE computed on imputed test sets.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Ratio of runs (over {num_trial * num_dataset} runs = {num_trial} trials x {num_dataset} datasets) where a prediction performance of a cond. imp.<br>method is better than {ref_imputer}, for {model}.<br>Evaluation based on WMAPE computed on complete test sets.")
+fig.update_xaxes(title="Types and Ratios of missing values")
+fig.update_yaxes(title="Ratio of runs")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+### Gain
+
+
+Graph for all predictors
+
+```python
+ref_imputer='ImputerMedian'
+# ref_imputer='ImputerShuffle'
+
+metric = f'wmape_gain_{ref_imputer}'
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None', ref_imputer]))
+                #  & (results_plot['dataset'].isin(['MiamiHousing2016', 'medical_charges']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['dataset', 'ratio_masked', 'imputer'],
+    add_annotation=False,
+    add_confidence_interval=True,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials.<br>Evaluation based on WMAPE computed on imputed test sets.<br>Baseline: {ref_imputer}")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials.<br>Evaluation based on WMAPE computed on complete test sets.<br>Baseline: {ref_imputer}")
+fig.update_xaxes(title="Datasets and Ratios of missing values")
+fig.update_yaxes(title="(WMAPE(P) - WMAPE(I+P))/WMAPE(P)")
+fig.update_layout(height=400, width=2000)
+fig
+```
+
+Graph for each predictor
+
+```python
+ref_imputer='ImputerMedian'
+# ref_imputer='ImputerShuffle'
+
+metric = f"wmape_relative_percentage_gain_{ref_imputer}"
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+model = 'HistGradientBoostingRegressor'
+# model = 'XGBRegressor'
+# model = 'Ridge'
+
+fig = imppred.plot_bar(
+    results_plot[(results_plot['predictor'].isin([model]))
+                & ~(results_plot['imputer'].isin(['None', ref_imputer]))
+                 & (results_plot['dataset'].isin(['MiamiHousing2016', 'medical_charges']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['dataset', 'ratio_masked', 'imputer'],
+    add_annotation=False,
+    add_confidence_interval=True,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials, for {model}.<br>Evaluation based on WMAPE computed on imputed test sets.<br>Baseline: {ref_imputer}")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Mean relative percentage gain of prediction performance over {num_trial} trials, for {model}.<br>Evaluation based on WMAPE computed on complete test sets.<br>Baseline: {ref_imputer}")
+fig.update_xaxes(title="Datasets and Ratios of missing values")
+fig.update_yaxes(title="(WMAPE(P) - WMAPE(I+P))/WMAPE(P)")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+#### The Wilcoxon signed-rank test on gains
+
+```python
+ref_imputer='ImputerMedian'
+# ref_imputer='ImputerShuffle'
+
+metric = f"wmape_gain_{ref_imputer}"
+
+type_set = 'nan'
+# type_set = 'notnan'
+
+results_plot_ = results_plot[~(results_plot['imputer'].isin(['None', ref_imputer]))].copy()
+groupby_cols = ['ratio_masked', 'predictor', 'imputer']
+num_runs = results_plot_.groupby(groupby_cols).count()[f'prediction_score_{type_set}_{metric}'].max()
+print(f'For a combinaison of {groupby_cols}, there are {num_runs} gains')
+wilcoxon_test = pd.DataFrame(results_plot_.groupby(groupby_cols).apply(lambda x: stats.wilcoxon(x[f'prediction_score_{type_set}_{metric}'], alternative='greater').statistic).rename('wilcoxon_test_statistic'))
+wilcoxon_test['wilcoxon_test_pvalue'] = pd.DataFrame(results_plot_.groupby(groupby_cols).apply(lambda x: stats.wilcoxon(x[f'prediction_score_{type_set}_{metric}'], alternative='greater').pvalue))
+
+wilcoxon_test['size_set'] = num_runs
+wilcoxon_test[wilcoxon_test['wilcoxon_test_pvalue'] < 0.05]
+# results_plot_wilcoxon_test
+```
+
+## Performance of imputers
+
+
+### Rescaling scores
+
+```python
+# def scale_score(row, score_col, metric, data_mean):
+#     scores_in = row[score_col][metric]
+#     scores_out = []
+#     for feature in scores_in:
+#         scores_out.append(scores_in[feature]/np.abs(data_mean[feature]))
+#     return np.mean(scores_out)
+
+# score_col_in = 'imputation_scores_trainset'
+# score_col_out = 'imputation_score_mae_scaled_train_set'
+
+# # score_col_in = 'imputation_scores_testset'
+# # score_col_out = 'imputation_score_mae_scaled_test_set'
+
+# metric = 'imputation_score_mae'
+
+# results_plot[score_col_out] = np.NAN
+# for dataset_name in results_plot['dataset'].unique():
+#     print(dataset_name)
+#     dataset = load_dataset("inria-soda/tabular-benchmark", data_files=f"reg_num/{dataset_name}.csv")
+#     data_mean = dataset["train"].to_pandas().abs().mean()
+#     index = results_plot[(results_plot['dataset']==dataset_name) & (results_plot['imputer']!='None')].index
+#     results_plot.loc[index, score_col_out] = results_plot.loc[index, :].apply(lambda x: scale_score(x, score_col = score_col_in, metric = metric, data_mean = data_mean), axis=1)
+
+#     # print(results_plot_features[results_plot_features['dataset']==dataset_name]['imputation_score_mae_scaled_train_set'].mean())
+```
+
+### Prediction peformance
+
+```python
+# metric = 'wmape'
+
+# for type_set in ['notnan', 'nan']:
+#     results_plot_ = results_plot[~(results_plot['imputer'].isin(['None']))].copy()
+
+#     results_plot_[f'prediction_score_{type_set}_{metric}_imputer_rank'] = results_plot_.groupby(['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask', 'predictor'])[f'prediction_score_{type_set}_{metric}'].rank()
+
+#     results_plot = results_plot.merge(results_plot_[[f'prediction_score_{type_set}_{metric}_imputer_rank']], left_index=True, right_index=True, how='left')
+```
+
+#### Average score
+
+```python
+metric = "wmape"
+
+type_set = "test_set_not_nan"
+# type_set = "test_set_with_nan"
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                 #& (results_plot['dataset'].isin(['Bike_Sharing_Demand', 'medical_charges']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['dataset', 'ratio_masked', 'imputer'],
+    add_annotation=False,
+    add_confidence_interval=True,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean,
+    yaxes_type='log')
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Average prediction performance over {num_predictor} predictors * {num_trial} trials.<br>Evaluation based on WMAPE computed on imputed test sets.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Average prediction performance over {num_predictor} predictors * {num_trial} trials.<br>Evaluation based on WMAPE computed on complete test sets.")
+fig.update_yaxes(title="WMAPE(P)")
+
+fig.update_xaxes(title="Datasets and Ratios of missing values")
+fig.update_layout(height=400, width=2000)
+fig
+```
+
+#### Ranking
+
+```python
+metric = 'wmape_imputer_rank'
+
+type_set = "test_set_not_nan"
+# type_set = "test_set_with_nan"
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['ratio_masked', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Average ranks of imputeurs for {num_dataset *num_trial *num_predictor *num_ratio_masked} rounds ({num_dataset} datasets * {num_ratio_masked} ratios of nan * {num_predictor} predictors * {num_trial} trials).<br>Evaluation based on prediction performance WMAPE computed on imputed test sets.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Average ranks of imputeurs for {num_dataset *num_trial *num_predictor *num_ratio_masked} rounds ({num_dataset} datasets * {num_ratio_masked} ratios of nan * {num_predictor} predictors * {num_trial} trials).<br>Evaluation based on prediction performance WMAPE computed on complete test sets.")
+
+fig.update_xaxes(title=f"Ratios of nan")
+fig.update_yaxes(title="Average rank")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+```python
+metric = 'wmape_imputer_rank'
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['ratio_masked', 'imputer', 'predictor'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Average ranks of imputeurs for {num_dataset *num_trial *num_ratio_masked} rounds ({num_dataset} datasets * {num_ratio_masked} ratios of nan * {num_trial} trials).<br>Evaluation based on prediction performance WMAPE computed on imputed test sets.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Average ranks of imputeurs for {num_dataset *num_trial *num_ratio_masked} rounds ({num_dataset} datasets * {num_ratio_masked} ratios of nan * {num_trial} trials).<br>Evaluation based on prediction performance WMAPE computed on complete test sets.")
+
+fig.update_xaxes(title=f"Ratios of nan")
+fig.update_yaxes(title="Average rank")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+##### Critical difference diagram of average score ranks
+
+```python
+metric = 'wmape'
+
+# type_set = "notnan"
+type_set = "nan"
+
+color_palette = dict([(key, value) for key, value in zip(results_plot['imputer'].unique(), np.random.rand(len(results_plot['imputer'].unique()),3))])
+
+values = results_plot['ratio_masked'].unique()[1:]
+for v in values:
+    ratio_masked = v
+    results_plot_ = results_plot[~(results_plot['hole_generator'].isin(['None'])) & ~(results_plot['imputer'].isin(['None'])) & (results_plot['ratio_masked'].isin([ratio_masked]))].copy()
+    if type_set=="notnan":
+        title=f'Average ranks for prediction performance, ratio of nan = {ratio_masked}. Evaluation based on complete test sets.'
+    if type_set=="nan":
+        title=f'Average ranks for prediction performance, ratio of nan = {ratio_masked}. Evaluation based on imputed test sets.'
+
+    out = imppred.plot_critical_difference_diagram(results_plot_, col_model='imputer', col_rank=f'prediction_score_{type_set}_{metric}_imputer_rank', col_value=f'prediction_score_{type_set}_{metric}', title=title, color_palette=color_palette, fig_size=(7, 1.5))
+```
+
+### Imputation performance
+
+
+#### Average score
+
+```python
+metric = "dist_corr_pattern"
+# metric = "wmape"
+
+# type_set = "test_set"
+type_set = "train_set"
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                #  & (results_plot['dataset'].isin(['Bike_Sharing_Demand', 'medical_charges']))
+                 ],
+    col_displayed=("imputation_score", type_set, metric),
+    cols_grouped=['dataset', 'ratio_masked', 'imputer'],
+    add_annotation=False,
+    add_confidence_interval=True,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean,
+    yaxes_type='log')
+
+fig.update_layout(title=f"Average imputation performance over {num_trial} trials.<br>Evaluation based on {dict_metric[metric]} computed on imputed {dict_type_set[type_set]}.")
+
+fig.update_yaxes(title=f"{dict_metric[metric]}(I)")
+fig.update_xaxes(title="Datasets and Ratios of missing values")
+fig.update_layout(height=400, width=2000)
+fig
+```
+
+#### Ranking
+
+```python
+# metric = 'wmape'
+
+# results_plot_ = results_plot[~(results_plot['imputer'].isin(['None']))].copy()
+
+# results_plot_[f'imputation_score_{metric}_rank_train_set'] = results_plot_.groupby(['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask', 'predictor'])[f'imputation_score_{metric}_train_set'].rank()
+# results_plot_[f'imputation_score_{metric}_rank_test_set'] = results_plot_.groupby(['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask', 'predictor'])[f'imputation_score_{metric}_test_set'].rank()
+
+# results_plot = results_plot.merge(results_plot_[[f'imputation_score_{metric}_rank_train_set', f'imputation_score_{metric}_rank_test_set']], left_index=True, right_index=True, how='left')
+```
+
+```python
+metric = "dist_corr_pattern"
+# metric = 'wmape'
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                 ],
+    cols_displayed=(("imputation_score", "test_set", f"{metric}_rank"),
+                   ("imputation_score", "train_set", f"{metric}_rank")),
+    cols_grouped=['ratio_masked', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    agg_func=pd.DataFrame.mean)
+
+fig.update_layout(title=f"Average ranks of imputeurs for {num_dataset *num_trial *num_ratio_masked} rounds ({num_dataset} datasets * {num_ratio_masked} ratios of nan * {num_trial} trials).<br>Evaluation based on imputation performance WMAPE computed on imputed test/train sets.")
+fig.update_xaxes(title=f"Imputers and ratios of nan")
+fig.update_yaxes(title="Average rank")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+##### Critical difference diagram of average score ranks
+
+```python
+# metric = "dist_corr_pattern"
+metric = 'wmape'
+
+type_set = "test_set"
+# type_set = "train_set"
+
+color_palette = dict([(key, value) for key, value in zip(results_plot['imputer'].unique(), np.random.rand(len(results_plot['imputer'].unique()),3))])
+
+values = results_plot['ratio_masked'].unique()[1:]
+for v in values:
+    ratio_masked = v
+    results_plot_ = results_plot[~(results_plot['hole_generator'].isin(['None'])) & ~(results_plot['imputer'].isin(['None'])) & (results_plot['ratio_masked'].isin([ratio_masked]))].copy()
+    if type_set=="test_set":
+        title=f'Average ranks for imputation performance, ratio of nan = {ratio_masked}. Evaluation based on imputed test sets.'
+    if type_set=="train_set":
+        title=f'Average ranks for imputation performance, ratio of nan = {ratio_masked}. Evaluation based on imputed train sets.'
+
+    out = imppred.plot_critical_difference_diagram(results_plot_, col_model='imputer', col_rank=f'imputation_score_{metric}_rank_{type_set}', col_value=f'imputation_score_{metric}_{type_set}', title=title, color_palette=color_palette, fig_size=(7, 1.5))
+```
+
+## Prediction performance of pairs imputer-predictor
+
+```python
+# metric = 'wmape'
+
+# for type_set in ['notnan', 'nan']:
+#     results_plot[f'prediction_score_{type_set}_{metric}_imputer_predictor_rank'] = results_plot.groupby(['dataset', 'n_fold', 'hole_generator', 'ratio_masked', 'n_mask'])[f'prediction_score_{type_set}_{metric}'].rank()
+```
+
+### Average score
+
+```python
+metric = "wmape"
+
+type_set = "test_set_not_nan"
+# type_set = "test_set_with_nan"
+
+fig = imppred.plot_bar(
+    results_plot[~(results_plot['imputer'].isin(['None']))
+                 & (results_plot['dataset'].isin(['Bike_Sharing_Demand', 'medical_charges']))
+                 ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['dataset', 'ratio_masked', 'imputer_predictor'],
+    add_annotation=False,
+    add_confidence_interval=True,
+    confidence_level=0.95,
+    agg_func=pd.DataFrame.mean,
+    yaxes_type='log')
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Average prediction performance over {num_trial} trials.<br>Evaluation based on WMAPE computed on imputed test sets.")
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Average prediction performance over {num_trial} trials.<br>Evaluation based on WMAPE computed on complete test sets.")
+fig.update_yaxes(title="WMAPE(P)")
+
+fig.update_xaxes(title="Datasets and Ratios of missing values")
+fig.update_layout(height=400, width=1000)
+fig
+```
+
+### Ranking
+
+```python
+# model = 'HistGradientBoostingRegressor'
+# model = 'XGBRegressor'
+# model = 'Ridge'
+
+metric = 'wmape_imputer_predictor_rank'
+
+# type_set = "test_set_not_nan"
+type_set = "test_set_with_nan"
+
+fig = imppred.plot_bar(
+    results_plot[
+        ~(results_plot['hole_generator'].isin(['None']))
+        # (results_plot['predictor'].isin([model ]))
+        ],
+    col_displayed=("prediction_score", type_set, metric),
+    cols_grouped=['predictor','ratio_masked', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    agg_func=pd.DataFrame.mean)
+
+if type_set == "test_set_with_nan":
+    fig.update_layout(title=f"Average ranks of {num_imputer * num_predictor} pairs imputer-predictor for {num_dataset * num_trial * num_ratio_masked} rounds ({num_dataset} datasets * {num_ratio_masked} ratios of nan * {num_trial} trials).<br>Evaluation based on prediction performance WMAPE computed on imputed test sets.")
+
+if type_set == "test_set_not_nan":
+    fig.update_layout(title=f"Average ranks of {num_imputer * num_predictor} pairs imputer-predictor for {num_dataset * num_trial * num_ratio_masked} rounds ({num_dataset} datasets * {num_ratio_masked} ratios of nan * {num_trial} trials).<br>Evaluation based on prediction performance WMAPE computed on complete test sets.")
+
+fig.update_xaxes(title=f"Predictors and ratios of nan")
+fig.update_yaxes(title="Average rank")
+fig.update_layout(height=500, width=2000)
+fig
+```
+
+#### Critical difference diagram of average score ranks
+
+```python
+metric = 'wmape'
+
+# type_set = "notnan"
+type_set = "nan"
+
+color_palette = dict([(key, value) for key, value in zip(results_plot['imputer_predictor'].unique(), np.random.rand(len(results_plot['imputer_predictor'].unique()),3))])
+
+values = results_plot['ratio_masked'].unique()[1:]
+for v in values:
+    ratio_masked = v
+    results_plot_ = results_plot[~(results_plot['hole_generator'].isin(['None'])) & ~(results_plot['imputer'].isin(['None'])) & (results_plot['ratio_masked'].isin([ratio_masked]))].copy()
+    if type_set=="notnan":
+        title=f'Average ranks for prediction performance, ratio of nan = {ratio_masked}. Evaluation based on complete test sets.'
+    if type_set=="nan":
+        title=f'Average ranks for prediction performance, ratio of nan = {ratio_masked}. Evaluation based on imputed test sets.'
+
+    out = imppred.plot_critical_difference_diagram(results_plot_, col_model='imputer_predictor', col_rank=f'prediction_score_{type_set}_{metric}_imputer_predictor_rank', col_value=f'prediction_score_{type_set}_{metric}', title=title, color_palette=color_palette, fig_size=(7, 3))
+```
+
+## Correlation
+
+
+### Scatter plot
+
+```python
+metric = 'wmape'
+type_set = 'notnan'
+
+fig = imppred.plot_scatter(results_plot, cond={}, col_x=f'imputation_score_{metric}_train_set', col_y=f'prediction_score_{type_set}_{metric}', col_legend='dataset')
+fig.update_layout(legend_title="Datasets")
+fig.update_xaxes(title=f"Imputation performance on the imputed train set")
+fig.update_yaxes(title="Prediction performance on the complet test set")
+fig.update_layout(title=f"Performance scores of all pairs imputer-predictor for {num_trial} trials. Evaluation based on WMAPE.")
+fig.update_layout(height=500, width=1000)
+
+fig
+```
+
+```python
+metric = 'wmape'
+type_set = 'nan'
+
+fig = imppred.plot_scatter(results_plot, cond={}, col_x=f'imputation_score_{metric}_test_set', col_y=f'prediction_score_{type_set}_{metric}', col_legend='dataset')
+fig.update_layout(legend_title="Datasets")
+fig.update_xaxes(title=f"Imputation performance on the imputed test set")
+fig.update_yaxes(title="Prediction performance on the imputed test set")
+fig.update_layout(title=f"Performance scores of all pairs imputer-predictor for {num_trial} trials. Evaluation based on WMAPE.")
+fig.update_layout(height=500, width=1000)
+
+fig
+```
+
+### Table of correlation
+
+```python
+# model = 'HistGradientBoostingRegressor'
+# model = 'XGBRegressor'
+model = 'Ridge'
+
+# groupby_col = 'ratio_masked'
+# groupby_col = 'dataset'
+# groupby_col = 'imputer'
+# groupby_col = 'predictor'
+groupby_col = None
+
+metric_imp = 'dist_corr_pattern'
+# metric_imp = 'wmape'
+metric_pred = 'wmape'
+
+results_plot_ = results_plot[~(results_plot['imputer'].isin(['None']))
+                            #  & (results_plot['predictor'].isin([model]))
+                             #& ~(results_plot['dataset'].isin(['Bike_Sharing_Demand', 'sulfur', 'MiamiHousing2016']))
+                             ].copy()
+score_cols = [f'imputation_score_{metric_imp}_train_set', f'imputation_score_{metric_imp}_test_set',f'prediction_score_notnan_{metric_pred}', f'prediction_score_nan_{metric_pred}']
+if groupby_col is None:
+    results_corr = results_plot_[score_cols].corr(method='spearman')
+else:
+    results_corr = results_plot_.groupby(groupby_col)[score_cols].corr(method='spearman')
+    print(f'#num_scores = {results_plot_.groupby(groupby_col).count().max().max()}')
+
+multi_index_columns = [
+    ('imputation', metric_imp, 'train_set'),
+    ('imputation', metric_imp, 'test_set'),
+    ('prediction', metric_pred, 'test_set_not_nan'),
+    ('prediction', metric_pred, 'test_set_with_nan'),
+]
+
+results_corr.columns = pd.MultiIndex.from_tuples(multi_index_columns)
+multi_index_rows = []
+if results_corr.index.shape[0] > results_corr.columns.shape[0]:
+    for row_index_0 in results_corr.index.get_level_values(0).unique():
+        for row_index_1 in multi_index_columns:
+            multi_index_rows.append([row_index_0] + list(row_index_1))
+    results_corr.index = pd.MultiIndex.from_tuples(multi_index_rows)
+else:
+    results_corr.index = pd.MultiIndex.from_tuples(multi_index_columns)
+
+if groupby_col is None:
+    results_corr.index.names = ['task', 'metric', 'set']
+    reorder_levels = ['task', 'metric', 'set']
+    hide_indices_test = (slice(None), slice(None), 'test_set')
+    hide_indices_train = (slice(None), slice(None), 'train_set')
+    level = 0
+else:
+    results_corr.index.names = [groupby_col, 'task', 'metric', 'set']
+    reorder_levels = ['task', 'metric', groupby_col, 'set']
+    hide_indices_test = (slice(None), slice(None), slice(None), 'test_set')
+    hide_indices_train = (slice(None), slice(None), slice(None), 'train_set')
+    level = 1
+
+results_corr.columns.names = ['task', 'metric', 'set']
+results_corr_plot = results_corr.xs('imputation', level=level, drop_level=False)[[('prediction', metric_pred, 'test_set_not_nan'), ('prediction', metric_pred, 'test_set_with_nan'),]].reorder_levels(reorder_levels)
+
+
+def mask_values(val):
+    return f"opacity: {0}"
+
+results_corr_plot\
+.style.applymap(
+    mask_values,
+    subset=(
+        hide_indices_test,
+        ('prediction', metric_pred, 'test_set_not_nan')
+    ),
+).applymap(
+    mask_values,
+    subset=(
+        hide_indices_train,
+        ('prediction', metric_pred, 'test_set_with_nan')
+    ),
+)
+```
+
+## Performance as a function of dataset
+
+```python
+metric = 'wmape'
+type_set = 'nan'
+
+results_plot_wilcoxon_test = results_plot[~(results_plot['imputer'].isin(['None']))
+                                          & (results_plot['predictor'].isin(['HistGradientBoostingRegressor','XGBRegressor']))].copy()
+groupby_cols = ['size_test_set', 'predictor', 'imputer']
+num_runs = results_plot_wilcoxon_test.groupby(groupby_cols).count()[f'prediction_score_{type_set}_{metric}_gain'].max()
+print(f'For a combinaison of {groupby_cols}, there are {num_runs} gains')
+results_plot_wilcoxon_test = pd.DataFrame(results_plot_wilcoxon_test.groupby(groupby_cols).apply(lambda x: stats.wilcoxon(x[f'prediction_score_{type_set}_{metric}_gain'], alternative='greater').pvalue).rename('wilcoxon_test_pvalue'))
+
+```
+
+```python
+results_plot_wilcoxon_test['wilcoxon_test_pvalue_count'] = results_plot_wilcoxon_test['wilcoxon_test_pvalue'].apply(lambda x: x < 0.05)
+```
+
+```python
+fig = go.Figure()
+
+for value in results_plot_wilcoxon_test.index.get_level_values('imputer').unique():
+    df_plot_ = results_plot_wilcoxon_test.xs(value, level='imputer')
+    fig.add_trace(
+        go.Scatter(
+            x=df_plot_.index.get_level_values(level='size_test_set'),
+            y=df_plot_['wilcoxon_test_pvalue'],
+            name=value,
+            mode="markers",
+            )
+        )
+
+fig
+```
+
+```python
+# model = 'HistGradientBoostingRegressor'
+# model = 'XGBRegressor'
+# model = 'Ridge'
+
+# metric = "mae_rank"
+metric = "wmape_rank"
+
+fig = imppred.plot_bar(
+    results_plot[
+        ~(results_plot['hole_generator'].isin(['None']))
+        # (results_plot['predictor'].isin([model ]))
+        ],
+    col_displayed=("prediction_score", "test_set_not_nan", metric),
+    cols_grouped=['dataset', 'predictor', 'imputer'],
+    add_annotation=True,
+    add_confidence_interval=False,
+    agg_func=pd.DataFrame.mean)
+
+
+fig.update_layout(title=f"Average prediction performance ranks of {num_imputer * num_predictor} pairs imputer-predictor for {num_dataset} datasets and {num_fold * num_mask} trials")
+# fig.update_xaxes(title=f"Ratios of nan with predictor={model}")
+fig.update_xaxes(title=f"Predictors and ratios of nan")
+fig.update_yaxes(title="Average rank")
+fig.update_layout(height=500, width=2000)
+fig
+```
+
+### Find best features
+
+```python
+from sklearn.ensemble import GradientBoostingRegressor
+
+k_top_features = []
+for dataset_name in results_plot['dataset'].unique():
+    print(dataset_name)
+    dataset = load_dataset("inria-soda/tabular-benchmark", data_files=f"reg_num/{dataset_name}.csv")
+    df_data = dataset["train"].to_pandas()
+
+    columns = df_data.columns.to_list()
+    df_data_x = df_data[columns[:-1]]
+    df_data_y = df_data[columns[-1]]
+
+    model = GradientBoostingRegressor().fit(df_data_x,df_data_y)
+
+    feature_importances = dict([(key, value) for key, value in zip(columns, model.feature_importances_)])
+
+    print(feature_importances)
+    break
+```
+
+```python
+
+```
diff --git a/examples/data/imp_pred/benchmark_Bike_Sharing_Demand.pkl b/examples/data/imp_pred/benchmark_Bike_Sharing_Demand.pkl
new file mode 100644
index 00000000..2b95bb9e
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diff --git a/examples/data/imp_pred/benchmark_Bike_Sharing_Demand_new.pkl b/examples/data/imp_pred/benchmark_Bike_Sharing_Demand_new.pkl
new file mode 100644
index 00000000..135b5df1
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diff --git a/examples/data/imp_pred/benchmark_Brazilian_houses.pkl b/examples/data/imp_pred/benchmark_Brazilian_houses.pkl
new file mode 100644
index 00000000..5ae52e6d
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diff --git a/examples/data/imp_pred/benchmark_Brazilian_houses_new.pkl b/examples/data/imp_pred/benchmark_Brazilian_houses_new.pkl
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index 00000000..45303215
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diff --git a/examples/data/imp_pred/benchmark_MiamiHousing2016.pkl b/examples/data/imp_pred/benchmark_MiamiHousing2016.pkl
new file mode 100644
index 00000000..cb6ac946
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diff --git a/examples/data/imp_pred/benchmark_MiamiHousing2016_new.pkl b/examples/data/imp_pred/benchmark_MiamiHousing2016_new.pkl
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diff --git a/examples/data/imp_pred/benchmark_all.csv b/examples/data/imp_pred/benchmark_all.csv
new file mode 100644
index 00000000..c258dbde
--- /dev/null
+++ b/examples/data/imp_pred/benchmark_all.csv
@@ -0,0 +1,892 @@
+dataset,hole_generator,ratio_masked,imputer,predictor,prediction_score_test_set_with_nan_mae,prediction_score_test_set_not_nan_mae,imputation_score_train_set_mae,imputation_score_train_set_KL_columnwise,imputation_score_test_set_mae,imputation_score_test_set_KL_columnwise,duration_prediction_fit,duration_prediction_transform,duration_imputation_fit,duration_imputation_transform_train,duration_imputation_transform_test
+Bike_Sharing_Demand,MCAR,0.1,ImputerDiffusion,HistGradientBoostingRegressor,93.61174685612481,89.89565620615842,3.2275098368448396,11.516938667070217,3.442105527638324,14.727448971735644,3.40793643951416,0.008757619857788087,13.295807943344116,28.090523805618286,7.852561893463135
+Bike_Sharing_Demand,MCAR,0.1,ImputerDiffusion,Ridge,119.61800185404556,118.85415910067015,3.2275098368448396,11.516938667070217,3.442105527638324,14.727448971735644,0.0019987678527832032,0.00023756027221679687,13.295807943344116,28.090523805618286,7.852561893463135
+Bike_Sharing_Demand,MCAR,0.1,ImputerDiffusion,XGBRegressor,97.64465072027667,93.39367160741656,3.2275098368448396,11.516938667070217,3.442105527638324,14.727448971735644,0.1327080535888672,0.0076274585723876956,13.295807943344116,28.090523805618286,7.852561893463135
+Bike_Sharing_Demand,MCAR,0.1,ImputerEM,HistGradientBoostingRegressor,93.88223898446792,90.17643826175377,3.076112818937022,10.305901439545227,3.3238341056400005,13.500789977906841,6.936937217712402,0.0771928596496582,22.225786390304567,0.01679126739501953,0.007805147171020508
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diff --git a/examples/data/imp_pred/benchmark_all.pkl b/examples/data/imp_pred/benchmark_all.pkl
new file mode 100644
index 00000000..24de7f39
Binary files /dev/null and b/examples/data/imp_pred/benchmark_all.pkl differ
diff --git a/examples/data/imp_pred/benchmark_all_new.csv b/examples/data/imp_pred/benchmark_all_new.csv
new file mode 100644
index 00000000..8736fa78
--- /dev/null
+++ b/examples/data/imp_pred/benchmark_all_new.csv
@@ -0,0 +1,892 @@
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+wine_quality,MCAR,0.5,ImputerRPCA,Ridge,0.1106883856523939,0.10255243724719305,0.25140026632789625,0.09264584660161593,0.2551003807800103,0.1646046606206891,0.007367334365844727,0.0003636646270751953,1.3902819633483887,0.4529782962799072,0.1653836727142334
+wine_quality,MCAR,0.5,ImputerRPCA,XGBRegressor,0.11515406929555218,0.10954351592643181,0.25140026632789625,0.09264584660161593,0.2551003807800103,0.1646046606206891,0.31387996673583984,0.004027652740478516,1.3902819633483887,0.4529782962799072,0.1653836727142334
+wine_quality,MCAR,0.5,ImputerShuffle,HistGradientBoostingRegressor,0.11043135000993917,0.10156730392705385,0.3846400334080238,0.2421349947449332,0.3368823392102097,0.24158541784943544,73.90297901153565,0.7800669193267822,0.007531719207763672,0.04535400390625,0.042860193252563475
+wine_quality,MCAR,0.5,ImputerShuffle,Ridge,0.11278026242587101,0.10484278809671689,0.3846400334080238,0.2421349947449332,0.3368823392102097,0.24158541784943544,0.005328912734985352,0.0008558368682861328,0.007531719207763672,0.04535400390625,0.042860193252563475
+wine_quality,MCAR,0.5,ImputerShuffle,XGBRegressor,0.1169121858482284,0.10811597257956121,0.3846400334080238,0.2421349947449332,0.3368823392102097,0.24158541784943544,0.2632977771759033,0.003597068786621094,0.007531719207763672,0.04535400390625,0.042860193252563475
+wine_quality,MCAR,0.5,None,HistGradientBoostingRegressor,0.10873644739329795,0.10203163792790894,,,,,62.496259994506836,0.5734039497375488,,,
+wine_quality,MCAR,0.5,None,Ridge,,,,,,,,,,,
+wine_quality,MCAR,0.5,None,XGBRegressor,0.11368251198867291,0.11646232883700412,,,,,0.3784504413604736,0.006693582534790039,,,
+wine_quality,MCAR,0.7,ImputerDiffusion,HistGradientBoostingRegressor,0.11423972345281,0.10800441743237194,0.263846174041283,0.18120658824239702,0.3195273221667817,0.2569667311584058,62.92931884765625,0.45221766471862795,5.811965246200561,12.989577264785767,4.145848779678345
+wine_quality,MCAR,0.7,ImputerDiffusion,Ridge,0.11169002292705724,0.1089574515909649,0.263846174041283,0.18120658824239702,0.3195273221667817,0.2569667311584058,0.0052704143524169925,0.0009280967712402343,5.811965246200561,12.989577264785767,4.145848779678345
+wine_quality,MCAR,0.7,ImputerDiffusion,XGBRegressor,0.12172706656437483,0.12356931347014424,0.263846174041283,0.18120658824239702,0.3195273221667817,0.2569667311584058,0.3820900249481201,0.0034369945526123045,5.811965246200561,12.989577264785767,4.145848779678345
+wine_quality,MCAR,0.7,ImputerEM,HistGradientBoostingRegressor,0.11427547299557313,0.1084280931025452,0.2679466584599553,0.19052982802688764,0.3226286009280674,0.25708854958221083,78.30621390342712,0.5103784942626953,18.94037247657776,0.030555000305175783,0.011758041381835938
+wine_quality,MCAR,0.7,ImputerEM,Ridge,0.11194076029652499,0.1091363902217528,0.2679466584599553,0.19052982802688764,0.3226286009280674,0.25708854958221083,0.004145116806030274,0.0005335617065429688,18.94037247657776,0.030555000305175783,0.011758041381835938
+wine_quality,MCAR,0.7,ImputerEM,XGBRegressor,0.12053437187928327,0.12208795036855437,0.2679466584599553,0.19052982802688764,0.3226286009280674,0.25708854958221083,0.3687705326080322,0.003876209259033203,18.94037247657776,0.030555000305175783,0.011758041381835938
+wine_quality,MCAR,0.7,ImputerKNN,HistGradientBoostingRegressor,0.11786935918651692,0.10811233706798123,0.28645455117137764,0.20963107399074685,0.26801101455232507,0.2506321680110996,47.1882497215271,0.22761104583740235,0.003959455490112305,4.098632230758667,0.22093585014343262
+wine_quality,MCAR,0.7,ImputerKNN,Ridge,0.1151460559749018,0.10550119898905365,0.28645455117137764,0.20963107399074685,0.26801101455232507,0.2506321680110996,0.007987089157104492,0.000627899169921875,0.003959455490112305,4.098632230758667,0.22093585014343262
+wine_quality,MCAR,0.7,ImputerKNN,XGBRegressor,0.1257656519071226,0.1207271536760573,0.28645455117137764,0.20963107399074685,0.26801101455232507,0.2506321680110996,0.3399976348876953,0.0036021900177001954,0.003959455490112305,4.098632230758667,0.22093585014343262
+wine_quality,MCAR,0.7,ImputerMICE,HistGradientBoostingRegressor,0.11469768099130173,0.10527604210777795,0.285401921374015,0.17937889871069324,0.2626116819367344,0.22173571710570308,62.61224781036377,0.26745800018310545,285.947346572876,286.81169292449954,196.59137125015258
+wine_quality,MCAR,0.7,ImputerMICE,Ridge,0.11277362586401474,0.10396894915882701,0.285401921374015,0.17937889871069324,0.2626116819367344,0.22173571710570308,0.004769563674926758,0.0004592132568359375,285.947346572876,286.81169292449954,196.59137125015258
+wine_quality,MCAR,0.7,ImputerMICE,XGBRegressor,0.12252983075787195,0.11766623552510451,0.285401921374015,0.17937889871069324,0.2626116819367344,0.22173571710570308,0.3509168529510498,0.0033605670928955078,285.947346572876,286.81169292449954,196.59137125015258
+wine_quality,MCAR,0.7,ImputerMedian,HistGradientBoostingRegressor,0.11431406577593999,0.1087721874376446,0.2702689702502104,0.23590701197563704,0.3116740561605658,0.2768751429264245,60.570545835495,0.3878726291656494,0.0115777587890625,0.01328925132751465,0.011876325607299804
+wine_quality,MCAR,0.7,ImputerMedian,Ridge,0.11214168621964354,0.11111294452883058,0.2702689702502104,0.23590701197563704,0.3116740561605658,0.2768751429264245,0.005994129180908203,0.0004080295562744141,0.0115777587890625,0.01328925132751465,0.011876325607299804
+wine_quality,MCAR,0.7,ImputerMedian,XGBRegressor,0.11885237347731697,0.1302164607061742,0.2702689702502104,0.23590701197563704,0.3116740561605658,0.2768751429264245,0.192882022857666,0.0033928966522216797,0.0115777587890625,0.01328925132751465,0.011876325607299804
+wine_quality,MCAR,0.7,ImputerRPCA,HistGradientBoostingRegressor,0.11476132063449278,0.10549629651323375,0.2595509521257555,0.13776535662300382,0.26137701734880286,0.2489087230769832,49.24661231994629,0.48497939109802246,1.3396017932891846,0.44469040870666504,0.15114766120910644
+wine_quality,MCAR,0.7,ImputerRPCA,Ridge,0.11320124726660598,0.10434060914142776,0.2595509521257555,0.13776535662300382,0.26137701734880286,0.2489087230769832,0.005352306365966797,0.00025470733642578123,1.3396017932891846,0.44469040870666504,0.15114766120910644
+wine_quality,MCAR,0.7,ImputerRPCA,XGBRegressor,0.12163229690441746,0.11558275575391724,0.2595509521257555,0.13776535662300382,0.26137701734880286,0.2489087230769832,0.3473171615600586,0.0036440467834472656,1.3396017932891846,0.44469040870666504,0.15114766120910644
+wine_quality,MCAR,0.7,ImputerShuffle,HistGradientBoostingRegressor,0.11499837535210906,0.10657925172506394,0.3871657405634141,0.3461525904163994,0.3359219919275697,0.3438018422563037,67.6965546131134,0.6099631309509277,0.007574357986450195,0.04650338172912598,0.044623069763183594
+wine_quality,MCAR,0.7,ImputerShuffle,Ridge,0.11474484446700202,0.10747746240096649,0.3871657405634141,0.3461525904163994,0.3359219919275697,0.3438018422563037,0.005731668472290039,0.0005390262603759766,0.007574357986450195,0.04650338172912598,0.044623069763183594
+wine_quality,MCAR,0.7,ImputerShuffle,XGBRegressor,0.12249284851408879,0.11618710296367495,0.3871657405634141,0.3461525904163994,0.3359219919275697,0.3438018422563037,0.29152962684631345,0.0036159610748291017,0.007574357986450195,0.04650338172912598,0.044623069763183594
+wine_quality,MCAR,0.7,None,HistGradientBoostingRegressor,0.11407939361098281,0.10848754142631525,,,,,71.10881216049194,0.6866734409332276,,,
+wine_quality,MCAR,0.7,None,Ridge,,,,,,,,,,,
+wine_quality,MCAR,0.7,None,XGBRegressor,0.11867873506614762,0.13150254101211345,,,,,0.3214874267578125,0.005470447540283203,,,
+wine_quality,None,0.0,None,HistGradientBoostingRegressor,0.09838801952758106,0.09838801952758106,,,,,38.153185033798216,0.4328769683837891,,,
+wine_quality,None,0.0,None,Ridge,0.10105572714081228,0.10105572714081228,,,,,0.001315927505493164,0.00021138191223144532,,,
+wine_quality,None,0.0,None,XGBRegressor,0.10438423120530807,0.10438423120530807,,,,,0.2091540813446045,0.003270244598388672,,,
diff --git a/examples/data/imp_pred/benchmark_all_new.pkl b/examples/data/imp_pred/benchmark_all_new.pkl
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diff --git a/examples/data/imp_pred/benchmark_elevators.pkl b/examples/data/imp_pred/benchmark_elevators.pkl
new file mode 100644
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index 00000000..0850d7e9
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index 00000000..f016f424
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diff --git a/examples/data/imp_pred/benchmark_houses.pkl b/examples/data/imp_pred/benchmark_houses.pkl
new file mode 100644
index 00000000..7e868fc4
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diff --git a/examples/data/imp_pred/benchmark_houses_add_nan_indicator.pkl b/examples/data/imp_pred/benchmark_houses_add_nan_indicator.pkl
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index 00000000..588cb052
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diff --git a/examples/data/imp_pred/benchmark_medical_charges.pkl b/examples/data/imp_pred/benchmark_medical_charges.pkl
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index 00000000..80a324c4
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diff --git a/examples/data/imp_pred/benchmark_medical_charges_new.pkl b/examples/data/imp_pred/benchmark_medical_charges_new.pkl
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index 00000000..0995bc7a
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diff --git a/examples/data/imp_pred/benchmark_plot.pkl b/examples/data/imp_pred/benchmark_plot.pkl
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diff --git a/examples/data/imp_pred/benchmark_sulfur.pkl b/examples/data/imp_pred/benchmark_sulfur.pkl
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diff --git a/examples/data/imp_pred/benchmark_sulfur_new.pkl b/examples/data/imp_pred/benchmark_sulfur_new.pkl
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index 00000000..750b117e
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diff --git a/examples/data/imp_pred/benchmark_wine_quality.pkl b/examples/data/imp_pred/benchmark_wine_quality.pkl
new file mode 100644
index 00000000..b86a9c2a
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diff --git a/examples/data/imp_pred/benchmark_wine_quality_new.pkl b/examples/data/imp_pred/benchmark_wine_quality_new.pkl
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diff --git a/examples/run_benchmark_plot.py b/examples/run_benchmark_plot.py
new file mode 100644
index 00000000..ec2e8a36
--- /dev/null
+++ b/examples/run_benchmark_plot.py
@@ -0,0 +1,218 @@
+import argparse
+import sys
+
+sys.path.append("/home/ec2-user/qolmat/")
+
+import pickle
+import pandas as pd
+import qolmat.benchmark.imputer_predictor as imppred
+
+results = pd.read_pickle("data/imp_pred/benchmark_all_new.pkl")
+results_plot = results.copy()
+
+num_dataset = len(results["dataset"].unique())
+num_predictor = len(results["predictor"].unique())
+num_imputer = len(results["imputer"].unique()) - 1
+num_fold = len(results["n_fold"].unique())
+# We remove the case [hole_generator=None, ratio_masked=0, n_mask=nan]
+num_mask = len(results["n_mask"].unique()) - 1
+num_ratio_masked = len(results["ratio_masked"].unique()) - 1
+num_trial = num_fold * num_mask
+
+print(f"datasets: {results['dataset'].unique()}")
+print(f"predictor: {results['predictor'].unique()}")
+print(f"imputer: {results['imputer'].unique()}")
+
+num_runs_each_predictor = (
+    results_plot.groupby(["hole_generator", "ratio_masked", "imputer", "predictor"])
+    .count()
+    .max()
+    .max()
+)
+num_runs_all_predictors = (
+    results_plot.groupby(["hole_generator", "ratio_masked", "imputer"]).count().max().max()
+)
+
+results_plot["imputer_predictor"] = results_plot["imputer"] + "_" + results_plot["predictor"]
+
+imputation_metrics = ["wmape", "dist_corr_pattern"]
+prediction_metrics = ["wmape"]
+
+for metric in prediction_metrics:
+    for type_set in ["notnan", "nan"]:
+
+        results_plot[
+            f"prediction_score_{type_set}_{metric}_relative_percentage_gain_data_complete"
+        ] = results_plot.apply(
+            lambda x: imppred.get_relative_score(
+                x,
+                results_plot,
+                col=f"prediction_score_{type_set}_{metric}",
+                method="relative_percentage_gain",
+                is_ref_hole_generator_none=True,
+            ),
+            axis=1,
+        )
+
+        results_plot[
+            f"prediction_score_{type_set}_{metric}_gain_data_complete"
+        ] = results_plot.apply(
+            lambda x: imppred.get_relative_score(
+                x,
+                results_plot,
+                col=f"prediction_score_{type_set}_{metric}",
+                method="gain",
+                is_ref_hole_generator_none=True,
+            ),
+            axis=1,
+        )
+        results_plot[
+            f"prediction_score_{type_set}_{metric}_gain_count_data_complete"
+        ] = results_plot.apply(
+            lambda x: 1
+            if x[f"prediction_score_{type_set}_{metric}_gain_data_complete"] > 0
+            else 0,
+            axis=1,
+        )
+
+        results_plot[f"prediction_score_{type_set}_{metric}_gain_ratio_data_complete"] = (
+            results_plot[f"prediction_score_{type_set}_{metric}_gain_count_data_complete"]
+            / num_runs_each_predictor
+        )
+
+for metric in prediction_metrics:
+    for type_set in ["notnan", "nan"]:
+
+        results_plot[
+            f"prediction_score_{type_set}_{metric}_relative_percentage_gain"
+        ] = results_plot.apply(
+            lambda x: imppred.get_relative_score(
+                x,
+                results_plot,
+                col=f"prediction_score_{type_set}_{metric}",
+                method="relative_percentage_gain",
+            ),
+            axis=1,
+        )
+
+        results_plot[f"prediction_score_{type_set}_{metric}_gain"] = results_plot.apply(
+            lambda x: imppred.get_relative_score(
+                x, results_plot, col=f"prediction_score_{type_set}_{metric}", method="gain"
+            ),
+            axis=1,
+        )
+        results_plot[f"prediction_score_{type_set}_{metric}_gain_count"] = results_plot.apply(
+            lambda x: 1 if x[f"prediction_score_{type_set}_{metric}_gain"] > 0 else 0, axis=1
+        )
+
+        results_plot[f"prediction_score_{type_set}_{metric}_gain_ratio"] = (
+            results_plot[f"prediction_score_{type_set}_{metric}_gain_count"]
+            / num_runs_each_predictor
+        )
+
+
+for metric in prediction_metrics:
+    for type_set in ["notnan", "nan"]:
+        for ref_imputer in ["ImputerMedian", "ImputerShuffle"]:
+
+            results_plot[
+                f"prediction_score_{type_set}_{metric}_relative_percentage_gain_{ref_imputer}"
+            ] = results_plot.apply(
+                lambda x: imppred.get_relative_score(
+                    x,
+                    results_plot,
+                    col=f"prediction_score_{type_set}_{metric}",
+                    method="relative_percentage_gain",
+                    ref_imputer=ref_imputer,
+                ),
+                axis=1,
+            )
+
+            results_plot[
+                f"prediction_score_{type_set}_{metric}_gain_{ref_imputer}"
+            ] = results_plot.apply(
+                lambda x: imppred.get_relative_score(
+                    x,
+                    results_plot,
+                    col=f"prediction_score_{type_set}_{metric}",
+                    method="gain",
+                    ref_imputer=ref_imputer,
+                ),
+                axis=1,
+            )
+            results_plot[
+                f"prediction_score_{type_set}_{metric}_gain_count_{ref_imputer}"
+            ] = results_plot.apply(
+                lambda x: 1
+                if x[f"prediction_score_{type_set}_{metric}_gain_{ref_imputer}"] > 0
+                else 0,
+                axis=1,
+            )
+
+            results_plot[f"prediction_score_{type_set}_{metric}_gain_ratio_{ref_imputer}_all"] = (
+                results_plot[f"prediction_score_{type_set}_{metric}_gain_count_{ref_imputer}"]
+                / num_runs_all_predictors
+            )
+
+            results_plot[f"prediction_score_{type_set}_{metric}_gain_ratio_{ref_imputer}_each"] = (
+                results_plot[f"prediction_score_{type_set}_{metric}_gain_count_{ref_imputer}"]
+                / num_runs_each_predictor
+            )
+
+
+# metric = 'mae'
+metric = "wmape"
+
+for metric in prediction_metrics:
+    for type_set in ["notnan", "nan"]:
+        results_plot_ = results_plot[~(results_plot["imputer"].isin(["None"]))].copy()
+
+        results_plot_[
+            f"prediction_score_{type_set}_{metric}_imputer_rank"
+        ] = results_plot_.groupby(
+            ["dataset", "n_fold", "hole_generator", "ratio_masked", "n_mask", "predictor"]
+        )[
+            f"prediction_score_{type_set}_{metric}"
+        ].rank()
+
+        results_plot = results_plot.merge(
+            results_plot_[[f"prediction_score_{type_set}_{metric}_imputer_rank"]],
+            left_index=True,
+            right_index=True,
+            how="left",
+        )
+
+for metric in imputation_metrics:
+    results_plot_ = results_plot[~(results_plot["imputer"].isin(["None"]))].copy()
+
+    results_plot_[f"imputation_score_{metric}_rank_train_set"] = results_plot_.groupby(
+        ["dataset", "n_fold", "hole_generator", "ratio_masked", "n_mask", "predictor"]
+    )[f"imputation_score_{metric}_train_set"].rank()
+    results_plot_[f"imputation_score_{metric}_rank_test_set"] = results_plot_.groupby(
+        ["dataset", "n_fold", "hole_generator", "ratio_masked", "n_mask", "predictor"]
+    )[f"imputation_score_{metric}_test_set"].rank()
+
+    results_plot = results_plot.merge(
+        results_plot_[
+            [
+                f"imputation_score_{metric}_rank_train_set",
+                f"imputation_score_{metric}_rank_test_set",
+            ]
+        ],
+        left_index=True,
+        right_index=True,
+        how="left",
+    )
+
+for metric in prediction_metrics:
+    for type_set in ["notnan", "nan"]:
+        results_plot[
+            f"prediction_score_{type_set}_{metric}_imputer_predictor_rank"
+        ] = results_plot.groupby(
+            ["dataset", "n_fold", "hole_generator", "ratio_masked", "n_mask"]
+        )[
+            f"prediction_score_{type_set}_{metric}"
+        ].rank()
+
+with open("data/imp_pred/benchmark_plot.pkl", "wb") as handle:
+    pickle.dump(results_plot, handle, protocol=pickle.HIGHEST_PROTOCOL)
diff --git a/examples/run_computational_time.py b/examples/run_computational_time.py
new file mode 100644
index 00000000..6badb39d
--- /dev/null
+++ b/examples/run_computational_time.py
@@ -0,0 +1,92 @@
+import time
+import pickle
+import pandas as pd
+import numpy as np
+from datasets import load_dataset
+
+from qolmat.imputations import imputers, imputers_pytorch
+from qolmat.imputations.diffusions import ddpms
+from qolmat.benchmark import missing_patterns
+
+from xgboost import XGBRegressor
+
+data_name = "house_sales"
+dataset = load_dataset("inria-soda/tabular-benchmark", data_files=f"reg_num/{data_name}.csv")
+df_data = dataset["train"].to_pandas()
+column_target = df_data.columns.to_list()[-1]
+columns_numerical = df_data.select_dtypes(include="number").columns.tolist()
+columns_categorical = df_data.select_dtypes(include="object").columns.tolist()
+
+list_imputers = [
+    imputers.ImputerMedian(),
+    imputers.ImputerShuffle(),
+    imputers.ImputerMICE(estimator=XGBRegressor(tree_method="hist", n_jobs=1), max_iter=100),
+    imputers.ImputerKNN(),
+    imputers.ImputerRPCA(max_iterations=100),
+    imputers.ImputerEM(max_iter_em=100, method="mle"),
+    imputers_pytorch.ImputerDiffusion(
+        model=ddpms.TabDDPM(num_sampling=50), batch_size=1000, epochs=100
+    ),
+]
+
+benchmark_duration_rows = []
+num_cols = 10
+for num_rows in [1000, 10000, 20000]:
+    df_sub_data = df_data.iloc[:num_rows, :num_cols]
+    hole_generator = missing_patterns.MCAR(ratio_masked=0.1)
+    df_sub_mask = hole_generator.split(df_sub_data)[0]
+    df_sub_data[df_sub_mask] = np.nan
+
+    for imputer in list_imputers:
+        start_time = time.time()
+        imputer = imputer.fit(df_sub_data)
+        duration_imputation_fit = time.time() - start_time
+
+        start_time = time.time()
+        df_imputed = imputer.transform(df_sub_data)
+        duration_imputation_transform = time.time() - start_time
+
+        benchmark_duration_rows.append(
+            {
+                "imputer": imputer.__class__.__name__,
+                "n_columns": df_sub_data.shape[1],
+                "size_data": df_sub_data.shape[0],
+                "duration_imputation_fit": duration_imputation_fit,
+                "duration_imputation_transform": duration_imputation_transform,
+            }
+        )
+
+        df_benchmark_rows = pd.DataFrame(benchmark_duration_rows)
+        with open(f"data/imp_pred/benchmark_time_rows_{data_name}.pkl", "wb") as handle:
+            pickle.dump(df_benchmark_rows, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+benchmark_duration_cols = []
+num_rows = 1000
+for num_cols in [5, 10, 15]:
+    df_sub_data = df_data.iloc[:num_rows, :num_cols]
+    hole_generator = missing_patterns.MCAR(ratio_masked=0.1)
+    df_sub_mask = hole_generator.split(df_sub_data)[0]
+    df_sub_data[df_sub_mask] = np.nan
+
+    for imputer in list_imputers:
+        start_time = time.time()
+        imputer = imputer.fit(df_sub_data)
+        duration_imputation_fit = time.time() - start_time
+
+        start_time = time.time()
+        df_imputed = imputer.transform(df_sub_data)
+        duration_imputation_transform = time.time() - start_time
+
+        benchmark_duration_cols.append(
+            {
+                "imputer": imputer.__class__.__name__,
+                "n_columns": df_sub_data.shape[1],
+                "size_data": df_sub_data.shape[0],
+                "duration_imputation_fit": duration_imputation_fit,
+                "duration_imputation_transform": duration_imputation_transform,
+            }
+        )
+
+        df_benchmark_cols = pd.DataFrame(benchmark_duration_cols)
+        with open(f"data/imp_pred/benchmark_time_cols_{data_name}.pkl", "wb") as handle:
+            pickle.dump(df_benchmark_cols, handle, protocol=pickle.HIGHEST_PROTOCOL)
diff --git a/examples/run_imputer_predictor.py b/examples/run_imputer_predictor.py
new file mode 100644
index 00000000..41b19f89
--- /dev/null
+++ b/examples/run_imputer_predictor.py
@@ -0,0 +1,182 @@
+import argparse
+import sys
+
+sys.path.append("/home/ec2-user/qolmat/")
+
+from datasets import load_dataset
+
+from sklearn.compose import ColumnTransformer
+from sklearn import preprocessing
+
+from sklearn.linear_model import Ridge, RidgeClassifier
+from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
+from xgboost import XGBClassifier, XGBRegressor
+
+from qolmat.benchmark import missing_patterns
+from qolmat.imputations import imputers, imputers_pytorch
+from qolmat.imputations.diffusions import ddpms
+
+from qolmat.benchmark.imputer_predictor import BenchmarkImputationPrediction
+
+parser = argparse.ArgumentParser(description="Tabular data benchmark")
+parser.add_argument("--data", type=str, help="Name of data")
+parser.add_argument("--path", type=str, help="Path to store benchmarks", default="data/imp_pred")
+parser.add_argument("--batch_size", type=int, help="Batch size", default=1000)
+parser.add_argument("--n_folds", type=int, help="#folds", default=5)
+parser.add_argument("--n_masks", type=int, help="#masks", default=5)
+
+args = parser.parse_args()
+
+dataset = load_dataset("inria-soda/tabular-benchmark", data_files=f"reg_num/{args.data}.csv")
+df_data = dataset["train"].to_pandas()
+column_target = df_data.columns.to_list()[-1]
+columns_numerical = df_data.select_dtypes(include="number").columns.tolist()
+columns_categorical = df_data.select_dtypes(include="object").columns.tolist()
+size_data = len(df_data)
+
+benchmark = BenchmarkImputationPrediction(
+    n_masks=args.n_masks,
+    n_folds=args.n_folds,
+    imputation_metrics=["wmape", "dist_corr_pattern"],
+    prediction_metrics=["wmape"],
+)
+
+# Hole generators
+hole_generators = [
+    None,
+    missing_patterns.MCAR(ratio_masked=0.1),
+    missing_patterns.MCAR(ratio_masked=0.3),
+    missing_patterns.MCAR(ratio_masked=0.5),
+    missing_patterns.MCAR(ratio_masked=0.7),
+    # missing_patterns.MAR(ratio_masked=0.1),
+    # missing_patterns.MAR(ratio_masked=0.3),
+    # missing_patterns.MAR(ratio_masked=0.5),
+    # missing_patterns.MAR(ratio_masked=0.7),
+    # missing_patterns.MNAR(ratio_masked=0.1),
+    # missing_patterns.MNAR(ratio_masked=0.3),
+    # missing_patterns.MNAR(ratio_masked=0.5),
+    # missing_patterns.MNAR(ratio_masked=0.7),
+]
+
+# Imputation pipelines
+transformers = []
+columns_numerical_ = [col for col in columns_numerical if col != column_target]
+if len(columns_numerical_) != 0:
+    transformers.append(("num", preprocessing.StandardScaler(), columns_numerical_))
+columns_categorical_ = [col for col in columns_categorical if col != column_target]
+if len(columns_categorical_) != 0:
+    transformers.append(("cat", preprocessing.OrdinalEncoder(), columns_categorical_))
+transformer_imputation_x = ColumnTransformer(transformers=transformers)
+
+# Format of prediction pipeline
+# {
+# "transformer_x": transformer_x/None,
+# "imputer": imputer,
+# }
+
+imputation_pipelines = [
+    None,
+    {"imputer": imputers.ImputerMedian()},
+    {"imputer": imputers.ImputerShuffle()},
+    {
+        "imputer": imputers.ImputerMICE(
+            estimator=XGBRegressor(tree_method="hist", n_jobs=1), max_iter=100
+        )
+    },
+    {"imputer": imputers.ImputerKNN()},
+    {"imputer": imputers.ImputerRPCA(max_iterations=100)},
+    {"imputer": imputers.ImputerEM(max_iter_em=100, method="mle")},
+    {
+        "imputer": imputers_pytorch.ImputerDiffusion(
+            model=ddpms.TabDDPM(num_sampling=50), batch_size=args.batch_size, epochs=100
+        )
+    },
+]
+
+# Prediction pipelines
+transformers = []
+columns_numerical_ = [col for col in columns_numerical if col != column_target]
+if len(columns_numerical_) != 0:
+    transformers.append(("num", preprocessing.StandardScaler(), columns_numerical_))
+columns_categorical_ = [col for col in columns_categorical if col != column_target]
+if len(columns_categorical) != 0:
+    transformers.append(("cat", preprocessing.OrdinalEncoder(), columns_categorical_))
+transformer_prediction_x = ColumnTransformer(transformers=transformers)
+
+target_prediction_pipeline_pairs = {}
+
+# Format of prediction pipeline
+# {
+# "transformer_x": transformer_x/None,
+# "transformer_y": transformer_y/None,
+# "predictor": RidgeClassifier(),
+# "handle_nan": True/False (default=False),
+# "add_nan_indicator": True/False (default=True)
+# }
+
+if column_target in columns_numerical:
+    transformer_prediction_y = ColumnTransformer(
+        transformers=[
+            ("y_num", preprocessing.StandardScaler(), [column_target]),
+        ]
+    )
+    target_prediction_pipeline_pairs[column_target] = [
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": Ridge(),
+            "handle_nan": False,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": HistGradientBoostingRegressor(),
+            "handle_nan": True,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": XGBRegressor(tree_method="hist", n_jobs=1),
+            "handle_nan": True,
+        },
+    ]
+
+if column_target in columns_categorical:
+    transformer_prediction_y = ColumnTransformer(
+        transformers=[
+            ("y_cat", preprocessing.OrdinalEncoder(), [column_target]),
+        ]
+    )
+    target_prediction_pipeline_pairs[column_target] = [
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": RidgeClassifier(),
+            "handle_nan": False,
+            "add_nan_indicator": True,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": HistGradientBoostingClassifier(),
+            "handle_nan": True,
+            "add_nan_indicator": True,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": XGBClassifier(tree_method="hist", n_jobs=1),
+            "handle_nan": True,
+            "add_nan_indicator": True,
+        },
+    ]
+
+results = benchmark.compare(
+    df_data=df_data,
+    columns_numerical=columns_numerical,
+    columns_categorical=columns_categorical,
+    file_path=f"{args.path}/benchmark_{args.data}.pkl",
+    hole_generators=hole_generators,
+    imputation_pipelines=imputation_pipelines,
+    target_prediction_pipeline_pairs=target_prediction_pipeline_pairs,
+)
diff --git a/examples/run_imputer_predictor_new.py b/examples/run_imputer_predictor_new.py
new file mode 100644
index 00000000..f448101d
--- /dev/null
+++ b/examples/run_imputer_predictor_new.py
@@ -0,0 +1,182 @@
+import argparse
+import sys
+
+sys.path.append("/home/ec2-user/qolmat/")
+
+from datasets import load_dataset
+
+from sklearn.compose import ColumnTransformer
+from sklearn import preprocessing
+
+from sklearn.linear_model import Ridge, RidgeClassifier
+from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor
+from xgboost import XGBClassifier, XGBRegressor
+
+from qolmat.benchmark import missing_patterns
+from qolmat.imputations import imputers, imputers_pytorch
+from qolmat.imputations.diffusions import ddpms
+
+from qolmat.benchmark.imputer_predictor import BenchmarkImputationPrediction
+
+parser = argparse.ArgumentParser(description="Tabular data benchmark")
+parser.add_argument("--data", type=str, help="Name of data")
+parser.add_argument("--path", type=str, help="Path to store benchmarks", default="data/imp_pred")
+parser.add_argument("--batch_size", type=int, help="Batch size", default=1000)
+parser.add_argument("--n_folds", type=int, help="#folds", default=5)
+parser.add_argument("--n_masks", type=int, help="#masks", default=5)
+
+args = parser.parse_args()
+
+dataset = load_dataset("inria-soda/tabular-benchmark", data_files=f"reg_num/{args.data}.csv")
+df_data = dataset["train"].to_pandas()
+column_target = df_data.columns.to_list()[-1]
+columns_numerical = df_data.select_dtypes(include="number").columns.tolist()
+columns_categorical = df_data.select_dtypes(include="object").columns.tolist()
+size_data = len(df_data)
+
+benchmark = BenchmarkImputationPrediction(
+    n_masks=args.n_masks,
+    n_folds=args.n_folds,
+    imputation_metrics=["wmape", "dist_corr_pattern"],
+    prediction_metrics=["wmape"],
+)
+
+# Hole generators
+hole_generators = [
+    None,
+    missing_patterns.MCAR(ratio_masked=0.1),
+    missing_patterns.MCAR(ratio_masked=0.3),
+    missing_patterns.MCAR(ratio_masked=0.5),
+    missing_patterns.MCAR(ratio_masked=0.7),
+    # missing_patterns.MAR(ratio_masked=0.1),
+    # missing_patterns.MAR(ratio_masked=0.3),
+    # missing_patterns.MAR(ratio_masked=0.5),
+    # missing_patterns.MAR(ratio_masked=0.7),
+    # missing_patterns.MNAR(ratio_masked=0.1),
+    # missing_patterns.MNAR(ratio_masked=0.3),
+    # missing_patterns.MNAR(ratio_masked=0.5),
+    # missing_patterns.MNAR(ratio_masked=0.7),
+]
+
+# Imputation pipelines
+transformers = []
+columns_numerical_ = [col for col in columns_numerical if col != column_target]
+if len(columns_numerical_) != 0:
+    transformers.append(("num", preprocessing.StandardScaler(), columns_numerical_))
+columns_categorical_ = [col for col in columns_categorical if col != column_target]
+if len(columns_categorical_) != 0:
+    transformers.append(("cat", preprocessing.OrdinalEncoder(), columns_categorical_))
+transformer_imputation_x = ColumnTransformer(transformers=transformers)
+
+# Format of prediction pipeline
+# {
+# "transformer_x": transformer_x/None,
+# "imputer": imputer,
+# }
+
+imputation_pipelines = [
+    None,
+    {"imputer": imputers.ImputerMedian()},
+    {"imputer": imputers.ImputerShuffle()},
+    {
+        "imputer": imputers.ImputerMICE(
+            estimator=XGBRegressor(tree_method="hist", n_jobs=1), max_iter=100
+        )
+    },
+    {"imputer": imputers.ImputerKNN()},
+    {"imputer": imputers.ImputerRPCA(max_iterations=100)},
+    {"imputer": imputers.ImputerEM(max_iter_em=100, method="mle")},
+    {
+        "imputer": imputers_pytorch.ImputerDiffusion(
+            model=ddpms.TabDDPM(num_sampling=50), batch_size=args.batch_size, epochs=100
+        )
+    },
+]
+
+# Prediction pipelines
+transformers = []
+columns_numerical_ = [col for col in columns_numerical if col != column_target]
+if len(columns_numerical_) != 0:
+    transformers.append(("num", preprocessing.StandardScaler(), columns_numerical_))
+columns_categorical_ = [col for col in columns_categorical if col != column_target]
+if len(columns_categorical) != 0:
+    transformers.append(("cat", preprocessing.OrdinalEncoder(), columns_categorical_))
+transformer_prediction_x = ColumnTransformer(transformers=transformers)
+
+target_prediction_pipeline_pairs = {}
+
+# Format of prediction pipeline
+# {
+# "transformer_x": transformer_x/None,
+# "transformer_y": transformer_y/None,
+# "predictor": RidgeClassifier(),
+# "handle_nan": True/False (default=False),
+# "add_nan_indicator": True/False (default=True)
+# }
+
+if column_target in columns_numerical:
+    transformer_prediction_y = ColumnTransformer(
+        transformers=[
+            ("y_num", preprocessing.StandardScaler(), [column_target]),
+        ]
+    )
+    target_prediction_pipeline_pairs[column_target] = [
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": Ridge(),
+            "handle_nan": False,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": HistGradientBoostingRegressor(),
+            "handle_nan": True,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": XGBRegressor(tree_method="hist", n_jobs=1),
+            "handle_nan": True,
+        },
+    ]
+
+if column_target in columns_categorical:
+    transformer_prediction_y = ColumnTransformer(
+        transformers=[
+            ("y_cat", preprocessing.OrdinalEncoder(), [column_target]),
+        ]
+    )
+    target_prediction_pipeline_pairs[column_target] = [
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": RidgeClassifier(),
+            "handle_nan": False,
+            "add_nan_indicator": True,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": HistGradientBoostingClassifier(),
+            "handle_nan": True,
+            "add_nan_indicator": True,
+        },
+        {
+            "transformer_x": transformer_prediction_x,
+            "transformer_y": transformer_prediction_y,
+            "predictor": XGBClassifier(tree_method="hist", n_jobs=1),
+            "handle_nan": True,
+            "add_nan_indicator": True,
+        },
+    ]
+
+results = benchmark.compare(
+    df_data=df_data,
+    columns_numerical=columns_numerical,
+    columns_categorical=columns_categorical,
+    file_path=f"{args.path}/benchmark_{args.data}_new.pkl",
+    hole_generators=hole_generators,
+    imputation_pipelines=imputation_pipelines,
+    target_prediction_pipeline_pairs=target_prediction_pipeline_pairs,
+)
diff --git a/examples/tutorials/Untitled.ipynb b/examples/tutorials/Untitled.ipynb
deleted file mode 100644
index 9db60061..00000000
--- a/examples/tutorials/Untitled.ipynb
+++ /dev/null
@@ -1,1285 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "607d62ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "from qolmat.benchmark import comparator, missing_patterns\n",
-    "from qolmat.imputations import imputers\n",
-    "from qolmat.utils import data, plot\n",
-    "\n",
-    "\n",
-    "df_data = data.get_data(\"Superconductor\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9145dd03",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean_atomic_mass</th>\n",
-       "      <th>wtd_mean_atomic_mass</th>\n",
-       "      <th>gmean_atomic_mass</th>\n",
-       "      <th>wtd_gmean_atomic_mass</th>\n",
-       "      <th>entropy_atomic_mass</th>\n",
-       "      <th>wtd_entropy_atomic_mass</th>\n",
-       "      <th>range_atomic_mass</th>\n",
-       "      <th>wtd_range_atomic_mass</th>\n",
-       "      <th>std_atomic_mass</th>\n",
-       "      <th>wtd_std_atomic_mass</th>\n",
-       "      <th>...</th>\n",
-       "      <th>mean_Valence</th>\n",
-       "      <th>wtd_mean_Valence</th>\n",
-       "      <th>gmean_Valence</th>\n",
-       "      <th>wtd_gmean_Valence</th>\n",
-       "      <th>entropy_Valence</th>\n",
-       "      <th>wtd_entropy_Valence</th>\n",
-       "      <th>wtd_range_Valence</th>\n",
-       "      <th>std_Valence</th>\n",
-       "      <th>wtd_std_Valence</th>\n",
-       "      <th>criticaltemp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.862692</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.116612</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.062396</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>31.794921</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.622535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.257143</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.219783</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.066221</td>\n",
-       "      <td>1.085714</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.437059</td>\n",
-       "      <td>29.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>92.729214</td>\n",
-       "      <td>58.518416</td>\n",
-       "      <td>73.132787</td>\n",
-       "      <td>36.396602</td>\n",
-       "      <td>1.449309</td>\n",
-       "      <td>1.057755</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>36.161939</td>\n",
-       "      <td>47.094633</td>\n",
-       "      <td>53.979870</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2.257143</td>\n",
-       "      <td>1.888175</td>\n",
-       "      <td>2.210679</td>\n",
-       "      <td>1.557113</td>\n",
-       "      <td>1.047221</td>\n",
-       "      <td>1.128571</td>\n",
-       "      <td>0.632456</td>\n",
-       "      <td>0.468606</td>\n",
-       "      <td>26.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.885242</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.122509</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>0.975980</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>35.741099</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.656268</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.271429</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.232679</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.029175</td>\n",
-       "      <td>1.114286</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.444697</td>\n",
-       "      <td>19.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.873967</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.119560</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.022291</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>33.768010</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.639405</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.264286</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.226222</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.048834</td>\n",
-       "      <td>1.100000</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.440952</td>\n",
-       "      <td>22.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.840143</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.110716</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.129224</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>27.848743</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.588771</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.242857</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.206963</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.096052</td>\n",
-       "      <td>1.057143</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.428809</td>\n",
-       "      <td>23.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21258</th>\n",
-       "      <td>106.957877</td>\n",
-       "      <td>53.095769</td>\n",
-       "      <td>82.515384</td>\n",
-       "      <td>43.135565</td>\n",
-       "      <td>1.177145</td>\n",
-       "      <td>1.254119</td>\n",
-       "      <td>146.88130</td>\n",
-       "      <td>15.504479</td>\n",
-       "      <td>65.764081</td>\n",
-       "      <td>43.202659</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>3.555556</td>\n",
-       "      <td>3.223710</td>\n",
-       "      <td>3.519911</td>\n",
-       "      <td>1.377820</td>\n",
-       "      <td>0.913658</td>\n",
-       "      <td>2.168889</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.496904</td>\n",
-       "      <td>2.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21259</th>\n",
-       "      <td>92.266740</td>\n",
-       "      <td>49.021367</td>\n",
-       "      <td>64.812662</td>\n",
-       "      <td>32.867748</td>\n",
-       "      <td>1.323287</td>\n",
-       "      <td>1.571630</td>\n",
-       "      <td>188.38390</td>\n",
-       "      <td>7.353333</td>\n",
-       "      <td>69.232655</td>\n",
-       "      <td>50.148287</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.047619</td>\n",
-       "      <td>2.168944</td>\n",
-       "      <td>2.038991</td>\n",
-       "      <td>1.594167</td>\n",
-       "      <td>1.337246</td>\n",
-       "      <td>0.904762</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.212959</td>\n",
-       "      <td>122.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21260</th>\n",
-       "      <td>99.663190</td>\n",
-       "      <td>95.609104</td>\n",
-       "      <td>99.433882</td>\n",
-       "      <td>95.464320</td>\n",
-       "      <td>0.690847</td>\n",
-       "      <td>0.530198</td>\n",
-       "      <td>13.51362</td>\n",
-       "      <td>53.041104</td>\n",
-       "      <td>6.756810</td>\n",
-       "      <td>5.405448</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>4.800000</td>\n",
-       "      <td>4.472136</td>\n",
-       "      <td>4.781762</td>\n",
-       "      <td>0.686962</td>\n",
-       "      <td>0.450561</td>\n",
-       "      <td>3.200000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>1.98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21261</th>\n",
-       "      <td>99.663190</td>\n",
-       "      <td>97.095602</td>\n",
-       "      <td>99.433882</td>\n",
-       "      <td>96.901083</td>\n",
-       "      <td>0.690847</td>\n",
-       "      <td>0.640883</td>\n",
-       "      <td>13.51362</td>\n",
-       "      <td>31.115202</td>\n",
-       "      <td>6.756810</td>\n",
-       "      <td>6.249958</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>4.690000</td>\n",
-       "      <td>4.472136</td>\n",
-       "      <td>4.665819</td>\n",
-       "      <td>0.686962</td>\n",
-       "      <td>0.577601</td>\n",
-       "      <td>2.210000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.462493</td>\n",
-       "      <td>1.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21262</th>\n",
-       "      <td>87.468333</td>\n",
-       "      <td>86.858500</td>\n",
-       "      <td>82.555758</td>\n",
-       "      <td>80.458722</td>\n",
-       "      <td>1.041270</td>\n",
-       "      <td>0.895229</td>\n",
-       "      <td>71.75500</td>\n",
-       "      <td>43.144000</td>\n",
-       "      <td>29.905282</td>\n",
-       "      <td>33.927941</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.00</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>4.762203</td>\n",
-       "      <td>4.242641</td>\n",
-       "      <td>1.054920</td>\n",
-       "      <td>0.970116</td>\n",
-       "      <td>1.800000</td>\n",
-       "      <td>1.414214</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>12.80</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>21263 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       mean_atomic_mass  wtd_mean_atomic_mass  gmean_atomic_mass   \n",
-       "0             88.944468             57.862692          66.361592  \\\n",
-       "1             92.729214             58.518416          73.132787   \n",
-       "2             88.944468             57.885242          66.361592   \n",
-       "3             88.944468             57.873967          66.361592   \n",
-       "4             88.944468             57.840143          66.361592   \n",
-       "...                 ...                   ...                ...   \n",
-       "21258        106.957877             53.095769          82.515384   \n",
-       "21259         92.266740             49.021367          64.812662   \n",
-       "21260         99.663190             95.609104          99.433882   \n",
-       "21261         99.663190             97.095602          99.433882   \n",
-       "21262         87.468333             86.858500          82.555758   \n",
-       "\n",
-       "       wtd_gmean_atomic_mass  entropy_atomic_mass  wtd_entropy_atomic_mass   \n",
-       "0                  36.116612             1.181795                 1.062396  \\\n",
-       "1                  36.396602             1.449309                 1.057755   \n",
-       "2                  36.122509             1.181795                 0.975980   \n",
-       "3                  36.119560             1.181795                 1.022291   \n",
-       "4                  36.110716             1.181795                 1.129224   \n",
-       "...                      ...                  ...                      ...   \n",
-       "21258              43.135565             1.177145                 1.254119   \n",
-       "21259              32.867748             1.323287                 1.571630   \n",
-       "21260              95.464320             0.690847                 0.530198   \n",
-       "21261              96.901083             0.690847                 0.640883   \n",
-       "21262              80.458722             1.041270                 0.895229   \n",
-       "\n",
-       "       range_atomic_mass  wtd_range_atomic_mass  std_atomic_mass   \n",
-       "0              122.90607              31.794921        51.968828  \\\n",
-       "1              122.90607              36.161939        47.094633   \n",
-       "2              122.90607              35.741099        51.968828   \n",
-       "3              122.90607              33.768010        51.968828   \n",
-       "4              122.90607              27.848743        51.968828   \n",
-       "...                  ...                    ...              ...   \n",
-       "21258          146.88130              15.504479        65.764081   \n",
-       "21259          188.38390               7.353333        69.232655   \n",
-       "21260           13.51362              53.041104         6.756810   \n",
-       "21261           13.51362              31.115202         6.756810   \n",
-       "21262           71.75500              43.144000        29.905282   \n",
-       "\n",
-       "       wtd_std_atomic_mass  ...  mean_Valence  wtd_mean_Valence   \n",
-       "0                53.622535  ...          2.25          2.257143  \\\n",
-       "1                53.979870  ...          2.00          2.257143   \n",
-       "2                53.656268  ...          2.25          2.271429   \n",
-       "3                53.639405  ...          2.25          2.264286   \n",
-       "4                53.588771  ...          2.25          2.242857   \n",
-       "...                    ...  ...           ...               ...   \n",
-       "21258            43.202659  ...          3.25          3.555556   \n",
-       "21259            50.148287  ...          2.20          2.047619   \n",
-       "21260             5.405448  ...          4.50          4.800000   \n",
-       "21261             6.249958  ...          4.50          4.690000   \n",
-       "21262            33.927941  ...          5.00          4.500000   \n",
-       "\n",
-       "       gmean_Valence  wtd_gmean_Valence  entropy_Valence  wtd_entropy_Valence   \n",
-       "0           2.213364           2.219783         1.368922             1.066221  \\\n",
-       "1           1.888175           2.210679         1.557113             1.047221   \n",
-       "2           2.213364           2.232679         1.368922             1.029175   \n",
-       "3           2.213364           2.226222         1.368922             1.048834   \n",
-       "4           2.213364           2.206963         1.368922             1.096052   \n",
-       "...              ...                ...              ...                  ...   \n",
-       "21258       3.223710           3.519911         1.377820             0.913658   \n",
-       "21259       2.168944           2.038991         1.594167             1.337246   \n",
-       "21260       4.472136           4.781762         0.686962             0.450561   \n",
-       "21261       4.472136           4.665819         0.686962             0.577601   \n",
-       "21262       4.762203           4.242641         1.054920             0.970116   \n",
-       "\n",
-       "       wtd_range_Valence  std_Valence  wtd_std_Valence  criticaltemp  \n",
-       "0               1.085714     0.433013         0.437059         29.00  \n",
-       "1               1.128571     0.632456         0.468606         26.00  \n",
-       "2               1.114286     0.433013         0.444697         19.00  \n",
-       "3               1.100000     0.433013         0.440952         22.00  \n",
-       "4               1.057143     0.433013         0.428809         23.00  \n",
-       "...                  ...          ...              ...           ...  \n",
-       "21258           2.168889     0.433013         0.496904          2.44  \n",
-       "21259           0.904762     0.400000         0.212959        122.10  \n",
-       "21260           3.200000     0.500000         0.400000          1.98  \n",
-       "21261           2.210000     0.500000         0.462493          1.84  \n",
-       "21262           1.800000     1.414214         1.500000         12.80  \n",
-       "\n",
-       "[21263 rows x 80 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "efb06475",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean_atomic_mass</th>\n",
-       "      <th>wtd_mean_atomic_mass</th>\n",
-       "      <th>gmean_atomic_mass</th>\n",
-       "      <th>wtd_gmean_atomic_mass</th>\n",
-       "      <th>entropy_atomic_mass</th>\n",
-       "      <th>wtd_entropy_atomic_mass</th>\n",
-       "      <th>range_atomic_mass</th>\n",
-       "      <th>wtd_range_atomic_mass</th>\n",
-       "      <th>std_atomic_mass</th>\n",
-       "      <th>wtd_std_atomic_mass</th>\n",
-       "      <th>...</th>\n",
-       "      <th>mean_Valence</th>\n",
-       "      <th>wtd_mean_Valence</th>\n",
-       "      <th>gmean_Valence</th>\n",
-       "      <th>wtd_gmean_Valence</th>\n",
-       "      <th>entropy_Valence</th>\n",
-       "      <th>wtd_entropy_Valence</th>\n",
-       "      <th>wtd_range_Valence</th>\n",
-       "      <th>std_Valence</th>\n",
-       "      <th>wtd_std_Valence</th>\n",
-       "      <th>criticaltemp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.862692</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.116612</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.062396</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>31.794921</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.622535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.257143</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.219783</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.066221</td>\n",
-       "      <td>1.085714</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.437059</td>\n",
-       "      <td>29.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>92.729214</td>\n",
-       "      <td>58.518416</td>\n",
-       "      <td>73.132787</td>\n",
-       "      <td>36.396602</td>\n",
-       "      <td>1.449309</td>\n",
-       "      <td>1.057755</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>36.161939</td>\n",
-       "      <td>47.094633</td>\n",
-       "      <td>53.979870</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2.257143</td>\n",
-       "      <td>1.888175</td>\n",
-       "      <td>2.210679</td>\n",
-       "      <td>1.557113</td>\n",
-       "      <td>1.047221</td>\n",
-       "      <td>1.128571</td>\n",
-       "      <td>0.632456</td>\n",
-       "      <td>0.468606</td>\n",
-       "      <td>26.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.885242</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.122509</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>0.975980</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>35.741099</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.656268</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.271429</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.232679</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.029175</td>\n",
-       "      <td>1.114286</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.444697</td>\n",
-       "      <td>19.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.873967</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.119560</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.022291</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>33.768010</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.639405</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.264286</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.226222</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.048834</td>\n",
-       "      <td>1.100000</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.440952</td>\n",
-       "      <td>22.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.840143</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.110716</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.129224</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>27.848743</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.588771</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.242857</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.206963</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.096052</td>\n",
-       "      <td>1.057143</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.428809</td>\n",
-       "      <td>23.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21258</th>\n",
-       "      <td>106.957877</td>\n",
-       "      <td>53.095769</td>\n",
-       "      <td>82.515384</td>\n",
-       "      <td>43.135565</td>\n",
-       "      <td>1.177145</td>\n",
-       "      <td>1.254119</td>\n",
-       "      <td>146.88130</td>\n",
-       "      <td>15.504479</td>\n",
-       "      <td>65.764081</td>\n",
-       "      <td>43.202659</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>3.555556</td>\n",
-       "      <td>3.223710</td>\n",
-       "      <td>3.519911</td>\n",
-       "      <td>1.377820</td>\n",
-       "      <td>0.913658</td>\n",
-       "      <td>2.168889</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.496904</td>\n",
-       "      <td>2.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21259</th>\n",
-       "      <td>92.266740</td>\n",
-       "      <td>49.021367</td>\n",
-       "      <td>64.812662</td>\n",
-       "      <td>32.867748</td>\n",
-       "      <td>1.323287</td>\n",
-       "      <td>1.571630</td>\n",
-       "      <td>188.38390</td>\n",
-       "      <td>7.353333</td>\n",
-       "      <td>69.232655</td>\n",
-       "      <td>50.148287</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.047619</td>\n",
-       "      <td>2.168944</td>\n",
-       "      <td>2.038991</td>\n",
-       "      <td>1.594167</td>\n",
-       "      <td>1.337246</td>\n",
-       "      <td>0.904762</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.212959</td>\n",
-       "      <td>122.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21260</th>\n",
-       "      <td>99.663190</td>\n",
-       "      <td>95.609104</td>\n",
-       "      <td>99.433882</td>\n",
-       "      <td>95.464320</td>\n",
-       "      <td>0.690847</td>\n",
-       "      <td>0.530198</td>\n",
-       "      <td>13.51362</td>\n",
-       "      <td>53.041104</td>\n",
-       "      <td>6.756810</td>\n",
-       "      <td>5.405448</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>4.800000</td>\n",
-       "      <td>4.472136</td>\n",
-       "      <td>4.781762</td>\n",
-       "      <td>0.686962</td>\n",
-       "      <td>0.450561</td>\n",
-       "      <td>3.200000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>1.98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21261</th>\n",
-       "      <td>99.663190</td>\n",
-       "      <td>97.095602</td>\n",
-       "      <td>99.433882</td>\n",
-       "      <td>96.901083</td>\n",
-       "      <td>0.690847</td>\n",
-       "      <td>0.640883</td>\n",
-       "      <td>13.51362</td>\n",
-       "      <td>31.115202</td>\n",
-       "      <td>6.756810</td>\n",
-       "      <td>6.249958</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>4.690000</td>\n",
-       "      <td>4.472136</td>\n",
-       "      <td>4.665819</td>\n",
-       "      <td>0.686962</td>\n",
-       "      <td>0.577601</td>\n",
-       "      <td>2.210000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.462493</td>\n",
-       "      <td>1.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21262</th>\n",
-       "      <td>87.468333</td>\n",
-       "      <td>86.858500</td>\n",
-       "      <td>82.555758</td>\n",
-       "      <td>80.458722</td>\n",
-       "      <td>1.041270</td>\n",
-       "      <td>0.895229</td>\n",
-       "      <td>71.75500</td>\n",
-       "      <td>43.144000</td>\n",
-       "      <td>29.905282</td>\n",
-       "      <td>33.927941</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.00</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>4.762203</td>\n",
-       "      <td>4.242641</td>\n",
-       "      <td>1.054920</td>\n",
-       "      <td>0.970116</td>\n",
-       "      <td>1.800000</td>\n",
-       "      <td>1.414214</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>12.80</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>21263 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       mean_atomic_mass  wtd_mean_atomic_mass  gmean_atomic_mass   \n",
-       "0             88.944468             57.862692          66.361592  \\\n",
-       "1             92.729214             58.518416          73.132787   \n",
-       "2             88.944468             57.885242          66.361592   \n",
-       "3             88.944468             57.873967          66.361592   \n",
-       "4             88.944468             57.840143          66.361592   \n",
-       "...                 ...                   ...                ...   \n",
-       "21258        106.957877             53.095769          82.515384   \n",
-       "21259         92.266740             49.021367          64.812662   \n",
-       "21260         99.663190             95.609104          99.433882   \n",
-       "21261         99.663190             97.095602          99.433882   \n",
-       "21262         87.468333             86.858500          82.555758   \n",
-       "\n",
-       "       wtd_gmean_atomic_mass  entropy_atomic_mass  wtd_entropy_atomic_mass   \n",
-       "0                  36.116612             1.181795                 1.062396  \\\n",
-       "1                  36.396602             1.449309                 1.057755   \n",
-       "2                  36.122509             1.181795                 0.975980   \n",
-       "3                  36.119560             1.181795                 1.022291   \n",
-       "4                  36.110716             1.181795                 1.129224   \n",
-       "...                      ...                  ...                      ...   \n",
-       "21258              43.135565             1.177145                 1.254119   \n",
-       "21259              32.867748             1.323287                 1.571630   \n",
-       "21260              95.464320             0.690847                 0.530198   \n",
-       "21261              96.901083             0.690847                 0.640883   \n",
-       "21262              80.458722             1.041270                 0.895229   \n",
-       "\n",
-       "       range_atomic_mass  wtd_range_atomic_mass  std_atomic_mass   \n",
-       "0              122.90607              31.794921        51.968828  \\\n",
-       "1              122.90607              36.161939        47.094633   \n",
-       "2              122.90607              35.741099        51.968828   \n",
-       "3              122.90607              33.768010        51.968828   \n",
-       "4              122.90607              27.848743        51.968828   \n",
-       "...                  ...                    ...              ...   \n",
-       "21258          146.88130              15.504479        65.764081   \n",
-       "21259          188.38390               7.353333        69.232655   \n",
-       "21260           13.51362              53.041104         6.756810   \n",
-       "21261           13.51362              31.115202         6.756810   \n",
-       "21262           71.75500              43.144000        29.905282   \n",
-       "\n",
-       "       wtd_std_atomic_mass  ...  mean_Valence  wtd_mean_Valence   \n",
-       "0                53.622535  ...          2.25          2.257143  \\\n",
-       "1                53.979870  ...          2.00          2.257143   \n",
-       "2                53.656268  ...          2.25          2.271429   \n",
-       "3                53.639405  ...          2.25          2.264286   \n",
-       "4                53.588771  ...          2.25          2.242857   \n",
-       "...                    ...  ...           ...               ...   \n",
-       "21258            43.202659  ...          3.25          3.555556   \n",
-       "21259            50.148287  ...          2.20          2.047619   \n",
-       "21260             5.405448  ...          4.50          4.800000   \n",
-       "21261             6.249958  ...          4.50          4.690000   \n",
-       "21262            33.927941  ...          5.00          4.500000   \n",
-       "\n",
-       "       gmean_Valence  wtd_gmean_Valence  entropy_Valence  wtd_entropy_Valence   \n",
-       "0           2.213364           2.219783         1.368922             1.066221  \\\n",
-       "1           1.888175           2.210679         1.557113             1.047221   \n",
-       "2           2.213364           2.232679         1.368922             1.029175   \n",
-       "3           2.213364           2.226222         1.368922             1.048834   \n",
-       "4           2.213364           2.206963         1.368922             1.096052   \n",
-       "...              ...                ...              ...                  ...   \n",
-       "21258       3.223710           3.519911         1.377820             0.913658   \n",
-       "21259       2.168944           2.038991         1.594167             1.337246   \n",
-       "21260       4.472136           4.781762         0.686962             0.450561   \n",
-       "21261       4.472136           4.665819         0.686962             0.577601   \n",
-       "21262       4.762203           4.242641         1.054920             0.970116   \n",
-       "\n",
-       "       wtd_range_Valence  std_Valence  wtd_std_Valence  criticaltemp  \n",
-       "0               1.085714     0.433013         0.437059         29.00  \n",
-       "1               1.128571     0.632456         0.468606         26.00  \n",
-       "2               1.114286     0.433013         0.444697         19.00  \n",
-       "3               1.100000     0.433013         0.440952         22.00  \n",
-       "4               1.057143     0.433013         0.428809         23.00  \n",
-       "...                  ...          ...              ...           ...  \n",
-       "21258           2.168889     0.433013         0.496904          2.44  \n",
-       "21259           0.904762     0.400000         0.212959        122.10  \n",
-       "21260           3.200000     0.500000         0.400000          1.98  \n",
-       "21261           2.210000     0.500000         0.462493          1.84  \n",
-       "21262           1.800000     1.414214         1.500000         12.80  \n",
-       "\n",
-       "[21263 rows x 80 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1fa40430",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean_atomic_mass</th>\n",
-       "      <th>wtd_mean_atomic_mass</th>\n",
-       "      <th>gmean_atomic_mass</th>\n",
-       "      <th>wtd_gmean_atomic_mass</th>\n",
-       "      <th>entropy_atomic_mass</th>\n",
-       "      <th>wtd_entropy_atomic_mass</th>\n",
-       "      <th>range_atomic_mass</th>\n",
-       "      <th>wtd_range_atomic_mass</th>\n",
-       "      <th>std_atomic_mass</th>\n",
-       "      <th>wtd_std_atomic_mass</th>\n",
-       "      <th>...</th>\n",
-       "      <th>mean_Valence</th>\n",
-       "      <th>wtd_mean_Valence</th>\n",
-       "      <th>gmean_Valence</th>\n",
-       "      <th>wtd_gmean_Valence</th>\n",
-       "      <th>entropy_Valence</th>\n",
-       "      <th>wtd_entropy_Valence</th>\n",
-       "      <th>wtd_range_Valence</th>\n",
-       "      <th>std_Valence</th>\n",
-       "      <th>wtd_std_Valence</th>\n",
-       "      <th>criticaltemp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.862692</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.116612</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.062396</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>31.794921</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.622535</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.257143</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.219783</td>\n",
-       "      <td>1.368922</td>\n",
-       "      <td>1.066221</td>\n",
-       "      <td>1.085714</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.437059</td>\n",
-       "      <td>29.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>92.729214</td>\n",
-       "      <td>58.518416</td>\n",
-       "      <td>73.132787</td>\n",
-       "      <td>36.396602</td>\n",
-       "      <td>1.449309</td>\n",
-       "      <td>1.057755</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>36.161939</td>\n",
-       "      <td>47.094633</td>\n",
-       "      <td>53.979870</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2.257143</td>\n",
-       "      <td>1.888175</td>\n",
-       "      <td>2.210679</td>\n",
-       "      <td>1.557113</td>\n",
-       "      <td>1.047221</td>\n",
-       "      <td>1.128571</td>\n",
-       "      <td>0.632456</td>\n",
-       "      <td>0.468606</td>\n",
-       "      <td>26.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.885242</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.122509</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>0.975980</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>35.741099</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.656268</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.271429</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.232679</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.029175</td>\n",
-       "      <td>1.114286</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.444697</td>\n",
-       "      <td>19.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.873967</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.119560</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.022291</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>33.768010</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.639405</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.264286</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.226222</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.048834</td>\n",
-       "      <td>1.100000</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.440952</td>\n",
-       "      <td>22.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>88.944468</td>\n",
-       "      <td>57.840143</td>\n",
-       "      <td>66.361592</td>\n",
-       "      <td>36.110716</td>\n",
-       "      <td>1.181795</td>\n",
-       "      <td>1.129224</td>\n",
-       "      <td>122.90607</td>\n",
-       "      <td>27.848743</td>\n",
-       "      <td>51.968828</td>\n",
-       "      <td>53.588771</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2.242857</td>\n",
-       "      <td>2.213364</td>\n",
-       "      <td>2.206963</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.096052</td>\n",
-       "      <td>1.057143</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.428809</td>\n",
-       "      <td>23.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21258</th>\n",
-       "      <td>106.957877</td>\n",
-       "      <td>53.095769</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>43.135565</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.254119</td>\n",
-       "      <td>146.88130</td>\n",
-       "      <td>15.504479</td>\n",
-       "      <td>65.764081</td>\n",
-       "      <td>43.202659</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>3.555556</td>\n",
-       "      <td>3.223710</td>\n",
-       "      <td>3.519911</td>\n",
-       "      <td>1.377820</td>\n",
-       "      <td>0.913658</td>\n",
-       "      <td>2.168889</td>\n",
-       "      <td>0.433013</td>\n",
-       "      <td>0.496904</td>\n",
-       "      <td>2.44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21259</th>\n",
-       "      <td>92.266740</td>\n",
-       "      <td>49.021367</td>\n",
-       "      <td>64.812662</td>\n",
-       "      <td>32.867748</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.571630</td>\n",
-       "      <td>188.38390</td>\n",
-       "      <td>7.353333</td>\n",
-       "      <td>69.232655</td>\n",
-       "      <td>50.148287</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>2.047619</td>\n",
-       "      <td>2.168944</td>\n",
-       "      <td>2.038991</td>\n",
-       "      <td>1.594167</td>\n",
-       "      <td>1.337246</td>\n",
-       "      <td>0.904762</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.212959</td>\n",
-       "      <td>122.10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21260</th>\n",
-       "      <td>99.663190</td>\n",
-       "      <td>95.609104</td>\n",
-       "      <td>99.433882</td>\n",
-       "      <td>95.464320</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.530198</td>\n",
-       "      <td>13.51362</td>\n",
-       "      <td>53.041104</td>\n",
-       "      <td>6.756810</td>\n",
-       "      <td>5.405448</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>4.800000</td>\n",
-       "      <td>4.472136</td>\n",
-       "      <td>4.781762</td>\n",
-       "      <td>0.686962</td>\n",
-       "      <td>0.450561</td>\n",
-       "      <td>3.200000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>1.98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21261</th>\n",
-       "      <td>99.663190</td>\n",
-       "      <td>97.095602</td>\n",
-       "      <td>99.433882</td>\n",
-       "      <td>96.901083</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.640883</td>\n",
-       "      <td>13.51362</td>\n",
-       "      <td>31.115202</td>\n",
-       "      <td>6.756810</td>\n",
-       "      <td>6.249958</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>4.690000</td>\n",
-       "      <td>4.472136</td>\n",
-       "      <td>4.665819</td>\n",
-       "      <td>0.686962</td>\n",
-       "      <td>0.577601</td>\n",
-       "      <td>2.210000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.462493</td>\n",
-       "      <td>1.84</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21262</th>\n",
-       "      <td>87.468333</td>\n",
-       "      <td>86.858500</td>\n",
-       "      <td>82.555758</td>\n",
-       "      <td>80.458722</td>\n",
-       "      <td>1.041270</td>\n",
-       "      <td>0.895229</td>\n",
-       "      <td>71.75500</td>\n",
-       "      <td>43.144000</td>\n",
-       "      <td>29.905282</td>\n",
-       "      <td>33.927941</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.00</td>\n",
-       "      <td>4.500000</td>\n",
-       "      <td>4.762203</td>\n",
-       "      <td>4.242641</td>\n",
-       "      <td>1.054920</td>\n",
-       "      <td>0.970116</td>\n",
-       "      <td>1.800000</td>\n",
-       "      <td>1.414214</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>12.80</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>21263 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       mean_atomic_mass  wtd_mean_atomic_mass  gmean_atomic_mass   \n",
-       "0             88.944468             57.862692          66.361592  \\\n",
-       "1             92.729214             58.518416          73.132787   \n",
-       "2             88.944468             57.885242          66.361592   \n",
-       "3             88.944468             57.873967          66.361592   \n",
-       "4             88.944468             57.840143          66.361592   \n",
-       "...                 ...                   ...                ...   \n",
-       "21258        106.957877             53.095769                NaN   \n",
-       "21259         92.266740             49.021367          64.812662   \n",
-       "21260         99.663190             95.609104          99.433882   \n",
-       "21261         99.663190             97.095602          99.433882   \n",
-       "21262         87.468333             86.858500          82.555758   \n",
-       "\n",
-       "       wtd_gmean_atomic_mass  entropy_atomic_mass  wtd_entropy_atomic_mass   \n",
-       "0                  36.116612             1.181795                 1.062396  \\\n",
-       "1                  36.396602             1.449309                 1.057755   \n",
-       "2                  36.122509             1.181795                 0.975980   \n",
-       "3                  36.119560             1.181795                 1.022291   \n",
-       "4                  36.110716             1.181795                 1.129224   \n",
-       "...                      ...                  ...                      ...   \n",
-       "21258              43.135565                  NaN                 1.254119   \n",
-       "21259              32.867748                  NaN                 1.571630   \n",
-       "21260              95.464320                  NaN                 0.530198   \n",
-       "21261              96.901083                  NaN                 0.640883   \n",
-       "21262              80.458722             1.041270                 0.895229   \n",
-       "\n",
-       "       range_atomic_mass  wtd_range_atomic_mass  std_atomic_mass   \n",
-       "0              122.90607              31.794921        51.968828  \\\n",
-       "1              122.90607              36.161939        47.094633   \n",
-       "2              122.90607              35.741099        51.968828   \n",
-       "3              122.90607              33.768010        51.968828   \n",
-       "4              122.90607              27.848743        51.968828   \n",
-       "...                  ...                    ...              ...   \n",
-       "21258          146.88130              15.504479        65.764081   \n",
-       "21259          188.38390               7.353333        69.232655   \n",
-       "21260           13.51362              53.041104         6.756810   \n",
-       "21261           13.51362              31.115202         6.756810   \n",
-       "21262           71.75500              43.144000        29.905282   \n",
-       "\n",
-       "       wtd_std_atomic_mass  ...  mean_Valence  wtd_mean_Valence   \n",
-       "0                53.622535  ...          2.25          2.257143  \\\n",
-       "1                53.979870  ...          2.00          2.257143   \n",
-       "2                53.656268  ...          2.25          2.271429   \n",
-       "3                53.639405  ...          2.25          2.264286   \n",
-       "4                53.588771  ...          2.25          2.242857   \n",
-       "...                    ...  ...           ...               ...   \n",
-       "21258            43.202659  ...          3.25          3.555556   \n",
-       "21259            50.148287  ...          2.20          2.047619   \n",
-       "21260             5.405448  ...          4.50          4.800000   \n",
-       "21261             6.249958  ...          4.50          4.690000   \n",
-       "21262            33.927941  ...          5.00          4.500000   \n",
-       "\n",
-       "       gmean_Valence  wtd_gmean_Valence  entropy_Valence  wtd_entropy_Valence   \n",
-       "0           2.213364           2.219783         1.368922             1.066221  \\\n",
-       "1           1.888175           2.210679         1.557113             1.047221   \n",
-       "2           2.213364           2.232679              NaN             1.029175   \n",
-       "3           2.213364           2.226222              NaN             1.048834   \n",
-       "4           2.213364           2.206963              NaN             1.096052   \n",
-       "...              ...                ...              ...                  ...   \n",
-       "21258       3.223710           3.519911         1.377820             0.913658   \n",
-       "21259       2.168944           2.038991         1.594167             1.337246   \n",
-       "21260       4.472136           4.781762         0.686962             0.450561   \n",
-       "21261       4.472136           4.665819         0.686962             0.577601   \n",
-       "21262       4.762203           4.242641         1.054920             0.970116   \n",
-       "\n",
-       "       wtd_range_Valence  std_Valence  wtd_std_Valence  criticaltemp  \n",
-       "0               1.085714     0.433013         0.437059         29.00  \n",
-       "1               1.128571     0.632456         0.468606         26.00  \n",
-       "2               1.114286     0.433013         0.444697         19.00  \n",
-       "3               1.100000     0.433013         0.440952         22.00  \n",
-       "4               1.057143     0.433013         0.428809         23.00  \n",
-       "...                  ...          ...              ...           ...  \n",
-       "21258           2.168889     0.433013         0.496904          2.44  \n",
-       "21259           0.904762     0.400000         0.212959        122.10  \n",
-       "21260           3.200000     0.500000         0.400000          1.98  \n",
-       "21261           2.210000     0.500000         0.462493          1.84  \n",
-       "21262           1.800000     1.414214         1.500000         12.80  \n",
-       "\n",
-       "[21263 rows x 80 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.add_holes(df_data, 0.1, 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "069532ab",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env_qolmat_dev",
-   "language": "python",
-   "name": "env_qolmat_dev"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.17"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/qolmat/benchmark/imputer_predictor.py b/qolmat/benchmark/imputer_predictor.py
new file mode 100644
index 00000000..9c39529e
--- /dev/null
+++ b/qolmat/benchmark/imputer_predictor.py
@@ -0,0 +1,1464 @@
+from typing import Dict, List, Tuple, Optional
+
+# import mlflow
+import pickle
+from pathlib import Path
+import pandas as pd
+import numpy as np
+import tqdm
+import re
+import scipy
+import time
+from scipy import stats
+
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import matplotlib.pyplot as plt
+import scikit_posthocs as sp
+
+from sklearn.model_selection import KFold
+
+from qolmat.benchmark.missing_patterns import _HoleGenerator
+from qolmat.benchmark import metrics as _imputation_metrics
+from qolmat.imputations.imputers import _Imputer
+
+from sklearn import linear_model
+
+
+class BenchmarkImputationPrediction:
+    def __init__(
+        self,
+        imputation_metrics: List = ["mae"],
+        prediction_metrics: List = ["mae"],
+        n_folds: int = 2,
+        n_masks: int = 2,
+    ):
+
+        self.imputation_metrics = imputation_metrics
+        self.prediction_metrics = prediction_metrics
+        self.n_folds = n_folds
+        self.n_masks = n_masks
+
+    def compare(
+        self,
+        df_data: pd.DataFrame,
+        columns_numerical: List[str],
+        columns_categorical: List[str],
+        file_path: str,
+        hole_generators: List,
+        imputation_pipelines,
+        target_prediction_pipeline_pairs,
+        imputation_columns: List[str] = [],
+    ):
+        self.columns = df_data.columns.to_list()
+        self.columns_numerical = columns_numerical
+        self.columns_categorical = columns_categorical
+        if len(imputation_columns) == 0:
+            self.imputation_columns = self.columns
+        else:
+            self.imputation_columns = imputation_columns
+
+        list_benchmark = []
+        for idx_fold, (idx_train, idx_test) in tqdm.tqdm(
+            enumerate(KFold(n_splits=self.n_folds).split(df_data)), position=0, desc="benchmark"
+        ):
+            df_train = df_data.iloc[idx_train, :]
+            df_test = df_data.iloc[idx_test, :]
+            for target_column, prediction_pipelines in tqdm.tqdm(
+                target_prediction_pipeline_pairs.items(),
+                position=1,
+                leave=True,
+                desc=f"n_fold={idx_fold}",
+            ):
+                feature_columns = [col for col in self.columns if col != target_column]
+                df_train_x = df_train[feature_columns]
+                df_test_x = df_test[feature_columns]
+
+                for hole_generator in tqdm.tqdm(
+                    hole_generators, position=1, leave=False, desc=f"target_column={target_column}"
+                ):
+                    if hole_generator is not None:
+                        hole_generator.subset = [
+                            col for col in feature_columns if col in self.imputation_columns
+                        ]
+                        hole_generator.n_splits = self.n_masks
+
+                        for idx_mask, (df_mask_train, df_mask_test) in enumerate(
+                            zip(hole_generator.split(df_train_x), hole_generator.split(df_test_x))
+                        ):
+                            for imputation_pipeline in tqdm.tqdm(
+                                imputation_pipelines, position=1, leave=False
+                            ):
+                                out_imputation, benchmark_imputation = self.benchmark_imputation(
+                                    imputation_pipeline,
+                                    feature_columns,
+                                    df_train,
+                                    df_test,
+                                    df_mask_train,
+                                    df_mask_test,
+                                )
+
+                                for prediction_pipeline in tqdm.tqdm(
+                                    prediction_pipelines, position=1, leave=False
+                                ):
+                                    benchmark_prediction = self.benchmark_prediction(
+                                        prediction_pipeline,
+                                        target_column,
+                                        feature_columns,
+                                        df_train,
+                                        df_test,
+                                        out_imputation["df_train_x_imputed"],
+                                        out_imputation["df_test_x_imputed"],
+                                        df_mask_train,
+                                        df_mask_test,
+                                    )
+
+                                    row_benchmark = self.get_row_benchmark(
+                                        df_train,
+                                        df_test,
+                                        idx_fold,
+                                        target_column,
+                                        hole_generator,
+                                        idx_mask,
+                                        imputation_pipeline,
+                                        prediction_pipeline,
+                                        benchmark_imputation,
+                                        benchmark_prediction,
+                                    )
+
+                                    list_benchmark.append(row_benchmark)
+                                    df_benchmark = pd.DataFrame(list_benchmark)
+                                    with open(file_path, "wb") as handle:
+                                        pickle.dump(
+                                            df_benchmark, handle, protocol=pickle.HIGHEST_PROTOCOL
+                                        )
+                    else:
+                        for prediction_pipeline in tqdm.tqdm(
+                            prediction_pipelines, position=1, leave=False
+                        ):
+                            benchmark_prediction = self.benchmark_prediction(
+                                prediction_pipeline,
+                                target_column,
+                                feature_columns,
+                                df_train,
+                                df_test,
+                                df_train_x,
+                                df_test_x,
+                                None,
+                                None,
+                            )
+
+                            row_benchmark = self.get_row_benchmark(
+                                df_train,
+                                df_test,
+                                idx_fold,
+                                target_column,
+                                None,
+                                np.nan,
+                                None,
+                                prediction_pipeline,
+                                None,
+                                benchmark_prediction,
+                            )
+
+                            list_benchmark.append(row_benchmark)
+                            df_benchmark = pd.DataFrame(list_benchmark)
+                            with open(file_path, "wb") as handle:
+                                pickle.dump(df_benchmark, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+        return df_benchmark
+
+    def compare_multiple(
+        self,
+        df_data: pd.DataFrame,
+        columns_numerical: List[str],
+        columns_categorical: List[str],
+        file_path: str,
+        hole_generators: List,
+        imputation_pipelines,
+        target_prediction_pipeline_pairs,
+        imputation_columns: List[str] = [],
+        n_imputations: int = 2,
+    ):
+        self.columns = df_data.columns.to_list()
+        self.columns_numerical = columns_numerical
+        self.columns_categorical = columns_categorical
+        if len(imputation_columns) == 0:
+            self.imputation_columns = self.columns
+        else:
+            self.imputation_columns = imputation_columns
+        self.n_imputations = n_imputations
+
+        list_benchmark = []
+        for idx_fold, (idx_train, idx_test) in tqdm.tqdm(
+            enumerate(KFold(n_splits=self.n_folds).split(df_data)), position=0, desc="benchmark"
+        ):
+            df_train = df_data.iloc[idx_train, :]
+            df_test = df_data.iloc[idx_test, :]
+            for target_column, prediction_pipelines in tqdm.tqdm(
+                target_prediction_pipeline_pairs.items(),
+                position=1,
+                leave=True,
+                desc=f"n_fold={idx_fold}",
+            ):
+                feature_columns = [col for col in self.columns if col != target_column]
+                df_train_x = df_train[feature_columns]
+                df_test_x = df_test[feature_columns]
+
+                for hole_generator in tqdm.tqdm(
+                    hole_generators, position=1, leave=False, desc=f"target_column={target_column}"
+                ):
+                    if hole_generator is not None:
+                        hole_generator.subset = [
+                            col for col in feature_columns if col in self.imputation_columns
+                        ]
+                        hole_generator.n_splits = self.n_masks
+
+                        for idx_mask, (df_mask_train, df_mask_test) in enumerate(
+                            zip(hole_generator.split(df_train_x), hole_generator.split(df_test_x))
+                        ):
+                            for imputation_pipeline in tqdm.tqdm(
+                                imputation_pipelines, position=1, leave=False
+                            ):
+                                (
+                                    out_imputations,
+                                    benchmark_imputations,
+                                ) = self.benchmark_imputation_multiple(
+                                    imputation_pipeline,
+                                    feature_columns,
+                                    df_train,
+                                    df_test,
+                                    df_mask_train,
+                                    df_mask_test,
+                                )
+                                for idx_imputation, (
+                                    out_imputation,
+                                    benchmark_imputation,
+                                ) in tqdm.tqdm(
+                                    enumerate(zip(out_imputations, benchmark_imputations)),
+                                    position=1,
+                                    leave=False,
+                                ):
+                                    for prediction_pipeline in tqdm.tqdm(
+                                        prediction_pipelines, position=1, leave=False
+                                    ):
+                                        benchmark_prediction = self.benchmark_prediction(
+                                            prediction_pipeline,
+                                            target_column,
+                                            feature_columns,
+                                            df_train,
+                                            df_test,
+                                            out_imputation["df_train_x_imputed"],
+                                            out_imputation["df_test_x_imputed"],
+                                            df_mask_train,
+                                            df_mask_test,
+                                        )
+
+                                        row_benchmark = self.get_row_benchmark(
+                                            df_train,
+                                            df_test,
+                                            idx_fold,
+                                            target_column,
+                                            hole_generator,
+                                            idx_mask,
+                                            imputation_pipeline,
+                                            prediction_pipeline,
+                                            benchmark_imputation,
+                                            benchmark_prediction,
+                                        )
+                                        row_benchmark["n_imputation"] = idx_imputation
+
+                                        list_benchmark.append(row_benchmark)
+                                        df_benchmark = pd.DataFrame(list_benchmark)
+                                        with open(file_path, "wb") as handle:
+                                            pickle.dump(
+                                                df_benchmark,
+                                                handle,
+                                                protocol=pickle.HIGHEST_PROTOCOL,
+                                            )
+                    else:
+                        for prediction_pipeline in tqdm.tqdm(
+                            prediction_pipelines, position=1, leave=False
+                        ):
+                            benchmark_prediction = self.benchmark_prediction(
+                                prediction_pipeline,
+                                target_column,
+                                feature_columns,
+                                df_train,
+                                df_test,
+                                df_train_x,
+                                df_test_x,
+                                None,
+                                None,
+                            )
+
+                            row_benchmark = self.get_row_benchmark(
+                                df_train,
+                                df_test,
+                                idx_fold,
+                                target_column,
+                                None,
+                                np.nan,
+                                None,
+                                prediction_pipeline,
+                                None,
+                                benchmark_prediction,
+                            )
+
+                            list_benchmark.append(row_benchmark)
+                            df_benchmark = pd.DataFrame(list_benchmark)
+                            with open(file_path, "wb") as handle:
+                                pickle.dump(df_benchmark, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+        return df_benchmark
+
+    def benchmark_imputation(
+        self,
+        imputation_pipeline,
+        feature_columns,
+        df_train,
+        df_test,
+        df_mask_train,
+        df_mask_test,
+    ):
+        feature_columns_ = [col for col in feature_columns if col in self.imputation_columns]
+        df_train_x = df_train[feature_columns]
+        df_test_x = df_test[feature_columns]
+
+        df_train_x_corrupted = df_train_x.copy()
+        df_train_x_corrupted[df_mask_train] = np.nan
+
+        df_test_x_corrupted = df_test_x.copy()
+        df_test_x_corrupted[df_mask_test] = np.nan
+
+        benchmark = None
+        df_train_x_imputed = df_train_x_corrupted
+        df_test_x_imputed = df_test_x_corrupted
+        if imputation_pipeline is not None:
+            if "transformer_x" in imputation_pipeline:
+                transformer_imputation_x = imputation_pipeline["transformer_x"]
+            else:
+                transformer_imputation_x = None
+            imputer = imputation_pipeline["imputer"]
+
+            if transformer_imputation_x is not None:
+                # Suppose that all categories/values are known
+                df_data_x = pd.concat([df_train_x, df_test_x], axis=0)
+                transformer_imputation_x = transformer_imputation_x.fit(df_data_x)
+
+                df_train_x_transformed_corrupted = pd.DataFrame(
+                    transformer_imputation_x.transform(df_train_x_corrupted),
+                    columns=transformer_imputation_x.get_feature_names_out(
+                        df_train_x_corrupted.columns
+                    ),
+                    index=df_train_x_corrupted.index,
+                )
+                df_test_x_transformed_corrupted = pd.DataFrame(
+                    transformer_imputation_x.transform(df_test_x_corrupted),
+                    columns=transformer_imputation_x.get_feature_names_out(
+                        df_test_x_corrupted.columns
+                    ),
+                    index=df_test_x_corrupted.index,
+                )
+            else:
+                df_train_x_transformed_corrupted = df_train_x_corrupted
+                df_test_x_transformed_corrupted = df_test_x_corrupted
+
+            start_time = time.time()
+            imputer = imputer.fit(df_train_x_transformed_corrupted)
+            duration_imputation_fit = time.time() - start_time
+
+            start_time = time.time()
+            df_train_x_transformed_imputed = imputer.transform(df_train_x_transformed_corrupted)
+            duration_imputation_transform_train = time.time() - start_time
+
+            start_time = time.time()
+            df_test_x_transformed_imputed = imputer.transform(df_test_x_transformed_corrupted)
+            duration_imputation_transform_test = time.time() - start_time
+
+            if transformer_imputation_x is not None:
+                df_train_x_imputed = self.inverse_transform(
+                    transformer_imputation_x, df_train_x_transformed_imputed
+                )
+                df_test_x_imputed = self.inverse_transform(
+                    transformer_imputation_x, df_test_x_transformed_imputed
+                )
+            else:
+                df_train_x_imputed = df_train_x_transformed_imputed
+                df_test_x_imputed = df_test_x_transformed_imputed
+
+            (
+                dict_imp_score_mean_train,
+                dict_imp_scores_train,
+            ) = self.get_imputation_scores_by_dataframe(
+                df_train_x[feature_columns_],
+                df_train_x_imputed[feature_columns_],
+                df_mask_train[feature_columns_],
+            )
+
+            (
+                dict_imp_score_mean_test,
+                dict_imp_scores_test,
+            ) = self.get_imputation_scores_by_dataframe(
+                df_test_x[feature_columns_],
+                df_test_x_imputed[feature_columns_],
+                df_mask_test[feature_columns_],
+            )
+
+            benchmark = {
+                "dict_imp_score_mean_train": dict_imp_score_mean_train,
+                "dict_imp_scores_train": dict_imp_scores_train,
+                "dict_imp_score_mean_test": dict_imp_score_mean_test,
+                "dict_imp_scores_test": dict_imp_scores_test,
+                "duration_imputation_fit": duration_imputation_fit,
+                "duration_imputation_transform_train": duration_imputation_transform_train,
+                "duration_imputation_transform_test": duration_imputation_transform_test,
+            }
+
+        output = {
+            "df_train_x_imputed": df_train_x_imputed,
+            "df_test_x_imputed": df_test_x_imputed,
+        }
+
+        return output, benchmark
+
+    def benchmark_imputation_multiple(
+        self,
+        imputation_pipeline,
+        feature_columns,
+        df_train,
+        df_test,
+        df_mask_train,
+        df_mask_test,
+    ):
+        feature_columns_ = [col for col in feature_columns if col in self.imputation_columns]
+        df_train_x = df_train[feature_columns]
+        df_test_x = df_test[feature_columns]
+
+        df_train_x_corrupted = df_train_x.copy()
+        df_train_x_corrupted[df_mask_train] = np.nan
+
+        df_test_x_corrupted = df_test_x.copy()
+        df_test_x_corrupted[df_mask_test] = np.nan
+
+        benchmark = None
+        df_train_x_imputed = df_train_x_corrupted
+        df_test_x_imputed = df_test_x_corrupted
+        if imputation_pipeline is not None:
+            if "transformer_x" in imputation_pipeline:
+                transformer_imputation_x = imputation_pipeline["transformer_x"]
+            else:
+                transformer_imputation_x = None
+            imputer = imputation_pipeline["imputer"]
+
+            if transformer_imputation_x is not None:
+                # Suppose that all categories/values are known
+                df_data_x = pd.concat([df_train_x, df_test_x], axis=0)
+                transformer_imputation_x = transformer_imputation_x.fit(df_data_x)
+
+                df_train_x_transformed_corrupted = pd.DataFrame(
+                    transformer_imputation_x.transform(df_train_x_corrupted),
+                    columns=transformer_imputation_x.get_feature_names_out(
+                        df_train_x_corrupted.columns
+                    ),
+                    index=df_train_x_corrupted.index,
+                )
+                df_test_x_transformed_corrupted = pd.DataFrame(
+                    transformer_imputation_x.transform(df_test_x_corrupted),
+                    columns=transformer_imputation_x.get_feature_names_out(
+                        df_test_x_corrupted.columns
+                    ),
+                    index=df_test_x_corrupted.index,
+                )
+            else:
+                df_train_x_transformed_corrupted = df_train_x_corrupted
+                df_test_x_transformed_corrupted = df_test_x_corrupted
+
+            start_time = time.time()
+            imputer = imputer.fit(df_train_x_transformed_corrupted)
+            duration_imputation_fit = time.time() - start_time
+
+            start_time = time.time()
+            df_train_x_transformed_imputed = imputer.transform(df_train_x_transformed_corrupted)
+            duration_imputation_transform_train = time.time() - start_time
+
+            start_time = time.time()
+            df_test_x_transformed_imputed = imputer.transform(df_test_x_transformed_corrupted)
+            duration_imputation_transform_test = time.time() - start_time
+
+            if transformer_imputation_x is not None:
+                df_train_x_imputed = self.inverse_transform(
+                    transformer_imputation_x, df_train_x_transformed_imputed
+                )
+                df_test_x_imputed = self.inverse_transform(
+                    transformer_imputation_x, df_test_x_transformed_imputed
+                )
+            else:
+                df_train_x_imputed = df_train_x_transformed_imputed
+                df_test_x_imputed = df_test_x_transformed_imputed
+
+            (
+                dict_imp_score_mean_train,
+                dict_imp_scores_train,
+            ) = self.get_imputation_scores_by_dataframe(
+                df_train_x[feature_columns_],
+                df_train_x_imputed[feature_columns_],
+                df_mask_train[feature_columns_],
+            )
+
+            (
+                dict_imp_score_mean_test,
+                dict_imp_scores_test,
+            ) = self.get_imputation_scores_by_dataframe(
+                df_test_x[feature_columns_],
+                df_test_x_imputed[feature_columns_],
+                df_mask_test[feature_columns_],
+            )
+
+            benchmark = {
+                "dict_imp_score_mean_train": dict_imp_score_mean_train,
+                "dict_imp_scores_train": dict_imp_scores_train,
+                "dict_imp_score_mean_test": dict_imp_score_mean_test,
+                "dict_imp_scores_test": dict_imp_scores_test,
+                "duration_imputation_fit": duration_imputation_fit,
+                "duration_imputation_transform_train": duration_imputation_transform_train,
+                "duration_imputation_transform_test": duration_imputation_transform_test,
+            }
+
+        output = {
+            "df_train_x_imputed": df_train_x_imputed,
+            "df_test_x_imputed": df_test_x_imputed,
+        }
+
+        return output, benchmark
+
+    def inverse_transform(self, transformer, df_transformed):
+        df_reversed = pd.DataFrame()
+        for transformer_values in transformer.transformers_:
+            cols_in = transformer_values[2]
+            cols_out = transformer.get_feature_names_out(cols_in)
+            df_reversed[cols_in] = transformer_values[1].inverse_transform(
+                df_transformed[cols_out]
+            )
+        df_reversed.index = df_transformed.index
+        return df_reversed
+
+    def benchmark_prediction(
+        self,
+        prediction_pipeline,
+        target_column,
+        feature_columns,
+        df_train,
+        df_test,
+        df_train_x_imputed,
+        df_test_x_imputed,
+        df_mask_train,
+        df_mask_test,
+    ):
+        predictor = prediction_pipeline["predictor"]
+        if "transformer_x" in prediction_pipeline:
+            transformer_prediction_x = prediction_pipeline["transformer_x"]
+        else:
+            transformer_prediction_x = None
+        if "transformer_y" in prediction_pipeline:
+            transformer_prediction_y = prediction_pipeline["transformer_y"]
+        else:
+            transformer_prediction_y = None
+        if "handle_nan" in prediction_pipeline:
+            handle_nan = prediction_pipeline["handle_nan"]
+        else:
+            handle_nan = False
+        if "add_nan_indicator" in prediction_pipeline:
+            add_nan_indicator = prediction_pipeline["add_nan_indicator"]
+        else:
+            add_nan_indicator = True
+
+        # df_train_x = df_train[feature_columns]
+        df_test_x = df_test[feature_columns]
+
+        df_train_y = df_train[[target_column]]
+        df_test_y = df_test[[target_column]]
+
+        if (
+            df_train_x_imputed.isna().sum().sum() > 0 or df_test_x_imputed.isna().sum().sum() > 0
+        ) and not handle_nan:
+            return None
+
+        if transformer_prediction_x is not None and transformer_prediction_y is not None:
+            # Suppose that all categories/values are known
+            df_data_x_imputed = pd.concat([df_train_x_imputed, df_test_x_imputed], axis=0)
+            df_data_y = pd.concat([df_train_y, df_test_y], axis=0)
+            transformer_prediction_x = transformer_prediction_x.fit(df_data_x_imputed)
+
+            df_train_x_transformed_imputed = transformer_prediction_x.transform(df_train_x_imputed)
+
+            # Evaluate prediction performance on imputed test set
+            df_test_x_transformed_imputed = transformer_prediction_x.transform(df_test_x_imputed)
+
+            # Evaluate prediction performance on reference test set
+            df_test_x_transformed_notnan = transformer_prediction_x.transform(df_test_x)
+
+            transformer_prediction_y = transformer_prediction_y.fit(df_data_y)
+
+            df_train_y_transformed = transformer_prediction_y.transform(df_train_y)
+
+        else:
+            df_train_x_transformed_imputed = df_train_x_imputed
+            df_train_y_transformed = df_train_y
+            df_test_x_transformed_imputed = df_test_x_imputed
+            df_test_x_transformed_notnan = df_test_x
+
+        # add indicator for missing values into x
+        if df_mask_train is not None and df_mask_test is not None and add_nan_indicator:
+            df_train_x_input = np.concatenate(
+                [df_train_x_transformed_imputed, df_mask_train.astype(int).values], axis=1
+            )
+            df_test_x_input_imputed = np.concatenate(
+                [df_test_x_transformed_imputed, df_mask_test.astype(int).values], axis=1
+            )
+            df_mask_test_ = np.zeros(df_mask_test.shape)
+            df_test_x_input_notnan = np.concatenate(
+                [df_test_x_transformed_notnan, df_mask_test_], axis=1
+            )
+        else:
+            df_train_x_input = df_train_x_transformed_imputed
+            df_test_x_input_imputed = df_test_x_transformed_imputed
+            df_test_x_input_notnan = df_test_x_transformed_notnan
+
+        # predictor fit
+        start_time = time.time()
+        predictor = predictor.fit(
+            df_train_x_input,
+            np.squeeze(df_train_y_transformed),
+        )
+        duration_prediction_fit = time.time() - start_time
+
+        if transformer_prediction_y is not None:
+            # predictor predict for test without nan
+            df_test_y_transformed_notnan_predicted = predictor.predict(df_test_x_input_notnan)
+            df_test_y_transformed_notnan_predicted = pd.DataFrame(
+                df_test_y_transformed_notnan_predicted,
+                columns=transformer_prediction_y.get_feature_names_out([target_column]),
+                index=df_test_y.index,
+            )
+            # predictor predict for test with nan
+            start_time = time.time()
+            df_test_y_transformed_imputed_predicted = predictor.predict(df_test_x_input_imputed)
+            duration_prediction_transform = time.time() - start_time
+
+            df_test_y_transformed_imputed_predicted = pd.DataFrame(
+                df_test_y_transformed_imputed_predicted,
+                columns=transformer_prediction_y.get_feature_names_out([target_column]),
+                index=df_test_y.index,
+            )
+
+            df_test_y_reversed_notnan_predicted = self.inverse_transform(
+                transformer_prediction_y, df_test_y_transformed_notnan_predicted
+            )
+            df_test_y_reversed_imputed_predicted = self.inverse_transform(
+                transformer_prediction_y, df_test_y_transformed_imputed_predicted
+            )
+        else:
+            # predictor predict for test without nan
+            df_test_y_reversed_notnan_predicted = predictor.predict(df_test_x_input_notnan)
+            df_test_y_reversed_notnan_predicted = pd.DataFrame(
+                df_test_y_reversed_notnan_predicted,
+                columns=[target_column],
+                index=df_test_y.index,
+            )
+
+            # predictor predict for test with nan
+            start_time = time.time()
+            df_test_y_reversed_imputed_predicted = predictor.predict(df_test_x_input_imputed)
+            duration_prediction_transform = time.time() - start_time
+
+            df_test_y_reversed_imputed_predicted = pd.DataFrame(
+                df_test_y_reversed_imputed_predicted,
+                columns=[target_column],
+                index=df_test_y.index,
+            )
+
+        (
+            dict_pred_score_mean_test_notnan,
+            dict_pred_scores_test_notnan,
+        ) = self.get_prediction_scores_by_column(
+            df_test_y, df_test_y_reversed_notnan_predicted, key="notnan"
+        )
+
+        (
+            dict_pred_score_mean_test_nan,
+            dict_pred_scores_test_nan,
+        ) = self.get_prediction_scores_by_column(
+            df_test_y, df_test_y_reversed_imputed_predicted, key="nan"
+        )
+
+        output = {
+            "dict_pred_score_mean_test_nan": dict_pred_score_mean_test_nan,
+            "dict_pred_scores_test_nan": dict_pred_scores_test_nan,
+            "dict_pred_score_mean_test_notnan": dict_pred_score_mean_test_notnan,
+            "dict_pred_scores_test_notnan": dict_pred_scores_test_notnan,
+            "duration_prediction_fit": duration_prediction_fit,
+            "duration_prediction_transform": duration_prediction_transform,
+        }
+
+        return output
+
+    def get_row_benchmark(
+        self,
+        df_train,
+        df_test,
+        idx_fold,
+        target_column,
+        hole_generator,
+        idx_mask,
+        imputation_pipeline,
+        prediction_pipeline,
+        benchmark_imputation,
+        benchmark_prediction,
+    ):
+
+        if target_column in self.columns_numerical:
+            prediction_task = "regression"
+        elif target_column in self.columns_categorical:
+            prediction_task = "classification"
+        else:
+            prediction_task = "unknown"
+
+        predictor = prediction_pipeline["predictor"]
+        if "transformer_x" in prediction_pipeline:
+            transformer_prediction_x = prediction_pipeline["transformer_x"]
+        else:
+            transformer_prediction_x = None
+        if "transformer_y" in prediction_pipeline:
+            transformer_prediction_y = prediction_pipeline["transformer_y"]
+        else:
+            transformer_prediction_y = None
+        if "handle_nan" in prediction_pipeline:
+            handle_nan = prediction_pipeline["handle_nan"]
+        else:
+            handle_nan = False
+        if "add_nan_indicator" in prediction_pipeline:
+            add_nan_indicator = prediction_pipeline["add_nan_indicator"]
+        else:
+            add_nan_indicator = True
+        if "add_nan_indicator" in prediction_pipeline:
+            add_nan_indicator = prediction_pipeline["add_nan_indicator"]
+        else:
+            add_nan_indicator = True
+        if transformer_prediction_x is not None:
+            tran_pre_name_x = "+".join(
+                set([tf[1].__class__.__name__ for tf in transformer_prediction_x.transformers])
+            )
+        else:
+            tran_pre_name_x = "None"
+        if transformer_prediction_y is not None:
+            tran_pre_name_y = "+".join(
+                set([tf[1].__class__.__name__ for tf in transformer_prediction_y.transformers])
+            )
+        else:
+            tran_pre_name_y = "None"
+
+        if hole_generator is not None:
+            if imputation_pipeline is not None:
+                if "transformer_x" in imputation_pipeline:
+                    transformer_imputation_x = imputation_pipeline["transformer_x"]
+                else:
+                    transformer_imputation_x = None
+                imputer = imputation_pipeline["imputer"]
+
+                if transformer_imputation_x is not None:
+                    tran_imp_name = "+".join(
+                        set(
+                            [
+                                tf[1].__class__.__name__
+                                for tf in transformer_imputation_x.transformers_
+                            ]
+                        )
+                    )
+                else:
+                    tran_imp_name = "None"
+                imputer_name = imputer.__class__.__name__
+            else:
+                tran_imp_name = "None"
+                imputer_name = "None"
+
+            hole_generator_name = hole_generator.__class__.__name__
+            ratio_masked = hole_generator.ratio_masked
+        else:
+            hole_generator_name = "None"
+            ratio_masked = 0
+            tran_imp_name = "None"
+            imputer_name = "None"
+
+        row_benchmark = {
+            "n_fold": idx_fold,
+            "size_train_set": len(df_train),
+            "size_test_set": len(df_test),
+            "n_columns": len(self.columns),
+            "n_mask": idx_mask,
+            "hole_generator": hole_generator_name,
+            "ratio_masked": ratio_masked,
+            "transformer_imputation": tran_imp_name,
+            "imputer": imputer_name,
+            "target_column": target_column,
+            "prediction_task": prediction_task,
+            "transformer_prediction_x": tran_pre_name_x,
+            "transformer_prediction_y": tran_pre_name_y,
+            "predictor": predictor.__class__.__name__,
+            "handle_nan": handle_nan,
+            "add_nan_indicator": add_nan_indicator,
+        }
+
+        if benchmark_imputation is not None:
+            dict_imp_score_mean_train = benchmark_imputation["dict_imp_score_mean_train"]
+            dict_imp_scores_train = benchmark_imputation["dict_imp_scores_train"]
+            dict_imp_score_mean_test = benchmark_imputation["dict_imp_score_mean_test"]
+            dict_imp_scores_test = benchmark_imputation["dict_imp_scores_test"]
+
+            dict_imp_score_mean_train_ = dict(
+                (f"{k}_train_set", v) for k, v in dict_imp_score_mean_train.items()
+            )
+            dict_imp_score_mean_test_ = dict(
+                (f"{k}_test_set", v) for k, v in dict_imp_score_mean_test.items()
+            )
+
+            row_benchmark = {**row_benchmark, **dict_imp_score_mean_train_}
+            row_benchmark = {**row_benchmark, **dict_imp_score_mean_test_}
+            row_benchmark["imputation_scores_trainset"] = dict_imp_scores_train
+            row_benchmark["imputation_scores_testset"] = dict_imp_scores_test
+            row_benchmark["duration_imputation_fit"] = benchmark_imputation[
+                "duration_imputation_fit"
+            ]
+            row_benchmark["duration_imputation_transform_train"] = benchmark_imputation[
+                "duration_imputation_transform_train"
+            ]
+            row_benchmark["duration_imputation_transform_test"] = benchmark_imputation[
+                "duration_imputation_transform_test"
+            ]
+
+        if benchmark_prediction is not None:
+            dict_pred_score_mean_test_nan = benchmark_prediction["dict_pred_score_mean_test_nan"]
+            dict_pred_scores_test_nan = benchmark_prediction["dict_pred_scores_test_nan"]
+            dict_pred_score_mean_test_notnan = benchmark_prediction[
+                "dict_pred_score_mean_test_notnan"
+            ]
+            dict_pred_scores_test_notnan = benchmark_prediction["dict_pred_scores_test_notnan"]
+            row_benchmark = {
+                **row_benchmark,
+                **dict_pred_score_mean_test_nan,
+                **dict_pred_score_mean_test_notnan,
+            }
+            row_benchmark["prediction_scores_testset_nan"] = dict_pred_scores_test_nan
+            row_benchmark["prediction_scores_testset_notnan"] = dict_pred_scores_test_notnan
+            row_benchmark["duration_prediction_fit"] = benchmark_prediction[
+                "duration_prediction_fit"
+            ]
+            row_benchmark["duration_prediction_transform"] = benchmark_prediction[
+                "duration_prediction_transform"
+            ]
+
+        # print({
+        #     "n_fold": idx_fold,
+        #     "target_column": target_column,
+        #     "hole_generator": hole_generator_name,
+        #     "ratio_masked": ratio_masked,
+        #     "n_mask": idx_mask,
+        #     "transformer_imputation": tran_imp_name,
+        #     "imputer": imputer_name,
+        #     "transformer_prediction": tran_pre_name,
+        #     "predictor": predictor.__class__.__name__,
+        # })
+
+        return row_benchmark
+
+    def get_imputation_scores_by_dataframe(self, df_true, df_imputed, df_mask):
+        dict_score_mean = {}
+        dict_scores = {}
+
+        columns_nan = df_mask.columns[(df_mask == True).any()].to_list()
+        for metric in self.imputation_metrics:
+            func_metric = _imputation_metrics.get_metric(metric)
+            score_by_col = func_metric(
+                df_true[columns_nan], df_imputed[columns_nan], df_mask[columns_nan]
+            )
+            dict_scores[f"imputation_score_{metric}"] = score_by_col.to_dict()
+            dict_score_mean[f"imputation_score_{metric}"] = score_by_col.mean()
+        return dict_score_mean, dict_scores
+
+    def get_prediction_scores_by_column(self, df_true, df_imputed, key=""):
+        dict_score_mean = {}
+        dict_scores = {}
+
+        df_mask = df_true.notnull()
+        for metric in self.prediction_metrics:
+            func_metric = _imputation_metrics.get_metric(metric)
+            try:
+                score_by_col = func_metric(df_true, df_imputed, df_mask)
+            except Exception:
+                score_by_col = pd.Series(
+                    [np.nan for col in df_true.columns], index=df_true.columns
+                )
+
+            dict_scores[f"prediction_score_{key}_{metric}"] = score_by_col.to_dict()
+            dict_score_mean[f"prediction_score_{key}_{metric}"] = score_by_col.mean()
+        return dict_score_mean, dict_scores
+
+
+def highlight_best(x, color="green"):
+    if re.search("|".join(["f1_score", "roc_auc_score"]), "_".join(x.name)):
+        return [f"background: {color}" if v == x.max() else "" for v in x]
+    else:
+        return [f"background: {color}" if v == x.min() else "" for v in x]
+
+
+def get_benchmark_aggregate(
+    df, cols_groupby=["imputer", "predictor"], agg_func=pd.DataFrame.mean, keep_values=False
+):
+    metrics = [col for col in df.columns if "_score_" in col]
+    durations = [col for col in df.columns if "duration_" in col]
+    if cols_groupby is None:
+        cols_groupby = [col for col in df.columns if col not in metrics and col not in durations]
+    df_groupby = df.groupby(cols_groupby)[metrics + durations].apply(agg_func)
+
+    if keep_values:
+        for metric in metrics:
+            df_groupby[f"{metric}_values"] = df.groupby(cols_groupby)[metric].apply(list)
+        for duration in durations:
+            df_groupby[f"{duration}_values"] = df.groupby(cols_groupby)[duration].apply(list)
+    cols_imputation = [col for col in df_groupby.columns if "imputation_score_" in col]
+    cols_prediction = [col for col in df_groupby.columns if "prediction_score_" in col]
+    cols_train_set = [col for col in df_groupby.columns if "_train_set" in col]
+    cols_test_set = [col for col in df_groupby.columns if "_test_set" in col]
+
+    cols_duration_imputation = [col for col in df_groupby.columns if "_imputation_" in col]
+    cols_duration_prediction = [col for col in df_groupby.columns if "_prediction_" in col]
+
+    cols_multi_index = []
+    for col in df_groupby.columns:
+        if col in cols_imputation and col in cols_train_set:
+            cols_multi_index.append(
+                (
+                    "imputation_score",
+                    "train_set",
+                    col.replace("imputation_score_", "").replace("_train_set", ""),
+                )
+            )
+        if col in cols_imputation and col in cols_test_set:
+            cols_multi_index.append(
+                (
+                    "imputation_score",
+                    "test_set",
+                    col.replace("imputation_score_", "").replace("_test_set", ""),
+                )
+            )
+        if col in cols_prediction:
+            if "notnan" in col:
+                cols_multi_index.append(
+                    (
+                        "prediction_score",
+                        "test_set_not_nan",
+                        col.replace("prediction_score_notnan_", ""),
+                    )
+                )
+            else:
+                cols_multi_index.append(
+                    (
+                        "prediction_score",
+                        "test_set_with_nan",
+                        col.replace("prediction_score_nan_", ""),
+                    )
+                )
+        if col in cols_duration_imputation:
+            cols_multi_index.append(
+                (
+                    "duration",
+                    "imputation",
+                    col.replace("duration_imputation_", ""),
+                )
+            )
+        if col in cols_duration_prediction:
+            cols_multi_index.append(
+                (
+                    "duration",
+                    "prediction",
+                    col.replace("duration_prediction_", ""),
+                )
+            )
+
+    df_groupby.columns = pd.MultiIndex.from_tuples(cols_multi_index)
+    return df_groupby
+
+
+# def visualize_mlflow(df, exp_name):
+#     cols_mean_on = ["n_fold", "n_mask"]
+#     cols_full_scores = [col for col in df.columns if "_scores" in col]
+#     metrics = [col for col in df.columns if "_score_" in col]
+#     cols_groupby = [
+#         col for col in df.columns if col not in metrics + cols_mean_on + cols_full_scores
+#     ]
+#     df_groupby = df.groupby(cols_groupby)[metrics].mean()
+
+#     experiment_id = mlflow.create_experiment(name=exp_name)
+#     num_index = np.prod([len(df[col].unique()) for col in cols_mean_on])
+#     for idx in df_groupby.index:
+#         dict_settings = dict(zip(df_groupby.index.names, idx))
+#         with mlflow.start_run(
+#             experiment_id=experiment_id, run_name=dict_settings["target_column"]
+#         ) as run:
+#             query = " and ".join([f"{k} == {repr(v)}" for k, v in dict_settings.items()])
+
+#             for col in cols_mean_on:
+#                 dict_settings[col] = len(df[col].unique())
+#                 dict_settings[f"{col}_values"] = df[col].unique()
+#             dict_scores = df_groupby.loc[idx][metrics].to_dict()
+
+#             mlflow.log_params(dict_settings)
+#             mlflow.log_metrics(dict_scores)
+
+#             df_query = df.query(query)
+#             for col_full_scores in cols_full_scores:
+#                 if df_query[col_full_scores].notna().all():
+#                     dict_full_scores = df_query[col_full_scores].values
+#                     list_scores = []
+#                     list_indices = []
+#                     num_index = 0
+#                     for dict_full_score_metric in dict_full_scores:
+#                         df_full_score_metric = pd.DataFrame(
+#                             list(dict_full_score_metric.values()),
+#                             index=list(dict_full_score_metric.keys()),
+#                         ).T
+#                         num_index = df_full_score_metric.shape[0]
+#                         list_scores.append(df_full_score_metric)
+
+#                     list_indices = [df_query[cols_mean_on] for i in range(num_index)]
+#                     df_scores = pd.concat(list_scores)
+#                     df_indices = pd.concat(list_indices)
+#                     df_indices.index = df_scores.index
+
+#                     df_scores = pd.concat([df_scores, df_indices], axis=1)
+#                     df_scores.index.name = "columns"
+#                     df_scores = df_scores.set_index(cols_mean_on, append=True)
+
+#                     file_path_html = Path(f"{run.info.artifact_uri[7:]}/{col_full_scores}.html")
+#                     file_path_html.parent.mkdir(parents=True, exist_ok=True)
+#                     df_scores.to_html(file_path_html)
+#                     mlflow.log_artifact(file_path_html)
+
+
+def visualize_plotly(df, selected_columns):
+    columns_numerical = df.select_dtypes(include=np.number).columns.tolist()
+    columns_categorical = [col for col in df.columns.to_list() if col not in columns_numerical]
+
+    df = df[selected_columns]
+    df = df.dropna()
+
+    dimensions = []
+    for col in selected_columns:
+        if col in columns_categorical:
+            dfg = pd.DataFrame({col: df[col].unique()})
+            dfg[f"{col}_dummy"] = dfg.index
+            df = pd.merge(df, dfg, on=col, how="left")
+
+    for col in selected_columns:
+        if col in columns_categorical:
+            dfg = pd.DataFrame({col: df[col].unique()})
+            dfg[f"{col}_dummy"] = dfg.index
+            dimensions.append(
+                dict(
+                    range=[0, df[f"{col}_dummy"].max()],
+                    tickvals=dfg[f"{col}_dummy"],
+                    ticktext=dfg[f"{col}"],
+                    label=col,
+                    values=df[f"{col}_dummy"],
+                ),
+            )
+        else:
+            dimensions.append(
+                dict(
+                    range=[df[f"{col}"].min(), df[f"{col}"].max()], label=col, values=df[f"{col}"]
+                ),
+            )
+    fig = go.Figure(data=go.Parcoords(dimensions=dimensions))
+
+    return fig
+
+
+def get_confidence_interval(x, confidence_level=0.95):
+    # https://www.statology.org/confidence-intervals-python/
+    interval = scipy.stats.norm.interval(
+        confidence=confidence_level, loc=np.mean(x), scale=scipy.stats.sem(x)
+    )
+    width = interval[1] - interval[0]
+    width_plus = interval[1] - np.mean(x)
+    width_minus = np.mean(x) - interval[0]
+    return [interval[0], interval[1], width, width_plus, width_minus]
+
+
+def plot_bar_y_1D(
+    df_agg,
+    col_displayed=("prediction_score", "test_set", "wmape"),
+    cols_grouped=["hole_generator", "imputer", "predictor"],
+    add_annotation=True,
+    add_confidence_interval=False,
+    confidence_level=0.95,
+    title="",
+):
+    df_agg_plot = df_agg.reset_index()
+    col_legend = cols_grouped[-1]
+    cols_x = [col for col in cols_grouped if col != col_legend]
+
+    fig = go.Figure()
+    for value in df_agg_plot[col_legend].unique():
+        df_agg_plot_ = df_agg_plot[df_agg_plot[col_legend] == value]
+
+        error_y = None
+        if add_confidence_interval:
+            value_ = list(col_displayed)
+            value_[2] = value_[2] + "_values"
+            error_y = np.array(
+                df_agg_plot_.loc[:, tuple(value_)]
+                .apply(lambda x: get_confidence_interval(x, confidence_level))
+                .to_list()
+            )
+
+            # error_y_width = dict(type="data", array=error_y[:, 3] / 2)
+            error_y_plus = error_y[:, 3]
+            array_y_minus = error_y[:, 4]
+
+            # error_y = error_y_width
+            error_y = dict(
+                type="data", symmetric=False, array=error_y_plus, arrayminus=array_y_minus
+            )
+
+        text = None
+        if add_annotation:
+            text = df_agg_plot_.loc[:, col_displayed]
+
+        fig.add_trace(
+            go.Bar(
+                x=np.squeeze([df_agg_plot_[col].astype(str).values for col in cols_x]),
+                y=df_agg_plot_.loc[:, col_displayed],
+                showlegend=True,
+                name=str(value),
+                text=text,
+                error_y=error_y,
+            )
+        )
+    metric_name = col_displayed[2]
+    if add_annotation:
+        fig.update_traces(texttemplate="%{text:.2}", textposition="outside")
+    fig.update_layout(barmode="group")
+    title_ = f'{metric_name} as a function of {"+".join(cols_grouped)}'
+    if title != "":
+        title_ = f"{title}, {title_}"
+    fig.update_layout(title=title_)
+    fig.update_xaxes(title="+".join(cols_grouped[:-1]))
+    fig.update_layout(legend_title_text=str(cols_grouped[-1]))
+
+    return fig
+
+
+def plot_bar_y_nD(
+    df_agg,
+    cols_displayed=[
+        ("imputation_score", "test_set", "wmape"),
+        ("prediction_score", "test_set", "wmape"),
+    ],
+    cols_grouped=["hole_generator", "imputer", "predictor"],
+    add_annotation=True,
+    add_confidence_interval=False,
+    confidence_level=0.95,
+    title="",
+):
+    col_legend_idx = []
+    for i in range(len(cols_displayed) - 1):
+        for j in range(len(cols_displayed[i])):
+            if cols_displayed[i][j] != cols_displayed[i + 1][j]:
+                col_legend_idx.append(j)
+
+    # fig = go.Figure()
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    for idx, value in enumerate(cols_displayed):
+        name = "_".join([value[i] for i in set(col_legend_idx)])
+        if "prediction" in name:
+            secondary_y = False
+        else:
+            secondary_y = True
+        offsetgroup = idx
+
+        error_y = None
+        if add_confidence_interval:
+            value_ = list(value)
+            value_[2] = value[2] + "_values"
+
+            error_y = np.array(
+                df_agg.loc[:, tuple(value_)]
+                .apply(lambda x: get_confidence_interval(x, confidence_level))
+                .to_list()
+            )
+            # error_y_width = dict(type="data", array=error_y[:, 2] / 2)
+            error_y_plus = error_y[:, 3]
+            array_y_minus = error_y[:, 4]
+
+            # error_y = error_y_width
+            error_y = dict(
+                type="data", symmetric=False, array=error_y_plus, arrayminus=array_y_minus
+            )
+
+        text = None
+        if add_annotation:
+            text = df_agg.loc[:, value]
+
+        fig.add_trace(
+            go.Bar(
+                name=name,
+                x=np.array(df_agg.index.to_list()).transpose(),
+                y=df_agg.loc[:, value],
+                text=text,
+                offsetgroup=offsetgroup,
+                error_y=error_y,
+            ),
+            secondary_y=secondary_y,
+        )
+
+    metric_names = set([col[2] for col in cols_displayed])
+
+    if add_annotation:
+        fig.update_traces(texttemplate="%{text:.2}", textposition="outside")
+    fig.update_layout(barmode="group")
+
+    col_y_inter = set(cols_displayed[0])
+    for s in cols_displayed[1:]:
+        col_y_inter.intersection_update(s[:2])
+    if len(col_y_inter) != 0:
+        title_ = f'{" and ".join(metric_names)} as a function of {"+".join(cols_grouped)}'
+        title_ += f'for {"+".join(list(col_y_inter))}'
+    else:
+        title_ = f'{" and ".join(metric_names)} as a function of {"+".join(cols_grouped)}'
+    if title != "":
+        title_ = f"{title}, {title_}"
+    fig.update_layout(title=title_)
+    type_names = "_".join(set([col[0] for col in cols_displayed]))
+    if "prediction_score" in type_names:
+        fig.update_yaxes(title_text="prediction_score", secondary_y=False)
+    fig.update_yaxes(title_text="imputation_score", secondary_y=True)
+    fig.update_xaxes(title="+".join(cols_grouped))
+    fig.update_layout(legend_title_text="Options")
+
+    return fig
+
+
+def plot_bar(
+    df,
+    col_displayed=("prediction_score", "test_set", "wmape"),
+    cols_displayed=None,
+    cols_grouped=["hole_generator", "imputer", "predictor"],
+    add_annotation=True,
+    add_confidence_interval=False,
+    confidence_level=0.95,
+    title="",
+    agg_func=pd.DataFrame.mean,
+    yaxes_type="-",
+):
+    df_agg = get_benchmark_aggregate(
+        df, cols_groupby=cols_grouped, agg_func=agg_func, keep_values=True
+    )
+
+    if cols_displayed is None:
+        fig = plot_bar_y_1D(
+            df_agg,
+            col_displayed,
+            cols_grouped,
+            add_annotation,
+            add_confidence_interval,
+            confidence_level,
+            title=title,
+        )
+    else:
+        fig = plot_bar_y_nD(
+            df_agg,
+            cols_displayed,
+            cols_grouped,
+            add_annotation,
+            add_confidence_interval,
+            confidence_level,
+            title=title,
+        )
+    fig.update_yaxes(type=yaxes_type)
+    fig.update_layout(hovermode="x")
+
+    return fig
+
+
+def plot_scatter(
+    df,
+    cond={},
+    col_x="imputation_score_mae_test_set",
+    col_y="prediction_score_nan_mae",
+    col_legend="ratio_masked",
+    add_trend_line=True,
+    model=linear_model.LinearRegression(),
+):
+
+    df_plot = df.copy()
+    for k, v in cond.items():
+        df_plot = df_plot[df_plot[k] == v]
+
+    df_plot = df_plot.dropna()
+
+    fig = go.Figure()
+    for value in df_plot[col_legend].unique():
+        df_plot_ = df_plot[df_plot[col_legend] == value]
+        fig.add_trace(
+            go.Scatter(
+                x=df_plot_[col_x],
+                y=df_plot_[col_y],
+                name=str(value),
+                mode="markers",
+            )
+        )
+
+    if add_trend_line:
+        model.fit(df_plot[[col_x]], df_plot[col_y])
+        df_plot[f"{col_y}_predict"] = model.predict(df_plot[[col_x]])
+        fig.add_trace(
+            go.Scatter(
+                x=df_plot[col_x],
+                y=df_plot[f"{col_y}_predict"],
+                name="trend",
+                mode="lines",
+                marker=dict(color="black"),
+            )
+        )
+
+    fig.update_layout(legend_title=col_legend)
+    fig.update_xaxes(title=col_x)
+    fig.update_yaxes(title=col_y)
+    title = f"{col_y} as a function of {col_x}"
+    if len(cond) != 0:
+        title += "<br>for "
+        for k, v in cond.items():
+            title += f"{k}={v}, "
+    fig.update_layout(title=title[:-2])
+
+    return fig
+
+
+def get_relative_score(
+    x, df, col, method="gain", ref_imputer="None", is_ref_hole_generator_none=False
+):
+    # https://en.wikipedia.org/wiki/Relative_change
+    x_row = x[col]
+    if is_ref_hole_generator_none:
+        x_ref = df[
+            (df["dataset"] == x["dataset"])
+            & (df["n_fold"] == x["n_fold"])
+            & (df["hole_generator"] == "None")
+            & (df["predictor"] == x["predictor"])
+            & (df["imputer"] == "None")
+        ][col]
+    else:
+        if x["hole_generator"] == "None":
+            x_ref = df[
+                (df["dataset"] == x["dataset"])
+                & (df["n_fold"] == x["n_fold"])
+                & (df["hole_generator"] == "None")
+                & (df["ratio_masked"] == x["ratio_masked"])
+                & (df["predictor"] == x["predictor"])
+                & (df["imputer"] == "None")
+            ][col]
+        else:
+            x_ref = df[
+                (df["dataset"] == x["dataset"])
+                & (df["n_fold"] == x["n_fold"])
+                & (df["hole_generator"] == x["hole_generator"])
+                & (df["ratio_masked"] == x["ratio_masked"])
+                & (df["n_mask"] == x["n_mask"])
+                & (df["predictor"] == x["predictor"])
+                & (df["imputer"] == ref_imputer)
+            ][col]
+
+    if method == "relative_percentage_gain":
+        x_out = ((x_ref - x_row)) / x_ref
+    elif method == "gain":
+        x_out = x_ref - x_row
+    else:
+        x_out = x_row - x_ref
+    return x_out.values
+
+
+def statistic_test(
+    df,
+    col_evaluated,
+    cols_grouped=[
+        "dataset",
+        "n_fold",
+        "hole_generator",
+        "ratio_masked",
+        "n_mask",
+        "predictor",
+        "imputer",
+    ],
+    cols_displayed=["ratio_masked", "predictor"],
+    func=stats.friedmanchisquare,
+):
+    df_values = df.groupby(cols_grouped)[col_evaluated].aggregate("first").unstack()
+    cols_displayed_ = cols_displayed
+    values = df_values.copy()
+
+    def get_value(values, df_values, cols_displayed):
+        col = cols_displayed[0]
+        if len(cols_displayed) > 1:
+            cols_displayed.remove(cols_displayed[0])
+            list_df = []
+            for v in df_values.index.get_level_values(col).unique():
+                df_out = get_value(df_values.xs(v, level=col), df_values, cols_displayed)
+                df_out[col] = v
+                list_df.append(df_out)
+
+            df_out = pd.concat(list_df)
+            first_col = df_out.pop(col)
+            df_out.insert(0, col, first_col)
+            return df_out
+        else:
+            list_out = []
+            for v in df_values.index.get_level_values(col).unique():
+                values_ = values.xs(v, level=col).values.T
+                res = func(*values_)
+                list_out.append(
+                    {
+                        col: v,
+                        "statistic": res.statistic,
+                        "pvalue": res.pvalue,
+                        "set_size": np.shape(values_),
+                    }
+                )
+            df_out = pd.DataFrame(list_out)
+            return df_out
+
+    return get_value(values, df_values, cols_displayed_)
+
+
+def plot_critical_difference_diagram(
+    df, col_model, col_rank, col_value, title="", color_palette=None, fig_size=(7, 2)
+):
+    df_avg_rank = df.groupby(col_model)[col_rank].mean()
+    df_values = df.groupby(col_model)[col_value].apply(list)
+    model_names = df_avg_rank.index
+
+    df_posthoc_conover_friedman = sp.posthoc_conover_friedman(np.array(list(df_values.values)).T)
+
+    df_posthoc_conover_friedman.index = model_names
+    df_posthoc_conover_friedman.columns = model_names
+
+    if color_palette is None:
+        color_palette = dict(
+            [(key, value) for key, value in zip(model_names, np.random.rand(len(model_names), 3))]
+        )
+    figure = plt.figure(figsize=fig_size)
+    plt.title(title)
+    _ = sp.critical_difference_diagram(
+        df_avg_rank, df_posthoc_conover_friedman, color_palette=color_palette
+    )
+
+    return figure
diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py
index 1dd4e0a0..c0b0048d 100644
--- a/qolmat/benchmark/metrics.py
+++ b/qolmat/benchmark/metrics.py
@@ -18,6 +18,56 @@
 ###########################
 
 
+def _get_numerical_features(df1: pd.DataFrame) -> List[str]:
+    """Get numerical features from dataframe
+
+    Parameters
+    ----------
+    df1 : pd.DataFrame
+
+    Returns
+    -------
+    List[str]
+        List of numerical features
+
+    Raises
+    ------
+    Exception
+        No numerical feature is found
+    """
+    cols_numerical = df1.select_dtypes(include=np.number).columns.tolist()
+    if len(cols_numerical) == 0:
+        raise Exception("No numerical feature is found.")
+    else:
+        return cols_numerical
+
+
+def _get_categorical_features(df1: pd.DataFrame) -> List[str]:
+    """Get categorical features from dataframe
+
+    Parameters
+    ----------
+    df1 : pd.DataFrame
+
+    Returns
+    -------
+    List[str]
+        List of categorical features
+
+    Raises
+    ------
+    Exception
+        No categorical feature is found
+    """
+
+    cols_numerical = df1.select_dtypes(include=np.number).columns.tolist()
+    cols_categorical = [col for col in df1.columns.to_list() if col not in cols_numerical]
+    if len(cols_categorical) == 0:
+        raise Exception("No categorical feature is found.")
+    else:
+        return cols_categorical
+
+
 def columnwise_metric(
     df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, metric: Callable, **kwargs
 ) -> pd.Series:
@@ -66,7 +116,10 @@ def mean_squared_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFra
     -------
     pd.Series
     """
-    return columnwise_metric(df1, df2, df_mask, skm.mean_squared_error)
+    cols_numerical = _get_numerical_features(df1)
+    return columnwise_metric(
+        df1[cols_numerical], df2[cols_numerical], df_mask[cols_numerical], skm.mean_squared_error
+    )
 
 
 def root_mean_squared_error(
@@ -87,7 +140,14 @@ def root_mean_squared_error(
     -------
     pd.Series
     """
-    return columnwise_metric(df1, df2, df_mask, skm.mean_squared_error, squared=False)
+    cols_numerical = _get_numerical_features(df1)
+    return columnwise_metric(
+        df1[cols_numerical],
+        df2[cols_numerical],
+        df_mask[cols_numerical],
+        skm.mean_squared_error,
+        squared=False,
+    )
 
 
 def mean_absolute_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> pd.Series:
@@ -106,7 +166,10 @@ def mean_absolute_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFr
     -------
     pd.Series
     """
-    return columnwise_metric(df1, df2, df_mask, skm.mean_absolute_error)
+    cols_numerical = _get_numerical_features(df1)
+    return columnwise_metric(
+        df1[cols_numerical], df2[cols_numerical], df_mask[cols_numerical], skm.mean_absolute_error
+    )
 
 
 def mean_absolute_percentage_error(
@@ -127,7 +190,13 @@ def mean_absolute_percentage_error(
     -------
     pd.Series
     """
-    return columnwise_metric(df1, df2, df_mask, skm.mean_absolute_percentage_error)
+    cols_numerical = _get_numerical_features(df1)
+    return columnwise_metric(
+        df1[cols_numerical],
+        df2[cols_numerical],
+        df_mask[cols_numerical],
+        skm.mean_absolute_percentage_error,
+    )
 
 
 def _weighted_mean_absolute_percentage_error_1D(values1: pd.Series, values2: pd.Series) -> float:
@@ -167,7 +236,13 @@ def weighted_mean_absolute_percentage_error(
     -------
     pd.Series
     """
-    return columnwise_metric(df1, df2, df_mask, _weighted_mean_absolute_percentage_error_1D)
+    cols_numerical = _get_numerical_features(df1)
+    return columnwise_metric(
+        df1[cols_numerical],
+        df2[cols_numerical],
+        df_mask[cols_numerical],
+        _weighted_mean_absolute_percentage_error_1D,
+    )
 
 
 def dist_wasserstein(
@@ -191,7 +266,13 @@ def dist_wasserstein(
         wasserstein distances
     """
     if method == "columnwise":
-        return columnwise_metric(df1, df2, df_mask, scipy.stats.wasserstein_distance)
+        cols_numerical = _get_numerical_features(df1)
+        return columnwise_metric(
+            df1[cols_numerical],
+            df2[cols_numerical],
+            df_mask[cols_numerical],
+            scipy.stats.wasserstein_distance,
+        )
     else:
         raise AssertionError(
             f"The parameter of the function wasserstein_distance should be one of"
@@ -199,56 +280,6 @@ def dist_wasserstein(
         )
 
 
-def _get_numerical_features(df1: pd.DataFrame) -> List[str]:
-    """Get numerical features from dataframe
-
-    Parameters
-    ----------
-    df1 : pd.DataFrame
-
-    Returns
-    -------
-    List[str]
-        List of numerical features
-
-    Raises
-    ------
-    Exception
-        No numerical feature is found
-    """
-    cols_numerical = df1.select_dtypes(include=np.number).columns.tolist()
-    if len(cols_numerical) == 0:
-        raise Exception("No numerical feature is found.")
-    else:
-        return cols_numerical
-
-
-def _get_categorical_features(df1: pd.DataFrame) -> List[str]:
-    """Get categorical features from dataframe
-
-    Parameters
-    ----------
-    df1 : pd.DataFrame
-
-    Returns
-    -------
-    List[str]
-        List of categorical features
-
-    Raises
-    ------
-    Exception
-        No categorical feature is found
-    """
-
-    cols_numerical = df1.select_dtypes(include=np.number).columns.tolist()
-    cols_categorical = [col for col in df1.columns.to_list() if col not in cols_numerical]
-    if len(cols_categorical) == 0:
-        raise Exception("No categorical feature is found.")
-    else:
-        return cols_categorical
-
-
 def kolmogorov_smirnov_test_1D(df1: pd.Series, df2: pd.Series) -> float:
     """Compute KS test statistic of the two-sample Kolmogorov-Smirnov test for goodness of fit.
     See more in https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html.
@@ -572,6 +603,60 @@ def mean_diff_corr_matrix_categorical_vs_numerical_features(
     return pd.Series(diff_corr, index=cols_categorical)
 
 
+def f1_score(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> pd.Series:
+    """
+
+    Parameters
+    ----------
+    df1 : pd.DataFrame
+        true dataframe
+    df2 : pd.DataFrame
+        predicted dataframe
+    df_mask : pd.DataFrame
+        Elements of the dataframes to compute on
+
+    Returns
+    -------
+    pd.Series
+        the F1 score
+    """
+    cols_categorical = _get_categorical_features(df1)
+    return columnwise_metric(
+        df1[cols_categorical],
+        df2[cols_categorical],
+        df_mask[cols_categorical],
+        skm.f1_score,
+        average="weighted",
+    )
+
+
+def roc_auc_score(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> pd.Series:
+    """
+
+    Parameters
+    ----------
+    df1 : pd.DataFrame
+        true dataframe
+    df2 : pd.DataFrame
+        predicted dataframe
+    df_mask : pd.DataFrame
+        Elements of the dataframes to compute on
+
+    Returns
+    -------
+    pd.Series
+        Area Under the Receiver Operating Characteristic Curve (ROC AUC)
+    """
+    cols_categorical = _get_categorical_features(df1)
+    return columnwise_metric(
+        df1[cols_categorical],
+        df2[cols_categorical],
+        df_mask[cols_categorical],
+        skm.roc_auc_score,
+        average="weighted",
+    )
+
+
 ###########################
 # Row-wise metrics        #
 ###########################
@@ -992,8 +1077,13 @@ def kl_divergence(
             min_n_rows=min_n_rows,
         )
     elif method == "random_forest":
+        cols_numerical = _get_numerical_features(df1)
         return pattern_based_weighted_mean_metric(
-            df1, df2, df_mask, kl_divergence_forest, min_n_rows=min_n_rows
+            df1[cols_numerical],
+            df2[cols_numerical],
+            df_mask[cols_numerical],
+            kl_divergence_forest,
+            min_n_rows=min_n_rows,
         )
     else:
         raise AssertionError(
@@ -1021,8 +1111,17 @@ def distance_anticorr(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFram
     float
         Distance correlation score
     """
-    df1 = df1.loc[df_mask.any(axis=1)]
-    df2 = df2.loc[df_mask.any(axis=1)]
+    cols_numerical = _get_numerical_features(df1)
+    df1 = df1[cols_numerical]
+    df2 = df2[cols_numerical]
+
+    df1 = df1[df_mask.any(axis=1)]
+    df2 = df2[df_mask.any(axis=1)]
+
+    if len(df1) > 30000:
+        df1 = df1.sample(20000)
+        df2 = df2.sample(20000)
+
     return (1 - dcor.distance_correlation(df1.values, df2.values)) / 2
 
 
@@ -1088,6 +1187,7 @@ def get_metric(name: str) -> Callable:
         "KL_gaussian": partial(kl_divergence, method="gaussian"),
         "KL_forest": partial(kl_divergence, method="random_forest"),
         "ks_test": kolmogorov_smirnov_test,
+        "total_variance_distance": total_variance_distance,
         "correlation_diff": mean_difference_correlation_matrix_numerical_features,
         "energy": sum_energy_distances,
         "frechet": frechet_distance,
@@ -1095,5 +1195,7 @@ def get_metric(name: str) -> Callable:
             pattern_based_weighted_mean_metric,
             metric=distance_anticorr,
         ),
+        "f1_score": f1_score,
+        "roc_auc_score": roc_auc_score,
     }
     return dict_metrics[name]
diff --git a/qolmat/benchmark/missing_patterns.py b/qolmat/benchmark/missing_patterns.py
index 69f07133..3ec57f57 100644
--- a/qolmat/benchmark/missing_patterns.py
+++ b/qolmat/benchmark/missing_patterns.py
@@ -9,6 +9,8 @@
 from sklearn import utils as sku
 from sklearn.utils import resample
 import math
+from scipy import optimize
+from scipy.special import expit
 
 from qolmat.utils.exceptions import NoMissingValue, SubsetIsAString
 
@@ -50,6 +52,50 @@ def get_sizes_max(values_isna: pd.Series) -> pd.Series[int]:
     return sizes_max
 
 
+def pick_coeffs(
+    X: np.ndarray,
+    idxs_obs: np.ndarray,
+    idxs_nas: np.ndarray,
+    self_mask: bool = False,
+) -> np.ndarray:
+    n, d = X.shape
+    if self_mask:
+        coeffs = np.random.rand(d)
+        Wx = X * coeffs
+        coeffs /= np.std(Wx, 0)
+    else:
+        d_obs = len(idxs_obs)
+        d_na = len(idxs_nas)
+        coeffs = np.random.rand(d_obs, d_na)
+        Wx = X[:, idxs_obs] @ coeffs
+        coeffs /= np.std(Wx, 0, keepdims=True)
+    return coeffs
+
+
+def fit_intercepts(
+    X: np.ndarray, coeffs: np.ndarray, p: float, self_mask: bool = False
+) -> np.ndarray:
+    if self_mask:
+        d = len(coeffs)
+        intercepts = np.zeros(d)
+        for j in range(d):
+
+            def f(x: np.ndarray) -> np.ndarray:
+                return expit(X * coeffs[j] + x).mean().item() - p
+
+            intercepts[j] = optimize.bisect(f, -1000, 1000)
+    else:
+        d_obs, d_na = coeffs.shape
+        intercepts = np.zeros(d_na)
+        for j in range(d_na):
+
+            def f(x: np.ndarray) -> np.ndarray:
+                return expit(np.dot(X, coeffs[:, j]) + x).mean().item() - p
+
+            intercepts[j] = optimize.bisect(f, -1000, 1000)
+    return intercepts
+
+
 class _HoleGenerator:
     """
     This abstract class implements the generic method to generate masks according to law of missing
@@ -726,3 +772,218 @@ def split(self, X: pd.DataFrame) -> List[pd.DataFrame]:
             list_masks.append(df_mask)
 
         return list_masks
+
+
+class MCAR(UniformHoleGenerator):
+    """This class implements a way to generate holes in a dataframe.
+    The holes are generated randomly, using the resample method of scikit learn.
+
+    Parameters
+    ----------
+    n_splits : int, optional
+        Number of splits, by default 1.
+    subset : Optional[List[str]], optional
+        Names of the columns for which holes must be created, by default None
+    ratio_masked : Optional[float], optional
+        Ratio of masked values ​​to add, by default 0.05.
+    random_state : Optional[int], optional
+        The seed used by the random number generator, by default 42.
+    """
+
+    def __init__(
+        self,
+        n_splits: int = 1,
+        subset: Optional[List[str]] = None,
+        ratio_masked: float = 0.05,
+        random_state: Union[None, int, np.random.RandomState] = None,
+    ):
+        super().__init__(
+            n_splits=n_splits, subset=subset, ratio_masked=ratio_masked, random_state=random_state
+        )
+
+    def _check_subset(self, X: pd.DataFrame):
+        if self.subset is None:
+            self.subset = X.columns
+        if isinstance(self.subset, str):
+            raise SubsetIsAString(self.subset)
+
+
+class MAR(_HoleGenerator):
+    """This class implements a way to generate holes in a dataframe.
+    Missing at random mechanism with a logistic masking model.
+    First, a subset of variables with *no* missing values is randomly selected.
+    The remaining variables have missing values according to a logistic model
+    with random weights, re-scaled so as to attain the desired proportion
+    of missing values on those variables.
+    This class is based on the function MAR_mask in
+    https://github.com/vanderschaarlab/hyperimpute/blob/main/src/hyperimpute/plugins/utils/simulate.py
+
+
+    Parameters
+    ----------
+    n_splits : int, optional
+        Number of splits, by default 1.
+    subset : Optional[List[str]], optional
+        Names of the columns for which holes must be created, by default None
+    ratio_masked : Optional[float], optional
+        Ratio of masked values ​​to add, by default 0.05.
+    ratio_observed : Optional[float], optional
+        Proportion of variables with *no* missing values that will be used
+        for the logistic masking model, by default 0.1.
+    sample_columns: bool, optional
+        Sample variables that will all be observed, by default True.
+    random_state : Optional[int], optional
+        The seed used by the random number generator, by default 42.
+    """
+
+    def __init__(
+        self,
+        n_splits: int = 1,
+        subset: Optional[List[str]] = None,
+        ratio_masked: float = 0.05,
+        ratio_observed: float = 0.1,
+        sample_columns: bool = True,
+        random_state: Union[None, int, np.random.RandomState] = None,
+    ):
+        super().__init__(
+            n_splits=n_splits, subset=subset, ratio_masked=ratio_masked, random_state=random_state
+        )
+        self.ratio_observed = ratio_observed
+        self.sample_columns = sample_columns
+
+    def _check_subset(self, X: pd.DataFrame):
+        if self.subset is None:
+            self.subset = X.columns
+        if isinstance(self.subset, str):
+            raise SubsetIsAString(self.subset)
+
+    def generate_mask(self, X: pd.DataFrame) -> pd.DataFrame:
+        df_mask = pd.DataFrame(False, index=X.index, columns=X.columns)
+
+        n, d = X.shape
+
+        d_obs = max(
+            int(self.ratio_observed * d), 1
+        )  # number of variables that will have no missing values (at least one variable)
+        d_na = d - d_obs  # number of variables that will have missing values
+
+        # Sample variables that will all be observed, and those with missing values:
+        if self.sample_columns:
+            idxs_obs = np.random.choice(d, d_obs, replace=False)
+        else:
+            idxs_obs = np.array(list(range(d_obs)))
+
+        idxs_nas = np.array([i for i in range(d) if i not in idxs_obs])
+
+        # Other variables will have NA proportions that depend on those observed variables,
+        # through a logistic model
+        # The parameters of this logistic model are random.
+
+        # Pick coefficients so that W^Tx has unit variance (avoids shrinking)
+        coeffs = pick_coeffs(X.values, idxs_obs, idxs_nas)
+        # Pick the intercepts to have a desired amount of missing values
+        intercepts = fit_intercepts(X.iloc[:, idxs_obs].values, coeffs, self.ratio_masked)
+
+        ps = expit(X.iloc[:, idxs_obs].values @ coeffs + intercepts)
+
+        ber = np.random.rand(n, d_na)
+        df_mask.iloc[:, idxs_nas] = ber < ps
+
+        return df_mask
+
+
+class MNAR(_HoleGenerator):
+    """This class implements a way to generate holes in a dataframe.
+    Missing not at random mechanism with a logistic masking model.
+    It implements two mechanisms:
+    (i) Missing probabilities are selected with a logistic model, taking all variables as inputs.
+    Hence, values that are inputs can also be missing.
+    (ii) Variables are split into a set of intputs for a logistic model, and a set whose missing
+    probabilities are determined by the logistic model.
+    Then inputs are then masked MCAR (hence, missing values from the second set will depend on
+    masked values.
+    In either case, weights are random and the intercept is selected to attain the desired
+    proportion of missing values.
+    This class is based on the function MNAR_mask_logistic in
+    https://github.com/vanderschaarlab/hyperimpute/blob/main/src/hyperimpute/plugins/utils/simulate.py
+
+
+    Parameters
+    ----------
+    n_splits : int, optional
+        Number of splits, by default 1.
+    subset : Optional[List[str]], optional
+        Names of the columns for which holes must be created, by default None
+    ratio_masked : Optional[float], optional
+        Ratio of masked values ​​to add, by default 0.05.
+    ratio_variable :Optional[float], optional
+        Proportion of variables that will be used for the logistic masking model
+        (only if exclude_inputs), by default 0.3.
+    exclude_inputs :
+        True: mechanism (ii) is used, False: (i)
+    random_state : Optional[int], optional
+        The seed used by the random number generator, by default 42.
+    """
+
+    def __init__(
+        self,
+        n_splits: int = 1,
+        subset: Optional[List[str]] = None,
+        ratio_masked: float = 0.05,
+        ratio_variable: float = 0.3,
+        exclude_inputs: bool = True,
+        random_state: Union[None, int, np.random.RandomState] = None,
+    ):
+        super().__init__(
+            n_splits=n_splits, subset=subset, ratio_masked=ratio_masked, random_state=random_state
+        )
+        self.ratio_variable = ratio_variable
+        self.exclude_inputs = exclude_inputs
+
+    def _check_subset(self, X: pd.DataFrame):
+        if self.subset is None:
+            self.subset = X.columns
+        if isinstance(self.subset, str):
+            raise SubsetIsAString(self.subset)
+
+    def generate_mask(self, X: pd.DataFrame) -> pd.DataFrame:
+        df_mask = pd.DataFrame(False, index=X.index, columns=X.columns)
+        n, d = X.shape
+        d_params = (
+            max(int(self.ratio_variable * d), 1) if self.exclude_inputs else d
+        )  # number of variables used as inputs (at least 1)
+        d_na = (
+            d - d_params if self.exclude_inputs else d
+        )  # number of variables masked with the logistic model
+
+        # Sample variables that will be parameters for the logistic regression:
+        idxs_params = (
+            np.random.choice(d, d_params, replace=False) if self.exclude_inputs else np.arange(d)
+        )
+        idxs_nas = (
+            np.array([i for i in range(d) if i not in idxs_params])
+            if self.exclude_inputs
+            else np.arange(d)
+        )
+
+        # Other variables will have NA proportions selected by a logistic model
+        # The parameters of this logistic model are random.
+
+        # Pick coefficients so that W^Tx has unit variance (avoids shrinking)
+        coeffs = pick_coeffs(X.values, idxs_params, idxs_nas)
+        # Pick the intercepts to have a desired amount of missing values
+        intercepts = fit_intercepts(X.iloc[:, idxs_params].values, coeffs, self.ratio_masked)
+
+        ps = expit(X.iloc[:, idxs_params].values @ coeffs + intercepts)
+
+        ber = np.random.rand(n, d_na)
+        df_mask.iloc[:, idxs_nas] = ber < ps
+
+        # If the inputs of the logistic model are excluded from MNAR missingness,
+        # mask some values used in the logistic model at random.
+        # This makes the missingness of other variables potentially dependent on masked values
+
+        if self.exclude_inputs:
+            df_mask.iloc[:, idxs_params] = np.random.rand(n, d_params) < self.ratio_masked
+
+        return df_mask
diff --git a/qolmat/imputations/em_sampler.py b/qolmat/imputations/em_sampler.py
index 7002b6ba..2678fd10 100644
--- a/qolmat/imputations/em_sampler.py
+++ b/qolmat/imputations/em_sampler.py
@@ -372,7 +372,11 @@ def transform(self, X: NDArray) -> NDArray:
         elif self.method == "sample":
             X_transformed = self._sample_ou(X, mask_na, estimate_params=False)
 
-        if np.all(np.isnan(X_transformed)):
+        X_transformed[np.isinf(X_transformed)] = 0.0
+        X_transformed[np.isnan(X_transformed)] = 0.0
+        if np.any(np.isinf(X_transformed)):
+            raise AssertionError("Result contains Inf. This is a bug.")
+        if np.any(np.isnan(X_transformed)):
             raise AssertionError("Result contains NaN. This is a bug.")
 
         return X_transformed
diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py
index 9f2ae1ec..4cc9dd04 100644
--- a/qolmat/imputations/imputers.py
+++ b/qolmat/imputations/imputers.py
@@ -164,6 +164,8 @@ def fit(self, X: pd.DataFrame, y=None) -> Self:
         if self.columnwise:
             for col in df.columns:
                 self._fit_allgroups(df[[col]], col=col)
+            # for col in cols_with_nans:
+            # self._fit_allgroups(df[[col]], col=col)
         else:
             self._fit_allgroups(df)
 
diff --git a/qolmat/utils/plot.py b/qolmat/utils/plot.py
index d37d3f46..98ec4438 100644
--- a/qolmat/utils/plot.py
+++ b/qolmat/utils/plot.py
@@ -300,6 +300,7 @@ def multibar(
         ax.bar_label(rect, padding=3, fmt=f"%.{decimals}f")
 
     plt.legend(loc=(1, 0))
+    plt.show()
 
 
 def plot_imputations(df: pd.DataFrame, dict_df_imputed: Dict[str, pd.DataFrame]):