diff --git a/baselines/bohb/data/utilities.py b/baselines/bohb/data/utilities.py
new file mode 100644
index 0000000..b353a2c
--- /dev/null
+++ b/baselines/bohb/data/utilities.py
@@ -0,0 +1,446 @@
+from argparse import Namespace
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import ConfigSpace
+import pandas as pd
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
+
+import dask.distributed
+
+import openml
+import numpy as np
+
+from sklearn.model_selection import train_test_split
+
+from smac.intensification.simple_intensifier import SimpleIntensifier
+from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost
+from smac.scenario.scenario import Scenario
+from smac.facade.smac_ac_facade import SMAC4AC
+from smac.runhistory.runhistory import RunHistory
+
+
+def get_data(
+    task_id: int,
+    val_share: float = 0.25,
+    test_size: float = 0.2,
+    seed: int = 11,
+) -> Tuple[pd.DataFrame, pd.DataFrame, np.ndarray, np.ndarray, dict, np.ndarray]:
+    """
+    Given  a task id and split size information, return
+    the dataset splits based on a seed for the main algorithm
+    to use.
+
+    Args:
+        task_id (int):
+            The id of the task which will be used for the run.
+        val_share (float):
+            The validation split size from the train set.
+        test_size (float):
+            The test split size from the whole dataset.
+        seed (int):
+            The seed used for the dataset preparation.
+
+    Returns:
+
+        X_train, X_test, y_train, y_test, resampling_strategy_args, categorical indicator
+            (tuple[pd.DataFrame, pd.DataFrame, np.ndarray, np.ndarray, dict, np.ndarray]):
+
+            The train examples, the test examples, the train labels, the test labels,
+            the resampling strategy to be used and the categorical indicator for the features.
+    """
+    task = openml.tasks.get_task(task_id=task_id)
+    dataset = task.get_dataset()
+    X, y, categorical_indicator, _ = dataset.get_data(
+        dataset_format='dataframe',
+        target=dataset.default_target_attribute,
+    )
+
+    # AutoPyTorch fails when it is given a y DataFrame with False and True values
+    # and category as dtype. In its inner workings it uses sklearn which cannot
+    # detect the column type.
+    if isinstance(y[1], bool):
+        y = y.astype('bool')
+
+    # uncomment only for np.arrays
+    """
+    # patch categorical values to string
+    for index_nr, categorical_feature in enumerate(categorical_indicator):
+        if categorical_feature:
+            X[index_nr] = X[index_nr].astype("category")
+    """
+    X_train, X_test, y_train, y_test = train_test_split(
+        X,
+        y,
+        test_size=test_size,
+        random_state=seed,
+        stratify=y,
+        shuffle=True,
+    )
+    resampling_strategy_args = {
+        'val_share': val_share,
+    }
+
+    """
+    This was an earlier fix to the AutoPyTorch failures for imbalanced datasets. In particular
+    having variables with only null values in the train set. Now this is handled inside AutoPyTorch.
+    
+    train_column_nan_info = X_train.isna().all()
+    test_column_nan_info = X_test.isna().all()
+    only_nan_columns = [label for label, value in train_column_nan_info.items() if value]
+    test_nan_columns = [label for label, value in test_column_nan_info.items() if value]
+    only_nan_columns.extend(test_nan_columns)
+    only_nan_columns = set(only_nan_columns)
+    X_train.drop(only_nan_columns, axis='columns', inplace=True)
+    X_test.drop(only_nan_columns, axis='columns', inplace=True)
+    """
+    # TODO turn this into a dictionary
+
+    return X_train, X_test, y_train, y_test, resampling_strategy_args, categorical_indicator
+
+
+def get_smac_object(
+    scenario_dict: Dict[str, Any],
+    seed: int,
+    ta: Callable,
+    ta_kwargs: Dict[str, Any],
+    n_jobs: int,
+    initial_budget: int,
+    max_budget: int,
+    dask_client: Optional[dask.distributed.Client],
+) -> SMAC4AC:
+    """
+    This function returns an SMAC object that is gonna be used as
+    optimizer of pipelines.
+
+    Args:
+        scenario_dict (typing.Dict[str, typing.Any]): constrain on how to run
+            the jobs.
+        seed (int): to make the job deterministic.
+        ta (typing.Callable): the function to be intensified by smac.
+        ta_kwargs (typing.Dict[str, typing.Any]): Arguments to the above ta.
+        n_jobs (int): Amount of cores to use for this task.
+        initial_budget (int):
+            The initial budget for a configuration.
+        max_budget (int):
+            The maximal budget for a configuration.
+        dask_client (dask.distributed.Client): User provided scheduler.
+
+    Returns:
+        (SMAC4AC): sequential model algorithm configuration object
+    """
+    # multi-fidelity is disabled, that is why initial_budget and max_budget
+    # are not used.
+    rh2EPM = RunHistory2EPM4LogCost
+
+    return SMAC4AC(
+        scenario=Scenario(scenario_dict),
+        rng=seed,
+        runhistory2epm=rh2EPM,
+        tae_runner=ta,
+        tae_runner_kwargs=ta_kwargs,
+        initial_configurations=None,
+        run_id=seed,
+        intensifier=SimpleIntensifier,
+        dask_client=dask_client,
+        n_jobs=n_jobs,
+    )
+
+
+def get_updates_for_regularization_cocktails(
+    categorical_indicator: np.ndarray,
+    args: Namespace,
+) -> Tuple[Dict, HyperparameterSearchSpaceUpdates, Dict]:
+    """
+    These updates replicate the regularization cocktail paper search space.
+
+    Args:
+        categorical_indicator (np.ndarray)
+            An array that indicates whether a feature is categorical or not.
+        args (Namespace):
+            The different updates for the setup of the run, mostly updates
+            for the different regularization ingredients.
+
+    Returns:
+    ________
+        pipeline_update, search_space_updates, include_updates (Tuple[dict, HyperparameterSearchSpaceUpdates, dict]):
+            The pipeline updates like number of epochs, budget, seed etc.
+            The search space updates like setting different hps to different values or ranges.
+            Lastly include updates, which can be used to include different features.
+    """
+    augmentation_names_to_trainers = {
+        'mixup': 'MixUpTrainer',
+        'cutout': 'RowCutOutTrainer',
+        'cutmix': 'RowCutMixTrainer',
+        'standard': 'StandardTrainer',
+        'adversarial': 'AdversarialTrainer',
+    }
+
+    include_updates = dict()
+    include_updates['network_embedding'] = ['NoEmbedding']
+    include_updates['network_init'] = ['NoInit']
+
+    has_cat_features = any(categorical_indicator)
+    has_numerical_features = not all(categorical_indicator)
+    search_space_updates = HyperparameterSearchSpaceUpdates()
+
+    # architecture head
+    search_space_updates.append(
+        node_name='network_head',
+        hyperparameter='__choice__',
+        value_range=['no_head'],
+        default_value='no_head',
+    )
+    search_space_updates.append(
+        node_name='network_head',
+        hyperparameter='no_head:activation',
+        value_range=['relu'],
+        default_value='relu',
+    )
+
+    # backbone architecture
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='__choice__',
+        value_range=['ShapedResNetBackbone'],
+        default_value='ShapedResNetBackbone',
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:resnet_shape',
+        value_range=['brick'],
+        default_value='brick',
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:num_groups',
+        value_range=[2],
+        default_value=2,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:blocks_per_group',
+        value_range=[2],
+        default_value=2,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:output_dim',
+        value_range=[512],
+        default_value=512,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:max_units',
+        value_range=[512],
+        default_value=512,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:activation',
+        value_range=['relu'],
+        default_value='relu',
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:shake_shake_update_func',
+        value_range=['even-even'],
+        default_value='even-even',
+    )
+
+    # training updates
+    search_space_updates.append(
+        node_name='lr_scheduler',
+        hyperparameter='__choice__',
+        value_range=['CosineAnnealingWarmRestarts'],
+        default_value='CosineAnnealingWarmRestarts',
+    )
+    search_space_updates.append(
+        node_name='lr_scheduler',
+        hyperparameter='CosineAnnealingWarmRestarts:n_restarts',
+        value_range=[3],
+        default_value=3,
+    )
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='__choice__',
+        value_range=['AdamWOptimizer'],
+        default_value='AdamWOptimizer',
+    )
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='AdamWOptimizer:lr',
+        value_range=[1e-3],
+        default_value=1e-3,
+    )
+    search_space_updates.append(
+        node_name='data_loader',
+        hyperparameter='batch_size',
+        value_range=[128],
+        default_value=128,
+    )
+
+    # preprocessing
+    search_space_updates.append(
+        node_name='feature_preprocessor',
+        hyperparameter='__choice__',
+        value_range=['NoFeaturePreprocessor'],
+        default_value='NoFeaturePreprocessor',
+    )
+
+    if has_numerical_features:
+        search_space_updates.append(
+            node_name='imputer',
+            hyperparameter='numerical_strategy',
+            value_range=['median'],
+            default_value='median',
+        )
+        search_space_updates.append(
+            node_name='scaler',
+            hyperparameter='__choice__',
+            value_range=['StandardScaler'],
+            default_value='StandardScaler',
+        )
+
+    if has_cat_features:
+        search_space_updates.append(
+            node_name='imputer',
+            hyperparameter='categorical_strategy',
+            value_range=['constant_!missing!'],
+            default_value='constant_!missing!',
+        )
+        search_space_updates.append(
+            node_name='encoder',
+            hyperparameter='__choice__',
+            value_range=['OneHotEncoder'],
+            default_value='OneHotEncoder',
+        )
+
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='AdamWOptimizer:beta1',
+        value_range=[0.9],
+        default_value=0.9,
+    )
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='AdamWOptimizer:beta2',
+        value_range=[0.999],
+        default_value=0.999,
+    )
+
+    # if the cash formulation of the cocktail is not activated,
+    # otherwise the methods activation will be chosen by the SMBO optimizer.
+    if not args.cash_cocktail:
+        # regularization ingredients updates
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:use_dropout',
+            value_range=args.use_dropout,
+            default_value=args.use_dropout[0],
+        )
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:use_batch_norm',
+            value_range=args.use_batch_normalization,
+            default_value=args.use_batch_normalization[0],
+        )
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:use_skip_connection',
+            value_range=args.use_skip_connection,
+            default_value=args.use_skip_connection[0],
+        )
+
+        multi_branch_choice = [args.mb_choice]
+
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:multi_branch_choice',
+            value_range=multi_branch_choice,
+            default_value=multi_branch_choice[0],
+        )
+
+        search_space_updates.append(
+            node_name='optimizer',
+            hyperparameter='AdamWOptimizer:use_weight_decay',
+            value_range=args.use_weight_decay,
+            default_value=args.use_weight_decay[0],
+        )
+
+        trainer_choice = [augmentation_names_to_trainers[args.augmentation]]
+
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter='__choice__',
+            value_range=trainer_choice,
+            default_value=trainer_choice[0],
+        )
+
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:weighted_loss',
+            value_range=[1],
+            default_value=1,
+        )
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:use_lookahead_optimizer',
+            value_range=args.use_lookahead,
+            default_value=args.use_lookahead[0],
+        )
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:use_stochastic_weight_averaging',
+            value_range=args.use_swa,
+            default_value=args.use_swa[0],
+        )
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:use_snapshot_ensemble',
+            value_range=args.use_se,
+            default_value=args.use_se[0],
+        )
+
+    # No early stopping and train on gpu
+    pipeline_update = {
+        'early_stopping': -1,
+        'min_epochs': args.epochs,
+        'epochs': args.epochs,
+        "device": 'cpu',
+    }
+
+    return pipeline_update, search_space_updates, include_updates
+
+
+def get_incumbent_results(
+    run_history_file: str,
+    search_space: ConfigSpace.ConfigurationSpace,
+) -> Tuple[ConfigSpace.Configuration, float]:
+    """
+    Get the incumbent configuration and performance from the previous run HPO
+    search with AutoPytorch.
+
+    Args:
+        run_history_file (str):
+            The path where the AutoPyTorch search data is located.
+        search_space (ConfigSpace.ConfigurationSpace):
+            The ConfigurationSpace that was previously used for the HPO
+            search space.
+
+    Returns:
+        config, incumbent_run_value (Tuple[ConfigSpace.Configuration, float]):
+            The incumbent configuration found from HPO search and the validation
+            performance it achieved.
+
+    """
+    run_history = RunHistory()
+    run_history.load_json(
+        run_history_file,
+        search_space,
+    )
+
+    run_history_data = run_history.data
+    sorted_runvalue_by_cost = sorted(run_history_data.items(), key=lambda item: item[1].cost)
+    incumbent_run_key, incumbent_run_value = sorted_runvalue_by_cost[0]
+    config = run_history.ids_config[incumbent_run_key.config_id]
+    return config, incumbent_run_value
diff --git a/cocktails/main_experiment.py b/cocktails/main_experiment.py
index 6beafd8..3c77311 100644
--- a/cocktails/main_experiment.py
+++ b/cocktails/main_experiment.py
@@ -24,6 +24,8 @@
 
 import numpy as np
 
+import utilities
+
 from utilities import \
     get_data, \
     get_incumbent_results, \
diff --git a/cocktails/utilities.py b/cocktails/utilities.py
new file mode 100644
index 0000000..b353a2c
--- /dev/null
+++ b/cocktails/utilities.py
@@ -0,0 +1,446 @@
+from argparse import Namespace
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import ConfigSpace
+import pandas as pd
+from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
+
+import dask.distributed
+
+import openml
+import numpy as np
+
+from sklearn.model_selection import train_test_split
+
+from smac.intensification.simple_intensifier import SimpleIntensifier
+from smac.runhistory.runhistory2epm import RunHistory2EPM4LogCost
+from smac.scenario.scenario import Scenario
+from smac.facade.smac_ac_facade import SMAC4AC
+from smac.runhistory.runhistory import RunHistory
+
+
+def get_data(
+    task_id: int,
+    val_share: float = 0.25,
+    test_size: float = 0.2,
+    seed: int = 11,
+) -> Tuple[pd.DataFrame, pd.DataFrame, np.ndarray, np.ndarray, dict, np.ndarray]:
+    """
+    Given  a task id and split size information, return
+    the dataset splits based on a seed for the main algorithm
+    to use.
+
+    Args:
+        task_id (int):
+            The id of the task which will be used for the run.
+        val_share (float):
+            The validation split size from the train set.
+        test_size (float):
+            The test split size from the whole dataset.
+        seed (int):
+            The seed used for the dataset preparation.
+
+    Returns:
+
+        X_train, X_test, y_train, y_test, resampling_strategy_args, categorical indicator
+            (tuple[pd.DataFrame, pd.DataFrame, np.ndarray, np.ndarray, dict, np.ndarray]):
+
+            The train examples, the test examples, the train labels, the test labels,
+            the resampling strategy to be used and the categorical indicator for the features.
+    """
+    task = openml.tasks.get_task(task_id=task_id)
+    dataset = task.get_dataset()
+    X, y, categorical_indicator, _ = dataset.get_data(
+        dataset_format='dataframe',
+        target=dataset.default_target_attribute,
+    )
+
+    # AutoPyTorch fails when it is given a y DataFrame with False and True values
+    # and category as dtype. In its inner workings it uses sklearn which cannot
+    # detect the column type.
+    if isinstance(y[1], bool):
+        y = y.astype('bool')
+
+    # uncomment only for np.arrays
+    """
+    # patch categorical values to string
+    for index_nr, categorical_feature in enumerate(categorical_indicator):
+        if categorical_feature:
+            X[index_nr] = X[index_nr].astype("category")
+    """
+    X_train, X_test, y_train, y_test = train_test_split(
+        X,
+        y,
+        test_size=test_size,
+        random_state=seed,
+        stratify=y,
+        shuffle=True,
+    )
+    resampling_strategy_args = {
+        'val_share': val_share,
+    }
+
+    """
+    This was an earlier fix to the AutoPyTorch failures for imbalanced datasets. In particular
+    having variables with only null values in the train set. Now this is handled inside AutoPyTorch.
+    
+    train_column_nan_info = X_train.isna().all()
+    test_column_nan_info = X_test.isna().all()
+    only_nan_columns = [label for label, value in train_column_nan_info.items() if value]
+    test_nan_columns = [label for label, value in test_column_nan_info.items() if value]
+    only_nan_columns.extend(test_nan_columns)
+    only_nan_columns = set(only_nan_columns)
+    X_train.drop(only_nan_columns, axis='columns', inplace=True)
+    X_test.drop(only_nan_columns, axis='columns', inplace=True)
+    """
+    # TODO turn this into a dictionary
+
+    return X_train, X_test, y_train, y_test, resampling_strategy_args, categorical_indicator
+
+
+def get_smac_object(
+    scenario_dict: Dict[str, Any],
+    seed: int,
+    ta: Callable,
+    ta_kwargs: Dict[str, Any],
+    n_jobs: int,
+    initial_budget: int,
+    max_budget: int,
+    dask_client: Optional[dask.distributed.Client],
+) -> SMAC4AC:
+    """
+    This function returns an SMAC object that is gonna be used as
+    optimizer of pipelines.
+
+    Args:
+        scenario_dict (typing.Dict[str, typing.Any]): constrain on how to run
+            the jobs.
+        seed (int): to make the job deterministic.
+        ta (typing.Callable): the function to be intensified by smac.
+        ta_kwargs (typing.Dict[str, typing.Any]): Arguments to the above ta.
+        n_jobs (int): Amount of cores to use for this task.
+        initial_budget (int):
+            The initial budget for a configuration.
+        max_budget (int):
+            The maximal budget for a configuration.
+        dask_client (dask.distributed.Client): User provided scheduler.
+
+    Returns:
+        (SMAC4AC): sequential model algorithm configuration object
+    """
+    # multi-fidelity is disabled, that is why initial_budget and max_budget
+    # are not used.
+    rh2EPM = RunHistory2EPM4LogCost
+
+    return SMAC4AC(
+        scenario=Scenario(scenario_dict),
+        rng=seed,
+        runhistory2epm=rh2EPM,
+        tae_runner=ta,
+        tae_runner_kwargs=ta_kwargs,
+        initial_configurations=None,
+        run_id=seed,
+        intensifier=SimpleIntensifier,
+        dask_client=dask_client,
+        n_jobs=n_jobs,
+    )
+
+
+def get_updates_for_regularization_cocktails(
+    categorical_indicator: np.ndarray,
+    args: Namespace,
+) -> Tuple[Dict, HyperparameterSearchSpaceUpdates, Dict]:
+    """
+    These updates replicate the regularization cocktail paper search space.
+
+    Args:
+        categorical_indicator (np.ndarray)
+            An array that indicates whether a feature is categorical or not.
+        args (Namespace):
+            The different updates for the setup of the run, mostly updates
+            for the different regularization ingredients.
+
+    Returns:
+    ________
+        pipeline_update, search_space_updates, include_updates (Tuple[dict, HyperparameterSearchSpaceUpdates, dict]):
+            The pipeline updates like number of epochs, budget, seed etc.
+            The search space updates like setting different hps to different values or ranges.
+            Lastly include updates, which can be used to include different features.
+    """
+    augmentation_names_to_trainers = {
+        'mixup': 'MixUpTrainer',
+        'cutout': 'RowCutOutTrainer',
+        'cutmix': 'RowCutMixTrainer',
+        'standard': 'StandardTrainer',
+        'adversarial': 'AdversarialTrainer',
+    }
+
+    include_updates = dict()
+    include_updates['network_embedding'] = ['NoEmbedding']
+    include_updates['network_init'] = ['NoInit']
+
+    has_cat_features = any(categorical_indicator)
+    has_numerical_features = not all(categorical_indicator)
+    search_space_updates = HyperparameterSearchSpaceUpdates()
+
+    # architecture head
+    search_space_updates.append(
+        node_name='network_head',
+        hyperparameter='__choice__',
+        value_range=['no_head'],
+        default_value='no_head',
+    )
+    search_space_updates.append(
+        node_name='network_head',
+        hyperparameter='no_head:activation',
+        value_range=['relu'],
+        default_value='relu',
+    )
+
+    # backbone architecture
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='__choice__',
+        value_range=['ShapedResNetBackbone'],
+        default_value='ShapedResNetBackbone',
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:resnet_shape',
+        value_range=['brick'],
+        default_value='brick',
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:num_groups',
+        value_range=[2],
+        default_value=2,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:blocks_per_group',
+        value_range=[2],
+        default_value=2,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:output_dim',
+        value_range=[512],
+        default_value=512,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:max_units',
+        value_range=[512],
+        default_value=512,
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:activation',
+        value_range=['relu'],
+        default_value='relu',
+    )
+    search_space_updates.append(
+        node_name='network_backbone',
+        hyperparameter='ShapedResNetBackbone:shake_shake_update_func',
+        value_range=['even-even'],
+        default_value='even-even',
+    )
+
+    # training updates
+    search_space_updates.append(
+        node_name='lr_scheduler',
+        hyperparameter='__choice__',
+        value_range=['CosineAnnealingWarmRestarts'],
+        default_value='CosineAnnealingWarmRestarts',
+    )
+    search_space_updates.append(
+        node_name='lr_scheduler',
+        hyperparameter='CosineAnnealingWarmRestarts:n_restarts',
+        value_range=[3],
+        default_value=3,
+    )
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='__choice__',
+        value_range=['AdamWOptimizer'],
+        default_value='AdamWOptimizer',
+    )
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='AdamWOptimizer:lr',
+        value_range=[1e-3],
+        default_value=1e-3,
+    )
+    search_space_updates.append(
+        node_name='data_loader',
+        hyperparameter='batch_size',
+        value_range=[128],
+        default_value=128,
+    )
+
+    # preprocessing
+    search_space_updates.append(
+        node_name='feature_preprocessor',
+        hyperparameter='__choice__',
+        value_range=['NoFeaturePreprocessor'],
+        default_value='NoFeaturePreprocessor',
+    )
+
+    if has_numerical_features:
+        search_space_updates.append(
+            node_name='imputer',
+            hyperparameter='numerical_strategy',
+            value_range=['median'],
+            default_value='median',
+        )
+        search_space_updates.append(
+            node_name='scaler',
+            hyperparameter='__choice__',
+            value_range=['StandardScaler'],
+            default_value='StandardScaler',
+        )
+
+    if has_cat_features:
+        search_space_updates.append(
+            node_name='imputer',
+            hyperparameter='categorical_strategy',
+            value_range=['constant_!missing!'],
+            default_value='constant_!missing!',
+        )
+        search_space_updates.append(
+            node_name='encoder',
+            hyperparameter='__choice__',
+            value_range=['OneHotEncoder'],
+            default_value='OneHotEncoder',
+        )
+
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='AdamWOptimizer:beta1',
+        value_range=[0.9],
+        default_value=0.9,
+    )
+    search_space_updates.append(
+        node_name='optimizer',
+        hyperparameter='AdamWOptimizer:beta2',
+        value_range=[0.999],
+        default_value=0.999,
+    )
+
+    # if the cash formulation of the cocktail is not activated,
+    # otherwise the methods activation will be chosen by the SMBO optimizer.
+    if not args.cash_cocktail:
+        # regularization ingredients updates
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:use_dropout',
+            value_range=args.use_dropout,
+            default_value=args.use_dropout[0],
+        )
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:use_batch_norm',
+            value_range=args.use_batch_normalization,
+            default_value=args.use_batch_normalization[0],
+        )
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:use_skip_connection',
+            value_range=args.use_skip_connection,
+            default_value=args.use_skip_connection[0],
+        )
+
+        multi_branch_choice = [args.mb_choice]
+
+        search_space_updates.append(
+            node_name='network_backbone',
+            hyperparameter='ShapedResNetBackbone:multi_branch_choice',
+            value_range=multi_branch_choice,
+            default_value=multi_branch_choice[0],
+        )
+
+        search_space_updates.append(
+            node_name='optimizer',
+            hyperparameter='AdamWOptimizer:use_weight_decay',
+            value_range=args.use_weight_decay,
+            default_value=args.use_weight_decay[0],
+        )
+
+        trainer_choice = [augmentation_names_to_trainers[args.augmentation]]
+
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter='__choice__',
+            value_range=trainer_choice,
+            default_value=trainer_choice[0],
+        )
+
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:weighted_loss',
+            value_range=[1],
+            default_value=1,
+        )
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:use_lookahead_optimizer',
+            value_range=args.use_lookahead,
+            default_value=args.use_lookahead[0],
+        )
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:use_stochastic_weight_averaging',
+            value_range=args.use_swa,
+            default_value=args.use_swa[0],
+        )
+        search_space_updates.append(
+            node_name='trainer',
+            hyperparameter=f'{trainer_choice[0]}:use_snapshot_ensemble',
+            value_range=args.use_se,
+            default_value=args.use_se[0],
+        )
+
+    # No early stopping and train on gpu
+    pipeline_update = {
+        'early_stopping': -1,
+        'min_epochs': args.epochs,
+        'epochs': args.epochs,
+        "device": 'cpu',
+    }
+
+    return pipeline_update, search_space_updates, include_updates
+
+
+def get_incumbent_results(
+    run_history_file: str,
+    search_space: ConfigSpace.ConfigurationSpace,
+) -> Tuple[ConfigSpace.Configuration, float]:
+    """
+    Get the incumbent configuration and performance from the previous run HPO
+    search with AutoPytorch.
+
+    Args:
+        run_history_file (str):
+            The path where the AutoPyTorch search data is located.
+        search_space (ConfigSpace.ConfigurationSpace):
+            The ConfigurationSpace that was previously used for the HPO
+            search space.
+
+    Returns:
+        config, incumbent_run_value (Tuple[ConfigSpace.Configuration, float]):
+            The incumbent configuration found from HPO search and the validation
+            performance it achieved.
+
+    """
+    run_history = RunHistory()
+    run_history.load_json(
+        run_history_file,
+        search_space,
+    )
+
+    run_history_data = run_history.data
+    sorted_runvalue_by_cost = sorted(run_history_data.items(), key=lambda item: item[1].cost)
+    incumbent_run_key, incumbent_run_value = sorted_runvalue_by_cost[0]
+    config = run_history.ids_config[incumbent_run_key.config_id]
+    return config, incumbent_run_value