def get_hyperparams(tree_type, dataset):
    """
    Input
        tree_type: str, Tree-ensemble type.
        dataset: str, dataset.

    Return
        - Dict of selected hyperparameters for the inputs.
    """
    lgb = {}
    lgb['adult'] = {'n_estimators': 100, 'num_leaves': 31, 'max_depth': -1}
    lgb['bank_marketing'] = {'n_estimators': 50, 'num_leaves': 31, 'max_depth': -1}
    lgb['bean'] = {'n_estimators': 25, 'num_leaves': 15, 'max_depth': -1}
    lgb['compas'] = {'n_estimators': 25, 'num_leaves': 91, 'max_depth': -1}
    lgb['concrete'] = {'n_estimators': 200, 'num_leaves': 15, 'max_depth': -1}
    lgb['credit_card'] = {'n_estimators': 50, 'num_leaves': 15, 'max_depth': -1}
    lgb['diabetes'] = {'n_estimators': 200, 'num_leaves': 31, 'max_depth': -1}
    lgb['energy'] = {'n_estimators': 200, 'num_leaves': 15, 'max_depth': -1}
    lgb['flight_delays'] = {'n_estimators': 100, 'num_leaves': 91, 'max_depth': -1}
    lgb['german_credit'] = {'n_estimators': 25, 'num_leaves': 15, 'max_depth': -1}
    lgb['htru2'] = {'n_estimators': 100, 'num_leaves': 15, 'max_depth': -1}
    lgb['life'] = {'n_estimators': 200, 'num_leaves': 61, 'max_depth': -1}
    lgb['naval'] = {'n_estimators': 200, 'num_leaves': 91, 'max_depth': -1}
    lgb['no_show'] = {'n_estimators': 50, 'num_leaves': 61, 'max_depth': -1}
    lgb['obesity'] = {'n_estimators': 200, 'num_leaves': 91, 'max_depth': -1}
    lgb['power'] = {'n_estimators': 200, 'num_leaves': 61, 'max_depth': -1}
    lgb['protein'] = {'n_estimators': 200, 'num_leaves': 91, 'max_depth': -1}
    lgb['spambase'] = {'n_estimators': 200, 'num_leaves': 31, 'max_depth': -1}
    lgb['surgical'] = {'n_estimators': 200, 'num_leaves': 15, 'max_depth': -1}
    lgb['twitter'] = {'n_estimators': 200, 'num_leaves': 91, 'max_depth': -1}
    lgb['vaccine'] = {'n_estimators': 100, 'num_leaves': 15, 'max_depth': -1}
    lgb['wine'] = {'n_estimators': 200, 'num_leaves': 91, 'max_depth': -1}

    sgb = {}
    sgb['adult'] = {'max_iter': 200, 'max_leaf_nodes': 15, 'max_bins': 250, 'max_depth': None}
    sgb['bank_marketing'] = {'max_iter': 50, 'max_leaf_nodes': 31, 'max_bins': 100, 'max_depth': None}
    sgb['bean'] = {'max_iter': 25, 'max_leaf_nodes': 15, 'max_bins': 50, 'max_depth': None}
    sgb['compas'] = {'max_iter': 50, 'max_leaf_nodes': 15, 'max_bins': 50, 'max_depth': None}
    sgb['concrete'] = {'max_iter': 200, 'max_leaf_nodes': 15, 'max_bins': 250, 'max_depth': None}
    sgb['credit_card'] = {'max_iter': 25, 'max_leaf_nodes': 15, 'max_bins': 100, 'max_depth': None}
    sgb['diabetes'] = {'max_iter': 200, 'max_leaf_nodes': 31, 'max_bins': 100, 'max_depth': None}
    sgb['energy'] = {'max_iter': 200, 'max_leaf_nodes': 15, 'max_bins': 50, 'max_depth': None}
    sgb['flight_delays'] = {'max_iter': 200, 'max_leaf_nodes': 61, 'max_bins': 250, 'max_depth': None}
    sgb['german_credit'] = {'max_iter': 25, 'max_leaf_nodes': 15, 'max_bins': 100, 'max_depth': None}
    sgb['htru2'] = {'max_iter': 50, 'max_leaf_nodes': 15, 'max_bins': 50, 'max_depth': None}
    sgb['life'] = {'max_iter': 200, 'max_leaf_nodes': 31, 'max_bins': 250, 'max_depth': None}
    sgb['naval'] = {'max_iter': 200, 'max_leaf_nodes': 91, 'max_bins': 250, 'max_depth': None}
    sgb['no_show'] = {'max_iter': 50, 'max_leaf_nodes': 61, 'max_bins': 100, 'max_depth': None}
    sgb['obesity'] = {'max_iter': 200, 'max_leaf_nodes': 91, 'max_bins': 250, 'max_depth': None}
    sgb['power'] = {'max_iter': 200, 'max_leaf_nodes': 61, 'max_bins': 250, 'max_depth': None}
    sgb['protein'] = {'max_iter': 200, 'max_leaf_nodes': 91, 'max_bins': 250, 'max_depth': None}
    sgb['spambase'] = {'max_iter': 200, 'max_leaf_nodes': 91, 'max_bins': 250, 'max_depth': None}
    sgb['surgical'] = {'max_iter': 100, 'max_leaf_nodes': 31, 'max_bins': 250, 'max_depth': None}
    sgb['twitter'] = {'max_iter': 200, 'max_leaf_nodes': 91, 'max_bins': 250, 'max_depth': None}
    sgb['vaccine'] = {'max_iter': 100, 'max_leaf_nodes': 15, 'max_bins': 50, 'max_depth': None}
    sgb['wine'] = {'max_iter': 200, 'max_leaf_nodes': 91, 'max_bins': 100, 'max_depth': None}

    cb = {}
    cb['adult'] = {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.6}
    cb['bank_marketing'] = {'n_estimators': 200, 'max_depth': 7, 'learning_rate': 0.1}
    cb['bean'] = {'n_estimators': 200, 'max_depth': 3, 'learning_rate': 0.3}
    cb['compas'] = {'n_estimators': 50, 'max_depth': 4, 'learning_rate': 0.3}
    cb['concrete'] = {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.3}
    cb['credit_card'] = {'n_estimators': 50, 'max_depth': 5, 'learning_rate': 0.1}
    cb['diabetes'] = {'n_estimators': 200, 'max_depth': 5, 'learning_rate': 0.3}
    cb['energy'] = {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.9}
    cb['flight_delays'] = {'n_estimators': 200, 'max_depth': 7, 'learning_rate': 0.3}
    cb['german_credit'] = {'n_estimators': 100, 'max_depth': 5, 'learning_rate': 0.1}
    cb['htru2'] = {'n_estimators': 200, 'max_depth': 3, 'learning_rate': 0.3}
    cb['life'] = {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.3}
    cb['naval'] = {'n_estimators': 200, 'max_depth': 7, 'learning_rate': 0.6}
    cb['no_show'] = {'n_estimators': 200, 'max_depth': 7, 'learning_rate': 0.3}
    cb['obesity'] = {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.6}
    cb['power'] = {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.6}
    cb['protein'] = {'n_estimators': 200, 'max_depth': 7, 'learning_rate': 0.3}
    cb['spambase'] = {'n_estimators': 200, 'max_depth': 5, 'learning_rate': 0.3}
    cb['surgical'] = {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.1}
    cb['twitter'] = {'n_estimators': 200, 'max_depth': 7, 'learning_rate': 0.6}
    cb['vaccine'] = {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.1}
    cb['wine'] = {'n_estimators': 200, 'max_depth': 7, 'learning_rate': 0.3}

    xgb = {}
    xgb['adult'] = {'n_estimators': 200, 'max_depth': 3}
    xgb['bank_marketing'] = {'n_estimators': 100, 'max_depth': 4}
    xgb['bean'] = {'n_estimators': 25, 'max_depth': 5}
    xgb['compas'] = {'n_estimators': 50, 'max_depth': 3}
    xgb['concrete'] = {'n_estimators': 200, 'max_depth': 4}
    xgb['credit_card'] = {'n_estimators': 10, 'max_depth': 3}
    xgb['diabetes'] = {'n_estimators': 200, 'max_depth': 3}
    xgb['energy'] = {'n_estimators': 200, 'max_depth': 5}
    xgb['flight_delays'] = {'n_estimators': 200, 'max_depth': 7}
    xgb['german_credit'] = {'n_estimators': 10, 'max_depth': 4}
    xgb['htru2'] = {'n_estimators': 100, 'max_depth': 2}
    xgb['life'] = {'n_estimators': 200, 'max_depth': 5}
    xgb['naval'] = {'n_estimators': 100, 'max_depth': 7}
    xgb['no_show'] = {'n_estimators': 100, 'max_depth': 5}
    xgb['obesity'] = {'n_estimators': 200, 'max_depth': 7}
    xgb['power'] = {'n_estimators': 200, 'max_depth': 7}
    xgb['protein'] = {'n_estimators': 200, 'max_depth': 7}
    xgb['spambase'] = {'n_estimators': 200, 'max_depth': 4}
    xgb['surgical'] = {'n_estimators': 50, 'max_depth': 5}
    xgb['twitter'] = {'n_estimators': 200, 'max_depth': 7}
    xgb['vaccine'] = {'n_estimators': 100, 'max_depth': 3}
    xgb['wine'] = {'n_estimators': 100, 'max_depth': 7}

    skrf = {}
    skrf['adult'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['bank_marketing'] = {'n_estimators': 100, 'max_depth': 7}
    skrf['bean'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['compas'] = {'n_estimators': 100, 'max_depth': 7}
    skrf['concrete'] = {'n_estimators': 100, 'max_depth': 7}
    skrf['credit_card'] = {'n_estimators': 50, 'max_depth': 7}
    skrf['diabetes'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['energy'] = {'n_estimators': 10, 'max_depth': 7}
    skrf['flight_delays'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['german_credit'] = {'n_estimators': 25, 'max_depth': 5}
    skrf['htru2'] = {'n_estimators': 50, 'max_depth': 7}
    skrf['life'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['naval'] = {'n_estimators': 100, 'max_depth': 7}
    skrf['no_show'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['obesity'] = {'n_estimators': 50, 'max_depth': 7}
    skrf['power'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['protein'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['spambase'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['surgical'] = {'n_estimators': 100, 'max_depth': 7}
    skrf['twitter'] = {'n_estimators': 50, 'max_depth': 7}
    skrf['vaccine'] = {'n_estimators': 200, 'max_depth': 7}
    skrf['wine'] = {'n_estimators': 200, 'max_depth': 5}

    hp = {}
    hp['lgb'] = lgb
    hp['sgb'] = sgb
    hp['cb'] = cb
    hp['xgb'] = xgb
    hp['skrf'] = skrf

    return hp[tree_type][dataset]