def load_data(folder, input_path='user_item', cut=40, high_cut=1000000, seed=None):
    '\n    loads the training,validation,test set from the folder, restricts the users with at least "cut" read articles and\n    returns the sets. The Format of the sets is pd.Series with index the UserID and value a list of ArticleIDs\n    :param folder/input_path: {folder}/{input_path} is the path to look for the *_train.pkl files\n    :param cut: value to cut off users with less than "cut" read articles\n    :return: three pd.Series. Index of each series is the UserID. The value is a list of ArticleIDs.\n    (look in create_split to see how the split is defines)\n    '
    (user_item_train, user_item_test, user_item_validation) = (pd.read_pickle(f'{folder}/{input_path}_train.pkl'), pd.read_pickle(f'{folder}/{input_path}_test.pkl'), pd.read_pickle(f'{folder}/{input_path}_validation.pkl'))
    user_item_train = user_item_train[(user_item_train.str.len() > (cut * 0.7))]
    user_item_train = user_item_train[(user_item_train.str.len() < (high_cut * 0.7))]
    user_item_test = user_item_test.loc[user_item_train.index]
    user_item_validation = user_item_validation.loc[user_item_train.index]
    return (user_item_train, user_item_test, user_item_validation)
loads the training,validation,test set from the folder, restricts the users with at least "cut" read articles and
returns the sets. The Format of the sets is pd.Series with index the UserID and value a list of ArticleIDs
:param folder/input_path: {folder}/{input_path} is the path to look for the *_train.pkl files
:param cut: value to cut off users with less than "cut" read articles
:return: three pd.Series. Index of each series is the UserID. The value is a list of ArticleIDs.
(look in create_split to see how the split is defines)
def load_data(folder, input_path='user_item', cut=40, high_cut=1000000, seed=None):
    '\n    loads the training,validation,test set from the folder, restricts the users with at least "cut" read articles and\n    returns the sets. The Format of the sets is pd.Series with index the UserID and value a list of ArticleIDs\n    :param folder/input_path: {folder}/{input_path} is the path to look for the *_train.pkl files\n    :param cut: value to cut off users with less than "cut" read articles\n    :return: three pd.Series. Index of each series is the UserID. The value is a list of ArticleIDs.\n    (look in create_split to see how the split is defines)\n    '
    (user_item_train, user_item_test, user_item_validation) = (pd.read_pickle(f'{folder}/{input_path}_train.pkl'), pd.read_pickle(f'{folder}/{input_path}_test.pkl'), pd.read_pickle(f'{folder}/{input_path}_validation.pkl'))
    user_item_train = user_item_train[(user_item_train.str.len() > (cut * 0.7))]
    user_item_train = user_item_train[(user_item_train.str.len() < (high_cut * 0.7))]
    user_item_test = user_item_test.loc[user_item_train.index]
    user_item_validation = user_item_validation.loc[user_item_train.index]
    return (user_item_train, user_item_test, user_item_validation)