data : np.ndarray — data to be used for simulation

data_config : dict — data configuration dictionary

clients_train_data : List[np.ndarray] — list of clients training data

clients_test_data : List[np.ndarray] — list of clients test data

clients_train_data_ms : List[np.ndarray] — list of clients training data with missing values

global_test : np.ndarray — global test data

client_seeds : List[int] — list of seeds for clients

stats : dict — simulation statistics

debug_mode : bool — whether to enable debug mode

create_simulated_scenario — Simulate missing data scenario

create_simulated_scenario_lite — Simulate missing data scenario

create_real_scenario — Create a real scenario from a list of pandas DataFrames

save

load

export_data

summarize_scenario

show_missing_data_details

visualize_missing_pattern

visualize_missing_distribution

visualize_data_heterogeneity

data : Union[np.array, pd.DataFrame] — data to be used for simulation

data_config : dict — data configuration dictionary

num_clients : int — number of clients

dp_strategy : str — data partition strategy, default: 'iid-even'

• iid-even, iid-dir, niid-dir, niid-path

dp_split_cols : Union[str, int, List[int]] — split columns option

• target, feature, default: target

dp_min_samples : Union[float, int] — minimum samples for clients, default: 50

dp_max_samples : Union[float, int] — maximum samples for clients, default: 2000

dp_sample_iid_direct : bool — sample iid data directly, default: False

dp_local_test_size : float — local test size ratio, default: 0.1

dp_global_test_size : float — global test size ratio, default: 0.1

dp_local_backup_size : float — local backup size ratio, default: 0.05

dp_reg_bins : int — regression bins, default: 50

ms_mech_type : str — missing mechanism type, default: 'mcar'

• mcar, mar_sigmoid, mnar_sigmoid, mar_quantile, mnar_quantile

ms_cols : Union[str, List[int]] — missing columns, default: 'all' - all, all-num, random

obs_cols : Union[str, List[int]] — fully observed columns for MAR, default: 'random' - random, rest

ms_global_mechanism : bool — global missing mechanism, default: False

ms_mr_dist_clients : str — missing ratio distribution, default: 'random' - 'random', 'random-int', 'normal', 'normal-int'

ms_mr_clients : Any — client-level missing ratio settings, default: (0.3, 0.7)

ms_mf_dist_clients : str — missing features distribution, default: 'identity' - 'identity', 'random', 'random2'

ms_mm_dist_clients : str — missing mechanism functions distribution, default: 'random' - 'identity', 'random', 'random2'

ms_missing_features : str — missing features strategy, default: 'all' - 'all', 'all-num'

ms_mr_lower : float — missing ratio lower clipping bound, default: 0.1

ms_mr_upper : float — missing ratio upper clipping bound, default: 0.9

ms_mm_funcs_bank : str — missing mechanism functions banks, default: 'lr' - None, 'lr', 'mt', 'all'

ms_mm_strictness : bool — missing adding probabilistic or deterministic, default: True

ms_mm_obs : bool — missing adding based on observed data, default: False

ms_mm_feature_option : str — missing mechanism associated with which features, default: 'allk=0.2' - 'self', 'all', 'allk=0.1'

ms_mm_beta_option : str — mechanism beta coefficient option, default: None - (mnar) self, sphere, randu, (mar) fixed, randu, randn

seed : int — random seed, default: 100330201

verbose : int — whether verbose the simulation process, default: 0

dict — dictionary of clients training data, test data, training data with missing values, global test data

ValueError

NotImplementedError

datas : List[pd.DataFrame] — list of pandas DataFrames

data_config : Dict — data configuration dictionary

seed : int — random seed, default: 100330201

verbose : int — whether verbose the simulation process, default: 0

ValueError