onlineml.autovw

AutoVW Objects

class AutoVW()

Class for the AutoVW algorithm.

__init__

def __init__(max_live_model_num: int, search_space: dict, init_config: Optional[dict] = {}, min_resource_lease: Optional[Union[str, float]] = "auto", automl_runner_args: Optional[dict] = {}, scheduler_args: Optional[dict] = {}, model_select_policy: Optional[str] = "threshold_loss_ucb", metric: Optional[str] = "mae_clipped", random_seed: Optional[int] = None, model_selection_mode: Optional[str] = "min", cb_coef: Optional[float] = None)

Constructor.

Arguments:

• max_live_model_num - An int to specify the maximum number of 'live' models, which, in other words, is the maximum number of models allowed to update in each learning iteraction.
• search_space - A dictionary of the search space. This search space includes both hyperparameters we want to tune and fixed hyperparameters. In the latter case, the value is a fixed value.
• init_config - A dictionary of a partial or full initial config, e.g. {'interactions': set(), 'learning_rate': 0.5}
• min_resource_lease - string or float | The minimum resource lease assigned to a particular model/trial. If set as 'auto', it will be calculated automatically.
• automl_runner_args - A dictionary of configuration for the OnlineTrialRunner. If set {}, default values will be used, which is equivalent to using the following configs.

Example:

automl_runner_args = {
    "champion_test_policy": 'loss_ucb', # the statistic test for a better champion
    "remove_worse": False,              # whether to do worse than test
}

• scheduler_args - A dictionary of configuration for the scheduler. If set {}, default values will be used, which is equivalent to using the following config.

Example:

scheduler_args = {
    "keep_challenger_metric": 'ucb',  # what metric to use when deciding the top performing challengers
    "keep_challenger_ratio": 0.5,     # denotes the ratio of top performing challengers to keep live
    "keep_champion": True,            # specifcies whether to keep the champion always running
}

• model_select_policy - A string in ['threshold_loss_ucb', 'threshold_loss_lcb', 'threshold_loss_avg', 'loss_ucb', 'loss_lcb', 'loss_avg'] to specify how to select one model to do prediction from the live model pool. Default value is 'threshold_loss_ucb'.
• metric - A string in ['mae_clipped', 'mae', 'mse', 'absolute_clipped', 'absolute', 'squared'] to specify the name of the loss function used for calculating the progressive validation loss in ChaCha.
• random_seed - An integer of the random seed used in the searcher (more specifically this the random seed for ConfigOracle).
• model_selection_mode - A string in ['min', 'max'] to specify the objective as minimization or maximization.
• cb_coef - A float coefficient (optional) used in the sample complexity bound.

predict

def predict(data_sample)

Predict on the input data sample.

Arguments:

• data_sample - one data example in vw format.

learn

def learn(data_sample)

Perform one online learning step with the given data sample.

Arguments:

• data_sample - one data example in vw format. It will be used to update the vw model.

get_ns_feature_dim_from_vw_example

@staticmethod
def get_ns_feature_dim_from_vw_example(vw_example) -> dict

Get a dictionary of feature dimensionality for each namespace singleton.