onlinerake.OnlineRakingMWU

class onlinerake.OnlineRakingMWU(targets, learning_rate: float = 1.0, min_weight: float = 0.001, max_weight: float = 100.0, n_steps: int = 3, verbose: bool = False, track_convergence: bool = True, convergence_window: int = 20, compute_weight_stats: bool | int = False)

Bases: OnlineRakingSGD

Online raking via multiplicative weights updates.

Parameters:

• targets (Targets) – Target population proportions for each feature.

• learning_rate (float, optional) – Step size used in the exponent of the multiplicative update. A typical default is learning_rate=1.0. The algorithm automatically clips extreme exponents based on the weights dtype to prevent numerical overflow/underflow, making it robust even with very large learning rates.

• min_weight (float, optional) – Lower bound applied to the weights after each update. This prevents weights from collapsing to zero. Must be positive.

• max_weight (float, optional) – Upper bound applied to the weights after each update. This prevents runaway weights. Must exceed min_weight.

• n_steps (int, optional) – Number of multiplicative updates applied each time a new observation arrives.

• compute_weight_stats (bool or int, optional) – Controls computation of weight distribution statistics for performance. If True, compute on every call. If False, never compute. If integer k, compute every k observations. Default is False.

__init__(targets, learning_rate: float = 1.0, min_weight: float = 0.001, max_weight: float = 100.0, n_steps: int = 3, verbose: bool = False, track_convergence: bool = True, convergence_window: int = 20, compute_weight_stats: bool | int = False) → None

Methods

__init__(targets[, learning_rate, ...])
check_convergence([tolerance])	Check if algorithm has converged based on loss stability.
detect_oscillation([threshold])	Detect if loss is oscillating rather than converging.
fit_one(obs)	Consume a single observation and update weights multiplicatively.
partial_fit(obs)	Consume a single observation and update weights multiplicatively.
partial_fit_batch(observations)	Process multiple observations in batch.

Attributes

converged	Return True if the algorithm has detected convergence.
convergence_step	Get step number where convergence was detected.
effective_sample_size	Return the effective sample size (ESS).
gradient_norm_history	Get history of gradient norms.
loss	Get current squared-error loss.
loss_moving_average	Return moving average of loss over convergence window.
margins	Get current weighted margins.
raw_margins	Get unweighted (raw) margins.
weight_distribution_stats	Return comprehensive weight distribution statistics.
weights	Get copy of current weight vector.

fit_one(obs: dict[str, Any] | Any) → None

Consume a single observation and update weights multiplicatively.

Parameters:

obs (dict or object) – An observation containing feature indicators. For dict input, keys should match feature names in targets. For object input, features are accessed as attributes. Values should be binary (0/1 or False/True).

Returns:

The internal state is updated in place.

Return type:

None

partial_fit(obs: dict[str, Any] | Any) → None

Consume a single observation and update weights multiplicatively.

Parameters:

obs (dict or object) – An observation containing feature indicators. For dict input, keys should match feature names in targets. For object input, features are accessed as attributes. Values should be binary (0/1 or False/True).

Returns:

The internal state is updated in place.

Return type:

None