model : StructuredModel
Object containing model structure. Has to implement loss, inference and loss_augmented_inference.
max_iter : int, default=100
Maximum number of passes over dataset to find constraints and perform updates.
C : float, default=1.
Regularization parameter
verbose : int, default=0
Verbosity.
learning_rate : float or ‘auto’, default=’auto’
Learning rate used in subgradient descent. If ‘auto’, the pegasos schedule is used, which starts with
learning_rate = n_samples * C.
momentum : float, default=0.0
Momentum used in subgradient descent.
n_jobs : int, default=1
Number of parallel jobs for inference. -1 means as many as cpus.
show_loss_every : int, default=0
Controlls how often the hamming loss is computed (for monitoring purposes). Zero means never, otherwise it will be computed very show_loss_every’th epoch.
decay_exponent : float, default=1
Exponent for decaying learning rate. Effective learning rate is
learning_rate / (decay_t0 + t)** decay_exponent. Zero means no decay.
decay_t0 : float, default=10
Offset for decaying learning rate. Effective learning rate is
learning_rate / (decay_t0 + t)** decay_exponent.
break_on_no_constraints : bool, default=True
Break when there are no new constraints found.
averaging : string, default=None
Whether and how to average weights. Possible options are ‘linear’, ‘squared’ and None. The string reflects the weighting of the averaging:
- linear:
w_avg ~ w_1 + 2 * w_2 + ... + t * w_t- squared:
w_avg ~ w_1 + 4 * w_2 + ... + t**2 * w_tUniform averaging is not implemented as it is worse than linear weighted averaging or no averaging.
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