model : StructuredModel
Object containing the model structure. Has to implement loss, inference and loss_augmented_inference.
max_iter : int
Maximum number of passes over dataset to find constraints.
C : float
Regularization parameter
check_constraints : bool (default=True)
Whether to check if the new “most violated constraint” is more violated than previous constraints. Helpful for stopping and debugging, but costly.
verbose : int (default=0)
Verbosity.
negativity_constraint: list of ints :
Indices of parmeters that are constraint to be negative. This is useful for learning submodular CRFs (inference is formulated as maximization in SSVMs, flipping some signs).
break_on_bad: bool (default=False) :
Whether to break (start debug mode) when inference was approximate.
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.
batch_size : int, default=100
Number of constraints after which we solve the QP again. batch_size=-1 means that an update is performed only after going once over the whole training set.
tol : float, default=-10
Convergence tolerance. If dual objective decreases less than tol, learning is stopped. The default corresponds to ignoring the behavior of the dual objective and stop only if no more constraints can be found.
inactive_threshold : float, default=1e-5
Threshold for dual variable of a constraint to be considered inactive.
inactive_window : float, default=50
Window for measuring inactivity. If a constraint is inactive for
inactive_windowiterations, it will be pruned from the QP. If set to 0, no constraints will be removed.
switch_to : None or string, default=None
Switch to the given inference method if the previous method does not find any more constraints.
logger : logger object, default=None
Pystruct logger for storing the model or extracting additional information.
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