model : StructuredModel
Object containing the model structure. Has to implement loss, inference and loss_augmented_inference.
max_iter : int, default=10000
Maximum number of passes over dataset to find constraints.
C : float, default=1
Regularization parameter.
check_constraints : bool
Whether to check if the new “most violated constraint” is more violated than previous constraints. Helpful for stopping and debugging, but costly.
verbose : int
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.
tol : float, default=1e-3
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.
inference_cache : int, default=0
How many results of loss_augmented_inference to cache per sample. If > 0 the most violating of the cached examples will be used to construct a global constraint. Only if this constraint is not violated, inference will be run again. This parameter poses a memory / computation tradeoff. Storing more constraints might lead to RAM being exhausted. Using inference_cache > 0 is only advisable if computation time is dominated by inference.
cache_tol : float, None or ‘auto’ default=’auto’
Tolerance when to reject a constraint from cache (and do inference). If None,
tolwill be used. Higher values might lead to faster learning. ‘auto’ uses a heuristic to determine the cache tolerance based on the duality gap, as described in [3].
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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