pystruct.learners.
OneSlackSSVM
(model, max_iter=10000, C=1.0, check_constraints=False, verbose=0, negativity_constraint=None, n_jobs=1, break_on_bad=False, show_loss_every=0, tol=0.001, inference_cache=0, inactive_threshold=1e05, inactive_window=50, logger=None, cache_tol='auto', switch_to=None)[source]¶Structured SVM solver for the 1slack QP with l1 slack penalty.
Implements margin rescaled structural SVM using the 1slack formulation and cutting plane method, solved using CVXOPT. The optimization is restarted in each iteration.
Parameters:  model : StructuredModel
max_iter : int, default=10000
C : float, default=1
check_constraints : bool
verbose : int
negativity_constraint : list of ints
break_on_bad : bool default=False
n_jobs : int, default=1
show_loss_every : int, default=0
tol : float, default=1e3
inference_cache : int, default=0
cache_tol : float, None or ‘auto’ default=’auto’
inactive_threshold : float, default=1e5
inactive_window : float, default=50
switch_to : None or string, default=None
logger : logger object, default=None


Attributes:  w : ndarray, shape=(model.size_joint_feature,)
old_solution : dict
``loss_curve_`` : list of float
``objective_curve_`` : list of float
``primal_objective_curve_`` : list of float
``timestamps_`` : list of int

References
Methods
fit (X, Y[, constraints, warm_start, initialize]) 
Learn parameters using cutting plane method. 
get_params ([deep]) 
Get parameters for this estimator. 
predict (X) 
Predict output on examples in X. 
prune_constraints (constraints, a) 

score (X, Y) 
Compute score as 1  loss over whole data set. 
set_params (**params) 
Set the parameters of this estimator. 
__init__
(model, max_iter=10000, C=1.0, check_constraints=False, verbose=0, negativity_constraint=None, n_jobs=1, break_on_bad=False, show_loss_every=0, tol=0.001, inference_cache=0, inactive_threshold=1e05, inactive_window=50, logger=None, cache_tol='auto', switch_to=None)[source]¶fit
(X, Y, constraints=None, warm_start=False, initialize=True)[source]¶Learn parameters using cutting plane method.
Parameters:  X : iterable
Y : iterable
contraints : ignored warm_start : bool, default=False
initialize : boolean, default=True


get_params
(deep=True)¶Get parameters for this estimator.
Parameters:  deep: boolean, optional :


Returns:  params : mapping of string to any

predict
(X)¶Predict output on examples in X.
Parameters:  X : iterable


Returns:  Y_pred : list

score
(X, Y)¶Compute score as 1  loss over whole data set.
Returns the average accuracy (in terms of model.loss) over X and Y.
Parameters:  X : iterable
Y : iterable


Returns:  score : float

set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
Returns:  self : 
