pystruct.learners.
NSlackSSVM
(model, max_iter=100, C=1.0, check_constraints=True, verbose=0, negativity_constraint=None, n_jobs=1, break_on_bad=False, show_loss_every=0, batch_size=100, tol=0.001, inactive_threshold=1e05, inactive_window=50, logger=None, switch_to=None)[source]¶Structured SVM solver for the nslack QP with l1 slack penalty.
Implements margin rescaled structural SVM using the nslack formulation and cutting plane method, solved using CVXOPT. The optimization is restarted in each iteration.
Parameters:  model : StructuredModel
max_iter : int
C : float
check_constraints : bool (default=True)
verbose : int (default=0)
negativity_constraint: list of ints :
break_on_bad: bool (default=False) :
n_jobs : int, default=1
show_loss_every : int, default=0
batch_size : int, default=100
tol : float, default=10
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
Altun, Yasemin and Singer, Yoram: Large margin methods for structured and interdependent output variables, JMLR 2006
Cuttingplane training of structural SVMs, JMLR 2009
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=100, C=1.0, check_constraints=True, verbose=0, negativity_constraint=None, n_jobs=1, break_on_bad=False, show_loss_every=0, batch_size=100, tol=0.001, inactive_threshold=1e05, inactive_window=50, logger=None, switch_to=None)[source]¶fit
(X, Y, constraints=None, warm_start=None, initialize=True)[source]¶Learn parameters using cutting plane method.
Parameters:  X : iterable
Y : iterable
contraints : iterable
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 : 
