pystruct.learners.LatentSSVM¶

class pystruct.learners.LatentSSVM(base_ssvm, latent_iter=5, logger=None)[source]¶

Stuctured SVM solver for latent-variable models.

This is a hard-EM-style algorithm that alternates between latent variable completion for the ground truth and learning the parameters with the latent variables held fixed using the base_ssvm solver.

The model is expected to know how to initialize itself - it should provide a init_latent procedure. Optionally the H_init parameter can be passed to fit, to explicitly initialize the latent variables in the first iteration.

If the base_ssvm is an n-slack SSVM, the current constraints will be adjusted after recomputing the latent variables H. If the base_ssvm is a 1-slack SSVM, the inference cache will be reused. Both methods drastically speed up learning.

If base_ssvm is an 1-slack SSVM, this corresponds to the approach of Yu and Joachims, Learning Structural SVMs with Latent Variables.

Parameters:	base_ssvm : object SSVM solver instance for solving the completed problem. latent_iter : int (default=5) Number of iterations in the completion / refit loop. logger : object Logger instance.
Attributes:	w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM.

Methods

fit(X, Y[, H_init, initialize])	Learn parameters using the concave-convex procedure.
get_params([deep])	Get parameters for this estimator.
predict(X)
predict_latent(X)
score(X, Y)	Compute score as 1 - loss over whole data set.
set_params(**params)	Set the parameters of this estimator.

__init__(base_ssvm, latent_iter=5, logger=None)[source]¶

fit(X, Y, H_init=None, initialize=True)[source]¶

Learn parameters using the concave-convex procedure.

If no H_init is given, the latent variables are initialized using the init_latent method of the model.

Parameters:

X : iterable Traing instances. Contains the structured input objects. No requirement on the particular form of entries of X is made. Y : iterable Training labels. Contains the strctured labels for inputs in X. Needs to have the same length as X. H_init : iterable, optional Initial values for the latent variables. initialize : boolean, default=True Whether to initialize the model for the data. Leave this true except if you really know what you are doing.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

score(X, Y)[source]¶

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 Evaluation data. Y : iterable True labels.
Returns:	score : float Average of 1 - loss over training examples.

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 :