pystruct.learners.PrimalDSStructuredSVM¶

class pystruct.learners.PrimalDSStructuredSVM(model, max_iter=100, C=1.0, verbose=0, n_jobs=1, show_loss_every=0, logger=None)[source]¶

Uses downhill simplex for optimizing an unconstraint primal.

This is basically a sanity check on all other implementations, as this is easier to check for correctness.

Methods

fit(X, Y)
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict output on examples in X.
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, verbose=0, n_jobs=1, show_loss_every=0, logger=None)¶

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.

predict(X)¶

Predict output on examples in X.

Parameters:	X : iterable Traing instances. Contains the structured input objects.
Returns:	Y_pred : list List of inference results for X using the learned parameters.

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 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 :