pystruct.models.LatentNodeCRF¶

class pystruct.models.LatentNodeCRF(n_labels=None, n_features=None, n_hidden_states=2, inference_method=None, class_weight=None, latent_node_features=False)[source]¶

CRF with latent variables.

Input x is tuple (features, edges, n_hidden) First features.shape[0] nodes are observed, then n_hidden unobserved nodes.

Currently unobserved nodes don’t have features.

Parameters:

n_labels : int, default=2 Number of states for observed variables. n_hidden_states : int, default=2 Number of states for hidden variables. n_features : int, default=None Number of features per node. None means n_states. inference_method : string, default=None Function to call to do inference and loss-augmented inference. Possible values are: ‘max-product’ for max-product belief propagation. Recommended for chains an trees. Loopy belief propagatin in case of a general graph. ‘lp’ for Linear Programming relaxation using cvxopt. ‘ad3’ for AD3 dual decomposition. ‘qpbo’ for QPBO + alpha expansion. ‘ogm’ for OpenGM inference algorithms. class_weight : None, or array-like Class weights. If an array-like is passed, it must have length n_classes. None means equal class weights. latent_node_features : bool, default=False Whether latent nodes have features. We assume that if True, the number of features is the same as for visible nodes.

Methods

base_loss(y, y_hat)
batch_inference(X, w[, relaxed])
batch_joint_feature(X, Y[, Y_true])
batch_loss(Y, Y_hat)
batch_loss_augmented_inference(X, Y, w[, ...])
continuous_loss(y, y_hat)
inference(x, w[, relaxed, return_energy])	Inference for x using parameters w.
init_latent(X, Y)
initialize(X, Y)
joint_feature(x, y)	Feature vector associated with instance (x, y).
label_from_latent(h)
latent(x, y, w)
loss(h, h_hat)
loss_augmented_inference(x, h, w[, relaxed, ...])
max_loss(h)

__init__(n_labels=None, n_features=None, n_hidden_states=2, inference_method=None, class_weight=None, latent_node_features=False)[source]¶

inference(x, w, relaxed=False, return_energy=False)¶

Inference for x using parameters w.

Finds (approximately) armin_y np.dot(w, joint_feature(x, y)) using self.inference_method.

Parameters:

x : tuple Instance of a graph with unary evidence. x=(unaries, edges) unaries are an nd-array of shape (n_nodes, n_states), edges are an nd-array of shape (n_edges, 2) w : ndarray, shape=(size_joint_feature,) Parameters for the CRF energy function. relaxed : bool, default=False Whether relaxed inference should be performed. Only meaningful if inference method is ‘lp’ or ‘ad3’. By default fractional solutions are rounded. If relaxed=True, fractional solutions are returned directly. return_energy : bool, default=False Whether to return the energy of the solution (x, y) that was found.

Returns:

y_pred : ndarray or tuple By default an inter ndarray of shape=(width, height) of variable assignments for x is returned. If relaxed=True and inference_method is lp or ad3, a tuple (unary_marginals, pairwise_marginals) containing the relaxed inference result is returned. unary marginals is an array of shape (width, height, n_states), pairwise_marginals is an array of shape (n_states, n_states) of accumulated pairwise marginals.

joint_feature(x, y)[source]¶

Feature vector associated with instance (x, y).

Feature representation joint_feature, such that the energy of the configuration (x, y) and a weight vector w is given by np.dot(w, joint_feature(x, y)).

Parameters:	x : tuple Unary evidence. y : ndarray or tuple Either y is an integral ndarray, giving a complete labeling for x. Or it is the result of a linear programming relaxation. In this case, y=(unary_marginals, pariwise_marginals).
Returns:	p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y).