pystruct.models.EdgeFeatureGraphCRF¶

class pystruct.models.EdgeFeatureGraphCRF(n_states=None, n_features=None, n_edge_features=None, inference_method=None, class_weight=None, symmetric_edge_features=None, antisymmetric_edge_features=None)[source]¶

Pairwise CRF with features/strength associated to each edge.

Pairwise potentials are asymmetric and shared over all edges. They are weighted by an edge-specific features, though. This allows for contrast sensitive potentials or directional potentials (using a {-1, +1} encoding of the direction for example).

More complicated interactions are also possible, of course.

Node features and edge features are given as a tuple of shape (n_nodes, n_features) and (n_edges, n_edge_features) respectively.

An instance x is represented as a tuple (node_features, edges, edge_features) where edges is an array of shape (n_edges, 2), representing the graph.

Labels y are given as array of shape (n_nodes)

Parameters:

n_states : int, default=None Number of states for all variables. Inferred from data if not provided. n_features : int, default=None Number of features per node. Inferred from data if not provided. n_edge_features : int, default=None Number of features per edge. Inferred from data if not provided. inference_method : string, default=”ad3” Function to call do do inference and loss-augmented inference. Possible values are: ‘max-product’ for max-product belief propagation. ‘lp’ for Linear Programming relaxation using cvxopt. ‘ad3’ for AD3 dual decomposition. ‘qpbo’ for QPBO + alpha expansion. 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. symmetric_edge_features : None or list Indices of edge features that are forced to be symmetric. Often the direction of the edge has no immediate meaning. antisymmetric_edge_features : None or list Indices of edge features that are forced to be anti-symmetric.

Methods

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.
initialize(X, Y)
joint_feature(x, y)	Feature vector associated with instance (x, y).
loss(y, y_hat)
loss_augmented_inference(x, y, w[, relaxed, ...])	Loss-augmented Inference for x relative to y using parameters w.
max_loss(y)

__init__(n_states=None, n_features=None, n_edge_features=None, inference_method=None, class_weight=None, symmetric_edge_features=None, antisymmetric_edge_features=None)[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 Input representation. 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).

loss_augmented_inference(x, y, w, relaxed=False, return_energy=False)¶

Loss-augmented Inference for x relative to y using parameters w.

Finds (approximately) armin_y_hat np.dot(w, joint_feature(x, y_hat)) + loss(y, y_hat) 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_features), edges are an nd-array of shape (n_edges, 2) y : ndarray, shape (n_nodes,) Ground truth labeling relative to which the loss will be measured. 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=(n_nodes) 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 (n_nodes, n_states), pairwise_marginals is an array of shape (n_states, n_states) of accumulated pairwise marginals.