pystruct.models.LatentGraphCRF

class pystruct.models.LatentGraphCRF(n_labels, n_features=None, n_states_per_label=2, inference_method='lp')

CRF with latent states for variables.

This is also called “hidden dynamics CRF”. For each output variable there is an additional variable which can encode additional states and interactions.

Parameters :

n_labels : int

Number of states of output variables.

n_featues : int or None (default=None).

Number of input features per input variable. None means it is equal to n_labels.

n_states_per_label : int or list (default=2)

Number of latent states associated with each observable state. Can be either an integer, which means the same number of hidden states will be used for all observable states, or a list of integers of length n_labels.

inference_method : string, default=”lp”

Function to call to do inference and loss-augmented inference. Possible values are:

• ‘qpbo’ for QPBO + alpha expansion.
• ‘dai’ for LibDAI bindings (which has another parameter).
• ‘lp’ for Linear Programming relaxation using GLPK.
• ‘ad3’ for AD3 dual decomposition.

Methods

base_loss(y, y_hat)
batch_inference(X, w[, relaxed])
batch_loss(Y, Y_hat)
batch_loss_augmented_inference(X, Y, w[, ...])
batch_psi(X, Y[, Y_true])
continuous_loss(y, y_hat)
get_edges(x)
get_features(x)
get_pairwise_potentials(x, w)	Computes pairwise potentials for x and w.
get_unary_potentials(x, w)	Computes unary potentials for x and w.
inference(x, w[, relaxed, return_energy])	Inference for x using parameters w.
init_latent(X, Y)
label_from_latent(h)
latent(x, y, w)
loss(h, h_hat)
loss_augmented_inference(x, h, w[, relaxed, ...])
max_loss(y)
psi(x, y)	Feature vector associated with instance (x, y).

__init__(n_labels, n_features=None, n_states_per_label=2, inference_method='lp')

get_pairwise_potentials(x, w)

Computes pairwise potentials for x and w.

Parameters :

x : tuple

Instance Representation.

w : ndarray, shape=(size_psi,)

Weight vector for CRF instance.

Returns :

pairwise : ndarray, shape=(n_states, n_states)

Pairwise weights.

get_unary_potentials(x, w)

Computes unary potentials for x and w.

Parameters :

x : tuple

Instance Representation.

w : ndarray, shape=(size_psi,)

Weight vector for CRF instance.

Returns :

unary : ndarray, shape=(n_states)

Unary weights.

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

Inference for x using parameters w.

Finds (approximately) armin_y np.dot(w, psi(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_psi,)

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.

psi(x, y)

Feature vector associated with instance (x, y).

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

Feature vector associated with state (x, y).