pystruct.models.MultiClassClf¶

class pystruct.models.MultiClassClf(n_features=None, n_classes=None, class_weight=None, rescale_C=False)[source]¶

Formulate linear multiclass SVM in C-S style in CRF framework.

Inputs x are simply feature arrays, labels y are 0 to n_classes.

Parameters:

n_features : int Number of features of inputs x. If None, it is inferred from data. n_classes : int, default=None Number of classes in dataset. If None, it is inferred from data. 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. rescale_C : bool, default=False Whether the class-weights should be used to rescale C (liblinear-style) or just rescale the loss.

Notes

No bias / intercept is learned. It is recommended to add a constant one feature to the data.

It is also highly recommended to use n_jobs=1 in the learner when using this model. Trying to parallelize the trivial inference will slow the infernce down a lot!

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[, y_true])	Compute joint feature vector of x and y.
loss(y, y_hat)
loss_augmented_inference(x, y, w[, relaxed, ...])	Loss-augmented inference for x and y using parameters w.
max_loss(y)

__init__(n_features=None, n_classes=None, class_weight=None, rescale_C=False)[source]¶

inference(x, w, relaxed=None, return_energy=False)[source]¶

Inference for x using parameters w.

Finds armin_y np.dot(w, joint_feature(x, y)), i.e. best possible prediction.

For an unstructured multi-class model (this model), this can easily done by enumerating all possible y.

Parameters:	x : ndarray, shape (n_features,) Input sample features. w : ndarray, shape=(size_joint_feature,) Parameters of the SVM. relaxed : ignored
Returns:	y_pred : int Predicted class label.

joint_feature(x, y, y_true=None)[source]¶

Compute joint feature vector of x and 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 : nd-array, shape=(n_features,) Input sample features. y : int Class label. Between 0 and n_classes. y_true : int True class label. Needed if rescale_C==True.
Returns:	p : ndarray, shape (size_joint_feature,) Feature vector associated with state (x, y).

loss_augmented_inference(x, y, w, relaxed=None, return_energy=False)[source]¶

Loss-augmented inference for x and y using parameters w.

Minimizes over y_hat: np.dot(joint_feature(x, y_hat), w) + loss(y, y_hat)

Parameters:	x : ndarray, shape (n_features,) Unary evidence / input to augment. y : int Ground truth labeling relative to which the loss will be measured. w : ndarray, shape (size_joint_feature,) Weights that will be used for inference.
Returns:	y_hat : int Label with highest sum of loss and score.