Formulate standard linear binary SVM in CRF framework.
Inputs x are simply feature arrays, labels y are -1 or 1.
Parameters:n_features : int or None, default=None
Number of features of inputs x. If None, it is inferred from data.
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) batch_joint_feature(X, Y) batch_loss(Y, Y_hat) batch_loss_augmented_inference(X, Y, w[, ...]) continuous_loss(y, y_hat) inference(x, w[, relaxed]) Inference for x using parameters w. initialize(X, Y) joint_feature(x, y) 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)[source]¶
inference(x, w, relaxed=None)[source]¶
Inference for x using parameters w.
Finds armin_y np.dot(w, joint_feature(x, y)), i.e. best possible prediction.
For a binary SVM, this is just sign(np.dot(w, x) + b))
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)[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
Returns:Class label, either +1 or -1.
p : ndarray, shape (size_joint_feature,)
Feature vector associated with state (x, y).
loss_augmented_inference(x, y, w, relaxed=None)[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) which is just sign(np.dot(x, w) + b - y)
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,)
Returns:Weights that will be used for inference.
y_hat : int
Label with highest sum of loss and score.
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