pystruct.learners.StructuredPerceptron¶

class pystruct.learners.StructuredPerceptron(model, max_iter=100, verbose=0, batch=False, decay_exponent=0, decay_t0=10, average=False, n_jobs=1, logger=None)[source]¶

Structured Perceptron training.

Implements a simple structured perceptron with optional averaging. The structured perceptron approximately minimizes the zero-one loss, therefore the learning does not take model.loss into account. It is just shown to illustrate the learning progress.

As the perceptron learning is not margin-based, the model does not need to provide loss_augmented_inference.

Parameters:

Parameters:	model : StructuredModel Object containing model structure. Has to implement loss, inference. max_iter : int (default=100) Maximum number of passes over dataset to find constraints and update parameters. verbose : int (default=0) Verbosity batch : bool (default=False) Whether to do batch learning or online learning. decay_exponent : float, default=0 Exponent for decaying learning rate. Effective learning rate is `(t0 + t) decay_exponent`. Zero means no decay. decay_t0 : float, default=10 Offset for decaying learning rate. Effective learning rate is `(t0 + t) decay_exponent`. Zero means no decay. average : bool or int, default=False Whether to average over all weight vectors obtained during training or simply keeping the last one. `average=False` does not perform any averaging. `average=True` averages over all epochs. `average=k` with `k >= 0` waits `k` epochs before averaging. `average=k` with `k < 0` averages over the last `k` epochs. So far `k = -1` is the only negative value supported. logger : logger object.
Attributes:	w : nd-array, shape=(model.size_joint_feature,) The learned weights of the SVM. ``loss_curve_`` : list of float List of loss values after each pass thorugh the dataset.

model : StructuredModel

Object containing model structure. Has to implement loss, inference.

max_iter : int (default=100)

Maximum number of passes over dataset to find constraints and update parameters.

verbose : int (default=0)

Verbosity

batch : bool (default=False)

Whether to do batch learning or online learning.

decay_exponent : float, default=0

Exponent for decaying learning rate. Effective learning rate is (t0 + t)** decay_exponent. Zero means no decay.

decay_t0 : float, default=10

Offset for decaying learning rate. Effective learning rate is (t0 + t)** decay_exponent. Zero means no decay.

average : bool or int, default=False

Whether to average over all weight vectors obtained during training or simply keeping the last one. average=False does not perform any averaging. average=True averages over all epochs. average=k with k >= 0 waits k epochs before averaging. average=k with k < 0 averages over the last k epochs. So far k = -1 is the only negative value supported.

logger : logger object.

Attributes:

w : nd-array, shape=(model.size_joint_feature,)

The learned weights of the SVM.

``loss_curve_`` : list of float

List of loss values after each pass thorugh the dataset.

Methods

`fit`(X, Y[, initialize])	Learn parameters using structured perceptron.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict output on examples in X.
`score`(X, Y)	Compute score as 1 - loss over whole data set.
`set_params`(**params)	Set the parameters of this estimator.

__init__(model, max_iter=100, verbose=0, batch=False, decay_exponent=0, decay_t0=10, average=False, n_jobs=1, logger=None)[source]¶

fit(X, Y, initialize=True)[source]¶

Learn parameters using structured perceptron.

Parameters:

Parameters:	X : iterable Traing instances. Contains the structured input objects. No requirement on the particular form of entries of X is made. Y : iterable Training labels. Contains the strctured labels for inputs in X. Needs to have the same length as X. initialize : boolean, default=True Whether to initialize the model for the data. Leave this true except if you really know what you are doing.

X : iterable

Traing instances. Contains the structured input objects. No requirement on the particular form of entries of X is made.

Y : iterable

Training labels. Contains the strctured labels for inputs in X. Needs to have the same length as X.

initialize : boolean, default=True

Whether to initialize the model for the data. Leave this true except if you really know what you are doing.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

Parameters:	deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)¶

Predict output on examples in X.

Parameters:

Parameters:	X : iterable Traing instances. Contains the structured input objects.
Returns:	Y_pred : list List of inference results for X using the learned parameters.

X : iterable

Traing instances. Contains the structured input objects.

Returns:

Y_pred : list

List of inference results for X using the learned parameters.

score(X, Y)¶

Compute score as 1 - loss over whole data set.

Returns the average accuracy (in terms of model.loss) over X and Y.

Parameters:

Parameters:	X : iterable Evaluation data. Y : iterable True labels.
Returns:	score : float Average of 1 - loss over training examples.

X : iterable

Evaluation data.

Y : iterable

True labels.

Returns:

score : float

Average of 1 - loss over training examples.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :