pulse2percept.model_selection

Model selection

optimizers

BaseOptimizer, GridSearchOptimizer, FunctionMinimizer, NotFittedError, ParticleSwarmOptimizer

class pulse2percept.model_selection.BaseOptimizer(estimator, search_params, **params)

Abstract base class for all optimizers.

Added in version 0.7.

Parameters:

• estimator (sklearn.base.estimator) – A scikit-learn estimator, such as a classifier or regressor, that contains fit and predict methods.

• serch_params (dict) – Initial values of all search parameters.

• has_loss_function ({False | True}) – If True, the estimator’s scoring function is really a loss function (where smaller values indicate better performance) rather than a true scoring function.

• verbose ({False | True}) – If True, will print debug information.

get_default_params()

Return a dict of user-settable model parameters

fit(X, y=None, fit_params=None)

Optimizes the values of the search parameters

Runs the optimizer to determine the optimal values of the search parameters. After optimization, two new attributes are available:

• best_score: contains the best score achieved with optimal parameter values

• best_params: a dict containing the optimal parameter values

Parameters:

• X (array-like of shape (n_samples, n_features)) – Sample data.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• fit_params (dict, optional) – Additional parameters that should be passed to the estimator’s fit method.

predict(X)

Predicts the labels of X

Uses the estimator’s predict method to predict the labels of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Sample data.

Returns:

y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted labels for X.

score(X, y, sample_weight=None)

Return the score of the model on the data X

Uses the estimator’s score method to determine the model score.

Parameters:

• X (array-like of shape (n_samples, n_features))

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

Returns:

score (float)

property is_fitted

A flag indicating whether the model has been fitted

class pulse2percept.model_selection.FunctionMinimizer(estimator, search_params, **params)

Loss function minimization

Function minimization using SciPy’s minimize to find the search_params that optimize an estimator’s score.

Added in version 0.7.

Parameters:

• estimator (sklearn.base.estimator) – A scikit-learn estimator, such as a classifier or regressor, that contains fit and predict methods.

• serch_params (dict of tupels (lower bound, upper bound)) – Dictionary of tupels containing the lower and upper bound of values for each search parameter.

• search_params_init (dict of floats, optional) – Initial values of all search parameters. If None, initialize to midpoint between lower and upper bounds

• method (str, optional) – Solving method to use (e.g., ‘Nelder-Mead’, ‘Powell’, ‘L-BFGS-B’)

• tol (float, optional) – Tolerance for termination. For detailed control, use solver-specific options.

• options (dict, optional) – A dictionary of solver-specific options.

• has_loss_function ({False | True}) – If True, the estimator’s scoring function is really a loss function (where smaller values indicate better performance) rather than a true scoring function.

• verbose ({False | True}) – If True, will print debug information.

get_default_params()

Return a dict of user-settable model parameters

fit(X, y=None, fit_params=None)

Optimizes the values of the search parameters

Runs the optimizer to determine the optimal values of the search parameters. After optimization, two new attributes are available:

• best_score: contains the best score achieved with optimal parameter values

• best_params: a dict containing the optimal parameter values

Parameters:

• X (array-like of shape (n_samples, n_features)) – Sample data.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• fit_params (dict, optional) – Additional parameters that should be passed to the estimator’s fit method.

property is_fitted

A flag indicating whether the model has been fitted

predict(X)

Predicts the labels of X

Uses the estimator’s predict method to predict the labels of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Sample data.

Returns:

y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted labels for X.

score(X, y, sample_weight=None)

Return the score of the model on the data X

Uses the estimator’s score method to determine the model score.

Parameters:

• X (array-like of shape (n_samples, n_features))

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

Returns:

score (float)

class pulse2percept.model_selection.GridSearchOptimizer(estimator, search_params, **params)

Performs a grid search

Exhaustive search over specified parameter values for an estimator.

Added in version 0.7.

Parameters:

• estimator (sklearn.base.estimator) – A scikit-learn estimator, such as a classifier or regressor, that contains fit and predict methods.

• serch_params (dict) – Dictionary of search parameters with a discrete number of values for each.

• has_loss_function ({False | True}) – If True, the estimator’s scoring function is really a loss function (where smaller values indicate better performance) rather than a true scoring function.

• verbose ({False | True}) – If True, will print debug information.

fit(X, y=None, fit_params=None)

Optimizes the values of the search parameters

Runs the optimizer to determine the optimal values of the search parameters. After optimization, two new attributes are available:

• best_score: contains the best score achieved with optimal parameter values

• best_params: a dict containing the optimal parameter values

Parameters:

• X (array-like of shape (n_samples, n_features)) – Sample data.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• fit_params (dict, optional) – Additional parameters that should be passed to the estimator’s fit method.

get_default_params()

Return a dict of user-settable model parameters

property is_fitted

A flag indicating whether the model has been fitted

predict(X)

Predicts the labels of X

Uses the estimator’s predict method to predict the labels of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Sample data.

Returns:

y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted labels for X.

score(X, y, sample_weight=None)

Return the score of the model on the data X

Uses the estimator’s score method to determine the model score.

Parameters:

• X (array-like of shape (n_samples, n_features))

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

Returns:

score (float)

exception pulse2percept.model_selection.NotFittedError

Exception class used to raise if optimizer is used before fitting

This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility.

add_note()

Exception.add_note(note) – add a note to the exception

name

attribute name

obj

object

with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class pulse2percept.model_selection.ParticleSwarmOptimizer(estimator, search_params, **params)

Performs particle swarm optimization

Optimizes the search parameter values using a particle swarm.

Added in version 0.7.

Parameters:

• estimator (sklearn.base.estimator) – A scikit-learn estimator, such as a classifier or regressor, that contains fit and predict methods.

• serch_params (dict of tupels (lower bound, upper bound)) – Dictionary of tupels containing the lower and upper bound of values for each search parameter.

• swarm_size (int, optional, default: 10 * number of search params) – The number of particles in the swarm.

• max_iter (int, optional, default: 100) – Maximum number of iterations for the swarm to search.

• min_func (float, optional, default: 0.01) – The minimum change of swarm’s best objective value before the search terminates.

• min_step (float, optional, default: 0.01) – The minimum step size of swarm’s best objective value before the search terminates.

• has_loss_function ({False | True}) – If True, the estimator’s scoring function is really a loss function (where smaller values indicate better performance) rather than a true scoring function.

• verbose (bool, optional, default: True) – Flag whether to print more stuff

get_default_params()

Return a dict of user-settable model parameters

fit(X, y=None, fit_params=None)

Optimizes the values of the search parameters

Runs the optimizer to determine the optimal values of the search parameters. After optimization, two new attributes are available:

• best_score: contains the best score achieved with optimal parameter values

• best_params: a dict containing the optimal parameter values

Parameters:

• X (array-like of shape (n_samples, n_features)) – Sample data.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• fit_params (dict, optional) – Additional parameters that should be passed to the estimator’s fit method.

property is_fitted

A flag indicating whether the model has been fitted

predict(X)

Predicts the labels of X

Uses the estimator’s predict method to predict the labels of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Sample data.

Returns:

y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted labels for X.

score(X, y, sample_weight=None)

Return the score of the model on the data X

Uses the estimator’s score method to determine the model score.

Parameters:

• X (array-like of shape (n_samples, n_features))

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

Returns:

score (float)