pactools.DARSklearn

class pactools.DARSklearn(fs, max_ordar, ordar=1, ordriv=0, normalize=False, ortho=True, center=True, iter_gain=10, eps_gain=0.0001, progress_bar=False, use_driver_phase=False, warn_gain_estimation_failure=False)

Different interface to DAR models, to use in scikit-learn’s GridSearchCV

Parameters

fsfloat

Sampling frequency

max_ordarint >= 0

Maximum ordar over a potential cross-validation scheme. The log-likelihood does not use the first max_ordar points in its computation, to fairly compare different ordar over cross-validation.

ordarint >= 0

Order of the autoregressive model (p)

ordrivint >= 0

Order of the taylor expansion for sigdriv (m)

normalizeboolean

If True, the basis vectors are normalized to unit energy.

orthoboolean

If True, the basis vectors are orthogonalized.

centerboolean

If True, we subtract the mean in sigin

iter_gainint >=0

Maximum number of iteration in gain estimation

eps_gainfloat >= 0

Threshold to stop iterations in gain estimation

use_driver_phaseboolean

If True, we divide the driver by its instantaneous amplitude.

Examples

>>> from sklearn.model_selection import GridSearchCV
>>> from pactools.grid_search import DARSklearn
>>> model = DARSklearn(fs=fs)
>>> param_grid = {'ordar': [10, 20, 30], 'ordriv': [0, 1, 2]}
>>> gscv = GridSearchCV(model, param_grid=param_grid)
>>> X = MultipleArray(sigin, sigdriv, sigdriv_imag)
>>> gscv.fit(X)
>>> print(gscv.cv_results_)

__init__(self, fs, max_ordar, ordar=1, ordriv=0, normalize=False, ortho=True, center=True, iter_gain=10, eps_gain=0.0001, progress_bar=False, use_driver_phase=False, warn_gain_estimation_failure=False)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, fs, max_ordar[, ordar, …])	Initialize self.
copy(self)	Creates a (deep) copy of a model
degrees_of_freedom(self)	Number of parameters of the fitted model
fit(self, X[, y])	Fit the DAR model
fit_transform(self, X[, y])	Fit the model and transform sigin into the residuals
get_criterion(self, criterion[, train])	Get the criterion (logL, AIC, BIC) of the fitted model
get_params(self[, deep])	Get parameters for this estimator.
get_title(self[, name, criterion])	Get the name and orders of the model
likelihood_ratio(self, ar0)	Computation of the likelihood ratio test
plot(self[, title, frange, mode, vmin, …])	Plot the PSD as a function of the driver
plot_lines(self[, title, frange, mode, ax, …])	Plot the PSD as a function of the driver
score(self, X[, y])	Difference in log-likelihood of this model and a model AR(0)
set_params(self, \*\*params)	Set the parameters of this estimator.
transform(self, X[, y])	Transform sigin into the residuals unexplained by the fitted model

Attributes

aic	Akaike information criterion (AIC) of the model
bic	Bayesian information criterion (BIC) of the model
logl	Log likelihood of the model
ordar_	AR order of the model, different from self.ordar if a model selection has been performed
tmax	Scaling of self.logl, self.aic, and self.bic

Examples using pactools.DARSklearn

Grid-search and cross-validation