mne.decoding.
GeneralizationAcrossTime
(picks=None, cv=5, clf=None, train_times=None, test_times=None, predict_method='predict', predict_mode='crossvalidation', scorer=None, score_mode='meanfoldwise', n_jobs=1)¶Generalize across time and conditions
Creates an estimator object used to 1) fit a series of classifiers on multidimensional timeresolved data, and 2) test the ability of each classifier to generalize across other time samples, as in [R26].
Parameters:  picks : arraylike of int  None
cv : int  object
clf : object  None
train_times : dict  None
test_times : ‘diagonal’  dict  None, optional
predict_method : str
predict_mode : {‘crossvalidation’, ‘meanprediction’}
scorer : object  None  str
score_mode : {‘foldwise’, ‘meanfoldwise’, ‘meansamplewise’}
n_jobs : int


See also
References
[R26]  (1, 2) JeanRemi King, Alexandre Gramfort, Aaron Schurger, Lionel Naccache and Stanislas Dehaene, “Two distinct dynamic modes subtend the detection of unexpected sounds”, PLoS ONE, 2014 DOI: 10.1371/journal.pone.0085791 
New in version 0.9.0.
Attributes
picks_ 
(arraylike of int  None) The channels indices to include. 
ch_names  (list, arraylike, shape (n_channels,)) Names of the channels used for training. 
y_train_ 
(list  ndarray, shape (n_samples,)) The categories used for training. 
train_times_ 
(dict) A dictionary that configures the training times: * slices : ndarray, shape (n_clfs,) Array of time slices (in indices) used for each classifier. If not given, computed from ‘start’, ‘stop’, ‘length’, ‘step’. * times : ndarray, shape (n_clfs,) The training times (in seconds). 
test_times_ 
(dict) A dictionary that configures the testing times for each training time: slices : ndarray, shape (n_clfs, n_testing_times) Array of time slices (in indices) used for each classifier. times : ndarray, shape (n_clfs, n_testing_times) The testing times (in seconds) for each training time. 
cv_ 
(CrossValidation object) The actual CrossValidation input depending on y. 
estimators_ 
(list of list of scikitlearn.base.BaseEstimator subclasses.) The estimators for each time point and each fold. 
y_pred_ 
(list of lists of arrays of floats, shape (n_train_times, n_test_times, n_epochs, n_prediction_dims)) The singletrial predictions estimated by self.predict() at each training time and each testing time. Note that the number of testing times per training time need not be regular, else np.shape(y_pred_) = (n_train_time, n_test_time, n_epochs). 
y_true_ 
(list  ndarray, shape (n_samples,)) The categories used for scoring y_pred_ . 
scorer_ 
(object) scikitlearn Scorer instance. 
scores_ 
(list of lists of float) The scores estimated by self.scorer_ at each training time and each testing time (e.g. mean accuracy of self.predict(X)). Note that the number of testing times per training time need not be regular; else, np.shape(scores) = (n_train_time, n_test_time) . 
Methods
__hash__ () <==> hash(x) 

fit (epochs[, y]) 
Train a classifier on each specified time slice. 
plot ([title, vmin, vmax, tlim, ax, cmap, ...]) 
Plotting function of GeneralizationAcrossTime object 
plot_diagonal ([title, xmin, xmax, ymin, ...]) 
Plotting function of GeneralizationAcrossTime object 
plot_times (train_time[, title, xmin, xmax, ...]) 
Plotting function of GeneralizationAcrossTime object 
predict (epochs) 
Classifiers’ predictions on each specified testing time slice. 
score ([epochs, y]) 
Score Epochs 
__hash__
() <==> hash(x)¶fit
(epochs, y=None)¶Train a classifier on each specified time slice.
Note
This function sets the picks_
, ch_names
, cv_
,
y_train
, train_times_
and estimators_
attributes.
Parameters:  epochs : instance of Epochs
y : list or ndarray of int, shape (n_samples,) or None, optional


Returns:  self : GeneralizationAcrossTime

Notes
If X and y are not Cordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.
If X is a dense array, then the other methods will not support sparse matrices as input.
plot
(title=None, vmin=None, vmax=None, tlim=None, ax=None, cmap='RdBu_r', show=True, colorbar=True, xlabel=True, ylabel=True)¶Plotting function of GeneralizationAcrossTime object
Plot the score of each classifier at each tested time window.
Parameters:  title : str  None
vmin : float  None
vmax : float  None
tlim : ndarray, (train_min, test_max)  None
ax : object  None
cmap : str  cmap object
show : bool
colorbar : bool
xlabel : bool
ylabel : bool


Returns:  fig : instance of matplotlib.figure.Figure

plot_diagonal
(title=None, xmin=None, xmax=None, ymin=None, ymax=None, ax=None, show=True, color=None, xlabel=True, ylabel=True, legend=True, chance=True, label='Classif. score')¶Plotting function of GeneralizationAcrossTime object
Plot each classifier score trained and tested at identical time windows.
Parameters:  title : str  None
xmin : float  None, optional
xmax : float  None, optional
ymin : float  None, optional
ymax : float  None, optional
ax : object  None
show : bool
color : str
xlabel : bool
ylabel : bool
legend : bool
chance : bool  float. Defaults to None
label : str


Returns:  fig : instance of matplotlib.figure.Figure

plot_times
(train_time, title=None, xmin=None, xmax=None, ymin=None, ymax=None, ax=None, show=True, color=None, xlabel=True, ylabel=True, legend=True, chance=True, label='Classif. score')¶Plotting function of GeneralizationAcrossTime object
Plot the scores of the classifier trained at specific training time(s).
Parameters:  train_time : float  list or array of float
title : str  None
xmin : float  None, optional
xmax : float  None, optional
ymin : float  None, optional
ymax : float  None, optional
ax : object  None
show : bool
color : str or list of str
xlabel : bool
ylabel : bool
legend : bool
chance : bool  float.
label : str


Returns:  fig : instance of matplotlib.figure.Figure

predict
(epochs)¶Classifiers’ predictions on each specified testing time slice.
Note
This function sets the y_pred_
and test_times_
attributes.
Parameters:  epochs : instance of Epochs


Returns:  y_pred : list of lists of arrays of floats, shape (n_train_t, n_test_t, n_epochs, n_prediction_dims)

score
(epochs=None, y=None)¶Score Epochs
Estimate scores across trials by comparing the prediction estimated for each trial to its true value.
Calls predict()
if it has not been already.
Note
The function updates the scorer_
, scores_
, and
y_true_
attributes.
Note
If predict_mode
is ‘meanprediction’, score_mode
is
automatically set to ‘meansamplewise’.
Parameters:  epochs : instance of Epochs  None, optional
y : list  ndarray, shape (n_epochs,)  None, optional


Returns:  scores : list of lists of float
