Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.decoding.SpaceNetClassifier¶

class nilearn.decoding.SpaceNetClassifier(penalty='graph-net', loss='logistic', l1_ratios=0.5, alphas=None, n_alphas=10, mask=None, target_affine=None, target_shape=None, low_pass=None, high_pass=None, t_r=None, max_iter=200, tol=0.0001, memory=None, memory_level=1, standardize=True, verbose=1, n_jobs=1, eps=0.001, cv=8, fit_intercept=True, screening_percentile=20.0, debias=False)[source]¶

Classification learners with sparsity and spatial priors.

SpaceNetClassifier implements Graph-Net and TV-L1 priors / penalties for classification problems. Thus, the penalty is a sum an L1 term and a spatial term. The aim of such a hybrid prior is to obtain weights maps which are structured (due to the spatial prior) and sparse (enforced by L1 norm).

Parameters:

penaltystring, optional (default ‘graph-net’): Penalty to used in the model. Can be ‘graph-net’ or ‘tv-l1’.
lossstring, optional (default “logistic”): Loss to be used in the classifier. Must be one of “mse”, or “logistic”.
l1_ratiosfloat or list of floats in the interval [0, 1]; optional (default .5): Constant that mixes L1 and spatial prior terms in penalization. l1_ratios == 1 corresponds to pure LASSO. The larger the value of this parameter, the sparser the estimated weights map. If list is provided, then the best value will be selected by cross-validation.
alphasfloat or list of floats, optional (default None): Choices for the constant that scales the overall regularization term. This parameter is mutually exclusive with the n_alphas parameter. If None or list of floats is provided, then the best value will be selected by cross-validation.
n_alphasint, optional (default 10).: Generate this number of alphas per regularization path. This parameter is mutually exclusive with the alphas parameter.
epsfloat, optional (default 1e-3): Length of the path. For example, eps=1e-3 means that alpha_min / alpha_max = 1e-3.
maskfilename, niimg, NiftiMasker instance, optional (default None): Mask to be used on data. If an instance of masker is passed, then its mask will be used. If no mask is it will be computed automatically by a MultiNiftiMasker with default parameters.
target_affinenumpy.ndarray, default=None: If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix.
target_shapetuple or list, default=None: If specified, the image will be resized to match this new shape. len(target_shape) must be equal to 3.

Note

If target_shape is specified, a target_affine of shape (4, 4) must also be given.
low_passfloat or None, default=None: Low cutoff frequency in Hertz. If specified, signals above this frequency will be filtered out. If None, no low-pass filtering will be performed.
high_passfloat, default=None: High cutoff frequency in Hertz. If specified, signals below this frequency will be filtered out.
t_rfloat or None, default=None: Repetition time, in seconds (sampling period). Set to None if not provided.
screening_percentilefloat in the interval [0, 100]; Optional (default 20): Percentile value for ANOVA univariate feature selection. A value of 100 means ‘keep all features’. This percentile is expressed w.r.t the volume of a standard (MNI152) brain, and so is corrected at runtime by premultiplying it with the ratio of the volume of the mask of the data and volume of a standard brain. If ‘100’ is given, all the features are used, regardless of the number of voxels.
standardizebool, optional (default True):: If set, then we’ll center the data (X, y) have mean zero along axis 0. This is here because nearly all linear models will want their data to be centered.
fit_interceptbool, optional (default True): Fit or not an intercept.
max_iterint (default 200): Defines the iterations for the solver.
tolfloat, default=1e-4.: Defines the tolerance for convergence.
verboseint, default=1: Verbosity level (0 means no message).
n_jobsint, default=1: The number of CPUs to use to do the computation. -1 means ‘all CPUs’.
memoryNone, instance of joblib.Memory, str, or pathlib.Path: Used to cache the masking process. By default, no caching is done. If a str is given, it is the path to the caching directory.
memory_levelint, default=1: Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Zero means no caching.
cvint, a cv generator instance, or None (default 8): The input specifying which cross-validation generator to use. It can be an integer, in which case it is the number of folds in a KFold, None, in which case 3 fold is used, or another object, that will then be used as a cv generator.
debiasbool, optional (default False): If set, then the estimated weights maps will be debiased.

Attributes:

all_coef_ndarray, shape (n_l1_ratios, n_folds, n_features): Coefficients for all folds and features.
alpha_grids_ndarray, shape (n_folds, n_alphas): Alpha values considered for selection of the best ones (saved in best_model_params_)
best_model_params_ndarray, shape (n_folds, n_parameter): Best model parameters (alpha, l1_ratio) saved for the different cross-validation folds.
classes_ndarray of labels (n_classes_): Labels of the classes
n_classes_int: Number of classes
coef_ndarray, shape: (1, n_features) for 2 class classification problems (i.e n_classes = 2) (n_classes, n_features) for n_classes > 2 Coefficient of the features in the decision function.
coef_img_nifti image: Masked model coefficients
mask_ndarray 3D: An array contains values of the mask image.
masker_instance of NiftiMasker: The nifti masker used to mask the data.
mask_img_Nifti like image: The mask of the data. If no mask was supplied by the user, this attribute is the mask image computed automatically from the data X.
memory_joblib memory cache
intercept_narray, shape: (1, ) for 2 class classification problems (i.e n_classes = 2) (n_classes, ) for n_classes > 2 Intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True.
cv_list of pairs of lists: Each pair is the list of indices for the train and test samples for the corresponding fold.
cv_scores_ndarray, shape (n_folds, n_alphas) or (n_l1_ratios, n_folds, n_alphas): Scores (misclassification) for each alpha, and on each fold
screening_percentile_float: Screening percentile corrected according to volume of mask, relative to the volume of standard brain.
w_ndarray, shape: (1, n_features + 1) for 2 class classification problems (i.e n_classes = 2) (n_classes, n_features + 1) for n_classes > 2 Model weights
ymean_array, shape (n_samples,): Mean of prediction targets
Xmean_array, shape (n_features,): Mean of X across samples
Xstd_array, shape (n_features,): Standard deviation of X across samples