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.
7.3.2. nilearn.decoding.SpaceNetRegressor¶

class
nilearn.decoding.
SpaceNetRegressor
(penalty='graphnet', 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=Memory(location=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)¶ Regression learners with sparsity and spatial priors.
SpaceNetRegressor implements GraphNet and TVL1 priors / penalties for regression 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 ‘graphnet’)
Penalty to used in the model. Can be ‘graphnet’ or ‘tvl1’.
 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_ratio == 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 crossvalidation.
 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 crossvalidation.
 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 1e3)
Length of the path. For example,
eps=1e3
means thatalpha_min / alpha_max = 1e3
 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_affine3x3 or 4x4 matrix, optional (default None)
This parameter is passed to image.resample_img. Please see the related documentation for details.
 target_shape3tuple of integers, optional (default None)
This parameter is passed to image.resample_img. Please see the related documentation for details.
 low_pass: None or float, optional
This parameter is passed to signal.clean. Please see the related documentation for details
 high_pass: None or float, optional
This parameter is passed to signal.clean. Please see the related documentation for details
 t_rfloat, optional (default None)
This parameter is passed to signal.clean. Please see the related documentation for details
 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 is expressed w.r.t the volume of a standard (MNI152) brain, and so is corrected at runtime to correspond to the volume of the usersupplied mask (which is typically smaller).
 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
Defines the tolerance for convergence. Defaults to 1e4.
 verboseint, optional (default 1)
Verbosity level.
 n_jobsint, optional (default 1)
Number of jobs in solving the subproblems.
 memory: instance of joblib.Memory or string
Used to cache the masking process. By default, no caching is done. If a string is given, it is the path to the caching directory.
 memory_level: integer, optional (default 1)
Rough estimator of the amount of memory used by caching. Higher value means more memory for caching.
 cvint, a cv generator instance, or None (default 8)
The input specifying which crossvalidation 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.
 debias: bool, 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 crossvalidation folds.
 `coef_`ndarray, shape (n_features,)
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)
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 (n_features,)
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

__init__
(self, penalty='graphnet', 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=Memory(location=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)¶ Initialize self. See help(type(self)) for accurate signature.

check_params
(self)¶ Makes sure parameters are sane

decision_function
(self, X)¶ Predict confidence scores for samples
The confidence score for a sample is the signed distance of that sample to the hyperplane.
 Parameters
 X{arraylike, sparse matrix}, shape = (n_samples, n_features)
Samples.
 Returns
 array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

fit
(self, X, y)¶ Fit the learner
 Parameters
 Xlist of Niimglike objects
See http://nilearn.github.io/manipulating_images/input_output.html Data on which model is to be fitted. If this is a list, the affine is considered the same for all.
 yarray or list of length n_samples
The dependent variable (age, sex, QI, etc.).
Notes
 selfSpaceNet object
Model selection is via crossvalidation with bagging.

get_params
(self, deep=True)¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, X)¶ Predict class labels for samples in X.
 Parameters
 Xlist of Niimglike objects
See http://nilearn.github.io/manipulating_images/input_output.html Data on prediction is to be made. If this is a list, the affine is considered the same for all.
 Returns
 y_predndarray, shape (n_samples,)
Predicted class label per sample.

score
(self, X, y, sample_weight=None)¶ Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  u/v), where u is the residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor will usemultioutput='uniform_average'
from version 0.23 to keep consistent withr2_score
. This will influence thescore
method of all the multioutput regressors (except forMultiOutputRegressor
). To specify the default value manually and avoid the warning, please either callr2_score
directly or make a custom scorer withmake_scorer
(the builtin scorer'r2'
usesmultioutput='uniform_average'
).

set_params
(self, **params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.