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='graph-net', 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 Graph-Net and TV-L1 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 ‘graph-net’)

Penalty to used in the model. Can be ‘graph-net’ or ‘tv-l1’.

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 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_affine3x3 or 4x4 matrix, optional (default None)

This parameter is passed to image.resample_img. Please see the related documentation for details.

target_shape3-tuple 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 user-supplied 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 1e-4.

verboseint, optional (default 1)

Verbosity level.

n_jobsint, optional (default 1)

Number of jobs in solving the sub-problems.

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 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.

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 cross-validation 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='graph-net', 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{array-like, 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 Niimg-like 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 cross-validation 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 Niimg-like 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
Xarray-like 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.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like 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 use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='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.