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.FREMClassifier

class nilearn.decoding.FREMClassifier(estimator='svc', mask=None, cv=30, param_grid=None, clustering_percentile=10, screening_percentile=20, scoring='roc_auc', smoothing_fwhm=None, standardize=True, target_affine=None, target_shape=None, mask_strategy='background', low_pass=None, high_pass=None, t_r=None, memory=None, memory_level=0, n_jobs=1, verbose=0)

State of the art decoding scheme applied to usual classifiers.

FREM uses an implicit spatial regularization through fast clustering and aggregates a high number of estimators trained on various splits of the training set, thus returning a very robust decoder at a lower computational cost than other spatially regularized methods 1.

Parameters

estimatorstr, optional

The estimator to choose among:

• svc: Linear support vector classifier with L2 penalty.

  svc = LinearSVC(penalty='l2',
                  max_iter=1e4)
  

• svc_l2: Linear support vector classifier with L2 penalty.

  Note

  Same as option svc.

• svc_l1: Linear support vector classifier with L1 penalty.

  svc_l1 = LinearSVC(penalty='l1',
                     dual=False,
                     max_iter=1e4)
  

• logistic: Logistic regression with L2 penalty.

  logistic = LogisticRegression(penalty='l2',
                                solver='liblinear')
  

• logistic_l1: Logistic regression with L1 penalty.

  logistic_l1 = LogisticRegression(penalty='l1',
                                   solver='liblinear')
  

• logistic_l2: Logistic regression with L2 penalty

  Note

  Same as option logistic.

• ridge_classifier: Ridge classifier.

  ridge_classifier = RidgeClassifierCV()
  

• dummy_classifier: Dummy classifier with stratified strategy.

  dummy = DummyClassifier(strategy='stratified',
                          random_state=0)
  

Default ‘svc’.

maskfilename, Nifti1Image, NiftiMasker, or MultiNiftiMasker, optional

Mask to be used on data. If an instance of masker is passed, then its mask and parameters will be used. If no mask is given, mask will be computed automatically from provided images by an inbuilt masker with default parameters. Refer to NiftiMasker or MultiNiftiMasker to check for default parameters. Default None

cvint or cross-validation generator, optional (default 30)

If int, number of stratified shuffled splits returned, which is usually the right way to train many different classifiers. A good trade-off between stability of the aggregated model and computation time is 50 splits. Shuffled splits are seeded by default for reproducibility. Can also be a cross-validation generator.

param_griddict of str to sequence, or sequence of such. Default None

The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values.

None or an empty dict signifies default parameters.

A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See scikit-learn documentation for more information, for example: https://scikit-learn.org/stable/modules/grid_search.html

clustering_percentileint, float, optional, in closed interval [0, 100]

Used to perform a fast ReNA clustering on input data as a first step of fit. It agglomerates similar features together to reduce their number down to this percentile. ReNA is typically efficient for cluster_percentile equal to 10. Default: 10.

screening_percentileint, float, optional, in closed interval [0, 100]

The percentage of brain volume that will be kept with respect to a full MNI template. In particular, if it is lower than 100, a univariate feature selection based on the Anova F-value for the input data will be performed. A float according to a percentile of the highest scores. Default: 20.

scoringstr, callable or None, optional. Default: ‘roc_auc’

The scoring strategy to use. See the scikit-learn documentation at https://scikit-learn.org/stable/modules/model_evaluation.html#the-scoring-parameter-defining-model-evaluation-rules If callable, takes as arguments the fitted estimator, the test data (X_test) and the test target (y_test) if y is not None. e.g. scorer(estimator, X_test, y_test)

For classification, valid entries are: ‘accuracy’, ‘f1’, ‘precision’, ‘recall’ or ‘roc_auc’. Default: ‘roc_auc’.

smoothing_fwhmfloat, optional.

If smoothing_fwhm is not None, it gives the full-width at half maximum in millimeters of the spatial smoothing to apply to the signal.

standardizebool, optional.

If standardize is True, the data are centered and normed: their mean is put to 0 and their variance is put to 1 in the time dimension. Default=True.

target_affinenumpy.ndarray, optional.

If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix. Default=None.

target_shapetuple or list, optional.

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.

Default=None.

low_passfloat or None, optional

Low cutoff frequency in Hertz. If specified, signals above this frequency will be filtered out. If None, no low-pass filtering will be performed. Default=None.

high_passfloat, optional

High cutoff frequency in Hertz. If specified, signals below this frequency will be filtered out. Default=None.

t_rfloat or None, optional

Repetition time, in seconds (sampling period). Set to None if not provided. Default=None.

mask_strategy{‘background’, ‘epi’, ‘whole-brain-template’,’gm-template’, ‘wm-template’}, optional

The strategy used to compute the mask:

• ‘background’: Use this option if your images present a clear homogeneous background.

• ‘epi’: Use this option if your images are raw EPI images

• ‘whole-brain-template’: This will extract the whole-brain part of your data by resampling the MNI152 brain mask for your data’s field of view.

  Note

  This option is equivalent to the previous ‘template’ option which is now deprecated.

• ‘gm-template’: This will extract the gray matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.

  New in version 0.8.1.

• ‘wm-template’: This will extract the white matter part of your data by resampling the corresponding MNI152 template for your data’s field of view.

  New in version 0.8.1.

Note

This parameter will be ignored if a mask image is provided.

Note

Depending on this value, the mask will be computed from nilearn.masking.compute_background_mask, nilearn.masking.compute_epi_mask, or nilearn.masking.compute_brain_mask.

Default is ‘background’.

memoryinstance of joblib.Memory or str

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, optional.

Rough estimator of the amount of memory used by caching. Higher value means more memory for caching. Default=0.

n_jobsint, optional.

The number of CPUs to use to do the computation. -1 means ‘all CPUs’. Default=1.

verboseint, optional

Verbosity level (0 means no message). Default=0.

See also

nilearn.decoding.Decoder

Classification strategies for Neuroimaging,

nilearn.decoding.FREMRegressor

State of the art regression pipeline for Neuroimaging

References

1

Andrés Hoyos-Idrobo, Gaël Varoquaux, Yannick Schwartz, and Bertrand Thirion. Frem – scalable and stable decoding with fast regularized ensemble of models. NeuroImage, 180:160–172, 2018. New advances in encoding and decoding of brain signals. URL: https://www.sciencedirect.com/science/article/pii/S1053811917308182, doi:https://doi.org/10.1016/j.neuroimage.2017.10.005.

__init__(estimator='svc', mask=None, cv=30, param_grid=None, clustering_percentile=10, screening_percentile=20, scoring='roc_auc', smoothing_fwhm=None, standardize=True, target_affine=None, target_shape=None, mask_strategy='background', low_pass=None, high_pass=None, t_r=None, memory=None, memory_level=0, n_jobs=1, verbose=0)

decision_function(X)

Predict class labels for samples in X.

Parameters

X: list 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_pred: ndarray, shape (n_samples,)

Predicted class label per sample.

fit(X, y, groups=None)

Fit the decoder (learner).

Parameters

X: list 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.

y: numpy.ndarray of shape=(n_samples) or list of length n_samples

The dependent variable (age, sex, IQ, yes/no, etc.). Target variable to predict. Must have exactly as many elements as 3D images in niimg.

groups: None

Group labels for the samples used while splitting the dataset into train/test set. Default None.

Note that this parameter must be specified in some scikit-learn cross-validation generators to calculate the number of splits, e.g. sklearn.model_selection.LeaveOneGroupOut or sklearn.model_selection.LeavePGroupsOut.

For more details see https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data

Attributes

`masker_`instance of NiftiMasker or MultiNiftiMasker

The NiftiMasker used to mask the data.

`mask_img_`Nifti1Image

Mask computed by the masker object.

`classes_`numpy.ndarray

Classes to predict. For classification only.

`screening_percentile_`float

Screening percentile corrected according to volume of mask, relative to the volume of standard brain.

`coef_`numpy.ndarray, shape=(n_classes, n_features)

Contains the mean of the models weight vector across fold for each class. Returns None for Dummy estimators.

`coef_img_`dict of Nifti1Image

Dictionary containing coef_ with class names as keys, and coef_ transformed in Nifti1Images as values. In the case of a regression, it contains a single Nifti1Image at the key ‘beta’. Ignored if Dummy estimators are provided.

`intercept_`ndarray, shape (nclasses,)

Intercept (a.k.a. bias) added to the decision function. Ignored if Dummy estimators are provided.

`cv_`list of pairs of lists

List of the (n_folds,) folds. For the corresponding fold, each pair is composed of two lists of indices, one for the train samples and one for the test samples.

`std_coef_`numpy.ndarray, shape=(n_classes, n_features)

Contains the standard deviation of the models weight vector across fold for each class. Note that folds are not independent, see https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data Ignored if Dummy estimators are provided.

`std_coef_img_`dict of Nifti1Image

Dictionary containing std_coef_ with class names as keys, and coef_ transformed in Nifti1Image as values. In the case of a regression, it contains a single Nifti1Image at the key ‘beta’. Ignored if Dummy estimators are provided.

`cv_params_`dict of lists

Best point in the parameter grid for each tested fold in the inner cross validation loop. The grid is empty when Dummy estimators are provided.

‘scorer_’function

Scorer function used on the held out data to choose the best parameters for the model.

`cv_scores_`dict, (classes, n_folds)

Scores (misclassification) for each parameter, and on each fold

`n_outputs_`int

Number of outputs (column-wise)

`dummy_output_`: ndarray, shape=(n_classes, 2) or shape=(1, 1) for regression

Contains dummy estimator attributes after class predictions using strategies of DummyClassifier (class_prior) and DummyRegressor (constant) from scikit-learn. This attribute is necessary for estimating class predictions after fit. Returns None if non-dummy estimators are provided.

get_params(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

paramsdict

Parameter names mapped to their values.

predict(X)

Predict a label for all X vectors indexed by the first axis.

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.

score(X, y, *args)

Compute the prediction score using the scoring metric defined by the scoring attribute.

Parameters

X{array-like, sparse matrix}, shape = (n_samples, n_features)

Samples.

yarray-like

Target values.

argsOptional arguments that can be passed to

scoring metrics. Example: sample_weight.

Returns

scorefloat

Prediction score.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). 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

selfestimator instance

Estimator instance.

Examples using nilearn.decoding.FREMClassifier

Decoding with FREM: face vs house object recognition

Decoding with FREM: face vs house object recognition