API Reference

This is the class and function reference of MVPy. Please also refer to the examples for further details.

Object	Description
__version__	mvpy
cross_val_score	Implements a shorthand for automated cross-validation scoring over estimators or pipelines. mvpy.crossvalidation
KFold	Implements a k-folds cross-validator. mvpy.crossvalidation
RepeatedKFold	Implements a repeated k-folds cross-validator. mvpy.crossvalidation
StratifiedKFold	Implements a stratified k-folds cross-validator. mvpy.crossvalidation
RepeatedStratifiedKFold	Implements a repeated stratified k-folds cross-validator. mvpy.crossvalidation
Validator	Implements automated cross-validation and scoring over estimators or pipelines. mvpy.crossvalidation
make_meeg_categorical	Create an M-EEG dataset based on continuous time-varying stimuli that group together into features and classes. mvpy.dataset
make_meeg_colours	Create spatial colours for sensor layout. mvpy.dataset
make_meeg_continuous	Create an M-EEG dataset based on continuous time-varying stimuli. mvpy.dataset
make_meeg_discrete	Create an M-EEG dataset based on stimuli defined as discrete events. mvpy.dataset
make_meeg_layout	Create a concentric channel layout for M-EEG sensors. mvpy.dataset
B2B	Implements a back-to-back regression to disentangle causal contributions of correlated features. mvpy.estimators
Classifier	Implements a wrapper for classifiers that handle one-versus-one (OvO) and one-versus-rest (OvR) classification schemes. mvpy.estimators
Covariance	Implements covariance and precision estimation as well as whitening of data. mvpy.estimators
KernelRidgeCV	Implements a kernel ridge regression with cross-validation. mvpy.estimators
ReceptiveField	Implements receptive field estimation (for multivariate temporal response functions or stimulus reconstruction). mvpy.estimators
RidgeClassifier	Implements a linear ridge classifier. mvpy.estimators
RidgeCV	Implements ridge regression with cross-validation. mvpy.estimators
RidgeDecoder	Implements a linear ridge decoder. mvpy.estimators
RidgeEncoder	Implements a linear ridge encoder. mvpy.estimators
RSA	Implements representational similarity analysis. mvpy.estimators
Sliding	Implements a sliding estimator that allows you to fit estimators iteratively over a set of dimensions. mvpy.estimators
SVC	Implements a support vector classifier. mvpy.estimators
TimeDelayed	Implements time delayed ridge regression (for multivariate temporal response functions or stimulus reconstruction). mvpy.estimators
accuracy	Compute accuracy between x and y. Note that accuracy is always computed over the final dimension. mvpy.math
cosine	Compute cosine similarities between x and y. Note that similarities are always computed over the final dimension. mvpy.math
cosine_d	Compute cosine distances between x and y. Note that distances are always computed over the final dimension. mvpy.math
cv_euclidean	Computes cross-validated euclidean distances between vectors in x and y. mvpy.math
cv_mahalanobis	Computes cross-validated mahalanobis distances between x and y. This is sometimes also referred to as the crossnobis distance. mvpy.math
euclidean	Computes euclidean distances between x and y. mvpy.math
kernel_linear	Compute the linear kernel function. mvpy.math
kernel_poly	Compute the polynomial kernel function. mvpy.math
kernel_rbf	Compute the radial basis kernel function. mvpy.math
kernel_sigmoid	Compute the sigmoid kernel function. mvpy.math
mahalanobis	Computes mahalanobis distance between x and y using inverse covariance matrix Σ. mvpy.math
pearsonr	Computes pearson correlations between x and y. Note that correlations are always computed over the final dimension. mvpy.math
pearsonr_d	Computes Pearson distance between x and y. Note that distances are always computed over the final dimension in your inputs. mvpy.math
r2	Rank data in x along its final feature dimension. Ties are computed as averages. mvpy.math
rank	Rank data in x along its final feature dimension. Ties are computed as averages. mvpy.math
roc_auc	Compute ROC AUC score between y_true and y_score. Note that ROC AUC is always computed over the final dimension. mvpy.math
spearmanr	Compute Spearman correlation between x and y. Note that correlations are always computed over the final dimension in your inputs. mvpy.math
spearmanr_d	Compute Spearman distance between x and y. Note that distances are always computed over the final dimension in your inputs. mvpy.math
Accuracy	Implements mvpy.math.accuracy() as a Metric. mvpy.metrics
Metric	mvpy.metrics
R2	Implements mvpy.math.r2() as a Metric. mvpy.metrics
Roc_auc	Implements mvpy.math.roc_auc() as a Metric. mvpy.metrics
score	mvpy.metrics
Hierarchical	Implements a hierarchical scoring procedure over all feature permutations in \(X\) describing \(y\). mvpy.model_selection
hierarchical_score	Implements a shorthand for hierarchical scoring over all feature permutations in \(X\) describing \(y\). mvpy.model_selection
Shapley	Implements a Shapley value scoring procedure over all feature permutations in \(X\) describing \(y\). mvpy.model_selection
shapley_score	Implements a shorthand for Shapley scoring over all feature permutations in \(X\) describing \(y\). mvpy.model_selection
Clamp	Implements a clamp to handle extreme values. mvpy.preprocessing
LabelBinariser	Class to create and handle multiclass and multifeature one-hot encodings. mvpy.preprocessing
RobustScaler	Implements a robust scaler that is invariant to outliers. mvpy.preprocessing
Scaler	A standard scaler akin to sklearn.preprocessing.StandardScaler. See notes for some differences. mvpy.preprocessing
hamming_window	mvpy.signal
hann_window	mvpy.signal
raised_cosine_window	mvpy.signal
numpy	Decorator that compiles a function with numba.jit, if available. mvpy.utilities.compile
torch	Decorator that compiles a function with torch.compile, if available. mvpy.utilities.compile
get_var	Grab var from environment, respecting defaults and flag. mvpy.utilities.env
is_enabled	Check if var is enabled in environment variables. mvpy.utilities.env
Progressbar	Simple class for progress bars that can be enabled or disabled. mvpy.utilities
compare	Compare two version strings. mvpy.utilities.version