hypertools.load

hypertools.load(dataset, reduce=None, ndims=None, align=None, normalize=None)[source]

Load a .geo file or example data

Parameters:

dataset : string

The name of the example dataset. Can be a .geo file, or one of a number of example datasets listed below. weights is an fmri dataset comprised of 36 subjects. For each subject, the rows are fMRI measurements and the columns are parameters of a model fit to the fMRI data. weights_sample is a sample of 3 subjects from that dataset. weights_avg is the dataset split in half and averaged into two groups. spiral is 3D spiral to highlight the procrustes function. mushrooms is an example dataset comprised of features (columns) of a collection of mushroomm samples (rows).

normalize : str or False or None

If set to ‘across’, the columns of the input data will be z-scored across lists (default). That is, the z-scores will be computed with with repect to column n across all arrays passed in the list. If set to ‘within’, the columns will be z-scored within each list that is passed. If set to ‘row’, each row of the input data will be z-scored. If set to False, the input data will be returned with no z-scoring.

reduce : str or dict

Decomposition/manifold learning model to use. Models supported: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, and MDS. Can be passed as a string, but for finer control of the model parameters, pass as a dictionary, e.g. reduce={‘model’ : ‘PCA’, ‘params’ : {‘whiten’ : True}}. See scikit-learn specific model docs for details on parameters supported for each model.

ndims : int

Number of dimensions to reduce

align : str or dict

If str, either ‘hyper’ or ‘SRM’. If ‘hyper’, alignment algorithm will be hyperalignment. If ‘SRM’, alignment algorithm will be shared response model. You can also pass a dictionary for finer control, where the ‘model’ key is a string that specifies the model and the params key is a dictionary of parameter values (default : ‘hyper’).

Returns:

data : Numpy Array

Example data