hypertools.analyze

hypertools.analyze(data, normalize=None, reduce=None, ndims=None, align=None, internal=False)

Wrapper function for normalize -> reduce -> align transformations.

Parameters:

datanumpy array, pandas df, or list of arrays/dfs

The data to analyze

normalizestr 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 respect 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.

reducestr 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.

ndimsint

Number of dimensions to reduce

alignstr 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:

analyzed_datalist of numpy arrays

The processed data