Multivariate Pattern Analysis in Python

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# Module Reference

This module reference extends the manual with a comprehensive overview of the currently available functionality, that is built into PyMVPA. However, instead of a full list including every single line of the PyMVPA code base, this reference limits itself to the relevant pieces of the application programming interface (API) that are of particular interest to users of this framework.

Each module in the package is documented by a general summary of its purpose and the list of classes and functions it provides.

## Entry Point

`mvpa2`

MultiVariate Pattern Analysis Package Organization ==================== The mvpa package contains the following subpackages and modules: :group Algorithms: algorithms :group Anatomical Atlases: atlases :group Basic Data Structures: datasets :group Classifiers (supervised learners): clfs :group Feature Selections: featsel :group Mappers (usually unsupervised learners): mappers :group Measures: measures :group Miscellaneous: base misc support :group Unittests: tests :author: Michael Hanke, Yaroslav Halchenko, `Per B.

## Basic Facilities

`base` | Base functionality of PyMVPA
`base.attributes` | Module with some special objects to be used as magic attributes with dedicated containers aka.
`base.collections` | Module with some special objects to be used as magic attributes with dedicated containers aka.
`base.config` | Registry-like monster
`base.dochelpers` | Various helpers to improve docstrings and textual output
`base.externals` | Helper to verify presence of external libraries and modules
`base.hdf5` | HDF5-based file IO for PyMVPA objects.
`base.info` | Provide system and PyMVPA information useful while reporting bugs
`base.learner` | Implementation of a common trainable processing object (Learner).
`base.node` | Implementation of a common processing object (node).
`base.param` | Parameter representation
`base.report` | Creating simple PDF reports using reportlab
`base.state` | Classes to control and store state information.
`base.types` | Things concerned with types and type-checking in PyMVPA
`base.verbosity` | Verbose output and debugging facility

## Datasets: Input, Output, Storage and Preprocessing

`base.dataset` | Multi-purpose dataset container with support for attributes.
`datasets.base` | PyMVPA’s common Dataset container.
`datasets.eventrelated` | Dataset for event-related samples.
`datasets.eep` | Dataset that gets its samples from an EEP binary file
`datasets.formats` | I/O helpers for some commonly used formats for datasets.
`datasets.mri` | Dataset for magnetic resonance imaging (MRI) data.
`datasets.miscfx` | Misc function performing operations on datasets.
`datasets.sources.sklearn_data` | Wrapper for sklearn datasets/data generators.

## Mappers: Data Transformations

`mappers` | Algorithms for (reversible) data transformation.
`mappers.base` | Basic, general purpose and meta mappers.
`mappers.boxcar` | Data mapper
`mappers.detrend` | Mapper for data detrending.
`mappers.filters` | Filtering mappers.
`mappers.flatten` | Data mapper
`mappers.fx` | Transform data by applying a function along samples or feature axis.
`mappers.fxy` |
`mappers.lle` | Local Linear Embedding Data mapper.
`mappers.mdp_adaptor` | MDP interface module.
`mappers.procrustean` | Procrustean rotation mapper
`mappers.projection` |
`mappers.prototype` | Prototype-based Mapper.
`mappers.shape` | Mappers that modify the shape of a dataset
`mappers.slicing` | Mappers for Dataset slicing.
`mappers.som` | Self-organizing map (SOM) mapper.
`mappers.svd` | Singular-value decomposition mapper
`mappers.wavelet` | Wavelet mappers
`mappers.zscore` | Mapper for data normalization by Z-Scoring.

## Generators: Repetitive Data Processing

`generators` | Nodes that generate multiple datasets.
`generators.base` | Generators for dataset resampling.
`generators.partition` |
`generators.permutation` | Generator nodes to permute datasets.
`generators.resampling` | Generators for dataset resampling.
`generators.splitters` | Generator nodes to split dataset into multiple parts.

## Classifiers and Errors

`clfs.base` | Base class for all XXX learners: classifiers and regressions.
`clfs.meta` | Classes for meta classifiers – classifiers which use other classifiers
`clfs.blr` | Bayesian Linear Regression (BLR).
`clfs.enet` | Elastic-Net (ENET) regression classifier.
`clfs.gda` | Gaussian Discriminant Analyses: LDA and QDA
`clfs.glmnet` | GLM-Net (GLMNET) regression and classifier.
`clfs.gnb` | Gaussian Naive Bayes Classifier Basic implementation of Gaussian Naive Bayes classifier.
`clfs.gpr` | Gaussian Process Regression (GPR).
`clfs.knn` | k-Nearest-Neighbour classifier.
`clfs.lars` | Least angle regression (LARS).
`clfs.model_selector` | Model selction.
`clfs.plr` | Penalized logistic regression classifier.
`clfs.ridge` | Ridge regression classifier.
`clfs.similarity` | Similarity functions for prototype-based projection.
`clfs.skl` | Classifiers provided by scikit-learn (skl) library
`clfs.smlr` | Sparse Multinomial Logistic Regression classifier.
`clfs.svm` | Importer for the available SVM and SVR machines.
`clfs.sg` | Classifiers provided by shogun (sg) library
`clfs.libsvmc` | Classifiers provied by LibSVM library
`clfs.distance` | Distance functions to be used in kernels and elsewhere
`clfs.similarity` | Similarity functions for prototype-based projection.
`clfs.stats` | Estimator for classifier error distributions.
`clfs.transerror` | Utility class to compute the transfer error of classifiers.
`clfs.warehouse` | Collection of classifiers to ease the exploration.

### Kernels

`kernels` | Import helper for PyMVPA kernels/similarities and alike
`kernels.base` | Base Kernel classes
`kernels.libsvm` | PyMVPA LibSVM-based kernels These kernels do not currently have the ability to run the calculations, so they are not translateable to other kernel types.
`kernels.np` | Kernels for Gaussian Process Regression and Classification.
`kernels.sg` | PyMVPA shogun-based kernels Provides interface to kernels defined in shogun toolbox.

## Measures: Searchlights and Sensitivties

`measures.base` | Base classes for measures: algorithms that quantify properties of datasets.
`measures.anova` | FeaturewiseMeasure performing a univariate ANOVA.
`measures.corrcoef` | FeaturewiseMeasure of correlation with the labels.
`measures.corrstability` | FeaturewiseMeasure of stability of labels across chunks based on correlation.
`measures.ds` | Dissimilarity measure.
`measures.irelief` | FeaturewiseMeasure performing multivariate Iterative RELIEF (I-RELIEF) algorithm.
`measures.noiseperturbation` | This is a `FeaturewiseMeasure` that uses a scalar `Measure` and selective noise perturbation to compute a sensitivity map.
`measures.pls` | PLS is not yet implemented
`measures.adhocsearchlightbase` | An efficient implementation of searchlight for GNB.
`measures.gnbsearchlight` | An efficient implementation of searchlight for GNB.
`measures.nnsearchlight` | An efficient implementation of searchlight for M1NN.
`measures.searchlight` | Implementation of the Searchlight algorithm
`measures.statsmodels_adaptor` |

## Feature Selection

`featsel.base` | Feature selection base class and related stuff base classes and helpers.
`featsel.ifs` | Incremental feature search (IFS).
`featsel.rfe` | Recursive feature elimination.
`featsel.helpers` |

## Additional Algorithms

`algorithms.hyperalignment` | Transformation of individual feature spaces into a common space

## Miscellaneous

`atlases` | Import helper for PyMVPA anatomical atlases
`misc.args` | Helpers for arguments handling.
`misc.attrmap` | Helper to map literal attribute to numerical ones (and back)
`misc.cmdline` | Common functions and options definitions for command line Conventions: Every option (instance of optparse.Option) has prefix “opt”.
`misc.data_generators` | Miscellaneous data generators for unittests and demos
`misc.dcov` | Compute dcov/dcorr measures for independence testing
`misc.errorfx` | Error functions helpers.
`misc.exceptions` | Exception classes which might get thrown
`misc.fx` | Misc.
`misc.neighborhood` | Neighborhood objects
`misc.sampleslookup` | Helper to map and validate samples’ origids into indices
`misc.stats` | Little statistics helper
`misc.support` | Support function – little helpers in everyday life
`misc.transformers` | Simply functors that transform something.
`misc.vproperty` | C++-like virtual properties

## Testing

`testing` | Helpers to unify/facilitate unittesting within PyMVPA
`testing.clfs` | Provides `clfs` dictionary with instances of all available classifiers.
`testing.datasets` | Provides convenience datasets for unittesting.
`testing.tools` | A Collection of tools found useful in unittests.
`testing.sweepargs`(**kwargs) | Decorator function to sweep over a given set of classifiers
`tests` | Unit test interface for PyMVPA

### Basic Plotting Utilities

`misc.plot` |
`misc.plot.base` |
`misc.plot.erp` |
`misc.plot.lightbox` |
`misc.plot.topo` |

### 3rd-party Interfaces

`misc.bv` | Import helper for Brain Voyager
`misc.bv.base` | Tiny snippets to interface with FSL easily.
`misc.fsl` | Import helper for FSL
`misc.fsl.base` | Tiny snippets to interface with FSL easily.
`misc.fsl.flobs` | Wrapper around FSLs halfcosbasis to generate HRF kernels
`misc.fsl.melodic` | Wrapper around the output of MELODIC (part of FSL)
`misc.io` | Import helper for IO helpers
`misc.io.base` | Some little helper for reading (and writing) common formats from and to disk.
`misc.io.hamster` | Helper for simple storage facility via cPickle and optionally zlib
`misc.io.meg` | IO helper for MEG datasets.

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