man pymvpa2-searchlight (1): traveling ROI analysis

SYNOPSIS

pymvpa2 ,searchlight /[,--version/] [,-h/] ,-i DATASET /[,DATASET /...] ,--payload PAYLOAD --neighbors SPEC /[,--nproc NPROC/] [,--multiproc-backend {native,hdf5}/] [,--aggregate-fx AGGREGATE_FX/] [,--ds-preproc-fx DS_PREPROC_FX/] [,--enable-ca NAME /[,NAME /...]] [,--disable-ca NAME /[,NAME /...]] [,--scatter-rois SPEC/] [,--roi-attr ATTR/EXPR /[,ATTR/EXPR /...]] [,--cv-learner CV_LEARNER/] [,--cv-learner-space CV_LEARNER_SPACE/] [,--cv-partitioner CV_PARTITIONER/] [,--cv-errorfx CV_ERRORFX/] [,--cv-avg-datafold-results/] [,--cv-balance-training CV_BALANCE_TRAINING/] [,--cv-sampling-repetitions CV_SAMPLING_REPETITIONS/] [,--cv-permutations CV_PERMUTATIONS/] [,--cv-prob-tail {left,right}/] ,-o OUTPUT /[,--hdf5-compression TYPE/]

DESCRIPTION

Traveling ROI analysis

OPTIONS

--version: show program's version and license information and exit
-h, --help, --help-np: show this help message and exit. --help-np forcefully disables the use of a pager for displaying the help.
-i DATASET [DATASET ...], --input DATASET [DATASET ...]: path(s) to one or more PyMVPA dataset files. All datasets will be merged into a single dataset (vstack'ed) in order of specification. In some cases this option may need to be specified more than once if multiple, but separate, input datasets are required.

Options for searchlight setup:

--payload PAYLOAD: switch to select a particular analysis type to be run in a searchlight fashion on a dataset. Depending on the choice the corresponding analysis setup options are evaluated. 'cv' computes a cross-validation analysis. Alternatively, the argument to this option can also be a script filename in which a custom measure is built that is then ran as a searchlight.
--neighbors SPEC: define the size and shape of an ROI with respect to a center/seed location. If a single integer number is given, it is interpreted as the radius (in number of grid elements) around a seed location. By default grid coordinates for features are taken from a 'voxel_indices' feature attribute in the input dataset. If coordinates shall be taken from a different attribute, the radius value can be prefixed with the attribute name, i.e. 'altcoords:2'. For ROI shapes other than spheres (with potentially additional parameters), the shape name can be specified as well, i.e. 'voxel_indices:HollowSphere:3:2'. All neighborhood objects from the mvpa2.misc.neighborhood module are supported. For custom ROI shapes it is also possible to pass a script filename, or an attribute name plus script filename combination, i.e. 'voxel_indices:myownshape.py' (advanced). It is possible to specify this option multiple times to define multi-space ROI shapes for, e.g., spatiotemporal searchlights.
--nproc NPROC: Use the specific number or worker processes for computing.
--multiproc-backend {native,hdf5}: Specifies the way results are provided back from a processing block in case of --nproc > 1. 'native' is pickling/unpickling of results, while 'hdf5' uses HDF5 based file storage. 'hdf5' might be more time and memory efficient in some cases.
--aggregate-fx AGGREGATE_FX: use a custom result aggregation function for the searchlight
--ds-preproc-fx DS_PREPROC_FX: custom preprocessing function to be applied immediately after loading the data

Options for conditional attributes:

--enable-ca NAME [NAME ...]: list of conditional attributes to be enabled
--disable-ca NAME [NAME ...]: list of conditional attributes to be disabled

Options for constraining the searchlight:

--scatter-rois SPEC: scatter ROI locations across the available space. The arguments supported by this option are identical to those of --neighbors. ROI locations are randomly picked from all possible locations with the constraint that the center coordinates of any ROI is NOT within the neighborhood (as defined by this option's argument) of a second ROI. Increasing the size of the neighborhood therefore increases the scarceness of the sampling.
--roi-attr ATTR/EXPR [ATTR/EXPR ...]: name of a feature attribute whose non-zero values define possible ROI seeds/centers. Alternatively, this can also be an expression like: parcellation_roi eq 16 (see the 'select' command on information what expressions are supported).

Options for cross-validation setup:

--cv-learner CV_LEARNER: select a learner (trainable node) via its description in the learner warehouse (see 'info' command for a listing), a colon-separated list of capabilities, or by a file path to a Python script that creates a classifier instance (advanced).
--cv-learner-space CV_LEARNER_SPACE: name of a sample attribute that defines the model to be learned by a learner. By default this is an attribute named 'targets'.
--cv-partitioner CV_PARTITIONER: select a data folding scheme. Supported arguments are: 'half' for split-half partitioning, 'oddeven' for partitioning into odd and even chunks, 'group-X' where X can be any positive integer for partitioning in X groups, 'n-X' where X can be any positive integer for leave-X-chunks out partitioning. By default partitioners operate on dataset chunks that are defined by a 'chunks' sample attribute. The name of the "chunking" attribute can be changed by appending a colon and the name of the attribute (e.g. 'oddeven:run'). optionally an argument to this option can also be a file path to a Python script that creates a custom partitioner instance (advanced).
--cv-errorfx CV_ERRORFX: error function to be applied to the targets and predictions of each cross-validation data fold. This can either be a name of any error function in PyMVPA's mvpa2.misc.errorfx module, or a file path to a Python script that creates a custom error function (advanced).
--cv-avg-datafold-results: average result values across data folds generated by the partitioner. For example to compute a mean prediction error across all folds of a crossvalidation procedure.
--cv-balance-training CV_BALANCE_TRAINING: If enabled, training samples are balanced within each data fold. If the keyword 'equal' is given as argument an equal number of random samples for each unique target value is chosen. The number of samples per category is determined by the category with the least number of samples in the respective training set. An integer argument will cause the a corresponding number of samples per category to be randomly selected. A floating point number argument (interval [0,1]) indicates what fraction of the available samples shall be selected.
--cv-sampling-repetitions CV_SAMPLING_REPETITIONS: If training set balancing is enabled, how often should random sample selection be performed for each data fold. Default: 1
--cv-permutations CV_PERMUTATIONS: Number of Monte-Carlo permutation runs to be computed for estimating an H0 distribution for all crossvalidation results. Enabling this option will make reports of corresponding p-values available in the result summary and output.
--cv-prob-tail {left,right}: which tail of the probability distribution to report p-values from when evaluating permutation test results. For example, a cross-validation computing mean prediction error could report left-tail p-value for a single-sided test.

Output options:

-o OUTPUT, --output OUTPUT: output filename ('.hdf5' extension is added automatically if necessary). NOTE: The output format is suitable for data exchange between PyMVPA commands, but is not recommended for long-term storage or exchange as its specific content may vary depending on the actual software environment. For long-term storage consider conversion into other data formats (see 'dump' command).
--hdf5-compression TYPE: compression type for HDF5 storage. Available values depend on the specific HDF5 installation. Typical values are: 'gzip', 'lzf', 'szip', or integers from 1 to 9 indicating gzip compression levels.

AUTHOR

Written by Michael Hanke & Yaroslav Halchenko, and numerous other contributors.

COPYRIGHT

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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