NAME¶
pymvpa2-searchlight - 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] [
--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
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¶
Copyright © 2006-2014 PyMVPA developers
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.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO
EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES
OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.