Ubuntu Manpage: scatter - cross-validation of a learner's performance

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NAME

       scatter -  cross-validation of a learner's performance

SYNOPSIS

       pymvpa2   crossval   [--version]  [-h]  -i  DATASET  [DATASET  ...]  --learner  LEARNER  [--learner-space
       LEARNER_SPACE] --partitioner PARTITIONER [--errorfx ERRORFX] [--avg-datafold-results] [--balance-training
       BALANCE_TRAINING]  [--sampling-repetitions  SAMPLING_REPETITIONS]  [--permutations PERMUTATIONS] [--prob-
       tail {left,right}] -o OUTPUT [--hdf5-compression TYPE]

DESCRIPTION

       scatter not available: No module named _tkinter, please install the python-tk package

       Cross-validation of a learner's performance

       A learner is repeatedly trained and tested on partitions of an input dataset  that  are  generated  by  a
       configurable  partitioning  scheme.   Partition  usually  constitute  training and testing portions.  The
       learner is trained on training portion of the dataset and then  learner's  generalization  is  tested  by
       comparing its predictions on the testing portion.

       A  summary  of  a  learner  performance  is  written  to STDOUT. Depending on the particular setup of the
       cross-validation analysis, either the learner's raw predictions or summary statistics are returned in  an
       output dataset.

       If  Monte-Carlo  permutation  testing  is  enabled  (see --permutations) a second output dataset with the
       corresponding p-values is stored as well (filename suffix '_nullprob').

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 cross-validation setup:
--learner 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).

--learner-space 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'.

--partitioner 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).

--errorfx 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).

--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.

--balance-training 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.

--sampling-repetitions SAMPLING_REPETITIONS
If training set balancing is enabled, how often should random sample selection be performed for
each data fold. Default: 1

--permutations 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.

--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.

pymvpa2-crossval 2.6.4

AUTHOR

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

COPYRIGHT

       Copyright © 2006-2016 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.

scatter not available: No module named _tkinter, pJanuaryn2018l the python-tk package                 SCATTER(1)