Cross-validation of a learner's performance
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\/]
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').
--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.
--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.
-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.
Written by Michael Hanke & Yaroslav Halchenko, and numerous other contributors.
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.
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