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PyMVPA for Matlab Users

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This content refers to the previous stable release of PyMVPA. Please visit for the most recent version of PyMVPA and its documentation.

Frequently Asked Questions


It is sloooooow. What can I do?

Have you tried running the Python interpreter with -O? PyMVPA provides lots of debug messages with information that is computed in addition to the work that really has to be done. However, if Python is running in optimized mode, PyMVPA will not waste time on this and really tries to be fast.

If you are already running it optimized, then maybe you are doing something really demanding...

I am tired of writing these endless import blocks. Any alternative?

Sure. Instead of individually importing all pieces that are required by a script, you can import them all at once. A simple:

>>> import mvpa.suite as mvpa

makes everything directly accessible through the mvpa namespace, e.g. mvpa.datasets.base.Dataset becomes mvpa.Dataset. Really lazy people can even do:

>>> from mvpa.suite import *

However, as always there is a price to pay for this convenience. In contrast to the individual imports there is some initial performance and memory cost. In the worst case you’ll get all external dependencies loaded (e.g. a full R session), just because you have them installed. Therefore, it might be better to limit this use to case where individual key presses matter and use individual imports for production scripts.

I feel like I want to contribute something, do you mind?

Not at all! If you think there is something that is not well explained in the documentation, send us an improvement. If you implemented a new algorithm using PyMVPA that you want to share, please share. If you have an idea for some other improvement (e.g. speed, functionality), but you have no time/cannot/do not want to implement it yourself, please post your idea to the PyMVPA mailing list.

I want to develop a new feature for PyMVPA. How can I do it efficiently?

The best way is to use Git for both, getting the latest code from the repository and preparing the patch. Here is a quick sketch of the workflow.

First get the latest code:

git clone git://

This will create a new PyMVPA subdirectory, that contains the complete repository. Enter this directory and run gitk –all to browse the full history and all branches that have ever been published.

You can run:

git fetch origin

in this directory at any time to get the latest changes from the main repository.

Next, you have to decide what you want to base your new feature on. In the simplest case this is the master branch (the one that contains the code that will become the next release). Creating a local branch based on the (remote) master branch is:

git checkout -b my_hack origin/master

Now you are ready to start hacking. You are free to use all powers of Git (and yours, of course). You can do multiple commits, fetch new stuff from the repository, and merge it into your local branch, ... To get a feeling what can be done, take a look very short description of Git or a more comprehensive Git tutorial.

When you are done with the new feature, you can prepare the patch for inclusion into PyMVPA. If you have done multiple commits you might want to squash them into a single patch containing the new feature. You can do this with git-rebase. In recent version git-rebase has an option –interactive, which allows you to easily pick, squash or even further edit any of the previous commits you have made. Rebase your local branch against the remote branch you started hacking on (origin/master in this example):

git rebase --interactive origin/master

When you are done, you can generate the final patch file:

git-format-patch origin/master

Above command will generate a file for each commit in you local branch that is not yet part of origin/master. The patch files can then be easily emailed.

The manual is quite insufficient. When will you improve it?

Writing a manual can be a tricky task if you already know the details and have to imagine what might be the most interesting information for someone who is just starting. If you feel that something is missing which has cost you some time to figure out, please drop us a note and we will add it as soon as possible. If you have developed some code snippets to demonstrate some feature or non-trivial behavior (maybe even trivial ones, which are not as obvious as they should be), please consider sharing this snippet with us and we will put it into the example collection or the manual. Thanks!

Data import, export and storage

What file formats are understood by PyMVPA?

Please see the Data Formats section.

What if there is no special file format for some particular datatype?

With the Hamster class, PyMVPA supports storing any kind of serializable data into a (compressed) file (see the class documentation for a trivial usage example). The facility is particularly useful for storing any number of intermediate analysis results, e.g. for post-processing.

Data preprocessing

Is there an easy way to remove invariant features from a dataset?

You might have to deal with invariant features in case like an fMRI dataset, where the brain mask is slightly larger than the thresholded fMRI timeseries image. Such invariant features (i.e. features with zero variance) are sometime a problem, e.g. they will lead to numerical difficulties when z-scoring the features of a dataset (i.e. division by zero).

The mvpa.datasets.miscfx module provides a convenience function removeInvariantFeatures() that strips such features from a dataset.

How can I do block-averaging of my block-design fMRI dataset?

The easiest way is to use a mapper to transform/average the respective samples. Suppose you have a dataset:

>>> dataset = normalFeatureDataset()
>>> dataset
<Dataset / float64 100 x 4 uniq: 2 labels 5 chunks labels_mapped>

Averaging all samples with the same label in each chunk individually is done by applying a samples mapper to the dataset.

>>> from mvpa.mappers.samplegroup import SampleGroupMapper
>>> from mvpa.misc.transformers import FirstAxisMean
>>> m = SampleGroupMapper(fx=FirstAxisMean)
>>> mapped_dataset = dataset.applyMapper(samplesmapper=m)
>>> mapped_dataset
<Dataset / float64 10 x 4 uniq: 2 labels 5 chunks labels_mapped>

SampleGroupMapper applies a function to every group of samples in each chunk individually. Using FirstAxisMean as function, therefore yields one sample of each label per chunk.

Data analysis

How do I know which features were finally selected by a classifier doing feature selection?

All classifier possess a state variable feature_ids. When enable, the classifier stores the ids of all features that were finally used to train the classifier.

>>> clf = FeatureSelectionClassifier(
...           kNN(k=5),
...           SensitivityBasedFeatureSelection(
...               SMLRWeights(SMLR(lm=1.0), transformer=Absolute),
...               FixedNElementTailSelector(1, tail='upper', mode='select')),
...           enable_states = ['feature_ids'])
>>> clf.train(dataset)
>>> final_dataset = dataset.selectFeatures(clf.feature_ids)
>>> final_dataset
<Dataset / float64 100 x 1 uniq: 2 labels 5 chunks labels_mapped>

In the above code snippet a kNN classifier is defined, that performs a feature selection step prior training. Features are selected according to the absolute magnitude of the weights of a SMLR classifier trained on the data (same training data that will also go into kNN). Absolute SMLR weights are used for feature selection as large negative values also indicate important information. Finally, the classifier is configured to select the single most important feature (given the SMLR weights). After enabling the feature_ids state, the classifier provides the desired information, that can e.g. be applied to generate a stripped dataset for an analysis of the similarity structure.

How do I extract sensitivities from a classifier used within a cross-validation?

CrossValidatedTransferError provides an interface to access any classifier-related information: harvest_attribs. Harvesting the sensitivities computed by all classifiers (without recomputing them again) looks like this:

>>> cv = CrossValidatedTransferError(
...       TransferError(SMLR()),
...       OddEvenSplitter(),
...       harvest_attribs=\
...        ['transerror.clf.getSensitivityAnalyzer(force_training=False)()'])
>>> merror = cv(dataset)
>>> sensitivities = cv.harvested.values()[0]
>>> N.array(sensitivities).shape == (2, dataset.nfeatures)

First, we define an instance of CrossValidatedTransferError that uses an SMLR classifier to perform the cross-validation on odd-even splits of a dataset. The important piece is the definition of the harvest_attribs. It takes a list of code snippets that will be executed in the local context of the cross-validation function. The TransferError instance used to train and test the classifier on each split is available via transerror. The rest is easy: TransferError provides access to its classifier and any classifier can in turn generate an appropriate Sensitivity instance via getSensitivityAnalyzer(). This generator method takes additional arguments to the constructor of the mvpa.measures.base.Sensitivity class. In this case we want to prevent retraining the classifiers, as they will be trained anyway by the TransferError instance they belong to.

The return values of all code snippets defined in harvest_attribs are available in the harvested state variable. harvested is a dictionary where the keys are the code snippets used to compute the value. As the key in this case is pretty long, we simply take the first (and only) value from the dictionary. The value is actually a list of sensitivity vectors, one per split.

Can PyMVPA deal with literal class labels?

Yes and no. In general the classifiers wrapped or implemented in PyMVPA are not capable of handling literal labels, some even might require binary labels. However, PyMVPA datasets provide functionality to map any set of literal labels to a corresponding set of numerical labels. Let’s take a look:

>>> # invent some samples (arbitrary in this example)
>>> samples = N.random.randn(3).reshape(3,1)

First we will construct a Dataset the usual way (3 samples with unique numerical labels, all in one chunk:

>>> Dataset(samples=samples, labels=range(3), chunks=1)
<Dataset / float64 3 x 1 uniq: 3 labels 1 chunks>

Now, we are trying to create the same dataset using literal labels:

>>> # now create the same dataset using literal labels
>>> ds = Dataset(samples=samples,
...              labels=['one', 'two', 'three'],
...              chunks=1)
>>> ds.labels[0]

This approach simply stored the literal labels in the dataset and will most likely lead to unpredictable behavior of classifiers that cannot handle them. A more flexible approach is to let the dataset map the literal labels to numerical ones:

>>> ds = Dataset(samples=samples,
...              labels=['one', 'two', 'three'],
...              chunks=1,
...              labels_map=True)
>>> ds
<Dataset / float64 3 x 1 uniq: 3 labels 1 chunks labels_mapped>
>>> ds.labels[0]
>>> for k in sorted(ds.labels_map.keys()):
...     print k, ds.labels_map[k]
one 0
three 1
two 2

With this approach the labels stored in the dataset are now numerical. However, the mapping between literal and numerical labels is somewhat arbitrary. If a fixed mapping is possible or intended (e.g. same mapping for multiple dataset), the mapping can be set explicitly:

>>> ds = Dataset(samples=samples,
...              labels=['one', 'two', 'three'],
...              chunks=1,
...              labels_map={'one': 1, 'two': 2, 'three': 3})
>>> for k in sorted(ds.labels_map.keys()):
...     print k, ds.labels_map[k]
one 1
three 3
two 2

PyMVPA will use the labels mapping to display literal instead of numerical labels e.g. in confusion matrices.