| Multivariate Pattern Analysis in Python

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

# PyMVPA Developer Guidelines

## Documentation

Documentation of the code and supplementary material (such as this file) should be done in reST (reStructuredText) light markup language. See Demo or a Cheatsheet for a quick demo.

### Code Documentation

Code must be documented in accordance to epydoc + reST usage guidelines However, the main focus should be put on a properly rendering Module reference. The module reference is also generated from the docstrings and there very similar to the API docs generated by epydoc. The main difference is that epydoc generates a much richer set of information (e.g. inheritance graphs), which might not be useful, but even counter-productive in a user-centered documentation. The module reference tries to limit the amount of information to a reasonable extent. it embeds technical docs into the user manual and therefore allows for easy (and automatic) cross-references between manual and module reference. A bunch of functions in doc/conf.py take care of converting the docstrings into the proper format to be processed by Sphinx. This is done to ensure both human-readable plain text docs (e.g. when using within IPython, and at the same time, an extensive use of Sphinx’s markup capabilities.

Parameter lists should be written as definition lists and not bulleted lists. For an example how to do it right, please see mvpa/datasets/dataset.py. Basically, it should look like this:

    :Parameters:
      <name of the parameter>: <optional description of its type
        an optional multiline description of the parameter

The same syntax has to be used for return value descriptions:

    :Returns:
      <what is returned>: <optional type or shape specs>
        optional multiline description

The textwidth of all docstrings should not exceed 72 characters, to ensure nicely looking docs on a 80 characters terminal in IPython.

### Examples

Examples should be complete and stand-alone scripts located in doc/examples. If an example involves any kind of interactive step, it should honor the `MVPA_EXAMPLES_INTERACTIVE` setting, to allow for automatic testing of all examples. In case of a matplotlib-based visualization such snippet should be sufficient:

    from mvpa import cfg
    if cfg.getboolean('examples', 'interactive', True):
        P.show()

All examples are automatically converted into RsT documents for inclusion in the manual. Each of them is preprocessed in the following way:

- Any header till the first docstring is stripped.
- Each top-level (non-assigned) docstring is taken as a text block in the generated RsT source file. Such a docstring might appear anywhere in the example, not just at the beginning. In this case, the code snippet is properly split and the text block is inserted at the corresponding location.
- All remaining lines are treated as code and inserted in the RsT source with appropriate markup.

The first docstring in each example must have a proper section heading (with ‘=’ markup).

Finally, each example should be added to the appropriate toctree in doc/examples.rst.

## Code Formatting

pylint

  Code should be conformant with Pylint driven by config located at doc/misc/pylintrc. It assumes camelback notation (classes start with capitals, functions with lowercase) and indentation using 4 spaces (i.e. no tabs) Variables are low-case and can have up to 2 _s. To engage, use 1 of 3 methods:

  - place it in ~/.pylintrc for user-wide installation

  - use within a call to pylint:

        pylint --rcfile=$PWD/doc/misc/pylintrc

  - export environment variable from mvpa sources top directory:

        export   PYLINTRC=$PWD/doc/misc/pylintrc

2 empty lines
  According to original python style guidelines: single empty line to separate methods within class, and 2 empty lines between classes BUT we do 2 empty between methods, 3 empty between classes
module docstring
  Each module should start with a docstring describing the module (which is not inside the hashed-comment of each file) look at mapper or neighbor for tentative organization if copyright/license has to be present in each file.
header
  Each file should contain a header from doc/misc/header.py.
notes

  Use following keywords will be caught by pylint to provide a summary of what yet to be done in the given file

  FIXME
    something which needs fixing (sooner than later)
  TODO
    future plan (i.e. later than sooner)
  XXX
    some concern/question
  YYY
    comment/answer to above mentioned XXX concern
  WiP
    Work in Progress: API and functionality might rapidly change

## Coding Conventions

__repr__
  most of the classes should provide meaningful and concise summary over their identity (name + parameters + some summary over results if any)

## Naming Conventions

### Function Arguments

dataset vs data

  Ones which are supposed to be derived from `Dataset` class should have suffix (or whole name) `dataset`. In contrast, if argument is expected to be simply a NumPy array, suffix should be `data`. For example:

      class Classifier(ClassWithCollections):
         ...
         def train(self, dataset):
         ...
         def predict(self, data):

      class FeatureSelection(ClassWithCollections):
         ...
         def __call__(self, dataset, testdataset):

  Such convention should be enforced in all `*train`, `*predict` functions of classifiers.

## Tests

- Every more or less “interesting” bugfix should be accompanied by a unittest which might help to prevent it in the future refactoring

- Every new feature should have a unittest

- Unit tests that might be non-deterministic (e.g. depending on classifier performance, which is turn is randomly initialized) should be made conditional like this:

      >>> from mvpa import cfg
      >>> if cfg.getboolean('tests', 'labile', default='yes'):
      ...     pass

## Extending PyMVPA

This section shall provide a developer with the necessary pieces of information for writing extensions to PyMVPA. The guidelines given here, must be obeyed to ensure a maximum of compatibilty and inter-operability. As a consequence, all modifications that introduce changes to the basic interfaces outlined below have to be documented here and also should be announced in the changelog.

### Adding an External Dependency

Introducing new external dependencies should be done in a completely optional fashion. This includes both build-dependencies and runtime dependencies. With mvpa.base.externals PyMVPA provides a simple framework to test the availability of certain external components and publish the results of the tests throughout PyMVPA.

### Adding a new Dataset type

> - Required interface for Mapper.
> - only new subclasses of MappedDataset + new Mappers (all other as improvements into the Dataset base class)?

go into mvpa/datasets/

### Adding a new Classifier

To add a new classifier implementation it is sufficient to create a new sub-class of `Classifier` and add implementations of the following methods:

__init__(**kwargs)
  Additional arguments and keyword arguments may be added, but the base-class contructor has to be called with **kwargs!
_train(dataset)
  Has to train the classifier when it is called with a `Dataset`. Successive calls to this methods always have to train the classifier on the respective datasets. An eventually existing prior training status has to be cleared automatically. Nothing is returned.
_predict(data)
  Unlike _train() the method is not called with a `Dataset` instance, but with any sequence of data samples (e.g. arrays). It has to return a sequence of predictions, one for each data sample.

With this minimal implementation the classifier provides some useful functionality, by automatically storing some relevant information upon request in state variables.

Supported states:

State Name | Description | Default
feature_ids | Feature IDS which were used for the actual training. | Disabled
predicting_time | Time (in seconds) which took classifier to predict. | Enabled
predictions | Most recent set of predictions. | Enabled
trained_dataset | The dataset it has been trained on. | Disabled
trained_labels | Set of unique labels it has been trained on. | Enabled
training_confusion | Confusion matrix of learning performance. | Disabled
training_time | Time (in seconds) which took classifier to train. | Enabled
values | Internal classifier values the most recent predictions are based on. | Disabled

If any intended functionality cannot be realized be implementing above methods. The `Classifier` class offers some additionals methods that might be overriden by sub-classes. For all methods described below it is strongly recommended to call the base-class methods at the end of the implementation in the sub-class to preserve the full functionality.

_pretrain(dataset)
  Called with the `Dataset` instance that shall be trained with, but before the actual training is performed.
_posttrain(dataset)
  Called with the `Dataset` instance the classifier was trained on, just after training was performed.
_prepredict(data)
  Called with the data samples the classifier should do a prediction with, just before the actual _prediction() call.
_postpredict(data, result)
  Called with the data sample for which predictions were made and the resulting predictions themselves.

Source code files of all classifier implementations go into mvpa/clfs/.

Outstanding Questions:

> - when states and when properties?

### Adding a new DatasetMeasure

There are few possible base-classes for new measures (former sensitivity analyzers). First, `DatasetMeasure` can directly be sub-classed. It is a base class for any measure to be computed on a `Dataset`. This is the more generic approach. In the most of the cases, measures are to be reported per each feature, thus `FeaturewiseDatasetMeasure` should serve as a base class in those cases. Furthermore, for measures that make use of some classifier and extract the sensitivities from it, `Sensitivity` (derived from `FeaturewiseDatasetMeasure`) is a more appropriate base-class, as it provides some additional useful functionality for this use case (e.g. training a classifier if needed).

All measures (actually all objects based on `DatasetMeasure`) support a transformer keyword argument to their constructor. The functor passed as its value is called with the to be returned results and its outcome is returned as the final results. By default no transformation is performed.

If a `DatasetMeasure` computes a characteristic, were both large positive and large negative values indicate high relevance, it should nevertheless not return absolute sensitivities, but set a default transformer instead that takes the absolute (e.g. plain N.absolute or a convinience wrapper Absolute).

To add a new measure implementation it is sufficient to create a new sub-class of `DatasetMeasure` (or `FeaturewiseDatasetMeasure`, or `Sensitivity`) and add an implementation of the _call(dataset) method. It will be called with an instance of `Dataset`. `FeaturewiseDatasetMeasure` (e.g. `Sensitivity` as well) has to return a vector of featurewise sensitivity scores.

Supported states:

State Name | Description | Default
null_prob | State variable. | Enabled
raw_results | Computed results before applying any transformation algorithm. | Disabled

Source code files of all sensitivity analyzer implementations go into mvpa/measures/.

#### Classifier-independent Sensitivity Analyzers

Nothing special.

#### Classifier-based Sensitivity Analyzers

A `Sensitivity` behaves exactly like its classifier-independent sibling, but additionally provides support for embedding the necessary classifier and handles its training upon request (boolean force_training keyword argument of the constructor). Access to the embedded classifier object is provided via the clf property.

Supported states:

State Name | Description | Default
base_sensitivities | Stores basic sensitivities if the sensitivity relies on combining multiple ones. | Disabled
null_prob | State variable. | Enabled
raw_results | Computed results before applying any transformation algorithm. | Disabled

Outstanding Questions:

> - What is a `mvpa.measures.base.ProxyClassifierSensitivityAnalyzer` useful for?
> - Shouldn’t there be a sensitivities state?

### Adding a new Algorithm

go into mvpa/algorithms/

## Git Repository

### Layout

The repository is structured by a number of branches. Each developer should prefix his/her branches with a unique string plus ‘/’ (maybe initials or similar). Currently there are:

> mh: | Michael Hanke
> per: | Per B. Sederberg
> yoh: | Yaroslav Halchenko

Each developer can have an infinite number of branches. If the number of branches causes gitk output to exceed a usual 19” screen, the respective developer has to spend some bucks (or euros) on new screens for all others ;-)

The main release branch is called master. This is a merge-only branch. Features finished or updated by some developer are merged from the corresponding branch into master. At a certain point the current state of master is tagged – a release is done.

Only usable feature should end-up in master. Ideally master should be releasable at all times. Something must not be merged into master if any unit test fails.

Additionally, there are packaging branches. They are labeled after the package target (e.g. debian for a Debian package). Releases are merged into the packaging branches, packaging get updated if necessary and the branch gets tagged when a package version is released. Maintenance (as well as backport) releases should be gone under maint/codename.flavor (e.g. maint/lenny, maint/lenny.security, maint/sarge.bpo).

### Commits

Please prefix all commit summaries with one (or more) of the following labels. This should help others to easily classify the commits into meaningful categories:

> - BF : bug fix
> - RF : refactoring
> - NF : new feature
> - BW : addresses backward-compatibility
> - OPT : optimization
> - BK : breaks something and/or tests fail
> - PL : making pylint happier
> - DOC: for all kinds of documentation related commits

### Merges

For easy tracking of what changes were absorbed during merge, we advice to enable merge summary within git:

> git-config merge.summary true

## Changelog

The PyMVPA changelog is located in the toplevel directory of the source tree in the Changelog file. The content of this file should be formated as restructured text to make it easy to put it into manual appendix and on the website.

This changelog should neither replicate the VCS commit log nor the distribution packaging changelogs (e.g. debian/changelog). It should be focused on the user perspective and is intended to list rather macroscopic and/or important changes to the module, like feature additions or bugfixes in the algorithms with implications to the performance or validity of results.

It may list references to 3rd party bugtrackers, in case the reported bugs match the criteria listed above.

Changelog entries should be tagged with the name of the developer(s) (mainly) involved in the modification – initials are sufficient for people contributing regularly.

Changelog entries should be added whenever something is ready to be merged into the master branch, not necessarily with a release already approaching.

## Developer-TODO

### Things to implement for the next release (Release goals)

- A part of below restructuring TODO but is separate due to it importance: come up with cleaner hierarchy and tagging of classifiers and regressions – now they are all Classifier

- Unify parameter naming across all classifiers and come up with a labeling guideline for future classifier implementations and wrappers:

      Numeric parameters can be part of .params Collection now, so they are
      joined together.

- Provide sufficient documentation about internal variable naming to make Harvester/Harvesting functionality usable. Currently the user is supposed to know, how a particular local variable is called to be able to harvest e.g. feature_ids of classifiers over cross-validation folds:

      class.HARVESTABLE={'blah' : ' some description'}

      Add information on HARVESTABLE and StateVariable
      Collectable -> Attribute

      base.attributes

- Restructure code base (incl. renaming and moving pieces)

  Let’s use the following list to come up with a nice structure for all logical components we have:

  - Datasets
  - Sensitivity analyzers (maybe: featurewise measures) * Classifier sensitivities (SVM, SMLR) -> respective classifiers * ANOVA -> mvpa.measures.anova * Noise perturbation -> -> mvpa.measures.noisepertrubation * meta-algorithms (splitting) -> mvpa.measures

  > DatasetMeasure -> Measure (transformers)

  > FeaturewiseDatasetMeasure?

  > combiners to be absorbed withing transformers? and then gone? {Classifier?}Sensitivity?

  - Mappers::

      mvpa.mappers (AKA mvpa.projections mvpa.transformers)

    - Along with PCA/ICA mappers, we should add a PLS mapper:

          PCA.train(learningdataset)
             .forward,
             .backward

          Package pychem for Debian, see how to use from PyMVPA! ;-) Same for MDP
          (i.e. use from pymvpa)

  - Feature selection algorithms
      - Simple thresholding
      - RFE
      - IFS

  - .mapper state variable

    > mvpa.featsel (NB no featsel.featsel.featsel more than 4 times!) mvpa.featsel.rfe mvpa.featsel.ifs

  - several base classes with framework infrastructure (Harvester, ClassWithCollections, virtual properties, ...)

  - Transfer error calculation

  - Cross-validation support

  - Monte-Carlo-based significance testing

  - Dataset splitter

  - Metrics and distance functions

  - Functions operating on dataset for preprocessing or transformations

  - Commandline interface support

  - Functions to generate artificial datasets

  - Error functions (i.e. for TransferError)

  - Custom exception types

  - Python 2.5 copy() aka external code shipped with PyMVPA

  - Several helpers for data IO

  - Left-over from the last attempt to establish a generic parameter interface

  - Detrending (operating on Datasets)

  - Result ‘Transformers’ to be used with ‘transformer=’ kwarg

  - Debugging and verbosity infrastructure

  - plus additional helpers, ranging from simple to complex scattered all over the place

- Resultant hierarchy:

  - mvpa
    - datasets
    - clfs
    - measures
    - featsel

- Add ability to add/modify custom attributes to a dataset.

- Possibly make NiftiDataset default to float32 when it sees that the data are ints.

- Add kernel methods as option to all classifiers, not just SVMs. For example, you should be able to run a predefined or custom kernel on the samples going into SMLR.

- TransferError needs to know what type of data to send to any specific ErrorFX. Right now there is only support for predictions and labels, but the area under the ROC and the correlation-based error functions expect to receive the “values” or “probabilities” from a classifier. Just to make this harder, every classifier is different. For example, a ridge regression’s predictions are continuous values, whereas for a SVM you need to pass in the probabilities.

  For binary: 1 value

    multiclass: 1 value, or N values

- In a related issue, the predictions and values states of the classifiers need to have a consitent format. Currently, SVM returns a list of dictionaries for values and SMLR returns a numpy ndarray.

### Long and medium term TODOs (aka stuff that has been here forever)

> - Agree upon sensitivities returned by the classifiers. Now SMLR/libsvm.SVM returns (nfeatures x X), (where X is either just 1 for binary problems, or nclasses in full multiclass in SMLR, or nclasses-1 for libsvm(?) or not-full SMLR). In case of sg.SVM and GPR (I believe) it is just (nfeatures,). MaskMapper puked on reverse in the first specification... think about combiner – should it or should not be there... etc

> - selected_ids -> implement via MaskMapper?

>   yoh:

>     it might be preferable to manipulate/expose MaskMapper instead of plain list of selected_ids within FeatureSelection classes

> - unify naming of working/testing

>   > - transerror.py for instance uses testdata/trainingdata
>   > - rfe.py dataset, testdataset

> - implement proper cloning of classifiers. untrain() doesn’t work in some cases, since we can create somewhat convolved object definitions so it is hard, if not impossible, to get to all used classifiers. See for instance clfswh[‘SVM/Multiclass+RFE’]. We can’t get all the way into classifier-based sensitivity analyzer. Thus instead of tracking all the way down in hierarchy, we should finally create proper ‘parametrization’ handling of classifiers, so we could easily clone basic ones (which might have active SWIG bindings), and top-level ones should implement .clone() themselves. or may be some other way, but things should be done. Or may be via proper implementation of __reduce__ etc

> - mvpa.misc.warning may be should use stock python warnings module instead of custom one?

> - ConfusionBasedError -> InternalError ?

> - Think about how to deal with Transformers to serve them with

>     basic_analyzers... May be transformer can be a an argument for any analyzer! Ha! Indeed... may be later

> - Renaming of the modules transerror.py -> errors.py

> - SVM: getSV and getSVCoef return very ‘packed’ presentation

>     whenever classifier is multiclass. Thus they have to be unpacked before proper use (unless it is simply a binary classifier).

> - Regression tests: for instance using sample dataset which we have

>     already, run doc/examples/searchlight.py and store output to validate against. Probably the best would be to create a regression test suite within unit tests which would load the dataset and run various algorithms on it a verify the results against previousely obtained (and dumped to the disk)

> - Agree on how to describe parameters to functions. Describe in NOTES.coding.

> - feature_selector – may be we should return a tuple (selected_ids, discarded_ids)?

>   Michael:

>     Is there any use case for that? ElementSelector can ‘select’ and ‘discard’ already. DO we need both simultaneously?

> - Basic documentation: Examples (more is better) describing various use cases

>     (everything in the cncre should be done in examples)

> - Non-linear SVM RFE

> - ParameterOptimizer (might be also OptimizedClassifier which uses parameterOptimizer internally but as the result there is a classifier which automatically optimizes its parameters. It is close in idea to classifier based on RFE)

> - provide for Dataset – Dataset.__featattr which has attributes for

>     features similar to __dsattr way.

> > in –> data -> dataShape out –> features ->

## Building a binary installer on MacOS X 10.5

A simple way to build a binary installer for Mac OS is bdist_mpkg. This is a setuptools extension that uses the proper native parts of MacOS to build the installer. However, for PyMVPA there are two problems with bdist_mpkg: 1. PyMVPA uses distutils not setuptools and 2. current bdist_mpkg 0.4.3 does not work for MacOS X 10.5 (Leopard). But both can be solved.

Per 1) A simple wrapper script in tools/mpkg_wrapper.py will enable the use of setuptools on top of distutils, while keeping the distutils part in a usable state.

Per 2) The following patch (against 0.4.3.) makes bdist_mpkg compatible with MacOS 10.5. It basically changes the way bdist_mpkg determined the GID of the admin group. 10.5 removed the nidump command:

    diff -rNu bdist_mpkg-0.4.3/bdist_mpkg/tools.py bdist_mpkg-0.4.3.leopard/bdist_mpkg/tools.py
    --- bdist_mpkg-0.4.3/bdist_mpkg/tools.py      2006-07-09 00:39:00.000000000 -0400
    +++ bdist_mpkg-0.4.3.leopard/bdist_mpkg/tools.py      2008-08-21 07:43:35.000000000 -0400
    @@ -79,15 +79,12 @@
                 yield os.path.join(root, fn)

     def get_gid(name, _cache={}):
    -    if not _cache:
    -        for line in os.popen('/usr/bin/nidump group .'):
    -            fields = line.split(':')
    -            if len(fields) >= 3:
    -                _cache[fields[0]] = int(fields[2])
    -    try:
    -        return _cache[name]
    -    except KeyError:
    -        raise ValueError('group %s not found' % (name,))
    +    for line in os.popen("dscl . -read /Groups/" + name + " PrimaryGroupID"):
    +        fields = [f.strip() for f in line.split(':')]
    +        if fields[0] == "PrimaryGroupID":
    +            return fields[1]
    +
    +    raise ValueError('group %s not found' % (name,))

     def find_root(path, base='/'):
         """

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