.. -*- mode: rst; fill-column: 79 -*-
.. ex: set sts=4 ts=4 sw=4 et tw=79:
  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
  #
  #   See COPYING file distributed along with the PyMVPA package for the
  #   copyright and license terms.
  #
  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###

.. _chap_devguide:

***************************
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
<http://docutils.sourceforge.net/docs/user/rst/cheatsheet.txt>`__ or a
`Cheatsheet <http://docutils.sourceforge.net/docs/user/rst/demo.txt>`__ for a
quick demo.


Code Documentation
------------------

Code must be documented in accordance to `epydoc + reST usage guidelines
<http://epydoc.sourceforge.net/manual-othermarkup.html>`__
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
:envvar:`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 <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 <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 :class:`~mvpa.datasets.base.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.

.. _NumPy: http://numpy.scipy.org/



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 :class:`~mvpa.clfs.base.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 :class:`~mvpa.datasets.base.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 :class:`~mvpa.datasets.base.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.

.. IncludeStates: clfs.base Classifier

Supported states:

================== ==============================================   =========
    State Name      Description                                      Default
------------------ ----------------------------------------------   ---------
feature_ids         Feature IDS which were used for the actual       Disabled
                    training.
predicting_time     Time (in seconds) which took classifier to       Enabled
                    predict.
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       Enabled
                    train.
values              Internal classifier values the most recent       Disabled
                    predictions are based on.
================== ==============================================   =========

If any intended functionality cannot be realized be implementing above methods.
The :class:`~mvpa.clfs.base.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 :class:`~mvpa.datasets.base.Dataset` instance that shall be trained with, but before
    the actual training is performed.

`_posttrain(dataset)`
    Called with the :class:`~mvpa.datasets.base.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, :class:`~mvpa.measures.base.DatasetMeasure` can directly be sub-classed. It is a base
class for any measure to be computed on a :class:`~mvpa.datasets.base.Dataset`. This is the more generic
approach. In the most of the cases, measures are to be reported per each
feature, thus :class:`~mvpa.measures.base.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, :class:`~mvpa.measures.base.Sensitivity` (derived from
:class:`~mvpa.measures.base.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 :class:`~mvpa.measures.base.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 :class:`~mvpa.measures.base.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 :class:`~mvpa.measures.base.DatasetMeasure` (or :class:`~mvpa.measures.base.FeaturewiseDatasetMeasure`, or :class:`~mvpa.measures.base.Sensitivity`) and add an
implementation of the `_call(dataset)` method. It will be called with an
instance of :class:`~mvpa.datasets.base.Dataset`. :class:`~mvpa.measures.base.FeaturewiseDatasetMeasure` (e.g. :class:`~mvpa.measures.base.Sensitivity` as well)
has to return a vector of featurewise sensitivity scores.

.. IncludeStates: measures.base DatasetMeasure

Supported states:

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

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


Classifier-independent Sensitivity Analyzers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Nothing special.


Classifier-based Sensitivity Analyzers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

A :class:`~mvpa.measures.base.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.

.. IncludeStates: measures.base Sensitivity

Supported states:

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


Outstanding Questions:

  * What is a :class:`mvpa.measures.base.ProxyClassifierSensitivityAnalyzer` useful for?
  * Shouldn't there be a `sensitivities` state?


.. _Absolute: api/mvpa.misc.transformers-module.html#Absolute



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

.. _reST: http://docutils.sourceforge.net/docs/ref/rst/restructuredtext.html
.. _EmacsreST: http://docutils.sourceforge.net/docs/user/emacs.html
.. _Pylint: http://packages.debian.org/unstable/python/pylint


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='/'):
       """

.. _bdist_mpkg: http://undefined.org/python/#bdist_mpkg