.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_searchlight_minimal:


Minimal Searchlight Example
===========================

.. index:: searchlight, cross-validation

The term :class:`~mvpa2.measures.searchlight.Searchlight` refers to an
algorithm that runs a scalar :class:`~mvpa2.measures.base.Measure` on
all possible spheres of a certain size within a dataset (that provides
information about distances between feature locations).  The measure
typically computed is a cross-validation of a classifier performance
(see :ref:`CrossValidation <sec_tutorial_crossvalidation>` section in
the tutorial). The idea to use a searchlight as a sensitivity analyzer
on fMRI datasets stems from :ref:`Kriegeskorte et al. (2006) <KGB06>`.

A searchlight analysis is can be easily performed. This examples shows a minimal
draft of a complete analysis.

First import a necessary pieces of PyMVPA -- this time each bit individually.

::

  import numpy as np

  from mvpa2.generators.partition import OddEvenPartitioner
  from mvpa2.clfs.svm import LinearCSVMC
  from mvpa2.measures.base import CrossValidation
  from mvpa2.measures.searchlight import sphere_searchlight
  from mvpa2.testing.datasets import datasets
  from mvpa2.mappers.fx import mean_sample


For the sake of simplicity, let's use a small artificial dataset.

::

  # Lets just use our tiny 4D dataset from testing battery
  dataset = datasets['3dlarge']


Now it only takes three lines for a searchlight analysis.

::

  # setup measure to be computed in each sphere (cross-validated
  # generalization error on odd/even splits)
  cv = CrossValidation(LinearCSVMC(), OddEvenPartitioner())

  # setup searchlight with 2 voxels radius and measure configured above
  sl = sphere_searchlight(cv, radius=2, space='myspace',
                          postproc=mean_sample())

  # run searchlight on dataset
  sl_map = sl(dataset)

  print 'Best performing sphere error:', np.min(sl_map.samples)


If this analysis is done on a fMRI dataset using `NiftiDataset` the resulting
searchlight map (`sl_map`) can be mapped back into the original dataspace
and viewed as a brain overlay. :ref:`Another example <example_searchlight>`
shows a typical application of this algorithm.

.. Mention the fact that it also is a special `SensitivityAnalyzer`

.. seealso::
  The full source code of this example is included in the PyMVPA source distribution (`doc/examples/searchlight_minimal.py`).
