[pymvpa] request for review OUPS WRONG PATCH

Valentin Haenel valentin.haenel at gmx.de
Fri Apr 24 09:14:41 UTC 2009


er, i think i attached the wrong patch. fracking git commit --amend :-)

here, use this one instead.

V-

* Valentin Haenel <valentin.haenel at gmx.de> [090424]:
> Hi,
> 
> i am working with FIR betas. Thus i have 8 timebins for which i need to rerun
> the searchlight. This should result in 8 accuracy maps, one for each timebin.
> 
> Although i could just iterate over datasets with the searchlight call, however
> in that case the searchlight indexes would be recomputed for each timebin.
> 
> I modified the Searchlight to operate also on lists of datasets, whereby the
> searchlight indexes are stored and used again for each timebin after the first
> this works for my particular use case, and reduces the computing time somewhat.
> 
> I patched the code such that the API remains backward compatible, and existing
> code should not break.
> 
> I do not know how else the Searchlight is used, so i am not sure if my
> modifications break something, and unfortunately the unit tests are still
> segfaulting on this end, and i'm not sure how to run only the unit test for the
> Searchlight.
> 
> Also i ran the code through pylint, and it complained mainly about functions
> prefixed with __.
> 
> I would also like to check that the datasets are of the same brain, not sure how
> to do that though, maybe check equality of the masks?
> 
> Furthermore i think there is a bug on line 84, where it says FIXME, however if
> this is the case i would probably send a separate patch for that.
> 
> Please have a look at the code.
> 
> cheers
> 
> V-
> 

> From 11e4fc5ad0f70a428ccb3eac67360e32bb5cf30c Mon Sep 17 00:00:00 2001
> From: Valentin Haenel <valentin.haenel at gmx.de>
> Date: Wed, 22 Apr 2009 16:13:08 +0200
> Subject: [PATCH] modifying searchlight to work with lists of datasets
> 
> ---
>  mvpa/measures/searchlight.py |  107 ++++++++++++++++++++++++++++++++---------
>  1 files changed, 83 insertions(+), 24 deletions(-)
> 
> diff --git a/mvpa/measures/searchlight.py b/mvpa/measures/searchlight.py
> index 5171dda..803e49b 100644
> --- a/mvpa/measures/searchlight.py
> +++ b/mvpa/measures/searchlight.py
> @@ -64,62 +64,121 @@ class Searchlight(DatasetMeasure):
>          self.__datameasure = datameasure
>          self.__radius = radius
>          self.__center_ids = center_ids
> -
> +        self.__searchlight_indexes = dict()
>  
>      __doc__ = enhancedDocString('Searchlight', locals(), DatasetMeasure)
>  
> -
> -    def _call(self, dataset):
> -        """Perform the spheresearch.
> -        """
> +    def __check_dataset_type(self, dataset):
> +        """ check that the dataset is mapped and has a metric """
>          if not isinstance(dataset, MappedDataset) \
>                 or dataset.mapper.metric is None:
>              raise ValueError, "Searchlight only works with MappedDatasets " \
>                                "that has metric assigned."
>  
> +
> +
> +    def __setup_housekeeping(self, dataset):
> +        """ setup house keeping for state monitoring and debugging """
>          if self.states.isEnabled('spheresizes'):
>              self.spheresizes = []
>  
>          if __debug__:
> -            nspheres = dataset.nfeatures
> -            sphere_count = 0
> -
> -        # collect the results in a list -- you never know what you get
> -        results = []
> +            self.nspheres = dataset.nfeatures
> +            self.sphere_count = 0
>  
>          # decide whether to run on all possible center coords or just a provided
>          # subset
>          if not self.__center_ids == None:
> -            generator = self.__center_ids
> +            self.generator = self.__center_ids
>          else:
> -            generator = xrange(dataset.nfeatures)
> +            self.generator = xrange(dataset.nfeatures)
> +
> +
> +
> +    def __get_searchlight_indexes(self,voxel_id, dataset):
> +        """ lazily compute searchlight indexes """
> +        # if the indexes for this voxel exist already return them
> +        if self.__searchlight_indexes.has_key(voxel_id):
> +            return self.__searchlight_indexes.get(voxel_id)
> +        # otherwise compute it now, and store for later
> +        else:
> +            indexes = dataset.mapper.getNeighbors(voxel_id, self.__radius)
> +            self.__searchlight_indexes[voxel_id] = indexes
> +
> +            # store the size of the sphere dataset
> +            if self.states.isEnabled('spheresizes'):
> +                self.spheresizes.append(indexes.nfeatures)
> +
> +            return indexes
> +
> +
> +
> +    def __single_dataset(self, dataset):
> +        """ perform the searchlight on a single dataset """
> +        self.__check_dataset_type(dataset)
> +
> +        self.__setup_housekeeping(dataset)
> +
> +        # collect the results in a list -- you never know what you get
> +        results = []
>  
>          # put spheres around all features in the dataset and compute the
>          # measure within them
> -        for f in generator:
> -            sphere = dataset.selectFeatures(
> -                dataset.mapper.getNeighbors(f, self.__radius),
> -                plain=True)
> +        for f in self.generator:
> +            sphere = dataset.selectFeatures(self.__get_searchlight_indexes(f,
> +                dataset), plain=True)
>  
>              # compute the datameasure and store in results
>              measure = self.__datameasure(sphere)
>              results.append(measure)
>  
> -            # store the size of the sphere dataset
> -            if self.states.isEnabled('spheresizes'):
> -                self.spheresizes.append(sphere.nfeatures)
> -
>              if __debug__:
> -                sphere_count += 1
> +                self.sphere_count += 1
>                  debug('SLC', "Doing %i spheres: %i (%i features) [%i%%]" \
> -                    % (nspheres,
> -                       sphere_count,
> +                    % (self.nspheres,
> +                       self.sphere_count,
>                         sphere.nfeatures,
> -                       float(sphere_count)/nspheres*100,), cr=True)
> +                       float(self.sphere_count)/self.nspheres*100,), cr=True)
>  
>          if __debug__:
>              debug('SLC', '')
>  
> +        return results
> +
> +
> +
> +    def __multiple_datasets(self, datasets):
> +        """ perform the searchlight on multiple datasets, reusing the voxel
> +        indices of the searchlights """
> +
> +        # check the correct dataset type for all datasets,
> +        # since we don't want it to crach halfway through
> +        for d in datasets:
> +            self.__check_dataset_type(d)
> +
> +        self.__setup_housekeeping(datasets[0])
> +
> +        # preallocate enough results lists
> +        results = []
> +
> +        # now do the actual analysis
> +
> +        if __debug__:
> +            debug('SLC', "found %i datasets", (len(datasets)))
> +        for (i, d) in enumerate(datasets):
> +            results.append(self.__single_dataset(d))
> +
> +        return results
> +
> +
> +
> +    def _call(self, dataset):
> +        """Perform the spheresearch. """
> +        if isinstance(dataset,list):
> +            results = self.__multiple_datasets(dataset)
> +        else:
> +            results = self.__single_dataset(dataset)
> +
>          # charge state
>          self.raw_results = results
>  
> -- 
> 1.5.6.5
> 

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