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- Committer: Amy Skerry
- Date: 2013-06-18 15:02:23 UTC
- Revision ID: amy.skerry@gmail.com-20130618150223-0q010udy3ka9u3lq
adding additional feature selection options to main classification stream
- files added:
- mvpaptb/compare_mvpastreams.m
- files modified:
- mvpaptb/feature_select_AES.m
added
removed
mvpaptb/feature_select_allcondandrestF.m
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function [subj] = feature_select_allcondandrestF(subj,data_patin,regsname,selsgroup, numfolds, fixed, fixednum, varargin)
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%performs voxel selection based on anova of all conditions including rest
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%as a final condition
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%
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% [SUBJ] = FEATURE_SELECT(SUBJ,DATA_PATIN,REGSNAME,SELSGROUP,...)
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%
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% Calls a statmap generation function multiple times, using
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% a different selector each time. This creates a group of
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% statmaps, which are then thresholded to create a group of
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% boolean masks, ready for use in no-peeking
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% cross-validation classification.
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%
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% Adds the following objects:
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% - pattern group of statmaps called NEW_MAP_PATNAME
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% - mask group based on the statmaps called
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% sprintf('%s%i',NEW_MASKSTEM,THRESH)
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%
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% DATA_PATIN should be the name of the pattern object that
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% contains voxel (or other feature) values that you want to
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% create a mask of. If DATA_PATIN is a group_name, then this
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% will use a different member of the group for each
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% iteration.
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%
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% REGSNAME should be a binary nConds x nTimepoints 1-of-n matrix
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%
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% SELSGROUP should be the name of a selectors group, such as
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% created by create_xvalid_indices
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%
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% For each iteration: call the ANOVA on the DATA_PATIN data,
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% which will produce a statmap, employing only the TRs
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% labelled with a 1 in the selector for that iteration
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%
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% NEW_MAP_PATNAME (optional, default = DATA_PATIN +
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% STRIPPED_NAME). The name of the new statmap pattern group
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% to be created. By default, this will be 'anova' if
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% STATMAP_FUNCT = 'statmap_anova' etc.
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%
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% NEW_MASKSTEM (optional, default = DATA_PATIN +
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% 'anovathresh'). The name of the new thresholded boolean
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% mask group to be created from the ANOVA statmap. You'll
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% need to create multiple mask groups if you want to try out
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% multiple thresholds, so adding the threshold to the name
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% is a good idea
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%
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% THRESH (optional, default = 0.05). Voxels that don't meet
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% this criterion value don't get included in the boolean
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% mask that gets created from the ANOVA statmap. If THRESH =
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% [], the thresholding doesn't get run
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%
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% STATMAP_FUNCT (optional, default = 'statmap_anova'). Feed
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% in a function name and this will create a function handle
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% to that and use it to create the statmaps instead of
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% statmap_anova
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%
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% STATMAP_ARG (optional, default = []). If you're using an
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% alternative voxel selection method, you can feed it a
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% single argument through this
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%
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% Need to implement a THRESH_TYPE argument (for p vs F
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% values), which would also set the toggle differently xxx
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%
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% e.g. subj = feature_select( ...
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% subj,'epi_z','conds','runs_nmo_xvalid','thresh',0.001)
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% License:
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%=====================================================================
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%
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% This is part of the Princeton MVPA toolbox, released under
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% the GPL. See http://www.csbmb.princeton.edu/mvpa for more
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% information.
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%
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% The Princeton MVPA toolbox is available free and
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% unsupported to those who might find it useful. We do not
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% take any responsibility whatsoever for any problems that
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% you have related to the use of the MVPA toolbox.
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%
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% ======================================================================
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defaults.new_map_patname = sprintf('');
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defaults.new_maskstem = sprintf('%s_thresh',data_patin);
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defaults.thresh = 0.05;
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defaults.statmap_funct = 'statmap_anova';
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defaults.statmap_arg = struct([]);
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args = propval(varargin,defaults);
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if isempty(args.new_map_patname)
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% get the name of the function being run, e.g. 'statmap_anova' -> 'anova'
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stripped_name = strrep(args.statmap_funct,'statmap_','');
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args.new_map_patname = sprintf('%s_%s',data_patin,stripped_name);
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end
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% append the thresh to the end of the name
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args.new_maskstem = sprintf( ...
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'%s%s',args.new_maskstem,num2str(args.thresh));
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% Find the selectors within the specified group
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selnames = find_group(subj,'selector',selsgroup);
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nIterations = length(selnames);
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[data_patnames isgroup] = find_group_single(subj,'pattern',data_patin,'repmat_times',nIterations);
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if length(data_patnames) ~= length(selnames)
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error('Different number of patterns and selectors');
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end
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if nIterations == 0
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error('No selectors in %s group',selsgroup);
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end
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% % this warning used to be here to remind people of the
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% % existence of peek_feature_select, but since there are good
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% % reasons why one might want to have just one selector
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% % without using peek_feature_select, i took it out
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% if nIterations == 1
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% warning('You''re only calling the anova once because you have one selector - use peek_feature_select instead?');
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% end
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if ~ischar(args.statmap_funct)
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error('The statmap function name has to be a string');
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end
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disp( sprintf('Starting %i %s iterations',nIterations,args.statmap_funct) );
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for n=1:nIterations
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fprintf(' %i',n);
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% Get the pattern for this iteration
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cur_data_patname = data_patnames{n};
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% Get the selector name for this iteration
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cur_selname = selnames{n};
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% Name the new statmap pattern and thresholded mask that will be created
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cur_maskname = sprintf('%s_%i',args.new_maskstem,n);
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cur_map_patname = sprintf('%s_%i',args.new_map_patname,n);
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% if a pattern with the same name already exists, it
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% will trigger an error later in init_object, but we
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% want to catch it here to save running the entire
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% statmap first
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if exist_object(subj,'pattern',cur_map_patname)
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error('A pattern called %s already exists',cur_map_patname);
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end
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if ~isempty(args.statmap_arg) && ~isstruct(args.statmap_arg)
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warning('Statmap_arg is supposed to be a struct');
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end
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% Add the current iteration number to the extra_arg, just in case
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% it's useful
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args.statmap_arg(1).cur_iteration = n;
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% Create a handle for the statmap function handle and then run it
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% to generate the statmaps
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statmap_fh = str2func(args.statmap_funct);
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subj = statmap_fh(subj,cur_data_patname,regsname,cur_selname,cur_map_patname,args.statmap_arg);
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subj = set_objfield(subj,'pattern',cur_map_patname,'group_name',args.new_map_patname);
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if fixed
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statvect=get_mat(subj, 'pattern', cur_map_patname);
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[sortedValues,sortIndex] = sort(statvect,'ascend');
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if fixednum<length(sortIndex)
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lowerbound=statvect(sortIndex(fixednum));
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else
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lowerbound=statvect(sortIndex(end));
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end
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cur_maskname=[data_patin '_top' num2str(fixednum) '_' num2str(n)];
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args.new_maskstem=[data_patin '_top' num2str(fixednum)];
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subj = create_thresh_mask(subj,cur_map_patname,cur_maskname,lowerbound);
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% subj = init_object(subj,'mask',cur_maskname);
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% subj = set_mat(subj,'mask',cur_maskname,statvect)
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subj = set_objfield(subj,'mask',cur_maskname,'group_name',args.new_maskstem);
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end
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if ~isempty(args.thresh) && ~fixed
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% Now, create a new thresholded binary mask from the p-values
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% statmap pattern returned by the anova
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subj = create_thresh_mask(subj,cur_map_patname,cur_maskname,args.thresh);
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subj = set_objfield(subj,'mask',cur_maskname,'group_name',args.new_maskstem);
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end
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end % i nIterations
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disp(' ');
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disp( sprintf('Pattern statmap group ''%s'' and mask group ''%s'' created by feature_select', ...
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args.new_map_patname,args.new_maskstem) );
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