Merge pull request #190 from nno/_enh/new_svm

NF: support for fitcsvm

mvpa/cosmo_check_external.m

@@ -16,7 +16,8 @@
 %                          'xunit'     xUnit unit test framework
 %                          'moxunit'   MOxUnit unit test framework
 %                          'matlabsvm' SVM classifier in matlab stats
-%                                      toolbox
+%                                      toolbox (prior 2018a)
+%                          'matlabcsvm'
 %                          'svm'       Either matlabsvm or libsvm
 %                          '@{name}'   Matlab toolbox {name}
 %                          It can also be '-list', '-tic', '-toc',' or
@@ -477,7 +478,9 @@
     externals.matlabsvm.is_present=@() (has_toolbox('stats') || ...
                                             has_toolbox('bioinfo')) && ...
                                         has('svmtrain') && ...
-                                        has('svmclassify');
+                                        has('svmclassify') && ...
+                                        is_matlab_prior_2018a();
+
     externals.matlabsvm.is_recent=yes;
     externals.matlabsvm.conflicts.neuroelf=@() isequal(...
                                                 path_of('svmtrain'),...
@@ -488,6 +491,14 @@
     externals.matlabsvm.label='Matlab stats or bioinfo toolbox';
     externals.matlabsvm.url='http://www.mathworks.com';
 
+    externals.matlabcsvm.is_present=@() cosmo_wtf('is_matlab') && ...
+                                        has('fitcsvm');
+
+    externals.matlabcsvm.is_recent=yes;
+    externals.matlabcsvm.label='Matlab stats or bioinfo toolbox';
+    externals.matlabcsvm.url='http://www.mathworks.com';
+
+
     externals.svm={'libsvm', 'matlabsvm'}; % need either
 
     externals.distatis.is_present=yes;
@@ -559,6 +570,24 @@
     externals.modox.authors={'N. N. Oosterhof'};
     externals.modox.url='https://github.com/MOdox/MOdox';
 
+
+function tf=is_matlab_prior_2018a()
+    this_version=cosmo_wtf('version_number');
+
+    matlab_pivot=[9, 4]; % verison 2018a
+    n_elem = numel(matlab_pivot);
+
+    delta = this_version(1:n_elem) - matlab_pivot;
+    if all(delta==0)
+        % this is version 2018a
+        tf=false;
+    else
+        idx=find(delta~=0,1);
+        % positive delta means that the current matlab version
+        % is later than 2018a
+        tf=delta(idx)<0;
+    end
+
 function tf=has_octave_package(label)
     tf=false;
     if ~cosmo_wtf('is_octave')

mvpa/cosmo_classify_matlabcsvm.m

@@ -0,0 +1,127 @@
+function predicted=cosmo_classify_matlabcsvm(samples_train, targets_train, samples_test, opt)
+% svm classifier wrapper (around fitcsvm)
+%
+% predicted=cosmo_classify_matlabcsvm(samples_train, targets_train, samples_test, opt)
+%
+% Inputs:
+%   samples_train      PxR training data for P samples and R features
+%   targets_train      Px1 training data classes
+%   samples_test       QxR test data
+%   opt                struct with options. supports any option that
+%                      fitcsvm supports
+%
+% Output:
+%   predicted          Qx1 predicted data classes for samples_test
+%
+% Notes:
+%  - this function uses Matlab's builtin fitcsvm function, which was the
+%    successor of svmtrain.
+%  - Matlab's SVM classifier is rather slow, especially for multi-class
+%    data (more than two classes). When classification takes a long time,
+%    consider using libsvm.
+%  - for a guide on svm classification, see
+%      http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
+%    Note that cosmo_crossvalidate and cosmo_crossvalidation_measure
+%    provide an option 'normalization' to perform data scaling
+%  - As of Matlab 2017a (maybe earlier), Matlab gives the warning that
+%      'svmtrain will be removed in a future release. Use fitcsvm instead.'
+%    however fitcsvm gives different results than svmtrain; as a result
+%    cosmo_classify_matlabcsvm gives different results than
+%    cosmo_classify_matlabsvm.
+%
+% See also fitcsvm, svmclassify, cosmo_classify_matlabsvm.
+%
+% #   For CoSMoMVPA's copyright information and license terms,   #
+% #   see the COPYING file distributed with CoSMoMVPA.           #
+
+    if nargin<4, opt=struct(); end
+
+    [ntrain, nfeatures]=size(samples_train);
+    [ntest, nfeatures_]=size(samples_test);
+    ntrain_=numel(targets_train);
+
+    if nfeatures~=nfeatures_ || ntrain_~=ntrain
+        error('illegal input size');
+    end
+
+    if ~cached_has_matlabcsvm()
+        cosmo_check_external('matlabcsvm');
+    end
+
+    [class_idxs,classes]=cosmo_index_unique(targets_train(:));
+    nclasses=numel(classes);
+
+    if nfeatures==0 || nclasses==1
+        % matlab's svm cannot deal with empty data, so predict all
+        % test samples as the class of the first sample
+        predicted=targets_train(1) * ones(ntest,1);
+        return
+    end
+
+
+    opt_cell=opt2cell(opt);
+
+    % number of pair-wise comparisons
+    ncombi=nclasses*(nclasses-1)/2;
+
+    % allocate space for all predictions
+    all_predicted=NaN(ntest, ncombi);
+
+    % Consider all pairwise comparisons (over classes)
+    % and store the predictions in all_predicted
+    pos=0;
+    for k=1:(nclasses-1)
+        for j=(k+1):nclasses
+            pos=pos+1;
+            % classify between 2 classes only
+            idxs=cat(1,class_idxs{k},class_idxs{j});
+
+
+            model=fitcsvm(samples_train(idxs,:), targets_train(idxs), ...
+                                    opt_cell{:});
+
+            pred=predict(model, samples_test(idxs, :));
+            all_predicted(idxs,pos)=pred;
+        end
+    end
+
+    assert(pos==ncombi);
+
+    % find the classes that were predicted most often.
+    % ties are handled by cosmo_winner_indices
+    [winners, test_classes]=cosmo_winner_indices(all_predicted);
+
+    predicted=test_classes(winners);
+
+
+
+    % helper function to convert cell to struct
+function opt_cell=opt2cell(opt)
+
+    if isempty(opt)
+        opt_cell=cell(0);
+        return;
+    end
+
+    fns=fieldnames(opt);
+
+    n=numel(fns);
+    opt_cell=cell(1,2*n);
+    for k=1:n
+        fn=fns{keep_id(k)};
+        opt_cell{k*2-1}=fn;
+        opt_cell{k*2}=opt.(fn);
+    end
+
+
+function tf=cached_has_matlabcsvm()
+    persistent cached_tf;
+
+    if isequal(cached_tf,true)
+        tf=true;
+        return
+    end
+
+    cached_tf=cosmo_check_external('matlabcsvm');
+    tf=cached_tf;
+

mvpa/cosmo_classify_matlabsvm.m

@@ -20,9 +20,18 @@
 %  - for a guide on svm classification, see
 %      http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
 %    note that cosmo_crossvalidate and cosmo_crossvalidation_measure
-%    provide an option 'normalization' to perform data scaling
+%    provide an option 'normalization' to perform data scaling.
+%  - As of Matlab 2017a (maybe earlier), Matlab gives the warning that
+%      'svmtrain will be removed in a future release. Use fitcsvm instead.'
+%    however fitcsvm gives different results than svmtrain; as a result
+%    cosmo_classify_matlabcsvm gives different results than
+%    cosmo_classify_matlabsvm. In this function the warning message is
+% .  suppressed.
+%  - As of Matlab 2018a, this function cannot be used anymore. Use
+%    cosmo_classify_matlabcsvm instead.
 %
-% See also svmtrain, svmclassify, cosmo_classify_svm, cosmo_classify_libsvm
+% See also svmtrain, svmclassify, cosmo_classify_svm,
+%          cosmo_classify_libsvm, cosmo_classify_matlabcsvm
 %
 % #   For CoSMoMVPA's copyright information and license terms,   #
 % #   see the COPYING file distributed with CoSMoMVPA.           #

mvpa/cosmo_classify_matlabsvm_2class.m

@@ -27,10 +27,15 @@
 %    provide an option 'normalization' to perform data scaling
 %  - As of Matlab 2017a (maybe earlier), Matlab gives the warning that
 %      'svmtrain will be removed in a future release. Use fitcsvm instead.'
-%    however fitcsvm gives different results than svmtrain. In this
-%    function the warning message is suppressed.
+%    however fitcsvm gives different results than svmtrain; as a result
+%    cosmo_classify_matlabcsvm gives different results than
+%    cosmo_classify_matlabsvm. In this function the warning message is
+% .  suppressed.
+%  - As of Matlab 2018a, this function cannot be used anymore. Use
+%    cosmo_classify_matlabcsvm instead.
 %
-% See also svmtrain, svmclassify, cosmo_classify_matlabsvm
+% See also svmtrain, svmclassify, cosmo_classify_matlabsvm,
+%          cosmo_classify_matlabcsvm
 %
 % #   For CoSMoMVPA's copyright information and license terms,   #
 % #   see the COPYING file distributed with CoSMoMVPA.           #
@@ -91,17 +96,15 @@
             % only show warning once (by default) if this is a
             % a stats:obsolete message
             suffix=['CoSMoMVPA note: the more recent '...
-                        'fitcsvm / svmsmoset classifiers'...
+                        'fitcsvm / svmsmoset classifiers produce '...
                         'different results '...
                         'than the older svmtrain function. '...
-                        'Currently there is no support '...
-                        'in CoSMoMVPA for using fitcsvm in a '...
-                        'classifier'];
+                        'To use fitcsvm, use cosmo_classify_matlabcsvm'];
             cosmo_warning('%s\n%s',warning_msg,suffix);
         elseif ~strcmp(warning_id,orig_lastid)
             % new warning was issued , different from stats:obsolete one;
             % show warning message
-            warning(warning_id,warning_msg);
+            cosmo_warning(warning_id,warning_msg);
         end
 
 

mvpa/cosmo_crossvalidation_measure.m

@@ -313,6 +313,7 @@
     folds=cat(1,fold_cell{:});
     fold_targets=cat(1,target_cell{:});
 
+    2;
 
 
 function winner_pred=compute_winner_predictions(pred)

tests/test_classify.m

@@ -9,6 +9,52 @@
     end
     initTestSuite;
 
+function test_general_classifiers_strong_signal()
+    % note: part of these checks are also implemented in 
+    % general_test_classifier (included below)
+
+    cfy_cell={{@cosmo_classify_lda,''},...
+              {@cosmo_classify_nn,''},...
+              {@cosmo_classify_naive_bayes,''},...
+              {@cosmo_classify_libsvm,'libsvm'},...
+              {@cosmo_classify_matlabsvm,'matlabsvm'},...
+              {@cosmo_classify_matlabcsvm,'matlabcsvm'}};
+
+    n_cfy=numel(cfy_cell);
+
+    no_information=0;
+    strong_information=2+rand();
+    for sigma=[no_information, strong_information]
+        [tr_s,tr_t, te_s, te_t]=generate_informative_data(sigma);
+
+        for k=1:n_cfy
+            cfy=cfy_cell{k}{1};
+            predictor_func=@()cfy(tr_s, tr_t, te_s);
+
+            external=cfy_cell{k}{2};
+            if ~strcmp(external,'')
+                if ~cosmo_check_external(external,false)
+                    assertExceptionThrown(predictor_func,'*');
+                    continue;
+                end
+            end
+
+            pred = cfy(tr_s, tr_t, te_s);
+            acc = mean(pred==te_t);
+
+            assertEqual(numel(pred), 400);
+
+            if sigma==no_information
+                assertTrue(0.4 < acc && acc < 0.6);
+            elseif sigma==strong_information
+                assertTrue(acc > 0.9);
+            else
+                assertFalse(true);
+            end
+        end
+    end
+
+
 function test_classify_lda
     cfy=@cosmo_classify_lda;
     handle=get_predictor(cfy);
@@ -113,6 +159,24 @@
     assert_predictions_equal(handle,[1 3 9 7 6 6 9 3 7 5 6 6 4 ...
                                     1 7 7 7 7 1 7 7 1 7 6 7 1 9]');
     general_test_classifier(cfy);
+
+function test_classify_matlabcsvm
+    warning_state=cosmo_warning();
+    cleaner=onCleanup(@()cosmo_warning(warning_state));
+    cosmo_warning('off');
+
+    cfy=@cosmo_classify_matlabcsvm;
+    handle=get_predictor(cfy);
+    if ~cosmo_check_external('matlabcsvm',false)
+        assert_throws_illegal_input_exceptions(cfy);
+        assertExceptionThrown(handle,'');
+        notify_test_skipped('matlabcsvm');
+        return;
+    end
+
+    assert_predictions_equal(handle,[1 2 3 4 5 6 7 8 9 1 2 3 4 ...
+                                    5 6 7 8 9 1 2 3 4 5 6 7 8 9]');
+    general_test_classifier(cfy);    
 
 function test_classify_matlabsvm_2class
     warning_state=cosmo_warning();
@@ -287,7 +351,7 @@ function assert_chance_null_data(cfy)
     assert_accuracy_in_range(cfy, 0, 0.3, 0.7);
 
 function assert_above_chance_informative_data(cfy)
-    assert_accuracy_in_range(cfy, 10, 0.8, 1);
+    assert_accuracy_in_range(cfy, 2, 0.8, 1);
 
 function assert_accuracy_in_range(cfy, sigma, min_val, max_val)
     [tr_s,tr_t, te_s, te_t]=generate_informative_data(sigma);