Add tests for mvpresults

skbold/core/tests/test_mvp_between.py

@@ -153,10 +153,3 @@ def test_mvp_between_regress_out_confounds(mvp1c, var):
     mvp1c.add_y(fpath, col_name='var_categorical', index_col=0, remove=999)
     mvp1c.regress_out_confounds(fpath, var)
 """
-
-# def test_teardown():
-
-    # setup code
-    # spaths = glob(op.join(testdata_path, 'mock_subjects',
-    #                      'sub*', 'run1.feat'))
-    # _ = [shutil.rmtree(s) for s in spaths]

skbold/postproc/mvp_results.py

@@ -212,8 +212,12 @@ def _extract_values_from_pipeline(self, pipe):
 
         if pipe.__class__.__name__ == 'GridSearchCV':
             pipe = pipe.best_estimator_
-
-        pipe_steps = copy(pipe.named_steps)
+        elif pipe.__class__.__name__ != 'Pipeline':
+            # hack to allow non-pipelines
+            pipe.idx_ = np.ones(pipe.coef_.size, dtype=bool)
+            pipe_steps = {pipe.__class__.__name__: pipe}
+        else:
+            pipe_steps = copy(pipe.named_steps)
 
         for name, step in pipe_steps.items():
 

skbold/postproc/tests/test_mvp_results.py

@@ -4,3 +4,40 @@
 import os
 import pytest
 import numpy as np
+from skbold.postproc import MvpResultsClassification
+from sklearn.cross_validation import StratifiedKFold
+from sklearn.svm import SVC
+
+dpath = op.join(testdata_path, 'mock_subjects', 'sub*', 'run1.feat', 'stats')
+bmask = op.join(roidata_path, 'GrayMatter.nii.gz')
+source = dict()
+source['Contrast1'] = {'path': op.join(testdata_path, 'mock_subjects',
+                                       'sub*', 'run1.feat', 'stats',
+                                       'cope1.nii.gz')}
+
+mvp = MvpBetween(source=source, subject_idf='sub???', mask=bmask)
+mvp.create()
+fpath = op.join(testdata_path, 'sample_behav.tsv')
+mvp.add_y(fpath, col_name='var_categorical', index_col=0, remove=999)
+
+
+def test_mvp_results_init():
+
+    clf = SVC(kernel='linear')
+    folds = StratifiedKFold(mvp.y, n_folds=2)
+    mvpr = MvpResultsClassification(mvp=mvp, n_iter=2, feature_scoring='fwm',
+                                    out_path=op.join(testdata_path))
+
+    for train_idx, test_idx in folds:
+        train_X, test_X = mvp.X[train_idx, :], mvp.X[test_idx, :]
+        train_y, test_y = mvp.y[train_idx], mvp.y[test_idx]
+        clf.fit(train_X, train_y)
+        pred = clf.predict(test_X)
+        mvpr.update(test_idx, pred, pipeline=clf)
+
+    mvpr.compute_scores()
+    mvpr.write(feature_viz=True)
+
+    for f in ['Contrast1.nii.gz', 'results.tsv', 'confmat.npy']:
+        assert(op.isfile(op.join(testdata_path, f)))
+        os.remove(op.join(testdata_path, f))