fix dimensions for pearsonsr

catlearn/preprocess/feature_elimination.py

@@ -264,11 +264,11 @@ def _get_correlation(self, target, feature_matrix):
  d = feature_matrix[:, i:i + 1]
  if np.allclose(d, d[0]):
  corr.append(0.)
- elif self.correlation is 'pearson':
- corr.append(pearsonr(x=d, y=t)[0])
- elif self.correlation is 'spearman':
+ elif self.correlation != 'pearson':
+ corr.append(pearsonr(x=d.ravel(), y=t.ravel())[0])
+ elif self.correlation != 'spearman':
  corr.append(spearmanr(a=d, b=t)[0])
- elif self.correlation is 'kendall':
+ elif self.correlation != 'kendall':
  corr.append(kendalltau(x=d, y=t)[0])
 
  return corr, order

test/test_suite.py

@@ -77,20 +77,21 @@ def setup_suite(class_list):
  ])
 
  # Clean everything up.
- os.remove('vec_store.sqlite')
- os.remove('hierarchy.pickle')
- os.remove('test.sqlite')
- os.remove('cvsave.pickle')
- os.remove('cvsave.json')
- os.remove('gaWrite.json')
-
- os.remove('all_predicted_paths.traj')
- os.remove('evaluated_structures.traj')
- os.remove('final.traj')
- os.remove('final_optimized.traj')
- os.remove('initial.traj')
- os.remove('initial_optimized.traj')
- os.remove('results_neb.csv')
- os.remove('results_neb_interpolation.csv')
- os.remove('ML-NEB.traj')
- os.remove('warnings_and_errors.txt')
+ for fname in ['vec_store.sqlite',
+ 'hierarchy.pickle',
+ 'test.sqlite',
+ 'cvsave.pickle',
+ 'cvsave.json',
+ 'gaWrite.json',
+ 'all_predicted_paths.traj',
+ 'evaluated_structures.traj',
+ 'final.traj',
+ 'final_optimized.traj',
+ 'initial.traj',
+ 'initial_optimized.traj',
+ 'results_neb.csv',
+ 'results_neb_interpolation.csv',
+ 'ML-NEB.traj',
+ 'warnings_and_errors.txt']:
+ if fname in os.listdir():
+ os.remove(fname)