v0.15: Fixed n=1 test bug and bugs with MSLL

pcntoolkit/normative.py

@@ -130,6 +130,13 @@ def evaluate(Y, Yhat, S2=None, mY=None, sY=None,
 
     feature_num = Y.shape[1]
 
+    # Remove metrics that cannot be computed with only a single data point 
+    if Y.shape[0] == 1:
+        if 'MSLL' in metrics:
+            metrics.remove('MSLL')
+        if 'SMSE' in metrics:
+            metrics.remove('SMSE')
+
     # find and remove bad variables from the response variables
     nz = np.where(np.bitwise_and(np.isfinite(Y).any(axis=0),
                                  np.var(Y, axis=0) != 0))[0]
@@ -163,8 +170,8 @@ def evaluate(Y, Yhat, S2=None, mY=None, sY=None,
         if ((S2 is not None) and (mY is not None) and (sY is not None)):
             MSLL = np.zeros(feature_num)
             MSLL[nz] = compute_MSLL(Y[:,nz], Yhat[:,nz], S2[:,nz], 
-                                    mY[nz].reshape(-1,1).T, 
-                                    (sY[nz]**2).reshape(-1,1).T)
+                                    mY.reshape(-1,1).T, 
+                                    (sY**2).reshape(-1,1).T)
             results['MSLL'] = MSLL
 
     return results
@@ -292,18 +299,18 @@ def estimate(covfile, respfile, **kwargs):
         run_cv = False
         cvfolds = 1
         Xte = fileio.load(testcov)
-        testids = range(X.shape[0], X.shape[0]+Xte.shape[0])
         if len(Xte.shape) == 1:
-            Xte = Xte[:, np.newaxis]
+            Xte = Xte[:, np.newaxis].T
         if testresp is not None:
             Yte, testmask = load_response_vars(testresp, maskfile)
             if len(Yte.shape) == 1:
-                Yte = Yte[:, np.newaxis]
+                Yte = Yte[:, np.newaxis].T
         else:
             sub_te = Xte.shape[0]
             Yte = np.zeros([sub_te, Nmod])
 
         # treat as a single train-test split
+        testids = range(X.shape[0], X.shape[0]+Xte.shape[0])
         splits = CustomCV((range(0, X.shape[0]),), (testids,))
 
         Y = np.concatenate((Y, Yte), axis=0)

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 setup(name='pcntoolkit',
-      version='0.14',
+      version='0.15',
       description='Predictive Clinical Neuroscience toolkit',
       url='http://github.com/amarquand/nispat',
       author='Andre Marquand',

tests/unit_tests.py

@@ -9,14 +9,14 @@
 # To fully evaluate the user test cases, this should be run in two ways:
 
 # 1. as a package
-from pcntoolkit.normative import estimate
-from pcntoolkit.normative_parallel import execute_nm, collect_nm, delete_nm
+#from pcntoolkit.normative import estimate
+#from pcntoolkit.normative_parallel import execute_nm, collect_nm, delete_nm
 
 ## 2. by appending to the path
 ##sys.path.clear()
-#sys.path.append('/home/preclineu/andmar/sfw/pcntoolkit/pcntoolkit')
-#from normative import estimate
-#from normative_parallel import execute_nm, collect_nm, delete_nm
+sys.path.append('/home/preclineu/andmar/sfw/PCNtoolkit/pcntoolkit')
+from normative import estimate
+from normative_parallel import execute_nm, collect_nm, delete_nm
 
 # ---------------- Config parameters -----------------------------------------
 
@@ -25,7 +25,7 @@
 python_path='/home/preclineu/andmar/sfw/anaconda3/envs/py36/bin/python'
 data_dir = '/home/preclineu/andmar/data/nispat_unit_test_data/'
 test_dir = '/home/preclineu/andmar/py.sandbox/unittests/unit_test_results'
-alt_alg = 'blr'             # alogrithm to test in addition to GPR
+alt_alg = 'blr'             # algorithm to test in addition to GPR
 
 # cluster paramateters
 job_name = 'nm_unit_test'