Merge pull request #106 from amarquand/dev

Dev

pcntoolkit/normative.py

@@ -1015,10 +1015,10 @@ def transfer(covfile, respfile, testcov=None, testresp=None, maskfile=None,
                 print("Warning: redundant batch effect parameterisation. Using HBR syntax")
 
             yhat, s2 = nm.predict(Xte, X, Y[:, i],
-                                  adaptcovfile = covfile,
-                                  adaptrespfile = respfile,
-                                  adaptvargroupfile = trbefile,
-                                  testvargroupfile = tsbefile,
+                                  adaptcov = X,
+                                  adaptresp = Y[:, i],
+                                  adaptvargroup = batch_effects_train,
+                                  testvargroup = batch_effects_test,
                                   **kwargs)
 
         if testcov is not None:
@@ -1060,7 +1060,6 @@ def transfer(covfile, respfile, testcov=None, testresp=None, maskfile=None,
         Z = (Yte - Yhat) / np.sqrt(S2)
 
         print("Evaluating the model ...")
-        #results = evaluate(Yte, Yhat, S2=S2, mY=mY, sY=sY)
         if meta_data and not warp:  
             results = evaluate(Yte, Yhat, S2=S2, mY=mY, sY=sY)
         else:    

pcntoolkit/normative_model/norm_blr.py

@@ -170,59 +170,72 @@ def predict(self, Xs, X=None, y=None, **kwargs):
         theta = self.theta # always use the estimated coefficients
         # remove from kwargs to avoid downstream problems
         kwargs.pop('theta', None)
-
-
 
         Phis = create_poly_basis(Xs, self._model_order)
 
         if X is None:
-            Phi =None
+            Phi = None
         else:
             Phi = create_poly_basis(X, self._model_order)
 
         # process variance groups for the test data
-        if 'testvargroupfile' in kwargs:
-            var_groups_test_file = kwargs.pop('testvargroupfile')
-            if var_groups_test_file.endswith('.pkl'):
-                var_groups_te = pd.read_pickle(var_groups_test_file)
-            else:
-                var_groups_te = np.loadtxt(var_groups_test_file)
+        if 'testvargroup' in kwargs:
+            var_groups_te = kwargs.pop('testvargroup')
         else:
-            var_groups_te = None
+            if 'testvargroupfile' in kwargs:
+                var_groups_test_file = kwargs.pop('testvargroupfile')
+                if var_groups_test_file.endswith('.pkl'):
+                    var_groups_te = pd.read_pickle(var_groups_test_file)
+                else:
+                    var_groups_te = np.loadtxt(var_groups_test_file)
+            else:
+                var_groups_te = None
 
         # process test variance covariates
-        if 'testvarcovfile' in kwargs:
-            var_cov_test_file = kwargs.get('testvarcovfile')
-            if var_cov_test_file.endswith('.pkl'):
-                var_cov_te = pd.read_pickle(var_cov_test_file)
-            else:
-                var_cov_te = np.loadtxt(var_cov_test_file)
+        if 'testvarcov' in kwargs:
+            var_cov_te = kwargs.pop('testvarcov')
         else:
-            var_cov_te = None
+            if 'testvarcovfile' in kwargs:
+                var_cov_test_file = kwargs.get('testvarcovfile')
+                if var_cov_test_file.endswith('.pkl'):
+                    var_cov_te = pd.read_pickle(var_cov_test_file)
+                else:
+                    var_cov_te = np.loadtxt(var_cov_test_file)
+            else:
+                var_cov_te = None
 
         # do we want to adjust the responses?
-        if 'adaptrespfile' in kwargs:
-            y_adapt = fileio.load(kwargs.pop('adaptrespfile'))
-            if len(y_adapt.shape) == 1:
-                y_adapt = y_adapt[:, np.newaxis]
+        if 'adaptresp' in kwargs:
+            y_adapt = kwargs.pop('adaptresp')
         else:
-            y_adapt = None
+            if 'adaptrespfile' in kwargs:
+                y_adapt = fileio.load(kwargs.pop('adaptrespfile'))
+                if len(y_adapt.shape) == 1:
+                    y_adapt = y_adapt[:, np.newaxis]
+            else:
+                y_adapt = None
 
-        if 'adaptcovfile' in kwargs:
-            X_adapt = fileio.load(kwargs.pop('adaptcovfile'))
+        if 'adaptcov' in kwargs:
+            X_adapt = kwargs.pop('adaptcov')
             Phi_adapt = create_poly_basis(X_adapt, self._model_order)
         else:
-            Phi_adapt = None
-
-        if 'adaptvargroupfile' in kwargs: 
-            var_groups_adapt_file = kwargs.pop('adaptvargroupfile')
-            if var_groups_adapt_file.endswith('.pkl'):
-                var_groups_ad = pd.read_pickle(var_groups_adapt_file)
+            if 'adaptcovfile' in kwargs:
+                X_adapt = fileio.load(kwargs.pop('adaptcovfile'))
+                Phi_adapt = create_poly_basis(X_adapt, self._model_order)
             else:
-                var_groups_ad = np.loadtxt(var_groups_adapt_file)
+                Phi_adapt = None
+
+        if 'adaptvargroup' in kwargs:
+            var_groups_ad = kwargs.pop('adaptvargroup')
         else:
-            var_groups_ad = None
-
+            if 'adaptvargroupfile' in kwargs: 
+                var_groups_adapt_file = kwargs.pop('adaptvargroupfile')
+                if var_groups_adapt_file.endswith('.pkl'):
+                    var_groups_ad = pd.read_pickle(var_groups_adapt_file)
+                else:
+                    var_groups_ad = np.loadtxt(var_groups_adapt_file)
+            else:
+                var_groups_ad = None
 
         if y_adapt is None:
             yhat, s2 = self.blr.predict(theta, Phi, y, Phis,