reverted changes to SHASH

pcntoolkit/normative.py

@@ -447,7 +447,7 @@ def estimate(covfile, respfile, **kwargs):
             kwargs['trbefile'] = 'be_kfold_tr_tempfile.pkl'
             kwargs['tsbefile'] = 'be_kfold_ts_tempfile.pkl'
 
-        # estimate the models for all response variables
+        # estimate the models for all subjects
         for i in range(0, len(nz)):              
             print("Estimating model ", i+1, "of", len(nz))
             nm = norm_init(Xz_tr, Yz_tr[:, i], alg=alg, **kwargs)
@@ -500,14 +500,7 @@ def estimate(covfile, respfile, **kwargs):
                     else:
                         Ytest = Y[ts, nz[i]] 
 
-                    if alg=='hbr':
-                        if outscaler in ['standardize', 'minmax', 'robminmax']:
-                            Ytestz = Y_scaler.transform(Ytest.reshape(-1,1), index=i)
-                        else:
-                            Ytestz = Ytest.reshape(-1,1)
-                        Z[ts, nz[i]] = nm.get_mcmc_zscores(Xz_ts, Ytestz, **kwargs)
-                    else:
-                        Z[ts, nz[i]] = (Ytest - Yhat[ts, nz[i]]) / \
+                    Z[ts, nz[i]] = (Ytest - Yhat[ts, nz[i]]) / \
                                     np.sqrt(S2[ts, nz[i]])       
 
             except Exception as e:
@@ -757,7 +750,6 @@ def predict(covfile, respfile, maskfile=None, **kwargs):
         Xz = X
 
     # estimate the models for all subjects
-    #TODO Z-scores adaptation for SHASH HBR
     for i, m in enumerate(models):
         print("Prediction by model ", i+1, "of", feature_num)      
         nm = norm_init(Xz)
@@ -814,7 +806,7 @@ def predict(covfile, respfile, maskfile=None, **kwargs):
 
                 warp_param = nm.blr.hyp[1:nm.blr.warp.get_n_params()+1] 
                 Yw[:,i] = nm.blr.warp.f(Y[:,i], warp_param)
-            Y = Yw
+            Y = Yw;
         else:
             warp = False
 
@@ -1071,7 +1063,6 @@ def transfer(covfile, respfile, testcov=None, testresp=None, maskfile=None,
         else:
             warp = False
 
-        #TODO Z-scores adaptation for SHASH HBR
         Z = (Yte - Yhat) / np.sqrt(S2)
 
         print("Evaluating the model ...")

pcntoolkit/normative_model/norm_hbr.py

@@ -488,25 +488,21 @@ def get_mcmc_quantiles(self, X, batch_effects=None, z_scores=None):
         return quantiles.mean(axis=-1)
 
 
-    def get_mcmc_zscores(self, X, y, **kwargs):
+    def get_mcmc_zscores(self, X, y, batch_effects=None):
 
         """
         Computes zscores of data given an estimated model
 
         Args:
             X ([N*p]ndarray): covariates
             y ([N*1]ndarray): response variables
+            batch_effects (ndarray): the batch effects corresponding to X
         """
-
+        # Set batch effects to zero if none are provided
         print(self.configs['likelihood'])
-
-        tsbefile = kwargs.get("tsbefile", None)
-        if tsbefile is not None:
-            batch_effects_test = fileio.load(tsbefile)
-        else:         # Set batch effects to zero if none are provided
-            print("Could not find batch-effects file! Initializing all as zeros ...")
-            batch_effects_test = np.zeros([X.shape[0], 1])
-
+        if batch_effects is None:
+            batch_effects = batch_effects_test = np.zeros([X.shape[0], 1])
+
         # Determine the variables to predict
         if self.configs["likelihood"] == "Normal":
             var_names = ["mu_samples", "sigma_samples","sigma_plus_samples"]
@@ -529,7 +525,7 @@ def get_mcmc_zscores(self, X, y, **kwargs):
         # Do a forward to get the posterior predictive in the idata
         self.hbr.predict(
             X=X,
-            batch_effects=batch_effects_test,
+            batch_effects=batch_effects,
             batch_effects_maps=self.batch_effects_maps,
             pred="single",
             var_names=var_names+["y_like"],
@@ -540,7 +536,7 @@ def get_mcmc_zscores(self, X, y, **kwargs):
             self.hbr.idata, "posterior_predictive", var_names=var_names
         )
 
-        # Remove superfluous var_names
+        # Remove superfluous var_nammes
         var_names.remove('sigma_samples')
         if 'delta_samples' in var_names:
             var_names.remove('delta_samples')
@@ -557,7 +553,7 @@ def get_mcmc_zscores(self, X, y, **kwargs):
             *array_of_vars,
             kwargs={"y": y, "likelihood": self.configs['likelihood']},
         )
-        return z_scores.mean(axis=-1).values
+        return z_scores.mean(axis=-1)
 
 
 

pcntoolkit/util/utils.py

@@ -1141,19 +1141,15 @@ def fit(self, X):
                 self.max[i] = np.median(np.sort(X[:,i])[-int(np.round(X.shape[0] * self.tail)):])   
 
 
-    def transform(self, X, index=None):
+    def transform(self, X):
 
         if self.scaler_type == 'standardize':
-            if index is None:
-                X = (X - self.m) / self.s 
-            else:
-                X = (X - self.m[index]) / self.s[index]
+
+            X = (X - self.m) / self.s 
 
         elif self.scaler_type in ['minmax', 'robminmax']:
-            if index is None:
-                X = (X - self.min) / (self.max - self.min)
-            else:
-                X = (X - self.min[index]) / (self.max[index] - self.min[index])
+
+            X = (X - self.min) / (self.max - self.min)
 
             if self.adjust_outliers:
 

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 setup(name='pcntoolkit',
-      version='0.29',
+      version='0.28',
       description='Predictive Clinical Neuroscience toolkit',
       url='http://github.com/amarquand/PCNtoolkit',
       author='Andre Marquand',