fixed issue with goptions --> wraper

qstack/regression/hyperparameters.py

@@ -46,8 +46,11 @@ def hyper_loop(sigma, eta):
                     print(s, e, mean, std, flush=True)
                 errors.append((mean, std, e, s))
         return errors
-
-    kernel = get_kernel(akernel, [gkernel, gdict])
+    if gkernel == None:
+        gwrap = None
+    else:
+        gwrap = [gkernel, gdict]
+    kernel = get_kernel(akernel, gwrap)
     if read_kernel is False:
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=0)
     else:
@@ -94,7 +97,7 @@ def main():
     parser.add_argument('--y',      type=str,   dest='prop',       required=True, help='path to the properties file')
     parser.add_argument('--test',   type=float, dest='test_size',  default=defaults.test_size, help='test set fraction (default='+str(defaults.test_size)+')')
     parser.add_argument('--akernel',     type=str,   dest='akernel',     default=defaults.kernel,    help='local kernel type (G for Gaussian, L for Laplacian, myL for Laplacian for open-shell systems) (default '+defaults.kernel+')')
-    parser.add_argument('--gkernel',     type=str,   dest='gkernel',     default=defaults.gkernel,    help='global kernel type (avg for average kernel, rem for REMatch kernel) (default '+defaults.gkernel+')')
+    parser.add_argument('--gkernel',     type=str,   dest='gkernel',     default=defaults.gkernel,    help='global kernel type (avg for average kernel, rem for REMatch kernel) (default )')
     parser.add_argument('--gdict',     nargs='*',   action=ParseKwargs, dest='gdict',     default=defaults.gdict,    help='dictionary like input string to initialize global kernel parameters')
     parser.add_argument('--splits', type=int,   dest='splits',     default=defaults.splits,    help='k in k-fold cross validation (default='+str(defaults.n_rep)+')')
     parser.add_argument('--print',  type=int,   dest='printlevel', default=0,                  help='printlevel')
@@ -110,7 +113,7 @@ def main():
 
     X = np.load(args.repr)
     y = np.loadtxt(args.prop)
-    errors = hyperparameters(X, y, read_kernel=args.readk, sigma=args.sigma, eta=args.eta, akernel=args.kernel, test_size=args.test_size, splits=args.splits, printlevel=args.printlevel, adaptive=args.adaptive)
+    errors = hyperparameters(X, y, read_kernel=args.readk, sigma=args.sigma, eta=args.eta, akernel=args.akernel, test_size=args.test_size, splits=args.splits, printlevel=args.printlevel, adaptive=args.adaptive)
 
     print()
     print('error        stdev          eta          sigma')

qstack/regression/kernel_utils.py

@@ -24,7 +24,7 @@ def __call__(self, parser, namespace, values, option_string=None):
   sigma=32.0,
   eta=1e-5,
   kernel='L',
-  gkernel='avg',
+  gkernel=None,
   gdict={'alpha':1.0, 'normalize':1},
   test_size=0.2,
   n_rep=5,
@@ -162,6 +162,7 @@ def get_global_K(X, Y, sigma, local_kernel, global_kernel, options):
     print(f"Final global kernel has size : {K_global.shape}", flush=True)
     return K_global
 
+
 def my_laplacian_kernel(X, Y, gamma):
   """ Compute Laplacian kernel between X and Y """
   def cdist(X, Y):

qstack/regression/regression.py

@@ -25,7 +25,7 @@ def regression(X, y, read_kernel=False, sigma=defaults.sigma, eta=defaults.eta,
 
     maes_all = []
     for size in train_size:
-        size_train = int(np.floor(len(y_train)*size))
+        size_train = int(np.floor(len(y_train)*size)) if size < 1.0 else size
         maes = []
         for rep in range(n_rep):
             train_idx = np.random.choice(all_indices_train, size = size_train, replace=False)