Fix test bugs

exps/LFNA/basic-his.py

@@ -82,7 +82,14 @@ def main(args):
         historical_x, historical_y = subsample(historical_x, historical_y)
         # build model
         mean, std = historical_x.mean().item(), historical_x.std().item()
-        model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
+        model_kwargs = dict(
+            input_dim=1,
+            output_dim=1,
+            act_cls="leaky_relu",
+            norm_cls="simple_norm",
+            mean=mean,
+            std=std,
+        )
         model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
         # build optimizer
         optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)

exps/LFNA/basic-same.py

@@ -78,7 +78,14 @@ def main(args):
         historical_y = env_info["{:}-y".format(idx)]
         # build model
         mean, std = historical_x.mean().item(), historical_x.std().item()
-        model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
+        model_kwargs = dict(
+            input_dim=1,
+            output_dim=1,
+            act_cls="leaky_relu",
+            norm_cls="simple_norm",
+            mean=mean,
+            std=std,
+        )
         model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
         # build optimizer
         optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)

exps/LFNA/lfna-v1.py

@@ -24,6 +24,8 @@ from models.xcore import get_model
 
 
 class Population:
+    """A population used to maintain models at different timestamps."""
+
     def __init__(self):
         self._time2model = dict()
 

tests/test_super_norm.py

@@ -64,7 +64,7 @@ class TestSuperSimpleNorm(unittest.TestCase):
         model.apply_verbose(True)
 
         print(model.super_run_type)
-        self.assertTrue(model[1].bias)
+        self.assertTrue(model[2].bias)
 
         inputs = torch.rand(20, 10)
         print("Input shape: {:}".format(inputs.shape))
@@ -80,6 +80,6 @@ class TestSuperSimpleNorm(unittest.TestCase):
         model.set_super_run_type(super_core.SuperRunMode.Candidate)
         model.apply_candidate(abstract_child)
 
-        output_shape = (20, abstract_child["1"]["_out_features"].value)
+        output_shape = (20, abstract_child["2"]["_out_features"].value)
         outputs = model(inputs)
         self.assertEqual(tuple(outputs.shape), output_shape)