Update LFNA version 1.0

exps/LFNA/vis-synthetic.py

@@ -23,7 +23,7 @@ lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
 
-
+from models.xcore import get_model
 from datasets.synthetic_core import get_synthetic_env
 from datasets.synthetic_example import create_example_v1
 from utils.temp_sync import optimize_fn, evaluate_fn
@@ -300,8 +300,20 @@ def compare_algs_v2(save_dir, alg_dir="./outputs/lfna-synthetic"):
 
     alg_name2dir = OrderedDict()
     alg_name2dir["Optimal"] = "use-same-timestamp"
-    alg_name2dir["Supervised Learning (History Data)"] = "use-all-past-data"
-    colors = ["r", "g"]
+    # alg_name2dir["Supervised Learning (History Data)"] = "use-all-past-data"
+    alg_name2all_containers = OrderedDict()
+    for idx_alg, (alg, xdir) in enumerate(alg_name2dir.items()):
+        ckp_path = Path(alg_dir) / xdir / "final-ckp.pth"
+        xdata = torch.load(ckp_path)
+        alg_name2all_containers[alg] = xdata["w_container_per_epoch"]
+    # load the basic model
+    model = get_model(
+        dict(model_type="simple_mlp"),
+        act_cls="leaky_relu",
+        norm_cls="identity",
+        input_dim=1,
+        output_dim=1,
+    )
 
     alg2xs, alg2ys = defaultdict(list), defaultdict(list)
     colors = ["r", "g"]
@@ -323,6 +335,7 @@ def compare_algs_v2(save_dir, alg_dir="./outputs/lfna-synthetic"):
         plot_scatter(cur_ax, allx, ally, "k", 0.99, linewidths, "Raw Data")
 
         for idx_alg, (alg, xdir) in enumerate(alg_name2dir.items()):
+            """
             ckp_path = (
                 Path(alg_dir)
                 / xdir
@@ -330,8 +343,12 @@ def compare_algs_v2(save_dir, alg_dir="./outputs/lfna-synthetic"):
             )
             assert ckp_path.exists()
             ckp_data = torch.load(ckp_path)
+            """
             with torch.no_grad():
-                predicts = ckp_data["model"](ori_allx)
+                # predicts = ckp_data["model"](ori_allx)
+                predicts = model.forward_with_container(
+                    ori_allx, alg_name2all_containers[alg][idx]
+                )
                 predicts = predicts.cpu()
                 # keep data
                 metric = MSEMetric()