Re-organize GeMOSA

exps/GeMOSA/side_utils.py

@@ -0,0 +1,50 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
+#####################################################
+import copy
+import torch
+from tqdm import tqdm
+from xautodl.procedures import prepare_seed, prepare_logger
+from xautodl.datasets.synthetic_core import get_synthetic_env
+
+
+def train_model(model, dataset, lr, epochs):
+    criterion = torch.nn.MSELoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True)
+    best_loss, best_param = None, None
+    for _iepoch in range(epochs):
+        preds = model(dataset.x)
+        optimizer.zero_grad()
+        loss = criterion(preds, dataset.y)
+        loss.backward()
+        optimizer.step()
+        # save best
+        if best_loss is None or best_loss > loss.item():
+            best_loss = loss.item()
+            best_param = copy.deepcopy(model.state_dict())
+    model.load_state_dict(best_param)
+    return best_loss
+
+
+class TimeData:
+    def __init__(self, timestamp, xs, ys):
+        self._timestamp = timestamp
+        self._xs = xs
+        self._ys = ys
+
+    @property
+    def x(self):
+        return self._xs
+
+    @property
+    def y(self):
+        return self._ys
+
+    @property
+    def timestamp(self):
+        return self._timestamp
+
+    def __repr__(self):
+        return "{name}(timestamp={timestamp}, with {num} samples)".format(
+            name=self.__class__.__name__, timestamp=self._timestamp, num=len(self._xs)
+        )