Update LFNA version 1.0

lib/datasets/synthetic_core.py

@@ -2,6 +2,7 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.05 #
 #####################################################
 from .synthetic_utils import TimeStamp
+from .synthetic_env import EnvSampler
 from .synthetic_env import SyntheticDEnv
 from .math_core import LinearFunc
 from .math_core import DynamicLinearFunc

lib/datasets/synthetic_env.py

@@ -2,7 +2,7 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
 #####################################################
 import math
-import abc
+import random
 import numpy as np
 from typing import List, Optional, Dict
 import torch
@@ -11,6 +11,28 @@ import torch.utils.data as data
 from .synthetic_utils import TimeStamp
 
 
+def is_list_tuple(x):
+    return isinstance(x, (tuple, list))
+
+
+def zip_sequence(sequence):
+    def _combine(*alist):
+        if is_list_tuple(alist[0]):
+            return [_combine(*xlist) for xlist in zip(*alist)]
+        else:
+            return torch.cat(alist, dim=0)
+
+    def unsqueeze(a):
+        if is_list_tuple(a):
+            return [unsqueeze(x) for x in a]
+        else:
+            return a.unsqueeze(dim=0)
+
+    with torch.no_grad():
+        sequence = [unsqueeze(a) for a in sequence]
+        return _combine(*sequence)
+
+
 class SyntheticDEnv(data.Dataset):
     """The synethtic dynamic environment."""
 
@@ -33,7 +55,7 @@ class SyntheticDEnv(data.Dataset):
         self._num_per_task = num_per_task
         if timestamp_config is None:
             timestamp_config = dict(mode=mode)
-        else:
+        elif "mode" not in timestamp_config:
             timestamp_config["mode"] = mode
 
         self._timestamp_generator = TimeStamp(**timestamp_config)
@@ -42,6 +64,7 @@ class SyntheticDEnv(data.Dataset):
         self._cov_functors = cov_functors
 
         self._oracle_map = None
+        self._seq_length = None
 
     @property
     def min_timestamp(self):
@@ -55,9 +78,18 @@ class SyntheticDEnv(data.Dataset):
     def timestamp_interval(self):
         return self._timestamp_generator.interval
 
+    def reset_max_seq_length(self, seq_length):
+        self._seq_length = seq_length
+
     def get_timestamp(self, index):
-        index, timestamp = self._timestamp_generator[index]
-        return timestamp
+        if index is None:
+            timestamps = []
+            for index in range(len(self._timestamp_generator)):
+                timestamps.append(self._timestamp_generator[index][1])
+            return tuple(timestamps)
+        else:
+            index, timestamp = self._timestamp_generator[index]
+            return timestamp
 
     def set_oracle_map(self, functor):
         self._oracle_map = functor
@@ -75,7 +107,14 @@ class SyntheticDEnv(data.Dataset):
     def __getitem__(self, index):
         assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
         index, timestamp = self._timestamp_generator[index]
-        return self.__call__(timestamp)
+        if self._seq_length is None:
+            return self.__call__(timestamp)
+        else:
+            timestamps = [
+                timestamp + i * self.timestamp_interval for i in range(self._seq_length)
+            ]
+            xdata = [self.__call__(timestamp) for timestamp in timestamps]
+            return zip_sequence(xdata)
 
     def __call__(self, timestamp):
         mean_list = [functor(timestamp) for functor in self._mean_functors]
@@ -88,10 +127,13 @@ class SyntheticDEnv(data.Dataset):
             mean_list, cov_matrix, size=self._num_per_task
         )
         if self._oracle_map is None:
-            return timestamp, torch.Tensor(dataset)
+            return torch.Tensor([timestamp]), torch.Tensor(dataset)
         else:
             targets = self._oracle_map.noise_call(dataset, timestamp)
-            return timestamp, (torch.Tensor(dataset), torch.Tensor(targets))
+            return torch.Tensor([timestamp]), (
+                torch.Tensor(dataset),
+                torch.Tensor(targets),
+            )
 
     def __len__(self):
         return len(self._timestamp_generator)
@@ -104,3 +146,20 @@ class SyntheticDEnv(data.Dataset):
             ndim=self._ndim,
             num_per_task=self._num_per_task,
         )
+
+
+class EnvSampler:
+    def __init__(self, env, batch, enlarge):
+        indexes = list(range(len(env)))
+        self._indexes = indexes * enlarge
+        self._batch = batch
+        self._iterations = len(self._indexes) // self._batch
+
+    def __iter__(self):
+        random.shuffle(self._indexes)
+        for it in range(self._iterations):
+            indexes = self._indexes[it * self._batch : (it + 1) * self._batch]
+            yield indexes
+
+    def __len__(self):
+        return self._iterations

lib/datasets/synthetic_utils.py

@@ -30,6 +30,7 @@ class UnifiedSplit:
             self._indexes = all_indexes[num_of_train + num_of_valid :]
         else:
             raise ValueError("Unkonwn mode of {:}".format(mode))
+        self._all_indexes = all_indexes
         self._mode = mode
 
     @property