initial test for get_lagged_subseq

test/model/deepar/test_lags.py

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+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+# Standard library imports
+import itertools
+
+# Third-party imports
+import torch
+
+# First-party imports
+from pts.model.deepar._network import DeepARTrainingNetwork
+
+
+def test_lagged_subsequences():
+    N = 8
+    T = 96
+    C = 2
+    lags = [1, 2, 3, 24, 48]
+    I = len(lags)
+    sequence = torch.randn((N, T, C))
+    S = 48
+
+    # (batch_size, sub_seq_len, target_dim, num_lags)
+    lagged_subsequences = DeepARTrainingNetwork.get_lagged_subsequences(
+        sequence=sequence,
+        sequence_length=sequence.shape[1],
+        indices=lags,
+        subsequences_length=S,
+    )
+
+    assert (N, S, C, I) == lagged_subsequences.shape
+
+    # checks that lags value behave as described as in the get_lagged_subsequences contract
+    for i, j, k in itertools.product(range(N), range(S), range(I)):
+        assert (
+            (
+                lagged_subsequences[i, j, :, k]
+                == sequence[i, -lags[k] - S + j, :]
+            )
+            .numpy()
+            .all()
+        )