added lstnet tests

pts/model/lstnet/lstnet_estimator.py

@@ -136,6 +136,6 @@ class LSTNetEstimator(PTSEstimator):
             prediction_net=prediction_network,
             batch_size=self.trainer.batch_size,
             freq=self.freq,
-            prediction_length=self.prediction_length,
+            prediction_length=self.horizon or self.prediction_length,
             device=device,
         )

pts/model/lstnet/lstnet_network.py

@@ -117,12 +117,12 @@ class LSTNetBase(nn.Module):
         # CNN
         c = F.relu(self.cnn(scaled_past_target.unsqueeze(1)))
         c = self.dropout(c)
-        c = c.squeeze()  # [B, C, T]
+        c = c.squeeze(2)  # [B, C, T]
 
         # RNN
         r = c.permute(2, 0, 1)  # [F (T), B, C]
         _, r = self.rnn(r)  # [1, B, H]
-        r = self.dropout(r.squeeze()) # [B, H]
+        r = self.dropout(r.squeeze(0)) # [B, H]
 
         # Skip-RNN
         skip_c = c[..., -self.conv_skip * self.skip_size :]

test/model/test_lstnet.py

@@ -0,0 +1,96 @@
+# 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.
+
+# Third-party imports
+import pytest
+import numpy as np
+import pandas as pd
+
+# First-party imports
+from pts.dataset.artificial import constant_dataset
+from pts.dataset import TrainDatasets, MultivariateGrouper
+from pts.evaluation import backtest_metrics
+from pts.model.lstnet import LSTNetEstimator
+from pts import Trainer
+from pts.evaluation import MultivariateEvaluator, make_evaluation_predictions
+
+
+NUM_SERIES = 10
+NUM_SAMPLES = 5
+
+
+def load_multivariate_constant_dataset():
+    metadata, train_ds, test_ds = constant_dataset()
+    grouper_train = MultivariateGrouper(max_target_dim=NUM_SERIES)
+    grouper_test = MultivariateGrouper(max_target_dim=NUM_SERIES)
+    return TrainDatasets(
+        metadata=metadata, train=grouper_train(train_ds), test=grouper_test(test_ds),
+    )
+
+
+dataset = load_multivariate_constant_dataset()
+freq = dataset.metadata.metadata.freq
+prediction_length = dataset.metadata.prediction_length
+
+
+@pytest.mark.parametrize("skip_size", [1, 2])
+@pytest.mark.parametrize("ar_window", [1, 2])
+@pytest.mark.parametrize(
+    "horizon, prediction_length",
+    [[prediction_length, None], [None, prediction_length]],
+)
+def test_lstnet(skip_size, ar_window, horizon, prediction_length):
+    estimator = LSTNetEstimator(
+        skip_size=skip_size,
+        ar_window=ar_window,
+        num_series=NUM_SERIES,
+        channels=6,
+        kernel_size=2,
+        context_length=4,
+        freq=freq,
+        horizon=horizon,
+        prediction_length=prediction_length,
+        trainer=Trainer(epochs=1, batch_size=2, learning_rate=0.01,),
+    )
+
+    predictor = estimator.train(dataset.train)
+    forecast_it, ts_it = make_evaluation_predictions(
+        dataset=dataset.test, predictor=predictor, num_samples=NUM_SAMPLES
+    )
+    forecasts = list(forecast_it)
+    tss = list(ts_it)
+    assert len(forecasts) == len(tss) == len(dataset.test)
+    test_ds = dataset.test.list_data[0]
+    for fct in forecasts:
+        assert fct.freq == freq
+        if estimator.horizon:
+            assert fct.samples.shape == (NUM_SAMPLES, 1, NUM_SERIES)
+        else:
+            assert fct.samples.shape == (NUM_SAMPLES, prediction_length, NUM_SERIES,)
+        assert (
+            fct.start_date
+            == pd.date_range(
+                start=str(test_ds["start"]),
+                periods=test_ds["target"].shape[1],  # number of test periods
+                freq=freq,
+                closed="right",
+            )[-(horizon or prediction_length)]
+        )
+
+    evaluator = MultivariateEvaluator(
+        quantiles=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
+    )
+    agg_metrics, item_metrics = evaluator(
+        iter(tss), iter(forecasts), num_series=len(dataset.test)
+    )
+    assert agg_metrics["ND"] < 0.21