HNiTS rename to match model naming convection (#1000)

* renamed nhits to match naming convention of other models

* renamed nhits to match naming convention of other models

* renamed nhits to match naming convention of other models

Author: gdevos010

README.md

@@ -141,7 +141,7 @@ Model | Univariate | Multivariate | Probabilistic | Multiple-series training | P
 `RNNModel` (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | | ✅ | [DeepAR paper](https://arxiv.org/abs/1704.04110)
 `BlockRNNModel` (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | |
 `NBEATSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)
-`NHiTS` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
+`NHiTSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
 `TCNModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
 `TransformerModel` | ✅ | ✅ | ✅ | ✅ | ✅ | |
 `TFTModel` (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)

darts/models/__init__.py

@@ -43,7 +43,7 @@
 try:
     from darts.models.forecasting.block_rnn_model import BlockRNNModel
     from darts.models.forecasting.nbeats import NBEATSModel
-    from darts.models.forecasting.nhits import NHiTS
+    from darts.models.forecasting.nhits import NHiTSModel
     from darts.models.forecasting.rnn_model import RNNModel
     from darts.models.forecasting.tcn_model import TCNModel
     from darts.models.forecasting.tft_model import TFTModel

darts/models/forecasting/nhits.py

@@ -456,7 +456,7 @@ def forward(self, x_in: Tuple):
         return y
 
 
-class NHiTS(PastCovariatesTorchModel):
+class NHiTSModel(PastCovariatesTorchModel):
     def __init__(
         self,
         input_chunk_length: int,

darts/tests/models/forecasting/test_nbeats_nhits.py

@@ -11,7 +11,7 @@
 
 try:
     from darts.models.forecasting.nbeats import NBEATSModel
-    from darts.models.forecasting.nhits import NHiTS
+    from darts.models.forecasting.nhits import NHiTSModel
 
     TORCH_AVAILABLE = True
 except ImportError:
@@ -39,7 +39,7 @@ def test_creation(self):
                 )
 
             with self.assertRaises(ValueError):
-                NHiTS(
+                NHiTSModel(
                     input_chunk_length=1,
                     output_chunk_length=1,
                     num_stacks=3,
@@ -50,7 +50,7 @@ def test_fit(self):
             large_ts = tg.constant_timeseries(length=100, value=1000)
             small_ts = tg.constant_timeseries(length=100, value=10)
 
-            for model_cls in [NBEATSModel, NHiTS]:
+            for model_cls in [NBEATSModel, NHiTSModel]:
                 # Test basic fit and predict
                 model = model_cls(
                     input_chunk_length=1,
@@ -88,7 +88,7 @@ def test_multivariate(self):
                 tg.linear_timeseries(length=100, start_value=0, end_value=0.5)
             )
 
-            for model_cls in [NBEATSModel, NHiTS]:
+            for model_cls in [NBEATSModel, NHiTSModel]:
                 model = model_cls(
                     input_chunk_length=3,
                     output_chunk_length=1,
@@ -131,7 +131,7 @@ def test_nhits_sampling_sizes(self):
             with self.assertRaises(ValueError):
 
                 # wrong number of coeffs for stacks and blocks
-                NHiTS(
+                NHiTSModel(
                     input_chunk_length=1,
                     output_chunk_length=1,
                     num_stacks=1,
@@ -140,7 +140,7 @@ def test_nhits_sampling_sizes(self):
                     n_freq_downsample=((1,), (1,)),
                 )
             with self.assertRaises(ValueError):
-                NHiTS(
+                NHiTSModel(
                     input_chunk_length=1,
                     output_chunk_length=1,
                     num_stacks=2,
@@ -150,7 +150,7 @@ def test_nhits_sampling_sizes(self):
                 )
 
             # it shouldn't fail with the right number of coeffs
-            _ = NHiTS(
+            _ = NHiTSModel(
                 input_chunk_length=1,
                 output_chunk_length=1,
                 num_stacks=2,
@@ -160,7 +160,7 @@ def test_nhits_sampling_sizes(self):
             )
 
             # default freqs should be such that last one is 1
-            model = NHiTS(
+            model = NHiTSModel(
                 input_chunk_length=1,
                 output_chunk_length=1,
                 num_stacks=2,
@@ -189,7 +189,7 @@ def test_logtensorboard(self):
         def test_activation_fns(self):
             ts = tg.constant_timeseries(length=50, value=10)
 
-            for model_cls in [NBEATSModel, NHiTS]:
+            for model_cls in [NBEATSModel, NHiTSModel]:
                 model = model_cls(
                     input_chunk_length=1,
                     output_chunk_length=1,