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unit8co · Sep 23, 2024 · 9bbd1c9 · 9bbd1c9
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diff --git a/darts/models/__init__.py b/darts/models/__init__.py
@@ -33,6 +33,7 @@
 from darts.models.forecasting.regression_model import RegressionModel
 from darts.models.forecasting.tbats_model import BATS, TBATS
 from darts.models.forecasting.theta import FourTheta, Theta
+from darts.models.forecasting.times_net_model import TimesNetModel
 from darts.models.forecasting.varima import VARIMA
 
 try:
@@ -50,7 +51,7 @@
     from darts.models.forecasting.tcn_model import TCNModel
     from darts.models.forecasting.tft_model import TFTModel
     from darts.models.forecasting.tide_model import TiDEModel
-    from darts.models.forecasting.time_net_model import TimeNetModel
+    from darts.models.forecasting.times_net_model import TimesNetModel
     from darts.models.forecasting.transformer_model import TransformerModel
     from darts.models.forecasting.tsmixer_model import TSMixerModel
 except ModuleNotFoundError:
@@ -72,7 +73,7 @@
     TFTModel = NotImportedModule(module_name="(Py)Torch", warn=False)
     TiDEModel = NotImportedModule(module_name="(Py)Torch", warn=False)
     TransformerModel = NotImportedModule(module_name="(Py)Torch", warn=False)
-    TimeNetModel = NotImportedModule(module_name="(Py)Torch", warn=False)
+    TimesNetModel = NotImportedModule(module_name="(Py)Torch", warn=False)
     TSMixerModel = NotImportedModule(module_name="(Py)Torch", warn=False)
 
 try:
@@ -153,7 +154,7 @@
     "TFTModel",
     "TiDEModel",
     "TransformerModel",
-    "TimeNetModel",
+    "TimesNetModel",
     "TSMixerModel",
     "Prophet",
     "CatBoostModel",

diff --git a/darts/models/components/embed.py b/darts/models/components/embed.py
@@ -2,8 +2,6 @@
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.utils import weight_norm
 
 
 class PositionalEmbedding(nn.Module):

diff --git a/darts/models/forecasting/__init__.py b/darts/models/forecasting/__init__.py
@@ -42,7 +42,7 @@
     - :class:`~darts.models.forecasting.nhits.NHiTSModel`
     - :class:`~darts.models.forecasting.tcn_model.TCNModel`
     - :class:`~darts.models.forecasting.transformer_model.TransformerModel`
-    - :class:`~darts.models.forecasting.time_net_model.TimeNetModel`
+    - :class:`~darts.models.forecasting.time_net_model.TimesNetModel`
     - :class:`~darts.models.forecasting.tft_model.TFTModel`
     - :class:`~darts.models.forecasting.dlinear.DLinearModel`
     - :class:`~darts.models.forecasting.nlinear.NLinearModel`

diff --git a/darts/models/forecasting/time_net_model.py → darts/models/forecasting/times_net_model.py b/darts/models/forecasting/time_net_model.py → darts/models/forecasting/times_net_model.py
@@ -1,11 +1,10 @@
 from typing import Tuple
 
-import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from darts.models.forecasting.embed import DataEmbedding
+from darts.models.components.embed import DataEmbedding
 from darts.models.forecasting.pl_forecasting_module import (
     PLPastCovariatesModule,
     io_processor,
@@ -107,7 +106,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return res
 
 
-class _TimeNetModule(PLPastCovariatesModule):
+class _TimesNetModule(PLPastCovariatesModule):
     def __init__(
         self,
         input_dim: int,
@@ -166,7 +165,7 @@ def forward(self, x_in: Tuple) -> torch.Tensor:
         return y
 
 
-class TimeNetModel(PastCovariatesTorchModel):
+class TimesNetModel(PastCovariatesTorchModel):
     def __init__(
         self,
         input_chunk_length: int,
@@ -179,7 +178,7 @@ def __init__(
         **kwargs,
     ):
         """
-        TimeNet model for time series forecasting.
+        TimesNet model for time series forecasting.
 
         Parameters:
         -----------
@@ -331,13 +330,13 @@ def encode_year(idx):
         Examples
         --------
         >>> from darts.datasets import WeatherDataset
-        >>> from darts.models import TimeNetModel
+        >>> from darts.models import TimesNetModel
         >>> series = WeatherDataset().load()
         >>> # predicting atmospheric pressure
         >>> target = series['p (mbar)'][:100]
         >>> # optionally, use past observed rainfall (pretending to be unknown beyond index 100)
         >>> past_cov = series['rain (mm)'][:100]
-        >>> model = TimeNetModel(
+        >>> model = TimesNetModel(
         >>>     input_chunk_length=6,
         >>>     output_chunk_length=6,
         >>>     n_epochs=20
@@ -368,7 +367,7 @@ def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module:
         output_dim = train_sample[-1].shape[1]
         nr_params = 1 if self.likelihood is None else self.likelihood.num_parameters
 
-        return _TimeNetModule(
+        return _TimesNetModule(
             input_dim=input_dim,
             output_dim=output_dim,
             nr_params=nr_params,

diff --git a/darts/tests/models/forecasting/test_global_forecasting_models.py b/darts/tests/models/forecasting/test_global_forecasting_models.py
@@ -33,9 +33,9 @@
     TCNModel,
     TFTModel,
     TiDEModel,
+    TimesNetModel,
     TransformerModel,
     TSMixerModel,
-    TimeNetModel,
 )
 from darts.models.forecasting.torch_forecasting_model import (
     DualCovariatesTorchModel,
@@ -161,7 +161,7 @@
         60.0,
     ),
     (
-        TimeNetModel,
+        TimesNetModel,
         {
             "n_epochs": 10,
             "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"],

diff --git a/darts/tests/models/forecasting/test_historical_forecasts.py b/darts/tests/models/forecasting/test_historical_forecasts.py
@@ -36,9 +36,9 @@
         TCNModel,
         TFTModel,
         TiDEModel,
+        TimesNetModel,
         TransformerModel,
         TSMixerModel,
-        TimeNetModel,
     )
     from darts.utils.likelihood_models import GaussianLikelihood, QuantileRegression
 
@@ -184,7 +184,7 @@
             "PastCovariates",
         ),
         (
-            TimeNetModel,
+            TimesNetModel,
             {
                 "input_chunk_length": IN_LEN,
                 "output_chunk_length": OUT_LEN,

diff --git a/darts/tests/models/forecasting/test_probabilistic_models.py b/darts/tests/models/forecasting/test_probabilistic_models.py
@@ -34,9 +34,9 @@
         TCNModel,
         TFTModel,
         TiDEModel,
+        TimeNetModel,
         TransformerModel,
         TSMixerModel,
-        TimeNetModel,
     )
     from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel
     from darts.utils.likelihood_models import (

diff --git a/darts/tests/models/forecasting/test_torch_forecasting_model.py b/darts/tests/models/forecasting/test_torch_forecasting_model.py
@@ -45,9 +45,10 @@
     TCNModel,
     TFTModel,
     TiDEModel,
+    TimesNetModel,
     TransformerModel,
     TSMixerModel,
-    TimeNetModel,
+    iTransformerModel,
 )
 from darts.models.components.layer_norm_variants import RINorm
 from darts.models.forecasting.global_baseline_models import _GlobalNaiveModel
@@ -103,7 +104,8 @@
     (TFTModel, {"add_relative_index": 2, **kwargs, **tft_light_kwargs}),
     (TiDEModel, kwargs),
     (TransformerModel, dict(kwargs, **trafo_light_kwargs)),
-    (TimeNetModel, kwargs),
+    (iTransformerModel, kwargs),
+    (TimesNetModel, kwargs),
     (TSMixerModel, kwargs),
     (GlobalNaiveSeasonal, kwargs),
     (GlobalNaiveAggregate, kwargs),