Add flag to toggle log scale feature for WaveNet model (#3012)

src/gluonts/torch/model/wavenet/estimator.py

@@ -85,6 +85,7 @@ def __init__(
         cardinality: List[int] = [1],
         seasonality: Optional[int] = None,
         embedding_dimension: int = 5,
+        use_log_scale_feature: bool = True,
         time_features: Optional[List[TimeFeature]] = None,
         lr: float = 1e-3,
         weight_decay: float = 1e-8,
@@ -140,6 +141,10 @@ def __init__(
         embedding_dimension, optional
             The dimension of the embeddings for categorical features,
             by default 5
+        use_log_scale_feature, optional
+            If True, logarithm of the scale of the past data will be used as an
+            additional static feature,
+            by default True
         time_features, optional
             List of time features, from :py:mod:`gluonts.time_feature`,
             by default None
@@ -187,6 +192,7 @@ def __init__(
         self.num_residual_channels = num_residual_channels
         self.num_skip_channels = num_skip_channels
         self.num_stacks = num_stacks
+        self.use_log_scale_feature = use_log_scale_feature
         self.time_features = unwrap_or(
             time_features, time_features_from_frequency_str(freq)
         )
@@ -382,6 +388,7 @@ def create_lightning_module(self) -> pl.LightningModule:
                 pred_length=self.prediction_length,
                 num_parallel_samples=self.num_parallel_samples,
                 temperature=self.temperature,
+                use_log_scale_feature=self.use_log_scale_feature,
             ),
         )
 

src/gluonts/torch/model/wavenet/module.py

@@ -130,6 +130,7 @@ def __init__(
         embedding_dimension: int = 5,
         num_parallel_samples: int = 100,
         temperature: float = 1.0,
+        use_log_scale_feature: bool = True,
     ):
         super().__init__()
 
@@ -141,8 +142,9 @@ def __init__(
             embedding_dimension * len(cardinality)
             + num_feat_dynamic_real
             + num_feat_static_real
-            + 1  # the log(scale)
+            + int(use_log_scale_feature)  # the log(scale)
         )
+        self.use_log_scale_feature = use_log_scale_feature
 
         # 1 extra bin to accounts for extreme values
         self.n_bins = len(bin_values) + 1
@@ -249,8 +251,11 @@ def get_full_features(
             network.
             Shape: (batch_size, num_features, receptive_field + pred_length)
         """
-        embedded_cat = self.feature_embedder(feat_static_cat.long())
-        static_feat = torch.cat([embedded_cat, torch.log(scale + 1.0)], dim=1)
+        static_feat = self.feature_embedder(feat_static_cat.long())
+        if self.use_log_scale_feature:
+            static_feat = torch.cat(
+                [static_feat, torch.log(scale + 1.0)], dim=1
+            )
         repeated_static_feat = torch.repeat_interleave(
             static_feat[..., None],
             self.prediction_length + self.receptive_field,