change all additivity check to false

docs/source/basic_ae.rst

@@ -4,4 +4,4 @@ Simple AE
 .. automodule:: flood_forecast.meta_models.basic_ae
     :members:
 
-A simple auto-encoder model.
+A simple auto-encoder model used primarily for making numerical embeddings.

docs/source/basic_utils.rst

@@ -1,7 +1,7 @@
 Basic Google Cloud Platform Utilities
 ================
 
-Flow Forecast natively integrates with Google Cloud Platform.
+Flow Forecast natively integrates with Google Cloud Platform (GCP) to provide a seamless experience for users. This module contains basic utilities for interacting with GCP services.
 
 .. automodule:: flood_forecast.gcp_integration.basic_utils
     :members:

docs/source/inference.rst

@@ -1,7 +1,7 @@
 Inference
 =========================
 
-This API makes it easy to run inference on trained PyTorchForecast modules. To use this code you 
+The inference API makes it easy to run inference on trained PyTorchForecast modules. To use this code you 
 need three main files: your model's configuration file, a CSV containing your data, and a path to 
 your model weights.
 

flood_forecast/explain_model_output.py

@@ -235,7 +235,7 @@ def deep_explain_model_heatmap(
     # heatmap one prediction sequence at datetime_start
     # (seq_len*forecast_len) per fop feature
     to_explain = history
-    shap_values = deep_explainer.shap_values(to_explain)
+    shap_values = deep_explainer.shap_values(to_explain, check_additivity=False)
     shap_values = fix_shap_values(shap_values, history)
     shap_values = np.stack(shap_values)
     if len(shap_values.shape) != 4:

flood_forecast/transformer_xl/data_embedding.py

@@ -5,7 +5,7 @@
 
 class PositionalEmbedding(nn.Module):
     def __init__(self, d_model, max_len=5000):
-        """[summary]
+        """Creates the positional embeddings 
 
         :param d_model: [description]
         :type d_model: int
@@ -68,6 +68,13 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class FixedEmbedding(nn.Module):
     def __init__(self, c_in: torch.Tensor, d_model):
+        """_summary_
+
+        :param c_in: _description_
+        :type c_in: torch.Tensor
+        :param d_model: _description_
+        :type d_model: _type_
+        """
         super(FixedEmbedding, self).__init__()
 
         w = torch.zeros(c_in, d_model).float()
@@ -130,6 +137,19 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class DataEmbedding(nn.Module):
     def __init__(self, c_in: int, d_model, embed_type='fixed', data=4, dropout=0.1):
+        """Creates a an embedding based on the input
+
+        :param c_in: _description_
+        :type c_in: int
+        :param d_model: _description_
+        :type d_model: _type_
+        :param embed_type: _description_, defaults to 'fixed'
+        :type embed_type: str, optional
+        :param data: _description_, defaults to 4
+        :type data: int, optional
+        :param dropout: _description_, defaults to 0.1
+        :type dropout: float, optional
+        """
         super(DataEmbedding, self).__init__()
 
         self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)

flood_forecast/transformer_xl/itransformer.py

@@ -38,7 +38,7 @@ def __init__(self, forecast_history, forecast_length, d_model, embed, dropout, n
         :type activation: str, optional
         :param factor: =n_, defaults to 1
         :type factor: int, optional
-        :param output_attention: Whether to output the scores, defaults to True
+        :param output_attention: Whether to output the scores, defaults to True.
         :type output_attention: bool, optional
         """
         class_strategy = 'projection'

flood_forecast/transformer_xl/transformer_bottleneck.py

@@ -54,6 +54,27 @@ def swish(x):
 
 class Attention(nn.Module):
     def __init__(self, n_head, n_embd, win_len, scale, q_len, sub_len, sparse=None, attn_pdrop=0.1, resid_pdrop=0.1):
+        """_summary_
+
+        :param n_head: _description_
+        :type n_head: _type_
+        :param n_embd: _description_
+        :type n_embd: _type_
+        :param win_len: _description_
+        :type win_len: _type_
+        :param scale: _description_
+        :type scale: _type_
+        :param q_len: _description_
+        :type q_len: _type_
+        :param sub_len: _description_
+        :type sub_len: _type_
+        :param sparse: _description_, defaults to None
+        :type sparse: _type_, optional
+        :param attn_pdrop: _description_, defaults to 0.1
+        :type attn_pdrop: float, optional
+        :param resid_pdrop: _description_, defaults to 0.1
+        :type resid_pdrop: float, optional
+        """
         super(Attention, self).__init__()
 
         if (sparse):