From 0d05f9af1bf8929ffd18568b47f1fba11d56a0e0 Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Wed, 19 Jun 2024 19:25:46 -0400
Subject: [PATCH 1/8] testing without additivity and stuff

---
 flood_forecast/explain_model_output.py          | 2 +-
 flood_forecast/transformer_xl/data_embedding.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index 68a15fc2b..a5d621551 100644
--- a/flood_forecast/explain_model_output.py
+++ b/flood_forecast/explain_model_output.py
@@ -105,7 +105,7 @@ def deep_explain_model_summary_plot(
     if isinstance(history, list):
         model.model = model.model.to("cpu")
         deep_explainer = shap.DeepExplainer(model.model, history)
-        shap_values = deep_explainer.shap_values(history)
+        shap_values = deep_explainer.shap_values(history, check_additivity=False)
         s_values_list.append(shap_values)
     else:
         deep_explainer = shap.DeepExplainer(model.model, background_tensor)
diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py
index 4004be390..23ac5af24 100644
--- a/flood_forecast/transformer_xl/data_embedding.py
+++ b/flood_forecast/transformer_xl/data_embedding.py
@@ -155,7 +155,7 @@ def forward(self, x, x_mark) -> torch:
         if x_mark is None:
             x = self.value_embedding(x)
         else:
-            # the potential to take covariates (e.g. timestamps) as tokens
+            # the potential to take covariates (e.g. timestamps) as tokens.
             x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
         # x: [Batch Variate d_model] t
         return self.dropout(x)

From 75dc3b3749334ee0d7dc54d1a804c8232cce8982 Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Wed, 19 Jun 2024 19:26:17 -0400
Subject: [PATCH 2/8] Revert "testing without additivity and stuff"

This reverts commit 0d05f9af1bf8929ffd18568b47f1fba11d56a0e0.
---
 flood_forecast/explain_model_output.py          | 2 +-
 flood_forecast/transformer_xl/data_embedding.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index a5d621551..68a15fc2b 100644
--- a/flood_forecast/explain_model_output.py
+++ b/flood_forecast/explain_model_output.py
@@ -105,7 +105,7 @@ def deep_explain_model_summary_plot(
     if isinstance(history, list):
         model.model = model.model.to("cpu")
         deep_explainer = shap.DeepExplainer(model.model, history)
-        shap_values = deep_explainer.shap_values(history, check_additivity=False)
+        shap_values = deep_explainer.shap_values(history)
         s_values_list.append(shap_values)
     else:
         deep_explainer = shap.DeepExplainer(model.model, background_tensor)
diff --git a/flood_forecast/transformer_xl/data_embedding.py b/flood_forecast/transformer_xl/data_embedding.py
index 23ac5af24..4004be390 100644
--- a/flood_forecast/transformer_xl/data_embedding.py
+++ b/flood_forecast/transformer_xl/data_embedding.py
@@ -155,7 +155,7 @@ def forward(self, x, x_mark) -> torch:
         if x_mark is None:
             x = self.value_embedding(x)
         else:
-            # the potential to take covariates (e.g. timestamps) as tokens.
+            # the potential to take covariates (e.g. timestamps) as tokens
             x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
         # x: [Batch Variate d_model] t
         return self.dropout(x)

From 045f001ab57ceebe21b0c273d3b9be51162c7fd8 Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Wed, 19 Jun 2024 19:27:50 -0400
Subject: [PATCH 3/8] fiing the code

---
 flood_forecast/explain_model_output.py                  | 7 +++----
 flood_forecast/transformer_xl/transformer_bottleneck.py | 2 +-
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index 68a15fc2b..943014afd 100644
--- a/flood_forecast/explain_model_output.py
+++ b/flood_forecast/explain_model_output.py
@@ -4,7 +4,6 @@
 import numpy as np
 import shap
 import torch
-
 import wandb
 from flood_forecast.plot_functions import (
     plot_shap_value_heatmaps,
@@ -27,7 +26,7 @@ def handle_dl_output(dl, dl_class: str, datetime_start: datetime, device: str) -
     :type datetime_start: datetime
     :param device: Typical device should be either cpu or cuda
     :type device: str
-    :return: Returns a tuple containing either a..
+    :return: Returns a tuple containing either a list of tensors or a single tensor, and an integer
     :rtype: Tuple[torch.Tensor, int]
     """
     if dl_class == "TemporalLoader":
@@ -105,7 +104,7 @@ def deep_explain_model_summary_plot(
     if isinstance(history, list):
         model.model = model.model.to("cpu")
         deep_explainer = shap.DeepExplainer(model.model, history)
-        shap_values = deep_explainer.shap_values(history)
+        shap_values = deep_explainer.shap_values(history, check_additivity=False)
         s_values_list.append(shap_values)
     else:
         deep_explainer = shap.DeepExplainer(model.model, background_tensor)
@@ -216,7 +215,7 @@ def deep_explain_model_heatmap(
     s_values_list = []
     if isinstance(history, list):
         deep_explainer = shap.DeepExplainer(model.model, history)
-        shap_values = deep_explainer.shap_values(history)
+        shap_values = deep_explainer.shap_values(history, check_additivity=False)
         s_values_list.append(shap_values)
     else:
         deep_explainer = shap.DeepExplainer(model.model, background_tensor)
diff --git a/flood_forecast/transformer_xl/transformer_bottleneck.py b/flood_forecast/transformer_xl/transformer_bottleneck.py
index 425809fe8..3812e3bff 100644
--- a/flood_forecast/transformer_xl/transformer_bottleneck.py
+++ b/flood_forecast/transformer_xl/transformer_bottleneck.py
@@ -39,7 +39,7 @@
 from flood_forecast.transformer_xl.lower_upper_config import activation_dict
 
 
-def gelu(x):
+def gelu(x: torch.Tensor):
     return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
 
 

From 98a37d56d834e516a58ec3b78123d2a1d11904c1 Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Wed, 19 Jun 2024 20:01:05 -0400
Subject: [PATCH 4/8] set additivity to false and all

---
 flood_forecast/explain_model_output.py                  | 4 ++--
 flood_forecast/transformer_xl/transformer_bottleneck.py | 1 -
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index 52b0f3dfd..75a6e399a 100644
--- a/flood_forecast/explain_model_output.py
+++ b/flood_forecast/explain_model_output.py
@@ -108,7 +108,7 @@ def deep_explain_model_summary_plot(
         s_values_list.append(shap_values)
     else:
         deep_explainer = shap.DeepExplainer(model.model, background_tensor)
-        shap_values = deep_explainer.shap_values(background_tensor)
+        shap_values = deep_explainer.shap_values(background_tensor, check_additivity=False)
     shap_values = fix_shap_values(shap_values, history)
     shap_values = np.stack(shap_values)
     # shap_values needs to be 4-dimensional
@@ -219,7 +219,7 @@ def deep_explain_model_heatmap(
         s_values_list.append(shap_values)
     else:
         deep_explainer = shap.DeepExplainer(model.model, background_tensor)
-        shap_values = deep_explainer.shap_values(background_tensor)
+        shap_values = deep_explainer.shap_values(background_tensor, check_additivity=False)
     shap_values = fix_shap_values(shap_values, history)
     shap_values = np.stack(shap_values)  # forecast_len x N x L x M
     if len(shap_values.shape) != 4:
diff --git a/flood_forecast/transformer_xl/transformer_bottleneck.py b/flood_forecast/transformer_xl/transformer_bottleneck.py
index 3812e3bff..158e47bb9 100644
--- a/flood_forecast/transformer_xl/transformer_bottleneck.py
+++ b/flood_forecast/transformer_xl/transformer_bottleneck.py
@@ -32,7 +32,6 @@
 import torch
 import torch.nn as nn
 import math
-# from torch.distributions.normal import Normal
 import copy
 from torch.nn.parameter import Parameter
 from typing import Dict

From 0889399c1ed0bdda108c4a1121dc345e48020476 Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Wed, 19 Jun 2024 20:12:16 -0400
Subject: [PATCH 5/8] remove final additivity check

---
 flood_forecast/evaluator.py            | 2 +-
 flood_forecast/explain_model_output.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/flood_forecast/evaluator.py b/flood_forecast/evaluator.py
index eb653a666..32b734d02 100644
--- a/flood_forecast/evaluator.py
+++ b/flood_forecast/evaluator.py
@@ -143,7 +143,7 @@ def evaluate_model(
                 else:
                     end_tensor = test_data.inverse_scale(end_tensor.detach().reshape(-1, 1))
                 end_tensor_list = flatten_list_function(end_tensor.numpy().tolist())
-                end_tensor = end_tensor.squeeze(1)  # Removing extra dim from reshape?
+                end_tensor = end_tensor.squeeze(1)  # Removing extra dim from reshape.?
             history_length = model.params["dataset_params"]["forecast_history"]
             if "n_targets" in model.params:
                 df_train_and_test.loc[df_train_and_test.index[history_length:],
diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index 75a6e399a..6b82be657 100644
--- a/flood_forecast/explain_model_output.py
+++ b/flood_forecast/explain_model_output.py
@@ -146,8 +146,8 @@ def deep_explain_model_summary_plot(
         hist.cpu().numpy(), names=["batches", "observations", "features"]
     )
 
-    shap_values = deep_explainer.shap_values(history)
-    shap_values = fix_shap_values(shap_values, history)
+    shap_values = deep_explainer.shap_values(history, check_additivity=False)
+    shap_values = fix_shap_values(shap_values, history, check_additivity=False)
     shap_values = np.stack(shap_values)
     if len(shap_values.shape) != 4:
         shap_values = np.expand_dims(shap_values, axis=0)

From d66a0fb4b755ea1e4ca7eef4442971289e9f2400 Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Wed, 19 Jun 2024 20:14:33 -0400
Subject: [PATCH 6/8] Revert "remove final additivity check"

This reverts commit 0889399c1ed0bdda108c4a1121dc345e48020476.
---
 flood_forecast/evaluator.py            | 2 +-
 flood_forecast/explain_model_output.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/flood_forecast/evaluator.py b/flood_forecast/evaluator.py
index 32b734d02..eb653a666 100644
--- a/flood_forecast/evaluator.py
+++ b/flood_forecast/evaluator.py
@@ -143,7 +143,7 @@ def evaluate_model(
                 else:
                     end_tensor = test_data.inverse_scale(end_tensor.detach().reshape(-1, 1))
                 end_tensor_list = flatten_list_function(end_tensor.numpy().tolist())
-                end_tensor = end_tensor.squeeze(1)  # Removing extra dim from reshape.?
+                end_tensor = end_tensor.squeeze(1)  # Removing extra dim from reshape?
             history_length = model.params["dataset_params"]["forecast_history"]
             if "n_targets" in model.params:
                 df_train_and_test.loc[df_train_and_test.index[history_length:],
diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index 6b82be657..75a6e399a 100644
--- a/flood_forecast/explain_model_output.py
+++ b/flood_forecast/explain_model_output.py
@@ -146,8 +146,8 @@ def deep_explain_model_summary_plot(
         hist.cpu().numpy(), names=["batches", "observations", "features"]
     )
 
-    shap_values = deep_explainer.shap_values(history, check_additivity=False)
-    shap_values = fix_shap_values(shap_values, history, check_additivity=False)
+    shap_values = deep_explainer.shap_values(history)
+    shap_values = fix_shap_values(shap_values, history)
     shap_values = np.stack(shap_values)
     if len(shap_values.shape) != 4:
         shap_values = np.expand_dims(shap_values, axis=0)

From a10ca4e9815bdd7d09630949036a022c0276c8ca Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Wed, 19 Jun 2024 20:16:35 -0400
Subject: [PATCH 7/8] fixing everything

---
 flood_forecast/evaluator.py            | 2 +-
 flood_forecast/explain_model_output.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/flood_forecast/evaluator.py b/flood_forecast/evaluator.py
index eb653a666..428ebd146 100644
--- a/flood_forecast/evaluator.py
+++ b/flood_forecast/evaluator.py
@@ -61,7 +61,7 @@ def get_model_r2_score(
 ):
     """
 
-    model_evaluate_function should call any necessary preprocessing
+    model_evaluate_function should call any necessary preprocessing.
     """
     test_river_data, baseline_mse = stream_baseline(river_flow_df, forecast_column)
 
diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index 75a6e399a..d598a51be 100644
--- a/flood_forecast/explain_model_output.py
+++ b/flood_forecast/explain_model_output.py
@@ -146,7 +146,7 @@ def deep_explain_model_summary_plot(
         hist.cpu().numpy(), names=["batches", "observations", "features"]
     )
 
-    shap_values = deep_explainer.shap_values(history)
+    shap_values = deep_explainer.shap_values(history, check_additivity=False)
     shap_values = fix_shap_values(shap_values, history)
     shap_values = np.stack(shap_values)
     if len(shap_values.shape) != 4:

From ff5cd9a8168fdeda6782d48105e8702585f962cf Mon Sep 17 00:00:00 2001
From: isaacmg <igodfried@isaac26.com>
Date: Mon, 24 Jun 2024 17:36:09 -0400
Subject: [PATCH 8/8] fixing all syha 2

---
 flood_forecast/explain_model_output.py | 2 +-
 requirements.txt                       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/flood_forecast/explain_model_output.py b/flood_forecast/explain_model_output.py
index 134a276a9..cfbe2ecff 100644
--- a/flood_forecast/explain_model_output.py
+++ b/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:
diff --git a/requirements.txt b/requirements.txt
index b3273e1e9..cd0ff50d3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,7 +12,7 @@ google-cloud-storage
 plotly~=5.20.0
 pytz>=2022.1
 setuptools~=69.5.1
-numpy>=1.21
+numpy==1.26
 requests
 torchvision>=0.6.0
 mpld3>=0.5