Merge branch 'main' into 1169-tft-tftfeature_importances-returns-erro…

…r-when-there-is-no-futr_exog_list
Nixtla · Oct 10, 2024 · e2a56e3 · e2a56e3
2 parents 6d749b6 + 137e3a8
commit e2a56e3
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diff --git a/.all-contributorsrc b/.all-contributorsrc
@@ -6,10 +6,10 @@
   "commit": false,
   "contributors": [
     {
-      "login": "FedericoGarza",
-      "name": "fede",
+      "login": "AzulGarza",
+      "name": "azul",
       "avatar_url": "https://avatars.githubusercontent.com/u/10517170?v=4",
-      "profile": "https://github.com/FedericoGarza",
+      "profile": "https://github.com/AzulGarza",
       "contributions": [
         "code",
         "maintenance"

diff --git a/README.md b/README.md
@@ -115,7 +115,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
 <table>
   <tbody>
     <tr>
-      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AzulGarza"><img src="https://avatars.githubusercontent.com/u/10517170?v=4?s=100" width="100px;" alt="Azul"/><br /><sub><b>fede</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=AzulGarza" title="Code">💻</a> <a href="#maintenance-AzulGarza" title="Maintenance">🚧</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AzulGarza"><img src="https://avatars.githubusercontent.com/u/10517170?v=4?s=100" width="100px;" alt="azul"/><br /><sub><b>azul</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=AzulGarza" title="Code">💻</a> <a href="#maintenance-AzulGarza" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/cchallu"><img src="https://avatars.githubusercontent.com/u/31133398?v=4?s=100" width="100px;" alt="Cristian Challu"/><br /><sub><b>Cristian Challu</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=cchallu" title="Code">💻</a> <a href="#maintenance-cchallu" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/jmoralez"><img src="https://avatars.githubusercontent.com/u/8473587?v=4?s=100" width="100px;" alt="José Morales"/><br /><sub><b>José Morales</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=jmoralez" title="Code">💻</a> <a href="#maintenance-jmoralez" title="Maintenance">🚧</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/mergenthaler"><img src="https://avatars.githubusercontent.com/u/4086186?v=4?s=100" width="100px;" alt="mergenthaler"/><br /><sub><b>mergenthaler</b></sub></a><br /><a href="https://github.com/Nixtla/neuralforecast/commits?author=mergenthaler" title="Documentation">📖</a> <a href="https://github.com/Nixtla/neuralforecast/commits?author=mergenthaler" title="Code">💻</a></td>

diff --git a/nbs/models.mlpmultivariate.ipynb b/nbs/models.mlpmultivariate.ipynb
@@ -210,7 +210,11 @@
     "            x = torch.cat(( x, futr_exog.reshape(batch_size, -1) ), dim=1)\n",
     "\n",
     "        if self.stat_exog_size > 0:\n",
-    "            x = torch.cat(( x, stat_exog.reshape(batch_size, -1) ), dim=1)\n",
+    "            stat_exog = stat_exog.reshape(-1)                   #   [N, S] -> [N * S]\n",
+    "            stat_exog = stat_exog.unsqueeze(0)\\\n",
+    "                                 .repeat(batch_size, \n",
+    "                                         1)                     #   [N * S] -> [B, N * S]            \n",
+    "            x = torch.cat((x, stat_exog), dim=1)\n",
     "\n",
     "        for layer in self.mlp:\n",
     "             x = torch.relu(layer(x))\n",
@@ -362,6 +366,8 @@
     "model = MLPMultivariate(h=12, \n",
     "            input_size=24,\n",
     "            n_series=2,\n",
+    "            stat_exog_list=['airline1'],\n",
+    "            futr_exog_list=['trend'],            \n",
     "            loss = MAE(),\n",
     "            scaler_type='robust',\n",
     "            learning_rate=1e-3,\n",

diff --git a/neuralforecast/models/mlpmultivariate.py b/neuralforecast/models/mlpmultivariate.py
@@ -160,7 +160,11 @@ def forward(self, windows_batch):
             x = torch.cat((x, futr_exog.reshape(batch_size, -1)), dim=1)
 
         if self.stat_exog_size > 0:
-            x = torch.cat((x, stat_exog.reshape(batch_size, -1)), dim=1)
+            stat_exog = stat_exog.reshape(-1)  #   [N, S] -> [N * S]
+            stat_exog = stat_exog.unsqueeze(0).repeat(
+                batch_size, 1
+            )  #   [N * S] -> [B, N * S]
+            x = torch.cat((x, stat_exog), dim=1)
 
         for layer in self.mlp:
             x = torch.relu(layer(x))