From 3ac793832053c7cf44c52627c8281b6376c771b1 Mon Sep 17 00:00:00 2001
From: Tyler Nisonoff <tylernisonoff@gmail.com>
Date: Mon, 18 Nov 2024 11:51:21 -0800
Subject: [PATCH] Set appropriate defaults for various keys in config_dict that
 previously were not saved

Fixes https://github.com/Nixtla/neuralforecast/issues/1206
---
 nbs/core.ipynb         | 19 +++++++++++++++----
 nbs/nbdev.yml          |  2 +-
 neuralforecast/core.py | 15 +++++++++++----
 3 files changed, 27 insertions(+), 9 deletions(-)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
index c9c12bb4b..3bb61dbdc 100644
--- a/nbs/core.ipynb
+++ b/nbs/core.ipynb
@@ -1626,15 +1626,26 @@
     "        except FileNotFoundError:\n",
     "            raise Exception('No configuration found in directory.')\n",
     "\n",
+    "        # in 1.6.4, `local_scaler_type` / `scalers_` lived on the dataset.\n",
+    "        # in order to preserve backwards-compatibility, we check to see if these are found on the dataset\n",
+    "        # in case they cannot be found in `config_dict`\n",
+    "        default_scalar_type = getattr(dataset, \"local_scaler_type\", None)\n",
+    "        default_scalars_ = getattr(dataset, \"scalers_\", None)\n",
+    "\n",
     "        # Create NeuralForecast object\n",
     "        neuralforecast = NeuralForecast(\n",
     "            models=models,\n",
     "            freq=config_dict['freq'],\n",
-    "            local_scaler_type=config_dict['local_scaler_type'],\n",
+    "            local_scaler_type=config_dict.get(\"local_scaler_type\", default_scalar_type),\n",
     "        )\n",
     "\n",
-    "        for attr in ['id_col', 'time_col', 'target_col']:\n",
-    "            setattr(neuralforecast, attr, config_dict[attr])\n",
+    "        attr_to_default = {\n",
+    "            \"id_col\": \"unique_id\",\n",
+    "            \"time_col\": \"ds\",\n",
+    "            \"target_col\": \"y\"\n",
+    "        }\n",
+    "        for attr, default in attr_to_default.items():\n",
+    "            setattr(neuralforecast, attr, config_dict.get(attr, default))\n",
     "        # only restore attribute if available\n",
     "        for attr in ['prediction_intervals', '_cs_df']:\n",
     "            if attr in config_dict.keys():\n",
@@ -1655,7 +1666,7 @@
     "        # Fitted flag\n",
     "        neuralforecast._fitted = config_dict['_fitted']\n",
     "\n",
-    "        neuralforecast.scalers_ = config_dict['scalers_']\n",
+    "        neuralforecast.scalers_ = config_dict.get(\"scalers_\", default_scalars_)\n",
     "\n",
     "        return neuralforecast\n",
     "    \n",
diff --git a/nbs/nbdev.yml b/nbs/nbdev.yml
index 190e738ad..768e9afbd 100644
--- a/nbs/nbdev.yml
+++ b/nbs/nbdev.yml
@@ -3,7 +3,7 @@ project:
 
 website:
   title: "neuralforecast"
-  site-url: "https://Nixtla.github.io/neuralforecast/"
+  site-url: "https://nixtlaverse.nixtla.io/neuralforecast/"
   description: "Time series forecasting suite using deep learning models"
   repo-branch: main
   repo-url: "https://github.com/Nixtla/neuralforecast/"
diff --git a/neuralforecast/core.py b/neuralforecast/core.py
index 85214f57a..fffe4bde5 100644
--- a/neuralforecast/core.py
+++ b/neuralforecast/core.py
@@ -1623,15 +1623,22 @@ def load(path, verbose=False, **kwargs):
         except FileNotFoundError:
             raise Exception("No configuration found in directory.")
 
+        # in 1.6.4, `local_scaler_type` / `scalers_` lived on the dataset.
+        # in order to preserve backwards-compatibility, we check to see if these are found on the dataset
+        # in case they cannot be found in `config_dict`
+        default_scalar_type = getattr(dataset, "local_scaler_type", None)
+        default_scalars_ = getattr(dataset, "scalers_", None)
+
         # Create NeuralForecast object
         neuralforecast = NeuralForecast(
             models=models,
             freq=config_dict["freq"],
-            local_scaler_type=config_dict["local_scaler_type"],
+            local_scaler_type=config_dict.get("local_scaler_type", default_scalar_type),
         )
 
-        for attr in ["id_col", "time_col", "target_col"]:
-            setattr(neuralforecast, attr, config_dict[attr])
+        attr_to_default = {"id_col": "unique_id", "time_col": "ds", "target_col": "y"}
+        for attr, default in attr_to_default.items():
+            setattr(neuralforecast, attr, config_dict.get(attr, default))
         # only restore attribute if available
         for attr in ["prediction_intervals", "_cs_df"]:
             if attr in config_dict.keys():
@@ -1652,7 +1659,7 @@ def load(path, verbose=False, **kwargs):
         # Fitted flag
         neuralforecast._fitted = config_dict["_fitted"]
 
-        neuralforecast.scalers_ = config_dict["scalers_"]
+        neuralforecast.scalers_ = config_dict.get("scalers_", default_scalars_)
 
         return neuralforecast