feat: replace token by api_key and environment by base_url (#275)

.github/workflows/ci.yaml

@@ -66,10 +66,10 @@ jobs:
         os: [ubuntu-latest, macos-latest, windows-latest]
         python-version: [3.9, '3.10']
     env:
-      TIMEGPT_TOKEN: ${{ secrets.TIMEGPT_TOKEN }}
-      TIMEGPT_API_URL: ${{ secrets.TIMEGPT_API_URL }}
-      TIMEGPT_CUSTOM_URL_TOKEN: ${{ secrets.TIMEGPT_CUSTOM_URL_TOKEN }}
-      TIMEGPT_CUSTOM_URL: ${{ secrets.TIMEGPT_CUSTOM_URL }}
+      NIXTLA_API_KEY: ${{ secrets.NIXTLA_API_KEY }}
+      NIXTLA_BASE_URL: ${{ secrets.NIXTLA_BASE_URL }}
+      NIXTLA_API_KEY_CUSTOM: ${{ secrets.NIXTLA_API_KEY_CUSTOM }}
+      NIXTLA_BASE_URL_CUSTOM: ${{ secrets.NIXTLA_BASE_URL_CUSTOM }}
       API_KEY_FRED: ${{ secrets.API_KEY_FRED }}
     steps:
       - name: Clone repo

.github/workflows/models-performance.yaml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
     env:
-      TIMEGPT_TOKEN: ${{ secrets.TIMEGPT_TOKEN }}
+      NIXTLA_API_KEY: ${{ secrets.NIXTLA_API_KEY }}
       PLOTS_REPO_URL: https://github.com/Nixtla/nixtla/blob/docs-figs-model-performance
     steps:
       - name: Clone repo

README.md

@@ -46,8 +46,8 @@ df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-tim
 
 from nixtlats import TimeGPT
 timegpt = TimeGPT(
-    # defaults to os.environ.get("TIMEGPT_TOKEN")
-    token = 'my_token_provided_by_nixtla'
+    # defaults to os.environ.get("NIXTLA_API_KEY")
+    api_key = 'my_api_key_provided_by_nixtla'
 )
 fcst_df = timegpt.forecast(df, h=24, level=[80, 90])
 ```

action_files/models_performance/main.py

@@ -141,7 +141,7 @@ def evaluate_timegpt(self, model: str) -> Tuple[pd.DataFrame, pd.DataFrame]:
         init_time = time()
         # A: this sould be replaced with
         # cross validation
-        timegpt = TimeGPT(token=os.environ["TIMEGPT_TOKEN"])
+        timegpt = TimeGPT()
         fcst_df = timegpt.forecast(
             df=self.df_train,
             X_df=self.df_test.drop(columns=self.target_col)
@@ -200,7 +200,7 @@ def evaluate_benchmark_performace(self) -> Tuple[pd.DataFrame, pd.DataFrame]:
 
     def plot_and_save_forecasts(self, cv_df: pd.DataFrame, plot_dir: str) -> str:
         """Plot ans saves forecasts, returns the path of the plot"""
-        timegpt = TimeGPT(token=os.environ["TIMEGPT_TOKEN"])
+        timegpt = TimeGPT()
         df = self.df.copy()
         df[self.time_col] = pd.to_datetime(df[self.time_col])
         if not self.has_id_col:

nbs/distributed.timegpt.ipynb

@@ -87,14 +87,14 @@
     "\n",
     "    def __init__(\n",
     "            self, \n",
-    "            token: Optional[str] = None, \n",
-    "            environment: Optional[str] = None,\n",
+    "            api_key: Optional[str] = None, \n",
+    "            base_url: Optional[str] = None,\n",
     "            max_retries: int = 6,\n",
     "            retry_interval: int = 10,\n",
     "            max_wait_time: int = 60,\n",
     "        ):\n",
-    "        self.token = token\n",
-    "        self.environment = environment\n",
+    "        self.api_key = api_key\n",
+    "        self.base_url = base_url\n",
     "        self.max_retries = max_retries\n",
     "        self.retry_interval = retry_interval\n",
     "        self.max_wait_time = max_wait_time\n",
@@ -164,7 +164,7 @@
     "            finetune_steps: int = 0,\n",
     "            finetune_loss: str = 'default',\n",
     "            clean_ex_first: bool = True,\n",
-    "            validate_token: bool = False,\n",
+    "            validate_api_key: bool = False,\n",
     "            add_history: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
@@ -182,7 +182,7 @@
     "            finetune_steps=finetune_steps,\n",
     "            finetune_loss=finetune_loss,\n",
     "            clean_ex_first=clean_ex_first,\n",
-    "            validate_token=validate_token,\n",
+    "            validate_api_key=validate_api_key,\n",
     "            add_history=add_history,\n",
     "            date_features=date_features,\n",
     "            date_features_to_one_hot=date_features_to_one_hot,\n",
@@ -214,7 +214,7 @@
     "            target_col: str = 'y',\n",
     "            level: Union[int, float] = 99,\n",
     "            clean_ex_first: bool = True,\n",
-    "            validate_token: bool = False,\n",
+    "            validate_api_key: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
     "            model: str = 'timegpt-1',\n",
@@ -227,7 +227,7 @@
     "            target_col=target_col,\n",
     "            level=level,\n",
     "            clean_ex_first=clean_ex_first,\n",
-    "            validate_token=validate_token,\n",
+    "            validate_api_key=validate_api_key,\n",
     "            date_features=date_features,\n",
     "            date_features_to_one_hot=date_features_to_one_hot,\n",
     "            model=model,\n",
@@ -257,7 +257,7 @@
     "            finetune_steps: int = 0,\n",
     "            finetune_loss: str = 'default',\n",
     "            clean_ex_first: bool = True,\n",
-    "            validate_token: bool = False,\n",
+    "            validate_api_key: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
     "            model: str = 'timegpt-1',\n",
@@ -276,7 +276,7 @@
     "            finetune_steps=finetune_steps,\n",
     "            finetune_loss=finetune_loss,\n",
     "            clean_ex_first=clean_ex_first,\n",
-    "            validate_token=validate_token,\n",
+    "            validate_api_key=validate_api_key,\n",
     "            date_features=date_features,\n",
     "            date_features_to_one_hot=date_features_to_one_hot,\n",
     "            model=model,\n",
@@ -303,8 +303,8 @@
     "    def _instantiate_timegpt(self):\n",
     "        from nixtlats.timegpt import _TimeGPT\n",
     "        timegpt = _TimeGPT(\n",
-    "            token=self.token, \n",
-    "            environment=self.environment,\n",
+    "            api_key=self.api_key, \n",
+    "            base_url=self.base_url,\n",
     "            max_retries=self.max_retries,\n",
     "            retry_interval=self.retry_interval,\n",
     "            max_wait_time=self.max_wait_time,\n",