support step_size selection in optimization.mlforecast_objective (#…

…419)

mlforecast/optimization.py

@@ -29,6 +29,7 @@ def mlforecast_objective(
     freq: Freq,
     n_windows: int,
     h: int,
+    step_size: Optional[int] = None,
     refit: Union[bool, int] = False,
     id_col: str = "unique_id",
     time_col: str = "ds",
@@ -53,6 +54,8 @@ def mlforecast_objective(
         Number of windows to evaluate.
     h : int
         Forecast horizon.
+    step_size : int, optional (default=None)
+        Step size between each cross validation window. If None it will be equal to `h`.
     refit : bool or int (default=False)
         Retrain model for each cross validation window.
         If False, the models are trained at the beginning and then used to predict each window.
@@ -82,6 +85,7 @@ def objective(trial: optuna.Trial) -> float:
             id_col=id_col,
             time_col=time_col,
             freq=freq,
+            step_size=step_size,
         )
         model_copy = clone(model)
         model_params = config["model_params"]

nbs/optimization.ipynb

@@ -80,6 +80,7 @@
     "    freq: Freq,\n",
     "    n_windows: int,\n",
     "    h: int,\n",
+    "    step_size: Optional[int] = None,\n",
     "    refit: Union[bool, int] = False,\n",
     "    id_col: str = 'unique_id',\n",
     "    time_col: str = 'ds',\n",
@@ -104,6 +105,8 @@
     "        Number of windows to evaluate.\n",
     "    h : int\n",
     "        Forecast horizon.\n",
+    "    step_size : int, optional (default=None)\n",
+    "        Step size between each cross validation window. If None it will be equal to `h`.\n",
     "    refit : bool or int (default=False)\n",
     "        Retrain model for each cross validation window.\n",
     "        If False, the models are trained at the beginning and then used to predict each window.\n",
@@ -132,6 +135,7 @@
     "            id_col=id_col,\n",
     "            time_col=time_col,\n",
     "            freq=freq,\n",
+    "            step_size=step_size,\n",
     "        )\n",
     "        model_copy = clone(model)\n",
     "        model_params = config['model_params']\n",