ensure coreforecast is installed for AutoDifferences (#314)

Nixtla · Feb 16, 2024 · 02a2a85 · 02a2a85
1 parent 6295a56
commit 02a2a85
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Showing 6 changed files with 54 additions and 40 deletions.
diff --git a/mlforecast/__init__.py b/mlforecast/__init__.py
@@ -1,3 +1,3 @@
-__version__ = "0.11.7"
+__version__ = "0.11.8"
 __all__ = ['MLForecast']
 from mlforecast.forecast import MLForecast
diff --git a/mlforecast/compat.py b/mlforecast/compat.py
@@ -17,8 +17,7 @@
     core_scalers = None
     CoreGroupedArray = None
 
-    class BaseLagTransform:
-        ...
+    class BaseLagTransform: ...
 
     Lag = None
 

diff --git a/mlforecast/lag_transforms.py b/mlforecast/lag_transforms.py
@@ -66,20 +66,16 @@ def __init__(self, window_size: int, min_samples: Optional[int] = None):
         self.min_samples = min_samples
 
 # %% ../nbs/lag_transforms.ipynb 7
-class RollingMean(_RollingBase):
-    ...
+class RollingMean(_RollingBase): ...
 
 
-class RollingStd(_RollingBase):
-    ...
+class RollingStd(_RollingBase): ...
 
 
-class RollingMin(_RollingBase):
-    ...
+class RollingMin(_RollingBase): ...
 
 
-class RollingMax(_RollingBase):
-    ...
+class RollingMax(_RollingBase): ...
 
 
 class RollingQuantile(_RollingBase):
@@ -119,20 +115,16 @@ def __init__(
         self.min_samples = min_samples
 
 # %% ../nbs/lag_transforms.ipynb 10
-class SeasonalRollingMean(_Seasonal_RollingBase):
-    ...
+class SeasonalRollingMean(_Seasonal_RollingBase): ...
 
 
-class SeasonalRollingStd(_Seasonal_RollingBase):
-    ...
+class SeasonalRollingStd(_Seasonal_RollingBase): ...
 
 
-class SeasonalRollingMin(_Seasonal_RollingBase):
-    ...
+class SeasonalRollingMin(_Seasonal_RollingBase): ...
 
 
-class SeasonalRollingMax(_Seasonal_RollingBase):
-    ...
+class SeasonalRollingMax(_Seasonal_RollingBase): ...
 
 
 class SeasonalRollingQuantile(_Seasonal_RollingBase):
@@ -154,24 +146,19 @@ def __init__(
 class _ExpandingBase(BaseLagTransform):
     """Expanding statistic"""
 
-    def __init__(self):
-        ...
+    def __init__(self): ...
 
 # %% ../nbs/lag_transforms.ipynb 13
-class ExpandingMean(_ExpandingBase):
-    ...
+class ExpandingMean(_ExpandingBase): ...
 
 
-class ExpandingStd(_ExpandingBase):
-    ...
+class ExpandingStd(_ExpandingBase): ...
 
 
-class ExpandingMin(_ExpandingBase):
-    ...
+class ExpandingMin(_ExpandingBase): ...
 
 
-class ExpandingMax(_ExpandingBase):
-    ...
+class ExpandingMax(_ExpandingBase): ...
 
 
 class ExpandingQuantile(_ExpandingBase):

diff --git a/mlforecast/target_transforms.py b/mlforecast/target_transforms.py
@@ -41,12 +41,10 @@ def update(self, df: DataFrame) -> DataFrame:
         raise NotImplementedError
 
     @abc.abstractmethod
-    def fit_transform(self, df: DataFrame) -> DataFrame:
-        ...
+    def fit_transform(self, df: DataFrame) -> DataFrame: ...
 
     @abc.abstractmethod
-    def inverse_transform(self, df: DataFrame) -> DataFrame:
-        ...
+    def inverse_transform(self, df: DataFrame) -> DataFrame: ...
 
 # %% ../nbs/target_transforms.ipynb 6
 class BaseGroupedArrayTargetTransform(abc.ABC):
@@ -59,16 +57,13 @@ def set_num_threads(self, num_threads: int) -> None:
         self.num_threads = num_threads
 
     @abc.abstractmethod
-    def update(self, ga: GroupedArray) -> GroupedArray:
-        ...
+    def update(self, ga: GroupedArray) -> GroupedArray: ...
 
     @abc.abstractmethod
-    def fit_transform(self, ga: GroupedArray) -> GroupedArray:
-        ...
+    def fit_transform(self, ga: GroupedArray) -> GroupedArray: ...
 
     @abc.abstractmethod
-    def inverse_transform(self, ga: GroupedArray) -> GroupedArray:
-        ...
+    def inverse_transform(self, ga: GroupedArray) -> GroupedArray: ...
 
     def inverse_transform_fitted(self, ga: GroupedArray) -> GroupedArray:
         return self.inverse_transform(ga)
@@ -137,6 +132,11 @@ class AutoDifferences(BaseGroupedArrayTargetTransform):
         Maximum number of differences to apply."""
 
     def __init__(self, max_diffs: int):
+        if not CORE_INSTALLED:
+            raise ImportError(
+                "coreforecast is required for this transformation. "
+                "Please follow the installation instructions at https://github.com/Nixtla/coreforecast/#installation"
+            )
         self.scaler_ = core_scalers.AutoDifferences(max_diffs)
 
     def fit_transform(self, ga: GroupedArray) -> GroupedArray:
@@ -172,6 +172,11 @@ class AutoSeasonalDifferences(AutoDifferences):
     def __init__(
         self, season_length: int, max_diffs: int, n_seasons: Optional[int] = 10
     ):
+        if not CORE_INSTALLED:
+            raise ImportError(
+                "coreforecast is required for this transformation. "
+                "Please follow the installation instructions at https://github.com/Nixtla/coreforecast/#installation"
+            )
         self.scaler_ = core_scalers.AutoSeasonalDifferences(
             season_length=season_length,
             max_diffs=max_diffs,
@@ -196,6 +201,11 @@ class AutoSeasonalityAndDifferences(AutoDifferences):
     def __init__(
         self, max_season_length: int, max_diffs: int, n_seasons: Optional[int] = 10
     ):
+        if not CORE_INSTALLED:
+            raise ImportError(
+                "coreforecast is required for this transformation. "
+                "Please follow the installation instructions at https://github.com/Nixtla/coreforecast/#installation"
+            )
         self.scaler_ = core_scalers.AutoSeasonalityAndDifferences(
             max_season_length=max_season_length,
             max_diffs=max_diffs,

diff --git a/nbs/target_transforms.ipynb b/nbs/target_transforms.ipynb
@@ -275,6 +275,11 @@
     "    max_diffs: int\n",
     "        Maximum number of differences to apply.\"\"\"\n",
     "    def __init__(self, max_diffs: int):\n",
+    "        if not CORE_INSTALLED:\n",
+    "            raise ImportError(\n",
+    "                \"coreforecast is required for this transformation. \"\n",
+    "                \"Please follow the installation instructions at https://github.com/Nixtla/coreforecast/#installation\"\n",
+    "            )\n",
     "        self.scaler_ = core_scalers.AutoDifferences(max_diffs)\n",
     "\n",
     "    def fit_transform(self, ga: GroupedArray) -> GroupedArray:\n",
@@ -301,6 +306,7 @@
    "outputs": [],
    "source": [
     "#| hide\n",
+    "#| core\n",
     "sc = AutoDifferences(1)\n",
     "ga = GroupedArray(np.arange(10), np.array([0, 10]))\n",
     "transformed = sc.fit_transform(ga)\n",
@@ -335,6 +341,11 @@
     "        If `None` will use all samples, otherwise `season_length` * `n_seasons samples` will be used for the test.\n",
     "        Smaller values will be faster but could be less accurate.\"\"\"\n",
     "    def __init__(self, season_length: int, max_diffs: int, n_seasons: Optional[int] = 10):\n",
+    "        if not CORE_INSTALLED:\n",
+    "            raise ImportError(\n",
+    "                \"coreforecast is required for this transformation. \"\n",
+    "                \"Please follow the installation instructions at https://github.com/Nixtla/coreforecast/#installation\"\n",
+    "            )        \n",
     "        self.scaler_ = core_scalers.AutoSeasonalDifferences(\n",
     "            season_length=season_length,\n",
     "            max_diffs=max_diffs,\n",
@@ -361,6 +372,7 @@
    ],
    "source": [
     "#| hide\n",
+    "#| core\n",
     "sc = AutoSeasonalDifferences(season_length=5, max_diffs=1)\n",
     "ga = GroupedArray(np.arange(5)[np.arange(10) % 5], np.array([0, 10]))\n",
     "transformed = sc.fit_transform(ga)\n",
@@ -390,6 +402,11 @@
     "        If `None` will use all samples, otherwise `max_season_length` * `n_seasons samples` will be used for the test.\n",
     "        Smaller values will be faster but could be less accurate.\"\"\"\n",
     "    def __init__(self, max_season_length: int, max_diffs: int, n_seasons: Optional[int] = 10):\n",
+    "        if not CORE_INSTALLED:\n",
+    "            raise ImportError(\n",
+    "                \"coreforecast is required for this transformation. \"\n",
+    "                \"Please follow the installation instructions at https://github.com/Nixtla/coreforecast/#installation\"\n",
+    "            )        \n",
     "        self.scaler_ = core_scalers.AutoSeasonalityAndDifferences(\n",
     "            max_season_length=max_season_length,\n",
     "            max_diffs=max_diffs,\n",
@@ -416,6 +433,7 @@
    ],
    "source": [
     "#| hide\n",
+    "#| core\n",
     "sc = AutoSeasonalityAndDifferences(max_season_length=5, max_diffs=1)\n",
     "ga = GroupedArray(np.arange(5)[np.arange(10) % 5], np.array([0, 10]))\n",
     "transformed = sc.fit_transform(ga)\n",

diff --git a/settings.ini b/settings.ini
@@ -8,7 +8,7 @@ author = José Morales
 author_email = [email protected]
 copyright = Nixtla
 branch = main
-version = 0.11.7
+version = 0.11.8
 min_python = 3.8
 audience = Developers
 language = English