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