make ForecastingDependenciesNotInstalledError a str message

autoPyTorch/constants.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 TABULAR_CLASSIFICATION = 1
 IMAGE_CLASSIFICATION = 2
 TABULAR_REGRESSION = 3
@@ -56,16 +54,9 @@
 CLASSIFICATION_OUTPUTS = [BINARY, MULTICLASS, MULTICLASSMULTIOUTPUT]
 REGRESSION_OUTPUTS = [CONTINUOUS, CONTINUOUSMULTIOUTPUT]
 
-
-# Constants for Forecasting Tasks
-# Exceptions
-class ForecastingDependenciesNotInstalledError(ModuleNotFoundError):
-    def __init__(self, msg: Optional[str] = None):
-        if msg is None:
-            msg = "Additional dependencies must be installed to work with time series forecasting" \
-                  "tasks! Please run \n pip install autoPyTorch[forecasting] \n to install the" \
-                  "corresponding dependencies!"
-        super().__init__(msg)
+ForecastingDependenciesNotInstalledMSG = "Additional dependencies must be installed to work with time series " \
+                                         "forecasting tasks! Please run \n pip install autoPyTorch[forecasting] \n to "\
+                                         "install the corresponding dependencies!"
 
 
 # The constant values for time series forecasting comes from

autoPyTorch/evaluation/abstract_evaluator.py

@@ -31,7 +31,7 @@
     CLASSIFICATION_TASKS,
     FORECASTING_BUDGET_TYPE,
     FORECASTING_TASKS,
-    ForecastingDependenciesNotInstalledError,
+    ForecastingDependenciesNotInstalledMSG,
     IMAGE_TASKS,
     MULTICLASS,
     REGRESSION_TASKS,
@@ -498,7 +498,7 @@ def __init__(self, backend: Backend,
         elif self.task_type in FORECASTING_TASKS:
             if isinstance(self.configuration, int):
                 if not forecasting_dependencies_installed:
-                    raise ForecastingDependenciesNotInstalledError
+                    raise ModuleNotFoundError(ForecastingDependenciesNotInstalledMSG)
                 self.pipeline_class = DummyTimeSeriesForecastingPipeline
             elif isinstance(self.configuration, str):
                 raise ValueError("Only tabular classifications tasks "

autoPyTorch/evaluation/tae.py

@@ -25,7 +25,7 @@
 from autoPyTorch.automl_common.common.utils.backend import Backend
 from autoPyTorch.constants import (
     FORECASTING_BUDGET_TYPE,
-    ForecastingDependenciesNotInstalledError,
+    ForecastingDependenciesNotInstalledMSG,
     STRING_TO_TASK_TYPES,
     TIMESERIES_FORECASTING,
 )
@@ -158,7 +158,7 @@ def __init__(
 
         if STRING_TO_TASK_TYPES.get(dm.task_type, -1) == TIMESERIES_FORECASTING:
             if not forecasting_dependencies_installed:
-                raise ForecastingDependenciesNotInstalledError
+                raise ModuleNotFoundError(ForecastingDependenciesNotInstalledMSG)
             eval_function: Callable = forecasting_eval_train_function
             if isinstance(self.resampling_strategy, (HoldoutValTypes, CrossValTypes)):
                 self.output_y_hat_optimization = output_y_hat_optimization

autoPyTorch/pipeline/components/training/metrics/utils.py

@@ -6,7 +6,7 @@
 from autoPyTorch.constants import (
     CLASSIFICATION_TASKS,
     FORECASTING_TASKS,
-    ForecastingDependenciesNotInstalledError,
+    ForecastingDependenciesNotInstalledMSG,
     REGRESSION_TASKS,
     STRING_TO_TASK_TYPES,
     TASK_TYPES,
@@ -49,7 +49,7 @@ def get_supported_metrics(dataset_properties: Dict[str, Any]) -> Dict[str, autoP
         return CLASSIFICATION_METRICS
     elif STRING_TO_TASK_TYPES[task_type] in FORECASTING_TASKS:
         if len(FORECASTING_METRICS) == 0:
-            raise ForecastingDependenciesNotInstalledError
+            raise ModuleNotFoundError(ForecastingDependenciesNotInstalledMSG)
         return FORECASTING_METRICS
     else:
         raise NotImplementedError(task_type)
@@ -129,7 +129,7 @@ def calculate_score(
     score_dict = dict()
     if task_type in FORECASTING_TASKS:
         if len(MASE_LOSSES) == 0:
-            raise ForecastingDependenciesNotInstalledError
+            raise ModuleNotFoundError(ForecastingDependenciesNotInstalledMSG)
         cprediction = sanitize_array(prediction)
         for metric_ in metrics:
             if metric_ in MASE_LOSSES and 'mase_coefficient' in score_kwargs:

autoPyTorch/utils/pipeline.py

@@ -6,7 +6,7 @@
 from autoPyTorch.constants import (
     CLASSIFICATION_TASKS,
     FORECASTING_TASKS,
-    ForecastingDependenciesNotInstalledError,
+    ForecastingDependenciesNotInstalledMSG,
     IMAGE_TASKS,
     REGRESSION_TASKS,
     STRING_TO_TASK_TYPES,
@@ -79,7 +79,7 @@ def get_dataset_requirements(info: Dict[str, Any],
                                                         )
     else:
         if not forecasting_dependencies_installed:
-            raise ForecastingDependenciesNotInstalledError
+            raise ModuleNotFoundError(ForecastingDependenciesNotInstalledMSG)
         return _get_forecasting_dataset_requirements(info,
                                                      include if include is not None else {},
                                                      exclude if exclude is not None else {},
@@ -137,7 +137,7 @@ def _get_forecasting_dataset_requirements(info: Dict[str, Any],
 
     if task_type in FORECASTING_TASKS:
         if not forecasting_dependencies_installed:
-            raise ForecastingDependenciesNotInstalledError
+            raise ModuleNotFoundError(ForecastingDependenciesNotInstalledMSG)
         return TimeSeriesForecastingPipeline(
             dataset_properties=info,
             include=include,