update fit_model for new prediction method with X_prediction

ml_garden/core/model_registry.py

@@ -1,26 +1,71 @@
 import importlib
 import logging
 import pkgutil
+from typing import Dict, Type
 
 from ml_garden.core.model import Model
 
 
 class ModelClassNotFoundError(Exception):
+    """Exception raised when a model class is not found in the registry."""
+
     pass
 
 
 class ModelRegistry:
     def __init__(self):
-        self._model_registry = {}
+        """
+        Initialize a new ModelRegistry instance.
+
+        Attributes
+        ----------
+        _model_registry : dict
+            A dictionary mapping model names to model classes.
+        logger : logging.Logger
+            Logger for the class.
+        """
+        self._model_registry: Dict[str, Type[Model]] = {}
         self.logger = logging.getLogger(__name__)
 
-    def register_model(self, model_class: type):
-        model_name = model_class.__name__
+    def register_model(self, model_class: Type[Model]) -> None:
+        """
+        Register a model class in the registry.
+
+        Parameters
+        ----------
+        model_class : Type[Model]
+            The model class to be registered.
+
+        Raises
+        ------
+        ValueError
+            If the model_class is not a subclass of Model.
+        """
+        model_name = model_class.__name__.lower()
         if not issubclass(model_class, Model):
             raise ValueError(f"{model_class} must be a subclass of Model")
         self._model_registry[model_name] = model_class
 
-    def get_model_class(self, model_name: str) -> type:
+    def get_model_class(self, model_name: str) -> Type[Model]:
+        """
+        Retrieve a model class from the registry.
+
+        Parameters
+        ----------
+        model_name : str
+            The name of the model class to retrieve.
+
+        Returns
+        -------
+        Type[Model]
+            The model class.
+
+        Raises
+        ------
+        ModelClassNotFoundError
+            If the model class is not found in the registry.
+        """
+        model_name = model_name.lower()
         if model_name in self._model_registry:
             return self._model_registry[model_name]
         else:
@@ -29,10 +74,31 @@ def get_model_class(self, model_name: str) -> type:
                 f" {list(self._model_registry.keys())}"
             )
 
-    def get_all_model_classes(self) -> dict:
+    def get_all_model_classes(self) -> Dict[str, Type[Model]]:
+        """
+        Get all registered model classes.
+
+        Returns
+        -------
+        dict
+            A dictionary of all registered model classes.
+        """
         return self._model_registry
 
-    def auto_register_models_from_package(self, package_name: str):
+    def auto_register_models_from_package(self, package_name: str) -> None:
+        """
+        Automatically register all model classes from a given package.
+
+        Parameters
+        ----------
+        package_name : str
+            The name of the package to search for model classes.
+
+        Raises
+        ------
+        ImportError
+            If the package cannot be imported.
+        """
         try:
             package = importlib.import_module(package_name)
             prefix = package.__name__ + "."

ml_garden/core/steps/fit_model.py

@@ -298,6 +298,7 @@ def predict(self, data: DataContainer) -> DataContainer:
             The updated data container
         """
         self.logger.info(f"Predicting with {self.model_class.__name__} model")
+        data.X_prediction = data.flow.drop(columns=data.columns_to_ignore_for_training)
         data.flow[data.prediction_column] = data.model.predict(data.X_prediction)
         data.predictions = data.flow[data.prediction_column]
         return data

ml_garden/implementation/tabular/autogluon/model.py

@@ -3,13 +3,14 @@
 import pandas as pd
 from autogluon.tabular import TabularPredictor
 
+from ml_garden.core.constants import Task
 from ml_garden.core.model import Model
 
 logger = logging.getLogger(__file__)
 
 
 class AutoGluon(Model):
-    TASKS = ["regression", "classification"]
+    TASKS = [Task.REGRESSION, Task.CLASSIFICATION]
 
     def __init__(self, **params):
         self.params = params