Minor comment cleanups

Author: eddiebergman

autosklearn/automl.py

@@ -578,9 +578,8 @@ def fit(
             example by `sklearn.preprocessing.LabelEncoder
             <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
 
-        dataset_name : Optional[str]
-            Create nicer output. If None, a string will be determined by the
-            md5 hash of the dataset.
+        dataset_name : str | None = None
+            Create nicer output. If None, a pseudo-random hash will be used
 
         only_return_configuration_space: bool = False
             If set to true, fit will only return the configuration space that will

autosklearn/estimators.py

@@ -575,82 +575,34 @@ def fit(
         Fit both optimizes the machine learning models and builds an ensemble
         out of them.
 
-        # TODO PR1213
-        #
-        #   `task: Optional[int]` and `is_classification`
-        #
-        #   `AutoML` tries to identify the task itself with `sklearn.type_of_target`,
-        #   leaving little for the subclasses to do.
-        #   Except this failes when type_of_target(y) == "multiclass".
-        #
-        #   "multiclass" be mean either REGRESSION or MULTICLASS_CLASSIFICATION,
-        #   and so this is where the subclasses are used to determine which.
-        #   However, this could also be deduced from the `is_classification`
-        #   parameter.
-        #
-        #   In the future, there is little need for the subclasses of `AutoML`
-        #   and no need for the `task` parameter. The extra functionality
-        #   provided by `AutoMLClassifier` in predict could be moved to
-        #   `AutoSklearnClassifier`, leaving `AutoML` to just produce raw
-        #   outputs and simplifying the heirarchy.
-        #
-        #  `load_models`
-        #
-        #   This parameter is likely not needed as they are loaded upon demand
-        #   throughout `AutoML`.
-        #   Creating a @property models that loads models into self.models_ is
-        #   not loaded would remove the need for this parameter and simplyify
-        #   the verification of `load if self.models_ is None` to one place.
-        #
-        #   `only_return_configuration_space`
-        #
-        #   This parameter is indicative of a need to create a seperate method
-        #   for this as the functionality of `fit` and what it returns can vary.
-
         Parameters
         ----------
-        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+        X : np.ndarray | pd.DataFrame | list | spmatrix
             The training input samples.
 
-        y : array-like, shape (n_samples) or (n_samples, n_outputs)
+        y : np.ndarray | pd.DataFrame | pd.Series | list
             The target classes.
 
-        task : Optional[int]
-            The identifier for the task AutoML is to perform.
-
-        X_test : Optional[{array-like, sparse matrix}, shape (n_samples, n_features)]
+        X_test : np.ndarray | pd.DataFrame | list | spmatrix | None = None
             Test data input samples. Will be used to save test predictions for
             all models. This allows to evaluate the performance of Auto-sklearn
             over time.
 
-        y_test : Optional[array-like, shape (n_samples) or (n_samples, n_outputs)]
+        y_test :  np.ndarray | pd.DataFrame | pd.Series | list | None = None
             Test data target classes. Will be used to calculate the test error
             of all models. This allows to evaluate the performance of
             Auto-sklearn over time.
 
-        feat_type : Optional[list],
+        feat_type : list[str] | None = None,
             List of str of `len(X.shape[1])` describing the attribute type.
             Possible types are `Categorical` and `Numerical`. `Categorical`
             attributes will be automatically One-Hot encoded. The values
             used for a categorical attribute must be integers, obtained for
             example by `sklearn.preprocessing.LabelEncoder
             <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
 
-        dataset_name : Optional[str]
-            Create nicer output. If None, a string will be determined by the
-            md5 hash of the dataset.
-
-        only_return_configuration_space: bool = False
-            If set to true, fit will only return the configuration space that will
-            be used for model search. Otherwise fitting will be performed and an
-            ensemble created.
-
-        load_models: bool = True
-            If true, this will load the models into memory once complete.
-
-        is_classification: bool = False
-            Indicates whether this is a classification task if True or a
-            regression task if False.
+        dataset_name : str | None = None
+            Create nicer output. If None, a pseudo-random hash will be used
 
         Returns
         -------