new engine 1

atom/data_cleaning.py

@@ -102,7 +102,7 @@ def __repr__(self, N_CHAR_MAX: Int = 700) -> str:
             and self.engine == EngineTuple()
             and sklearn.get_config()["print_changed_only"]
         ):
-            out = re.sub(r"engine=Engine\(\)", "", out)
+            out = re.sub(r"engine={data='numpy', estimator='sklearn'}", "", out)
             out = re.sub(r"((?<=\(),\s|,\s(?=\))|,\s(?=,\s))", "", out)  # Drop comma-spaces
 
         return out
@@ -3033,24 +3033,12 @@ class Scaler(TransformerMixin, OneToOneFeatureMixin, _SetOutputMixin):
         `#!python device="gpu"` to use the GPU. Read more in the
         [user guide][gpu-acceleration].
 
-    engine: str, dict or None, default=None
-        Execution engine to use for [data][data-acceleration] and
-        [estimators][estimator-acceleration]. The value should be
-        one of the possible values to change one of the two engines,
-        or a dictionary with keys `data` and `estimator`, with their
-        corresponding choice as values to change both engines. If
-        None, the default values are used. Choose from:
-
-        - "data":
+    engine: str, default="sklearn"
+        Execution engine for [estimators][estimator-acceleration].
+        Choose from:
 
-            - "pandas" (default)
-            - "pyarrow"
-            - "modin"
-
-        - "estimator":
-
-            - "sklearn" (default)
-            - "cuml"
+        - "sklearn"
+        - "cuml"
 
     verbose: int, default=0
         Verbosity level of the class. Choose from:
@@ -3119,7 +3107,7 @@ def __init__(
         *,
         include_binary: Bool = False,
         device: str = "cpu",
-        engine: Engine = None,
+        engine: Engine = "sklearn",
         verbose: Verbose = 0,
         **kwargs,
     ):

atom/utils/types.py

@@ -114,10 +114,8 @@ class EngineTuple(NamedTuple):
     estimator: EngineEstimatorOptions = "sklearn"
 
     def __repr__(self) -> str:
-        """Hide default values and change the name of the class."""
-        data = f"data={self[0]}" if self[0] != EngineTuple().data else ""
-        estimator = f"estimator={self[1]}" if self[1] != EngineTuple().estimator else ""
-        return f"Engine({data}{', ' if data else ''}{estimator})"
+        """Print representation as dictionary."""
+        return self._asdict().__repr__()
 
 
 class SPTuple(NamedTuple):

atom/utils/utils.py

@@ -2857,8 +2857,8 @@ def wrapper(self, *args, **kwargs):
             # For sktime estimators, we are interested in y, not X
             X = args[0] if len(args) > 0 else kwargs.get("X")
 
-            # We add the attributes and methods after running fit
-            # to avoid deleting them with .reset() calls
+            # We add the attributes and methods after running
+            # fit to avoid deleting them with .reset() calls
             if X is not None:
                 if not hasattr(self, "feature_names_in_"):
                     BaseEstimator._check_feature_names(self, X, reset=True)