Last few mypy errors knocked out

Author: eddiebergman

autosklearn/automl.py

@@ -98,13 +98,7 @@
 from autosklearn.smbo import AutoMLSMBO
 from autosklearn.util import RE_PATTERN, pipeline
 from autosklearn.util.dask import Dask, LocalDask, UserDask
-from autosklearn.util.data import (
-    DatasetCompressionSpec,
-    default_dataset_compression_arg,
-    reduce_dataset_size_if_too_large,
-    supported_precision_reductions,
-    validate_dataset_compression_arg,
-)
+from autosklearn.util.data import DatasetCompression
 from autosklearn.util.logging_ import (
     PicklableClientLogger,
     get_named_client_logger,
@@ -252,15 +246,14 @@ def __init__(
                 )
 
         # Validate dataset_compression and set its values
-        self._dataset_compression: DatasetCompressionSpec | None = None
-        if isinstance(dataset_compression, bool):
-            if dataset_compression is True:
-                self._dataset_compression = default_dataset_compression_arg
-        else:
-            self._dataset_compression = validate_dataset_compression_arg(
-                dataset_compression,
-                memory_limit=memory_limit,
-            )
+        self._dataset_compression: DatasetCompression | None = None
+        if dataset_compression is not False:
+
+            if memory_limit is None:
+                raise ValueError("Must provide a `memory_limit` for data compression")
+
+            spec = {} if dataset_compression is True else dataset_compression
+            self._dataset_compression = DatasetCompression(**spec, limit=memory_limit)
 
         # If we got something callable for `get_trials_callback`, wrap it so SMAC
         # will accept it.
@@ -667,30 +660,13 @@ def fit(
                 X_test, y_test = input_validator.transform(X_test, y_test)
 
             # We don't support size reduction on pandas type object yet
-            if (
-                self._dataset_compression is not None
-                and not isinstance(X, pd.DataFrame)
-                and not (isinstance(y, pd.Series) or isinstance(y, pd.DataFrame))
-            ):
-                methods = self._dataset_compression["methods"]
-                memory_allocation = self._dataset_compression["memory_allocation"]
-
-                # Remove precision reduction if we can't perform it
-                if (
-                    "precision" in methods
-                    and X.dtype not in supported_precision_reductions
-                ):
-                    methods = [method for method in methods if method != "precision"]
-
+            if self._dataset_compression and self._dataset_compression.supports(X, y):
                 with warnings_to(self.logger):
-                    X, y = reduce_dataset_size_if_too_large(
+                    X, y = self._dataset_compression.compress(
                         X=X,
                         y=y,
-                        memory_limit=self._memory_limit,
-                        is_classification=self.is_classification,
+                        stratify=self.is_classification,
                         random_state=self._seed,
-                        operations=methods,
-                        memory_allocation=memory_allocation,
                     )
 
             # Check the re-sampling strategy