perf: implement one hot encoder and imputer using polars (#768)

### Summary of Changes

The one hot encoder and imputer are now also implemented using polars,
providing better performance.

Tests should pass again now. We'll maximize coverage over the coming
days.

---------

Co-authored-by: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>

.mega-linter.yml

@@ -15,6 +15,8 @@ JSON_PRETTIER_FILE_EXTENSIONS:
   - .html
 #  - .md
 
+PYTHON_RUFF_CONFIG_FILE: pyproject.toml
+
 # Commands
 PRE_COMMANDS:
   - command: npm i @lars-reimann/prettier-config

src/safeds/data/labeled/containers/_image_dataset.py

@@ -290,6 +290,7 @@ def shuffle(self) -> ImageDataset[T]:
 
 class _TableAsTensor:
     def __init__(self, table: Table) -> None:
+        import polars as pl
         import torch
 
         _init_default_device()
@@ -298,7 +299,7 @@ def __init__(self, table: Table) -> None:
         if table.number_of_rows == 0:
             self._tensor = torch.empty((0, table.number_of_columns), dtype=torch.float32).to(_get_device())
         else:
-            self._tensor = table._data_frame.to_torch().to(_get_device())
+            self._tensor = table._data_frame.to_torch(dtype=pl.Float32).to(_get_device())
 
         if not torch.all(self._tensor.sum(dim=1) == torch.ones(self._tensor.size(dim=0))):
             raise ValueError(
@@ -345,6 +346,7 @@ def _to_table(self) -> Table:
 
 class _ColumnAsTensor:
     def __init__(self, column: Column) -> None:
+        import polars as pl
         import torch
 
         _init_default_device()
@@ -360,9 +362,9 @@ def __init__(self, column: Column) -> None:
             # TODO: should not one-hot-encode the target. label encoding without order is sufficient. should also not
             #  be done automatically?
             self._one_hot_encoder = OneHotEncoder().fit(column_as_table, [self._column_name])
-        self._tensor = torch.Tensor(self._one_hot_encoder.transform(column_as_table)._data_frame.to_torch()).to(
-            _get_device(),
-        )
+        self._tensor = torch.Tensor(
+            self._one_hot_encoder.transform(column_as_table)._data_frame.to_torch(dtype=pl.Float32),
+        ).to(_get_device())
 
     def __eq__(self, other: object) -> bool:
         import torch

src/safeds/data/labeled/containers/_tabular_dataset.py

@@ -53,7 +53,7 @@ class TabularDataset(Dataset):
 
     Examples
     --------
-    >>> from safeds.data.labeled.containers import TabularDataset
+    >>> from safeds.data.tabular.containers import Table
     >>> table = Table(
     ...     {
     ...         "id": [1, 2, 3],

src/safeds/data/tabular/containers/_column.py

@@ -1009,14 +1009,7 @@ def mode(
         >>> from safeds.data.tabular.containers import Column
         >>> column = Column("test", [3, 1, 2, 1, 3])
         >>> column.mode()
-        +------+
-        | test |
-        |  --- |
-        |  i64 |
-        +======+
-        |    1 |
-        |    3 |
-        +------+
+        [1, 3]
         """
         import polars as pl
 

src/safeds/data/tabular/containers/_table.py

@@ -331,7 +331,7 @@ def __eq__(self, other: object) -> bool:
         if self is other:
             return True
 
-        return self._data_frame.frame_equal(other._data_frame)
+        return self._data_frame.equals(other._data_frame)
 
     def __hash__(self) -> int:
         return _structural_hash(self.schema, self.number_of_rows)
@@ -859,7 +859,7 @@ def rename_column(self, old_name: str, new_name: str) -> Table:
     def replace_column(
         self,
         old_name: str,
-        new_columns: Column | list[Column],
+        new_columns: Column | list[Column] | Table,
     ) -> Table:
         """
         Return a new table with a column replaced by zero or more columns.
@@ -871,7 +871,7 @@ def replace_column(
         old_name:
             The name of the column to replace.
         new_columns:
-            The new column or columns.
+            The new columns.
 
         Returns
         -------
@@ -922,11 +922,13 @@ def replace_column(
         |   9 |  12 |   6 |
         +-----+-----+-----+
         """
-        _check_columns_exist(self, old_name)
-        _check_columns_dont_exist(self, [column.name for column in new_columns], old_name=old_name)
-
         if isinstance(new_columns, Column):
             new_columns = [new_columns]
+        elif isinstance(new_columns, Table):
+            new_columns = new_columns.to_columns()
+
+        _check_columns_exist(self, old_name)
+        _check_columns_dont_exist(self, [column.name for column in new_columns], old_name=old_name)
 
         if len(new_columns) == 0:
             return self.remove_columns(old_name)
@@ -1033,9 +1035,6 @@ def remove_duplicate_rows(self) -> Table:
         |   2 |   5 |
         +-----+-----+
         """
-        if self.number_of_columns == 0:
-            return self  # Workaround for https://github.com/pola-rs/polars/issues/16207
-
         return Table._from_polars_lazy_frame(
             self._lazy_frame.unique(maintain_order=True),
         )

src/safeds/data/tabular/plotting/_table_plotter.py

@@ -24,7 +24,7 @@ class TablePlotter:
     Examples
     --------
     >>> from safeds.data.tabular.containers import Table
-    >>> table = Table("test", [1, 2, 3])
+    >>> table = Table({"test": [1, 2, 3]})
     >>> plotter = table.plot
     """
 

src/safeds/data/tabular/transformation/_invertible_table_transformer.py

@@ -15,9 +15,11 @@ class InvertibleTableTransformer(TableTransformer):
     @abstractmethod
     def inverse_transform(self, transformed_table: Table) -> Table:
         """
-        Undo the learned transformation.
+        Undo the learned transformation as well as possible.
 
-        The table is not modified.
+        Column order and types may differ from the original table. Likewise, some values might not be restored.
+
+        **Note:** The given table is not modified.
 
         Parameters
         ----------

src/safeds/data/tabular/transformation/_label_encoder.py

@@ -37,13 +37,14 @@ def __init__(self, *, partial_order: list[Any] | None = None) -> None:
         self._partial_order = partial_order
 
         # Internal state
-        self._mapping: dict[str, dict[Any, int]] | None = None
-        self._inverse_mapping: dict[str, dict[int, Any]] | None = None
+        self._mapping: dict[str, dict[Any, int]] | None = None  # Column name -> value -> label
+        self._inverse_mapping: dict[str, dict[int, Any]] | None = None  # Column name -> label -> value
 
     def __hash__(self) -> int:
         return _structural_hash(
             super().__hash__(),
             self._partial_order,
+            # Leave out the internal state for faster hashing
         )
 
     # ------------------------------------------------------------------------------------------------------------------
@@ -61,7 +62,7 @@ def fit(self, table: Table, column_names: list[str] | None) -> LabelEncoder:
         table:
             The table used to fit the transformer.
         column_names:
-            The list of columns from the table used to fit the transformer. If `None`, all columns are used.
+            The list of columns from the table used to fit the transformer. If `None`, all non-numeric columns are used.
 
         Returns
         -------
@@ -76,14 +77,13 @@ def fit(self, table: Table, column_names: list[str] | None) -> LabelEncoder:
             If the table contains 0 rows.
         """
         if column_names is None:
-            column_names = table.column_names
+            column_names = [name for name in table.column_names if not table.get_column_type(name).is_numeric]
         else:
             _check_columns_exist(table, column_names)
+            _warn_if_columns_are_numeric(table, column_names)
 
         if table.number_of_rows == 0:
-            raise ValueError("The LabelEncoder cannot transform the table because it contains 0 rows")
-
-        _warn_if_columns_are_numeric(table, column_names)
+            raise ValueError("The LabelEncoder cannot be fitted because the table contains 0 rows")
 
         # Learn the transformation
         mapping = {}
@@ -142,7 +142,10 @@ def transform(self, table: Table) -> Table:
 
         _check_columns_exist(table, self._column_names)
 
-        columns = [pl.col(name).replace(self._mapping[name], return_dtype=pl.UInt32) for name in self._column_names]
+        columns = [
+            pl.col(name).replace(self._mapping[name], default=None, return_dtype=pl.UInt32)
+            for name in self._column_names
+        ]
 
         return Table._from_polars_lazy_frame(
             table._lazy_frame.with_columns(columns),
@@ -186,7 +189,7 @@ def inverse_transform(self, transformed_table: Table) -> Table:
             operation="inverse-transform with a LabelEncoder",
         )
 
-        columns = [pl.col(name).replace(self._inverse_mapping[name]) for name in self._column_names]
+        columns = [pl.col(name).replace(self._inverse_mapping[name], default=None) for name in self._column_names]
 
         return Table._from_polars_lazy_frame(
             transformed_table._lazy_frame.with_columns(columns),