feat: improve transformers for tabular data (#108)

Closes #61.
Closes #90.

### Summary of Changes

* Common superclasses `TableTransformer` and
`InvertibleTableTransformer`
* Common interface for `fit`, `transform`, `fit_transform`,
`inverse_transform`
* Return new transformer when calling `fit`
* More thorough tests

---------

Co-authored-by: lars-reimann <[email protected]>

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

@@ -1,3 +1,3 @@
-from ._imputer import Imputer
+from ._imputer import Imputer, ImputerStrategy
 from ._label_encoder import LabelEncoder
 from ._one_hot_encoder import OneHotEncoder

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

@@ -5,17 +5,18 @@
 
 import pandas as pd
 from safeds.data.tabular.containers import Table
-from sklearn.impute import SimpleImputer
+from safeds.data.tabular.transformation._table_transformer import TableTransformer
+from safeds.exceptions import NotFittedError, UnknownColumnNameError
+from sklearn.impute import SimpleImputer as sk_SimpleImputer
 
 
 class ImputerStrategy(ABC):
     @abstractmethod
-    def _augment_imputer(self, imputer: SimpleImputer) -> None:
+    def _augment_imputer(self, imputer: sk_SimpleImputer) -> None:
         pass
 
 
-# noinspection PyProtectedMember
-class Imputer:
+class Imputer(TableTransformer):
     """
     Impute the data for a given Table.
 
@@ -39,7 +40,10 @@ class Constant(ImputerStrategy):
             def __init__(self, value: Any):
                 self._value = value
 
-            def _augment_imputer(self, imputer: SimpleImputer) -> None:
+            def __str__(self) -> str:
+                return f"Constant({self._value})"
+
+            def _augment_imputer(self, imputer: sk_SimpleImputer) -> None:
                 imputer.strategy = "constant"
                 imputer.fill_value = self._value
 
@@ -48,97 +52,112 @@ class Mean(ImputerStrategy):
             An imputation strategy for imputing missing data with mean values.
             """
 
-            def _augment_imputer(self, imputer: SimpleImputer) -> None:
+            def __str__(self) -> str:
+                return "Mean"
+
+            def _augment_imputer(self, imputer: sk_SimpleImputer) -> None:
                 imputer.strategy = "mean"
 
         class Median(ImputerStrategy):
             """
             An imputation strategy for imputing missing data with median values.
             """
 
-            def _augment_imputer(self, imputer: SimpleImputer) -> None:
+            def __str__(self) -> str:
+                return "Median"
+
+            def _augment_imputer(self, imputer: sk_SimpleImputer) -> None:
                 imputer.strategy = "median"
 
         class Mode(ImputerStrategy):
             """
             An imputation strategy for imputing missing data with mode values.
             """
 
-            def _augment_imputer(self, imputer: SimpleImputer) -> None:
+            def __str__(self) -> str:
+                return "Mode"
+
+            def _augment_imputer(self, imputer: sk_SimpleImputer) -> None:
                 imputer.strategy = "most_frequent"
 
     def __init__(self, strategy: ImputerStrategy):
-        self._imp = SimpleImputer()
-        strategy._augment_imputer(self._imp)
-        self._column_names: list[str] = []
+        self._strategy = strategy
 
-    def fit(self, table: Table, column_names: Optional[list[str]] = None) -> None:
+        self._wrapped_transformer: Optional[sk_SimpleImputer] = None
+        self._column_names: Optional[list[str]] = None
+
+    # noinspection PyProtectedMember
+    def fit(self, table: Table, column_names: Optional[list[str]] = None) -> Imputer:
         """
-        Fit the imputer on the dataset.
+        Learn a transformation for a set of columns in a table.
 
         Parameters
         ----------
         table : Table
-            The table used to learn the imputation values.
+            The table used to fit the transformer.
         column_names : Optional[list[str]]
-            An optional list of column names, if the imputer is only supposed to run on specific columns.
+            The list of columns from the table used to fit the transformer. If `None`, all columns are used.
+
+        Returns
+        -------
+        fitted_transformer : TableTransformer
+            The fitted transformer.
         """
         if column_names is None:
-            column_names = table.schema.get_column_names()
+            column_names = table.get_column_names()
+        else:
+            missing_columns = set(column_names) - set(table.get_column_names())
+            if len(missing_columns) > 0:
+                raise UnknownColumnNameError(list(missing_columns))
 
-        if self._imp.strategy == "most_frequent":
+        if isinstance(self._strategy, Imputer.Strategy.Mode):
             for name in column_names:
-                if 1 < len(table.get_column(name).mode()):
-                    raise IndexError(
-                        "There are multiple frequent values in a column given for the Imputer"
-                    )
+                if len(table.get_column(name).mode()) > 1:
+                    raise IndexError("There are multiple most frequent values in a column given for the Imputer")
+
+        indices = [table.schema._get_column_index_by_name(name) for name in column_names]
+
+        wrapped_transformer = sk_SimpleImputer()
+        self._strategy._augment_imputer(wrapped_transformer)
+        wrapped_transformer.fit(table._data[indices])
+
+        result = Imputer(self._strategy)
+        result._wrapped_transformer = wrapped_transformer
+        result._column_names = column_names
 
-        self._column_names = column_names
-        indices = [
-            table.schema._get_column_index_by_name(name) for name in self._column_names
-        ]
-        self._imp.fit(table._data[indices])
+        return result
 
+    # noinspection PyProtectedMember
     def transform(self, table: Table) -> Table:
         """
-        Impute the missing values on the dataset.
+        Apply the learned transformation to a table.
 
         Parameters
         ----------
         table : Table
-            The dataset to be imputed.
+            The table to which the learned transformation is applied.
 
         Returns
         -------
-        table : Table
-            The dataset with missing values imputed by the given strategy.
-        """
-        data = table._data.copy()
-        indices = [
-            table.schema._get_column_index_by_name(name) for name in self._column_names
-        ]
-        data[indices] = pd.DataFrame(
-            self._imp.transform(data[indices]), columns=indices
-        )
-        return Table(data, table.schema)
+        transformed_table : Table
+            The transformed table.
 
-    def fit_transform(
-        self, table: Table, column_names: Optional[list[str]] = None
-    ) -> Table:
+        Raises
+        ----------
+        NotFittedError
+            If the transformer has not been fitted yet.
         """
-        Fit the imputer on the dataset and impute the missing values.
 
-        Parameters
-        ----------
-        table : Table
-            The table used to learn the imputation values.
-        column_names : Optional[list[str]]
-            An optional list of column names, if the imputer is only supposed to run on specific columns.
+        # Transformer has not been fitted yet
+        if self._wrapped_transformer is None or self._column_names is None:
+            raise NotFittedError()
 
-        Returns
-        -------
-        table : Table
-            The dataset with missing values imputed by the given strategy.
-        """
-        self.fit(table, column_names)
-        return self.transform(table)
+        # Input table does not contain all columns used to fit the transformer
+        missing_columns = set(self._column_names) - set(table.get_column_names())
+        if len(missing_columns) > 0:
+            raise UnknownColumnNameError(list(missing_columns))
+
+        data = table._data.copy()
+        indices = [table.schema._get_column_index_by_name(name) for name in self._column_names]
+        data[indices] = pd.DataFrame(self._wrapped_transformer.transform(data[indices]), columns=indices)
+        return Table(data, table.schema)