feat: discretize table

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

@@ -1,5 +1,6 @@
 """Classes for transforming tabular data."""
 
+from ._discretizer import Discretizer
 from ._imputer import Imputer
 from ._label_encoder import LabelEncoder
 from ._one_hot_encoder import OneHotEncoder
@@ -14,5 +15,6 @@
     "InvertibleTableTransformer",
     "TableTransformer",
     "RangeScaler",
+    "Discretizer",
     "StandardScaler",
 ]

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

@@ -0,0 +1,205 @@
+from __future__ import annotations
+
+from sklearn.preprocessing import KBinsDiscretizer as sk_KBinsDiscretizer
+
+from safeds.data.tabular.containers import Table
+from safeds.data.tabular.transformation._table_transformer import TableTransformer
+from safeds.exceptions import NonNumericColumnError, TransformerNotFittedError, UnknownColumnNameError
+
+
+class Discretizer(TableTransformer):
+    """
+    The Discretizer bins continuous data into intervals.
+
+    Parameters
+    ----------
+    number_of_bins: float
+        The number of bins to be created.
+
+    Raises
+    ------
+    ValueError
+        If the given number_of_bins is less than 2.
+    """
+
+    def __init__(self, number_of_bins: float = 5):
+        self._column_names: list[str] | None = None
+        self._wrapped_transformer: sk_KBinsDiscretizer | None = None
+
+        if number_of_bins < 2:
+            raise ValueError("Parameter 'number_of_bins' must be >= 2.")
+        self._number_of_bins = number_of_bins
+
+    def fit(self, table: Table, column_names: list[str] | None) -> Discretizer:
+        """
+        Learn a transformation for a set of columns in a table.
+
+        This transformer is not modified.
+
+        Parameters
+        ----------
+        table : Table
+            The table used to fit the transformer.
+        column_names : list[str] | None
+            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.
+
+        Raises
+        ------
+        ValueError
+            If the table is empty.
+        NonNumericColumnError
+            If one of the columns, that should be fitted is non-numeric.
+        UnknownColumnNameError
+            If one of the columns, that should be fitted is not in the table.
+        """
+        if table.number_of_rows == 0:
+            raise ValueError("The Discretizer cannot be fitted because the table contains 0 rows")
+
+        if column_names is None:
+            column_names = table.column_names
+        else:
+            missing_columns = set(column_names) - set(table.column_names)
+            if len(missing_columns) > 0:
+                raise UnknownColumnNameError(
+                    sorted(
+                        missing_columns,
+                        key={val: ix for ix, val in enumerate(column_names)}.__getitem__,
+                    ),
+                )
+
+            for column in column_names:
+                if not table.get_column(column).type.is_numeric():
+                    raise NonNumericColumnError(f"{column} is of type {table.get_column(column).type}.")
+
+        wrapped_transformer = sk_KBinsDiscretizer(n_bins=self._number_of_bins, encode="ordinal")
+        wrapped_transformer.fit(table._data[column_names])
+
+        result = Discretizer(self._number_of_bins)
+        result._wrapped_transformer = wrapped_transformer
+        result._column_names = column_names
+
+        return result
+
+    def transform(self, table: Table) -> Table:
+        """
+        Apply the learned transformation to a table.
+
+        The table is not modified.
+
+        Parameters
+        ----------
+        table : Table
+            The table to which the learned transformation is applied.
+
+        Returns
+        -------
+        transformed_table : Table
+            The transformed table.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        ValueError
+            If the table is empty.
+        UnknownColumnNameError
+            If one of the columns, that should be transformed is not in the table.
+        NonNumericColumnError
+            If one of the columns, that should be fitted is non-numeric.
+        """
+        # Transformer has not been fitted yet
+        if self._wrapped_transformer is None or self._column_names is None:
+            raise TransformerNotFittedError
+
+        if table.number_of_rows == 0:
+            raise ValueError("The table cannot be transformed because it contains 0 rows")
+
+        # Input table does not contain all columns used to fit the transformer
+        missing_columns = set(self._column_names) - set(table.column_names)
+        if len(missing_columns) > 0:
+            raise UnknownColumnNameError(
+                sorted(
+                    missing_columns,
+                    key={val: ix for ix, val in enumerate(self._column_names)}.__getitem__,
+                ),
+            )
+
+        for column in self._column_names:
+            if not table.get_column(column).type.is_numeric():
+                raise NonNumericColumnError(f"{column} is of type {table.get_column(column).type}.")
+
+        data = table._data.copy()
+        data.columns = table.column_names
+        data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
+        return Table._from_pandas_dataframe(data)
+
+    def is_fitted(self) -> bool:
+        """
+        Check if the transformer is fitted.
+
+        Returns
+        -------
+        is_fitted : bool
+            Whether the transformer is fitted.
+        """
+        return self._wrapped_transformer is not None
+
+    def get_names_of_added_columns(self) -> list[str]:
+        """
+        Get the names of all new columns that have been added by the Discretizer.
+
+        Returns
+        -------
+        added_columns : list[str]
+            A list of names of the added columns, ordered as they will appear in the table.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        if not self.is_fitted():
+            raise TransformerNotFittedError
+        return []
+
+    # (Must implement abstract method, cannot instantiate class otherwise.)
+    def get_names_of_changed_columns(self) -> list[str]:
+        """
+         Get the names of all columns that may have been changed by the Discretizer.
+
+        Returns
+        -------
+        changed_columns : list[str]
+             The list of (potentially) changed column names, as passed to fit.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        if self._column_names is None:
+            raise TransformerNotFittedError
+        return self._column_names
+
+    def get_names_of_removed_columns(self) -> list[str]:
+        """
+        Get the names of all columns that have been removed by the Discretizer.
+
+        Returns
+        -------
+        removed_columns : list[str]
+            A list of names of the removed columns, ordered as they appear in the table the Discretizer was fitted on.
+
+        Raises
+        ------
+        TransformerNotFittedError
+            If the transformer has not been fitted yet.
+        """
+        if not self.is_fitted():
+            raise TransformerNotFittedError
+        return []