fix type in featureselection converters

DashAI/back/converters/category/feature_selection.py

@@ -1,8 +1,12 @@
-from typing import Final
+from typing import TYPE_CHECKING, Final, Union
 
 from DashAI.back.converters.base_converter import BaseConverter
 from DashAI.back.core.utils import MultilingualString
 from DashAI.back.static.icons import Icon
+from DashAI.back.types.dashai_data_type import DashAIDataType
+
+if TYPE_CHECKING:
+    from DashAI.back.dataloaders.classes.dashai_dataset import DashAIDataset
 
 
 class FeatureSelectionConverter(BaseConverter):
@@ -15,6 +19,10 @@ class FeatureSelectionConverter(BaseConverter):
 
     Use these converters to reduce overfitting, speed up training, and improve
     model interpretability by retaining only the most informative features.
+
+    These converters only drop columns; the retained columns keep their
+    original values untouched, so their data types must be preserved instead of
+    being coerced to float.
     """
 
     CATEGORY = MultilingualString(
@@ -26,3 +34,57 @@ class FeatureSelectionConverter(BaseConverter):
     )
     ICON: Final[str] = Icon.FilterList.value
     COLOR: Final[str] = "rgb(255, 206, 86)"
+
+    def fit(
+        self, x: "DashAIDataset", y: Union["DashAIDataset", None] = None
+    ) -> "FeatureSelectionConverter":
+        """Fit the selector while remembering the input column types.
+
+        Feature selection only keeps a subset of the input columns without
+        modifying their values, so the original types are captured here to be
+        returned later by ``get_output_type``. Types are recorded during ``fit``
+        (rather than ``transform``) because scikit-learn auto-wraps ``transform``
+        on subclasses and would coerce its output back to a pandas DataFrame.
+
+        Parameters
+        ----------
+        x : DashAIDataset
+            The input dataset to fit the selector on.
+        y : DashAIDataset, optional
+            Target values for the supervised selectors. Defaults to None.
+
+        Returns
+        -------
+        FeatureSelectionConverter
+            The fitted selector instance (self).
+        """
+        if hasattr(x, "types") and x.types is not None:
+            self._input_types = dict(x.types)
+        return super().fit(x, y)
+
+    def get_output_type(self, column_name: str = None) -> DashAIDataType:
+        """Return the original DashAI data type of a retained column.
+
+        Since feature selection leaves the retained columns' values unchanged,
+        the output type matches the input type of that column.
+
+        Parameters
+        ----------
+        column_name : str, optional
+            The name of the retained column. Defaults to None.
+
+        Returns
+        -------
+        DashAIDataType
+            The original type of the column. Falls back to ``float64`` when the
+            input type is unknown (feature selectors only operate on numbers).
+        """
+        input_types = getattr(self, "_input_types", None)
+        if input_types is not None and column_name in input_types:
+            return input_types[column_name]
+
+        import pyarrow as pa
+
+        from DashAI.back.types.value_types import Float
+
+        return Float(arrow_type=pa.float64())

DashAI/back/converters/scikit_learn/generic_univariate_select.py

@@ -14,7 +14,6 @@
 )
 from DashAI.back.core.schema_fields.base_schema import BaseSchema
 from DashAI.back.core.utils import MultilingualString
-from DashAI.back.types.dashai_data_type import DashAIDataType
 from DashAI.back.types.value_types import Float, Integer
 
 
@@ -83,21 +82,3 @@ class GenericUnivariateSelect(
     )
     IMAGE_PREVIEW = "generic_univariate_select.png"
     metadata = {"allowed_types": [Float, Integer], "allowed_dtypes": []}
-
-    def get_output_type(self, column_name: str = None) -> DashAIDataType:
-        """Return the DashAI data type produced by this converter for a column.
-
-        Parameters
-        ----------
-        column_name : str, optional
-            Not used; all output columns share the
-            same type. Defaults to None.
-
-        Returns
-        -------
-        DashAIDataType
-            A Float type backed by ``pyarrow.float64()``.
-        """
-        import pyarrow as pa
-
-        return Float(arrow_type=pa.float64())

DashAI/back/converters/scikit_learn/select_fdr.py

@@ -5,7 +5,6 @@
 from DashAI.back.core.schema_fields import float_field, schema_field
 from DashAI.back.core.schema_fields.base_schema import BaseSchema
 from DashAI.back.core.utils import MultilingualString
-from DashAI.back.types.dashai_data_type import DashAIDataType
 from DashAI.back.types.value_types import Float, Integer
 
 
@@ -104,21 +103,3 @@ def __init__(self, **kwargs):
             schema fields. Forwarded to the underlying scikit-learn class.
         """
         super().__init__(**kwargs)
-
-    def get_output_type(self, column_name: str = None) -> DashAIDataType:
-        """Return the DashAI data type produced by this converter for a column.
-
-        Parameters
-        ----------
-        column_name : str, optional
-            Not used; all output columns share the
-            same type. Defaults to None.
-
-        Returns
-        -------
-        DashAIDataType
-            A Float type backed by ``pyarrow.float64()``.
-        """
-        import pyarrow as pa
-
-        return Float(arrow_type=pa.float64())

DashAI/back/converters/scikit_learn/select_fpr.py

@@ -5,7 +5,6 @@
 from DashAI.back.core.schema_fields import float_field, schema_field
 from DashAI.back.core.schema_fields.base_schema import BaseSchema
 from DashAI.back.core.utils import MultilingualString
-from DashAI.back.types.dashai_data_type import DashAIDataType
 from DashAI.back.types.value_types import Float, Integer
 
 
@@ -95,21 +94,3 @@ def __init__(self, **kwargs):
             schema fields. Forwarded to the underlying scikit-learn class.
         """
         super().__init__(**kwargs)
-
-    def get_output_type(self, column_name: str = None) -> DashAIDataType:
-        """Return the DashAI data type produced by this converter for a column.
-
-        Parameters
-        ----------
-        column_name : str, optional
-            Not used; all output columns share the
-            same type. Defaults to None.
-
-        Returns
-        -------
-        DashAIDataType
-            A Float type backed by ``pyarrow.float64()``.
-        """
-        import pyarrow as pa
-
-        return Float(arrow_type=pa.float64())

DashAI/back/converters/scikit_learn/select_fwe.py

@@ -5,7 +5,6 @@
 from DashAI.back.core.schema_fields import float_field, schema_field
 from DashAI.back.core.schema_fields.base_schema import BaseSchema
 from DashAI.back.core.utils import MultilingualString
-from DashAI.back.types.dashai_data_type import DashAIDataType
 from DashAI.back.types.value_types import Float, Integer
 
 
@@ -96,24 +95,6 @@ class SelectFwe(FeatureSelectionConverter, SklearnWrapper, SelectFweOperation):
     IMAGE_PREVIEW = "select_fwe.png"
     metadata = {"allowed_types": [Float, Integer], "allowed_dtypes": []}
 
-    def get_output_type(self, column_name: str = None) -> DashAIDataType:
-        """Return the DashAI data type produced by this converter for a column.
-
-        Parameters
-        ----------
-        column_name : str, optional
-            Not used; all output columns share the
-            same type. Defaults to None.
-
-        Returns
-        -------
-        DashAIDataType
-            A Float type backed by ``pyarrow.float64()``.
-        """
-        import pyarrow as pa
-
-        return Float(arrow_type=pa.float64())
-
     def __init__(self, **kwargs):
         """Initialize the SelectFwe converter.
 

DashAI/back/converters/scikit_learn/select_k_best.py

@@ -10,7 +10,6 @@
 )
 from DashAI.back.core.schema_fields.base_schema import BaseSchema
 from DashAI.back.core.utils import MultilingualString
-from DashAI.back.types.dashai_data_type import DashAIDataType
 from DashAI.back.types.value_types import Float, Integer
 
 
@@ -83,24 +82,6 @@ class SelectKBest(FeatureSelectionConverter, SklearnWrapper, SelectKBestOperatio
     IMAGE_PREVIEW = "select_k_best.png"
     metadata = {"allowed_types": [Float, Integer], "allowed_dtypes": []}
 
-    def get_output_type(self, column_name: str = None) -> DashAIDataType:
-        """Return the DashAI data type produced by this converter for a column.
-
-        Parameters
-        ----------
-        column_name : str, optional
-            Not used; all output columns share the
-            same type. Defaults to None.
-
-        Returns
-        -------
-        DashAIDataType
-            A Float type backed by ``pyarrow.float64()``.
-        """
-        import pyarrow as pa
-
-        return Float(arrow_type=pa.float64())
-
     def __init__(self, **kwargs):
         """Initialize the SelectKBest converter.
 

DashAI/back/converters/scikit_learn/select_percentile.py

@@ -5,7 +5,6 @@
 from DashAI.back.core.schema_fields import int_field, schema_field
 from DashAI.back.core.schema_fields.base_schema import BaseSchema
 from DashAI.back.core.utils import MultilingualString
-from DashAI.back.types.dashai_data_type import DashAIDataType
 from DashAI.back.types.value_types import Float, Integer
 
 
@@ -87,24 +86,6 @@ class SelectPercentile(
     IMAGE_PREVIEW = "select_percentile.png"
     metadata = {"allowed_types": [Float, Integer], "allowed_dtypes": []}
 
-    def get_output_type(self, column_name: str = None) -> DashAIDataType:
-        """Return the DashAI data type produced by this converter for a column.
-
-        Parameters
-        ----------
-        column_name : str, optional
-            Not used; all output columns share the
-            same type. Defaults to None.
-
-        Returns
-        -------
-        DashAIDataType
-            A Float type backed by ``pyarrow.float64()``.
-        """
-        import pyarrow as pa
-
-        return Float(arrow_type=pa.float64())
-
     def __init__(self, **kwargs):
         """Initialize the SelectPercentile converter.
 

DashAI/back/converters/scikit_learn/variance_threshold.py

@@ -96,25 +96,35 @@ class VarianceThreshold(
     def fit(
         self, x: "DashAIDataset", y: Union["DashAIDataset", None] = None
     ) -> "VarianceThreshold":
-        """Fit the transformer, allowing all features to be removed if none pass.
+        """Fit the selector, remembering input types and tolerating empty output.
 
-        sklearn raises a ValueError when no feature meets the threshold; we catch
-        it and return self instead so that ``transform`` can legitimately return a
-        dataset with zero columns.  ``self.variances_`` is already populated by
-        sklearn before it raises, so the internal state is correct.
+        VarianceThreshold only drops low-variance columns without modifying the
+        retained columns' values, so their original types are captured here to be
+        returned later by ``get_output_type`` instead of coercing to float. Types
+        are recorded during ``fit`` (rather than ``transform``) because
+        scikit-learn auto-wraps ``transform`` on subclasses and would coerce its
+        output back to a pandas DataFrame.
+
+        Additionally, sklearn raises a ValueError when no feature meets the
+        threshold; we catch it and return self instead so that ``transform`` can
+        legitimately return a dataset with zero columns. ``self.variances_`` is
+        already populated by sklearn before it raises, so the internal state is
+        correct.
 
         Parameters
         ----------
         x : DashAIDataset
-            Input dataset.
+            The input dataset to fit the selector on.
         y : DashAIDataset, optional
             Ignored; present for API consistency.
 
         Returns
         -------
         VarianceThreshold
-            The fitted instance.
+            The fitted selector instance (self).
         """
+        if hasattr(x, "types") and x.types is not None:
+            self._input_types = dict(x.types)
         try:
             return super().fit(x, y)
         except ValueError as e:
@@ -125,19 +135,26 @@ def fit(
             return self
 
     def get_output_type(self, column_name: str = None) -> DashAIDataType:
-        """Return the DashAI data type produced by this converter for a column.
+        """Return the original DashAI data type of a retained column.
+
+        Since the selection leaves the retained columns' values unchanged, the
+        output type matches the input type of that column.
 
         Parameters
         ----------
         column_name : str, optional
-            Not used; all output columns share the
-            same type. Defaults to None.
+            The name of the retained column. Defaults to None.
 
         Returns
         -------
         DashAIDataType
-            A Float type backed by ``pyarrow.float64()``.
+            The original type of the column. Falls back to ``float64`` when the
+            input type is unknown (the selector only operates on numbers).
         """
+        input_types = getattr(self, "_input_types", None)
+        if input_types is not None and column_name in input_types:
+            return input_types[column_name]
+
         import pyarrow as pa
 
         return Float(arrow_type=pa.float64())