import pyarrow as pa
from sklearn.feature_selection import VarianceThreshold as VarianceThresholdOperation
from DashAI.back.converters.category.dimensionality_reduction import (
DimensionalityReductionConverter,
)
from DashAI.back.converters.sklearn_wrapper import SklearnWrapper
from DashAI.back.core.schema_fields import float_field, schema_field
from DashAI.back.core.schema_fields.base_schema import BaseSchema
from DashAI.back.types.dashai_data_type import DashAIDataType
from DashAI.back.types.value_types import Float
class VarianceThresholdSchema(BaseSchema):
threshold: schema_field(
float_field(ge=0.0),
0.0,
"Features with a variance lower than this threshold will be removed.",
) # type: ignore
[docs]
class VarianceThreshold(
DimensionalityReductionConverter, SklearnWrapper, VarianceThresholdOperation
):
"""Scikit-learn's VarianceThreshold wrapper for DashAI."""
SCHEMA = VarianceThresholdSchema
DESCRIPTION = "Feature selector that removes all low-variance features."
CATEGORY = "Dimensionality Reduction"
DISPLAY_NAME = "Variance Threshold"
def get_output_type(self, column_name: str = None) -> DashAIDataType:
"""Returns Float64 as the output type for selected features."""
return Float(arrow_type=pa.float64())
IMAGE_PREVIEW = "variance_threshold.png"