import pyarrow as pa
from sklearn.preprocessing import MaxAbsScaler as MaxAbsScalerOperation
from DashAI.back.converters.category.scaling_and_normalization import (
ScalingAndNormalizationConverter,
)
from DashAI.back.converters.sklearn_wrapper import SklearnWrapper
from DashAI.back.core.schema_fields import bool_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 MaxAbsScalerSchema(BaseSchema):
use_copy: schema_field(
bool_field(),
True,
"Set to False to perform inplace scaling.",
alias="copy",
) # type: ignore
[docs]
class MaxAbsScaler(
ScalingAndNormalizationConverter, SklearnWrapper, MaxAbsScalerOperation
):
"""Scikit-learn's MaxAbsScaler wrapper for DashAI."""
SCHEMA = MaxAbsScalerSchema
DESCRIPTION = "Scale each feature by its maximum absolute value."
CATEGORY = "Scaling & Normalization"
DISPLAY_NAME = "Max Abs Scaler"
def get_output_type(self, column_name: str = None) -> DashAIDataType:
"""Returns Float64 as the output type for scaled data."""
return Float(arrow_type=pa.float64())
IMAGE_PREVIEW = "max_abs_scaler.png"