Source code for

from typing import List

import numpy as np
import pandas as pd

from import Preprocessor
from ray.util.annotations import PublicAPI

[docs]@PublicAPI(stability="alpha") class PowerTransformer(Preprocessor): """Apply a `power transform <>`_ to make your data more normally distributed. Some models expect data to be normally distributed. By making your data more Gaussian-like, you might be able to improve your model's performance. This preprocessor supports the following transformations: * `Yeo-Johnson <>`_ * `Box-Cox <>`_ Box-Cox requires all data to be positive. .. warning:: You need to manually specify the transform's power parameter. If you choose a bad value, the transformation might not work well. Args: columns: The columns to separately transform. power: A parameter that determines how your data is transformed. Practioners typically set ``power`` between :math:`-2.5` and :math:`2.5`, although you may need to try different values to find one that works well. method: A string representing which transformation to apply. Supports ``"yeo-johnson"`` and ``"box-cox"``. If you choose ``"box-cox"``, your data needs to be positive. Defaults to ``"yeo-johnson"``. """ # noqa: E501 _valid_methods = ["yeo-johnson", "box-cox"] _is_fittable = False def __init__(self, columns: List[str], power: float, method: str = "yeo-johnson"): self.columns = columns self.method = method self.power = power if method not in self._valid_methods: raise ValueError( f"Method {method} is not supported." f"Supported values are: {self._valid_methods}" ) def _transform_pandas(self, df: pd.DataFrame): def column_power_transformer(s: pd.Series): if self.method == "yeo-johnson": result = np.zeros_like(s, dtype=np.float64) pos = s >= 0 # binary mask if self.power != 0: result[pos] = (np.power(s[pos] + 1, self.power) - 1) / self.power else: result[pos] = np.log(s[pos] + 1) if self.power != 2: result[~pos] = -(np.power(-s[~pos] + 1, 2 - self.power) - 1) / ( 2 - self.power ) else: result[~pos] = -np.log(-s[~pos] + 1) return result else: # box-cox if self.power != 0: return (np.power(s, self.power) - 1) / self.power else: return np.log(s) df.loc[:, self.columns] = df.loc[:, self.columns].transform( column_power_transformer ) return df def __repr__(self): return ( f"{self.__class__.__name__}(columns={self.columns!r}, " f"power={self.power!r}, method={self.method!r})" )