Source code for evalml.pipelines.components.transformers.scalers.standard_scaler

import pandas as pd
from sklearn.preprocessing import StandardScaler as SkScaler
from woodwork.logical_types import Boolean, Categorical, Integer

from evalml.pipelines.components.transformers import Transformer
from evalml.utils import (
    _retain_custom_types_and_initalize_woodwork,
    infer_feature_types,
)


[docs]class StandardScaler(Transformer): """A transformer that standardizes input features by removing the mean and scaling to unit variance. Arguments: random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Standard Scaler" hyperparameter_ranges = {} """{}""" def __init__(self, random_seed=0, **kwargs): parameters = {} parameters.update(kwargs) scaler = SkScaler(**parameters) super().__init__( parameters=parameters, component_obj=scaler, random_seed=random_seed )
[docs] def transform(self, X, y=None): X = infer_feature_types(X) original_ltypes = X.ww.schema.logical_types X = X.ww.select_dtypes(exclude=["datetime"]) X_t = self._component_obj.transform(X) X_t_df = pd.DataFrame(X_t, columns=X.columns, index=X.index) return _retain_custom_types_and_initalize_woodwork( original_ltypes, X_t_df, ltypes_to_ignore=[Integer, Categorical, Boolean] )
[docs] def fit_transform(self, X, y=None): X = infer_feature_types(X) X = X.select_dtypes(exclude=["datetime"]) return self.fit(X, y).transform(X, y)