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

"""A transformer that standardizes input features by removing the mean and scaling to unit variance."""
from sklearn.preprocessing import StandardScaler as SkScaler

from evalml.pipelines.components.transformers import Transformer
from evalml.utils import infer_feature_types


class StandardScaler(Transformer):
    """A transformer that standardizes input features by removing the mean and scaling to unit variance.

    Args:
        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)
        self._supported_types = [
            "Age",
            "AgeNullable",
            "Double",
            "Integer",
            "IntegerNullable",
        ]
        scaler = SkScaler(**parameters)
        super().__init__(
            parameters=parameters,
            component_obj=scaler,
            random_seed=random_seed,
        )

    def fit(self, X, y=None):
        """Fits the standard scalar on the given data.

        Args:
            X (pd.DataFrame): The input training data of shape [n_samples, n_features].
            y (pd.Series, optional): The target training data of length [n_samples].

        Returns:
            self
        """
        X = infer_feature_types(X)
        X_scalable = X.ww.select(self._supported_types)
        self.scaled_columns = list(X_scalable.columns)
        if X_scalable.empty:
            return self
        self._component_obj.fit(X_scalable)
        return self

    def transform(self, X, y=None):
        """Transform data using the fitted standard scaler.

        Args:
            X (pd.DataFrame): The input training data of shape [n_samples, n_features].
            y (pd.Series, optional): The target training data of length [n_samples].

        Returns:
            pd.DataFrame: Transformed data.
        """
        X = infer_feature_types(X)

        X = X.ww.select(exclude=["datetime"])
        if not self.scaled_columns:
            return X
        X_scaled_columns = X.ww[self.scaled_columns]
        scaled = self._component_obj.transform(X_scaled_columns)
        X[self.scaled_columns] = scaled
        X.ww.set_types(logical_types={col: "Double" for col in self.scaled_columns})
        return X

    def fit_transform(self, X, y=None):
        """Fit and transform data using the standard scaler component.

        Args:
            X (pd.DataFrame): The input training data of shape [n_samples, n_features].
            y (pd.Series, optional): The target training data of length [n_samples].

        Returns:
            pd.DataFrame: Transformed data.
        """
        X = infer_feature_types(X)
        return self.fit(X, y).transform(X, y)