Source code for evalml.pipelines.components.transformers.column_selectors

from abc import abstractmethod

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


class ColumnSelector(Transformer):
    """
    Initalizes an transformer that drops specified columns in input data.

    Arguments:
        columns (list(string)): List of column names, used to determine which columns to select.
        random_seed (int): Seed for the random number generator. Defaults to 0.
    """

    def __init__(self, columns=None, random_seed=0, **kwargs):
        if columns and not isinstance(columns, list):
            raise ValueError(
                f"Parameter columns must be a list. Received {type(columns)}."
            )

        parameters = {"columns": columns}
        parameters.update(kwargs)
        super().__init__(
            parameters=parameters, component_obj=None, random_seed=random_seed
        )

    def _check_input_for_columns(self, X):
        cols = self.parameters.get("columns") or []

        column_names = X.columns

        missing_cols = set(cols) - set(column_names)
        if missing_cols:
            raise ValueError(
                "Columns {} not found in input data".format(
                    ", ".join(f"'{col_name}'" for col_name in missing_cols)
                )
            )

    @abstractmethod
    def _modify_columns(self, cols, X, y=None):
        """How the transformer modifies the columns of the input data."""

    def fit(self, X, y=None):
        """Fits the transformer by checking if column names are present in the dataset.

        Arguments:
            X (pd.DataFrame): Data to check.
            y (pd.Series, optional): Targets.

        Returns:
            self
        """
        X = infer_feature_types(X)
        self._check_input_for_columns(X)
        return self

    def transform(self, X, y=None):
        X = infer_feature_types(X)
        self._check_input_for_columns(X)
        cols = self.parameters.get("columns") or []
        modified_cols = self._modify_columns(cols, X, y)
        return infer_feature_types(modified_cols)


class DropColumns(ColumnSelector):
    """
    Drops specified columns in input data.

    Arguments:
        columns (list(string)): List of column names, used to determine which columns to drop.
        random_seed (int): Seed for the random number generator. Defaults to 0.
    """

    name = "Drop Columns Transformer"
    hyperparameter_ranges = {}
    """{}"""
    needs_fitting = False

    def _modify_columns(self, cols, X, y=None):
        return X.ww.drop(cols)

    def transform(self, X, y=None):
        """Transforms data X by dropping columns.

        Arguments:
            X (pd.DataFrame): Data to transform.
            y (pd.Series, optional): Targets.

        Returns:
            pd.DataFrame: Transformed X.
        """
        return super().transform(X, y)


class SelectColumns(ColumnSelector):
    """
    Selects specified columns in input data.

    Arguments:
        columns (list(string)): List of column names, used to determine which columns to select.
        random_seed (int): Seed for the random number generator. Defaults to 0.
    """

    name = "Select Columns Transformer"
    hyperparameter_ranges = {}
    """{}"""
    needs_fitting = False

    def _modify_columns(self, cols, X, y=None):
        return X.ww[cols]

    def transform(self, X, y=None):
        """Transforms data X by selecting columns.

        Arguments:
            X (pd.DataFrame): Data to transform.
            y (pd.Series, optional): Targets.

        Returns:
            pd.DataFrame: Transformed X.
        """
        return super().transform(X, y)