Source code for foreshadow.estimators.meta

"""Wrapped Estimator."""

from foreshadow.base import BaseEstimator
from foreshadow.serializers import ConcreteSerializerMixin
from foreshadow.utils import check_df


class MetaEstimator(BaseEstimator, ConcreteSerializerMixin):
    """Wrapper that allows data preprocessing on the response variable(s).

    Args:
        estimator: An instance of a subclass of
            :obj:`sklearn.base.BaseEstimator`
        preprocessor: An instance of
            :obj:`foreshadow.preprocessor.Preprocessor`

    """

    def __init__(self, estimator, preprocessor):
        self.estimator = estimator
        self.preprocessor = preprocessor

    def dict_serialize(self, deep=False):  # noqa
        return super().dict_serialize(deep=False)

    def fit(self, X, y=None):
        """Fit the AutoEstimator instance using a selected AutoML estimator.

        Args:
            X (:obj:`pandas.DataFrame` or :obj:`numpy.ndarray` or list): The
                input feature(s)
            y (:obj:`pandas.DataFrame` or :obj:`numpy.ndarray` or list): The
                response feature(s)

        Returns:
            self

        """
        X = check_df(X)
        y = check_df(y)
        y = self.preprocessor.fit_transform(y)
        self.estimator.fit(X, y)
        return self

    def predict(self, X):
        """Use the trained estimator to predict the response.

        Args:
            X (pandas.DataFrame or :obj:`numpy.ndarray` or list): The input
                feature(s)

        Returns:
            :obj:`pandas.DataFrame`: The response feature(s) (transformed)

        """
        X = check_df(X)
        return self.preprocessor.inverse_transform(self.estimator.predict(X))

    def predict_proba(self, X):
        """Use the trained estimator to predict the response probabilities.

        Args:
            X (:obj:`pandas.DataFrame` or :obj:`numpy.ndarray` or list): The
                input feature(s)

        Returns:
            :obj:`pandas.DataFrame`: The probability associated with each \
                feature

        """
        X = check_df(X)
        return self.estimator.predict_proba(X)

    def score(self, X, y):
        """Use the trained estimator to compute the evaluation score.

        Note: sample weights are not supported

        Args:
            X (:obj:`pandas.DataFrame` or :obj:`numpy.ndarray` or list): The
                input feature(s)
            y (:obj:`pandas.DataFrame` or :obj:`numpy.ndarray` or list): The
                response feature(s)

        Returns:
            float: A computed prediction fitness score

        """
        X = check_df(X)
        y = check_df(y)
        y = self.preprocessor.transform(y)

        return self.estimator.score(X, y)