Source code for sklego.preprocessing.randomadder

from sklearn.base import BaseEstimator
from sklearn.utils import check_array, check_X_y
from sklearn.utils.validation import FLOAT_DTYPES, check_random_state, check_is_fitted

from sklego.common import TrainOnlyTransformerMixin


class RandomAdder(TrainOnlyTransformerMixin, BaseEstimator):
    def __init__(self, noise=1, random_state=None):
        self.noise = noise
        self.random_state = random_state

    def fit(self, X, y):
        super().fit(X, y)
        X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES)
        self.dim_ = X.shape[1]

        return self

    def transform_train(self, X):
        rs = check_random_state(self.random_state)
        check_is_fitted(self, ["dim_"])

        X = check_array(X, estimator=self, dtype=FLOAT_DTYPES)

        return X + rs.normal(0, self.noise, size=X.shape)