3335032e5cbf3776fd48cce2a9e1a597f3b90867,category_encoders/count.py,CountEncoder,__init__,#CountEncoder#,11

Before Change

        ----------

        
        if (
            isinstance(min_group_size, float)
            and (min_group_size >= 1.0)
            and (min_group_size <= 0.0)
        ):
            raise ValueError(
                "If `min_group_size` is float, "
                "it must be in the range (0, 1)."
            )

        self.return_df = return_df
        self.drop_invariant = drop_invariant
        self.drop_cols = []
        self.verbose = verbose

After Change

        self.min_group_size = min_group_size
        self.min_group_name = min_group_name
        self.combine_min_nan_groups = combine_min_nan_groups
        self._min_group_categories = {}

    def fit(self, X, y=None, **kwargs):
        Fit encoder according to X.

Instances