def fit_transform_disk(self, input_path, output_path, target, chunksize,
                           metric=None, metric_special=0, metric_missing=0,
                           show_digits=2, **kwargs):
        """Fit the binning process according to the given training data on
        disk, then transform it and save to comma-separated values (csv) file.

        Parameters
        ----------
        input_path : str
            Any valid string path to a file with extension .csv.

        output_path : str
            Any valid string path to a file with extension .csv.

        target : str
            Target column.

        chunksize :
            Rows to read, transform and write at a time.

        metric : str or None, (default=None)
            The metric used to transform the input vector. If None, the default
            transformation metric for each target type is applied. For binary
            target options are: "woe" (default), "event_rate", "indices" and
            "bins". For continuous target options are: "mean" (default),
            "indices" and "bins". For multiclass target options are:
            "mean_woe" (default), "weighted_mean_woe", "indices" and "bins".

        metric_special : float or str (default=0)
            The metric value to transform special codes in the input vector.
            Supported metrics are "empirical" to use the empirical WoE or
            event rate for a binary target, and any numerical value for other
            targets.

        metric_missing : float or str (default=0)
            The metric value to transform missing values in the input vector.
            Supported metrics are "empirical" to use the empirical WoE or
            event rate for a binary target, and any numerical value for other
            targets.

        show_digits : int, optional (default=2)
            The number of significant digits of the bin column. Applies when
            ``metric="bins"``.

        **kwargs : keyword arguments
            Keyword arguments for ``pandas.read_csv``.

        Returns
        -------
        self : BinningProcess
            Fitted binning process.
        """
        return self.fit_disk(input_path, target, **kwargs).transform_disk(
            input_path, output_path, chunksize, metric, metric_special,
            metric_missing, show_digits, **kwargs)
Fit the binning process according to the given training data on
        disk, then transform it and save to comma-separated values (csv) file.

        Parameters
        ----------
        input_path : str
            Any valid string path to a file with extension .csv.

        output_path : str
            Any valid string path to a file with extension .csv.

        target : str
            Target column.

        chunksize :
            Rows to read, transform and write at a time.

        metric : str or None, (default=None)
            The metric used to transform the input vector. If None, the default
            transformation metric for each target type is applied. For binary
            target options are: "woe" (default), "event_rate", "indices" and
            "bins". For continuous target options are: "mean" (default),
            "indices" and "bins". For multiclass target options are:
            "mean_woe" (default), "weighted_mean_woe", "indices" and "bins".

        metric_special : float or str (default=0)
            The metric value to transform special codes in the input vector.
            Supported metrics are "empirical" to use the empirical WoE or
            event rate for a binary target, and any numerical value for other
            targets.

        metric_missing : float or str (default=0)
            The metric value to transform missing values in the input vector.
            Supported metrics are "empirical" to use the empirical WoE or
            event rate for a binary target, and any numerical value for other
            targets.

        show_digits : int, optional (default=2)
            The number of significant digits of the bin column. Applies when
            ``metric="bins"``.

        **kwargs : keyword arguments
            Keyword arguments for ``pandas.read_csv``.

        Returns
        -------
        self : BinningProcess
            Fitted binning process.
        
optbinning/binning/binning_process.py