(self, X, Y, W, target_class, domain)
return num_possible < general_validator.min_covered_examples
Data stopping.

        If the minimum number of required covered examples can no longer
        be met, return True and conclude rule induction.

        Important
        ---------
        This function is inherently linked to the GuardianValidator. If
        a different general validator is used, it is imperative that the
        minimum number of required covered examples is enforced or this
        function overridden. The parameter min_covered_examples must be
        accessible, else an exception is raised.

        Parameters
        ----------
        X, Y, W : ndarray
            Learning data.
        target_class : int
            Index of the class to model.

        Returns
        -------
        res : bool
            Whether or not rule induction will be stopped.
Data stopping.
Data stopping .
def positive_remaining_data_stopping(self, X, Y, W, target_class, domain):
        """
        Data stopping.

        If the minimum number of required covered examples can no longer
        be met, return True and conclude rule induction.

        Important
        ---------
        This function is inherently linked to the GuardianValidator. If
        a different general validator is used, it is imperative that the
        minimum number of required covered examples is enforced or this
        function overridden. The parameter min_covered_examples must be
        accessible, else an exception is raised.

        Parameters
        ----------
        X, Y, W : ndarray
            Learning data.
        target_class : int
            Index of the class to model.

        Returns
        -------
        res : bool
            Whether or not rule induction will be stopped.
        """
        tc = target_class
        dist = get_dist(Y, W, domain)
        general_validator = self.rule_finder.general_validator
        num_possible = dist[tc] if tc is not None else dist.sum()
        return num_possible < general_validator.min_covered_examples
def positive_remaining_data_stopping ( self , X , Y , W , target_class , domain ) : tc = target_class dist = get_dist ( Y , W , domain ) general_validator = self . rule_finder . general_validator num_possible = dist [ tc ] if tc is not None else dist . sum ( ) return num_possible < general_validator . min_covered_examples