sklearn_utils/preprocessing/standard_scale_by_label.py
def partial_fit(self, X, y):
        """
        :X: {array-like, sparse matrix}, shape [n_samples, n_features]
            The data used to compute the mean and standard deviation
            used for later scaling along the features axis.
        :y: Healthy 'h' or 'sick_name'
        """
        X, y = filter_by_label(X, y, self.reference_label)
        super().partial_fit(X, y)
        return self
def partial_fit(self, X, y):
        """
        :X: {array-like, sparse matrix}, shape [n_samples, n_features]
            The data used to compute the mean and standard deviation
            used for later scaling along the features axis.
        :y: Healthy 'h' or 'sick_name'
        """
        X, y = filter_by_label(X, y, self.reference_label)
        super().partial_fit(X, y)
        return self
:X: {array-like, sparse matrix}, shape [n_samples, n_features]
            The data used to compute the mean and standard deviation
            used for later scaling along the features axis.
        :y: Healthy 'h' or 'sick_name'