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
def partial_fit ( self , X , y ) : 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'
: 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