def explained_variance_visualizer(X, y=None, ax=None, scale=True, 
                                  center=True, colormap=palettes.DEFAULT_SEQUENCE,
                                  **kwargs):
        """Produce a plot of the explained variance produced by a dimensionality 
        reduction algorithm using n=1 to n=n_components dimensions. This is a single 
        plot to help identify the best trade off between number of dimensions
        and amount of information retained within the data.

        Parameters
        ----------
        X : ndarray or DataFrame of shape n x m
            A matrix of n rows with m features

        y : ndarray or Series of length n
            An array or Series of target or class values
        
        ax : matplotlib Axes, default: None
            The aces to plot the figure on

        scale : bool, default: True
            Boolean that indicates if the values of X should be scaled.

        colormap : string or cmap, default: None
            optional string or matplotlib cmap to colorize lines
            Use either color to colorize the lines on a per class basis or
            colormap to color them on a continuous scale.

        kwargs : dict
            Keyword arguments that are passed to the base class and may influence
            the visualization as defined in other Visualizers.
        
        Examples
        --------
        >>> from sklearn import datasets
        >>> bc = datasets.load_breast_cancer()
        >>> X = bc = bc.data
        >>> explained_variance_visualizer(X, scale=True, center=True, colormap='RdBu_r')
        
        """

        # Instantiate the visualizer
        visualizer = ExplainedVariance(X=X)

        # Fit and transform the visualizer (calls draw)
        visualizer.fit(X, y, **kwargs)
        visualizer.transform(X)

        # Return the axes object on the visualizer
        return visualizer.poof()
Produce a plot of the explained variance produced by a dimensionality 
        reduction algorithm using n=1 to n=n_components dimensions. This is a single 
        plot to help identify the best trade off between number of dimensions
        and amount of information retained within the data.

        Parameters
        ----------
        X : ndarray or DataFrame of shape n x m
            A matrix of n rows with m features

        y : ndarray or Series of length n
            An array or Series of target or class values
        
        ax : matplotlib Axes, default: None
            The aces to plot the figure on

        scale : bool, default: True
            Boolean that indicates if the values of X should be scaled.

        colormap : string or cmap, default: None
            optional string or matplotlib cmap to colorize lines
            Use either color to colorize the lines on a per class basis or
            colormap to color them on a continuous scale.

        kwargs : dict
            Keyword arguments that are passed to the base class and may influence
            the visualization as defined in other Visualizers.
        
        Examples
        --------
        >>> from sklearn import datasets
        >>> bc = datasets.load_breast_cancer()
        >>> X = bc = bc.data
        >>> explained_variance_visualizer(X, scale=True, center=True, colormap='RdBu_r')