Defining a new plotting function in GPyΒΆ

GPy has a wrapper for different plotting backends. There are some functions you can use for standard plotting. Anything going beyond the scope of the AbstractPlottingLibrary classes plot definitions should be considered carefully and maybe is a special case for your plotting library only.

All plotting related code lives in GPy.plotting and beneath. No plotting related code needs to be anywhere else in GPy.

As examples are always the easiest way to learn how to, we will implement an example of a plotting function, which plots the covariance of a kernel.

Write your plotting function into a module under GPy.plotting.gpy_plot .<module_name> using the plotting routines provided in GPy.plotting.plotting_library. I like to from . import plotting_library as pl and the allways use pl(). to access functionality of the plotting library.

For the covariance plot we define the function in GPy.plotting.kernel_plots.

The first thing is to define the function parameters and write the documentation for them! The first argument of the plotting function is always self for the class this plotting function will be attached to (we will get to attaching the function to a class that in detail later on):

def plot_covariance(kernel, x=None, label=None,
             plot_limits=None, visible_dims=None, resolution=None,
             projection=None, levels=20, **kwargs):
    """
    Plot a kernel covariance w.r.t. another x.

    :param array-like x: the value to use for the other kernel argument (kernels are a function of two variables!)
    :param plot_limits: the range over which to plot the kernel
    :type plot_limits: Either (xmin, xmax) for 1D or (xmin, xmax, ymin, ymax) / ((xmin, xmax), (ymin, ymax)) for 2D
    :param array-like visible_dims: input dimensions (!) to use for x. Make sure to select 2 or less dimensions to plot.
    :resolution: the resolution of the lines used in plotting. for 2D this defines the grid for kernel evaluation.
    :param {2d|3d} projection: What projection shall we use to plot the kernel?
    :param int levels: for 2D projection, how many levels for the contour plot to use?
    :param kwargs:  valid kwargs for your specific plotting library
    """

Having defined the outline of the function we can start implementing the real plotting.

First, we will write the necessary logic behind getting the covariance function. This involves getting an Xgrid to plot with and the second x to compare the covariance to:

from .plot_util import helper_for_plot_data
X = np.ones((2, kernel.input_dim)) * [-4, 4]
_, free_dims, Xgrid, xx, yy, _, _, resolution = helper_for_plot_data(kernel, X, plot_limits, visible_dims, None, resolution)
from numbers import Number
if x is None:
    x = np.zeros((1, kernel.input_dim))
elif isinstance(x, Number):
    x = np.ones((1, kernel.input_dim))*x
K = kernel.K(Xgrid, x)

free_dims holds the free dimensions after selecting from the visible_dims, Xgrid is the grid for the covariance, xx, yy are the grid positions for 2D plotting and x is the X2 for the kernel and K holds the kernel covariance for all positions between Xgrid and x.

Then we need a canvas to plot on. Always push the keyword arguments of the specifig library through GPy.plotting.abstract_plotting_library.AbstractPlottingLibrary.new_canvas:

if projection == '3d':
    zlabel = "k(X, {!s})" % (np.asanyarray(x).tolist())
    xlabel = 'X[:,0]'
    ylabel = 'X[:,1]'
else:
    xlabel = 'X'
    ylabel = "k(X, {!s})" % (np.asanyarray(x).tolist())

canvas, kwargs = pl().new_canvas(projection=projection, xlabel=xlabel, ylabel=ylabel, zlabel=zlabel, **kwargs)

Also very important is to use the defaults, which are defined for all plotting libraries implemented. This is done by updating the kwargs from the defaults. There is a helper function which takes care for existing keyword arguments. In this case we will just use the default for plotting a mean function for the covariance plot as well. If you want to define your own defaults add them to the defaults for each library and add it in here. See for example the defaults for matplotlib in GPy.plotting.matplot_dep.defaults. There is also the default for the meanplot_1d, which we are for the 1d plot:

from .plot_util import update_not_existing_kwargs
update_not_existing_kwargs(kwargs, pl().defaults.meanplot_1d)  # @UndefinedVariable

The full definition of the plotting then looks like this:

if len(free_dims)<=2:
    if len(free_dims)==1:
        # 1D plotting:
        update_not_existing_kwargs(kwargs, pl().defaults.meanplot_1d)  # @UndefinedVariable
        plots = dict(covariance=[pl().plot(canvas, Xgrid[:, free_dims], K, label=label, **kwargs)])
    else:
        if projection == '2d':
            update_not_existing_kwargs(kwargs, pl().defaults.meanplot_2d)  # @UndefinedVariable
            plots = dict(covariance=[pl().contour(canvas, xx[:, 0], yy[0, :],
                                           K.reshape(resolution, resolution),
                                           levels=levels, label=label, **kwargs)])
        elif projection == '3d':
            update_not_existing_kwargs(kwargs, pl().defaults.meanplot_3d)  # @UndefinedVariable
            plots = dict(covariance=[pl().surface(canvas, xx, yy,
                                           K.reshape(resolution, resolution),
                                           label=label,
                                           **kwargs)])
    return pl().add_to_canvas(canvas, plots)

else:
    raise NotImplementedError("Cannot plot a kernel with more than two input dimensions")

Where we return whatever is returned by GPy.plotting.abstract_plotting_library.AbstractPlottingLibrary.add_to_canvas, so that the plotting library can choose what to do with the plot later, when we want to show it. In order to show a plot, we can just call GPy.plotting.show with the output of the plot above.

Now we want to add the plot to the GPy.kern.src.kern.Kern. In order to do that, we inject the plotting function into the class in the GPy.plotting.__init__, which will make sure that the on the fly change of the backend works smoothly. Thus, in GPy.plotting.__init__ we add the line:

from ..kern import Kern
Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance

And that’s it. The plot can be shown in plotly by calling:

GPy.plotting.change_plotting_library('plotly')

k = GPy.kern.RBF(1) + GPy.kern.Matern32(1)
k.randomize()
fig = k.plot()
GPy.plotting.show(fig, <plot_library specific **kwargs>)

k = GPy.kern.RBF(2) + GPy.kern.Matern32(2)
k.randomize()
fig = k.plot()
GPy.plotting.show(fig, <plot_library specific **kwargs>)

k = GPy.kern.RBF(1) + GPy.kern.Matern32(2)
k.randomize()
fig = k.plot(projection='3d')
GPy.plotting.show(fig, <plot_library specific **kwargs>)

This explains the next thing. Changing the backend works on-the-fly. To show the above example in matplotlib, we just exchange the first line by GPy.plotting.change_plotting_library('matplotlib').