Source code for GPy.plotting.gpy_plot.kernel_plots

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# Copyright (c) 2015, Max Zwiessele
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import numpy as np
from . import plotting_library as pl
from .. import Tango
from .plot_util import update_not_existing_kwargs, helper_for_plot_data
from ...kern.src.kern import Kern, CombinationKernel

[docs]def plot_ARD(kernel, filtering=None, legend=False, canvas=None, **kwargs): """ If an ARD kernel is present, plot a bar representation using matplotlib :param fignum: figure number of the plot :param filtering: list of names, which to use for plotting ARD parameters. Only kernels which match names in the list of names in filtering will be used for plotting. :type filtering: list of names to use for ARD plot """ Tango.reset() ard_params = np.atleast_2d(kernel.input_sensitivity(summarize=False)) bottom = 0 last_bottom = bottom x = np.arange(kernel._effective_input_dim) parts = [] def visit(x): if (not isinstance(x, CombinationKernel)) and isinstance(x, Kern): parts.append(x) kernel.traverse(visit) if filtering is None: filtering = [k.name for k in parts] bars = [] kwargs = update_not_existing_kwargs(kwargs, pl().defaults.ard) if canvas is None: canvas, kwargs = pl().new_canvas(xlim=(-.5, kernel._effective_input_dim-.5), xlabel='input dimension', ylabel='ard contribution', **kwargs) for i in range(ard_params.shape[0]): if parts[i].name in filtering: c = Tango.nextMedium() bars.append(pl().barplot(canvas, x, ard_params[i,:], color=c, label=parts[i].name, bottom=bottom, **kwargs)) last_bottom = ard_params[i,:] bottom += last_bottom else: print("filtering out {}".format(parts[i].name)) #add_bar_labels(fig, ax, [bars[-1]], bottom=bottom-last_bottom) return pl().add_to_canvas(canvas, bars, legend=legend)
[docs]def plot_covariance(kernel, x=None, label=None, plot_limits=None, visible_dims=None, resolution=None, projection='2d', 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 """ X = np.ones((2, kernel._effective_input_dim)) * [[-3], [3]] _, 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: from ...kern.src.stationary import Stationary x = np.ones((1, kernel._effective_input_dim)) * (not isinstance(kernel, Stationary)) elif isinstance(x, Number): x = np.ones((1, kernel._effective_input_dim))*x K = kernel.K(Xgrid, x) if projection == '3d': xlabel = 'X[:,0]' ylabel = 'X[:,1]' zlabel = "k(X, {!s})".format(np.asanyarray(x).tolist()) else: xlabel = 'X' ylabel = "k(X, {!s})".format(np.asanyarray(x).tolist()) zlabel = None canvas, kwargs = pl().new_canvas(projection=projection, xlabel=xlabel, ylabel=ylabel, zlabel=zlabel, **kwargs) 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")