#===============================================================================
# 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")