def testMovingSum(self):
x = num.arange(5)
assert numeq(
trace.moving_sum(x, 3, mode='valid'),
[3, 6, 9], 0.001)
assert numeq(
trace.moving_sum(x, 3, mode='full'),
[0, 1, 3, 6, 9, 7, 4], 0.001)
def testMovingSum(self):
x = num.arange(5)
assert numeq(
trace.moving_sum(x, 3, mode='valid'),
[3, 6, 9], 0.001)
assert numeq(
trace.moving_sum(x, 3, mode='full'),
[0, 1, 3, 6, 9, 7, 4], 0.001)
def draw_figures(self, environ):
nwindow = 200
show_raw_acceptance_rates = False
optimiser = environ.get_optimiser()
problem = environ.get_problem()
history = environ.get_history()
chains = optimiser.chains(problem, history)
chains.load()
acceptance = chains.acceptance_history
nmodels_rate = history.nmodels - (nwindow - 1)
if nmodels_rate < 1:
logger.warning(
'Cannot create plot acceptance: insufficient number of tested '
'models.')
return
acceptance_rate = num.zeros((history.nchains, nmodels_rate))
for ichain in range(history.nchains):
acceptance_rate[ichain, :] = trace.moving_sum(
acceptance[ichain, :], nwindow, mode='valid') / float(nwindow)
acceptance_n = num.sum(acceptance, axis=0)
acceptance_any = num.minimum(acceptance_n, 1)
acceptance_any_rate = trace.moving_sum(
acceptance_any, nwindow, mode='valid') / float(nwindow)
acceptance_p = acceptance_n / float(history.nchains)
popularity = trace.moving_sum(
acceptance_p, nwindow, mode='valid') \
/ float(nwindow) / acceptance_any_rate
mpl_init(fontsize=self.font_size)
fig = plt.figure(figsize=self.size_inch)
labelpos = mpl_margins(fig, w=7., h=5., units=self.font_size)
axes = fig.add_subplot(1, 1, 1)
labelpos(axes, 2.5, 2.0)
imodels = num.arange(history.nmodels)
imodels_rate = imodels[nwindow - 1:]
axes.plot(acceptance_n / history.nchains * 100.,
'.',
ms=2.0,
color=mpl_color('skyblue2'),
label='Popularity of Accepted Models',
alpha=0.3)
if show_raw_acceptance_rates:
for ichain in range(chains.nchains):
axes.plot(imodels_rate,
acceptance_rate[ichain, :] * 100.,
color=mpl_color('scarletred2'),
alpha=0.2)
axes.plot(imodels_rate,
popularity * 100.,
color=mpl_color('skyblue2'),
label='Popularity (moving average)')
axes.plot(imodels_rate,
acceptance_any_rate * 100.,
color='black',
label='Acceptance Rate (any chain)')
axes.legend()
axes.set_xlabel('Iteration')
axes.set_ylabel('Acceptance Rate, Model Popularity')
axes.set_ylim(0., 100.)
axes.set_xlim(0., history.nmodels - 1)
axes.grid(alpha=.2)
axes.yaxis.set_major_formatter(FuncFormatter(lambda v, p: '%d%%' % v))
iiter = 0
bgcolors = [mpl_color('aluminium1'), mpl_color('aluminium2')]
for iphase, phase in enumerate(optimiser.sampler_phases):
axes.axvspan(iiter,
iiter + phase.niterations,
color=bgcolors[iphase % len(bgcolors)])
iiter += phase.niterations
yield (PlotItem(name='acceptance',
description=u'''
Acceptance rate (black line) within a moving window of %d iterations.
A model is considered accepted, if it is accepted in at least one chain. The
popularity of accepted models is shown as blue dots. Popularity is defined as
the percentage of chains accepting the model (100%% meaning acceptance in all
chains). A moving average of the popularities is shown as blue line (same
averaging interval as for the acceptance rate). Different background colors
represent different sampler phases.
''' % nwindow), fig)
mpl_init(fontsize=self.font_size)
fig = plt.figure(figsize=self.size_inch)
labelpos = mpl_margins(fig, w=7., h=5., units=self.font_size)
axes = fig.add_subplot(1, 1, 1)
labelpos(axes, 2.5, 2.0)
nwindow2 = max(1, int(history.nmodels / (self.size_inch[1] * 100)))
nmodels_rate2 = history.nmodels - (nwindow2 - 1)
acceptance_rate2 = num.zeros((history.nchains, nmodels_rate2))
for ichain in range(history.nchains):
acceptance_rate2[ichain, :] = trace.moving_sum(
acceptance[ichain, :], nwindow2, mode='valid') \
/ float(nwindow2)
imodels_rate2 = imodels[nwindow2 - 1:]
axes.pcolormesh(imodels_rate2,
num.arange(history.nchains),
num.log(0.01 + acceptance_rate2),
cmap='GnBu')
if history.sampler_contexts is not None:
axes.plot(imodels,
history.sampler_contexts[:, 1],
'.',
ms=2.0,
color='black',
label='Breeding Chain',
alpha=0.3)
axes.set_xlabel('Iteration')
axes.set_ylabel('Bootstrap Chain')
axes.set_xlim(0, history.nmodels - 1)
axes.set_ylim(0, history.nchains - 1)
axes.xaxis.grid(alpha=.4)
yield (PlotItem(name='acceptance_img',
description=u'''
Model acceptance per bootstrap chain averaged over %d models (background color,
low to high acceptance as light to dark colors).
Black dots mark the base chains used when sampling new models (directed sampler
phases only).
''' % nwindow2), fig)
