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import numpy as np
from .. import monitor
from ..plotutil import coordinated_colors
from .plot_view import PlotView
class ConvergenceMonitor(monitor.Monitor):
"""
Gather statistics about the best, worst, median and +/- 1 interquartile
range. This will be the input for the convergence plot.
"""
def __init__(self):
self.pop = []
def config_history(self, history):
history.requires(population_values=1, value=1)
def __call__(self, history):
best = history.value[0]
try:
pop = history.population_values[0]
n = len(pop)
p = np.sort(pop)
(
QI,
Qmid,
) = int(0.2 * n), int(0.5 * n)
self.pop.append((best, p[0], p[QI], p[Qmid], p[-1 - QI], p[-1]))
except (AttributeError, TypeError):
self.pop.append((best,))
def progress(self):
if not self.pop:
return dict(pop=np.empty((0, 1), "d"))
else:
return dict(pop=np.array(self.pop))
class ConvergenceView(PlotView):
title = "Convergence"
def plot(self):
if not self.plot_state:
return
pop, best = self.plot_state
with self.pylab_interface as pylab:
pylab.clf()
ni, npop = pop.shape
iternum = np.arange(1, ni + 1)
tail = int(0.25 * ni)
c = coordinated_colors(base=(0.4, 0.8, 0.2))
if npop == 5:
pylab.fill_between(iternum[tail:], pop[tail:, 1], pop[tail:, 3], color=c["light"], label="_nolegend_")
pylab.plot(iternum[tail:], pop[tail:, 2], label="80% range", color=c["base"])
pylab.plot(iternum[tail:], pop[tail:, 0], label="_nolegend_", color=c["base"])
pylab.plot(iternum[tail:], best[tail:], label="best", color=c["dark"])
pylab.xlabel("iteration number")
pylab.ylabel("chisq")
pylab.legend()
# pylab.gca().set_yscale('log')
pylab.draw()
def update(self, best, pop):
self.plot_state = pop, best
self.plot()
def OnFitProgress(self, event):
if event.problem != self.model:
return
pop = 2 * event.pop / self.model.dof
self.update(pop[:, 0], pop[:, 1:])
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