# Copyright (c) 2012-2014, Max Zwiessele.
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from __future__ import print_function
import numpy as np
import sys
import time
import datetime
def exponents(fnow, current_grad):
exps = [np.abs(np.float(fnow)), 1 if current_grad is np.nan else current_grad]
return np.sign(exps) * np.log10(exps).astype(int)
class VerboseOptimization(object):
def __init__(self, model, opt, maxiters, verbose=False, current_iteration=0, ipython_notebook=True, clear_after_finish=False):
self.verbose = verbose
if self.verbose:
self.model = model
self.iteration = current_iteration
self.p_iter = self.iteration
self.maxiters = maxiters
self.len_maxiters = len(str(maxiters))
self.opt_name = opt.opt_name
self.model.add_observer(self, self.print_status)
self.status = 'running'
self.clear = clear_after_finish
self.deltat = .2
self.update()
try:
from IPython.display import display
from IPython.html.widgets import IntProgress, HTML, Box, VBox, HBox, FlexBox
self.text = HTML(width='100%')
self.progress = IntProgress(min=0, max=maxiters)
#self.progresstext = Text(width='100%', disabled=True, value='0/{}'.format(maxiters))
self.model_show = HTML()
self.ipython_notebook = ipython_notebook
except:
# Not in Ipython notebook
self.ipython_notebook = False
if self.ipython_notebook:
left_col = VBox(children=[self.progress, self.text], padding=2, width='40%')
right_col = Box(children=[self.model_show], padding=2, width='60%')
self.hor_align = FlexBox(children = [left_col, right_col], width='100%', orientation='horizontal')
display(self.hor_align)
try:
self.text.set_css('width', '100%')
left_col.set_css({
'padding': '2px',
'width': "100%",
})
right_col.set_css({
'padding': '2px',
})
self.hor_align.set_css({
'width': "100%",
})
self.hor_align.remove_class('vbox')
self.hor_align.add_class('hbox')
left_col.add_class("box-flex1")
right_col.add_class('box-flex0')
except:
pass
#self.text.add_class('box-flex2')
#self.progress.add_class('box-flex1')
else:
self.exps = exponents(self.fnow, self.current_gradient)
print('Running {} Code:'.format(self.opt_name))
print(' {3:7s} {0:{mi}s} {1:11s} {2:11s}'.format("i", "f", "|g|", "runtime", mi=self.len_maxiters))
def __enter__(self):
self.start = time.time()
return self
def print_out(self, seconds):
if seconds<60:
ms = (seconds%1)*100
self.timestring = "{s:0>2d}s{ms:0>2d}".format(s=int(seconds), ms=int(ms))
else:
m, s = divmod(seconds, 60)
if m>59:
h, m = divmod(m, 60)
if h>23:
d, h = divmod(h, 24)
self.timestring = '{d:0>2d}d{h:0>2d}h{m:0>2d}'.format(m=int(m), h=int(h), d=int(d))
else:
self.timestring = '{h:0>2d}h{m:0>2d}m{s:0>2d}'.format(m=int(m), s=int(s), h=int(h))
else:
ms = (seconds%1)*100
self.timestring = '{m:0>2d}m{s:0>2d}s{ms:0>2d}'.format(m=int(m), s=int(s), ms=int(ms))
if self.ipython_notebook:
names_vals = [['optimizer', "{:s}".format(self.opt_name)],
['runtime', "{:>s}".format(self.timestring)],
['evaluation', "{:>0{l}}".format(self.iteration, l=self.len_maxiters)],
['objective', "{: > 12.3E}".format(self.fnow)],
['||gradient||', "{: >+12.3E}".format(float(self.current_gradient))],
['status', "{:s}".format(self.status)],
]
#message = "Lik:{:5.3E} Grad:{:5.3E} Lik:{:5.3E} Len:{!s}".format(float(m.log_likelihood()), np.einsum('i,i->', grads, grads), float(m.likelihood.variance), " ".join(["{:3.2E}".format(l) for l in m.kern.lengthscale.values]))
html_begin = """
"
html_body = ""
for name, val in names_vals:
html_body += ""
html_body += "| {} | ".format(name)
html_body += "{} | ".format(val)
html_body += "
"
self.text.value = html_begin + html_body + html_end
self.progress.value = (self.iteration+1)
#self.progresstext.value = '0/{}'.format((self.iteration+1))
self.model_show.value = self.model._repr_html_()
else:
n_exps = exponents(self.fnow, self.current_gradient)
if self.iteration - self.p_iter >= 20 * np.random.rand():
a = self.iteration >= self.p_iter * 2.78
b = np.any(n_exps < self.exps)
if a or b:
self.p_iter = self.iteration
print('')
if b:
self.exps = n_exps
print('\r', end=' ')
print('{3:} {0:>0{mi}g} {1:> 12e} {2:> 12e}'.format(self.iteration, float(self.fnow), float(self.current_gradient), "{:>8s}".format(self.timestring), mi=self.len_maxiters), end=' ') # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
sys.stdout.flush()
def print_status(self, me, which=None):
self.update()
seconds = time.time()-self.start
#sys.stdout.write(" "*len(self.message))
self.deltat += seconds
if self.deltat > .2:
self.print_out(seconds)
self.deltat = 0
self.iteration += 1
def update(self):
self.fnow = self.model.objective_function()
if self.model.obj_grads is not None:
grad = self.model.obj_grads
self.current_gradient = np.dot(grad, grad)
else:
self.current_gradient = np.nan
def finish(self, opt):
self.status = opt.status
if self.verbose and self.ipython_notebook:
if 'conv' in self.status.lower():
self.progress.bar_style = 'success'
elif self.iteration >= self.maxiters:
self.progress.bar_style = 'warning'
else:
self.progress.bar_style = 'danger'
def __exit__(self, type, value, traceback):
if self.verbose:
self.stop = time.time()
self.model.remove_observer(self)
self.print_out(self.stop - self.start)
if not self.ipython_notebook:
print()
print('Runtime: {}'.format("{:>9s}".format(self.timestring)))
print('Optimization status: {0}'.format(self.status))
print()
elif self.clear:
self.hor_align.close()