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LinearCF Psi Stat not working yet, strange bug in psi computations

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Max Zwiessele 2013-05-01 17:09:38 +01:00
parent c502b66ea3
commit 42474f0044
8 changed files with 353 additions and 244 deletions
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51
GPy/inference/conjugate_gradient_descent.py
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@ -4,14 +4,14 @@ Created on 24 Apr 2013
@author: maxz
'''
from GPy.inference.gradient_descent_update_rules import FletcherReeves
import numpy
from multiprocessing import Value
from scipy.optimize.linesearch import line_search_wolfe1, line_search_wolfe2
from multiprocessing.synchronize import Event
from multiprocessing.queues import Queue
from Queue import Empty
import sys
from multiprocessing import Value
from multiprocessing.queues import Queue
from multiprocessing.synchronize import Event
from scipy.optimize.linesearch import line_search_wolfe1, line_search_wolfe2
from threading import Thread
import numpy
import sys
RUNNING = "running"
CONVERGED = "converged"
@ -20,10 +20,9 @@ MAX_F_EVAL = "maximum number of function calls reached"
LINE_SEARCH = "line search failed"
KBINTERRUPT = "interrupted"
SENTINEL = None
class _Async_Optimization(Thread):
def __init__(self, f, df, x0, update_rule, runsignal,
def __init__(self, f, df, x0, update_rule, runsignal, SENTINEL,
report_every=10, messages=0, maxiter=5e3, max_f_eval=15e3,
gtol=1e-6, outqueue=None, *args, **kw):
"""
@ -42,6 +41,7 @@ class _Async_Optimization(Thread):
self.maxiter = maxiter
self.max_f_eval = max_f_eval
self.gtol = gtol
self.SENTINEL = SENTINEL
self.runsignal = runsignal
# self.parent = parent
# self.result = None
@ -70,7 +70,7 @@ class _Async_Optimization(Thread):
def callback_return(self, *a):
self.callback(*a)
self.outq.put(SENTINEL)
self.outq.put(self.SENTINEL)
self.runsignal.clear()
def run(self, *args, **kwargs):
@ -136,7 +136,7 @@ class _CGDAsync(_Async_Optimization):
if gfi is not None:
gi = gfi
if fi_old > fi:
if numpy.isnan(fi) or fi_old < fi:
gi, ur, si = self.reset(xi, *a, **kw)
else:
xi += numpy.dot(alphai, si)
@ -145,22 +145,23 @@ class _CGDAsync(_Async_Optimization):
sys.stdout.flush()
sys.stdout.write("iteration: {0:> 6g} f:{1:> 12e} |g|:{2:> 12e}".format(it, fi, numpy.dot(gi.T, gi)))
if it % self.report_every == 0:
self.callback(xi, fi, it, self.f_call.value, self.df_call.value, status)
if it % self.report_every == 0:
self.callback(xi, fi, gi, it, self.f_call.value, self.df_call.value, status)
it += 1
else:
status = MAXITER
# self.result = [xi, fi, it, self.f_call.value, self.df_call.value, status]
self.callback_return(xi, fi, it, self.f_call.value, self.df_call.value, status)
self.callback_return(xi, fi, gi, it, self.f_call.value, self.df_call.value, status)
self.result = [xi, fi, gi, it, self.f_call.value, self.df_call.value, status]
class Async_Optimize(object):
callback = lambda *x: None
runsignal = Event()
SENTINEL = "SENTINEL"
def async_callback_collect(self, q):
while self.runsignal.is_set():
try:
for ret in iter(lambda: q.get(timeout=1), SENTINEL):
for ret in iter(lambda: q.get(timeout=1), self.SENTINEL):
self.callback(*ret)
except Empty:
pass
@ -169,12 +170,12 @@ class Async_Optimize(object):
messages=0, maxiter=5e3, max_f_eval=15e3, gtol=1e-6,
report_every=10, *args, **kwargs):
self.runsignal.set()
outqueue = Queue(5)
outqueue = Queue()
if callback:
self.callback = callback
c = Thread(target=self.async_callback_collect, args=(outqueue,))
c.start()
p = _CGDAsync(f, df, x0, update_rule, self.runsignal,
p = _CGDAsync(f, df, x0, update_rule, self.runsignal, self.SENTINEL,
report_every=report_every, messages=messages, maxiter=maxiter,
max_f_eval=max_f_eval, gtol=gtol, outqueue=outqueue, *args, **kwargs)
p.run()
@ -189,12 +190,14 @@ class Async_Optimize(object):
while self.runsignal.is_set():
try:
p.join(1)
c.join(1)
# c.join(1)
except KeyboardInterrupt:
# print "^C"
self.runsignal.clear()
p.join()
c.join()
if c.is_alive():
print "WARNING: callback still running, optimisation done!"
return p.result
class CGD(Async_Optimize):
'''
@ -215,7 +218,7 @@ class CGD(Async_Optimize):
callback gets called every `report_every` iterations
callback(xi, fi, iteration, function_calls, gradient_calls, status_message)
callback(xi, fi, gi, iteration, function_calls, gradient_calls, status_message)
if df returns tuple (grad, natgrad) it will optimize according
to natural gradient rules
@ -233,7 +236,7 @@ class CGD(Async_Optimize):
**calls**
---------
callback(x_opt, f_opt, iteration, function_calls, gradient_calls, status_message)
callback(x_opt, f_opt, g_opt, iteration, function_calls, gradient_calls, status_message)
at end of optimization!
"""
@ -247,7 +250,7 @@ class CGD(Async_Optimize):
Minimize f, calling callback every `report_every` iterations with following syntax:
callback(xi, fi, iteration, function_calls, gradient_calls, status_message)
callback(xi, fi, gi, iteration, function_calls, gradient_calls, status_message)
if df returns tuple (grad, natgrad) it will optimize according
to natural gradient rules
@ -260,7 +263,7 @@ class CGD(Async_Optimize):
**returns**
---------
x_opt, f_opt, iteration, function_calls, gradient_calls, status_message
x_opt, f_opt, g_opt, iteration, function_calls, gradient_calls, status_message
at end of optimization
"""