class SimulatedAnnealing:
'''
1. easy to dump the object as a resulting model, and the trajectory?
2. linear cooling rate
'''
def __init__( self, scorefxn, steps, temp, cooling_rate ):
''' '''
assert isScoreFxn( scorefxn )
self._log = ""
self._scorefxn = scorefxn
self._steps = steps # MC steps per annealing cycle
self._temperature = temp
# declare objects
self.__fragidx_pose = FragIdxPose()
self._initialization = "random"
self._cooling_rate = cooling_rate
self._anneal_temp = 0.0
# recover_low ( quench )
self._quench = False
self._lowest_score_pose = FragIdxPose()
self._record_trajectory = False
def set_annealing_temperature( self ):
''' '''
self._anneal_temp = round( self._temperature*self._cooling_rate, 1 )
self._temperature -= self._anneal_temp
def run( self, runid ):
''' run id is a identification for a model to dump '''
self.__fragidx_pose.clear()
self.__fragidx_pose.initialization( self._scorefxn.get_density_score_dict(), self._initialization ) # default initialize by random
#print self.__fragidx_pose.show_state( "initialization" )
mc = MonteCarlo( self._scorefxn, self._temperature )
mc.apply( self.__fragidx_pose, self._steps ) # to prevent a silly bug that at the very first state SCORE==0, such that you wouldn't lower than that during high temperatures sampling
self._lowest_score_pose = self.__fragidx_pose.clone()
tracker = TrajectoryTracker( runid )
tag = "model_" + str( runid )
while self._temperature >=0 : # it would never reach this criteria
if self._quench:
self.recover_low() # retrieve the lowest-score pose from previous runs
mc.apply( self.__fragidx_pose, self._steps )
residual_pose = self._scorefxn.residualize_pose() # residual_pose will be filled with
#print residual_pose.show_state( tag + "_" + str( self._temperature ) )
self.set_annealing_temperature() # update self._temperature
if self._temperature == mc.get_temperature(): break
mc.set_temperature( self._temperature )
if self._record_trajectory: tracker.save( self._temperature, self.__fragidx_pose )
tracker.save( self._temperature, self._scorefxn.residualize_pose() )
tracker.dump_pickle( tag )
residual_pose.show_state( tag + "_final", True ) # True for verbose showing all residues states
def set_to_quench( self ):
self._quench = True
def record_trajectory( self ):
self._record_trajectory = True
def recover_low( self ): # recover_low
'''
last accept score pose, and lowest score pose; the MC.recover_low has this is because it uses Metropolis to thermally accept pose
'''
# this is for the first around
# need to think about a better way to initiate it
if not self._lowest_score_pose._initialized:
self._lowest_score_pose._initialized = self.__fragidx_pose
# .total_score() method will re-evaluate pose whenever it is called
if self.__fragidx_pose.total_score() > self._lowest_score_pose.total_score():
stdout.write("recover previous found lowest-score pose with score: %s\n" % self._lowest_score_pose.total_score() )
self.__fragidx_pose = self._lowest_score_pose.clone()
else:
stdout.write("found a newer lowest-score pose\n")
self._lowest_score_pose = self.__fragidx_pose.clone()
def dump( self, id ):
return
def quench( self ):
return
