def fromProtoModel(cls,
protomodel,
nsteps=10000,
strategy="aggressive",
walkerid=0,
dump_training=False,
dbpath="<rundir>/database.pcl",
expected=False,
select="all",
catch_exceptions=True,
keep_meta=True,
rundir=None,
do_combine=False,
seed=None,
stopTeleportationAfter=-1):
ret = cls( walkerid, nsteps=nsteps, dbpath = dbpath, expected=expected,
select=select, catch_exceptions = catch_exceptions, rundir = rundir,
do_combine = do_combine, seed = seed, stopTeleportationAfter = \
stopTeleportationAfter )
ret.manipulator.M = protomodel
ret.manipulator.setWalkerId(walkerid)
ret.manipulator.backupModel()
if dump_training:
## we use the accelerator only to dump the training data
from accelerator import Accelerator
ret.accelerator = Accelerator(walkerid=walkerid,
dump_training=True,
is_trained=False)
return ret
def __init__(self,
walkerid=0,
nsteps=10000,
strategy="aggressive",
dump_training=False,
cheatcode=0,
dbpath="./database.pcl",
expected=False,
select="all",
catch_exceptions=True,
rundir=None,
nevents=100000,
do_combine=False,
record_history=False,
seed=None,
stopTeleportationAfter=-1):
""" initialise the walker
:param nsteps: maximum number of steps to perform, negative is infinity
:param cheatcode: cheat mode. 0 is no cheating, 1 is with ranges, 2
is the Z323 model.
:param expected: remove possible signals from database
:param select: select only subset of results (all for all, em for efficiency maps only, ul for upper limits only, alternatively select for txnames via e.g. "txnames:T1,T2"
:param catch_exceptions: should we catch exceptions
:param nevents: number of MC events when computing cross-sections
:param do_combine: if true, then also perform combinations, either via
simplified likelihoods or via pyhf
:param record_history: if true, attach a history recorder class
:param seed: random seed, int or None
:param stopTeleportationAfter: int or None. we stop teleportation after this step nr.
If negative or None, we dont teleport at all
"""
if type(walkerid) != int or type(nsteps) != int or type(
strategy) != str:
self.pprint("Wrong call of constructor: %s, %s, %s" %
(walkerid, nsteps, strategy))
sys.exit(-2)
self.walkerid = walkerid ## walker id, for parallel runs
self.rundir = rundir
if rundir == None:
self.rundir = "./"
if seed is not None:
from ptools import helpers
helpers.seedRandomNumbers(seed + walkerid)
self.pprint(f"setting random seed to {seed}")
#Initialize Predictor
self.predictor = Predictor(self.walkerid,
dbpath=dbpath,
expected=expected,
select=select,
do_combine=do_combine)
#Initialize Hiscore (with access to the predictor)
self.hiscoreList = Hiscore(walkerid,
True,
"%s/H%d.hi" % (self.rundir, walkerid),
backup=False,
predictor=self.predictor)
self.hiscoreList.nkeep = 1
#Initialize ProtoModel and Manipulator:
protomodel = ProtoModel(
self.walkerid,
keep_meta=True,
nevents=nevents,
dbversion=self.predictor.database.databaseVersion)
self.manipulator = Manipulator(protomodel,
strategy,
do_record=record_history,
seed=seed)
self.catch_exceptions = catch_exceptions
self.maxsteps = nsteps
if stopTeleportationAfter == None:
stopTeleportationAfter = -1
# stopTeleportationAfter = self.maxsteps/3.
self.stopTeleportationAfter = stopTeleportationAfter
self.accelerator = None
if record_history:
from ptools.history import History
self.recorder = History(f"{self.rundir}/history{walkerid}.list")
self.manipulator.do_record = True
jobid = "unknown"
if "SLURM_JOBID" in os.environ:
jobid = os.environ["SLURM_JOBID"]
self.pprint("Ramping up with slurm jobid %s" % jobid)
if cheatcode <= 0:
self.takeStep() # the first step should be considered as "taken"
#Set current Z and K values to threshold values
self.currentZ = -0.1
self.currentK = -20.0
else:
self.manipulator.cheat(cheatcode)
self.predictor.predict(self.protomodel)
self.pprint ( "Cheat model gets Z=%.2f, K=%.2f" % \
( self.manipulator.M.Z, self.manipulator.M.K ) )
# self.printStats ( substep=4 )
self.manipulator.backupModel()
self.hiscoreList.newResult(self.manipulator)
self.printStats(substep=5)
self.currentK = self.manipulator.M.K
self.currentZ = self.manipulator.M.Z
if dump_training:
from accelerator import Accelerator
## we use the accelerator only to dump the training data
self.accelerator = Accelerator(walkerid=walkerid,
dump_training=True,
is_trained=False)
