def __init__(self,
in_dim,
emb_dim,
pb_limits=None,
batch_sz=128,
log_dir="/tmp/"):
self.frozen = MiscUtils.SmallEncoder1d(in_dim,
emb_dim,
num_hidden=3,
non_lin="leaky_relu",
use_bn=False)
self.frozen.eval()
self.learnt = MiscUtils.SmallEncoder1d(in_dim,
emb_dim,
num_hidden=5,
non_lin="leaky_relu",
use_bn=False)
self.optimizer = torch.optim.Adam(self.learnt.parameters(), lr=1e-2)
self.archive = None
self.pop = None
self.batch_sz = batch_sz
self.epoch = 0
self.log_dir = log_dir
if pb_limits is not None:
MiscUtils.make_networks_divergent(self.frozen,
self.learnt,
pb_limits,
iters=50)
def run_playbook(self):
self.check_for_extension()
os.system(self.__ansible__command__ + " " + self.pb)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + Constants.openedx_run_playbook +
self.pb)
def do_action(self):
if self.command == 'change_root_password':
user = self.args[3]
strQuery = 'mysql -u root -p -e "SET PASSWORD FOR ' + user + "@'localhost' = PASSWORD('" + self.args[4] + "');"
os.system("sudo " + strQuery)
MiscUtils.write_to_log(Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " + Constants.change_user_pass + " " + self.args[4])
def run_script(self):
if self.__does_script_exist():
os.system(self.dir + self.script)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.execute_shell_script + " " + self.dir + self.script)
else:
print(Constants.script_not_found)
def do_lms_asset_recompile():
print(Constants.header + Constants.openedx_compile_assets)
os.system(
"cd /edx/app/edxapp/edx-platform && paver update_assets lms --settings=aws"
)
sv = Supervisor.Supervisor("restart")
sv.cmd = sv.cmd + " edxapp:"
sv.run()
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.openedx_compile_assets_lms_log)
def handle_alexa(event, content):
intent_name = event['request']['intent']['name']
slots = event['request']['intent']['slots']
session_attrs = event['session'].get('attributes', {})
set_user_response = MiscUtils.set_session_user_id_and_type(event, True)
if set_user_response is not None:
return set_user_response
if LexUtils.is_yes(session_attrs[CC.SESS_ATTR_ANONYMOUS]):
message = "You must provide an email address or phone number to use this feature."
return AlexaUtils.build_response(
session_attrs,
AlexaUtils.build_speechlet_response("Sorry", message, None, False))
user = User.User(session_attrs[CC.SESS_ATTR_USER_ID],
session_attrs[CC.SESS_ATTR_USER_ID_TYPE])
points_period_days = 30
if AlexaUtils.is_slot_present(slots, CC.SLOT_POINTS_TIME_PERIOD):
points_time_period = slots[CC.SLOT_POINTS_TIME_PERIOD]
if points_time_period == CC.WEEKLY:
points_period_days = 7
elif points_time_period == CC.YEARLY:
points_period_days = 365
# TODO get points from user opportunities + points per opportunity
message = "The point period is {}.".format(points_period_days)
return AlexaUtils.build_response(
session_attrs,
AlexaUtils.build_speechlet_response("Point Period", message, None,
False))
def run_me():
yield """
<script type="text/javascript">
function stb() {
window.scrollBy(0,document.body.scrollHeight);
}
var success = true;
</script>
<style>body {color:#fff;font-family: monospace;}</style>
<pre>"""
args = (caseobj["case_appsmount"], )
p = Thread(target=parser.begin_scan, args=args)
p.start()
while True:
res = q.get()
if res == "FIN":
break
elif res == "FINERR":
yield "<b>Error occurred, canceling scan!<b>"
yield "<script>success = false;</script>"
self.db.remove_case(caseobj["case_id"])
return
yield res + "\n<script>stb();</script>"
yield "<b>Finished, Saving Content please wait...</b>"
yield "</pre>"
store = parser.get_extractedstore()
storepath = store.gen_storepath(caseobj["case_name"],
caseobj["case_id"])
self.db.add_case_storepath(caseobj["case_id"], storepath)
store_file = store.save_store(storepath)
self.db.add_case_file(case_id, store_file,
MiscUtils.genfilesig(store_file))
self.add_store(store, case_id)
def handle_lex(event, content):
intent_name = event['currentIntent']['name']
slots = event['currentIntent']['slots']
session_attrs = event['sessionAttributes'] if event[
'sessionAttributes'] is not None else {}
set_user_response = MiscUtils.set_session_user_id_and_type(event, False)
if set_user_response is not None:
return set_user_response
if LexUtils.is_yes(session_attrs[CC.SESS_ATTR_ANONYMOUS]):
message = "You must provide an email address or phone number to use this feature."
return LexUtils.close(CC.EMPTY_OBJ, True, message, CC.EMPTY_OBJ)
user = User.User(session_attrs[CC.SESS_ATTR_USER_ID],
session_attrs[CC.SESS_ATTR_USER_ID_TYPE])
points_period_days = 30
if LexUtils.is_slot_present(slots, CC.SLOT_POINTS_TIME_PERIOD):
points_time_period = slots[CC.SLOT_POINTS_TIME_PERIOD]
if points_time_period == CC.WEEKLY:
points_period_days = 7
elif points_time_period == CC.YEARLY:
points_period_days = 365
# TODO get points from user opportunities + points per opportunity
message = "The point period is {}.".format(points_period_days)
return LexUtils.close(CC.EMPTY_OBJ, True, message, CC.EMPTY_OBJ)
def sendParser(gui,message):
"""
Retrieves the text from the entry field, parses it and sends it to
the server in a bytearray form.
Keyword arguments:
event - The return-key was pressent
Side-effects:
Checks if the command is valid. If so, sends it to the server
"""
options = initiateSwitch()
argumentString = MiscUtils.messageSplit(message)
if (argumentString[0] == "/connect"):
connect(argumentString,gui)
elif (gui.socketStatus != "ok"):
TabUtils.writeMessage(gui,"You are not connected to a server, use /connect IP")
gui.message.delete(0,END)
else:
if argumentString[0] in options:
options[argumentString[0]](argumentString,gui)
else:
msg_temp = gui.currentTab + " " + message+'\n'
sendToServer(gui,msg_temp)
def del_user(self, username, email):
cmd = "sudo /edx/bin/python.edxapp /edx/bin/manage.edxapp lms manage_user " + username + " " + email + " --settings=aws"
if MiscUtils.isValidEmail(email):
os.system(cmd)
else:
print(Constants.openedx_user_invalidemail)
def run_me():
yield """
<script type="text/javascript">
function stb() {
window.scrollBy(0,document.body.scrollHeight);
}
var success = true;
</script>
<style>body {color:#fff;font-family: monospace;}</style>
<pre>"""
args = (caseobj["case_appsmount"],)
p = Thread(target=parser.begin_scan, args=args)
p.start()
while True:
res = q.get()
if res == "FIN":
break
elif res == "FINERR":
yield "<b>Error occurred, canceling scan!<b>"
yield "<script>success = false;</script>"
self.db.remove_case(caseobj["case_id"])
return
yield res + "\n<script>stb();</script>"
yield "<b>Finished, Saving Content please wait...</b>"
yield "</pre>"
store = parser.get_extractedstore()
storepath = store.gen_storepath( caseobj["case_name"],
caseobj["case_id"] )
self.db.add_case_storepath( caseobj["case_id"], storepath )
store_file = store.save_store( storepath )
self.db.add_case_file(case_id, store_file,
MiscUtils.genfilesig(store_file))
self.add_store(store, case_id)
def get_accurate_tree_score(mask_matrix, parent_vector, dft, log_scores):
"""Accurately evaluate mutation tree.
For detailed explanation of the fast_matrix please see docstring of get_fast_tree_score.
Args:
mask_matrix (np.ndarray): Matrix of 'count of probabilities' of type P(D_ij = x|E_ij = 0) and
P(D_ij = x|E_ij = 1) from which fast_matrix can be obtained if log scores matrix is known.
parent_vector (np.ndarray): Parent vector representation of mutation tree.
dft (np.ndarray): Depth first traversal of mutation tree.
log_scores (np.ndarray): Log scores matrix corresponding to error probabilities.
Returns:
float: Accurate score of mutation tree.
np.ndarray: fast_matrix containing probabilities of type PI(i=1 -> n) P(D_ij|A(T)_i ro_j) that relate to the
attachment of every cell to every node.
"""
# Create a copy
mask_matrix_copy = np.copy(mask_matrix)
# Use DFS to visit all nodes
# Parent will be always evaluated before child, so we can calculate the score of the child from the score of the
# parent, root is already evaluated so it can be skipped
for i in dft[1:]:
mask_matrix_copy[i, :, :] += mask_matrix_copy[parent_vector[i], :, :]
# Transformation of log_scores_matrix to analogous vector representation
log_scores_vector = np.hstack((log_scores[:, 0], log_scores[:, 1]))
# Transform counts to probabilities by using log_scores_vector
fast_matrix = MiscUtils.dot_3d(mask_matrix_copy, log_scores_vector)
return get_score_from_fast_matrix(fast_matrix), fast_matrix
def test_dot_3d(self):
a = np.random.randint(0, 100, (15, 3000, 8), dtype=np.int32)
b = np.log(np.random.random((8,)))
dot_product = MiscUtils.dot_3d(a, b)
dot_product_test = np.dot(a, b)
self.assertTrue(check_max_variation_matrix(dot_product, dot_product_test, 1e-15))
def checkQueue(gui):
"""
Parses the answer from the server and performes the corresponding
command.
Side effects:
Can be one of the following: A new tab is created,
the username is changed, a message is printed in the current window.
"""
stopSign = 1
respons = gui.thread.returnQueue()
if(respons[0][0] == "{"):
options = initDictionary()
temp = respons[1:len(respons)-2]
if " " in temp:
commandString = MiscUtils.messageSplit(temp)
if commandString[0] == 'disconnected':
stopSign = 0
disconnected(gui)
else:
if commandString[0] in options:
options[commandString[0]](gui,commandString[1])
else:
fillUserList(gui,commandString)
else:
fillRoomList(gui,temp)
else:
printMessage(gui,respons)
if stopSign == 1:
gui.master.after(50,checkQueue,gui)
def delegate(self, gridPassPhrase='', verbose=False):
"""method to upload a valid proxy credential in a myproxy server.
The proxy must to have voms attributes.
Only the pilot owner will be allowed to retrieve the proxy,
specified by -Z option.
The DN of the user is used as username.
"""
if self.servername == '':
raise MyProxyError(2100)
if self.vomsattributes == '':
raise MyProxyError(2101)
if self.pilotownerDN == '':
raise MyProxyError(2103)
cmd = 'myproxy-init'
# credname
credname = re.sub('-', '', MiscUtils.wrappedUuidGen())
print "=== upload proxy for glexec"
# command options
cmd += ' -s %s' % self.servername # myproxy sever name
cmd += " -x -Z '%s'" % self.pilotownerDN # only user with this DN
# is allowed to retrieve it
cmd += ' --voms %s' % self.vomsattributes # voms attributes
cmd += ' -d' # uses DN as username
cmd += ' --credname %s' % credname
if gridPassPhrase == '':
if sys.stdin.isatty():
gridPassPhrase = getpass.getpass('Enter GRID pass phrase:')
else:
sys.stdout.write('Enter GRID pass phrase:')
sys.stdout.flush()
gridPassPhrase = sys.stdin.readline().rstrip()
print
gridPassPhrase = gridPassPhrase.replace('$', '\$')
cmd = 'echo "%s" | %s -S' % (gridPassPhrase, cmd)
cmd = self.srcFile + ' unset GT_PROXY_MODE; ' + cmd
self.__command = cmd # for testing purpose
if verbose:
cmdStr = cmd
if gridPassPhrase != '':
cmdStr = re.sub(gridPassPhrase, '*****', cmd)
print cmdStr
status, out = commands.getstatusoutput(cmd)
if verbose:
print out
if status != 0:
if out.find(
'Warning: your certificate and proxy will expire') == -1:
if not verbose:
print out
raise MyProxyError(2300)
return credname
def test_get_mirrored_beta(self):
betas = np.array([0, 0.5, 1, -0.3, 1.3, -2.5, 2.5, -3.7, 3.7, -4.8, 4.8, -5.6, 5.6], dtype=np.float64)
betas_mirrored = np.empty((betas.size,), dtype=np.float64)
for beta_index in range(betas.size):
betas_mirrored[beta_index] = MiscUtils.get_mirrored_beta(betas[beta_index])
betas_mirrored_test = np.array([0, 0.5, 1, 0.3, 0.7, 0.5, 0.5, 0.3, 0.3, 0.8, 0.8, 0.4, 0.4], dtype=np.float64)
self.assertTrue(check_max_variation_array(betas_mirrored, betas_mirrored_test, 1e-15))
def delegate(self,gridPassPhrase='',verbose=False):
"""method to upload a valid proxy credential in a myproxy server.
The proxy must to have voms attributes.
Only the pilot owner will be allowed to retrieve the proxy,
specified by -Z option.
The DN of the user is used as username.
"""
if self.servername == '' :
raise MyProxyError(2100)
if self.vomsattributes == '' :
raise MyProxyError(2101)
if self.pilotownerDN == '' :
raise MyProxyError(2103)
cmd = 'myproxy-init'
# credname
credname = re.sub('-','',MiscUtils.wrappedUuidGen())
print "=== upload proxy for glexec"
# command options
cmd += ' -s %s' % self.servername # myproxy sever name
cmd += " -x -Z '%s'" % self.pilotownerDN # only user with this DN
# is allowed to retrieve it
cmd += ' --voms %s' % self.vomsattributes # voms attributes
cmd += ' -d' # uses DN as username
cmd += ' --credname %s' % credname
if gridPassPhrase == '':
if sys.stdin.isatty():
gridPassPhrase = getpass.getpass('Enter GRID pass phrase:')
else:
sys.stdout.write('Enter GRID pass phrase:')
sys.stdout.flush()
gridPassPhrase = sys.stdin.readline().rstrip()
print
gridPassPhrase = gridPassPhrase.replace('$','\$')
cmd = 'echo "%s" | %s -S' % (gridPassPhrase,cmd)
cmd = self.srcFile + ' unset GT_PROXY_MODE; ' + cmd
self.__command = cmd # for testing purpose
if verbose:
cmdStr = cmd
if gridPassPhrase != '':
cmdStr = re.sub(gridPassPhrase,'*****',cmd)
print cmdStr
status,out = commands.getstatusoutput( cmd )
if verbose:
print out
if status != 0:
if out.find('Warning: your certificate and proxy will expire') == -1:
if not verbose:
print out
raise MyProxyError(2300)
return credname
def join(argument,gui):
success = 0
if (" " in argument[1]):
argumentString2 = MiscUtils.messageSplit(argument[1])
if MiscUtils.noDuplicate(gui.windowList,argumentString2[0]):
success = 1
arguments = 2
else:
if MiscUtils.noDuplicate(gui.windowList,argument[1]):
success = 1
arguments = 1
if success == 1:
if (arguments == 2):
msg_temp = argument[0] + " " + argument[0]+ " " + argument[1]+'\n'
else:
msg_temp = argument[1] + " " + argument[0]+ " " + argument[1]+'\n'
sendToServer(gui,msg_temp)
else:
TabUtils.writeMessage(gui,"You are already a member of that room!")
gui.message.delete(0,END)
def leave(argument,gui):
if argument[1] == "global":
TabUtils.writeMessage("You cannot leave global!")
gui.message.delete(0,END)
else:
if(not MiscUtils.noDuplicate(gui.windowList,argument[1])):
TabUtils.deleteTab(gui,argument[1])
gui.windowList.pop(argument[1],None)
gui.menues.updateRoomList(gui.windowList)
msg_temp ="global" + " " + argument[0] + " " +argument[1] +'\n'
sendToServer(gui,msg_temp)
else:
gui.writeMessage("You are not a member of the room " + argument[1] +"!")
gui.message.delete(0,END)
def run_management_script_update(self):
print(Constants.header + " " + Constants.management_script_upgrade)
os.system("git config --global credential.helper \"cache --timeout=3600\"")
os.system("cd " + MiscUtils.get_python_dir() + " && sudo git reset --hard HEAD")
os.system("cd " + MiscUtils.get_python_dir() + " && sudo git clean -f")
os.system("cd " + MiscUtils.get_python_dir() + " && sudo git pull")
print(Constants.header + Constants.done)
MiscUtils.write_to_log(Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " + Constants.management_script_upgrade_log)
def add_user(self, username, email, isStaff, isSuperUser):
cmd = "sudo /edx/bin/python.edxapp /edx/bin/manage.edxapp lms manage " + username + " " + email
if isStaff:
cmd = cmd + " --staff"
if isSuperUser:
cmd = cmd + " --superuser"
cmd = cmd + " --settings=aws"
if MiscUtils.isValidEmail(email):
os.system(cmd)
else:
print(Constants.openedx_user_invalidemail)
def propose_new_beta(self, mutation_matrix, mask_matrix, log_scores,
best_tree_log_score, jump_stdev, beta_params):
"""Construct a beta move in Metropolis-Hastings algorithm and accept or reject it.
Args:
mutation_matrix (np.ndarray): Mutation matrix (D matrix).
mask_matrix (np.ndarray): Matrix of 'count of probabilities' of type P(D_ij = x|E_ij = 0) and
P(D_ij = x|E_ij = 1) from which fast_matrix can be obtained if log scores matrix is known.
log_scores (np.ndarray): Log scores matrix corresponding to error probabilities.
best_tree_log_score (float): Best tree log score so far.
jump_stdev (float): Beta jump normal random variable standard deviation.
beta_params (list): Beta distribution parameters for beta.
Returns:
np.ndarray: Log scores matrix corresponding to error probabilities.
"""
# Find new beta
proposed_beta = self.beta + np.random.normal(0, jump_stdev)
# Mirror value if not on interval [0, 1]
proposed_beta = MiscUtils.get_mirrored_beta(proposed_beta)
# Calculate score of the proposed beta
proposed_beta_log_score = ScoreUtils.get_beta_score(
proposed_beta, beta_params)
# Update log scores matrix
proposed_log_scores = ScoreUtils.get_updated_log_scores_matrix(
log_scores, proposed_beta)
# Calculate score of the mutation tree with the new beta
proposed_tree_log_score, proposed_fast_matrix = ScoreUtils.get_fast_tree_score(
mutation_matrix, mask_matrix, self.parent_vector, self.dft,
proposed_log_scores, best_tree_log_score)
# Accept move
if ScoreUtils.check_accept_move(
proposed_beta_log_score + proposed_tree_log_score,
self.combined_log_score):
self.update_beta(proposed_beta, proposed_beta_log_score,
proposed_tree_log_score, proposed_fast_matrix)
return proposed_log_scores
# Reject move
return log_scores
def run_db_backup(self):
print(Constants.header + " " + Constants.backup_log)
if self.__hasMaintenanceStarted__ is True:
if MiscUtils.check_for_backup_dir():
cur_logtime = MiscUtils.get_logtime_format()
os.system("mongodump -h 127.0.0.1:" + Constants.mongodb_port + " -o " + MiscUtils.get_backup_dir() + cur_logtime + "_mongo-backup")
os.system("mongodump -h 127.0.0.1:" + Constants.mongodb_port + " -o " + Constants.edx_backup_loc)
os.system("mysqldump -u " + Constants.mysql_db_backup_user + " -p --all-databases > " + MiscUtils.get_backup_dir() + cur_logtime + "_mongo-backup/" + cur_logtime + "_mysql-backup.sql")
shutil.make_archive(cur_logtime, "zip", MiscUtils.get_backup_dir() + cur_logtime + "_mongo-backup")
os.system("sudo rm -rf " + MiscUtils.get_backup_dir() + cur_logtime + "_mongo-backup")
os.system("sudo cp " + cur_logtime + ".zip " + MiscUtils.get_backup_dir() + "db/")
os.system("sudo rm " + cur_logtime + ".zip")
print(Constants.header + " " + Constants.backup_log_complete + MiscUtils.get_backup_dir())
else:
print(Constants.maintenance_error)
def run_mark_packages(self):
if self.do_mark_packages:
print(Constants.header + Constants.system_updates_mark_package)
for item in self.packages:
os.system("sudo apt-mark hold " + item)
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.system_updates_mark_package_log)
else:
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.system_updates_mark_package_log_unsuccessful)
def run_upgrades(self):
if self.do_upgrade:
print(Constants.header + Constants.system_updates_run_upgrades)
os.system(
"sudo apt-get update -y && apt-get upgrade -y && apt-get autoremove"
)
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.system_updates_run_upgrades_successful)
else:
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.system_updates_run_upgrades_unsuccessful)
def __init__(self,
archive,
nov_estimator,
mutator,
problem,
selector,
n_pop,
n_offspring,
agent_factory,
visualise_bds_flag,
map_type="scoop",
logs_root="/tmp/ns_log/",
compute_parent_child_stats=False):
"""
archive Archive object implementing the Archive interface. Can be None if novelty is LearnedNovelty1d/LearnedNovelty2d
nov_estimator NoveltyEstimator object implementing the NoveltyEstimator interface.
problem Problem object that provides
- __call__ function taking individual_index returning (fitness, behavior_descriptors, task_solved_or_not)
- a dist_thresh (that is determined from its bds) which specifies the minimum distance that should separate a point x from
its nearest neighbour in the archive+pop in order for the point to be considered as novel. It is also used as a threshold on novelty
when updating the archive.
- optionally, a visualise_bds function.
mutator Mutator
selector function
n_pop int
n_offspring int
agent_factory function
visualise_bds_flag int
map_type string different options for sequential/parallel mapping functions. supported values currently are
"scoop" distributed map from futures.map
"std" buildin python map
logs_root str the logs diretory will be created inside logs_root
"""
self.archive = archive
if archive is not None:
self.archive.reset()
self.nov_estimator = nov_estimator
self.problem = problem
self.map_type = map_type
self._map = futures.map if map_type == "scoop" else map
print(
colored("[NS info] Using map_type " + map_type,
"green",
attrs=["bold"]))
self.mutator = mutator
self.selector = selector
self.n_offspring = n_offspring
self.agent_factory = agent_factory
initial_pop = [self.agent_factory() for i in range(n_pop)]
initial_pop = self.generate_new_agents(initial_pop, generation=0)
self._initial_pop = copy.deepcopy(initial_pop)
assert n_offspring >= len(
initial_pop), "n_offspring should be larger or equal to n_pop"
self.visualise_bds_flag = visualise_bds_flag
if os.path.isdir(logs_root):
self.logs_root = logs_root
self.log_dir_path = MiscUtils.create_directory_with_pid(
dir_basename=logs_root + "/NS_log_",
remove_if_exists=True,
no_pid=False)
print(
colored("[NS info] NS log directory was created: " +
self.log_dir_path,
"green",
attrs=["bold"]))
else:
raise Exception(
"Root dir for logs not found. Please ensure that it exists before launching the script."
)
self.task_solvers = {}
self.compute_parent_child_stats = compute_parent_child_stats
self.save_archive_to_file = True
self.disable_tqdm = False
def main():
parser = argparse.ArgumentParser(description='Novelty Search.')
parser.add_argument('--config',
type=str,
help="yaml config file for ns",
default="")
args = parser.parse_args()
if not len(args.config):
raise Exception("You need to provide a yaml config file")
if len(args.config):
with open(args.config, "r") as fl:
config = yaml.load(fl, Loader=yaml.FullLoader)
if config["problem"]["name"] == "hardmaze":
max_steps = config["problem"]["max_steps"]
bd_type = config["problem"]["bd_type"]
assets = config["problem"]["assets"]
import HardMaze
problem = HardMaze.HardMaze(bd_type=bd_type,
max_steps=max_steps,
assets=assets)
elif config["problem"]["name"] == "large_ant_maze" or config[
"problem"]["name"] == "huge_ant_maze":
max_steps = config["problem"]["max_steps"]
bd_type = config["problem"]["bd_type"]
assets = config["problem"]["assets"]
pb_type = "huge" if config["problem"][
"name"] == "huge_ant_maze" else "large"
import LargeAntMaze
problem = LargeAntMaze.LargeAntMaze(pb_type=pb_type,
bd_type=bd_type,
max_steps=max_steps,
assets=assets)
else:
raise NotImplementedError("Problem type")
if config["novelty_estimator"]["type"] == "archive_based":
nov_estimator = NoveltyEstimators.ArchiveBasedNoveltyEstimator(
k=config["hyperparams"]["k"])
arch_types = {"list_based": Archives.ListArchive}
arch = arch_types[config["archive"]["type"]](
max_size=config["archive"]["max_size"],
growth_rate=config["archive"]["growth_rate"],
growth_strategy=config["archive"]["growth_strategy"],
removal_strategy=config["archive"]["removal_strategy"])
elif config["novelty_estimator"]["type"] == "learned":
bd_dims = problem.get_bd_dims()
embedding_dims = 2 * bd_dims
nov_estimator = NoveltyEstimators.LearnedNovelty1d(
in_dim=bd_dims,
emb_dim=embedding_dims,
pb_limits=problem.get_behavior_space_boundaries())
arch = None
if config["selector"]["type"] == "elitist_with_thresh":
selector = functools.partial(
MiscUtils.selBest, k=config["hyperparams"]["population_size"])
elif config["selector"]["type"] == "roulette_with_thresh":
roulette_msg = "Usage currently not supported: it ends up chosing the same element many times, this duplicates agent._ids etc"
roulette_msg += " fixing this bug is not a priority since selBest with thresholding actually works well"
raise Exception(roulette_msg)
elif config["selector"]["type"] == "nsga2_with_thresh":
selector = MiscUtils.NSGA2(
k=config["hyperparams"]["population_size"])
elif config["selector"]["type"] == "elitist":
selector = functools.partial(
MiscUtils.selBest,
k=config["hyperparams"]["population_size"],
automatic_threshold=False)
else:
raise NotImplementedError("selector")
in_dims = problem.dim_obs
out_dims = problem.dim_act
num_pop = config["hyperparams"]["population_size"]
if config["population"]["individual_type"] == "simple_fw_fc":
normalise_output_with = ""
num_hidden = 3
hidden_dim = 10
if "large_ant_maze" == config["problem"]["name"]:
normalise_output_with = "tanh"
num_hidden = 4
hidden_dim = 10
def make_ag():
return Agents.SmallFC_FW(
in_d=in_dims,
out_d=out_dims,
num_hidden=num_hidden,
hidden_dim=hidden_dim,
output_normalisation=normalise_output_with)
elif config["population"]["individual_type"] == "agent1d":
def make_ag():
return Agents.Agent1d(min(problem.env.phi_vals),
max(problem.env.phi_vals))
mutator_type = config["mutator"]["type"]
genotype_len = make_ag().get_genotype_len()
if mutator_type == "gaussian_same":
mutator_conf = config["mutator"]["gaussian_params"]
mu, sigma, indpb = mutator_conf["mu"], mutator_conf[
"sigma"], mutator_conf["indpb"]
mus = [mu] * genotype_len
sigmas = [sigma] * genotype_len
mutator = functools.partial(deap_tools.mutGaussian,
mu=mus,
sigma=sigmas,
indpb=indpb)
elif mutator_type == "poly_same":
mutator_conf = config["mutator"]["poly_params"]
eta, low, up, indpb = mutator_conf["eta"], mutator_conf[
"low"], mutator_conf["up"], mutator_conf["indpb"]
if config["population"]["individual_type"] == "agent1d":
dummy_ag = make_ag()
low = dummy_ag.min_val
up = dummy_ag.max_val
mutator = functools.partial(deap_tools.mutPolynomialBounded,
eta=eta,
low=low,
up=up,
indpb=indpb)
else:
raise NotImplementedError("mutation type")
map_t = "scoop" if config["use_scoop"] else "std"
visualise_bds = config["visualise_bds"]
ns = NoveltySearch(
archive=arch,
nov_estimator=nov_estimator,
mutator=mutator,
problem=problem,
selector=selector,
n_pop=num_pop,
n_offspring=config["hyperparams"]["offspring_size"],
agent_factory=make_ag,
visualise_bds_flag=visualise_bds,
map_type=map_t,
logs_root=config["ns_log_root"],
compute_parent_child_stats=config["compute_parent_child_stats"])
MiscUtils.bash_command(
["cp", args.config, ns.log_dir_path + "/config.yaml"])
stop_on_reaching_task = config["stop_when_task_solved"]
nov_estimator.log_dir = ns.log_dir_path
ns.disable_tqdm = config["disable_tqdm"]
ns.save_archive_to_file = config["archive"]["save_to_file"]
if ns.disable_tqdm:
print(
colored("[NS info] tqdm is disabled.",
"magenta",
attrs=["bold"]))
final_pop, solutions = ns(
iters=config["hyperparams"]["num_generations"],
stop_on_reaching_task=stop_on_reaching_task,
save_checkpoints=config["save_checkpoints"])
def __call__(self,
iters,
stop_on_reaching_task=True,
reinit=False,
save_checkpoints=0):
"""
iters int number of iterations
"""
print(
f"Starting NS with pop_sz={len(self._initial_pop)}, offspring_sz={self.n_offspring}"
)
print("Evaluation will take time.")
if save_checkpoints:
raise NotImplementedError(
"checkpoint save/load not implemented yet")
if reinit and self.archive is not None:
self.archive.reset()
parents = copy.deepcopy(self._initial_pop)
self.eval_agents(parents)
self.nov_estimator.update(archive=[], pop=parents)
novs = self.nov_estimator()
for ag_i in range(len(parents)):
parents[ag_i]._nov = novs[ag_i]
tqdm_gen = tqdm.trange(iters,
desc='',
leave=True,
disable=self.disable_tqdm)
for it in tqdm_gen:
offsprings = self.generate_new_agents(parents, generation=it + 1)
task_solvers, _ = self.eval_agents(offsprings)
pop = parents + offsprings
for x in pop:
if x._age == -1:
x._age = it + 1 - x._created_at_gen
else:
x._age += 1
self.nov_estimator.update(archive=self.archive, pop=pop)
novs = self.nov_estimator()
for ag_i in range(len(pop)):
pop[ag_i]._nov = novs[ag_i]
parents_next = self.selector(individuals=pop, fit_attr="_nov")
if self.compute_parent_child_stats:
for x in parents_next:
if x._bd_dist_to_parent_bd == -1 and x._created_at_gen > 0:
xp = next((s for s in pop if s._idx == x._parent_idx),
None)
if xp is None:
raise Exception("this shouldn't happen")
x._bd_dist_to_parent_bd = self.problem.bd_extractor.distance(
x._behavior_descr, xp._behavior_descr)
parents = parents_next
if hasattr(self.nov_estimator, "train"):
self.nov_estimator.train(parents)
if self.archive is not None:
self.archive.update(parents,
offsprings,
thresh=self.problem.dist_thresh,
boundaries=[0, 600],
knn_k=15)
if self.save_archive_to_file:
self.archive.dump(self.log_dir_path + f"/archive_{it}")
self.visualise_bds(parents +
[x for x in offsprings if x._solved_task])
MiscUtils.dump_pickle(self.log_dir_path + f"/population_gen_{it}",
parents)
if len(task_solvers):
print(
colored("[NS info] found task solvers (generation " +
str(it) + ")",
"magenta",
attrs=["bold"]))
self.task_solvers[it] = task_solvers
if stop_on_reaching_task:
break
tqdm_gen.set_description(
f"Generation {it}/{iters}, archive_size=={len(self.archive) if self.archive is not None else -1}"
)
tqdm_gen.refresh()
return parents, self.task_solvers
def printMessage(gui,message):
argumentString = MiscUtils.messageSplit(message)
gui.windowList[argumentString[0]].config(state=NORMAL)
gui.windowList[argumentString[0]].insert(END,MiscUtils.GetTime() + argumentString[1])
gui.windowList[argumentString[0]].yview(END)
gui.windowList[argumentString[0]].config(state=DISABLED)
def run_logs_backup(self):
print(Constants.header + " " + Constants.logs_backup)
cur_logtime = MiscUtils.get_logtime_format()
shutil.make_archive("logs_backup_" + cur_logtime, "zip", Constants.logs_loc)
os.system("mv logs_backup_" + cur_logtime + ".zip " + MiscUtils.get_backup_dir() + "logs/")
MiscUtils.write_to_log(Constants.mgmt_system_log, MiscUtils.get_current_user() + " " + Constants.logs_loc)
def run(self):
os.system(self.__sv__ + " " + self.cmd)
MiscUtils.write_to_log(Constants.mgmt_system_log, MiscUtils.get_current_user() + " " + Constants.supervisor_log + " " + self.cmd)
def do_command(self):
if self.command == "add_edx_superuser":
if self.totalLen > 4:
print(Constants.header + Constants.add_edx_user)
self.add_user(self.args[3], self.args[4], True, True)
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.add_edx_user_log + " " + self.args[3] + " " +
self.args[4])
elif self.command == "add_edx_user":
print(Constants.header + Constants.add_edx_user)
self.add_user(self.args[3], self.args[4], False, False)
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.add_edx_user_log + " " + self.args[3] + " " +
self.args[4])
elif self.command == "add_edx_staff":
print(Constants.header + Constants.add_edx_user)
self.add_user(self.args[3], self.args[4], True, False)
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.add_edx_user_log + " " + self.args[3] + " " +
self.args[4])
elif self.command == "delete_edx_user":
print(Constants.header + Constants.del_edx_user)
self.del_user(self.args[3], self.args[4])
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.del_edx_user_log + " " + self.args[3] + " " +
self.args[4])
elif self.command == "change_password":
print(Constants.header + Constants.change_password_edx_user)
self.change_password(self.args[3])
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " +
Constants.change_password_edx_user_log + " " + self.args[3])
else:
print(Constants.command_not_found)
def invited(gui,arguments):
if(MiscUtils.noDuplicate(gui.windowList,arguments)):
TabUtils.addTab(gui,arguments)
def compare_execution_time(min_d_e, max_d_e, pop_sz=50, offspring_sz=100, archive_size=8000,num_tests=50):
global_time_networks = []
global_time_archive = []
bd_size_range = [2**x for x in range(min_d_e,max_d_e+1,1)]
print(bd_size_range)
for descr_dim in bd_size_range:
num_gens = num_tests
emb_dim = descr_dim * 2
pop_bds = torch.rand(offspring_sz + pop_sz, descr_dim)
frozen = MiscUtils.SmallEncoder1d(descr_dim,
emb_dim,
num_hidden=3,
non_lin="leaky_relu",
use_bn=False)
frozen.eval()
learnt = MiscUtils.SmallEncoder1d(descr_dim,
emb_dim,
num_hidden=5,
non_lin="leaky_relu",
use_bn=False)
time_hist = []
frozen.eval()
optimizer = torch.optim.Adam(
[x for x in learnt.parameters() if x.requires_grad], lr=1e-2)
batch_sz = 256
for i in range(num_gens):
t1_n = time.time()
learnt.eval()
for batch_i in range(0, pop_bds.shape[0], batch_sz):
batch = pop_bds[batch_i:batch_i + batch_sz]
with torch.no_grad():
e_frozen = frozen(batch)
e_pred = learnt(batch)
nov = (e_pred - e_frozen).norm(dim=1)
learnt.train()
#this is how training is done, note that we can further reduce runtime by removing the extra frozen forward passes that we've made before when computing novelty
for _ in range(5):#In the experiments presented in the paper, usually this was set to 3 so BR-NS in the paper should be slightly faster
for batch_i in range(0, pop_bds.shape[0], batch_sz):
batch = pop_bds[batch_i:batch_i + batch_sz]
with torch.no_grad():
e_frozen = frozen(batch)
optimizer.zero_grad()
e_l = learnt(batch)
loss = (e_l - e_frozen).norm()**2
loss /= batch_sz
loss.backward()
optimizer.step()
t2_n = time.time()
time_hist.append(t2_n - t1_n)
mean_t_nets = np.array(time_hist).mean()
print(descr_dim, mean_t_nets)
global_time_networks.append(mean_t_nets)
if 1:
knn_k = 15
kdt_bds = np.random.rand(archive_size, descr_dim)
times = []
for i in range(num_gens):
t1_a = time.time()
#note that the kdtree has to be created everytime as after adding elements, you can't just reuse the same kdtree (some cells might have become much more dense)
kdt = KDTree(kdt_bds, leaf_size=20, metric='euclidean')
dists, ids = kdt.query(pop_bds, knn_k, return_distance=True)
t2_a = time.time()
times.append(t2_a - t1_a)
mean_t_arch = np.array(times).mean()
global_time_archive.append(mean_t_arch)
print(descr_dim, mean_t_arch)
if 1:
gt_arc_ms = [x * 1000 for x in global_time_archive]
gt_net_ms = [x * 1000 for x in global_time_networks]
plt.plot(bd_size_range,
gt_arc_ms,
"r",
label=f"Archive-based NS (size=={archive_size})",
linewidth=5)
plt.plot(bd_size_range, gt_net_ms, "b", label="BR-NS", linewidth=5)
plt.grid("on")
plt.legend(fontsize=28)
plt.xlabel("behavior descriptor dimensionality", fontsize=28)
plt.ylabel("time (ms)", fontsize=28)
plt.xticks(fontsize=28)
plt.yticks(fontsize=28)
plt.xlim(0, 2**max_d_e)
plt.show()
def end_maintenance(self):
if self.__hasMaintenanceStarted__ is True:
MiscUtils.write_to_log(Constants.maintenance_log, MiscUtils.get_current_user() + " has ended Maintenance") #TODO: Create Constant
os.system("sudo service nginx start")
self.__hasMaintenanceStarted__ = False
def username(gui,arguments):
gui.configList["userName"] = arguments
gui.menues.setName(arguments)
MiscUtils.rename(gui)
def run_maintenance_script(self,user): #Use only if you do something special with Maintenance (though you shouldn't need to!)
MiscUtils.write_to_log(Constants.mgmt_system_log, MiscUtils.get_current_user() + " is running User Maintenance script : " + user) #TODO: Create Constant
su = shell_functions.Shell_Script(shell_functions.get_script_dir(), user + "-maintenance-script.sh")
su.run_script()
def success(gui,arguments):
command = MiscUtils.messageSplit(arguments)
gui.rooms[command[0]] = [command[0],command[1]]
TabUtils.addTab(gui,command[0])
def get_reports_dir():
thedir = os.path.join(get_data_dir(), 'reports')
ut.createdir(thedir)
assert os.path.exists(thedir)
return thedir
def restart_service(self):
print(Constants.header + self.service + " " + Constants.restart)
if self.service == "forum":
self.__openedx__forum__res()
elif self.service == "mongodb" or self.service == "mongo" or self.service == "mongod":
self.__mongodb__res()
elif self.service == "ssh":
self.__ssh__res()
elif self.service == "nginx":
self.__nginx__res()
elif self.service == "denyhosts":
self.__denyhosts__res()
elif self.service == "mysql":
self.__mysql__res()
elif self.service == "nginx":
self.__nginx__res()
elif self.service == "all":
self.__openedx__res()
self.__openedx__analytics__res()
self.__openedx__certs__res()
self.__openedx__ecommerce__res()
self.__openedx__ecomworker__res()
self.__openedx__insights__res()
self.__openedx__notifier_celery__res()
self.__openedx__notifier_scheduler__res()
self.__openedx__programs__res()
self.__openedx__xqueue__res()
self.__openedx__xqueue_consumer__res()
self.__mongodb__res()
self.__ssh__res()
self.__nginx__res()
self.__denyhosts__res()
self.__mysql__res()
self.__nginx__res()
elif self.service == "edxapp":
self.__openedx__edxapp__res()
elif self.service == "analytics_api":
self.__openedx__analytics__res()
elif self.service == "certs":
self.__openedx__certs__res()
elif self.service == "ecommerce":
self.__openedx__ecommerce__res()
elif self.service == "ecommerce_workers":
self.__openedx__ecomworker__res()
elif self.service == "insights":
self.__openedx__insights__res()
elif self.service == "notifier-celery-workers":
self.__openedx__notifier_celery__res()
elif self.service == "notifier-scheduler":
self.__openedx__notifier_scheduler__res()
elif self.service == "programs":
self.__openedx__programs__res()
elif self.service == "xqueue":
self.__openedx__xqueue__res()
elif self.service == "xqueue_consumer":
self.__openedx__xqueue_consumer__res()
elif self.service == "rabbitmq":
self.__rabbitmq_server__res()
elif self.service == "edxapp-worker" or self.service == "edxapp-workers" or self.service == "edxapp_worker" or self.service == "edxapp_workers":
self.__openedx__edxapp_worker__res()
else:
print(Constants.option_not_found)
print(Constants.header + Constants.done)
MiscUtils.write_to_log(
Constants.mgmt_system_log,
MiscUtils.get_current_user() + " " + Constants.restart_log + " " +
self.service)
