def add_customers(circus, params):
logging.info(" adding customers")
customers = circus.create_population(
name="customers",
size=params["n_customers"],
ids_gen=SequencialGenerator(prefix="CUST_"))
logging.info(" adding 'possible sites' mobility relationship to customers")
mobility_rel = customers.create_relationship("POSSIBLE_SITES")
# probability of each site to be chosen, based on geo_level1 population
site_weight = circus.actors["sites"] \
.get_attribute("GEO_LEVEL_1_POPULATION") \
.get_values(None)
customer_gen = NumpyRandomGenerator(method="choice",
seed=next(circus.seeder),
a=customers.ids,
replace=False)
site_gen = NumpyRandomGenerator(method="choice",
seed=next(circus.seeder),
a=circus.actors["sites"].ids,
p=site_weight.values / sum(site_weight))
mobility_weight_gen = NumpyRandomGenerator(method="exponential",
scale=1.,
seed=next(circus.seeder))
# Everybody gets at least one site
mobility_rel.add_relations(from_ids=customers.ids,
to_ids=site_gen.generate(customers.size),
weights=mobility_weight_gen.generate(
customers.size))
# at each iteration, give a new site to a sample of people
# the sample will be of proportion p
p = 0.5
# to get an average site per customer of mean_known_sites_per_customer,
# if each iteration samples with p,
# we need mean_known_sites_per_customer/p iterations
#
# we remove one iteration that already happened for everybody here above
for i in range(int(params["mean_known_sites_per_customer"] / p) - 1):
sample = customer_gen.generate(int(customers.size * p))
mobility_rel.add_relations(from_ids=sample,
to_ids=site_gen.generate(len(sample)),
weights=mobility_weight_gen.generate(
len(sample)))
logging.info(" assigning a first random site to each customer")
customers.create_attribute(name="CURRENT_SITE",
init_relationship="POSSIBLE_SITES")
return customers
class _MaybeBackToDefault(SideEffectOnly):
"""
This is an internal operation of story, that transits members
back to default with probability as declared in
back_to_default_probability
"""
def __init__(self, story):
self.judge = NumpyRandomGenerator(method="uniform", seed=1234)
self.story = story
def side_effect(self, story_data):
# only transiting members that have ran during this clock tick
active_timer = self.story.timer.loc[story_data.index]
non_default_ids = active_timer[
active_timer["state"] != "default"].index
if non_default_ids.shape[0] == 0:
return
back_prob = self.story.get_param("back_to_default_probability",
non_default_ids)
if np.all(back_prob == 0):
cond = [False] * non_default_ids.shape[0]
elif np.all(back_prob == 1):
cond = [True] * non_default_ids.shape[0]
else:
baseline = self.judge.generate(back_prob.shape[0])
cond = back_prob > baseline
member_ids = back_prob[cond].index
states = ["default"] * member_ids.shape[0]
self.story.transit_to_state(ids=member_ids, states=states)
def connect_agent_to_dealer(self, agents, dealers):
"""
Relationship from agents to dealers
"""
logging.info("Randomly connecting agents to dealer ")
deg_prob = params["average_agent_degree"] / params[
"n_agents"] * params["n_dealers"]
agent_weight_gen = NumpyRandomGenerator(method="exponential",
scale=1.,
seed=1)
agent_customer_df = pd.DataFrame.from_records(
make_random_bipartite_data(agents.ids,
dealers.ids,
deg_prob,
seed=next(self.seeder)),
columns=["AGENT", "DEALER"])
agent_customer_rel = agents.create_relationship(name="DEALERS")
agent_customer_rel.add_relations(from_ids=agent_customer_df["AGENT"],
to_ids=agent_customer_df["DEALER"],
weights=agent_weight_gen.generate(
agent_customer_df.shape[0]))
# every agent is also connected to the "broke dealer", to make sure this
# one gets out of stock quickly
agent_customer_rel.add_relations(from_ids=agents.ids,
to_ids=np.repeat(
"broke_dealer", agents.ids.shape),
weights=4)
def step4():
"""
Woah, this got drastically slower
"""
example1 = circus.Circus(name="example1",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
person = example1.create_population(
name="person",
size=1000,
ids_gen=SequencialGenerator(prefix="PERSON_"))
person.create_attribute("NAME",
init_gen=FakerGenerator(method="name",
seed=next(
example1.seeder)))
sites = SequencialGenerator(prefix="SITE_").generate(1000)
random_site_gen = NumpyRandomGenerator(method="choice",
a=sites,
seed=next(example1.seeder))
allowed_sites = person.create_relationship(name="sites")
for i in range(5):
allowed_sites \
.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size))
hello_world = example1.create_story(
name="hello_world",
initiating_population=person,
member_id_field="PERSON_ID",
# after each story, reset the timer to 0, so that it will get
# executed again at the next clock tick (next hour)
timer_gen=ConstantDependentGenerator(value=0))
duration_gen = NumpyRandomGenerator(method="exponential",
scale=60,
seed=next(example1.seeder))
hello_world.set_operations(
person.ops.lookup(id_field="PERSON_ID", select={"NAME": "NAME"}),
ConstantGenerator(value="hello world").ops.generate(named_as="HELLO"),
duration_gen.ops.generate(named_as="DURATION"),
allowed_sites.ops.select_one(from_field="PERSON_ID", named_as="SITE"),
example1.clock.ops.timestamp(named_as="TIME"),
FieldLogger(log_id="hello"))
example1.run(duration=pd.Timedelta("48h"),
log_output_folder="output/example1",
delete_existing_logs=True)
with open("output/example1/hello.csv") as f:
print("Logged {} lines".format(len(f.readlines()) - 1))
def numpy_generators_read_from_disk_should_generate_same_sequence_as_original(
):
with path.tempdir() as p:
# making sure we're not using the default seed
tested = NumpyRandomGenerator(method="normal",
loc=10,
scale=4,
seed=123456)
gen_file = os.path.join(p, "tested2.json")
tested.save_to(gen_file)
reloaded = Generator.load_generator(gen_type="NumpyRandomGenerator",
input_file=gen_file)
assert tested.generate(size=10000) == reloaded.generate(size=10000)
def create_field_agents(circus, params):
logging.info(" adding {} field agents".format(params["n_field_agents"]))
field_agents = circus.create_population(
name="field_agents",
size=params["n_field_agents"],
ids_gen=SequencialGenerator(prefix="FA_"))
logging.info(" adding mobility relationships to field agents")
mobility_rel = field_agents.create_relationship("POSSIBLE_SITES")
# TODO: make sure the number of sites per field agent is "reasonable"
mobility_df = pd.DataFrame.from_records(make_random_bipartite_data(
field_agents.ids,
circus.actors["sites"].ids,
0.4,
seed=next(circus.seeder)),
columns=["FA_ID", "SID"])
mobility_weight_gen = NumpyRandomGenerator(method="exponential",
scale=1.,
seed=next(circus.seeder))
mobility_rel.add_relations(from_ids=mobility_df["FA_ID"],
to_ids=mobility_df["SID"],
weights=mobility_weight_gen.generate(
mobility_df.shape[0]))
# Initialize the mobility by allocating one first random site to each
# field agent among its network
field_agents.create_attribute(name="CURRENT_SITE",
init_relationship="POSSIBLE_SITES")
return field_agents
def add_mobility(self, subs, cells):
"""
adds a CELL attribute to the customer population + a mobility story that
randomly moves customers from CELL to CELL among their used cells.
"""
logging.info("Adding mobility ")
# mobility time profile: assign high mobility activities to busy hours
# of the day
mov_prof = [
1., 1., 1., 1., 1., 1., 1., 1., 5., 10., 5., 1., 1., 1., 1., 1.,
1., 5., 10., 5., 1., 1., 1., 1.
]
mobility_time_gen = CyclicTimerGenerator(
clock=self.clock,
seed=next(self.seeder),
config=CyclicTimerProfile(
profile=mov_prof,
profile_time_steps="1H",
start_date=pd.Timestamp("12 September 2016 00:00.00")))
# Mobility network, i.e. choice of cells per user, i.e. these are the
# weighted "used cells" (as in "most used cells) for each user
mobility_weight_gen = NumpyRandomGenerator(method="exponential",
scale=1.,
seed=next(self.seeder))
mobility_rel = subs.create_relationship("POSSIBLE_CELLS")
logging.info(" creating bipartite graph ")
mobility_df = pd.DataFrame.from_records(make_random_bipartite_data(
subs.ids, cells.ids, 0.4, seed=next(self.seeder)),
columns=["USER_ID", "CELL"])
logging.info(" adding mobility relationships to customer")
mobility_rel.add_relations(from_ids=mobility_df["USER_ID"],
to_ids=mobility_df["CELL"],
weights=mobility_weight_gen.generate(
mobility_df.shape[0]))
logging.info(" creating customer's CELL attribute ")
# Initialize the mobility by allocating one first random cell to each
# customer among its network
subs.create_attribute(name="CELL", init_relationship="POSSIBLE_CELLS")
# Mobility story itself, basically just a random hop from cell to cell,
# that updates the "CELL" attributes + generates mobility logs
logging.info(" creating mobility story")
mobility_story = self.create_story(
name="mobility",
initiating_population=subs,
member_id_field="A_ID",
timer_gen=mobility_time_gen,
)
logging.info(" adding operations")
mobility_story.set_operations(
subs.ops.lookup(id_field="A_ID", select={"CELL": "PREV_CELL"}),
# selects a destination cell (or maybe the same as current... ^^)
mobility_rel.ops.select_one(from_field="A_ID",
named_as="NEW_CELL"),
# update the CELL attribute of the customers accordingly
subs.get_attribute("CELL").ops.update(member_id_field="A_ID",
copy_from_field="NEW_CELL"),
self.clock.ops.timestamp(named_as="TIME"),
# create mobility logs
operations.FieldLogger(
log_id="mobility_logs",
cols=["TIME", "A_ID", "PREV_CELL", "NEW_CELL"]),
)
logging.info(" done")
def create_subs_and_sims(self):
"""
Creates the subs and sims + a relationship between them + an agent
relationship.
We have at least one sim per subs: sims.size >= subs.size
The sims population contains the "OPERATOR", "MAIN_ACCT" and "MSISDN" attributes.
The subs population has a "SIMS" relationship that points to the sims owned by
each subs.
The sims population also has a relationship to the set of agents where this sim
can be topped up.
"""
npgen = RandomState(seed=next(self.seeder))
# subs are empty here but will receive a "CELLS" and "EXCITABILITY"
# attributes later on
subs = self.create_population(
name="subs",
size=self.params["n_subscribers"],
ids_gen=SequencialGenerator(prefix="SUBS_"))
number_of_operators = npgen.choice(a=range(1, 5), size=subs.size)
operator_ids = build_ids(size=4, prefix="OPERATOR_", max_length=1)
def pick_operators(qty):
"""
randomly choose a set of unique operators of specified size
"""
return npgen.choice(a=operator_ids,
p=[.8, .05, .1, .05],
size=qty,
replace=False).tolist()
# set of operators of each subs
subs_operators_list = map(pick_operators, number_of_operators)
# Dataframe with 4 columns for the 1rst, 2nd,... operator of each subs.
# Since subs_operators_list don't all have the size, some entries of this
# dataframe contains None, which are just discarded by the stack() below
subs_operators_df = pd.DataFrame(data=list(subs_operators_list),
index=subs.ids)
# same info, vertically: the index contains the sub id (with duplicates)
# and "operator" one of the operators of this subs
subs_ops_mapping = subs_operators_df.stack()
subs_ops_mapping.index = subs_ops_mapping.index.droplevel(level=1)
# SIM population, each with an OPERATOR and MAIN_ACCT attributes
sims = self.create_population(
name="sims",
size=subs_ops_mapping.size,
ids_gen=SequencialGenerator(prefix="SIMS_"))
sims.create_attribute("OPERATOR", init_values=subs_ops_mapping.values)
recharge_gen = ConstantGenerator(value=1000.)
sims.create_attribute(name="MAIN_ACCT", init_gen=recharge_gen)
# keeping track of the link between population and sims as a relationship
sims_of_subs = subs.create_relationship("SIMS")
sims_of_subs.add_relations(from_ids=subs_ops_mapping.index,
to_ids=sims.ids)
msisdn_gen = MSISDNGenerator(
countrycode="0032",
prefix_list=["472", "473", "475", "476", "477", "478", "479"],
length=6,
seed=next(self.seeder))
sims.create_attribute(name="MSISDN", init_gen=msisdn_gen)
# Finally, adding one more relationship that defines the set of possible
# shops where we can topup each SIM.
# TODO: to make this a bit more realistic, we should probably generate
# such relationship first from the subs to their favourite shops, and then
# copy that info to each SIM, maybe with some fluctuations to account
# for the fact that not all shops provide topups of all operators.
agents = build_ids(self.params["n_agents"],
prefix="AGENT_",
max_length=3)
agent_df = pd.DataFrame.from_records(make_random_bipartite_data(
sims.ids, agents, 0.3, seed=next(self.seeder)),
columns=["SIM_ID", "AGENT"])
logging.info(" creating random sim/agent relationship ")
sims_agents_rel = sims.create_relationship("POSSIBLE_AGENTS")
agent_weight_gen = NumpyRandomGenerator(method="exponential",
scale=1.,
seed=next(self.seeder))
sims_agents_rel.add_relations(from_ids=agent_df["SIM_ID"],
to_ids=agent_df["AGENT"],
weights=agent_weight_gen.generate(
agent_df.shape[0]))
return subs, sims, recharge_gen
def step7():
example1 = circus.Circus(name="example1",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
person = example1.create_population(
name="person",
size=1000,
ids_gen=SequencialGenerator(prefix="PERSON_"))
person.create_attribute("NAME",
init_gen=FakerGenerator(method="name",
seed=next(
example1.seeder)))
person.create_attribute("POPULARITY",
init_gen=NumpyRandomGenerator(
method="uniform",
low=0,
high=1,
seed=next(example1.seeder)))
sites = SequencialGenerator(prefix="SITE_").generate(1000)
random_site_gen = NumpyRandomGenerator(method="choice",
a=sites,
seed=next(example1.seeder))
allowed_sites = person.create_relationship(name="sites")
# SITES ------------------
# Add HOME sites
allowed_sites.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size),
weights=0.4)
# Add WORK sites
allowed_sites.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size),
weights=0.3)
# Add OTHER sites
for i in range(3):
allowed_sites \
.add_relations(from_ids=person.ids,
to_ids=random_site_gen.generate(person.size),
weights=0.1)
# FRIENDS ------------------
friends = person.create_relationship(name="friends")
friends_df = pd.DataFrame.from_records(
make_random_bipartite_data(
person.ids,
person.ids,
p=0.005, # probability for a node to be connected to
# another one : 5 friends on average = 5/1000
seed=next(example1.seeder)),
columns=["A", "B"])
friends.add_relations(from_ids=friends_df["A"], to_ids=friends_df["B"])
# PRICE ------------------
def price(story_data):
result = pd.DataFrame(index=story_data.index)
result["PRICE"] = story_data["DURATION"] * 0.05
result["CURRENCY"] = "EUR"
return result
# STORIES ------------------
hello_world = example1.create_story(
name="hello_world",
initiating_population=person,
member_id_field="PERSON_ID",
# after each story, reset the timer to 0, so that it will get
# executed again at the next clock tick (next hour)
timer_gen=ConstantDependentGenerator(value=0))
duration_gen = NumpyRandomGenerator(method="exponential",
scale=60,
seed=next(example1.seeder))
hello_world.set_operations(
person.ops.lookup(id_field="PERSON_ID", select={"NAME": "NAME"}),
ConstantGenerator(value="hello world").ops.generate(named_as="HELLO"),
duration_gen.ops.generate(named_as="DURATION"),
friends.ops.select_one(
from_field="PERSON_ID",
named_as="COUNTERPART_ID",
weight=person.get_attribute_values("POPULARITY"),
# For people that do not have friends, it will try to find
# the POPULARITY attribute of a None and crash miserably
# Adding this flag will discard people that do not have friends
discard_empty=True),
person.ops.lookup(id_field="COUNTERPART_ID",
select={"NAME": "COUNTER_PART_NAME"}),
allowed_sites.ops.select_one(from_field="PERSON_ID", named_as="SITE"),
allowed_sites.ops.select_one(from_field="COUNTERPART_ID",
named_as="COUNTERPART_SITE"),
Apply(source_fields=["DURATION", "SITE", "COUNTERPART_SITE"],
named_as=["PRICE", "CURRENCY"],
f=price,
f_args="dataframe"),
example1.clock.ops.timestamp(named_as="TIME"),
FieldLogger(log_id="hello"))
example1.run(duration=pd.Timedelta("48h"),
log_output_folder="output/example1",
delete_existing_logs=True)
with open("output/example1/hello.csv") as f:
print("Logged {} lines".format(len(f.readlines()) - 1))
def test_numpy_random_generator_should_delegate_to_numpy_correctly():
# basic "smoke" test, if it does not crash it at least proves it's able
# to load the appropriate method
tested = NumpyRandomGenerator(method="normal", loc=10, scale=4, seed=1)
assert len(tested.generate(size=10)) == 10
