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 load_empirical_discrete_generator(namespace, gen_id, seed):
root_folder = _empirical_discrete_gen_folder(namespace)
gen_file_path = os.path.join(root_folder, "%s.csv" % gen_id)
df = pd.read_csv(gen_file_path)
gen = NumpyRandomGenerator(method="choice",
a=df["x"].tolist(),
p=df["px"].tolist(),
seed=seed)
return gen
def _add_message_story(self):
story_timer_gen = DefaultDailyTimerGenerator(
clock=self.clock,
seed=next(self.seeder))
low_activity = story_timer_gen.activity(n=3, per=pd.Timedelta("1 day"))
med_activity = story_timer_gen.activity(n=10, per=pd.Timedelta("1 day"))
high_activity = story_timer_gen.activity(n=20, per=pd.Timedelta("1 day"))
activity_gen = NumpyRandomGenerator(
method="choice",
a=[low_activity, med_activity, high_activity],
p=[.2, .7, .1],
seed=next(self.seeder))
hello_world = self.create_story(
name="hello_world",
initiating_population=self.populations["person"],
member_id_field="PERSON_ID",
timer_gen=story_timer_gen,
activity_gen=activity_gen
)
hello_world.set_operations(
self.clock.ops.timestamp(named_as="TIME"),
self.populations["person"].get_relationship("quotes")
.ops.select_one(from_field="PERSON_ID",named_as="MESSAGE"),
self.populations["person"]
.get_relationship("friends")
.ops.select_one(from_field="PERSON_ID", named_as="OTHER_PERSON"),
self.populations["person"]
.ops.lookup(id_field="PERSON_ID", select={"NAME": "EMITTER_NAME"}),
self.populations["person"]
.ops.lookup(id_field="OTHER_PERSON", select={"NAME": "RECEIVER_NAME"}),
operations.FieldLogger(log_id="hello_6")
)
def _add_person_population(self):
id_gen = SequencialGenerator(prefix="PERSON_")
age_gen = NumpyRandomGenerator(method="normal", loc=3, scale=5,
seed=next(self.seeder))
name_gen = FakerGenerator(method="name", seed=next(self.seeder))
person = self.create_population(name="person", size=1000, ids_gen=id_gen)
person.create_attribute("NAME", init_gen=name_gen)
person.create_attribute("AGE", init_gen=age_gen)
quote_generator = FakerGenerator(method="sentence", nb_words=6, variable_nb_words=True,
seed=next(self.seeder))
quotes_rel = self.populations["person"].create_relationship("quotes")
for w in range(4):
quotes_rel.add_relations(
from_ids=person.ids,
to_ids=quote_generator.generate(size=person.size),
weights=w
)
def __init__(self, story):
self.judge = NumpyRandomGenerator(method="uniform", seed=1234)
self.story = story
from tabulate import tabulate
from core import container, operations
from core.random_generators import SequencialGenerator, FakerGenerator, NumpyRandomGenerator, ConstantDependentGenerator, ConstantGenerator
import core.util_functions as util_functions
util_functions.setup_logging()
example_circus = container.Container(name="example",
master_seed=12345,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
id_gen = SequencialGenerator(prefix="PERSON_")
age_gen = NumpyRandomGenerator(method="normal",
loc=3,
scale=5,
seed=next(example_circus.seeder))
name_gen = FakerGenerator(method="name", seed=next(example_circus.seeder))
person = example_circus.create_population(name="person",
size=1000,
ids_gen=id_gen)
person.create_attribute("NAME", init_gen=name_gen)
person.create_attribute("AGE", init_gen=age_gen)
hello_world = example_circus.create_story(
name="hello_world",
initiating_population=example_circus.populations["person"],
member_id_field="PERSON_ID",
timer_gen=ConstantDependentGenerator(value=1))
from core.random_generators import SequencialGenerator, FakerGenerator, NumpyRandomGenerator, ConstantDependentGenerator, ConstantGenerator
import core.util_functions as util_functions
util_functions.setup_logging()
example_container = container.Container(name="example1",
master_seed=1234,
start=pd.Timestamp("1 Jan 2017 00:00"),
step_duration=pd.Timedelta("1h"))
id_gen = SequencialGenerator(prefix="PERSON_")
name_gen = FakerGenerator(method="name", seed=next(example_container.seeder))
age_gen = NumpyRandomGenerator(method="choice",
a=[20, 30, 40, 60],
p=[0.2, 0.3, 0.2, 0.3],
seed=next(example_container.seeder))
gender_gen = NumpyRandomGenerator(method="choice",
a=["male", "female"],
p=[0.6, 0.4],
seed=next(example_container.seeder))
job_type_gen = NumpyRandomGenerator(
method="choice",
a=["salaried", "non_salaried", "job_type3", "job_type4", "job_type5"],
p=[0.2, 0.2, 0.2, 0.2, 0.2],
seed=next(example_container.seeder))
person = example_container.create_population(name="person",
size=1000,
def build_healthy_level_gen(seed):
return NumpyRandomGenerator(method="beta", a=1, b=999, seed=seed)