def pearl_after_remove_(s, sprime, y, aprime, mprime):
p = 0.0
for ua, un, um, us in product(UA.domains.get_all(),
UN.domains.get_all(),
UM.domains.get_all(),
US.domains.get_all()):
e = Event({
UA: ua,
UN: un,
UM: um,
US: us,
A: aprime,
M: mprime,
S: sprime
})
c = Event({A: aprime, M: mprime, S: sprime})
ps = event.get_group(tuple(
e.values()))['prob'].sum() / condition.get_group(
tuple(c.values()))['prob'].sum()
for a, n, m in product(A.domains.get_all(), N.domains.get_all(),
M.domains.get_all()):
p += cbn.find_prob (Event ({A: a}), Event ({UA: ua})) * \
cbn.find_prob (Event ({M: m}), Event ({S: s, A: a, UM: um})) * \
cbn.find_prob (Event ({N: n}), Event ({S: s, A: a, UN: un})) * \
cbn.find_prob (Event ({Y: y}), Event ({S: s, A: a, N: n, M: m, UY: 1})) * \
ps
return p
def random_assign(self, event: Event, target: Event, p: float):
convert(event)
convert(target)
groupby_object = self.df.groupby(event.keys())
name = tuple(event.values())
if name.__len__() == 1:
name = name[0]
try:
index = groupby_object.get_group(name).index
target_index = random.sample(list(index),
int(round(index.__len__() * p)))
col = self.df.columns.get_loc(target.keys()[0])
self.df.ix[target_index, col] = target.values()[0]
except KeyError:
pass
def get_marginal_prob(self, event: Event):
convert(event)
groupby_object = self.df.groupby(event.keys())
name = tuple(event.values())
if name.__len__() == 1:
name = name[0]
try:
return groupby_object.get_group(name).__len__()
except:
return 0
def count(self, event: Event, condition: Event, relationship: str):
convert(event)
convert(condition)
groupby_object = self.df.groupby(condition.keys())
name = tuple(condition.values())
if name.__len__() == 1:
name = name[0]
try:
group = groupby_object.get_group(name)
key0 = event.keys()[0]
key1 = event.keys()[1]
if relationship == 'equal':
sub_group = group[group[key0] == group[key1]]
return sub_group.__len__() / group.__len__()
else:
sub_group = group[group[key0] != group[key1]]
return sub_group.__len__() / group.__len__()
except:
return 0