def test_CRAN1(self):
bn = gum.fastBN("w->x->z->y;w->z")
bn.cpt("w")[:] = [0.5, 0.5]
bn.cpt("x")[:] = [[0.4, 0.6], [0.3, 0.7]]
bn.cpt("z")[{'w': 0, 'x': 0}] = [0.2, 0.8]
bn.cpt("z")[{'w': 0, 'x': 1}] = [0.1, 0.9]
bn.cpt("z")[{'w': 1, 'x': 0}] = [0.4, 0.6]
bn.cpt("z")[{'w': 1, 'x': 1}] = [0.5, 0.5]
bn.cpt("y")[:] = [[0.1, 0.9], [0.2, 0.8]]
d = csl.CausalModel(bn, [("lat1", ["x", "y"])])
# from the formula \sum_{ z ,w }{ P(z\mid w,x) \cdot P(w) \cdot
# \sum_{ x' }{ P(y\mid w,x',z) \cdot P(x'\mid w) }
# }
marg = (bn.cpt("x") * bn.cpt("y")).margSumOut(["x"])
marg = (marg * bn.cpt("w") * bn.cpt("z")).margSumOut(["z", "w"])
_, pot, _ = csl.causalImpact(d, on={"y"}, doing={"x"}) # when x=1
self.assertEqual(pot, marg)
_, pot, _ = csl.causalImpact(d, on="y", doing="x") # when x=1
self.assertEqual(pot, marg)
def testFromWHY19(self):
bn = gum.fastBN("X->Y;U")
d = csl.CausalModel(bn, [('Z', ["X", "Y", "U"])], True)
_, pot, _ = csl.causalImpact(d, on="Y", doing="X")
self.assertIsNone(pot) # HedgeException
# Causality, Pearl, 2009, p66
bn = gum.fastBN("Z1->Z2->Z3->Y<-X->Z2;Z2->Y;Z1->X->Z3<-Z1")
c = csl.CausalModel(bn, [("Z0", ("X", "Z1", "Z3"))], False)
_, pot, explanation = csl.causalImpact(c, "Y", "X")
self.assertIsNotNone(pot)
self.assertEqual(explanation, "Do-calculus computations")
def test_simpson(self):
m1 = gum.fastBN("Gender->Drug->Patient;Gender->Patient")
# Gender = 0 == male
# Gender = 1 == female
# Drug == 0 - not taking the Drug
# Drug == 1 - taking the Drug
# Patient = 0 -- not healed
# Patient = 1 - healed
m1.cpt("Gender")[:] = [0.5, 0.5]
m1.cpt("Drug")[:] = [[0.25, 0.75], # Gender=0
[0.75, 0.25]] # Gender=1
m1.cpt("Patient")[{'Drug': 0, 'Gender': 0}] = [
0.2, 0.8] # No Drug, Male -> healed in 0.8 of cases
# No Drug, Female -> healed in 0.4 of cases
m1.cpt("Patient")[{'Drug': 0, 'Gender': 1}] = [0.6, 0.4]
m1.cpt("Patient")[{'Drug': 1, 'Gender': 0}] = [
0.3, 0.7] # Drug, Male -> healed 0.7 of cases
m1.cpt("Patient")[{'Drug': 1, 'Gender': 1}] = [
0.8, 0.2] # Drug, Female -> healed in 0.2 of cases
d1 = csl.CausalModel(m1)
# from formula : \sum_{ Gender }{ P(Patient\mid Drug,Gender) \cdot P(Gender) }
margPatient = (m1.cpt("Patient") * m1.cpt("Gender")).margSumOut(["Gender"])
lat, pot, expl = csl.causalImpact(
d1, on="Patient", doing={"Drug"}) # when drug=1
self.assertEqual(pot, margPatient)
def setUp(self):
m = gum.fastBN("z2->x->z1->y;z2->z1;z2->z3->y")
m.cpt("z2")[:] = [0.5, 0.5]
m.cpt("x")[:] = [
[0.4, 0.6], # z2=0
[0.4, 0.6]
] # z2=1
m.cpt("z3")[:] = [
[0.3, 0.7], # z2=0
[0.3, 0.7]
] # z2=1
m.cpt("z1")[{"z2": 0, "x": 0}] = [0.2, 0.8]
m.cpt("z1")[{"z2": 0, "x": 1}] = [0.25, 0.75]
m.cpt("z1")[{"z2": 1, "x": 0}] = [0.1, 0.9]
m.cpt("z1")[{"z2": 1, "x": 1}] = [0.15, 0.85]
m.cpt("y")[{"z1": 0, "z3": 0}] = [0.5, 0.5]
m.cpt("y")[{"z1": 0, "z3": 1}] = [0.45, 0.55]
m.cpt("y")[{"z1": 1, "z3": 0}] = [0.4, 0.6]
m.cpt("y")[{"z1": 1, "z3": 1}] = [0.35, 0.65]
self.d = csl.CausalModel(m,
[("X-Z2", ["x", "z2"]), ("X-Z3", ["x", "z3"]),
("X-Y", ["x", "y"]),
("Y-Z2", ["y", "z2"])], True)
try:
formula, result, msg = csl.causalImpact(self.d,
on={"y", "z2", "z1", "z3"},
doing={"x"})
except csl.HedgeException as h:
self.fail("Should not raise")
def test_DoCalculusp213(self):
fd = gum.fastBN("w->z->x->y")
fdModele = csl.CausalModel(fd, [("u1", ["w", "x"]),
("u2", ["w", "y"])], True)
formula, impact, explanation = csl.causalImpact(fdModele,
on="y",
doing="x")
def getCausalImpact(model: csl.CausalModel, on: Union[str, NameSet], doing: Union[str, NameSet],
knowing: Optional[NameSet] = None, values: Optional[Dict[str, int]] = None):
"""
return a HTML representing of the three values defining a causal impact : formula, value, explanation
Parameters
----------
model: CausalModel
the causal model
on: str | Set[str]
the impacted variable(s)
doing: str | Set[str]
the interventions
knowing: str | Set[str]
the observations
values: Dict[str,int] default=None
value for certain variables
Returns
-------
HTML
"""
formula, impact, explanation = csl.causalImpact(model, on, doing, knowing, values)
gnb.flow.clear()
gnb.flow.add(getCausalModel(model),caption="Causal Model")
if formula is None:
gnb.flow.add(explanation,caption="Impossible")
else:
gnb.flow.add('$$\\begin{equation*}' + formula.toLatex() + '\\end{equation*}$$',caption="Explanation : "+explanation )
gnb.flow.add("No result" if formula is None else impact,caption="Impact") # : $" + ("?" if formula is None else formula.latexQuery(values)) + "$")
return gnb.flow.html()
def test_CRAN2(self):
bn = gum.fastBN("Z1->X->Z2->Y")
d = csl.CausalModel(bn, [("L1", ["Z1", "X"]),
("L2", ["Z1", "Z2"]),
("L3", ["Z1", "Y"]),
("L4", ["Y", "X"])],
True)
_, pot, _ = csl.causalImpact(d, on="Y", doing={"X"})
self.assertIsNone(pot)
def test_tobacco1(self):
obs1 = gum.fastBN("Smoking->Cancer")
obs1.cpt("Smoking")[:] = [0.6, 0.4]
obs1.cpt("Cancer")[{"Smoking": 0}] = [0.9, 0.1]
obs1.cpt("Cancer")[{"Smoking": 1}] = [0.7, 0.3]
modele1 = csl.CausalModel(obs1)
lat, pot, expl = csl.causalImpact(
modele1, "Cancer", "Smoking") # when doing is 1
self.assertEqual(pot, obs1.cpt("Cancer"))
modele2 = csl.CausalModel(obs1, [("Genotype", ["Smoking", "Cancer"])])
lat, pot, expl = csl.causalImpact(
modele2, "Cancer", {"Smoking"}, values={"Smoking": 1})
margCancer = (obs1.cpt("Smoking") * obs1.cpt("Cancer")
).margSumOut(["Smoking"])
self.assertEqual(pot, margCancer)
modele3 = csl.CausalModel(
obs1, [("Genotype", ["Smoking", "Cancer"])], True)
lat, pot, expl = csl.causalImpact(
modele3, "Cancer", {"Smoking"}, values={"Smoking": 1})
self.assertIsNone(pot)
def testFromNotebooks(self):
bn = gum.fastBN("w->x->z->y;w->z")
bn.cpt("w")[:] = [0.7, 0.3]
bn.cpt("x")[:] = [[0.4, 0.6], [0.3, 0.7]]
bn.cpt("z")[{'w': 0, 'x': 0}] = [0.2, 0.8]
bn.cpt("z")[{'w': 0, 'x': 1}] = [0.1, 0.9]
bn.cpt("z")[{'w': 1, 'x': 0}] = [0.9, 0.1]
bn.cpt("z")[{'w': 1, 'x': 1}] = [0.5, 0.5]
bn.cpt("y")[:] = [[0.1, 0.9], [0.8, 0.2]]
d = csl.CausalModel(bn, [("lat1", ["x", "y"])])
_, pot, _ = csl.causalImpact(d, on="y", doing="x")
self.assertIsNotNone(pot)
def test_Carouselp115(self):
ab = gum.fastBN('Elapsed time[11]->Bag on Carousel<-Bag on Plane')
ab.cpt("Bag on Plane").fillWith(1).normalize()
ab.cpt("Elapsed time").fillWith(1).normalize()
ab.cpt("Bag on Carousel").fillWith(
[1.0, 0.0] * 11 +
[1 - i / 20 if i % 2 == 0 else (i - 1) / 20 for i in range(22)])
abModele = csl.CausalModel(ab)
formula, impact, explanation = csl.causalImpact(
abModele,
on={"Bag on Plane"},
doing={"Elapsed time"},
knowing={"Bag on Carousel"},
values={
"Elapsed time": 7,
"Bag on Carousel": 0
})
self.assertAlmostEqual(impact[0], 0.7692, 4)
def test_tobacco2(self):
obs2 = gum.fastBN("Smoking->Tar->Cancer;Smoking->Cancer")
obs2.cpt("Smoking")[:] = [0.6, 0.4]
obs2.cpt("Tar")[{"Smoking": 0}] = [0.9, 0.1]
obs2.cpt("Tar")[{"Smoking": 1}] = [0.7, 0.3]
obs2.cpt("Cancer")[{"Tar": 0, "Smoking": 0}] = [0.9, 0.1]
obs2.cpt("Cancer")[{"Tar": 1, "Smoking": 0}] = [0.8, 0.2]
obs2.cpt("Cancer")[{"Tar": 0, "Smoking": 1}] = [0.7, 0.3]
obs2.cpt("Cancer")[{"Tar": 1, "Smoking": 1}] = [0.6, 0.4]
modele4 = csl.CausalModel(obs2, [("Genotype", ["Smoking", "Cancer"])])
# from formula : \sum_{ Tar }{ P(Tar\mid Smoking) \cdot
# \sum_{ Smoking' }{ P(Cancer\mid Smoking',Tar) \cdot P(Smoking') }
# }
margCancer = (obs2.cpt("Cancer") * obs2.cpt("Smoking")
).margSumOut(['Smoking'])
margCancer = (margCancer * obs2.cpt("Tar")).margSumOut(['Tar'])
lat, pot, _ = csl.causalImpact(
modele4, "Cancer", "Smoking") # when smoking=1
self.assertEqual(pot, margCancer)
def test_AccentsInVariables(self):
bn = gum.fastBN("héhé->hoho")
cm = csl.CausalModel(bn)
formula, impact, explanation = csl.causalImpact(cm, "héhé", "hoho")
def test_CausalImpactWithObservation(self):
evs = {'Gender'}
latex, impact, explain = csl.causalImpact(self.model,
"Patient",
doing="Drug",
knowing=evs)