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cpd arguments should match case of parents
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@tom4everitt
tom4everitt committed Dec 21, 2021
1 parent b48c74d commit e50ee06
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -38,7 +38,7 @@ cid = pycid.CID([
# specify the causal relationships with CPDs using keyword arguments
cid.add_cpds(S = pycid.discrete_uniform([-1, 1]), # S is -1 or 1 with equal probability
D=[-1, 1], # the permitted action choices for D are -1 and 1
U=lambda s, d: s * d) # U is the product of S and D (arguments lowercase the variable names)
U=lambda S, D: S * D) # U is the product of S and D (argument names match parent names)

# Draw the result
cid.draw()
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1,558 changes: 844 additions & 714 deletions notebooks/CID_Basics_Tutorial.ipynb
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3,392 changes: 1,822 additions & 1,570 deletions notebooks/CID_Incentives_Tutorial.ipynb
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2,850 changes: 1,530 additions & 1,320 deletions notebooks/MACID_Basics_Tutorial.ipynb
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165 changes: 0 additions & 165 deletions notebooks/PyCID basic example.ipynb
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54 changes: 27 additions & 27 deletions notebooks/Why_fair_labels_may_yield_unfair_models_AAAI_22.ipynb
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Expand Up @@ -68,7 +68,7 @@
"import networkx as nx\n",
"import random"
],
"execution_count": 1,
"execution_count": null,
"outputs": []
},
{
Expand Down Expand Up @@ -109,7 +109,7 @@
" \"\"\"ITV is possible under P-admissible loss\"\"\"\n",
" return theorem9_gc(cid) and not 'A' in cid.get_parents(list(cid.decisions)[0]) and cid.is_active_trail('A', 'U', [list(cid.decisions)[0]] + cid.get_parents(list(cid.decisions)[0]))"
],
"execution_count": 2,
"execution_count": null,
"outputs": []
},
{
Expand All @@ -132,7 +132,7 @@
"metadata": {
"id": "0RCzeQhGlaPz"
},
"execution_count": 3,
"execution_count": null,
"outputs": []
},
{
Expand Down Expand Up @@ -168,9 +168,9 @@
"\n",
"hiring.add_cpds(\n",
" A = pycid.bernoulli(0.5),\n",
" D = lambda a: pycid.noisy_copy(a, 0.8),\n",
" Y = lambda d: pycid.bernoulli(0.49 + 0.02*d),\n",
" U = lambda y, yh: int(y==yh),\n",
" D = lambda A: pycid.noisy_copy(A, 0.8),\n",
" Y = lambda D: pycid.bernoulli(0.49 + 0.02*D),\n",
" U = lambda Y, Yh: int(Y==Yh),\n",
" Yh = [0, 1],\n",
")\n",
"print(hiring.check_model())\n",
Expand All @@ -185,7 +185,7 @@
"id": "0g2o4MFqli5q",
"outputId": "2fbe277c-148e-4aa7-d626-6000ebf0ecb7"
},
"execution_count": 4,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
Expand Down Expand Up @@ -251,18 +251,18 @@
"music_cid.add_cpds(\n",
" A = pycid.bernoulli(0.5),\n",
" M = pycid.bernoulli(0.5),\n",
" T = lambda a, m: {0: None, # this becomes 1-P(T=1)\n",
" 1: (0.05 if m==0 else \n",
" 0.9 if m==1 and a==1 else\n",
" T = lambda A, M: {0: None, # this becomes 1-P(T=1)\n",
" 1: (0.05 if M==0 else \n",
" 0.9 if M==1 and A==1 else\n",
" 1)},\n",
" Y = lambda m: pycid.noisy_copy(m, 0.95),\n",
" U = lambda y, yh: -(yh-y)**2,\n",
" Y = lambda M: pycid.noisy_copy(M, 0.95),\n",
" U = lambda Y, Yh: -(Yh-Y)**2,\n",
" Yh = [0, 0.25, 0.5, 0.75, 1]\n",
" )\n",
"music_cid.draw()\n",
"assess_unfairness(music_cid)"
],
"execution_count": 5,
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -313,12 +313,12 @@
"music_a_feature.add_cpds(\n",
" A = pycid.bernoulli(0.5),\n",
" M = pycid.bernoulli(0.5),\n",
" T = lambda a, m: {0: None, # this becomes 1-P(T=1)\n",
" 1: (0.05 if m==0 else \n",
" 0.9 if m==1 and a==1 else\n",
" T = lambda A, M: {0: None, # this becomes 1-P(T=1)\n",
" 1: (0.05 if M==0 else \n",
" 0.9 if M==1 and A==1 else\n",
" 1)},\n",
" Y = lambda m: pycid.noisy_copy(m, 0.95),\n",
" U = lambda y, yh: int(y == yh),\n",
" Y = lambda M: pycid.noisy_copy(M, 0.95),\n",
" U = lambda Y, Yh: int(Y == Yh),\n",
" Yh = [0, 1]\n",
" )\n",
"music_a_feature.draw()\n",
Expand All @@ -332,7 +332,7 @@
"id": "muy3bNXdQ8z7",
"outputId": "08a2632b-bd34-44de-fac7-a93acf592d1e"
},
"execution_count": 6,
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -385,7 +385,7 @@
"id": "9l0xrtCCWD_J",
"outputId": "de8588f5-05f3-477c-fec5-a5a99e01f1ed"
},
"execution_count": 7,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
Expand Down Expand Up @@ -454,7 +454,7 @@
" # add random cpds and a 0-1 loss function\n",
" cid.add_cpds(\n",
" Yh = [0, 1],\n",
" U = lambda yh, y: int(yh==y), # 0-1 loss function\n",
" U = lambda Yh, Y: int(Yh==Y), # 0-1 loss function\n",
" A = pycid.random_cpd.RandomCPD(),\n",
" Y = pycid.random_cpd.RandomCPD(),\n",
" )\n",
Expand All @@ -463,7 +463,7 @@
"\n",
" return cid"
],
"execution_count": 8,
"execution_count": null,
"outputs": []
},
{
Expand Down Expand Up @@ -492,7 +492,7 @@
"cid.draw()\n",
"assess_unfairness(cid)"
],
"execution_count": 9,
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -554,7 +554,7 @@
" else:\n",
" continue # if the CID doesn't satisfy the graphical condition, we resample"
],
"execution_count": 10,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
Expand Down Expand Up @@ -612,7 +612,7 @@
" else:\n",
" continue # if the CID doesn't satisfy the graphical condition, we resample"
],
"execution_count": 11,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
Expand Down Expand Up @@ -650,8 +650,8 @@
"source": [
""
],
"execution_count": 11,
"execution_count": null,
"outputs": []
}
]
}
}
283 changes: 0 additions & 283 deletions notebooks/fairness.ipynb
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23 changes: 1 addition & 22 deletions pycid/core/causal_bayesian_network.py
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Expand Up @@ -90,15 +90,6 @@ def __init__(self, edges: Iterable[Tuple[str, str]]):
self.model = self.Model(self)
super().__init__(ebunch=edges)

self._lowercase_to_variable: Dict[str, str] = {}
for node in self.nodes:
if node.lower() in self._lowercase_to_variable:
raise ValueError(
f'Name conflict: Both "{node}" and "{self._lowercase_to_variable[node.lower()]}" '
f'have the same lowercase "{node.lower()}".'
)
self._lowercase_to_variable[node.lower()] = node

def remove_edge(self, u: str, v: str) -> None:
"""removes an edge u to v that exists from the CBN"""
super().remove_edge(u, v)
Expand All @@ -116,7 +107,7 @@ def add_cpds(self, *cpds: TabularCPD, **relationships: Relationship) -> None:
Add the given CPDs and initialize StochasticFunctionCPDs
"""
for cpd in cpds:
self.model.__setitem__(cpd.variable, cpd) # type: ignore
self.model[cpd.variable] = cpd # type: ignore
self.model.update(relationships)

def remove_cpds(self, *cpds: Union[str, TabularCPD]) -> None:
Expand All @@ -137,15 +128,6 @@ def is_structural_causal_model(self) -> bool:
return False
return True

def _fix_lowercase_variables(self, outcome_dict: Dict[str, Outcome]) -> None:
"""
Outcomes are sometimes specified in terms of lowercase versions of variable names.
They need to be converted, before passed to factor.query
"""
for var in set(outcome_dict).intersection(self._lowercase_to_variable):
outcome_dict[self._lowercase_to_variable[var]] = outcome_dict[var]
del outcome_dict[var]

def query(
self, query: Iterable[str], context: Dict[str, Outcome], intervention: Dict[str, Outcome] = None
) -> BeliefPropagation:
Expand All @@ -162,8 +144,6 @@ def query(
intervention: Interventions to apply. A dictionary mapping node => outcome.
"""
self._fix_lowercase_variables(context)

for variable, outcome in context.items():
if outcome not in self.model.domain[variable]:
raise ValueError(f"The outcome {outcome} is not in the domain of {variable}")
Expand Down Expand Up @@ -202,7 +182,6 @@ def intervene(self, intervention: Dict[str, Outcome]) -> None:
----------
intervention: Interventions to apply. A dictionary mapping node => value.
"""
self._fix_lowercase_variables(intervention)
for variable in intervention:
for p in self.get_parents(variable): # remove ingoing edges
self.remove_edge(p, variable)
Expand Down
11 changes: 5 additions & 6 deletions pycid/core/cpd.py
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Expand Up @@ -60,8 +60,8 @@ def __init__(
stochastic_function: A stochastic function that maps parent outcomes to a distribution
over outcomes for this variable (see doc-string for class).
The different parents are identified by name: the arguments to the function must
be lowercase versions of the names of the parent variables. For example, if X has
parents Y, S1, and Obs, the arguments to function must be y, s1, and obs.
match the names of the parent variables. For example, if X has
parents Y, S1, and Obs, the arguments to function must be Y, S1, and Obs.
domain: An optional specification of the variable's domain.
Must include all values this variable can take as a result of its function.
Expand Down Expand Up @@ -149,11 +149,10 @@ def check_function_arguments_match_parent_names(self) -> None:
sig = inspect.signature(self.stochastic_function).parameters
arg_kinds = [arg_kind.kind.name for arg_kind in sig.values()]
args = set(sig)
lower_case_parents = {p.lower() for p in self.cbn.get_parents(self.variable)}
if "VAR_KEYWORD" not in arg_kinds and args != lower_case_parents:
if "VAR_KEYWORD" not in arg_kinds and args != set(self.cbn.get_parents(self.variable)):
raise ValueError(
f"function for {self.variable} mismatch parents on"
f" {args.symmetric_difference(lower_case_parents)}, "
f" {args.symmetric_difference(set(self.cbn.get_parents(self.variable)))}, "
)

def parent_values(self) -> Iterator[Dict[str, Outcome]]:
Expand All @@ -165,7 +164,7 @@ def parent_values(self) -> Iterator[Dict[str, Outcome]]:
except KeyError:
raise ParentsNotReadyException(f"Parent {p} of {self.variable} not yet instantiated")
for parent_values in itertools.product(*parent_values_list):
yield {p.lower(): parent_values[i] for i, p in enumerate(self.cbn.get_parents(self.variable))}
yield {p: parent_values[i] for i, p in enumerate(self.cbn.get_parents(self.variable))}

def possible_values(self) -> List[Outcome]:
"""The possible values this variable can take, given the values the parents can take"""
Expand Down
8 changes: 3 additions & 5 deletions pycid/core/macid_base.py
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Expand Up @@ -151,8 +151,6 @@ def query(
intervention: Interventions to apply. A dictionary mapping node => outcome.
"""

self._fix_lowercase_variables(context)
for variable, outcome in context.items():
if outcome not in self.get_cpds(variable).domain:
raise ValueError(f"The outcome {outcome} is not in the domain of {variable}")
Expand Down Expand Up @@ -290,7 +288,7 @@ def arg2idx(pv: Dict[str, Outcome]) -> int:
idx = 0
for i, parent in enumerate(parents):
name_to_no: Dict[Outcome, int] = self.get_cpds(parent).name_to_no[parent]
idx += name_to_no[pv[parent.lower()]] * int(np.product(parent_cardinalities[:i]))
idx += name_to_no[pv[parent]] * int(np.product(parent_cardinalities[:i]))
assert 0 <= idx <= number_of_decision_contexts
return idx

Expand Down Expand Up @@ -400,8 +398,8 @@ def impute_conditional_expectation_decision(self, decision: str, y: str) -> None

@lru_cache(maxsize=1000)
def cond_exp_policy(**pv: Outcome) -> float:
if y.lower() in pv:
return pv[y.lower()] # type: ignore
if y in pv:
return pv[y] # type: ignore
else:
return copy.expected_value([y], pv)[0]

Expand Down
8 changes: 4 additions & 4 deletions pycid/examples/simple_cbns.py
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Expand Up @@ -4,23 +4,23 @@

def get_3node_cbn() -> CausalBayesianNetwork:
cbn = CausalBayesianNetwork([("S", "D"), ("S", "U"), ("D", "U")])
cbn.add_cpds(S=discrete_uniform([-1, 1]), D=lambda s: s + 1, U=lambda s, d: s * d)
cbn.add_cpds(S=discrete_uniform([-1, 1]), D=lambda S: S + 1, U=lambda S, D: S * D)
return cbn


def get_3node_uniform_cbn() -> CausalBayesianNetwork:
cbn = CausalBayesianNetwork([("A", "C"), ("A", "B"), ("B", "C")])
cbn.add_cpds(A=bernoulli(0.5), B=bernoulli(0.5), C=lambda a, b: a * b)
cbn.add_cpds(A=bernoulli(0.5), B=bernoulli(0.5), C=lambda A, B: A * B)
return cbn


def get_minimal_cbn() -> CausalBayesianNetwork:
cbn = CausalBayesianNetwork([("A", "B")])
cbn.add_cpds(A=discrete_uniform([0, 1]), B=lambda a: a)
cbn.add_cpds(A=discrete_uniform([0, 1]), B=lambda A: A)
return cbn


def get_fork_cbn() -> CausalBayesianNetwork:
cbn = CausalBayesianNetwork([("A", "C"), ("B", "C")])
cbn.add_cpds(A=discrete_uniform([1, 2]), B=discrete_uniform([3, 4]), C=lambda a, b: a * b)
cbn.add_cpds(A=discrete_uniform([1, 2]), B=discrete_uniform([3, 4]), C=lambda A, B: A * B)
return cbn

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