def test_do_accepts_all_state_probabilities(self, bn):
"""Do should accept a map of state->p and update p accordingly"""
ie = InferenceEngine(bn)
ie.do_intervention("d", {False: 0.7, True: 0.3})
assert math.isclose(ie.query()["d"][False], 0.7)
assert math.isclose(ie.query()["d"][True], 0.3)
def intervention(cls, input):
"""Users can apply an intervention to any node in the data, updating its distribution
using a do operator, examining the effect of that intervention by querying marginals
and resetting any interventions
Args:
input (a list of dictionaries): The data on which to do the interventions.
"""
from causalnex.inference import InferenceEngine
bn = cls.get_model()
ie = InferenceEngine(bn)
i_node = input["node"]
i_states = input["states"]
i_target = input["target_node"]
print(i_node, i_states, i_target)
lst = []
# i_states is a list of dict
for state in i_states:
state = {int(k): int(v) for k, v in state.items()}
ie.do_intervention(i_node, state)
intervention_result = ie.query()[i_target]
lst.append(intervention_result)
print("Updated marginal", intervention_result)
ie.reset_do(i_node)
return lst
def test_do_reflected_in_query(self, bn):
"""Do should adjust marginals returned by query when given a different observation"""
ie = InferenceEngine(bn)
assert ie.query({"a": "b"})["d"][True] != 1
ie.do_intervention("d", True)
assert ie.query({"a": "b"})["d"][True] == 1
def test_do_expects_all_state_probabilities_sum_to_one(self, bn):
"""Do should accept only state probabilities where the full distribution is provided"""
ie = InferenceEngine(bn)
with pytest.raises(
ValueError, match="The cpd for the provided observation must sum to 1"
):
ie.do_intervention("d", {False: 0.7, True: 0.4})
def test_do_expects_all_states_have_a_probability(self, bn):
"""Do should accept only state probabilities where all states in the original cpds are present"""
ie = InferenceEngine(bn)
with pytest.raises(
ValueError, match="The cpd states do not match expected states*"
):
ie.do_intervention("d", {False: 1})
def test_do_prevents_new_states_being_added(self, bn):
"""Do should not allow the introduction of new states"""
ie = InferenceEngine(bn)
with pytest.raises(
ValueError, match="The cpd states do not match expected states*"
):
ie.do_intervention("d", {False: 0.7, True: 0.3, "other": 0.0})
def test_do_sets_state_probability_to_one(self, train_model, train_data_idx):
"""Do should update the probability of the given observation=state to 1"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
ie.do_intervention("d", 1)
assert math.isclose(ie.query()["d"][1], 1)
def test_do_accepts_all_state_probabilities(self, train_model, train_data_idx):
"""Do should accept a map of state->p and update p accordingly"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
ie.do_intervention("d", {0: 0.7, 1: 0.3})
assert math.isclose(ie.query()["d"][0], 0.7)
assert math.isclose(ie.query()["d"][1], 0.3)
def test_do_sets_other_state_probabilitys_to_zero(self, train_model,
train_data_idx):
"""Do should update the probability of every other state for the observation to zero"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
ie.do_intervention("d", 1)
assert ie.query()["d"][0] == 0
def test_do_on_node_with_no_effects_not_allowed(self, bn):
"""It should not be possible to create an isolated node in the network"""
ie = InferenceEngine(bn)
with pytest.raises(
ValueError,
match="Do calculus cannot be applied because it would result in an isolate",
):
ie.do_intervention("a", "b")
def test_do_reflected_in_query(self, train_model, train_data_idx):
"""Do should adjust marginals returned by query when given a different observation"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
assert ie.query({"a": 1})["d"][1] != 1
ie.do_intervention("d", 1)
assert ie.query({"a": 1})["d"][1] == 1
def test_reset_do_sets_probabilities_back_to_initial_state(
self, bn, train_data_discrete_marginals):
"""Resetting Do operator should re-introduce the original conditional dependencies"""
ie = InferenceEngine(bn)
ie.do_intervention("d", {False: 0.7, True: 0.3})
ie.reset_do("d")
assert math.isclose(ie.query()["d"][False],
train_data_discrete_marginals["d"][False])
assert math.isclose(ie.query()["d"][False],
train_data_discrete_marginals["d"][False])
def test_do_prevents_new_states_being_added(self, train_model, train_data_idx):
"""Do should not allow the introduction of new states"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
with pytest.raises(
ValueError, match="The cpd states do not match expected states*"
):
ie.do_intervention("d", {0: 0.7, 1: 0.3, 2: 0.0})
def test_do_expects_all_state_probabilities_sum_to_one(
self, train_model, train_data_idx):
"""Do should accept only state probabilities where the full distribution is provided"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
with pytest.raises(
ValueError,
match="The cpd for the provided observation must sum to 1"):
ie.do_intervention("d", {0: 0.7, 1: 0.4})
def test_do_expects_all_states_have_a_probability(self, train_model,
train_data_idx):
"""Do should accept only state probabilities where all states in the original cpds are present"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
with pytest.raises(
ValueError,
match="The cpd states do not match expected states*"):
ie.do_intervention("d", {1: 1})
def test_do_on_node_with_no_effects_not_allowed(self, train_model, train_data_idx):
"""It should not be possible to create an isolated node in the network"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
with pytest.raises(
ValueError,
match="Do calculus cannot be applied because it would result in an isolate",
):
ie.do_intervention("a", 1)
def test_reset_do_sets_probabilities_back_to_initial_state(
self, train_model, train_data_idx, train_data_idx_marginals
):
"""Resetting Do operator should re-introduce the original conditional dependencies"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
ie.do_intervention("d", {0: 0.7, 1: 0.3})
ie.reset_do("d")
assert math.isclose(ie.query()["d"][0], train_data_idx_marginals["d"][0])
assert math.isclose(ie.query()["d"][1], train_data_idx_marginals["d"][1])
def test_do_expects_all_state_probabilities_within_0_and_1(
self, train_model, train_data_idx):
"""Do should accept only state probabilities where the full distribution is provided"""
bn = BayesianNetwork(train_model)
bn.fit_node_states(train_data_idx).fit_cpds(train_data_idx)
ie = InferenceEngine(bn)
with pytest.raises(
ValueError,
match=
"The cpd for the provided observation must be between 0 and 1",
):
ie.do_intervention("d", {0: -1.0, 1: 2.0})
def test_query_after_do_intervention_has_split_graph(self, chain_network):
"""
chain network: a → b → c → d → e
test 1.
- do intervention on node c generates 2 graphs (a → b) and (c → d → e)
- assert the query can be run (it used to hang before)
- assert rest_do works
"""
ie = InferenceEngine(chain_network)
original_margs = ie.query()
var = "c"
state_dict = {0: 1.0, 1: 0.0}
ie.do_intervention(var, state_dict)
# assert the intervention node has indeed the right state
assert ie.query()[var][0] == state_dict[0]
assert ie.query()[var][1] == state_dict[1]
# assert the upstream nodes have the default marginals (no info
# propagates in the upstream graph)
assert ie.query()["a"][0] == original_margs["a"][0]
assert ie.query()["a"][1] == original_margs["a"][1]
assert ie.query()["b"][0] == original_margs["b"][0]
assert ie.query()["b"][1] == original_margs["b"][1]
# assert the _cpds of the upstream nodes are stored correctly
orig_cpds = ie._cpds_original # pylint: disable=protected-access
upstream_cpds = ie._detached_cpds # pylint: disable=protected-access
assert orig_cpds["a"] == upstream_cpds["a"]
assert orig_cpds["b"] == upstream_cpds["b"]
ie.reset_do(var)
reset_margs = ie.query()
for node in original_margs.keys():
dict_left = original_margs[node]
dict_right = reset_margs[node]
for (kl, kr) in zip(dict_left.keys(), dict_right.keys()):
assert math.isclose(dict_left[kl], dict_right[kr])
# repeating above tests intervening on b, so that there is one single
# isolate
var_b = "b"
state_dict_b = {0: 1.0, 1: 0.0}
ie.do_intervention(var_b, state_dict_b)
# assert the intervention node has indeed the right state
assert ie.query()[var_b][0] == state_dict[0]
assert ie.query()[var_b][1] == state_dict[1]
# assert the upstream nodes have the default marginals (no info
# propagates in the upstream graph)
assert ie.query()["a"][0] == original_margs["a"][0]
assert ie.query()["a"][1] == original_margs["a"][1]
# assert the _cpds of the upstream nodes are stored correctly
orig_cpds = ie._cpds_original # pylint: disable=protected-access
upstream_cpds = ie._detached_cpds # pylint: disable=protected-access
assert orig_cpds["a"] == upstream_cpds["a"]
ie.reset_do(var_b)
reset_margs = ie.query()
for node in original_margs.keys():
dict_left = original_margs[node]
dict_right = reset_margs[node]
for (kl, kr) in zip(dict_left.keys(), dict_right.keys()):
assert math.isclose(dict_left[kl], dict_right[kr])
def test_do_sets_other_state_probabilitys_to_zero(self, bn):
"""Do should update the probability of every other state for the observation to zero"""
ie = InferenceEngine(bn)
ie.do_intervention("d", True)
assert ie.query()["d"][False] == 0
def test_do_sets_state_probability_to_one(self, bn):
"""Do should update the probability of the given observation=state to 1"""
ie = InferenceEngine(bn)
ie.do_intervention("d", True)
assert math.isclose(ie.query()["d"][True], 1)
marginals_long = ie.query({"studytime": "long-studytime"})
print("Marginal G1 | Short Studtyime", marginals_short["G1"])
print("Marginal G1 | Long Studytime", marginals_long["G1"])
"""
Marginal G1 | Short Studtyime {'Fail': 0.2776556433482524, 'Pass': 0.7223443566517477}
Marginal G1 | Long Studytime {'Fail': 0.15504850337837614, 'Pass': 0.8449514966216239}
=> 공부를 더 많이 한 경우 G1 시험 통과 확률이 더 높다.
"""
# Higher 변수(고등 교육 선호도)에 Intervention(개입, 조작, 조종) 수행
# -> 임의로 해당 변수의 분포를 통제(도메인 지식 개입))
# 여기서는 모두가 고등 교육을 선호할 것이다라고 가정하고 해당 변수의 분포를 강제로 변경
print("distribution before do", ie.query()["higher"])
ie.do_intervention("higher",
{'yes': 1.0,
'no': 0.0})
print("distribution after do", ie.query()["higher"])
"""
distribution before do {'no': 0.10752688172043011, 'yes': 0.8924731182795698}
distribution after do {'no': 0.0, 'yes': 0.9999999999999998}
=> higher 변수를 임의로 marginal까지 조정하여 강제로 변화시킨 결과
"""
# Intervention을 다시 되돌리고 싶을 때
ie.reset_do("higher")
# 변수에 Intervention을 수행한 후 확률 계산 변화
print("marginal G1", ie.query()["G1"])
ie.do_intervention("higher",
{'yes': 1.0,
print('Marginal G1 | Long Studytime', marginalDist_long['G1'])
# %% markdown [markdown]
# ## Interventions with Do Calculus
# Do-Calculus, allows us to specify interventions.
#
# ### Updating a Node Distribution
# Can apply an intervention to any node in our data, updating its distribution using a `do` operator, which means asking our mdoel "what if" something were different.
#
# For example, can ask what would happen if 100% of students wanted to go on to do higher education.
# %% codecell
print("'higher' marginal distribution before DO: ", eng.query()['higher'])
# Make the intervention on the network
eng.do_intervention(node='higher', state={
'yes': 1.0,
'no': 0.0
}) # all students yes
print("'higher' marginal distribution after DO: ", eng.query()['higher'])
# %% markdown [markdown]
# ### Resetting a Node Distribution
# We can reset any interventions that we make using `reset_intervention` method and providing the node we want to reset:
# %% codecell
eng.reset_do('higher')
eng.query()['higher'] # same as before
# %% markdown [markdown]
# ### Effect of DO on Marginals
# We can use `query` to find the effect that an intervention has on our marginal likelihoods of OTHER variables, not just on the INTERVENED variable.
#
