def test_set_start_state_list(self, check_state):
model = MC(['b', 'a'], [1, 2])
check_state.return_value = True
model.set_start_state([State('a', 0), State('b', 1)])
model_state = [State('b', 1), State('a', 0)]
check_state.assert_called_once_with(model, model_state)
self.assertEqual(model.state, model_state)
def test_generate_sample_less_arg(self, random_state, sample_discrete):
model = MC(['a', 'b'], [2, 2])
model.transition_models['a'] = {
0: {
0: 0.1,
1: 0.9
},
1: {
0: 0.2,
1: 0.8
}
}
model.transition_models['b'] = {
0: {
0: 0.3,
1: 0.7
},
1: {
0: 0.4,
1: 0.6
}
}
random_state.return_value = [State('a', 0), State('b', 1)]
sample_discrete.side_effect = [[1], [0]] * 2
gen = model.generate_sample(size=2)
samples = [sample for sample in gen]
expected_samples = [[State('a', 1), State('b', 0)]] * 2
self.assertEqual(samples, expected_samples)
def test_sample(self):
model = MC(['a', 'b'], [2, 2])
model.transition_models['a'] = {
0: {
0: 0.1,
1: 0.9
},
1: {
0: 0.2,
1: 0.8
}
}
model.transition_models['b'] = {
0: {
0: 0.3,
1: 0.7
},
1: {
0: 0.4,
1: 0.6
}
}
sample = model.sample(start_state=[State('a', 0),
State('b', 1)],
size=2)
self.assertEqual(len(sample), 2)
self.assertEqual(list(sample.columns), ['a', 'b'])
self.assertTrue(
list(sample.loc[0]) in [[0, 0], [0, 1], [1, 0], [1, 1]])
self.assertTrue(
list(sample.loc[1]) in [[0, 0], [0, 1], [1, 0], [1, 1]])
def test_sample_less_arg(self, random_state):
model = MC(['a', 'b'], [2, 2])
random_state.return_value = [State('a', 0), State('b', 1)]
sample = model.sample(size=1)
random_state.assert_called_once_with(model)
self.assertEqual(model.state, random_state.return_value)
self.assertEqual(len(sample), 1)
self.assertEqual(list(sample.columns), ['a', 'b'])
self.assertEqual(list(sample.loc[0]), [0, 1])
def test_sample_less_arg(self, random_state):
self.gibbs.state = None
random_state.return_value = [
State('diff', 0),
State('intel', 0),
State('grade', 0)
]
sample = self.gibbs.sample(size=2)
random_state.assert_called_once_with(self.gibbs)
self.assertEqual(len(sample), 2)
def test_sample(self):
start_state = [State("diff", 0), State("intel", 0), State("grade", 0)]
sample = self.gibbs.sample(start_state, 2)
self.assertEquals(len(sample), 2)
self.assertEquals(len(sample.columns), 3)
self.assertIn("diff", sample.columns)
self.assertIn("intel", sample.columns)
self.assertIn("grade", sample.columns)
self.assertTrue(set(sample["diff"]).issubset({0, 1}))
self.assertTrue(set(sample["intel"]).issubset({0, 1}))
self.assertTrue(set(sample["grade"]).issubset({0, 1, 2}))
def test_generate_sample(self):
start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
gen = self.gibbs.generate_sample(start_state, 2)
samples = [sample for sample in gen]
self.assertEqual(len(samples), 2)
self.assertEqual(
{samples[0][0].var, samples[0][1].var, samples[0][2].var},
{'diff', 'intel', 'grade'})
self.assertEqual(
{samples[1][0].var, samples[1][1].var, samples[1][2].var},
{'diff', 'intel', 'grade'})
def test_sample(self):
start_state = [State('diff', 0), State('intel', 0), State('grade', 0)]
sample = self.gibbs.sample(start_state, 2)
self.assertEquals(len(sample), 2)
self.assertEquals(len(sample.columns), 3)
self.assertIn('diff', sample.columns)
self.assertIn('intel', sample.columns)
self.assertIn('grade', sample.columns)
self.assertTrue(set(sample['diff']).issubset({0, 1}))
self.assertTrue(set(sample['intel']).issubset({0, 1}))
self.assertTrue(set(sample['grade']).issubset({0, 1, 2}))
def setUp(self):
self.variables = ['intel', 'diff', 'grade']
self.card = [3, 2, 3]
self.cardinalities = {'intel': 3, 'diff': 2, 'grade': 3}
self.intel_tm = {
0: {
0: 0.1,
1: 0.25,
2: 0.65
},
1: {
0: 0.5,
1: 0.3,
2: 0.2
},
2: {
0: 0.3,
1: 0.3,
2: 0.4
}
}
self.intel_tm_matrix = np.array([[0.1, 0.25, 0.65], [0.5, 0.3, 0.2],
[0.3, 0.3, 0.4]])
self.diff_tm = {0: {0: 0.3, 1: 0.7}, 1: {0: 0.75, 1: 0.25}}
self.diff_tm_matrix = np.array([[0.3, 0.7], [0.75, 0.25]])
self.grade_tm = {
0: {
0: 0.4,
1: 0.2,
2: 0.4
},
1: {
0: 0.9,
1: 0.05,
2: 0.05
},
2: {
0: 0.1,
1: 0.4,
2: 0.5
}
}
self.grade_tm_matrix = [[0.4, 0.2, 0.4], [0.9, 0.05, 0.05],
[0.1, 0.4, 0.5]]
self.start_state = [
State('intel', 0),
State('diff', 1),
State('grade', 2)
]
self.model = MC()
self.sample = DataFrame(index=range(200), columns=['a', 'b'])
self.sample.a = [1] * 100 + [0] * 100
self.sample.b = [0] * 100 + [1] * 100
def generate_time_series(
sampler: BayesianModelSampling,
length: int,
labels: typing.List[str],
seed: int = 42,
):
# Initialize progress bar
pbar = notebook.tqdm(total=length)
# Generate first sample given no evidence
with io.capture_output() as captured:
# When no evidence is provided, the function under-the-hood performs forward sampling
sample = sampler.rejection_sample(seed=seed)
sample = sample.reindex(sorted(sample.columns), axis=1)
# Split sample in 'current' and 'next' slices:
# - the 'current' slice will be the first row of the generated time series
# - the 'next' slice is added as the second row, and will be used as
# evidence for subsequent predictions
df_synth = sample.filter(regex="_T$")
next_slice = sample.filter(regex="_T\+1").iloc[0].values.tolist()
df_synth = df_synth.append(pd.Series(next_slice, index=df_synth.columns),
ignore_index=True)
evidence = [
State(n, v) for n, v in zip(df_synth.columns.values, next_slice)
]
# Update progress bar
pbar.update(2)
for _ in range(2, length):
# Generate new data
with io.capture_output() as captured:
sample = sampler.rejection_sample(evidence=evidence)
sample = sample.reindex(sorted(sample.columns), axis=1)
# Append 'next' slice to the generated time series, and use it as new evidence
next_slice = sample.filter(regex="_T\+1").iloc[0].values.tolist()
df_synth = df_synth.append(pd.Series(next_slice,
index=df_synth.columns),
ignore_index=True)
evidence = [
State(n, v) for n, v in zip(df_synth.columns.values, next_slice)
]
# Update progress bar
pbar.update(1)
# Close progress bar
pbar.close()
# Update column names
df_synth.columns = labels
return df_synth
def test_generate_sample(self):
start_state = [State("diff", 0), State("intel", 0), State("grade", 0)]
gen = self.gibbs.generate_sample(start_state, 2)
samples = [sample for sample in gen]
self.assertEqual(len(samples), 2)
self.assertEqual(
{samples[0][0].var, samples[0][1].var, samples[0][2].var},
{"diff", "intel", "grade"},
)
self.assertEqual(
{samples[1][0].var, samples[1][1].var, samples[1][2].var},
{"diff", "intel", "grade"},
)
def set_start_state(self, start_state):
"""
Set the start state of the Markov Chain. If the start_state is given as a array-like iterable, its contents
are reordered in the internal representation.
Parameters:
-----------
start_state: dict or array-like iterable object
Dict (or list) of tuples representing the starting states of the variables.
Examples:
---------
>>> from pgmpy.models import MarkovChain as MC
>>> from pgmpy.factors.discrete import State
>>> model = MC(['a', 'b'], [2, 2])
>>> model.set_start_state([State('a', 0), State('b', 1)])
"""
if start_state is not None:
if not hasattr(start_state, '__iter__') or isinstance(start_state, six.string_types):
raise ValueError('start_state must be a non-string iterable.')
# Must be an array-like iterable. Reorder according to self.variables.
state_dict = {var: st for var, st in start_state}
start_state = [State(var, state_dict[var]) for var in self.variables]
if start_state is None or self._check_state(start_state):
self.state = start_state
def sample(self, start_state=None, size=1):
"""
Sample from the Markov Chain.
Parameters:
-----------
start_state: dict or array-like iterable
Representing the starting states of the variables. If None is passed, a random start_state is chosen.
size: int
Number of samples to be generated.
Return Type:
------------
pandas.DataFrame
Examples:
---------
>>> from pgmpy.models import MarkovChain as MC
>>> from pgmpy.factors.discrete import State
>>> model = MC(['intel', 'diff'], [2, 3])
>>> model.set_start_state([State('intel', 0), State('diff', 2)])
>>> intel_tm = {0: {0: 0.25, 1: 0.75}, 1: {0: 0.5, 1: 0.5}}
>>> model.add_transition_model('intel', intel_tm)
>>> diff_tm = {0: {0: 0.1, 1: 0.5, 2: 0.4}, 1: {0: 0.2, 1: 0.2, 2: 0.6 }, 2: {0: 0.7, 1: 0.15, 2: 0.15}}
>>> model.add_transition_model('diff', diff_tm)
>>> model.sample(size=5)
intel diff
0 0 2
1 1 0
2 0 1
3 1 0
4 0 2
"""
if start_state is None:
if self.state is None:
self.state = self.random_state()
# else use previously-set state
else:
self.set_start_state(start_state)
sampled = DataFrame(index=range(size), columns=self.variables)
sampled.loc[0] = [st for var, st in self.state]
var_states = defaultdict(dict)
var_values = defaultdict(dict)
samples = defaultdict(dict)
for var in self.transition_models.keys():
for st in self.transition_models[var]:
var_states[var][st] = list(self.transition_models[var][st].keys())
var_values[var][st] = list(self.transition_models[var][st].values())
samples[var][st] = sample_discrete(var_states[var][st], var_values[var][st], size=size)
for i in range(size - 1):
for j, (var, st) in enumerate(self.state):
next_st = samples[var][st][i]
self.state[j] = State(var, next_st)
sampled.loc[i + 1] = [st for var, st in self.state]
return sampled
def rejection_estimate(n):
inferences = BayesianModelSampling(disease_model)
evidences = [
State(var='Fatigue', state=0),
State(var='Fever', state=0),
State(var='FluShot', state=0)
]
p = inferences.rejection_sample(evidences, n)
i = 0
for t in range(n):
if p['Flu'][t] == float(0):
i = i + 1
plt.plot(t, (i / n), 'bo')
plt.ylabel('Evolving esimate')
plt.xlabel('Number of samples')
plt.show()
def test_rejection_sample_basic(self):
sample = self.sampling_inference.rejection_sample(
[State('A', 1), State('J', 1),
State('R', 1)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 6)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertTrue(set(sample.A).issubset({1}))
self.assertTrue(set(sample.J).issubset({1}))
self.assertTrue(set(sample.R).issubset({1}))
self.assertTrue(set(sample.Q).issubset({0, 1}))
self.assertTrue(set(sample.G).issubset({0, 1}))
self.assertTrue(set(sample.L).issubset({0, 1}))
def test_likelihood_weighted_sample(self):
sample = self.sampling_inference.likelihood_weighted_sample(
[State('A', 0), State('J', 1),
State('R', 0)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 7)
self.assertIn('A', sample.columns)
self.assertIn('J', sample.columns)
self.assertIn('R', sample.columns)
self.assertIn('Q', sample.columns)
self.assertIn('G', sample.columns)
self.assertIn('L', sample.columns)
self.assertIn('_weight', sample.columns)
self.assertTrue(set(sample.A).issubset({0, 1}))
self.assertTrue(set(sample.J).issubset({0, 1}))
self.assertTrue(set(sample.R).issubset({0, 1}))
self.assertTrue(set(sample.Q).issubset({0, 1}))
self.assertTrue(set(sample.G).issubset({0, 1}))
self.assertTrue(set(sample.L).issubset({0, 1}))
def test_rejection_sample_basic(self):
sample = self.sampling_inference.rejection_sample()
sample = self.sampling_inference.rejection_sample(
[State("A", 1), State("J", 1),
State("R", 1)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 6)
self.assertIn("A", sample.columns)
self.assertIn("J", sample.columns)
self.assertIn("R", sample.columns)
self.assertIn("Q", sample.columns)
self.assertIn("G", sample.columns)
self.assertIn("L", sample.columns)
self.assertTrue(set(sample.A).issubset({1}))
self.assertTrue(set(sample.J).issubset({1}))
self.assertTrue(set(sample.R).issubset({1}))
self.assertTrue(set(sample.Q).issubset({0, 1}))
self.assertTrue(set(sample.G).issubset({0, 1}))
self.assertTrue(set(sample.L).issubset({0, 1}))
def test_likelihood_weighted_sample(self):
sample = self.sampling_inference.likelihood_weighted_sample()
sample = self.sampling_inference.likelihood_weighted_sample(
[State("A", 0), State("J", 1),
State("R", 0)], 25)
self.assertEquals(len(sample), 25)
self.assertEquals(len(sample.columns), 7)
self.assertIn("A", sample.columns)
self.assertIn("J", sample.columns)
self.assertIn("R", sample.columns)
self.assertIn("Q", sample.columns)
self.assertIn("G", sample.columns)
self.assertIn("L", sample.columns)
self.assertIn("_weight", sample.columns)
self.assertTrue(set(sample.A).issubset({0, 1}))
self.assertTrue(set(sample.J).issubset({0, 1}))
self.assertTrue(set(sample.R).issubset({0, 1}))
self.assertTrue(set(sample.Q).issubset({0, 1}))
self.assertTrue(set(sample.G).issubset({0, 1}))
self.assertTrue(set(sample.L).issubset({0, 1}))
def is_stationarity(self, tolerance=0.2, sample=None):
"""
Checks if the given markov chain is stationary and checks the steady state
probablity values for the state are consistent.
Parameters:
-----------
tolerance: float
represents the diff between actual steady state value and the computed value
sample: [State(i,j)]
represents the list of state which the markov chain has sampled
Return Type:
------------
Boolean
True, if the markov chain converges to steady state distribution within the tolerance
False, if the markov chain does not converge to steady state distribution within tolerance
Examples:
---------
>>> from pgmpy.models.MarkovChain import MarkovChain
>>> from pgmpy.factors.discrete import State
>>> model = MarkovChain()
>>> model.add_variables_from(['intel', 'diff'], [3, 2])
>>> intel_tm = {0: {0: 0.2, 1: 0.4, 2:0.4}, 1: {0: 0, 1: 0.5, 2: 0.5}, 2: {0: 0.3, 1: 0.3, 2: 0.4}}
>>> model.add_transition_model('intel', intel_tm)
>>> diff_tm = {0: {0: 0.5, 1: 0.5}, 1: {0: 0.25, 1:0.75}}
>>> model.add_transition_model('diff', diff_tm)
>>> model.is_stationarity()
True
"""
keys = self.transition_models.keys()
return_val = True
for k in keys:
# convert dict to numpy matrix
transition_mat = np.array([np.array(list(self.transition_models[k][i].values()))
for i in self.transition_models[k].keys()], dtype=np.float)
S, U = eig(transition_mat.T)
stationary = np.array(U[:, np.where(np.abs(S - 1.) < 1e-8)[0][0]].flat)
stationary = (stationary / np.sum(stationary)).real
probabilites = []
window_size = 10000 if sample is None else len(sample)
for i in range(0, transition_mat.shape[0]):
probabilites.extend(self.prob_from_sample([State(k, i)], window_size=window_size))
if any(np.abs(i) > tolerance for i in np.subtract(probabilites, stationary)):
return_val = return_val and False
else:
return_val = return_val and True
return return_val
def test_is_stationarity_failure(self):
model = MC(['intel', 'diff'], [2, 3])
model.set_start_state([State('intel', 0), State('diff', 2)])
intel_tm = {0: {0: 0.25, 1: 0.75}, 1: {0: 0.5, 1: 0.5}}
model.add_transition_model('intel', intel_tm)
diff_tm = {
0: {
0: 0.1,
1: 0.5,
2: 0.4
},
1: {
0: 0.2,
1: 0.2,
2: 0.6
},
2: {
0: 0.7,
1: 0.15,
2: 0.15
}
}
model.add_transition_model('diff', diff_tm)
self.assertFalse(model.is_stationarity(0.002, None))
def random_state(self):
"""
Generates a random state of the Markov Chain.
Return Type:
------------
List of namedtuples, representing a random assignment to all variables of the model.
Examples:
---------
>>> from pgmpy.models import MarkovChain as MC
>>> model = MC(['intel', 'diff'], [2, 3])
>>> model.random_state()
[State('diff', 2), State('intel', 1)]
"""
return [State(var, np.random.randint(self.cardinalities[var])) for var in self.variables]
def sample_slots(model_info_file, mr_slot_names):
model_info = helpers.load_from_pickle(model_info_file)
model = model_info['model']
inference = BayesianModelSampling(model)
# use the missing mr slots as evidence
all_slots = model_info['all_slots']
missing_slots = [mr for mr in all_slots if mr not in mr_slot_names]
evidence = [State(mr, 0) for mr in missing_slots]
inference = BayesianModelSampling(model)
# don't allow empty samples
sampled_slots = []
while (sampled_slots == []):
sample = inference.rejection_sample(evidence=evidence,
size=1,
return_type='recarray')
# return a list of the column names which had presence
sampled_slots = [
name for var, name in zip(sample.view('<i8'), sample.dtype.names)
if var == 1
]
return sampled_slots
def generate_sample(self, start_state=None, size=1):
"""
Generator version of self.sample
Returns
-------
List of State namedtuples, representing the assignment to all variables of the model.
Examples
--------
>>> from pgmpy.models.MarkovChain import MarkovChain
>>> from pgmpy.factors.discrete import State
>>> model = MarkovChain()
>>> model.add_variables_from(['intel', 'diff'], [3, 2])
>>> intel_tm = {0: {0: 0.2, 1: 0.4, 2:0.4}, 1: {0: 0, 1: 0.5, 2: 0.5}, 2: {0: 0.3, 1: 0.3, 2: 0.4}}
>>> model.add_transition_model('intel', intel_tm)
>>> diff_tm = {0: {0: 0.5, 1: 0.5}, 1: {0: 0.25, 1:0.75}}
>>> model.add_transition_model('diff', diff_tm)
>>> gen = model.generate_sample([State('intel', 0), State('diff', 0)], 2)
>>> [sample for sample in gen]
[[State(var='intel', state=2), State(var='diff', state=1)],
[State(var='intel', state=2), State(var='diff', state=0)]]
"""
if start_state is None:
if self.state is None:
self.state = self.random_state()
# else use previously-set state
else:
self.set_start_state(start_state)
# sampled.loc[0] = [self.state[var] for var in self.variables]
for i in range(size):
for j, (var, st) in enumerate(self.state):
next_st = sample_discrete(
list(self.transition_models[var][st].keys()),
list(self.transition_models[var][st].values()),
)[0]
self.state[j] = State(var, next_st)
yield self.state[:]
def test_check_state_bad_var_value(self):
model = MC(['a'], [2])
# value of variable >= cardinaliity
self.assertRaises(ValueError, model._check_state, [State('a', 3)])
trainData = modelData.iloc[0:int(0.85 * modelData.shape[0])].copy()
model.fit(trainData, estimator=MaximumLikelihoodEstimator)
#for cpd in model.get_cpds():
# print("CPD of {variable}:".format(variable=cpd.variable))
# print(cpd)
model_sample = BayesianModelSampling(model)
pickle.dump(model_sample, open('results/sampler.p', 'wb'))
# open the nhts sample and add the inferred resType requirements
nhtsSample = pd.read_csv('results/nhtsSample.csv')
resType = []
for ind, row in nhtsSample.iterrows():
evidence = [
State('IncomeQ', min(row['hh_income'] - 1, 10)),
State('HhSize', min(row['hh_size'] - 1, 5))
]
sample = model_sample.likelihood_weighted_sample(evidence=evidence, size=1)
resType.extend([int(sample['Bedrooms']) * 3 + int(sample['RentQ'])])
nhtsSample['resType'] = resType
os.chdir('..')
nhtsSample[nhtsSample['occupation_type'] == 1].sample(
n=50, replace=True).to_csv('ABM/includes/pop_occat_1.csv', index=False)
nhtsSample[nhtsSample['occupation_type'] == 2].sample(
n=50, replace=True).to_csv('ABM/includes/pop_occat_2.csv', index=False)
nhtsSample[nhtsSample['occupation_type'] == 3].sample(
n=50, replace=True).to_csv('ABM/includes/pop_occat_3.csv', index=False)
nhtsSample[nhtsSample['occupation_type'] == 4].sample(
n=50, replace=True).to_csv('ABM/includes/pop_occat_4.csv', index=False)
nhtsSample[nhtsSample['occupation_type'] == 5].sample(
def test_check_state_success(self):
model = MC(['a'], [2])
self.assertTrue(model._check_state([State('a', 1)]))
def test_copy(self):
model = MC(['a', 'b'], [2, 2], [State('a', 0), State('b', 1)])
model.add_transition_model('a', {
0: {
0: 0.1,
1: 0.9
},
1: {
0: 0.2,
1: 0.8
}
})
model.add_transition_model('b', {
0: {
0: 0.3,
1: 0.7
},
1: {
0: 0.4,
1: 0.6
}
})
copy = model.copy()
self.assertIsInstance(copy, MC)
self.assertEqual(sorted(model.variables), sorted(copy.variables))
self.assertEqual(model.cardinalities, copy.cardinalities)
self.assertEqual(model.transition_models, copy.transition_models)
self.assertEqual(model.state, copy.state)
model.add_variable('p', 1)
model.set_start_state([State('a', 0), State('b', 1), State('p', 0)])
model.add_transition_model('p', {0: {0: 1}})
self.assertNotEqual(sorted(model.variables), sorted(copy.variables))
self.assertEqual(sorted(['a', 'b']), sorted(copy.variables))
self.assertNotEqual(model.cardinalities, copy.cardinalities)
self.assertEqual({'a': 2, 'b': 2}, copy.cardinalities)
self.assertNotEqual(model.state, copy.state)
self.assertEqual([State('a', 0), State('b', 1)], copy.state)
self.assertNotEqual(model.transition_models, copy.transition_models)
self.assertEqual(len(copy.transition_models), 2)
self.assertEqual(copy.transition_models['a'], {
0: {
0: 0.1,
1: 0.9
},
1: {
0: 0.2,
1: 0.8
}
})
self.assertEqual(copy.transition_models['b'], {
0: {
0: 0.3,
1: 0.7
},
1: {
0: 0.4,
1: 0.6
}
})
def test_prob_from_sample(self, sample):
model = MC(['a', 'b'], [2, 2])
sample.return_value = self.sample
probabilites = model.prob_from_sample([State('a', 1), State('b', 0)])
self.assertEqual(list(probabilites), [1] * 50 + [0] * 50)
corr_mat.style.background_gradient(cmap="coolwarm").set_precision(2)
# eaxmple: if we condition on "child_screen_time"..
# ..then "child_physical_activity" becomes independent of "parent_education":
corr_mat = simulated_sample.query("child_screen_time==1").drop(
"child_screen_time", axis=1).corr()
corr_mat.style.background_gradient(cmap="coolwarm").set_precision(2)
corr_mat = simulated_sample.query("child_screen_time==0").drop(
"child_screen_time", axis=1).corr()
corr_mat.style.background_gradient(cmap="coolwarm").set_precision(2)
# suppose that we are interested in measuring the average causal effect of "child_screen_time" on "obesity"
# we can estimate this by simulating from the system:
simulated_sample_lowScreentime = inference.rejection_sample(
evidence=[State(var='child_screen_time', state="low")], size=10_000)
simulated_sample_highScreentime = inference.rejection_sample(
evidence=[State(var='child_screen_time', state="high")], size=10_000)
# the observed effect of high screen time on prob. of high child obesity is:
((simulated_sample_highScreentime["child_obesity"] == "high").sum() /
len(simulated_sample_highScreentime)) / (
(simulated_sample_lowScreentime["child_obesity"] == "high").sum() /
len(simulated_sample_lowScreentime))
# i.e. around 2x
infer_adjusted = CausalInference(pg_model)
print(
infer_adjusted.query(variables=["child_obesity"],
do={"child_screen_time": "high"}))
# we can estimate this effect from the observed data using a logistic regression model:
def test_check_state_bad_vars(self):
model = MC()
# state_vars and model_vars differ
self.assertRaises(ValueError, model._check_state, [State(1, 2)])
