def test_get_independencies(self):
chain = BayesianModel([('X', 'Y'), ('Y', 'Z')])
self.assertEqual(chain.get_independencies(), Independencies(('X', 'Z', 'Y'), ('Z', 'X', 'Y')))
fork = BayesianModel([('Y', 'X'), ('Y', 'Z')])
self.assertEqual(fork.get_independencies(), Independencies(('X', 'Z', 'Y'), ('Z', 'X', 'Y')))
collider = BayesianModel([('X', 'Y'), ('Z', 'Y')])
self.assertEqual(collider.get_independencies(), Independencies(('X', 'Z'), ('Z', 'X')))
def test_estimate_from_independencies(self):
ind = Independencies(['B', 'C'], ['A', ['B', 'C'], 'D'])
ind = ind.closure()
model = ConstraintBasedEstimator.estimate_from_independencies("ABCD", ind)
self.assertSetEqual(set(model.edges()),
set([('B', 'D'), ('A', 'D'), ('C', 'D')]))
model1 = BayesianModel([('A', 'C'), ('B', 'C'), ('B', 'D'), ('C', 'E')])
model2 = ConstraintBasedEstimator.estimate_from_independencies(
model1.nodes(),
model1.get_independencies())
self.assertTrue(set(model2.edges()) == set(model1.edges()) or
set(model2.edges()) == set([('B', 'C'), ('A', 'C'), ('C', 'E'), ('D', 'B')]))
def test_build_skeleton(self):
ind = Independencies(['B', 'C'], ['A', ['B', 'C'], 'D'])
ind = ind.closure()
skel1, sep_sets1 = ConstraintBasedEstimator.build_skeleton("ABCD", ind)
self.assertTrue(self._edge_list_equal(skel1.edges(), [('A', 'D'), ('B', 'D'), ('C', 'D')]))
sep_sets_ref1 = {frozenset({'A', 'C'}): (), frozenset({'A', 'B'}): (), frozenset({'C', 'B'}): ()}
self.assertEqual(sep_sets1, sep_sets_ref1)
model = BayesianModel([('A', 'C'), ('B', 'C'), ('B', 'D'), ('C', 'E')])
skel2, sep_sets2 = ConstraintBasedEstimator.build_skeleton(model.nodes(), model.get_independencies())
self.assertTrue(self._edge_list_equal(skel2, [('D', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'E')]))
sep_sets_ref2 = {frozenset({'D', 'C'}): ('B',),
frozenset({'E', 'B'}): ('C',),
frozenset({'A', 'D'}): (),
frozenset({'E', 'D'}): ('C',),
frozenset({'E', 'A'}): ('C',),
frozenset({'A', 'B'}): ()}
# witnesses/seperators might change on each run, so we cannot compare directly
self.assertEqual(sep_sets2.keys(), sep_sets_ref2.keys())
self.assertEqual([len(v) for v in sorted(sep_sets2.values())],
[len(v) for v in sorted(sep_sets_ref2.values())])
def bayesnet():
"""
References:
https://class.coursera.org/pgm-003/lecture/17
http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
http://www3.cs.stonybrook.edu/~sael/teaching/cse537/Slides/chapter14d_BP.pdf
http://www.cse.unsw.edu.au/~cs9417ml/Bayes/Pages/PearlPropagation.html
https://github.com/pgmpy/pgmpy.git
http://pgmpy.readthedocs.org/en/latest/
http://nipy.bic.berkeley.edu:5000/download/11
"""
# import operator as op
# # Enumerate all possible events
# varcard_list = list(map(op.attrgetter('variable_card'), cpd_list))
# _esdat = list(ut.iprod(*map(range, varcard_list)))
# _escol = list(map(op.attrgetter('variable'), cpd_list))
# event_space = pd.DataFrame(_esdat, columns=_escol)
# # Custom compression of event space to inspect a specific graph
# def compress_space_flags(event_space, var1, var2, var3, cmp12_):
# """
# var1, var2, cmp_ = 'Lj', 'Lk', op.eq
# """
# import vtool as vt
# data = event_space
# other_cols = ut.setdiff_ordered(data.columns.tolist(), [var1, var2, var3])
# case_flags12 = cmp12_(data[var1], data[var2]).values
# # case_flags23 = cmp23_(data[var2], data[var3]).values
# # case_flags = np.logical_and(case_flags12, case_flags23)
# case_flags = case_flags12
# case_flags = case_flags.astype(np.int64)
# subspace = np.hstack((case_flags[:, None], data[other_cols].values))
# sel_ = vt.unique_row_indexes(subspace)
# flags = np.logical_and(mask, case_flags)
# return flags
# # Build special cases
# case_same = event_space.loc[compress_space_flags(event_space, 'Li', 'Lj', 'Lk', op.eq)]
# case_diff = event_space.loc[compress_space_flags(event_space, 'Li', 'Lj', 'Lk', op.ne)]
# special_cases = [
# case_same,
# case_diff,
# ]
from pgmpy.factors import TabularCPD
from pgmpy.models import BayesianModel
import pandas as pd
from pgmpy.inference import BeliefPropagation # NOQA
from pgmpy.inference import VariableElimination # NOQA
name_nice = ['n1', 'n2', 'n3']
score_nice = ['low', 'high']
match_nice = ['diff', 'same']
num_names = len(name_nice)
num_scores = len(score_nice)
nid_basis = list(range(num_names))
score_basis = list(range(num_scores))
semtype2_nice = {
'score': score_nice,
'name': name_nice,
'match': match_nice,
}
var2_cpd = {
}
globals()['semtype2_nice'] = semtype2_nice
globals()['var2_cpd'] = var2_cpd
name_combo = np.array(list(ut.iprod(nid_basis, nid_basis)))
combo_is_same = name_combo.T[0] == name_combo.T[1]
def get_expected_scores_prob(level1, level2):
part1 = combo_is_same * level1
part2 = (1 - combo_is_same) * (1 - (level2))
expected_scores_level = part1 + part2
return expected_scores_level
# def make_cpd():
def name_cpd(aid):
from pgmpy.factors import TabularCPD
cpd = TabularCPD(
variable='N' + aid,
variable_card=num_names,
values=[[1.0 / num_names] * num_names])
cpd.semtype = 'name'
return cpd
name_cpds = [name_cpd('i'), name_cpd('j'), name_cpd('k')]
var2_cpd.update(dict(zip([cpd.variable for cpd in name_cpds], name_cpds)))
if True:
num_same_diff = 2
samediff_measure = np.array([
# get_expected_scores_prob(.12, .2),
# get_expected_scores_prob(.88, .8),
get_expected_scores_prob(0, 0),
get_expected_scores_prob(1, 1),
])
samediff_vals = (samediff_measure / samediff_measure.sum(axis=0)).tolist()
def samediff_cpd(aid1, aid2):
cpd = TabularCPD(
variable='A' + aid1 + aid2,
variable_card=num_same_diff,
values=samediff_vals,
evidence=['N' + aid1, 'N' + aid2], # [::-1],
evidence_card=[num_names, num_names]) # [::-1])
cpd.semtype = 'match'
return cpd
samediff_cpds = [samediff_cpd('i', 'j'), samediff_cpd('j', 'k'), samediff_cpd('k', 'i')]
var2_cpd.update(dict(zip([cpd.variable for cpd in samediff_cpds], samediff_cpds)))
if True:
def score_cpd(aid1, aid2):
semtype = 'score'
evidence = ['A' + aid1 + aid2, 'N' + aid1, 'N' + aid2]
evidence_cpds = [var2_cpd[key] for key in evidence]
evidence_nice = [semtype2_nice[cpd.semtype] for cpd in evidence_cpds]
evidence_card = list(map(len, evidence_nice))
evidence_states = list(ut.iprod(*evidence_nice))
variable_basis = semtype2_nice[semtype]
variable_values = []
for mystate in variable_basis:
row = []
for state in evidence_states:
if state[0] == state[1]:
if state[2] == 'same':
val = .2 if mystate == 'low' else .8
else:
val = 1
# val = .5 if mystate == 'low' else .5
elif state[0] != state[1]:
if state[2] == 'same':
val = .5 if mystate == 'low' else .5
else:
val = 1
# val = .9 if mystate == 'low' else .1
row.append(val)
variable_values.append(row)
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=len(variable_basis),
values=variable_values,
evidence=evidence, # [::-1],
evidence_card=evidence_card) # [::-1])
cpd.semtype = semtype
return cpd
else:
score_values = [
[.8, .1],
[.2, .9],
]
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['A' + aid1 + aid2], # [::-1],
evidence_card=[num_same_diff]) # [::-1])
cpd.semtype = 'score'
return cpd
score_cpds = [score_cpd('i', 'j'), score_cpd('j', 'k')]
cpd_list = name_cpds + score_cpds + samediff_cpds
else:
score_measure = np.array([get_expected_scores_prob(level1, level2)
for level1, level2 in
zip(np.linspace(.1, .9, num_scores),
np.linspace(.2, .8, num_scores))])
score_values = (score_measure / score_measure.sum(axis=0)).tolist()
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['N' + aid1, 'N' + aid2],
evidence_card=[num_names, num_names])
cpd.semtype = 'score'
return cpd
score_cpds = [score_cpd('i', 'j'), score_cpd('j', 'k')]
cpd_list = name_cpds + score_cpds
pass
input_graph = []
for cpd in cpd_list:
if cpd.evidence is not None:
for evar in cpd.evidence:
input_graph.append((evar, cpd.variable))
name_model = BayesianModel(input_graph)
name_model.add_cpds(*cpd_list)
var2_cpd.update(dict(zip([cpd.variable for cpd in cpd_list], cpd_list)))
globals()['var2_cpd'] = var2_cpd
varnames = [cpd.variable for cpd in cpd_list]
# --- PRINT CPDS ---
cpd = score_cpds[0]
def print_cpd(cpd):
print('CPT: %r' % (cpd,))
index = semtype2_nice[cpd.semtype]
if cpd.evidence is None:
columns = ['None']
else:
basis_lists = [semtype2_nice[var2_cpd[ename].semtype] for ename in cpd.evidence]
columns = [','.join(x) for x in ut.iprod(*basis_lists)]
data = cpd.get_cpd()
print(pd.DataFrame(data, index=index, columns=columns))
for cpd in name_model.get_cpds():
print('----')
print(cpd._str('phi'))
print_cpd(cpd)
# --- INFERENCE ---
Ni = name_cpds[0]
event_space_combos = {}
event_space_combos[Ni.variable] = 0 # Set ni to always be Fred
for cpd in cpd_list:
if cpd.semtype == 'score':
event_space_combos[cpd.variable] = list(range(cpd.variable_card))
evidence_dict = ut.all_dict_combinations(event_space_combos)
# Query about name of annotation k given different event space params
def pretty_evidence(evidence):
return [key + '=' + str(semtype2_nice[var2_cpd[key].semtype][val])
for key, val in evidence.items()]
def print_factor(factor):
row_cards = factor.cardinality
row_vars = factor.variables
values = factor.values.reshape(np.prod(row_cards), 1).flatten()
# col_cards = 1
# col_vars = ['']
basis_lists = list(zip(*list(ut.iprod(*[range(c) for c in row_cards]))))
nice_basis_lists = []
for varname, basis in zip(row_vars, basis_lists):
cpd = var2_cpd[varname]
_nice_basis = ut.take(semtype2_nice[cpd.semtype], basis)
nice_basis = ['%s=%s' % (varname, val) for val in _nice_basis]
nice_basis_lists.append(nice_basis)
row_lbls = [', '.join(sorted(x)) for x in zip(*nice_basis_lists)]
print(ut.repr3(dict(zip(row_lbls, values)), precision=3, align=True, key_order_metric='-val'))
# name_belief = BeliefPropagation(name_model)
name_belief = VariableElimination(name_model)
import pgmpy
import six # NOQA
def try_query(evidence):
print('--------')
query_vars = ut.setdiff_ordered(varnames, list(evidence.keys()))
evidence_str = ', '.join(pretty_evidence(evidence))
probs = name_belief.query(query_vars, evidence)
factor_list = probs.values()
joint_factor = pgmpy.factors.factor_product(*factor_list)
print('P(' + ', '.join(query_vars) + ' | ' + evidence_str + ')')
# print(six.text_type(joint_factor))
factor = joint_factor # NOQA
# print_factor(factor)
# import utool as ut
print(ut.hz_str([(f._str(phi_or_p='phi')) for f in factor_list]))
for evidence in evidence_dict:
try_query(evidence)
evidence = {'Aij': 1, 'Ajk': 1, 'Aki': 1, 'Ni': 0}
try_query(evidence)
evidence = {'Aij': 0, 'Ajk': 0, 'Aki': 0, 'Ni': 0}
try_query(evidence)
globals()['score_nice'] = score_nice
globals()['name_nice'] = name_nice
globals()['score_basis'] = score_basis
globals()['nid_basis'] = nid_basis
print('Independencies')
print(name_model.get_independencies())
print(name_model.local_independencies([Ni.variable]))
# name_belief = BeliefPropagation(name_model)
# # name_belief = VariableElimination(name_model)
# for case in special_cases:
# test_data = case.drop('Lk', axis=1)
# test_data = test_data.reset_index(drop=True)
# print('----')
# for i in range(test_data.shape[0]):
# evidence = test_data.loc[i].to_dict()
# probs = name_belief.query(['Lk'], evidence)
# factor = probs['Lk']
# probs = factor.values
# evidence_ = evidence.copy()
# evidence_['Li'] = name_nice[evidence['Li']]
# evidence_['Lj'] = name_nice[evidence['Lj']]
# evidence_['Sij'] = score_nice[evidence['Sij']]
# evidence_['Sjk'] = score_nice[evidence['Sjk']]
# nice2_prob = ut.odict(zip(name_nice, probs.tolist()))
# ut.print_python_code('P(Lk | {evidence}) = {cpt}'.format(
# evidence=(ut.repr2(evidence_, explicit=True, nobraces=True, strvals=True)),
# cpt=ut.repr3(nice2_prob, precision=3, align=True, key_order_metric='-val')
# ))
# for case in special_cases:
# test_data = case.drop('Lk', axis=1)
# test_data = test_data.drop('Lj', axis=1)
# test_data = test_data.reset_index(drop=True)
# print('----')
# for i in range(test_data.shape[0]):
# evidence = test_data.loc[i].to_dict()
# query_vars = ['Lk', 'Lj']
# probs = name_belief.query(query_vars, evidence)
# for queryvar in query_vars:
# factor = probs[queryvar]
# print(factor._str('phi'))
# probs = factor.values
# evidence_ = evidence.copy()
# evidence_['Li'] = name_nice[evidence['Li']]
# evidence_['Sij'] = score_nice[evidence['Sij']]
# evidence_['Sjk'] = score_nice[evidence['Sjk']]
# nice2_prob = ut.odict(zip([queryvar + '=' + x for x in name_nice], probs.tolist()))
# ut.print_python_code('P({queryvar} | {evidence}) = {cpt}'.format(
# query_var=query_var,
# evidence=(ut.repr2(evidence_, explicit=True, nobraces=True, strvals=True)),
# cpt=ut.repr3(nice2_prob, precision=3, align=True, key_order_metric='-val')
# ))
# _ draw model
import plottool as pt
import networkx as netx
fig = pt.figure() # NOQA
fig.clf()
ax = pt.gca()
netx_nodes = [(node, {}) for node in name_model.nodes()]
netx_edges = [(etup[0], etup[1], {}) for etup in name_model.edges()]
netx_graph = netx.DiGraph()
netx_graph.add_nodes_from(netx_nodes)
netx_graph.add_edges_from(netx_edges)
# pos = netx.graphviz_layout(netx_graph)
pos = netx.pydot_layout(netx_graph, prog='dot')
netx.draw(netx_graph, pos=pos, ax=ax, with_labels=True)
pt.plt.savefig('foo.png')
ut.startfile('foo.png')
variable_card=2,
values=[[0.65, 0.3], [0.35, 0.7]],
evidence=['Cancer'],
evidence_card=[2])
# Associating the parameters with the model structure.
cancer_model.add_cpds(cpd_poll, cpd_smoke, cpd_cancer, cpd_xray, cpd_dysp)
# Checking if the cpds are valid for the model.
print(cancer_model.check_model())
# Check d-separations. This is only meant for those interested. You do not need to understand this to do the project.
print(cancer_model.is_active_trail('Pollution', 'Smoker'))
print(cancer_model.is_active_trail('Pollution', 'Smoker', observed=['Cancer']))
print(cancer_model.local_independencies('Xray'))
print(cancer_model.get_independencies())
# Print model information
print(cancer_model.edges())
print(cancer_model.nodes())
print(cancer_model.get_cpds())
# Doing exact inference using Variable Elimination
from pgmpy.inference import VariableElimination
cancer_infer = VariableElimination(cancer_model)
# Query
print(
cancer_infer.query(variables=['Dyspnoea'],
joint=False,
evidence={'Cancer': 0})['Dyspnoea'])
def bayesnet():
"""
References:
https://class.coursera.org/pgm-003/lecture/17
http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
http://www3.cs.stonybrook.edu/~sael/teaching/cse537/Slides/chapter14d_BP.pdf
http://www.cse.unsw.edu.au/~cs9417ml/Bayes/Pages/PearlPropagation.html
https://github.com/pgmpy/pgmpy.git
http://pgmpy.readthedocs.org/en/latest/
http://nipy.bic.berkeley.edu:5000/download/11
"""
# import operator as op
# # Enumerate all possible events
# varcard_list = list(map(op.attrgetter('variable_card'), cpd_list))
# _esdat = list(ut.iprod(*map(range, varcard_list)))
# _escol = list(map(op.attrgetter('variable'), cpd_list))
# event_space = pd.DataFrame(_esdat, columns=_escol)
# # Custom compression of event space to inspect a specific graph
# def compress_space_flags(event_space, var1, var2, var3, cmp12_):
# """
# var1, var2, cmp_ = 'Lj', 'Lk', op.eq
# """
# import vtool as vt
# data = event_space
# other_cols = ut.setdiff_ordered(data.columns.tolist(), [var1, var2, var3])
# case_flags12 = cmp12_(data[var1], data[var2]).values
# # case_flags23 = cmp23_(data[var2], data[var3]).values
# # case_flags = np.logical_and(case_flags12, case_flags23)
# case_flags = case_flags12
# case_flags = case_flags.astype(np.int64)
# subspace = np.hstack((case_flags[:, None], data[other_cols].values))
# sel_ = vt.unique_row_indexes(subspace)
# flags = np.logical_and(mask, case_flags)
# return flags
# # Build special cases
# case_same = event_space.loc[compress_space_flags(event_space, 'Li', 'Lj', 'Lk', op.eq)]
# case_diff = event_space.loc[compress_space_flags(event_space, 'Li', 'Lj', 'Lk', op.ne)]
# special_cases = [
# case_same,
# case_diff,
# ]
from pgmpy.factors import TabularCPD
from pgmpy.models import BayesianModel
import pandas as pd
from pgmpy.inference import BeliefPropagation # NOQA
from pgmpy.inference import VariableElimination # NOQA
name_nice = ['n1', 'n2', 'n3']
score_nice = ['low', 'high']
match_nice = ['diff', 'same']
num_names = len(name_nice)
num_scores = len(score_nice)
nid_basis = list(range(num_names))
score_basis = list(range(num_scores))
semtype2_nice = {
'score': score_nice,
'name': name_nice,
'match': match_nice,
}
var2_cpd = {
}
globals()['semtype2_nice'] = semtype2_nice
globals()['var2_cpd'] = var2_cpd
name_combo = np.array(list(ut.iprod(nid_basis, nid_basis)))
combo_is_same = name_combo.T[0] == name_combo.T[1]
def get_expected_scores_prob(level1, level2):
part1 = combo_is_same * level1
part2 = (1 - combo_is_same) * (1 - (level2))
expected_scores_level = part1 + part2
return expected_scores_level
# def make_cpd():
def name_cpd(aid):
from pgmpy.factors import TabularCPD
cpd = TabularCPD(
variable='N' + aid,
variable_card=num_names,
values=[[1.0 / num_names] * num_names])
cpd.semtype = 'name'
return cpd
name_cpds = [name_cpd('i'), name_cpd('j'), name_cpd('k')]
var2_cpd.update(dict(zip([cpd.variable for cpd in name_cpds], name_cpds)))
if True:
num_same_diff = 2
samediff_measure = np.array([
# get_expected_scores_prob(.12, .2),
# get_expected_scores_prob(.88, .8),
get_expected_scores_prob(0, 0),
get_expected_scores_prob(1, 1),
])
samediff_vals = (samediff_measure / samediff_measure.sum(axis=0)).tolist()
def samediff_cpd(aid1, aid2):
cpd = TabularCPD(
variable='A' + aid1 + aid2,
variable_card=num_same_diff,
values=samediff_vals,
evidence=['N' + aid1, 'N' + aid2], # [::-1],
evidence_card=[num_names, num_names]) # [::-1])
cpd.semtype = 'match'
return cpd
samediff_cpds = [samediff_cpd('i', 'j'), samediff_cpd('j', 'k'), samediff_cpd('k', 'i')]
var2_cpd.update(dict(zip([cpd.variable for cpd in samediff_cpds], samediff_cpds)))
if True:
def score_cpd(aid1, aid2):
semtype = 'score'
evidence = ['A' + aid1 + aid2, 'N' + aid1, 'N' + aid2]
evidence_cpds = [var2_cpd[key] for key in evidence]
evidence_nice = [semtype2_nice[cpd.semtype] for cpd in evidence_cpds]
evidence_card = list(map(len, evidence_nice))
evidence_states = list(ut.iprod(*evidence_nice))
variable_basis = semtype2_nice[semtype]
variable_values = []
for mystate in variable_basis:
row = []
for state in evidence_states:
if state[0] == state[1]:
if state[2] == 'same':
val = .2 if mystate == 'low' else .8
else:
val = 1
# val = .5 if mystate == 'low' else .5
elif state[0] != state[1]:
if state[2] == 'same':
val = .5 if mystate == 'low' else .5
else:
val = 1
# val = .9 if mystate == 'low' else .1
row.append(val)
variable_values.append(row)
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=len(variable_basis),
values=variable_values,
evidence=evidence, # [::-1],
evidence_card=evidence_card) # [::-1])
cpd.semtype = semtype
return cpd
else:
score_values = [
[.8, .1],
[.2, .9],
]
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['A' + aid1 + aid2], # [::-1],
evidence_card=[num_same_diff]) # [::-1])
cpd.semtype = 'score'
return cpd
score_cpds = [score_cpd('i', 'j'), score_cpd('j', 'k')]
cpd_list = name_cpds + score_cpds + samediff_cpds
else:
score_measure = np.array([get_expected_scores_prob(level1, level2)
for level1, level2 in
zip(np.linspace(.1, .9, num_scores),
np.linspace(.2, .8, num_scores))])
score_values = (score_measure / score_measure.sum(axis=0)).tolist()
def score_cpd(aid1, aid2):
cpd = TabularCPD(
variable='S' + aid1 + aid2,
variable_card=num_scores,
values=score_values,
evidence=['N' + aid1, 'N' + aid2],
evidence_card=[num_names, num_names])
cpd.semtype = 'score'
return cpd
score_cpds = [score_cpd('i', 'j'), score_cpd('j', 'k')]
cpd_list = name_cpds + score_cpds
pass
input_graph = []
for cpd in cpd_list:
if cpd.evidence is not None:
for evar in cpd.evidence:
input_graph.append((evar, cpd.variable))
name_model = BayesianModel(input_graph)
name_model.add_cpds(*cpd_list)
var2_cpd.update(dict(zip([cpd.variable for cpd in cpd_list], cpd_list)))
globals()['var2_cpd'] = var2_cpd
varnames = [cpd.variable for cpd in cpd_list]
# --- PRINT CPDS ---
cpd = score_cpds[0]
def print_cpd(cpd):
print('CPT: %r' % (cpd,))
index = semtype2_nice[cpd.semtype]
if cpd.evidence is None:
columns = ['None']
else:
basis_lists = [semtype2_nice[var2_cpd[ename].semtype] for ename in cpd.evidence]
columns = [','.join(x) for x in ut.iprod(*basis_lists)]
data = cpd.get_cpd()
print(pd.DataFrame(data, index=index, columns=columns))
for cpd in name_model.get_cpds():
print('----')
print(cpd._str('phi'))
print_cpd(cpd)
# --- INFERENCE ---
Ni = name_cpds[0]
event_space_combos = {}
event_space_combos[Ni.variable] = 0 # Set ni to always be Fred
for cpd in cpd_list:
if cpd.semtype == 'score':
event_space_combos[cpd.variable] = list(range(cpd.variable_card))
evidence_dict = ut.all_dict_combinations(event_space_combos)
# Query about name of annotation k given different event space params
def pretty_evidence(evidence):
return [key + '=' + str(semtype2_nice[var2_cpd[key].semtype][val])
for key, val in evidence.items()]
def print_factor(factor):
row_cards = factor.cardinality
row_vars = factor.variables
values = factor.values.reshape(np.prod(row_cards), 1).flatten()
# col_cards = 1
# col_vars = ['']
basis_lists = list(zip(*list(ut.iprod(*[range(c) for c in row_cards]))))
nice_basis_lists = []
for varname, basis in zip(row_vars, basis_lists):
cpd = var2_cpd[varname]
_nice_basis = ut.take(semtype2_nice[cpd.semtype], basis)
nice_basis = ['%s=%s' % (varname, val) for val in _nice_basis]
nice_basis_lists.append(nice_basis)
row_lbls = [', '.join(sorted(x)) for x in zip(*nice_basis_lists)]
print(ut.repr3(dict(zip(row_lbls, values)), precision=3, align=True, key_order_metric='-val'))
# name_belief = BeliefPropagation(name_model)
name_belief = VariableElimination(name_model)
import pgmpy
import six # NOQA
def try_query(evidence):
print('--------')
query_vars = ut.setdiff_ordered(varnames, list(evidence.keys()))
evidence_str = ', '.join(pretty_evidence(evidence))
probs = name_belief.query(query_vars, evidence)
factor_list = probs.values()
joint_factor = pgmpy.factors.factor_product(*factor_list)
print('P(' + ', '.join(query_vars) + ' | ' + evidence_str + ')')
# print(six.text_type(joint_factor))
factor = joint_factor # NOQA
# print_factor(factor)
# import utool as ut
print(ut.hz_str([(f._str(phi_or_p='phi')) for f in factor_list]))
for evidence in evidence_dict:
try_query(evidence)
evidence = {'Aij': 1, 'Ajk': 1, 'Aki': 1, 'Ni': 0}
try_query(evidence)
evidence = {'Aij': 0, 'Ajk': 0, 'Aki': 0, 'Ni': 0}
try_query(evidence)
globals()['score_nice'] = score_nice
globals()['name_nice'] = name_nice
globals()['score_basis'] = score_basis
globals()['nid_basis'] = nid_basis
print('Independencies')
print(name_model.get_independencies())
print(name_model.local_independencies([Ni.variable]))
# name_belief = BeliefPropagation(name_model)
# # name_belief = VariableElimination(name_model)
# for case in special_cases:
# test_data = case.drop('Lk', axis=1)
# test_data = test_data.reset_index(drop=True)
# print('----')
# for i in range(test_data.shape[0]):
# evidence = test_data.loc[i].to_dict()
# probs = name_belief.query(['Lk'], evidence)
# factor = probs['Lk']
# probs = factor.values
# evidence_ = evidence.copy()
# evidence_['Li'] = name_nice[evidence['Li']]
# evidence_['Lj'] = name_nice[evidence['Lj']]
# evidence_['Sij'] = score_nice[evidence['Sij']]
# evidence_['Sjk'] = score_nice[evidence['Sjk']]
# nice2_prob = ut.odict(zip(name_nice, probs.tolist()))
# ut.print_python_code('P(Lk | {evidence}) = {cpt}'.format(
# evidence=(ut.repr2(evidence_, explicit=True, nobraces=True, strvals=True)),
# cpt=ut.repr3(nice2_prob, precision=3, align=True, key_order_metric='-val')
# ))
# for case in special_cases:
# test_data = case.drop('Lk', axis=1)
# test_data = test_data.drop('Lj', axis=1)
# test_data = test_data.reset_index(drop=True)
# print('----')
# for i in range(test_data.shape[0]):
# evidence = test_data.loc[i].to_dict()
# query_vars = ['Lk', 'Lj']
# probs = name_belief.query(query_vars, evidence)
# for queryvar in query_vars:
# factor = probs[queryvar]
# print(factor._str('phi'))
# probs = factor.values
# evidence_ = evidence.copy()
# evidence_['Li'] = name_nice[evidence['Li']]
# evidence_['Sij'] = score_nice[evidence['Sij']]
# evidence_['Sjk'] = score_nice[evidence['Sjk']]
# nice2_prob = ut.odict(zip([queryvar + '=' + x for x in name_nice], probs.tolist()))
# ut.print_python_code('P({queryvar} | {evidence}) = {cpt}'.format(
# query_var=query_var,
# evidence=(ut.repr2(evidence_, explicit=True, nobraces=True, strvals=True)),
# cpt=ut.repr3(nice2_prob, precision=3, align=True, key_order_metric='-val')
# ))
# _ draw model
import plottool as pt
import networkx as netx
fig = pt.figure() # NOQA
fig.clf()
ax = pt.gca()
netx_nodes = [(node, {}) for node in name_model.nodes()]
netx_edges = [(etup[0], etup[1], {}) for etup in name_model.edges()]
netx_graph = netx.DiGraph()
netx_graph.add_nodes_from(netx_nodes)
netx_graph.add_edges_from(netx_edges)
# pos = netx.graphviz_layout(netx_graph)
pos = netx.pydot_layout(netx_graph, prog='dot')
netx.draw(netx_graph, pos=pos, ax=ax, with_labels=True)
pt.plt.savefig('foo.png')
ut.startfile('foo.png')
heartDisease = heartDisease.replace('?', np.nan)
heartDisease.dtypes
print(heartDisease.dtypes)
heartDisease.columns
from pgmpy.models import BayesianModel
from pgmpy.estimators import MaximumLikelihoodEstimator, BayesianEstimator
model = BayesianModel([('age', 'trestbps'), ('age', 'fbs'),
('sex', 'trestbps'), ('sex', 'trestbps'),
('exang', 'trestbps'), ('trestbps', 'heartdisease'),
('fbs', 'heartdisease'), ('heartdisease', 'restecg'),
('heartdisease', 'thalach'), ('heartdisease', 'chol')])
# Learning CPDs using Maximum Likelihood Estimators
model.fit(heartDisease, estimator=MaximumLikelihoodEstimator)
print(model.get_cpds('age'))
print(model.get_cpds('chol'))
print(model.get_cpds('sex'))
model.get_independencies()
# Doing exact inference using Variable Elimination
from pgmpy.inference import VariableElimination
HeartDisease_infer = VariableElimination(model)
# Computing the probability of bronc given smoke.
q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'age': 28})
print(q['heartdisease'])
q = HeartDisease_infer.query(variables=['heartdisease'],
evidence={'chol': 100})
print(q['heartdisease'])
cpd_dysp = TabularCPD(variable='Dyspnoea',
variable_card=2,
values=[[0.65, 0.3], [0.35, 0.7]],
evidence=['Cancer'],
evidence_card=[2])
# Associating the parameters with the model structure.
cancer_model.add_cpds(cpd_poll, cpd_smoke, cpd_cancer, cpd_xray, cpd_dysp)
print('Model generated by adding conditional probability disttributions(cpds)')
# Checking if the cpds are valid for the model.
print('Checking for Correctness of model : ', end='')
print(cancer_model.check_model())
print('All local idependencies are as follows')
cancer_model.get_independencies()
print('Displaying CPDs')
print(cancer_model.get_cpds('Pollution'))
print(cancer_model.get_cpds('Smoker'))
print(cancer_model.get_cpds('Cancer'))
print(cancer_model.get_cpds('Xray'))
print(cancer_model.get_cpds('Dyspnoea'))
##Inferencing with Bayesian Network
# Computing the probability of Cancer given smoke.
cancer_infer = VariableElimination(cancer_model)
print('\nInferencing with Bayesian Network')
