def bql_pdf_joint(bdb, generator_id, modelno, *args):
metamodel = core.bayesdb_generator_metamodel(bdb, generator_id)
# A nonexistent (`unobserved') row id.
fake_row_id = core.bayesdb_generator_fresh_row_id(bdb, generator_id)
i = 0
targets = []
while i < len(args):
if args[i] == -1:
i += 1
break
if i + 1 == len(args):
raise ValueError('Missing logpdf target value: %r' % (args[i],))
t_colno = args[i]
t_value = args[i + 1]
targets.append((fake_row_id, t_colno, t_value))
i += 2
constraints = []
while i < len(args):
if i + 1 == len(args):
raise ValueError('Missing logpdf constraint value: %r' %
(args[i],))
c_colno = args[i]
c_value = args[i + 1]
constraints.append((fake_row_id, c_colno, c_value))
i += 2
logp = metamodel.logpdf_joint(bdb, generator_id, targets, constraints,
modelno)
return ieee_exp(logp)
def bql_row_column_predictive_probability(
bdb, population_id, generator_id, modelnos, rowid, targets,
constraints):
targets = json.loads(targets)
constraints = json.loads(constraints)
modelnos = _retrieve_modelnos(modelnos)
# Build the constraints and query from rowid, using a fresh rowid.
fresh_rowid = core.bayesdb_population_fresh_row_id(bdb, population_id)
def retrieve_values(colnos):
values = [
core.bayesdb_population_cell_value(bdb, population_id, rowid, colno)
for colno in colnos
]
return [(c,v) for (c,v) in zip (colnos, values) if v is not None]
cgpm_targets = retrieve_values(targets)
# If all targets have NULL values, return None.
if len(cgpm_targets) == 0:
return None
cgpm_constraints = retrieve_values(constraints)
def generator_predprob(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.logpdf_joint(
bdb, generator_id, modelnos, fresh_rowid, cgpm_targets,
cgpm_constraints)
generator_ids = _retrieve_generator_ids(bdb, population_id, generator_id)
predprobs = map(generator_predprob, generator_ids)
r = logmeanexp(predprobs)
return ieee_exp(r)
def bql_pdf_joint(bdb, population_id, generator_id, modelnos, *args):
modelnos = _retrieve_modelnos(modelnos)
i = 0
targets = []
while i < len(args):
if args[i] is None:
i += 1
break
if i + 1 == len(args):
raise ValueError('Missing logpdf target value: %r' % (args[i],))
t_colno = args[i]
t_value = args[i + 1]
targets.append((t_colno, t_value))
i += 2
constraints = []
while i < len(args):
if i + 1 == len(args):
raise ValueError('Missing logpdf constraint value: %r' %
(args[i],))
c_colno = args[i]
c_value = args[i + 1]
constraints.append((c_colno, c_value))
i += 2
logp = _bql_logpdf(bdb, population_id, generator_id, modelnos, targets,
constraints)
return ieee_exp(logp)
def bql_row_column_predictive_probability(bdb, generator_id, modelno, rowid,
colno):
metamodel = core.bayesdb_generator_metamodel(bdb, generator_id)
value = core.bayesdb_generator_cell_value(
bdb, generator_id, rowid, colno)
if value is None:
return None
r = metamodel.logpdf_joint(
bdb, generator_id, [(rowid, colno, value)], [], modelno)
return ieee_exp(r)
def bql_column_value_probability(bdb, population_id, generator_id, colno,
value, *constraint_args):
# A nonexistent (`unobserved') row id.
fake_row_id = core.bayesdb_population_fresh_row_id(bdb, population_id)
constraints = []
i = 0
while i < len(constraint_args):
if i + 1 == len(constraint_args):
raise ValueError('Odd constraint arguments: %s' %
(constraint_args,))
constraint_colno = constraint_args[i]
constraint_value = constraint_args[i + 1]
constraints.append((fake_row_id, constraint_colno, constraint_value))
i += 2
targets = [(fake_row_id, colno, value)]
logp = _bql_logpdf(bdb, population_id, generator_id, targets, constraints)
return ieee_exp(logp)
def bql_column_value_probability(bdb, population_id, generator_id, modelnos,
colno, value, *constraint_args):
modelnos = _retrieve_modelnos(modelnos)
constraints = []
i = 0
while i < len(constraint_args):
if i + 1 == len(constraint_args):
raise ValueError('Odd constraint arguments: %s' %
(constraint_args, ))
constraint_colno = constraint_args[i]
constraint_value = constraint_args[i + 1]
constraints.append((constraint_colno, constraint_value))
i += 2
targets = [(colno, value)]
logp = _bql_logpdf(bdb, population_id, generator_id, modelnos, targets,
constraints)
return ieee_exp(logp)
def bql_column_value_probability(
bdb, population_id, generator_id, modelnos, colno, value,
*constraint_args):
modelnos = _retrieve_modelnos(modelnos)
constraints = []
i = 0
while i < len(constraint_args):
if i + 1 == len(constraint_args):
raise ValueError(
'Odd constraint arguments: %s' % (constraint_args,))
constraint_colno = constraint_args[i]
constraint_value = constraint_args[i + 1]
constraints.append((constraint_colno, constraint_value))
i += 2
targets = [(colno, value)]
logp = _bql_logpdf(bdb, population_id, generator_id, modelnos, targets,
constraints)
return ieee_exp(logp)
def bql_column_value_probability(bdb, generator_id, modelno, colno, value,
*constraint_args):
metamodel = core.bayesdb_generator_metamodel(bdb, generator_id)
# A nonexistent (`unobserved') row id.
fake_row_id = core.bayesdb_generator_fresh_row_id(bdb, generator_id)
constraints = []
i = 0
while i < len(constraint_args):
if i + 1 == len(constraint_args):
raise ValueError('Odd constraint arguments: %s' %
(constraint_args, ))
constraint_colno = constraint_args[i]
constraint_value = constraint_args[i + 1]
constraints.append((fake_row_id, constraint_colno, constraint_value))
i += 2
targets = [(fake_row_id, colno, value)]
r = metamodel.logpdf_joint(bdb, generator_id, targets, constraints,
modelno)
return ieee_exp(r)
def bql_column_value_probability(bdb, generator_id, modelno, colno, value,
*constraint_args):
metamodel = core.bayesdb_generator_metamodel(bdb, generator_id)
# A nonexistent (`unobserved') row id.
fake_row_id = core.bayesdb_generator_fresh_row_id(bdb, generator_id)
constraints = []
i = 0
while i < len(constraint_args):
if i + 1 == len(constraint_args):
raise ValueError('Odd constraint arguments: %s' %
(constraint_args,))
constraint_colno = constraint_args[i]
constraint_value = constraint_args[i + 1]
constraints.append((fake_row_id, constraint_colno, constraint_value))
i += 2
targets = [(fake_row_id, colno, value)]
r = metamodel.logpdf_joint(
bdb, generator_id, targets, constraints, modelno)
return ieee_exp(r)
def bql_pdf_joint(bdb, population_id, generator_id, *args):
i = 0
targets = []
while i < len(args):
if args[i] is None:
i += 1
break
if i + 1 == len(args):
raise ValueError('Missing logpdf target value: %r' % (args[i], ))
t_colno = args[i]
t_value = args[i + 1]
targets.append((t_colno, t_value))
i += 2
constraints = []
while i < len(args):
if i + 1 == len(args):
raise ValueError('Missing logpdf constraint value: %r' %
(args[i], ))
c_colno = args[i]
c_value = args[i + 1]
constraints.append((c_colno, c_value))
i += 2
logp = _bql_logpdf(bdb, population_id, generator_id, targets, constraints)
return ieee_exp(logp)
