def test_bayesdb_generator_fresh_row_id():
with bayesdb_generator(bayesdb(), 't1', 't1_cc', t1_schema, lambda x: 0,\
columns=['label CATEGORICAL', 'age NUMERICAL', 'weight NUMERICAL'])\
as (bdb, generator_id):
assert core.bayesdb_generator_fresh_row_id(bdb, generator_id) == 1
t1_data(bdb)
assert core.bayesdb_generator_fresh_row_id(bdb, generator_id) == \
len(t1_rows) + 1
def test_bayesdb_generator_fresh_row_id():
with bayesdb_generator(bayesdb(), 't1', 't1_cc', t1_schema, lambda x: 0,\
columns=['label CATEGORICAL', 'age NUMERICAL', 'weight NUMERICAL'])\
as (bdb, generator_id):
assert core.bayesdb_generator_fresh_row_id(bdb, generator_id) == 1
t1_data(bdb)
assert core.bayesdb_generator_fresh_row_id(bdb, generator_id) == \
len(t1_rows) + 1
def test_bayesdb_generator_fresh_row_id():
with bayesdb_generator(
bayesdb(),
"t1",
"t1_cc",
t1_schema,
lambda x: 0,
columns=["label CATEGORICAL", "age NUMERICAL", "weight NUMERICAL"],
) as (bdb, generator_id):
assert core.bayesdb_generator_fresh_row_id(bdb, generator_id) == 1
t1_data(bdb)
assert core.bayesdb_generator_fresh_row_id(bdb, generator_id) == len(t1_rows) + 1
def bayesdb_simulate(bdb,
generator_id,
constraints,
colnos,
modelno=None,
numpredictions=1):
"""Simulate rows from a generative model, subject to constraints.
Returns a list of `numpredictions` tuples, with a value for each
column specified in the list `colnos`, conditioned on the
constraints in the list `constraints` of tuples ``(colno,
value)``.
The results are simulated from the predictive distribution on
fresh rows.
"""
metamodel = core.bayesdb_generator_metamodel(bdb, generator_id)
fake_rowid = core.bayesdb_generator_fresh_row_id(bdb, generator_id)
targets = [(fake_rowid, colno) for colno in colnos]
if constraints is not None:
constraints = [(fake_rowid, colno, val) for colno, val in constraints]
return metamodel.simulate_joint(bdb,
generator_id,
targets,
constraints,
modelno,
num_predictions=numpredictions)
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_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 column_mutual_information(self, bdb, genid, modelno, colno0, colno1,
numsamples=None):
if numsamples is None:
numsamples = self.n_samples
# XXX Aggregator only.
row_id = core.bayesdb_generator_fresh_row_id(bdb, genid)
X = [(row_id, colno0)]
W = [(row_id, colno1)]
Z = Y = []
if modelno is None:
modelnos = core.bayesdb_generator_modelnos(bdb, genid)
else:
modelnos = [modelno]
with bdb.savepoint():
mi = sum(self.conditional_mutual_information(
bdb, genid, modelno, X, W, Z, Y)
for modelno in modelnos) / float(len(modelnos))
return mi
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 bayesdb_simulate(bdb, generator_id, constraints, colnos,
modelno=None, numpredictions=1):
"""Simulate rows from a generative model, subject to constraints.
Returns a list of `numpredictions` tuples, with a value for each
column specified in the list `colnos`, conditioned on the
constraints in the list `constraints` of tuples ``(colno,
value)``.
The results are simulated from the predictive distribution on
fresh rows.
"""
metamodel = core.bayesdb_generator_metamodel(bdb, generator_id)
fake_rowid = core.bayesdb_generator_fresh_row_id(bdb, generator_id)
targets = [(fake_rowid, colno) for colno in colnos]
if constraints is not None:
constraints = [(fake_rowid, colno, val)
for colno, val in constraints]
return metamodel.simulate_joint(bdb, generator_id, targets,
constraints, modelno, num_predictions=numpredictions)
def predict_confidence(self,
bdb,
generator_id,
modelno,
colno,
rowid,
numsamples=None):
if not numsamples:
numsamples = 2
assert numsamples > 0
def _impute_categorical(sample):
counts = Counter(s[0] for s in sample)
mode_count = max(counts[v] for v in counts)
pred = iter(v for v in counts if counts[v] == mode_count).next()
conf = float(mode_count) / numsamples
return pred, conf
def _impute_numerical(sample):
pred = sum(s[0] for s in sample) / float(len(sample))
conf = 0 # XXX Punt confidence for now
return pred, conf
constraints = []
# If rowid is a hypothetical cell for cgpm (did not exist at the time
# of INITIALIZE), but exists in the base table (by INSERT INTO), then
# retrieve all values for rowid as the constraints.
exists = rowid < core.bayesdb_generator_fresh_row_id(bdb, generator_id)
max_cgpm_rowid = bdb.sql_execute(
'''
SELECT MAX(table_rowid) FROM bayesdb_cgpm_individual
WHERE generator_id = ?
''', (generator_id, )).fetchall()[0][0]
hypothetical = rowid > max_cgpm_rowid
if exists and hypothetical:
population_id = core.bayesdb_generator_population(
bdb, generator_id)
# Retrieve all other variables except colno, and ignore latents in
# generator_id, and place them in the constraints.
pop_names = core.bayesdb_variable_names(bdb, population_id, None)
avoid_name = core.bayesdb_variable_name(bdb, population_id, colno)
constraints_names = [n for n in pop_names if n != avoid_name]
# Obtain the row.
qt_names = str.join(',', map(sqlite3_quote_name,
constraints_names))
qt_table = sqlite3_quote_name(
core.bayesdb_population_table(bdb, population_id))
data = bdb.sql_execute(
'''
SELECT %s FROM %s WHERE oid = ?
''' % (
qt_names,
qt_table,
), (rowid, )).fetchall()[0]
# Build the constraints.
pop_nos = core.bayesdb_variable_numbers(bdb, population_id, None)
constraints_nos = [n for n in pop_nos if n != colno]
# import ipdb; ipdb.set_trace()
assert len(data) == len(constraints_nos)
constraints = [(rowid, c, v)
for c, v in zip(constraints_nos, data)
if (v is not None) and v]
# Retrieve the samples.
sample = self.simulate_joint(bdb, generator_id, [(rowid, colno)],
constraints, modelno, numsamples)
# Determine the imputation strategy (mode or mean).
stattype = core.bayesdb_variable_stattype(
bdb, core.bayesdb_generator_population(bdb, generator_id), colno)
if _is_categorical(stattype):
return _impute_categorical(sample)
else:
return _impute_numerical(sample)
