def test_nig_normal_latent_numbering():
with bayesdb_open(':memory:') as bdb:
bayesdb_register_metamodel(bdb, NIGNormalMetamodel())
bdb.sql_execute('create table t(id integer primary key, x, y)')
for x in xrange(100):
bdb.sql_execute('insert into t(x, y) values(?, ?)',
(x, x * x - 100))
bdb.execute('''
create population p for t(id ignore; model x,y as numerical)
''')
assert core.bayesdb_has_population(bdb, 'p')
pid = core.bayesdb_get_population(bdb, 'p')
assert core.bayesdb_variable_numbers(bdb, pid, None) == [1, 2]
bdb.execute('create generator g0 for p using nig_normal')
bdb.execute('''
create generator g1 for p using nig_normal(xe deviation(x))
''')
assert core.bayesdb_has_generator(bdb, pid, 'g0')
g0 = core.bayesdb_get_generator(bdb, pid, 'g0')
assert core.bayesdb_has_generator(bdb, pid, 'g1')
g1 = core.bayesdb_get_generator(bdb, pid, 'g1')
assert core.bayesdb_variable_numbers(bdb, pid, None) == [1, 2]
assert core.bayesdb_variable_numbers(bdb, pid, g0) == [1, 2]
assert core.bayesdb_generator_column_numbers(bdb, g0) == [1, 2]
assert core.bayesdb_variable_numbers(bdb, pid, g1) == [-1, 1, 2]
assert core.bayesdb_generator_column_numbers(bdb, g1) == [-1, 1, 2]
def test_nig_normal_smoke():
with bayesdb_open(':memory:') as bdb:
bayesdb_register_metamodel(bdb, NIGNormalMetamodel())
bdb.sql_execute('create table t(x)')
for x in xrange(100):
bdb.sql_execute('insert into t(x) values(?)', (x, ))
bdb.execute('create population p for t(x numerical)')
bdb.execute('create generator g for p using nig_normal')
bdb.execute('initialize 1 model for g')
bdb.execute('analyze g for 1 iteration wait')
bdb.execute('estimate probability density of x = 50 from p').fetchall()
bdb.execute('simulate x from p limit 1').fetchall()
bdb.execute('drop models from g')
bdb.execute('drop generator g')
bdb.execute('drop population p')
bdb.execute('drop table t')
def test_nig_normal_latent_smoke():
with bayesdb_open(':memory:') as bdb:
bayesdb_register_metamodel(bdb, NIGNormalMetamodel())
bdb.sql_execute('create table t(x)')
for x in xrange(100):
bdb.sql_execute('insert into t(x) values(?)', (x, ))
bdb.execute('create population p for t(x numerical)')
bdb.execute('create generator g0 for p using nig_normal')
bdb.execute('''
create generator g1 for p using nig_normal(xe deviation(x))
''')
bdb.execute('initialize 1 model for g0')
bdb.execute('analyze g0 for 1 iteration wait')
bdb.execute('initialize 1 model for g1')
bdb.execute('analyze g1 for 1 iteration wait')
# PROBABILITY DENSITY OF x = v
bdb.execute('estimate probability density of x = 50 within p') \
.fetchall()
with pytest.raises(BQLError):
bdb.execute('estimate probability density of xe = 1 within p') \
.fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 1 within p modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of xe = 1 within p modelled by g1
''').fetchall()
# PREDICTIVE PROBABILITY OF x
bdb.execute('estimate predictive probability of x from p').fetchall()
with pytest.raises(BQLError):
bdb.execute(
'estimate predictive probability of xe from p').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate predictive probability of xe from p modelled by g0
''').fetchall()
for r, p_xe in bdb.execute('''
estimate rowid, predictive probability of xe from p modelled by g1
'''):
assert p_xe is None, 'rowid %r p(xe) %r' % (r, p_xe)
# INFER/PREDICT
bdb.execute(
'INFER EXPLICIT PREDICT x CONFIDENCE x_c FROM p').fetchall()
with pytest.raises(BQLError):
bdb.execute(
'INFER EXPLICIT PREDICT xe CONFIDENCE xe_c FROM p').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
INFER EXPLICIT PREDICT xe CONFIDENCE xe_c FROM p
MODELLED BY g0
''').fetchall()
bdb.execute('''
INFER EXPLICIT PREDICT xe CONFIDENCE xe_c FROM p
MODELLED BY g1
''').fetchall()
# SIMULATE x
bdb.execute('simulate x from p limit 1').fetchall()
with pytest.raises(BQLError):
bdb.execute('simulate x, xe from p limit 1').fetchall()
with pytest.raises(BQLError):
bdb.execute(
'simulate x, xe from p modelled by g0 limit 1').fetchall()
bdb.execute('simulate x, xe from p modelled by g1 limit 1').fetchall()
assert 100 == len(
bdb.execute('''
estimate similarity in the context of x from pairwise p limit 100
''').fetchall())
assert 1 == len(
bdb.execute('''
estimate similarity in the context of x
from pairwise p modelled by g0 limit 1
''').fetchall())
# No such column xe in g0.
with pytest.raises(BQLError):
assert 1 == len(
bdb.execute('''
estimate similarity in the context of xe
from pairwise p modelled by g0 limit 1
''').fetchall())
# Column xe exists in g1.
assert 1 == len(
bdb.execute('''
estimate similarity in the context of xe
from pairwise p modelled by g1 limit 1
''').fetchall())
bdb.execute('drop models from g0')
bdb.execute('drop generator g0')
bdb.execute('drop models from g1')
bdb.execute('drop generator g1')
bdb.execute('drop population p')
bdb.execute('drop table t')
def test_nig_normal_latent_2var2lat_conditional_smoke():
with bayesdb_open(':memory:') as bdb:
bayesdb_register_metamodel(bdb, NIGNormalMetamodel())
bdb.sql_execute('create table t(x, y)')
for x in xrange(100):
bdb.sql_execute('insert into t(x, y) values(?, ?)',
(x, x * x - 100))
bdb.execute('create population p for t(x numerical; y numerical)')
bdb.execute('create generator g0 for p using nig_normal')
bdb.execute('''
create generator g1 for p using nig_normal(
xe deviation(x),
ye deviation(y)
)
''')
bdb.execute('initialize 1 model for g0')
bdb.execute('analyze g0 for 1 iteration wait')
bdb.execute('initialize 1 model for g1')
bdb.execute('analyze g1 for 1 iteration wait')
# latent given latent
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 1 given (ye = -1) within p
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 1 given (ye = -1) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of xe = 1 given (ye = -1) within p
modelled by g1
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
simulate xe from p given ye = -1 limit 1
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
simulate xe from p modelled by g0 given ye = -1 limit 1
''').fetchall()
bdb.execute('''
simulate xe from p modelled by g1 given ye = -1 limit 1
''').fetchall()
with pytest.raises(BQLError):
bdb.execute(
'estimate dependence probability of xe with ye within p')
with pytest.raises(BQLError):
bdb.execute('''
estimate dependence probability of xe with ye within p
modelled by g0
''')
bdb.execute('''
estimate dependence probability of xe with ye within p
modelled by g1
''')
with pytest.raises(BQLError):
bdb.execute('estimate mutual information of xe with ye within p')
with pytest.raises(BQLError):
bdb.execute('''
estimate mutual information of xe with ye within p
modelled by g0
''')
bdb.execute('''
estimate mutual information of xe with ye within p
modelled by g1
''')
bdb.execute('drop models from g0')
bdb.execute('drop generator g0')
bdb.execute('drop models from g1')
bdb.execute('drop generator g1')
bdb.execute('drop population p')
bdb.execute('drop table t')
def test_nig_normal_latent_2var_conditional_smoke():
with bayesdb_open(':memory:') as bdb:
bayesdb_register_metamodel(bdb, NIGNormalMetamodel())
bdb.sql_execute('create table t(x, y)')
for x in xrange(100):
bdb.sql_execute('insert into t(x, y) values(?, ?)',
(x, x * x - 100))
bdb.execute('create population p for t(x numerical; y numerical)')
# CORRELATION, CORRELATION PVALUE, without generators.
assert 4 == len(
bdb.execute('''
estimate correlation, correlation pvalue
from pairwise variables of p
''').fetchall())
bdb.execute('create generator g0 for p using nig_normal')
bdb.execute('''
create generator g1 for p using nig_normal(xe deviation(x))
''')
bdb.execute('initialize 1 model for g0')
bdb.execute('analyze g0 for 1 iteration wait')
bdb.execute('initialize 1 model for g1')
bdb.execute('analyze g1 for 1 iteration wait')
# observed given other observed
bdb.execute('''
estimate probability density of x = 50 given (y = 49) within p
''').fetchall()
bdb.execute('''
estimate probability density of x = 50 given (y = 49) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of x = 50 given (y = 49) within p
modelled by g1
''').fetchall()
bdb.execute('simulate x from p given y = 49 limit 1').fetchall()
bdb.execute('''
simulate x from p modelled by g0 given y = 49 limit 1
''').fetchall()
bdb.execute('''
simulate x from p modelled by g1 given y = 49 limit 1
''').fetchall()
# observed given related latent
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of x = 50 given (xe = 1) within p
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of x = 50 given (xe = 1) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of x = 50 given (xe = 1) within p
modelled by g1
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('simulate x from p given xe = 1 limit 1').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
simulate x from p modelled by g0 given xe = 1 limit 1
''').fetchall()
bdb.execute('''
simulate x from p modelled by g1 given xe = 1 limit 1
''').fetchall()
# observed given unrelated latent
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of y = 50 given (xe = 1) within p
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of y = 50 given (xe = 1) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of y = 50 given (xe = 1) within p
modelled by g1
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('simulate y from p given xe = 1 limit 1').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
simulate y from p modelled by g0 given xe = 1 limit 1
''').fetchall()
bdb.execute('''
simulate y from p modelled by g1 given xe = 1 limit 1
''').fetchall()
# latent given related observed
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 1 given (x = 50) within p
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 1 given (x = 50) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of xe = 1 given (x = 50) within p
modelled by g1
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('simulate xe from p given x = 50 limit 1').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
simulate xe from p modelled by g0 given x = 50 limit 1
''').fetchall()
bdb.execute('''
simulate xe from p modelled by g1 given x = 50 limit 1
''').fetchall()
# latent given unrelated observed
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 1 given (y = 50) within p
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 1 given (y = 50) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of xe = 1 given (y = 50) within p
modelled by g1
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('simulate xe from p given y = 50 limit 1').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
simulate xe from p modelled by g0 given y = 50 limit 1
''').fetchall()
bdb.execute('''
simulate xe from p modelled by g1 given y = 50 limit 1
''').fetchall()
bdb.execute('drop models from g0')
bdb.execute('drop generator g0')
bdb.execute('drop models from g1')
bdb.execute('drop generator g1')
bdb.execute('drop population p')
bdb.execute('drop table t')
def test_nig_normal_latent_2var_smoke():
with bayesdb_open(':memory:') as bdb:
bayesdb_register_metamodel(bdb, NIGNormalMetamodel())
bdb.sql_execute('create table t(x, y)')
for x in xrange(100):
bdb.sql_execute('insert into t(x, y) values(?, ?)',
(x, x * x - 100))
bdb.execute('create population p for t(x numerical; y numerical)')
# CORRELATION, CORRELATION PVALUE, without generators.
assert 4 == len(
bdb.execute('''
estimate correlation, correlation pvalue
from pairwise variables of p
''').fetchall())
bdb.execute('create generator g0 for p using nig_normal')
bdb.execute('''
create generator g1 for p using nig_normal(xe deviation(x))
''')
bdb.execute('initialize 1 model for g0')
bdb.execute('analyze g0 for 1 iteration wait')
bdb.execute('initialize 1 model for g1')
bdb.execute('analyze g1 for 1 iteration wait')
# CORRELATION, CORRELATION PVALUE, with generators.
assert 4 == len(
bdb.execute('''
estimate correlation, correlation pvalue
from pairwise variables of p
''').fetchall())
assert 4 == len(
bdb.execute('''
estimate correlation, correlation pvalue
from pairwise variables of p modelled by g0
''').fetchall())
with pytest.raises(BQLError):
assert 4 == len(
bdb.execute('''
estimate correlation, correlation pvalue
from pairwise variables of p modelled by g1
''').fetchall())
# DEPENDENCE PROBABILITY, MUTUAL INFORMATION
assert 4 == len(
bdb.execute('''
estimate dependence probability, mutual information
from pairwise variables of p
''').fetchall())
assert 4 == len(
bdb.execute('''
estimate dependence probability, mutual information
from pairwise variables of p modelled by g0
''').fetchall())
assert 9 == len(
bdb.execute('''
estimate dependence probability, mutual information
from pairwise variables of p modelled by g1
''').fetchall())
# SIMULATE LATENT VARIABLE
assert 10 == len(
bdb.execute('''
simulate xe from p modeled by g1 limit 10;
''').fetchall())
assert 10 == len(
bdb.execute('''
simulate y, xe from p modeled by g1 limit 10;
''').fetchall())
# Cannot simulate the latent xe from the population p.
with pytest.raises(BQLError):
assert 10 == len(
bdb.execute('''
simulate xe from p limit 10;
''').fetchall())
# Cannot simulate the latent xe from the generator g0.
with pytest.raises(BQLError):
assert 10 == len(
bdb.execute('''
simulate xe from p modeled by g0 limit 10;
''').fetchall())
bdb.execute('drop models from g0')
bdb.execute('drop generator g0')
bdb.execute('drop models from g1')
bdb.execute('drop generator g1')
bdb.execute('drop population p')
bdb.execute('drop table t')
def test_nig_normal_latent_conditional_smoke():
with bayesdb_open(':memory:') as bdb:
bayesdb_register_metamodel(bdb, NIGNormalMetamodel())
bdb.sql_execute('create table t(x)')
for x in xrange(100):
bdb.sql_execute('insert into t(x) values(?)', (x, ))
bdb.execute('create population p for t(x numerical)')
bdb.execute('create generator g0 for p using nig_normal')
bdb.execute('''
create generator g1 for p using nig_normal(xe deviation(x))
''')
bdb.execute('initialize 1 model for g0')
bdb.execute('analyze g0 for 1 iteration wait')
bdb.execute('initialize 1 model for g1')
bdb.execute('analyze g1 for 1 iteration wait')
# observed given observed
bdb.execute('''
estimate probability density of x = 50 given (x = 50) within p
''').fetchall()
bdb.execute('''
estimate probability density of x = 50 given (x = 50) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of x = 50 given (x = 50) within p
modelled by g1
''').fetchall()
# observed given latent
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of x = 50 given (xe = 50) within p
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of x = 50 given (xe = 50) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of x = 50 given (xe = 50) within p
modelled by g1
''').fetchall()
# latent given observed
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 50 given (x = 50) within p
''').fetchall()
with pytest.raises(BQLError):
bdb.execute('''
estimate probability density of xe = 50 given (x = 50) within p
modelled by g0
''').fetchall()
bdb.execute('''
estimate probability density of xe = 50 given (x = 50) within p
modelled by g1
''').fetchall()
bdb.execute('drop models from g0')
bdb.execute('drop generator g0')
bdb.execute('drop models from g1')
bdb.execute('drop generator g1')
bdb.execute('drop population p')
bdb.execute('drop table t')