def test_naive_bayes_independence_lovecat():
rng = gu.gen_rng(1)
D = rng.normal(size=(10, 1))
T = np.repeat(D, 10, axis=1)
Ci = list(itertools.combinations(range(10), 2))
state = State(T, cctypes=['normal'] * 10, Ci=Ci, rng=gu.gen_rng(0))
state.transition(N=10, progress=0)
vu.validate_crp_constrained_partition(state.Zv(), [], Ci, {}, {})
state.transition_lovecat(N=100, progress=0)
vu.validate_crp_constrained_partition(state.Zv(), [], Ci, {}, {})
def test_complex_independent_relationships():
rng = gu.gen_rng(1)
D = rng.normal(size=(10, 1))
T = np.repeat(D, 10, axis=1)
Ci = [(2, 8), (0, 3)]
state = State(T, cctypes=['normal'] * 10, Ci=Ci, rng=rng)
state.transition(N=10, progress=0)
vu.validate_crp_constrained_partition(state.Zv(), [], Ci, {}, {})
def test_simple_dependence_constraint(Ci):
rng = gu.gen_rng(1)
D = rng.normal(size=(10, 1))
T = np.repeat(D, 10, axis=1)
Cd = [(2, 0), (8, 3)]
state = State(T, cctypes=['normal'] * 10, Ci=Ci, Cd=Cd, rng=rng)
with pytest.raises(ValueError):
# Cannot transition columns with dependencies.
state.transition(N=10, kernels=['columns'], progress=0)
state.transition(
N=10,
kernels=['rows', 'alpha', 'column_hypers', 'alpha', 'view_alphas'],
progress=False)
vu.validate_crp_constrained_partition(state.Zv(), Cd, Ci, {}, {})
def test_categorical_forest():
state = State(
T, cctypes=CCTYPES, distargs=DISTARGS, rng=gu.gen_rng(1))
state.transition(N=1, progress=False)
cat_id = CCTYPES.index('categorical')
# If cat_id is singleton migrate first.
if len(state.view_for(cat_id).dims) == 1:
distargs = DISTARGS[cat_id].copy()
state.unincorporate_dim(cat_id)
state.incorporate_dim(
T[:,cat_id], outputs=[cat_id], cctype='categorical',
distargs=distargs, v=0)
state.update_cctype(cat_id, 'random_forest', distargs=distargs)
bernoulli_id = CCTYPES.index('bernoulli')
state.incorporate_dim(
T[:,bernoulli_id], outputs=[191], cctype='bernoulli',
v=state.Zv(cat_id))
state.update_cctype(191, 'random_forest', distargs={'k':2})
# Run valid transitions.
state.transition(
N=2, kernels=['rows','column_params','column_hypers'],
views=[state.Zv(cat_id)], progress=False)
# Running column transition should raise.
with pytest.raises(ValueError):
state.transition(N=1, kernels=['columns'], progress=False)
# Updating cctype in singleton View should raise.
distargs = DISTARGS[cat_id].copy()
state.incorporate_dim(
T[:,CCTYPES.index('categorical')], outputs=[98],
cctype='categorical', distargs=distargs, v=max(state.views)+1)
with pytest.raises(Exception):
state.update_cctype(98, 'random_forest', distargs=distargs)
def test_independence_inference_quality_lovecat():
rng = gu.gen_rng(584)
column_view_1 = rng.normal(loc=0, size=(50, 1))
column_view_2 = np.concatenate((
rng.normal(loc=10, size=(25, 1)),
rng.normal(loc=20, size=(25, 1)),
))
data_view_1 = np.repeat(column_view_1, 4, axis=1)
data_view_2 = np.repeat(column_view_2, 4, axis=1)
data = np.column_stack((data_view_1, data_view_2))
Zv0 = {i: 0 for i in xrange(8)}
state = State(data, Zv=Zv0, cctypes=['normal'] * 8, rng=gu.gen_rng(10))
state.transition_lovecat(N=100, progress=1)
for col in [
0,
1,
2,
3,
]:
assert state.Zv(col) == state.Zv(0)
for col in [4, 5, 6, 7]:
assert state.Zv(col) == state.Zv(4)
assert state.Zv(0) != state.Zv(4)
# Get lovecat to merge the dependent columns into one view.
Cd = [(0, 1), (2, 3), (4, 5), (6, 7)]
Zv0 = {0: 0, 1: 0, 2: 1, 3: 1, 4: 2, 5: 2, 6: 3, 7: 3}
state = State(data,
Zv=Zv0,
cctypes=['normal'] * 8,
Cd=Cd,
rng=gu.gen_rng(1))
state.transition_lovecat(N=100, progress=1)
for col in [
0,
1,
2,
3,
]:
assert state.Zv(col) == state.Zv(0)
for col in [4, 5, 6, 7]:
assert state.Zv(col) == state.Zv(4)
assert state.Zv(0) != state.Zv(4)
def test_incorporate_state():
state = State(
T[:,:2], cctypes=CCTYPES[:2], distargs=DISTARGS[:2], rng=gu.gen_rng(0))
state.transition(N=5)
target = state.views.keys()[0]
# Incorporate a new dim into view[0].
state.incorporate_dim(
T[:,2], outputs=[2], cctype=CCTYPES[2], distargs=DISTARGS[2], v=target)
assert state.Zv(2) == target
state.transition(N=1)
# Incorporate a new dim into view[0] with a non-contiguous output.
state.incorporate_dim(
T[:,2], outputs=[10], cctype=CCTYPES[2], distargs=DISTARGS[2], v=target)
assert state.Zv(10) == target
state.transition(N=1)
# Some crash testing queries.
state.logpdf(-1, {10:1}, constraints={0:2, 1:1})
state.simulate(-1, [10], constraints={0:2})
# Incorporating with a duplicated output should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[10], cctype=CCTYPES[2], distargs=DISTARGS[2],
v=target)
# Multivariate incorporate should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[10, 2], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Missing output should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Wrong number of rows should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2][:-1], outputs=[11], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Inputs should raise.
with pytest.raises(ValueError):
state.incorporate_dim(
T[:,2], outputs=[11], inputs=[2], cctype=CCTYPES[2],
distargs=DISTARGS[2], v=target)
# Incorporate dim into a newly created singleton view.
target = max(state.views)+1
state.incorporate_dim(
T[:,3], outputs=[3], cctype=CCTYPES[3],
distargs=DISTARGS[3], v=target)
assert state.Zv(3) == target
state.transition(N=1)
# Incorporate dim without specifying a view.
state.incorporate_dim(T[:,4], outputs=[4],
cctype=CCTYPES[4], distargs=DISTARGS[4])
state.transition(N=1)
# Unincorporate first dim.
previous = state.n_cols()
state.unincorporate_dim(0)
assert state.n_cols() == previous-1
state.transition(N=1)
# Reincorporate dim without specifying a view.
state.incorporate_dim(
T[:,0], outputs=[0], cctype=CCTYPES[0], distargs=DISTARGS[0])
state.transition(N=1)
# Incorporate dim into singleton view, remove it, assert destroyed.
target = max(state.views)+1
state.incorporate_dim(
T[:,5], outputs=[5], cctype=CCTYPES[5], distargs=DISTARGS[5],
v=target)
previous = len(state.views)
state.unincorporate_dim(5)
assert len(state.views) == previous-1
state.transition(N=1)
# Reincorporate dim into a singleton view.
target = max(state.views)+1
state.incorporate_dim(T[:,5], outputs=[5], cctype=CCTYPES[5],
distargs=DISTARGS[5], v=target)
state.transition(N=1)
# Incorporate the rest of the dims in the default way.
for i in xrange(6, len(CCTYPES)):
state.incorporate_dim(
T[:,i], outputs=[max(state.outputs)+1],
cctype=CCTYPES[i], distargs=DISTARGS[i])
state.transition(N=1)
# Unincorporating non-existent dim should raise.
with pytest.raises(ValueError):
state.unincorporate_dim(9999)
# Unincorporate all the dims, except the last one.
for o in state.outputs[:-1]:
state.unincorporate_dim(o)
assert state.n_cols() == 1
state.transition(N=1)
# Unincorporating last dim should raise.
with pytest.raises(ValueError):
state.unincorporate_dim(state.outputs[0])
