def test_two_views_column_partition_normal__ci_(lovecat):
D = retrieve_normal_dataset()
engine = Engine(D.T,
outputs=[5, 0, 1, 2, 3, 4],
cctypes=['normal'] * len(D),
rng=gu.gen_rng(12),
num_states=64)
if lovecat:
engine.transition_lovecat(N=200)
else:
engine.transition(N=200)
P = engine.dependence_probability_pairwise()
R1 = engine.row_similarity_pairwise(cols=[5, 0, 1])
R2 = engine.row_similarity_pairwise(cols=[2, 3, 4])
pu.plot_clustermap(P)
pu.plot_clustermap(R1)
pu.plot_clustermap(R2)
P_THEORY = [
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
]
return engine
def test_two_views_row_partition_bernoulli__ci_(lovecat):
D = retrieve_bernoulli_dataset()
if lovecat:
engine = Engine(D.T,
cctypes=['categorical'] * len(D),
distargs=[{
'k': 2
}] * len(D),
Zv={
0: 0,
1: 0,
2: 1,
3: 1
},
rng=gu.gen_rng(12),
num_states=64)
engine.transition_lovecat(N=100,
kernels=[
'row_partition_assignments',
'row_partition_hyperparameters',
'column_hyperparameters',
])
else:
engine = Engine(D.T,
cctypes=['bernoulli'] * len(D),
Zv={
0: 0,
1: 0,
2: 1,
3: 1
},
rng=gu.gen_rng(12),
num_states=64)
engine.transition(N=100,
kernels=[
'view_alphas',
'rows',
'column_hypers',
])
R1 = engine.row_similarity_pairwise(cols=[0, 1])
R2 = engine.row_similarity_pairwise(cols=[2, 3])
pu.plot_clustermap(R1)
pu.plot_clustermap(R2)
return engine
def test_two_views_row_partition_normal__ci_(lovecat):
D = retrieve_normal_dataset()
engine = Engine(D.T,
cctypes=['normal'] * len(D),
Zv={
0: 0,
1: 0,
2: 0,
3: 1,
4: 1,
5: 1
},
rng=gu.gen_rng(12),
num_states=64)
if lovecat:
engine.transition_lovecat(N=100,
kernels=[
'row_partition_assignments',
'row_partition_hyperparameters',
'column_hyperparameters',
])
else:
engine.transition(N=100,
kernels=[
'view_alphas',
'rows',
'column_hypers',
])
R1 = engine.row_similarity_pairwise(cols=[0, 1, 2])
R2 = engine.row_similarity_pairwise(cols=[3, 4, 5])
pu.plot_clustermap(R1)
pu.plot_clustermap(R2)
return engine
def test_two_views_column_partition_bernoulli__ci_(lovecat):
D = retrieve_bernoulli_dataset()
engine = Engine(D.T,
cctypes=['categorical'] * len(D),
distargs=[{
'k': 2
}] * len(D),
rng=gu.gen_rng(12),
num_states=64)
if lovecat:
engine.transition_lovecat(N=200)
else:
# engine = Engine(
# D.T,
# cctypes=['bernoulli']*len(D),
# rng=gu.gen_rng(12),
# num_states=64)
engine.transition(N=200)
P = engine.dependence_probability_pairwise()
R1 = engine.row_similarity_pairwise(cols=[0, 1])
R2 = engine.row_similarity_pairwise(cols=[2, 3])
pu.plot_clustermap(P)
pu.plot_clustermap(R1)
pu.plot_clustermap(R2)
P_THEORY = [
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 1, 1],
]
return engine
