def test_marg():
spn = example_spns.get_gender_spn()
spn1 = fn.marg(spn, [2])
fn.plot_spn(spn1, "marg1.pdf")
spn2 = fn.marg(spn, [0])
fn.plot_spn(spn2, "marg2.pdf")
spn3 = fn.marg(spn, [1])
fn.plot_spn(spn3, "marg3.pdf")
spn4 = fn.marg(spn, [1, 2])
fn.plot_spn(spn4, "marg4.pdf")
rang = [None, NominalRange([1]), None]
prob, spn5 = fn.marg_rang(spn, rang)
fn.plot_spn(spn5, "marg5.pdf")
rang = [None, NominalRange([1]), NumericRange([[10, 12]])]
prob, spn6 = fn.marg_rang(spn, rang)
fn.plot_spn(spn6, "marg6.pdf")
rang = [NominalRange([0]), NominalRange([1]), None]
prob = fn.prob(spn, rang)
print(prob)
prob = fn.prob(spn6, rang)
print(prob)
def test_sampling():
spn = example_spns.get_gender_spn()
'''
Always same random number generator
'''
samples = fn.sampling(spn, n_samples=10, random_seed=1)
print(samples)
samples = fn.sampling_rang(spn,
rang=[None, None, None, None],
n_samples=10,
random_seed=1)
print(samples)
samples = fn.sampling_rang(
spn,
rang=[None, None, NumericRange([[10, 11], [29, 30]])],
n_samples=10,
random_seed=1)
print(samples)
samples = fn.sampling_rang(
spn,
rang=[NominalRange([0]), None,
NumericRange([[14, 15], [29, 30]])],
n_samples=10,
random_seed=1)
print(samples)
def test_expect():
spn = example_spns.get_gender_spn()
rang = [None, None, None]
expect = fn.expect(spn, feature_id=2, rang=rang)
print(expect)
rang = [NominalRange([0]), None, None]
expect = fn.expect(spn, feature_id=2, rang=rang)
print(expect)
rang = [NominalRange([1]), None, None]
expect = fn.expect(spn, feature_id=2, rang=rang)
print(expect)
rang = [None, NominalRange([0]), None]
expect = fn.expect(spn, feature_id=2, rang=rang)
print(expect)
feature_scope = {2}
data = np.array([[np.nan, np.nan, np.nan]])
expect = fn.expects_spnflow(spn, feature_scope, data)
print(expect)
feature_scope = {2}
data = np.array([np.nan, np.nan, np.nan])
expect = fn.expect_spnflow(spn, feature_scope, data)
print(expect)
def test_prob():
spn = example_spns.get_gender_spn()
rang = [None, None, None]
prob = fn.prob(spn, rang)
print(prob)
rang = [NominalRange([0]), NominalRange([1]), NumericRange([[20]])]
prob = fn.prob(spn, rang)
print(prob)
ranges = np.array([[None, None, NumericRange([[0, 20]])],
[NominalRange([0]), None, None],
[None, NominalRange([1]), None]])
probs = fn.probs(spn, ranges)
print(probs)
inst = [0, np.nan, np.nan]
prob = fn.prob_spflow(spn, inst)
print(prob)
data = np.array([[0, np.nan, np.nan], [0, 1, np.nan]])
probs = fn.probs_spflow(spn, data)
print(probs)
for interval in zip(val_space[1:], val_space[:-1]):
conds.append(NumericRange([list(interval)]))
labels.append(str(list(interval)))
else:
raise Exception(
"Not implemented for other than discrete or numeric ...: " +
str(value_dict[target_id][0]))
return conds, labels
if __name__ == '__main__':
from simple_spn.example import example_spns
from util import io
spn = example_spns.get_gender_spn()
loc = "_spns"
ident = "rdc=" + str(0.3) + "_mis=" + str(0.1)
spn, value_dict, _ = io.load(ident, "titanic", loc)
spn = fn.marg(spn, keep=[0, 1, 2, 3, 6])
#visualize_sub_populations(spn)
#visualize_overall_distribution(spn, value_dict=value_dict, save_path="overall_visualization.pdf")
#visualize_target_based_overall_distribution_single(spn, 0, value_dict=value_dict, save_path="overall_visualization_target_based.pdf")
#visualize_target_based_overall_distribution_compact(spn, 0, value_dict=value_dict, save_path="overall_visualization_target_based_compact.pdf")
visualize_expected_sub_populations(spn,
value_dict=value_dict,
save_path="expectation_line_plot.pdf")
visualized_target_based_expected_sub_populations(
spn,
def test_plot():
spn = example_spns.get_gender_spn()
fn.plot_spn(spn, "sample_spn.pdf")
spn = example_spns.get_credit_spn()()
fn.plot_spn(spn, "sample_spn2.pdf")
def test_get_overall_population():
spn = example_spns.get_gender_spn()
overall_pop = fn.get_overall_population(spn)
print(overall_pop)
def test_generate_value_dict():
spn = example_spns.get_gender_spn()
val_dict = fn.generate_adhoc_value_dict(spn)
print(val_dict)
def test_get_subpopulations():
spn = example_spns.get_gender_spn()
#rang = [NominalRange([0]), NominalRange([1]), None]
sub_pops = fn.get_sub_populations(spn)
print(sub_pops)