from zincbase import KB
kb = KB()
kb.seed(555)
kb.from_csv('./assets/countries_s1_train.csv', delimiter='\t')
# specifying both a ~ and a truthiness < 0 is probably unnecessary.
kb.store('~locatedin(canada, africa)', {'truthiness': -1.})
kb.build_kg_model(cuda=False,
embedding_size=100,
pred_attributes=['truthiness'])
kb.train_kg_model(steps=1000, batch_size=4, neg_ratio=0.01)
canada_in_africa = kb.estimate_triple_prob('canada', 'locatedin', 'africa')
canada_in_asia = kb.estimate_triple_prob('canada', 'locatedin', 'asia')
canada_in_america = kb.estimate_triple_prob('canada', 'locatedin',
'northern_america')
assert 2 * canada_in_africa < canada_in_asia
assert canada_in_america > 2 * canada_in_asia
clafrica_truthiness = kb.estimate_triple_prob_with_attrs(
'canada', 'locatedin', 'africa', 'truthiness')
clasia_truthiness = kb.estimate_triple_prob_with_attrs('canada', 'locatedin',
'asia', 'truthiness')
assert clafrica_truthiness < clasia_truthiness
print('All truthiness tests passed.')
# prediction test)
# # # # # # # # # # # # # # # # # # # # # # # #
bay_prob = kb.estimate_triple_prob('tom', 'lives_in', 'bay_area')
sea_prob = kb.estimate_triple_prob('tom', 'lives_in', 'seattle')
assert bay_prob > sea_prob
bay_prob = kb.estimate_triple_prob('shamala', 'lives_in', 'bay_area')
sea_prob = kb.estimate_triple_prob('shamala', 'lives_in', 'seattle')
assert bay_prob > 2 * sea_prob
sea_prob = kb.estimate_triple_prob('other1', 'lives_in', 'seattle')
bay_prob = kb.estimate_triple_prob('other1', 'lives_in', 'bay_area')
assert sea_prob > 2 * bay_prob
sea_prob = kb.estimate_triple_prob('other2', 'lives_in', 'seattle')
bay_prob = kb.estimate_triple_prob('other2', 'lives_in', 'bay_area')
assert sea_prob > 2 * bay_prob
x = kb.estimate_triple_prob_with_attrs('tom', 'lives_in', 'bay_area',
'formerly')
y = kb.estimate_triple_prob_with_attrs('john', 'lives_in', 'bay_area',
'formerly')
z = kb.estimate_triple_prob_with_attrs('mary', 'lives_in', 'bay_area',
'formerly')
assert x > y
assert x > z
x = kb.estimate_triple_prob_with_attrs('tom', 'knows', 'john', 'formerly')
y = kb.estimate_triple_prob('tom', 'knows', 'john')
assert y > x
print('Neural network tests passed.')