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
print('First suite of neural network tests passed.')
# # # # # # # # # # # # # # # # # # # # # # # #
# Test predicate attributes
# # # # # # # # # # # # # # # # # # # # # # # #
kb.store(
'lives_in(tom, seattle)'
) ## TODO seems weird to have to add this fact then stick 'formerly' on it
# on the next line.
kb.edge_attr('tom', 'lives_in', 'seattle', {'formerly': 1.0})
kb.seed(555)
kb.build_kg_model(cuda=False,
embedding_size=50,
node_attributes=['owns_a_raincoat', 'doesnt_own_raincoat'],
pred_attributes=['formerly'],
attr_loss_to_graph_loss=0.9,
pred_loss_to_graph_loss=5.0)
kb.train_kg_model(steps=8001, batch_size=2, neg_to_pos=4)
# # # # # # # # # # # # # # # # # # # # # # # #
# People from Seattle should be more likely to
# own an umbrella (attribute prediction test)
# # # # # # # # # # # # # # # # # # # # # # # #
x = kb._kg_model.run_embedding(kb.get_embedding('other1'), 'owns_a_raincoat')
y = kb._kg_model.run_embedding(kb.get_embedding('other1'),
import context
from zincbase import KB
kb = KB()
kb.store('a(b,c)')
kb.attr('b', {'is_letter': 1.0})
assert kb.node('b') == {'is_letter': 1.0}
kb.edge_attr('b', 'a', 'c', {'both_alpha': 1.0})
assert kb.edge('b', 'a', 'c') == {'both_alpha': 1.0}
assert kb.to_triples() == [('b', 'a', 'c')]
triples = kb.to_triples(data=True)
assert triples == [('b', 'a', 'c', {'is_letter': 1.0}, {'both_alpha': 1.0}, {}, False)]
kb.attr('c', {'is_letter': 0.9})
triples = kb.to_triples(data=True)
assert triples == [('b', 'a', 'c', {'is_letter': 1.0}, {'both_alpha': 1.0}, {'is_letter': 0.9}, False)]
neg_rule_idx = kb.store('~a(b,c)')
triples = kb.to_triples(data=True)
assert triples == [('b', 'a', 'c', {'is_letter': 1.0}, {'both_alpha': 1.0}, {'is_letter': 0.9}, True)]
kb.delete_rule(neg_rule_idx)
triples = kb.to_triples(data=True)
assert triples == [('b', 'a', 'c', {'is_letter': 1.0}, {'both_alpha': 1.0}, {'is_letter': 0.9}, False)]
kb.edge_attr('b', 'a', 'c', {'truthiness':-1})
triples = kb.to_triples(data=True)
assert triples == [('b', 'a', 'c', {'is_letter': 1.0}, {'both_alpha': 1.0, 'truthiness': -1}, {'is_letter': 0.9}, True)]
kb.delete_edge_attr('b', 'a', 'c', ['truthiness'])
triples = kb.to_triples(data=True)
assert triples == [('b', 'a', 'c', {'is_letter': 1.0}, {'both_alpha': 1.0}, {'is_letter': 0.9}, False)]
print('All graph tests passed.')