def test_make_activations_in(self):
slipnet = Slipnet.empty() # slipnet contents are ignored in this test
self.assertEqual(slipnet.make_activations_in(['a']), {'a': 1.0})
a_in = dict(a=1.5, b=0.5, c=1.2)
self.assertEqual(
slipnet.make_activations_in(features=['a', 'b', 'd'],
activations_in=a_in),
dict(a=1.5, b=1.0, c=1.2, d=1.0))
self.assertEqual(a_in, dict(a=1.5, b=0.5, c=1.2))
def test_topna_and_top(self):
slipnet = Slipnet.empty() # slipnet contents are ignored in this test
d = {'a': 0.9, 'b': 0.2, 'c': 0.3, 'd': 0.4, 'e': 0.5, 'f': 0.6}
pred = lambda x: x > 'b'
self.assertEqual(slipnet.topna(d, pred=pred, k=None), [
NodeA('f', 0.6),
NodeA('e', 0.5),
NodeA('d', 0.4),
NodeA('c', 0.3)
])
self.assertEqual(slipnet.top(d, pred=pred, k=None),
['f', 'e', 'd', 'c'])
self.assertEqual(slipnet.top1(d, pred=pred, k=None), 'f')
self.assertEqual(slipnet.topna(d, pred=pred, k=2),
[NodeA('f', 0.6), NodeA('e', 0.5)])
self.assertEqual(
slipnet.topna(d, pred=pred), # default k: pick only the top 1
[NodeA('f', 0.6)])
self.assertEqual(slipnet.topna(d, k=2),
[NodeA('a', 0.9), NodeA('f', 0.6)])
def test_filter_by_type(self):
slipnet = Slipnet.empty() # slipnet contents are ignored in this test
d = {'a': 0.2, 'b': 0.3, 1: 0.2, 2: 0.3}
self.assertEqual(slipnet.top(d, pred=str), ['b'])
self.assertEqual(slipnet.top(d, pred=int), [2])
# features.
out_d = self.sl.dquery(features=[Equation.make([6, 4], plus)])
self.assertCountEqual(
self.sl.top(out_d, pred=int, k=3),
[6, 4, 10]
)
self.assertEqual(
self.sl.top1(out_d, pred=After),
After(10)
)
self.assertCountEqual(
self.sl.top(out_d, pred=Before, k=2),
[Before(6), Before(4)]
)
sl = Slipnet.empty()
def run(features, p=p, g=eqn_graph, k=30):
global sl
sl = Slipnet(g, p)
pr(as_dict(p))
print()
print('Input:')
pr(features)
print()
t0 = process_time()
out_d = sl.dquery(features=features)
t1 = process_time()
pts(sl.topna(out_d, k=k))
print()
pts(sl.topna(out_d, pred=Equation, k=k))