Use more models and fewere passes in test_loom_metamodel.test_loom_four_var
@@ -261,8 +261,8 @@ def test_loom_four_var():
create population p for t(x numerical; xx numerical;
y numerical; z categorical)''')
bdb.execute('create generator g for p using loom')
- bdb.execute('initialize 2 model for g')
- bdb.execute('analyze g for 500 iterations wait')
+ bdb.execute('initialize 10 model for g')
+ bdb.execute('analyze g for 20 iterations wait')
with pytest.raises(BQLError):
relevance = bdb.execute('''
@@ -334,9 +334,9 @@ def test_loom_four_var():
if col1 == col2:
assert d_val == 1
elif col1 in ['xx', 'x'] and col2 in ['xx', 'x']:
- assert d_val > 0.99
+ assert d_val > 0.80
else:
- assert d_val == 0
+ assert d_val < 0.20
predict_confidence = bdb.execute(
'infer explicit predict x confidence x_c FROM p').fetchall()
predictions, confidences = zip(*predict_confidence)
