def test_over_cat_limit(self):
y = pd.Series(np.random.choice([0, 1], size=(500, )))
X = pd.DataFrame({
'a': np.random.normal(size=500),
'b': np.random.normal(size=500),
'c': np.random.choice([0, 1], size=500),
'd': np.random.choice(['a', 'b', 'c', 'd'], size=500),
'e': np.random.choice([7, 8, 9, 10, 11], size=500),
'f': np.random.choice(['x', 'y'], size=500),
'g': np.random.choice([0, 1], size=500),
'h': np.random.choice(['q', 'r', 's'], size=500)
})
inds = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
cats = ['c', 'd', 'e', 'f', 'g', 'h']
hinds = ['a', 'd']
ca = CausalAnalysis(inds, cats, hinds, upper_bound_on_cat_expansion=2)
ca.fit(X, y)
# columns 'd', 'e', 'h' have too many values
self.assertEqual([res.feature_name for res in ca._results],
['a', 'b', 'c', 'f', 'g'])
inds = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
cats = ['c', 'd', 'e', 'f', 'g', 'h']
hinds = ['a', 'd']
ca = CausalAnalysis(inds, cats, hinds, upper_bound_on_cat_expansion=3)
ca.fit(X, y)
# columns 'd', 'e' have too many values
self.assertEqual([res.feature_name for res in ca._results],
['a', 'b', 'c', 'f', 'g', 'h'])
ca.upper_bound_on_cat_expansion = 2
ca.fit(X, y, warm_start=True)
# lowering bound shouldn't affect already fit columns when warm starting
self.assertEqual([res.feature_name for res in ca._results],
['a', 'b', 'c', 'f', 'g', 'h'])
ca.upper_bound_on_cat_expansion = 4
ca.fit(X, y, warm_start=True)
# column d is now okay, too
self.assertEqual([res.feature_name for res in ca._results],
['a', 'b', 'c', 'd', 'f', 'g', 'h'])
def test_invalid_inds(self):
X = np.zeros((300, 6))
y = np.random.normal(size=(300, ))
# first column: 10 ones, this is fine
X[np.random.choice(300, 10, replace=False),
0] = 1 # ten ones, should be fine
# second column: 6 categories, plenty of random instances of each
# this is fine only if we increase the cateogry limit
X[:, 1] = np.random.choice(6, 300) # six categories
# third column: nine ones, lots of twos, not enough unless we disable check
X[np.random.choice(300, 100, replace=False), 2] = 2
X[np.random.choice(300, 9, replace=False), 2] = 1
# fourth column: 5 ones, also not enough but barely works even with forest heterogeneity
X[np.random.choice(300, 5, replace=False), 3] = 1
# fifth column: 2 ones, ensures that we will change number of folds for linear heterogeneity
# forest heterogeneity won't work
X[np.random.choice(300, 2, replace=False), 4] = 1
# sixth column: just 1 one, not enough even without check
X[np.random.choice(300, 1), 5] = 1 # one instance of
col_names = ['a', 'b', 'c', 'd', 'e', 'f']
X = pd.DataFrame(X, columns=col_names)
for n in ['linear', 'automl']:
for h in ['linear', 'forest']:
for warm_start in [True, False]:
ca = CausalAnalysis(col_names,
col_names,
col_names,
verbose=1,
nuisance_models=n,
heterogeneity_model=h)
ca.fit(X, y)
self.assertEqual(ca.trained_feature_indices_,
[0]) # only first column okay
self.assertEqual(ca.untrained_feature_indices_,
[(1, 'upper_bound_on_cat_expansion'),
(2, 'cat_limit'), (3, 'cat_limit'),
(4, 'cat_limit'), (5, 'cat_limit')])
# increase bound on cat expansion
ca.upper_bound_on_cat_expansion = 6
ca.fit(X, y, warm_start=warm_start)
self.assertEqual(ca.trained_feature_indices_,
[0, 1]) # second column okay also
self.assertEqual(ca.untrained_feature_indices_,
[(2, 'cat_limit'), (3, 'cat_limit'),
(4, 'cat_limit'), (5, 'cat_limit')])
# skip checks (reducing folds accordingly)
ca.skip_cat_limit_checks = True
ca.fit(X, y, warm_start=warm_start)
if h == 'linear':
self.assertEqual(
ca.trained_feature_indices_,
[0, 1, 2, 3, 4]) # all but last col okay
self.assertEqual(ca.untrained_feature_indices_,
[(5, 'cat_limit')])
else:
self.assertEqual(ca.trained_feature_indices_,
[0, 1, 2, 3]) # can't handle last two
self.assertEqual(ca.untrained_feature_indices_,
[(4, 'cat_limit'), (5, 'cat_limit')])
