def test_fcbf(self):
scorer = FCBF()
weights = scorer(self.zoo, None)
found = [
self.zoo.domain[attr].name
for attr in reversed(weights.argsort()[-5:])
]
reference = ['legs', 'backbone', 'toothed', 'hair', 'aquatic']
self.assertEqual(found, reference)
def _fit(self):
warnings.simplefilter(action='ignore')
table = self.data.to_table()
if self.params["eval_method"] == "relief":
scores = ReliefF(table, n_iterations=100)
elif self.params["eval_method"] == "fcbf":
scores = FCBF(table)
else:
scores = RandomForestLearner().score_data(table)[0]
for attr, score in zip(table.domain.attributes, scores):
self.feature_importances[attr.name] = score
def test_fcbf(self):
scorer = FCBF()
weights = scorer(self.zoo, None)
found = [
self.zoo.domain[attr].name
for attr in reversed(weights.argsort()[-5:])
]
reference = ['legs', 'milk', 'toothed', 'feathers', 'backbone']
self.assertEqual(found, reference)
# GH-1916
data = Table(
Domain([ContinuousVariable('1'),
ContinuousVariable('2')], DiscreteVariable('target')),
np.full((2, 2), np.nan), np.r_[0., 1])
weights = scorer(data, None)
np.testing.assert_equal(weights, np.nan)
def test_fcbf(self):
scorer = FCBF()
weights = scorer(self.zoo, None)
found = [self.zoo.domain[attr].name for attr in reversed(weights.argsort()[-5:])]
reference = ['legs', 'milk', 'toothed', 'feathers', 'backbone']
self.assertEqual(found, reference)
# GH-1916
data = Table(Domain([ContinuousVariable('1'), ContinuousVariable('2')],
DiscreteVariable('target')),
np.full((2, 2), np.nan),
np.r_[0., 1])
with warnings.catch_warnings():
# these warnings are expected
warnings.filterwarnings("ignore", "invalid value.*double_scalars")
warnings.filterwarnings("ignore", "invalid value.*true_divide")
weights = scorer(data, None)
np.testing.assert_equal(weights, np.nan)
def test_fcbf(self):
scorer = FCBF()
weights = scorer(self.zoo, None)
found = [
self.zoo.domain[attr].name
for attr in reversed(weights.argsort()[-5:])
]
reference = ["legs", "milk", "toothed", "feathers", "backbone"]
self.assertEqual(found, reference)
# GH-1916
data = Table(
Domain(
[ContinuousVariable("1"),
ContinuousVariable("2")],
DiscreteVariable("target"),
),
np.full((2, 2), np.nan),
np.r_[0.0, 1],
)
weights = scorer(data, None)
np.testing.assert_equal(weights, np.nan)