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# Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import unittest
import numpy as np
from Orange.data import Table
from Orange.preprocess.score import ANOVA, Gini, UnivariateLinearRegression, \
Chi2
from Orange.preprocess import SelectBestFeatures, Impute, SelectRandomFeatures
from Orange.tests import test_filename
class TestFSS(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.titanic = Table('titanic')
cls.heart_disease = Table('heart_disease')
cls.iris = Table('iris')
cls.imports = Table(test_filename('datasets/imports-85.tab'))
def test_select_1(self):
gini = Gini()
s = SelectBestFeatures(method=gini, k=1)
data2 = s(self.titanic)
best = max((gini(self.titanic, f), f) for f in self.titanic.domain.attributes)[1]
self.assertEqual(data2.domain.attributes[0], best)
def test_select_2(self):
gini = Gini()
# 100th percentile = selection of top1 attribute
sel1 = SelectBestFeatures(method=gini, k=1.0)
data2 = sel1(self.titanic)
best = max((gini(self.titanic, f), f) for f in self.titanic.domain.attributes)[1]
self.assertEqual(data2.domain.attributes[0], best)
# no k and no threshold, select all attributes
sel2 = SelectBestFeatures(method=gini, k=0)
data2 = sel2(self.titanic)
self.assertEqual(len(data2.domain.attributes), len(self.titanic.domain.attributes))
# 31% = selection of top (out of 3) attributes
sel3 = SelectBestFeatures(method=gini, k=0.31)
data2 = sel3(self.titanic)
self.assertEqual(len(data2.domain.attributes), 1)
# 35% = selection of top (out of 3) attributes
sel3 = SelectBestFeatures(method=gini, k=0.35)
data2 = sel3(self.titanic)
self.assertEqual(len(data2.domain.attributes), 1)
# 1% = select one (out of 3) attributes
sel3 = SelectBestFeatures(method=gini, k=0.01)
data2 = sel3(self.titanic)
self.assertEqual(len(data2.domain.attributes), 1)
# number of selected attrs should be relative to number of current input attrs
sel3 = SelectBestFeatures(method=gini, k=1.0)
data2 = sel3(self.heart_disease)
self.assertEqual(len(data2.domain.attributes), 13)
def test_select_threshold(self):
anova = ANOVA()
t = 30
data2 = SelectBestFeatures(method=anova, threshold=t)(self.heart_disease)
self.assertTrue(all(anova(self.heart_disease, f) >= t
for f in data2.domain.attributes))
def test_error_when_using_regression_score_on_classification_data(self):
s = SelectBestFeatures(method=UnivariateLinearRegression(), k=3)
with self.assertRaises(ValueError):
s(self.heart_disease)
def test_discrete_scores_on_continuous_features(self):
c = self.iris.columns
for method in (Gini(), Chi2()):
d1 = SelectBestFeatures(method=method)(self.iris)
expected = \
(c.petal_length, c.petal_width, c.sepal_length, c.sepal_width)
self.assertSequenceEqual(d1.domain.attributes, expected)
scores = method(d1)
self.assertEqual(len(scores), 4)
score = method(d1, c.petal_length)
self.assertEqual(score.ndim, 0) # a scalar
self.assertTrue(np.issubdtype(score.dtype, float))
def test_continuous_scores_on_discrete_features(self):
data = Impute()(self.imports)
with self.assertRaises(ValueError):
UnivariateLinearRegression()(data)
d1 = SelectBestFeatures(method=UnivariateLinearRegression())(data)
self.assertEqual(len(d1.domain.variables), len(data.domain.variables))
def test_defaults(self):
fs = SelectBestFeatures(k=3)
data2 = fs(Impute()(self.imports))
self.assertTrue(all(a.is_continuous for a in data2.domain.attributes))
data2 = fs(self.iris)
self.assertTrue(all(a.is_continuous for a in data2.domain.attributes))
data2 = fs(self.titanic)
self.assertTrue(all(a.is_discrete for a in data2.domain.attributes))
class TestSelectRandomFeatures(unittest.TestCase):
def test_select_random_features(self):
data = Table("heart_disease")
for k_features, n_attributes in ((3, 3), (0.35, 4)):
srf = SelectRandomFeatures(k=k_features)
new_data = srf(data)
self.assertEqual(len(new_data.domain.attributes), n_attributes)
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