def test_classless(self):
classless = Table(Domain(self.zoo.domain.attributes), self.zoo[:,
0:-1])
scorers = [scoring.Gini(), scoring.InfoGain(), scoring.GainRatio()]
for scorer in scorers:
with self.assertRaises(ValueError):
scorer(0, classless)
def test_wrong_class_type(self):
scorers = [scoring.Gini(), scoring.InfoGain(), scoring.GainRatio()]
for scorer in scorers:
with self.assertRaises(ValueError):
scorer(0, self.housing)
class TestMeasureAttr_InfoGain(testing.MeasureAttributeTestCase):
MEASURE = scoring.InfoGain()
def test_info_gain(self):
scorer = scoring.InfoGain()
correct = [0.79067, 0.71795, 0.83014, 0.97432, 0.46970]
np.testing.assert_almost_equal([scorer(a, self.zoo) for a in range(5)],
correct,
decimal=5)
# initialize Qt application
# NOTE: Needed by the save_treegraph_image() function.
import sys
from OWWidget import QApplication
a = QApplication(sys.argv)
# find out the current file's location so it can be used to compute the
# location of other files/directories
cur_dir = os.path.dirname(os.path.abspath(__file__))
path_prefix = os.path.abspath(os.path.join(cur_dir, "../../"))
base_learners = OrderedDict()
import Orange.classification.tree as octree
import Orange.feature.scoring as fscoring
split_const = octree.SplitConstructor_ExhaustiveBinary(
measure=fscoring.InfoGain())
base_learners["orange_tree"] = octree.TreeLearner(split=split_const,
store_instances=True)
base_learners["orange_tree_pruned"] = octree.TreeLearner(
split=split_const,
min_instances=10,
same_majority_pruning=True,
store_instances=True)
measures = []
measures.append("CA")
measures.append("AUC")
learners = OrderedDict()
learners["Tree"] = bin_exp.TreeMarkedAndMergedLearner()
learners["ForcedTree"] = bin_exp.ForcedFirstSplitMTLLearner(