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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.evaluation import CrossValidation, CA, RMSE
from Orange.classification import RandomForestLearner
from Orange.regression import RandomForestRegressionLearner
from Orange.tests import test_filename
class RandomForestTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.iris = Table('iris')
cls.housing = Table('housing')
def test_RandomForest(self):
forest = RandomForestLearner()
cv = CrossValidation(k=10)
results = cv(self.iris, [forest])
ca = CA(results)
self.assertGreater(ca, 0.9)
self.assertLess(ca, 0.99)
def test_predict_single_instance(self):
forest = RandomForestLearner()
c = forest(self.iris)
for ins in self.iris:
c(ins)
val, prob = c(ins, c.ValueProbs)
def test_predict_table(self):
forest = RandomForestLearner()
c = forest(self.iris)
c(self.iris)
vals, probs = c(self.iris, c.ValueProbs)
def test_predict_numpy(self):
forest = RandomForestLearner()
c = forest(self.iris)
c(self.iris.X)
vals, probs = c(self.iris.X, c.ValueProbs)
def test_RandomForestRegression(self):
forest = RandomForestRegressionLearner()
cv = CrossValidation(k=10)
results = cv(self.housing, [forest])
_ = RMSE(results)
def test_predict_single_instance_reg(self):
forest = RandomForestRegressionLearner()
model = forest(self.housing)
for ins in self.housing:
pred = model(ins)
self.assertGreater(pred, 0)
def test_predict_table_reg(self):
forest = RandomForestRegressionLearner()
model = forest(self.housing)
pred = model(self.housing)
self.assertEqual(len(self.housing), len(pred))
self.assertGreater(all(pred), 0)
def test_predict_numpy_reg(self):
forest = RandomForestRegressionLearner()
model = forest(self.housing)
pred = model(self.housing.X)
self.assertEqual(len(self.housing), len(pred))
self.assertGreater(all(pred), 0)
def test_classification_scorer(self):
learner = RandomForestLearner()
scores = learner.score_data(self.iris)
self.assertEqual(scores.shape[1], len(self.iris.domain.attributes))
self.assertNotEqual(sum(scores[0]), 0)
self.assertEqual(['petal length', 'petal width'],
sorted([self.iris.domain.attributes[i].name
for i in np.argsort(scores[0])[-2:]]))
def test_regression_scorer(self):
learner = RandomForestRegressionLearner()
scores = learner.score_data(self.housing)
self.assertEqual(['LSTAT', 'RM'],
sorted([self.housing.domain.attributes[i].name
for i in np.argsort(scores[0])[-2:]]))
def test_scorer_feature(self):
np.random.seed(42)
data = Table(test_filename('datasets/test4.tab'))
learner = RandomForestLearner()
scores = learner.score_data(data)
for i, attr in enumerate(data.domain.attributes):
np.random.seed(42)
score = learner.score_data(data, attr)
np.testing.assert_array_almost_equal(score, scores[:, i])
def test_get_classification_trees(self):
n = 5
forest = RandomForestLearner(n_estimators=n)
model = forest(self.iris)
self.assertEqual(len(model.trees), n)
tree = model.trees[0]
self.assertEqual(tree(self.iris[0]), 0)
def test_get_regression_trees(self):
n = 5
forest = RandomForestRegressionLearner(n_estimators=n)
model = forest(self.housing)
self.assertEqual(len(model.trees), n)
tree = model.trees[0]
tree(self.housing[0])
def test_max_features_cls(self):
data = Table("heart_disease")
forest_1 = RandomForestLearner(random_state=0, max_features=1)
model_1 = forest_1(data[1:])
forest_2 = RandomForestLearner(random_state=0, max_features=1.)
model_2 = forest_2(data[1:])
diff = np.sum(np.abs(model_1(data[:1], ret=model_2.Probs) -
model_2(data[:1], ret=model_2.Probs)))
self.assertGreaterEqual(diff, 0.2)
def test_max_features_reg(self):
data = self.housing
forest_1 = RandomForestRegressionLearner(random_state=0, max_features=1)
model_1 = forest_1(data[2:])
forest_2 = RandomForestRegressionLearner(random_state=0, max_features=1.)
model_2 = forest_2(data[2:])
self.assertNotEqual(model_1(data[:2]).tolist(),
model_2(data[:2]).tolist())
def test_supports_weights(self):
self.assertTrue(RandomForestRegressionLearner().supports_weights)
self.assertTrue(RandomForestLearner().supports_weights)
if __name__ == "__main__":
unittest.main()
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