Esempio n. 1
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 def test_basic_cv(self):
     c = self.make_config()
     n_folds = random.randint(2,10)
     repeat = 2
     scores = models.cv(self.ds, c, n_folds, repeat)
     self.assertAlmostEqual(scores.mean(), 0)
     self.assertEqual(len(scores), n_folds*repeat)
Esempio n. 2
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 def test_basic_cv_bad_valid(self):
     # shouldnt matter
     c = self.make_config(target='2a_plus_b_valid_diff')
     n_folds = random.randint(2,10)
     repeat = 2
     scores = models.cv(self.ds, c, n_folds, repeat)
     self.assertAlmostEqual(scores.mean(), 0)
     self.assertEqual(len(scores), n_folds*repeat)
Esempio n. 3
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 def test_basic_cv_no_relationship(self):
     c = self.make_config(features=[
         'c'
         ])
     n_folds = random.randint(2,10)
     repeat = 2
     scores = models.cv(self.ds, c, n_folds, repeat)
     self.assertTrue(3 < scores.mean() < 6)
     self.assertEqual(len(scores), n_folds*repeat)
Esempio n. 4
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 def test_blending(self):
     pcnf = Configuration(
         target='b',
         features=[
             '2a_plus_b_valid_diff',
             'a'],
         model= TestPredictor(linear_model.LinearRegression())
     )
     c = self.make_config(
             target='b',
             features=[
                 Predictions(pcnf)
                 ])
     scores = models.cv(self.ds, c)
     self.assertEqual(pcnf.model.n_fit, 5)
     print scores
     self.assertTrue( 3 < scores.mean() < 5)