def test_training(self):
a= 0
# Create a new learner, but use the same data for each run
lr = LogisticRegression(self.X_train, self.Y_train, alpha=a)
training_reconstruction = [lr.predict(x) for x in self.X_train]
residual = sum([ x-y for x,y in zip(training_reconstruction,self.Y_train)])
self.assertTrue(residual<1e-5)
def test_training(self):
a = 0
# Create a new learner, but use the same data for each run
lr = LogisticRegression(self.X_train, self.Y_train, alpha=a)
training_reconstruction = [lr.predict(x) for x in self.X_train]
residual = sum(
[x - y for x, y in zip(training_reconstruction, self.Y_train)])
self.assertTrue(residual < 1e-5)
def test_multi_surrogate_models(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.surrogate = {
'd': KrigingSurrogate(),
'c': LogisticRegression()
}
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
metamodel.a = simple.a = 3.
metamodel.b = simple.b = 4.
metamodel.train_next = True
simple.run()
metamodel.run()
self.assertTrue(isinstance(metamodel.d, NormalDistribution))
self.assertTrue(isinstance(metamodel.c, float))
def test_default_surrogate_change(self):
metamodel = MetaModel()
mmins = set(metamodel.list_inputs())
mmouts = set(metamodel.list_outputs())
metamodel.default_surrogate = KrigingSurrogate()
metamodel.model = Simple()
metamodel.default_surrogate = LogisticRegression()
attrs = metamodel.get_attributes(io_only=False)
for s in attrs['Slots']:
self.assertNotEqual(s['name'], 'c')
self.assertNotEqual(s['name'], 'd')
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['a', 'b']))
self.assertEquals(outputs - mmouts, set(['c', 'd']))
for i in range(3):
metamodel.train_next = True
metamodel.run()
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue(len(metamodel._training_data['d']) == 3)
self.assertTrue(len(metamodel._training_input_history) == 3)
metamodel.includes = ['a', 'b', 'c', 'd']
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue(len(metamodel._training_data['d']) == 3)
self.assertTrue(len(metamodel._training_input_history) == 3)
# removing an output should not clobber the rest of the training data
metamodel.includes = ['a', 'b', 'c']
self.assertTrue(len(metamodel._training_data['c']) == 3)
self.assertTrue('d' not in metamodel._training_data)
self.assertTrue(len(metamodel._training_input_history) == 3)
# now put a different model in with the same inputs/outputs
metamodel.model = SimpleMatch()
metamodel.includes = ['a', 'b', 'c', 'd']
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['a', 'b']))
self.assertEquals(outputs - mmouts, set(['c', 'd']))
self.assertTrue(len(metamodel._training_data['c']) == 0)
self.assertTrue(len(metamodel._training_data['d']) == 0)
self.assertTrue(len(metamodel._training_input_history) == 0)
# now put a different model in
metamodel.model = Simple2()
metamodel.includes = ['w', 'x', 'y', 'z']
inputs = set(metamodel.list_inputs())
outputs = set(metamodel.list_outputs())
self.assertEquals(inputs - mmins, set(['w', 'x']))
self.assertEquals(outputs - mmouts, set(['y', 'z']))
def test_uncertain_value(self):
lr = LogisticRegression(self.X_train, self.Y_train)
self.assertEqual(lr.get_uncertain_value(1.0),1.0)
def test_uncertain_value(self):
lr = LogisticRegression()
self.assertEqual(lr.get_uncertain_value(1.0), 1.0)
