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_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_default_execute(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.default_surrogate = KrigingSurrogate()
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
#import pdb; pdb.set_trace()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
self.assertEqual(metamodel.c.getvalue(), 3.)
self.assertEqual(metamodel.d.getvalue(), -1.)
self.assertEqual(metamodel.c.getvalue(), simple.c)
self.assertEqual(metamodel.d.getvalue(), simple.d)
def test_default_execute(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.default_surrogate = KrigingSurrogate()
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
#import pdb; pdb.set_trace()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
metamodel.a = simple.a = 1.
metamodel.b = simple.b = 2.
metamodel.train_next = True
simple.run()
metamodel.run()
self.assertEqual(metamodel.c.getvalue(), 3.)
self.assertEqual(metamodel.d.getvalue(), -1.)
self.assertEqual(metamodel.c.getvalue(), simple.c)
self.assertEqual(metamodel.d.getvalue(), simple.d)
def test_warm_start(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.default_surrogate = KrigingSurrogate()
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
cases = []
metamodel.a = 1.
metamodel.b = 2.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a', metamodel.a), ('meta2.b', metamodel.b)]
outputs = [('meta2.c', metamodel.c.mu), ('meta2.d', metamodel.d.mu)]
cases.append(Case(inputs=inputs, outputs=outputs))
metamodel.a = 3.
metamodel.b = 5.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a', metamodel.a), ('meta2.b', metamodel.b)]
outputs = [('meta2.c', metamodel.c.mu), ('meta2.d', metamodel.d.mu)]
cases.append(Case(inputs=inputs, outputs=outputs))
case_iter = ListCaseIterator(cases)
metamodel2 = MetaModel()
metamodel2.name = 'meta2'
metamodel2.default_surrogate = KrigingSurrogate()
metamodel2.model = Simple()
metamodel2.recorder = DumbRecorder()
metamodel2.warm_start_data = case_iter
metamodel2.a = simple.a = 1
metamodel2.b = simple.b = 2
metamodel.train_next = True
metamodel2.run()
simple.run()
self.assertEqual(metamodel2.c.getvalue(), 3.)
self.assertEqual(metamodel2.d.getvalue(), -1.)
self.assertEqual(metamodel2.c.getvalue(), simple.c)
self.assertEqual(metamodel2.d.getvalue(), simple.d)
def test_warm_start(self):
metamodel = MetaModel()
metamodel.name = 'meta'
metamodel.default_surrogate = KrigingSurrogate()
metamodel.model = Simple()
metamodel.recorder = DumbRecorder()
simple = Simple()
cases = []
metamodel.a = 1.
metamodel.b = 2.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a',metamodel.a),('meta2.b',metamodel.b)]
outputs = [('meta2.c',metamodel.c.mu),('meta2.d',metamodel.d.mu)]
cases.append(Case(inputs=inputs,outputs=outputs))
metamodel.a = 3.
metamodel.b = 5.
metamodel.train_next = True
metamodel.run()
inputs = [('meta2.a',metamodel.a),('meta2.b',metamodel.b)]
outputs = [('meta2.c',metamodel.c.mu),('meta2.d',metamodel.d.mu)]
cases.append(Case(inputs=inputs,outputs=outputs))
case_iter = ListCaseIterator(cases)
metamodel2 = MetaModel()
metamodel2.name = 'meta2'
metamodel2.default_surrogate = KrigingSurrogate()
metamodel2.model = Simple()
metamodel2.recorder = DumbRecorder()
metamodel2.warm_start_data = case_iter
metamodel2.a = simple.a = 1
metamodel2.b = simple.b = 2
metamodel.train_next = True
metamodel2.run()
simple.run()
self.assertEqual(metamodel2.c.getvalue(), 3.)
self.assertEqual(metamodel2.d.getvalue(), -1.)
self.assertEqual(metamodel2.c.getvalue(), simple.c)
self.assertEqual(metamodel2.d.getvalue(), simple.d)
def test_proxy(self):
meta = MetaModel()
meta.default_surrogate = KrigingSurrogate()
meta.model = SimpleProxy()
for i in range(3):
meta.train_next = True
meta.run()
self.assertEqual(len(meta._training_data['f_x']), 3)
def test_proxy(self):
meta = MetaModel()
meta.default_surrogate = KrigingSurrogate()
meta.model = SimpleProxy()
for i in range(3):
meta.train_next = True
meta.run()
self.assertEqual(len(meta._training_data['f_x']), 3)
