def test_train_mp(self): isa = ISA(5, 10) params = isa.default_parameters() params['training_method'] = 'MP' params['mp']['num_coeff'] = 4 samples = isa.sample(100) states = isa.matching_pursuit(samples, params) # simple sanity checks self.assertEqual(states.shape[1], 100) self.assertEqual(states.shape[0], 10) self.assertFalse(any(sum(states > 0., 0) > 4)) # make sure training with MP doesn't throw any errors isa.train(isa.sample(1011), params)
def test_train_mp(self):
isa = ISA(5, 10)
params = isa.default_parameters()
params['training_method'] = 'MP'
params['mp']['num_coeff'] = 4
samples = isa.sample(100)
states = isa.matching_pursuit(samples, params)
# simple sanity checks
self.assertEqual(states.shape[1], 100)
self.assertEqual(states.shape[0], 10)
self.assertFalse(any(sum(states > 0., 0) > 4))
# make sure training with MP doesn't throw any errors
isa.train(isa.sample(1011), params)
def test_callback(self):
isa = ISA(2)
# callback function
def callback(i, isa_):
callback.count += 1
self.assertTrue(isa == isa_)
callback.count = 0
# set callback function
parameters = {
'verbosity': 0,
'max_iter': 7,
'callback': callback,
'sgd': {
'max_iter': 0
}
}
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, parameters['max_iter'] + 1)
def callback(i, isa_):
if i == 5:
return False
callback.count += 1
callback.count = 0
parameters['callback'] = callback
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, 5)
# make sure referece counts stay stable
self.assertEqual(sys.getrefcount(isa) - 1, 1)
self.assertEqual(sys.getrefcount(callback) - 1, 2)
def test_train_lbfgs(self): isa = ISA(2) isa.initialize() isa.A = eye(2) samples = isa.sample(10000) # initialize close to original parameters isa.A = asarray([[cos(0.4), sin(0.4)], [-sin(0.4), cos(0.4)]]) params = isa.default_parameters() params['training_method'] = 'LBFGS' params['train_prior'] = False params['max_iter'] = 1 params['lbfgs']['max_iter'] = 50 isa.train(samples, params) # L-BFGS should be able to recover the parameters self.assertLess(sqrt(sum(square(isa.A.flatten() - eye(2).flatten()))), 0.1)
def test_merge(self): isa1 = ISA(5, ssize=2) isa2 = ISA(5) isa1.initialize() isa1.orthogonalize() isa2.initialize() isa2.A = isa1.A params = isa2.default_parameters() params['train_basis'] = False params['merge_subspaces'] = True params['merge']['verbosity'] = 0 isa2.train(isa1.sample(10000), params) ssizes1 = [gsm.dim for gsm in isa1.subspaces()] ssizes2 = [gsm.dim for gsm in isa2.subspaces()] # algorithm should be able to recover subspace sizes self.assertTrue(all(sort(ssizes1) == sort(ssizes2)))
def test_merge(self):
isa1 = ISA(5, ssize=2)
isa2 = ISA(5)
isa1.initialize()
isa1.orthogonalize()
isa2.initialize()
isa2.A = isa1.A
params = isa2.default_parameters()
params['train_basis'] = False
params['merge_subspaces'] = True
params['merge']['verbosity'] = 0
isa2.train(isa1.sample(10000), params)
ssizes1 = [gsm.dim for gsm in isa1.subspaces()]
ssizes2 = [gsm.dim for gsm in isa2.subspaces()]
# algorithm should be able to recover subspace sizes
self.assertTrue(all(sort(ssizes1) == sort(ssizes2)))
def test_train_lbfgs(self):
isa = ISA(2)
isa.initialize()
isa.A = eye(2)
samples = isa.sample(10000)
# initialize close to original parameters
isa.A = asarray([[cos(0.4), sin(0.4)], [-sin(0.4), cos(0.4)]])
params = isa.default_parameters()
params['training_method'] = 'LBFGS'
params['train_prior'] = False
params['max_iter'] = 1
params['lbfgs']['max_iter'] = 50
isa.train(samples, params)
# L-BFGS should be able to recover the parameters
self.assertLess(sqrt(sum(square(isa.A.flatten() - eye(2).flatten()))),
0.1)
def test_callback(self):
isa = ISA(2)
# callback function
def callback(i, isa_):
callback.count += 1
self.assertTrue(isa == isa_)
callback.count = 0
# set callback function
parameters = {
'verbosity': 0,
'max_iter': 7,
'callback': callback,
'sgd': {'max_iter': 0}
}
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, parameters['max_iter'] + 1)
def callback(i, isa_):
if i == 5:
return False
callback.count += 1
callback.count = 0
parameters['callback'] = callback
isa.train(randn(2, 1000), parameters=parameters)
# test how often callback function was called
self.assertEqual(callback.count, 5)
# make sure referece counts stay stable
self.assertEqual(sys.getrefcount(isa) - 1, 1)
self.assertEqual(sys.getrefcount(callback) - 1, 2)
def test_train(self): # make sure train() doesn't throw any errors isa = ISA(2) params = isa.default_parameters() params['verbosity'] = 0 params['max_iter'] = 2 params['training_method'] = 'SGD' params['sgd']['max_iter'] = 1 params['sgd']['batch_size'] = 57 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params) isa = ISA(4, ssize=2) isa.initialize(randn(4, 1000)) isa.train(randn(4, 1000), params) isa = ISA(2, 3) params['gibbs']['ini_iter'] = 2 params['gibbs']['num_iter'] = 2 params['verbosity'] = 0 params['gibbs']['verbosity'] = 0 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params)
def test_train(self):
# make sure train() doesn't throw any errors
isa = ISA(2)
params = isa.default_parameters()
params['verbosity'] = 0
params['max_iter'] = 2
params['training_method'] = 'SGD'
params['sgd']['max_iter'] = 1
params['sgd']['batch_size'] = 57
isa.initialize(randn(2, 1000))
isa.train(randn(2, 1000), params)
isa = ISA(4, ssize=2)
isa.initialize(randn(4, 1000))
isa.train(randn(4, 1000), params)
isa = ISA(2, 3)
params['gibbs']['ini_iter'] = 2
params['gibbs']['num_iter'] = 2
params['verbosity'] = 0
params['gibbs']['verbosity'] = 0
isa.initialize(randn(2, 1000))
isa.train(randn(2, 1000), params)