def test_pickle(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
order = [(i // 2, i % 2) for i in permutation(4)]
model0 = FVBN(2, 2, xmask, ymask, order, GLM(sum(xmask), LogisticFunction, Bernoulli))
samples = model0.sample(1000)
tmp_file = mkstemp()[1]
# store model
with open(tmp_file, 'w') as handle:
dump({'model': model0}, handle)
# load model
with open(tmp_file) as handle:
model1 = load(handle)['model']
# make sure parameters haven't changed
self.assertEqual(model0.rows, model1.rows)
self.assertEqual(model0.cols, model1.cols)
for i in range(model0.rows):
for j in range(model0.cols):
if model0[i, j].dim_in > 0:
self.assertLess(max(abs(model0[i, j].weights - model1[i, j].weights)), 1e-8)
self.assertLess(max(abs(model0[i, j].bias - model1[i, j].bias)), 1e-8)
self.assertAlmostEqual(
mean(model0.loglikelihood(samples)),
mean(model1.loglikelihood(samples)))
def test_fvbn(self):
xmask = ones([8, 8], dtype='bool')
ymask = zeros([8, 8], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model = FVBN(8, 8, xmask, ymask)
self.assertLess(max(abs(model.input_mask() - xmask)), 1e-8)
self.assertLess(max(abs(model.output_mask() - ymask)), 1e-8)
for i in range(8):
for j in range(8):
self.assertEqual(model[i, j].dim_in, (i + 1) * (j + 1) - 1)
self.assertTrue(isinstance(model[i, j], GLM))
def test_fvbn(self): xmask = ones([8, 8], dtype='bool') ymask = zeros([8, 8], dtype='bool') xmask[-1, -1] = False ymask[-1, -1] = True model = FVBN(8, 8, xmask, ymask) self.assertLess(max(abs(model.input_mask() - xmask)), 1e-8) self.assertLess(max(abs(model.output_mask() - ymask)), 1e-8) for i in range(8): for j in range(8): self.assertEqual(model[i, j].dim_in, (i + 1) * (j + 1) - 1) self.assertTrue(isinstance(model[i, j], GLM))
def test_fvbn_train(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model = FVBN(2,
2,
xmask,
ymask,
model=GLM(sum(xmask), LogisticFunction, Bernoulli))
# checkerboard
data = array([[0, 1], [1, 0]], dtype='bool').reshape(-1, 1)
data = tile(data, (1, 1000))
logloss = model.evaluate(data)
model.initialize(data, parameters={'max_iter': 100})
# training should converge in much less than 2000 iterations
self.assertTrue(model.train(data, parameters={'max_iter': 2000}))
# negative log-likelihood should have decreased
self.assertLess(model.evaluate(data), logloss)
def test_pickle(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
order = [(i // 2, i % 2) for i in permutation(4)]
model0 = FVBN(2, 2, xmask, ymask, order,
GLM(sum(xmask), LogisticFunction, Bernoulli))
samples = model0.sample(1000)
tmp_file = mkstemp()[1]
# store model
with open(tmp_file, 'w') as handle:
dump({'model': model0}, handle)
# load model
with open(tmp_file) as handle:
model1 = load(handle)['model']
# make sure parameters haven't changed
self.assertEqual(model0.rows, model1.rows)
self.assertEqual(model0.cols, model1.cols)
for i in range(model0.rows):
for j in range(model0.cols):
if model0[i, j].dim_in > 0:
self.assertLess(
max(abs(model0[i, j].weights - model1[i, j].weights)),
1e-8)
self.assertLess(
max(abs(model0[i, j].bias - model1[i, j].bias)), 1e-8)
self.assertAlmostEqual(mean(model0.loglikelihood(samples)),
mean(model1.loglikelihood(samples)))
def test_fvbn_train(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model = FVBN(2, 2, xmask, ymask, model=GLM(sum(xmask), LogisticFunction, Bernoulli))
# checkerboard
data = array([[0, 1], [1, 0]], dtype='bool').reshape(-1, 1)
data = tile(data, (1, 1000))
logloss = model.evaluate(data)
model.initialize(data, parameters={'max_iter': 100})
# training should converge in much less than 2000 iterations
self.assertTrue(model.train(data, parameters={'max_iter': 2000}))
# negative log-likelihood should have decreased
self.assertLess(model.evaluate(data), logloss)