def test_patchmcbm_loglikelihood(self): xmask = ones([2, 2], dtype='bool') ymask = zeros([2, 2], dtype='bool') xmask[-1, -1] = False ymask[-1, -1] = True model = PatchMCBM(2, 2, xmask, ymask) samples = model.sample(10000) self.assertFalse(isnan(mean(model.loglikelihood(samples)))) model = PatchMCBM(2, 2, xmask, ymask, max_pcs=2) model.initialize(samples) logLik = mean(model.loglikelihood(samples)) preconditioners = model.preconditioners model.preconditioners = preconditioners self.assertAlmostEqual(mean(model.loglikelihood(samples)), logLik) samples = model.sample(100)
def test_patchmcbm_loglikelihood(self):
xmask = ones([2, 2], dtype="bool")
ymask = zeros([2, 2], dtype="bool")
xmask[-1, -1] = False
ymask[-1, -1] = True
model = PatchMCBM(2, 2, xmask, ymask)
samples = model.sample(10000)
self.assertFalse(isnan(mean(model.loglikelihood(samples))))
model = PatchMCBM(2, 2, xmask, ymask, max_pcs=2)
model.initialize(samples)
logLik = mean(model.loglikelihood(samples))
preconditioners = model.preconditioners
model.preconditioners = preconditioners
self.assertAlmostEqual(mean(model.loglikelihood(samples)), logLik)
samples = model.sample(100)
def test_patchmcbm_pickle(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model0 = PatchMCBM(2, 2, xmask, ymask)
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)
self.assertLess(max(abs(model0[0, 0].priors - model1[0, 0].priors)), 1e-20)
self.assertLess(max(abs(model0[1, 0].weights - model1[1, 0].weights)), 1e-20)
self.assertLess(max(abs(model0[1, 0].features - model1[1, 0].features)), 1e-20)
self.assertLess(max(abs(model0[0, 1].predictors - model1[0, 1].predictors)), 1e-20)
self.assertLess(max(abs(model0[1, 1].input_bias - model1[1, 1].input_bias)), 1e-20)
self.assertLess(max(abs(model0[1, 1].output_bias - model1[1, 1].output_bias)), 1e-20)
xmask = ones([10, 10], dtype='bool')
ymask = zeros([10, 10], dtype='bool')
xmask[5, 5] = False
ymask[5, 5] = True
model0 = PatchMCBM(4, 5, xmask, ymask, max_pcs=3,
order=[(i // 5, i % 5) for i in permutation(20)])
model0.initialize(randn(20, 100))
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)
self.assertLess(max(abs(asarray(model1.order) - asarray(model0.order))), 1e-20)
self.assertLess(max(abs(model0.input_mask() - model1.input_mask())), 1e-20)
self.assertLess(max(abs(model0.output_mask() - model1.output_mask())), 1e-20)
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].priors - model1[i, j].priors)), 1e-20)
self.assertLess(max(abs(model0[i, j].weights - model1[i, j].weights)), 1e-20)
self.assertLess(max(abs(model0[i, j].features - model1[i, j].features)), 1e-20)
self.assertLess(max(abs(model0[i, j].predictors - model1[i, j].predictors)), 1e-20)
self.assertLess(max(abs(model0[i, j].input_bias - model1[i, j].input_bias)), 1e-20)
self.assertLess(max(abs(model0[i, j].output_bias - model1[i, j].output_bias)), 1e-20)
model0.initialize(samples)
logLik = mean(model0.loglikelihood(samples))
# 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']
self.assertAlmostEqual(mean(model1.loglikelihood(samples)), logLik)
rows, cols = 7, 5
order = [(i // cols, i % cols) for i in permutation(rows * cols)]
model0 = PatchMCBM(rows, cols, xmask, ymask, order)
# 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']
for i in range(rows):
for j in range(cols):
self.assertEqual(model1.input_mask(i, j).sum(), model1[i, j].dim_in)
def test_patchmcbm_pickle(self):
xmask = ones([2, 2], dtype="bool")
ymask = zeros([2, 2], dtype="bool")
xmask[-1, -1] = False
ymask[-1, -1] = True
model0 = PatchMCBM(2, 2, xmask, ymask)
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)
self.assertLess(max(abs(model0[0, 0].priors - model1[0, 0].priors)), 1e-20)
self.assertLess(max(abs(model0[1, 0].weights - model1[1, 0].weights)), 1e-20)
self.assertLess(max(abs(model0[1, 0].features - model1[1, 0].features)), 1e-20)
self.assertLess(max(abs(model0[0, 1].predictors - model1[0, 1].predictors)), 1e-20)
self.assertLess(max(abs(model0[1, 1].input_bias - model1[1, 1].input_bias)), 1e-20)
self.assertLess(max(abs(model0[1, 1].output_bias - model1[1, 1].output_bias)), 1e-20)
xmask = ones([10, 10], dtype="bool")
ymask = zeros([10, 10], dtype="bool")
xmask[5, 5] = False
ymask[5, 5] = True
model0 = PatchMCBM(4, 5, xmask, ymask, max_pcs=3, order=[(i // 5, i % 5) for i in permutation(20)])
model0.initialize(randn(20, 100))
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)
self.assertLess(max(abs(asarray(model1.order) - asarray(model0.order))), 1e-20)
self.assertLess(max(abs(model0.input_mask() - model1.input_mask())), 1e-20)
self.assertLess(max(abs(model0.output_mask() - model1.output_mask())), 1e-20)
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].priors - model1[i, j].priors)), 1e-20)
self.assertLess(max(abs(model0[i, j].weights - model1[i, j].weights)), 1e-20)
self.assertLess(max(abs(model0[i, j].features - model1[i, j].features)), 1e-20)
self.assertLess(max(abs(model0[i, j].predictors - model1[i, j].predictors)), 1e-20)
self.assertLess(max(abs(model0[i, j].input_bias - model1[i, j].input_bias)), 1e-20)
self.assertLess(max(abs(model0[i, j].output_bias - model1[i, j].output_bias)), 1e-20)
model0.initialize(samples)
logLik = mean(model0.loglikelihood(samples))
# 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"]
self.assertAlmostEqual(mean(model1.loglikelihood(samples)), logLik)
rows, cols = 7, 5
order = [(i // cols, i % cols) for i in permutation(rows * cols)]
model0 = PatchMCBM(rows, cols, xmask, ymask, order)
# 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"]
for i in range(rows):
for j in range(cols):
self.assertEqual(model1.input_mask(i, j).sum(), model1[i, j].dim_in)
