def test_learn_h_graph(self):
experiment = load_test_experiment()
emission = likelihood.NegBinBetaBinLikelihood()
emission.h = experiment.h
emission.learn_parameters(experiment.x, experiment.l)
N = experiment.l.shape[0]
M = experiment.h.shape[0]
prior = cn_model.CopyNumberPrior(N, M, perfect_cn_prior(experiment.cn))
prior.set_lengths(experiment.l)
model = cn_model.HiddenMarkovModel(N, M, emission, prior)
_, cn_init = model.optimal_state()
graph = genome_graph.GenomeGraph(emission, prior, experiment.adjacencies, experiment.breakpoints)
graph.init_copy_number(cn_init)
h_init = experiment.h + experiment.h * 0.05 * np.random.randn(*experiment.h.shape)
estimator = em.HardAssignmentEstimator(num_em_iter=1)
estimator.learn_param(graph, 'h', h_init)
def test_learn_h(self):
experiment = load_test_experiment()
h_init = experiment.h * (1. +
0.05 * np.random.randn(*experiment.h.shape))
emission = likelihood.NegBinBetaBinLikelihood(experiment.x,
experiment.l)
emission.h = h_init
print experiment.h, h_init
N = experiment.l.shape[0]
M = experiment.h.shape[0]
prior = cn_model.CopyNumberPrior(experiment.l)
model = cn_model.HiddenMarkovModel(N,
M,
emission,
prior,
experiment.chains,
normal_contamination=False)
estimator = em.ExpectationMaximizationEstimator()
estimator.learn_param(model, emission.h_param)
def test_evaluate_q_derivative(self):
cn, h, l, phi, r, x = generate_simple_data()
emission = likelihood.NegBinBetaBinLikelihood(x, l)
emission.h = h
N = l.shape[0]
M = h.shape[0]
prior = cn_model.CopyNumberPrior(perfect_cn_prior(cn))
prior.set_lengths(l)
model = cn_model.HiddenMarkovModel(N, M, emission, prior, [(0, N)])
log_posterior, cns, resps = model.posterior_marginals()
estimator = em.ExpectationMaximizationEstimator()
params = [emission.h_param, emission.r_param, emission.M_param, emission.z_param, emission.hdel_mu_param, emission.loh_p_param]
value = np.concatenate([p.value for p in params])
idxs = np.array([p.length for p in params]).cumsum() - params[0].length
remixt.tests.utils.assert_grad_correct(
estimator.evaluate_q, estimator.evaluate_q_derivative,
value, model, cns, resps, params, idxs)
def test_learn_phi(self):
experiment = load_test_experiment()
emission = likelihood.NegBinLikelihood()
emission.h = experiment.h
emission.phi = experiment.phi
emission.r = experiment.negbin_r
N = experiment.l.shape[0]
M = experiment.h.shape[0]
prior = cn_model.CopyNumberPrior(N, M, perfect_cn_prior(experiment.cn))
prior.set_lengths(experiment.l)
model = cn_model.HiddenMarkovModel(N, M, emission, prior)
phi_init = experiment.phi + experiment.phi * 0.02 * np.random.randn(*experiment.phi.shape)
estimator = em.ExpectationMaximizationEstimator(num_em_iter=1)
estimator.learn_param(model, 'phi', phi_init)
def test_build_cn(self):
prior = cn_model.CopyNumberPrior(1, 3, uniform_cn_prior())
model = cn_model.HiddenMarkovModel(1, 3, None, prior)
cn_max = 6
cn_dev_max = 1
model.cn_max = cn_max
model.cn_dev_max = cn_dev_max
# Build cn list using the method from CopyNumberModel
built_cns = set()
for cn in model.build_cn_states():
cn_tuple = list()
for m in xrange(3):
for ell in xrange(2):
cn_tuple.append(cn[0, m, ell])
cn_tuple = tuple(cn_tuple)
self.assertNotIn(cn_tuple, built_cns)
built_cns.add(cn_tuple)
# Build the naive way
expected_cns = set()
for b1 in xrange(cn_max + 1):
for b2 in xrange(cn_max + 1):
for d1 in xrange(-cn_dev_max, cn_dev_max + 1):
for d2 in xrange(-cn_dev_max, cn_dev_max + 1):
if b1 + d1 < 0 or b2 + d2 < 0 or b1 + d1 > cn_max or b2 + d2 > cn_max:
continue
if (b1 != b2 or b1 + d1 != b2 + d2) and (
b1 <= b2 and b1 + d1 <= b2 + d2):
continue
cn_tuple = (1, 1, b1, b2, b1 + d1, b2 + d2)
self.assertIn(cn_tuple, built_cns)
expected_cns.add(cn_tuple)
self.assertTrue(expected_cns == built_cns)
