def test_floats_equal(self):
l = functions.get_sublist_greater_than([2.2, 0.1, 1.1], 1.1)
self.assertEqual(l, [2.2])
def flat_result_iter(self, observed_index, prior_index,
include_tau_exclusion_info = False):
glm_failures = []
for sim_idx, result in enumerate(self.result_iter(observed_index,
prior_index)):
r = self.result_to_flat_dict(result)
if include_tau_exclusion_info:
div_times = sorted([r['PRI.t.' + str(i)] for i in range(1,
self.num_taxon_pairs + 1)])
model_index = r['model_mode']
try:
model_index_glm = int(round(r['model_mode_glm']))
except ValueError:
model_index_glm = model_index
glm_failures.append(sim_idx)
tau_max = self.prior_configs[model_index].tau.maximum
tau_max_glm = self.prior_configs[model_index_glm].tau.maximum
prob_of_exclusion = 0.0
prob_of_exclusion_glm = 0.0
prior_prob_of_exclusion = 0.0
model_prior = float(1) / len(self.prior_configs.keys())
bf_tau_max = []
for i in self.prior_configs.iterkeys():
if max(div_times) > self.prior_configs[i].tau.maximum:
prob_of_exclusion += r['model_{0}_prob'.format(i)]
prob_of_exclusion_glm += r['model_{0}_prob_glm'.format(i)]
prior_prob_of_exclusion += model_prior
bf_tau_max.append(self.prior_configs[i].tau.maximum)
if len(bf_tau_max) < 1:
bf_tau_max = float('inf')
else:
bf_tau_max = max(bf_tau_max)
ex = functions.get_sublist_greater_than(div_times, tau_max)
ex_glm = functions.get_sublist_greater_than(div_times,
tau_max_glm)
prior_odds = (prior_prob_of_exclusion /
(1 - prior_prob_of_exclusion))
post_odds = (prob_of_exclusion / (1 - prob_of_exclusion))
post_odds_glm = (prob_of_exclusion_glm /
(1 - prob_of_exclusion_glm))
if probability.almost_equal(prior_odds, 0.0):
bf_of_exclusion = float('inf')
bf_of_exclusion_glm = float('inf')
if probability.almost_equal(bf_of_exclusion,
0.0):
bf_of_exclusion = 0.0
if probability.almost_equal(
bf_of_exclusion_glm, 0.0):
bf_of_exclusion_glm = 0.0
else:
bf_of_exclusion = post_odds / prior_odds
bf_of_exclusion_glm = post_odds_glm / prior_odds
bf_ex = []
bf_ex_glm = []
if bf_of_exclusion > 1.0:
bf_ex = functions.get_sublist_greater_than(div_times,
bf_tau_max)
if bf_of_exclusion_glm > 1.0:
bf_ex_glm = functions.get_sublist_greater_than(div_times,
bf_tau_max)
r['prob_of_exclusion'] = prob_of_exclusion
r['prob_of_exclusion_glm'] = prob_of_exclusion_glm
r['prior_prob_of_exclusion'] = prior_prob_of_exclusion
r['bf_of_exclusion'] = bf_of_exclusion
r['bf_of_exclusion_glm'] = bf_of_exclusion_glm
r['tau_max'] = tau_max
r['tau_max_glm'] = tau_max_glm
r['num_excluded'] = len(ex)
r['num_excluded_glm'] = len(ex_glm)
r['bf_num_excluded'] = len(bf_ex)
r['bf_num_excluded_glm'] = len(bf_ex_glm)
yield r
if len(glm_failures) > 0:
_LOG.warning('WARNING: there were GLM-regression failures:\n'
'For observed index {0} prior index {1}, there were '
'failures at the following simulation indices:\n'
'{2}'.format(observed_index, prior_index, glm_failures))
def flat_result_iter(self,
observed_index,
prior_index,
include_tau_exclusion_info=False):
glm_failures = []
for sim_idx, result in enumerate(
self.result_iter(observed_index, prior_index)):
r = self.result_to_flat_dict(result)
if include_tau_exclusion_info:
div_times = sorted([
r['PRI.t.' + str(i)]
for i in range(1, self.num_taxon_pairs + 1)
])
model_index = r['model_mode']
try:
model_index_glm = int(round(r['model_mode_glm']))
except ValueError:
model_index_glm = model_index
glm_failures.append(sim_idx)
tau_max = self.prior_configs[model_index].tau.maximum
tau_max_glm = self.prior_configs[model_index_glm].tau.maximum
prob_of_exclusion = 0.0
prob_of_exclusion_glm = 0.0
prior_prob_of_exclusion = 0.0
model_prior = float(1) / len(self.prior_configs.keys())
bf_tau_max = []
for i in self.prior_configs.iterkeys():
if max(div_times) > self.prior_configs[i].tau.maximum:
prob_of_exclusion += r['model_{0}_prob'.format(i)]
prob_of_exclusion_glm += r['model_{0}_prob_glm'.format(
i)]
prior_prob_of_exclusion += model_prior
bf_tau_max.append(self.prior_configs[i].tau.maximum)
if len(bf_tau_max) < 1:
bf_tau_max = float('inf')
else:
bf_tau_max = max(bf_tau_max)
ex = functions.get_sublist_greater_than(div_times, tau_max)
ex_glm = functions.get_sublist_greater_than(
div_times, tau_max_glm)
prior_odds = (prior_prob_of_exclusion /
(1 - prior_prob_of_exclusion))
post_odds = (prob_of_exclusion / (1 - prob_of_exclusion))
post_odds_glm = (prob_of_exclusion_glm /
(1 - prob_of_exclusion_glm))
if probability.almost_equal(prior_odds, 0.0):
bf_of_exclusion = float('inf')
bf_of_exclusion_glm = float('inf')
if probability.almost_equal(bf_of_exclusion, 0.0):
bf_of_exclusion = 0.0
if probability.almost_equal(bf_of_exclusion_glm, 0.0):
bf_of_exclusion_glm = 0.0
else:
bf_of_exclusion = post_odds / prior_odds
bf_of_exclusion_glm = post_odds_glm / prior_odds
bf_ex = []
bf_ex_glm = []
if bf_of_exclusion > 1.0:
bf_ex = functions.get_sublist_greater_than(
div_times, bf_tau_max)
if bf_of_exclusion_glm > 1.0:
bf_ex_glm = functions.get_sublist_greater_than(
div_times, bf_tau_max)
r['prob_of_exclusion'] = prob_of_exclusion
r['prob_of_exclusion_glm'] = prob_of_exclusion_glm
r['prior_prob_of_exclusion'] = prior_prob_of_exclusion
r['bf_of_exclusion'] = bf_of_exclusion
r['bf_of_exclusion_glm'] = bf_of_exclusion_glm
r['tau_max'] = tau_max
r['tau_max_glm'] = tau_max_glm
r['num_excluded'] = len(ex)
r['num_excluded_glm'] = len(ex_glm)
r['bf_num_excluded'] = len(bf_ex)
r['bf_num_excluded_glm'] = len(bf_ex_glm)
yield r
if len(glm_failures) > 0:
_LOG.warning('WARNING: there were GLM-regression failures:\n'
'For observed index {0} prior index {1}, there were '
'failures at the following simulation indices:\n'
'{2}'.format(observed_index, prior_index,
glm_failures))
def test_floats_equal(self):
l = functions.get_sublist_greater_than([2.2, 0.1, 1.1], 1.1)
self.assertEqual(l, [2.2])
