def simulate(self, num_simulated_cohorts, cohort_size, time_steps):
"""
:param num_simulated_cohorts: number of cohorts to simulate
:param cohort_size: population size of cohorts
:param time_steps: simulation length
:param cohort_ids: ids of cohort to simulate
:return:
"""
# resample cohort IDs and mortality probabilities based on weights
sampled_row_indices = np.random.choice(a=range(0, len(self._weights)),
size=num_simulated_cohorts,
replace=True,
p=self._weights)
# use the sampled indices to populate the list of cohort IDs and mortality probabilities
resampled_IDs = []
resampled_mortalityprobs = []
for i in sampled_row_indices:
resampled_IDs.append(self._cohortIDs[i])
resampled_mortalityprobs.append(self._mortalityProbs[i])
# simulate the desired number of cohorts
self._multiCohorts = SurvivalCls.MultiCohort(
ids=resampled_IDs,
pop_sizes=[cohort_size] * num_simulated_cohorts,
mortality_probs=resampled_mortalityprobs)
# simulate all the cohorts
self._multiCohorts.simulate(time_steps)
def sample_posterior(self):
""" sample the posterior distribution of the mortality probability """
# find values of mortality probability at which the posterior should be evaluated
self._mortalitySamples = np.random.uniform(low=POST_L,
high=POST_U,
size=POST_N)
# create a multi cohort
multiCohort = SurvivalCls.MultiCohort(
ids=self._cohortIDs,
pop_sizes=[POST_N] * POST_N,
mortality_probs=self._mortalitySamples)
# simulate the multi cohort
multiCohort.simulate(TIME_STEPS)
# calculate the likelihood of each simulated cohort
for i in self._cohortIDs:
# get the 5-year OS for this cohort
survival = multiCohort.get_cohort_FIVEyear_OS(i)
# construct weight utilizing study's k and n; and simulated five-year OS
weight = stat.binom.pmf(k=STUDY_K, n=STUDY_N, p=survival)
# store the weight
self._weights.append(weight)
# normalize the likelihood weights
sum_weights = np.sum(self._weights)
self._normalizedWeights = np.divide(self._weights, sum_weights)
# re-sample mortality probability (with replacement) according to likelihood weights
self._mortalityResamples = np.random.choice(a=self._mortalitySamples,
size=NUM_SIM_COHORTS,
replace=True,
p=self._normalizedWeights)
# produce the list to report the results
for i in range(0, len(self._mortalitySamples)):
self._csvRows.append([
self._cohortIDs[i], self._normalizedWeights[i],
self._mortalitySamples[i]
])
# write the calibration result into a csv file
InOutSupport.write_csv('CalibrationResults.csv', self._csvRows)