def sample_posterior(self):
self._survivalSamples = np.random.uniform(low=RunCalib.POST_L,
high=RunCalib.POST_H,
size=RunCalib.COHORT_SIZE)
multiCohort = Cls.MultiCohort(ids=self._cohortIDs,
mortality_probs=self._survivalSamples,
pop_sizes=[RunCalib.SIMULATION_SIZE] *
RunCalib.COHORT_SIZE)
multiCohort.simulate(RunCalib.TIME_STEPS)
for cohort_id in self._cohortIDs:
mean = MultiCohort.get_overall_mean_survival(cohort_id)
weight = binom.pmf(k=RunCalib.K,
n=RunCalib.SAMPLE_SIZE,
p=RunCalib.SURVIVAL_PROB)
self._weights.append(weight)
sum_weights = np.sum(self._weights)
self._normalizedWeights = np.divide(self._weights, sum_weights)
self._survivalResamples = np.random.choice(
a=self._survivalSamples,
size=RunCalib.SIMULATION_SIZE,
replace=True,
p=self._normalizedWeights)
for i in range(0, len(self._survivalSamples)):
self._csvRows.append([
self._cohortIDs[i], self._normalizedWeights[i],
self._survivalSamples[i]
])
InOutSupport.write_csv('CalibrationResult.csv', self._csvRows)
def sample_posterior(self):
""" sample the posterior distribution of the mortality probability """
# find values of mortality probability at which the posterior should be evaluated
# HR: this is a prior p, since we just simulate the parameter without having any observations
self._mortalitySamples = np.random.uniform(
low=CalibSets.POST_L,
high=CalibSets.POST_U,
size=CalibSets.POST_N)
# create a multi cohort
multiCohort = SurvivalCls.MultiCohort(
ids=self._cohortIDs,
mortality_probs=self._mortalitySamples,
pop_sizes=[CalibSets.SIM_POP_SIZE]*CalibSets.POST_N
)
# simulate the multi cohort
multiCohort.simulate(CalibSets.TIME_STEPS)
# calculate the likelihood of each simulated cohort
for cohort_id in self._cohortIDs:
# get the survival percentage for this cohort
survivalrate= multiCohort.get_cohort_survival_percentages(cohort_id)
# construct a gaussian likelihood
# with mean calculated from the simulated data and standard deviation from the clinical study.
# evaluate this pdf (probability density function) at the mean reported in the clinical study.
weight = stat.binom.pmf(
k=CalibSets.OBS_SUR,
p=survivalrate*0.01,
n=CalibSets.OBS_N
)
# 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=CalibSets.NUM_SIM_COHORTS,
# HR: size is the number of resampled cohorts we would get eventually, this is not the number of cohorts we simulate first time
replace=True,
# HR: values with higher weights get sampled more often
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)
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=CalibSets.POST_L,
high=CalibSets.POST_U,
size=CalibSets.POST_N)
# create a multi cohort
multiCohort = SurvivalCls.MultiCohort(
ids=self._cohortIDs,
mortality_probs=self._mortalitySamples,
pop_sizes=[CalibSets.SIM_POP_SIZE]*CalibSets.POST_N
)
# simulate the multi cohort
multiCohort.simulate(CalibSets.TIME_STEPS)
# calculate the likelihood of each simulated cohort
for cohort_id in self._cohortIDs:
# get the average survival time for this cohort
mean = multiCohort.get_cohort_mean_survival(cohort_id)
percent = multiCohort.get_percents_5yr(cohort_id)
self._5yr_survival.append(percent)
# construct a gaussian likelihood
# with mean calculated from the simulated data and standard deviation from the clinical study.
# evaluate this pdf (probability density function) at the mean reported in the clinical study.
weight = stat.binom.pmf(
k=CalibSets.OBS_K,
n=CalibSets.OBS_n,
p=percent)
# 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=CalibSets.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)
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=CalibrationSettings.POST_L,
high=CalibrationSettings.POST_U,
size=CalibrationSettings.POST_N)
# create a multi cohort
multiCohort = SurvivalCls.MultiCohort(
ids=self._cohortIDs,
mortality_probs=self._mortalitySamples,
pop_sizes=[CalibrationSettings.SIM_POP_SIZE] *
CalibrationSettings.POST_N)
# simulate the multi cohort
multiCohort.simulate(CalibrationSettings.TIME_STEPS)
# calculate the likelihood of each simulated cohort
for cohort_id in self._cohortIDs:
# get the average survival time for this cohort
proportion = multiCohort.get_5yr_survtime(
cohort_id) ## modify to get 5 year survival
weight = stat.binom.pmf(k=CalibrationSettings.OBS_SURV,
n=CalibrationSettings.OBS_N,
p=proportion,
loc=0)
# 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=CalibrationSettings.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)
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)
from scr import InOutFunctions as OutSupport
myList = [['Col1', 'Col2', 'Col3']]
for i in range(1, 10):
myList.append([i, 2 * i, 3 * i])
OutSupport.write_csv('myCSV', myList)
