How to use the pymc.runiform function in pymc
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aflaxman / gbd / tests / validate_age_group.py View on Github 
def simulate_age_group_data(N=50, delta_true=150, pi_true=true_rate_function):
""" generate simulated data
"""
# start with a simple model with N rows of data
model = data_simulation.simple_model(N)
# record the true age-specific rates
model.ages = pl.arange(0, 101, 1)
model.pi_age_true = pi_true(model.ages)
# choose age groups randomly
age_width = mc.runiform(1, 100, size=N)
age_mid = mc.runiform(age_width/2, 100-age_width/2, size=N)
age_width[:10] = 10
age_mid[:10] = pl.arange(5, 105, 10)
#age_width[10:20] = 10
#age_mid[10:20] = pl.arange(5, 105, 10)
age_start = pl.array(age_mid - age_width/2, dtype=int)
age_end = pl.array(age_mid + age_width/2, dtype=int)
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
# choose effective sample size uniformly at random
n = mc.runiform(100, 10000, size=N)
model.input_data['effective_sample_size'] = ndef validate_age_pattern_model_sim(N=500, delta_true=.15, pi_true=quadratic):
## generate simulated data
a = pl.arange(0, 101, 1)
pi_age_true = pi_true(a)
model = data_simulation.simple_model(N)
model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
age_list = pl.array(mc.runiform(0, 100, size=N), dtype=int)
p = pi_age_true[age_list]
n = mc.runiform(100, 10000, size=N)
model.input_data['age_start'] = age_list
model.input_data['age_end'] = age_list
model.input_data['effective_sample_size'] = n
model.input_data['true'] = p
model.input_data['value'] = mc.rnegative_binomial(n*p, delta_true*n*p) / n
## Then fit the model and compare the estimates to the truth
model.vars = {}
model.vars['p'] = data_model.data_model('p', model, 'p', 'all', 'total', 'all', None, None, None)
model.map, model.mcmc = fit_model.fit_data_model(model.vars['p'], iter=10000, burn=5000, thin=25, tune_interval=100)
graphics.plot_one_ppc(model.vars['p'], 'p')
graphics.plot_convergence_diag(model.vars)def generate_data(N, delta_true, pi_true, heterogeneity, bias, sigma_prior):
a = pl.arange(0, 101, 1)
pi_age_true = pi_true(a)
model = data_simulation.simple_model(N)
model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
model.parameters['p']['smoothness'] = dict(amount='Moderately')
model.parameters['p']['heterogeneity'] = heterogeneity
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
age_weights = pl.ones_like(a)
sum_pi_wt = pl.cumsum(pi_age_true*age_weights)
sum_wt = pl.cumsum(age_weights)
p = (sum_pi_wt[age_end] - sum_pi_wt[age_start]) / (sum_wt[age_end] - sum_wt[age_start])
# correct cases where age_start == age_end
i = age_start == age_end
if pl.any(i):
p[i] = pi_age_true[age_start[i]]
n = mc.runiform(10000, 100000, size=N)
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
model.input_data['effective_sample_size'] = npi_age_true = pi_true(a)
import dismod3
import simplejson as json
model = data.ModelData.from_gbd_jsons(json.loads(dismod3.disease_json.DiseaseJson().to_json()))
gbd_hierarchy = model.hierarchy
model = data_simulation.simple_model(N)
model.hierarchy = gbd_hierarchy
model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
model.parameters['p']['smoothness'] = dict(amount=smoothness)
model.parameters['p']['heterogeneity'] = heterogeneity
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
age_weights = pl.ones_like(a)
sum_pi_wt = pl.cumsum(pi_age_true*age_weights)
sum_wt = pl.cumsum(age_weights*1.)
p = (sum_pi_wt[age_end] - sum_pi_wt[age_start]) / (sum_wt[age_end] - sum_wt[age_start])
# correct cases where age_start == age_end
i = age_start == age_end
if pl.any(i):
p[i] = pi_age_true[age_start[i]]
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
model.input_data['effective_sample_size'] = mc.runiform(100, 10000, size=N)types = pl.array(['i', 'r', 'f', 'p'])
## generate simulated data
model = data_simulation.simple_model(N)
model.input_data['effective_sample_size'] = 1.
model.input_data['value'] = 0.
# coarse knot spacing for fast testing
for t in types:
model.parameters[t]['parameter_age_mesh'] = range(0, 101, 20)
sim = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
for t in 'irf':
for i, k_i in enumerate(sim[t]['knots']):
sim[t]['gamma'][i].value = pl.log(true[t](k_i))
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
data_type = types[mc.rcategorical(pl.ones(len(types), dtype=float) / float(len(types)), size=N)]
a = pl.arange(101)
age_weights = pl.ones_like(a)
sum_wt = pl.cumsum(age_weights)
p = pl.zeros(N)
for t in types:
mu_t = sim[t]['mu_age'].value
sum_mu_wt = pl.cumsum(mu_t*age_weights)
p_t = (sum_mu_wt[age_end] - sum_mu_wt[age_start]) / (sum_wt[age_end] - sum_wt[age_start])def validate_age_integrating_model_sim(N=500, delta_true=.15, pi_true=quadratic):
## generate simulated data
a = pl.arange(0, 101, 1)
pi_age_true = pi_true(a)
model = data_simulation.simple_model(N)
#model.parameters['p']['parameter_age_mesh'] = range(0, 101, 10)
#model.parameters['p']['smoothness'] = dict(amount='Very')
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
age_weights = pl.ones_like(a)
sum_pi_wt = pl.cumsum(pi_age_true*age_weights)
sum_wt = pl.cumsum(age_weights)
p = (sum_pi_wt[age_end] - sum_pi_wt[age_start]) / (sum_wt[age_end] - sum_wt[age_start])
# correct cases where age_start == age_end
i = age_start == age_end
if pl.any(i):
p[i] = pi_age_true[age_start[i]]
n = mc.runiform(100, 10000, size=N)
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_enddef simulate_age_group_data(N=50, delta_true=150, pi_true=true_rate_function):
""" generate simulated data
"""
# start with a simple model with N rows of data
model = data_simulation.simple_model(N)
# record the true age-specific rates
model.ages = pl.arange(0, 101, 1)
model.pi_age_true = pi_true(model.ages)
# choose age groups randomly
age_width = mc.runiform(1, 100, size=N)
age_mid = mc.runiform(age_width/2, 100-age_width/2, size=N)
age_width[:10] = 10
age_mid[:10] = pl.arange(5, 105, 10)
#age_width[10:20] = 10
#age_mid[10:20] = pl.arange(5, 105, 10)
age_start = pl.array(age_mid - age_width/2, dtype=int)
age_end = pl.array(age_mid + age_width/2, dtype=int)
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
# choose effective sample size uniformly at random
n = mc.runiform(100, 10000, size=N)
model.input_data['effective_sample_size'] = ntypes = pl.array(['i', 'r', 'f', 'p'])
## generate simulated data
model = data_simulation.simple_model(N)
model.input_data['effective_sample_size'] = 1.
model.input_data['value'] = 0.
for t in types:
model.parameters[t]['parameter_age_mesh'] = range(0, 101, 20)
sim = consistent_model.consistent_model(model, 'all', 'total', 'all', {})
for t in 'irf':
for i, k_i in enumerate(sim[t]['knots']):
sim[t]['gamma'][i].value = pl.log(true[t](k_i))
age_start = pl.array(mc.runiform(0, 100, size=N), dtype=int)
age_end = pl.array(mc.runiform(age_start, 100, size=N), dtype=int)
data_type = types[mc.rcategorical(pl.ones(len(types), dtype=float) / float(len(types)), size=N)]
a = pl.arange(101)
age_weights = pl.ones_like(a)
sum_wt = pl.cumsum(age_weights)
p = pl.zeros(N)
for t in types:
mu_t = sim[t]['mu_age'].value
sum_mu_wt = pl.cumsum(mu_t*age_weights)
p_t = (sum_mu_wt[age_end] - sum_mu_wt[age_start]) / (sum_wt[age_end] - sum_wt[age_start])
# correct cases where age_start == age_endage_width = mc.runiform(1, 100, size=N)
age_mid = mc.runiform(age_width/2, 100-age_width/2, size=N)
age_width[:10] = 10
age_mid[:10] = pl.arange(5, 105, 10)
#age_width[10:20] = 10
#age_mid[10:20] = pl.arange(5, 105, 10)
age_start = pl.array(age_mid - age_width/2, dtype=int)
age_end = pl.array(age_mid + age_width/2, dtype=int)
model.input_data['age_start'] = age_start
model.input_data['age_end'] = age_end
# choose effective sample size uniformly at random
n = mc.runiform(100, 10000, size=N)
model.input_data['effective_sample_size'] = n
# integrate true age-specific rate across age groups to find true group rate
model.input_data['true'] = pl.nan
model.input_data['age_weights'] = ''
for i in range(N):
beta = mc.rnormal(0., .025**-2)
# TODO: clean this up, it is computing more than is necessary
age_weights = pl.exp(beta*model.ages)
sum_pi_wt = pl.cumsum(model.pi_age_true*age_weights)
sum_wt = pl.cumsum(age_weights)
p = (sum_pi_wt[age_end] - sum_pi_wt[age_start]) / (sum_wt[age_end] - sum_wt[age_start])pymc
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