\(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\W}[1]{ \; #1 \; }\)
user_age_avg_split.py#
View page sourceNon-uniform Age Average Grid#
Purpose#
This example demonstrate one reason for using the age_avg_split option to create an Age Average Grid that is not uniformly spaced.
Variables#
For this case there is only one rate omega for the parent node and there are no other model_variables .
Prior#
The prior for omega is mean 0.1 and standard deviation
.01 for ages 0.0 and 0.9 .
The prior for omega is mean 0.01 and standard deviation
0.001 for ages 1.1 and 100.0 .
Data#
There is no data for this model; i.e., the prior is the only information available.
Fit#
A fit is done, and this should make fit_var_table equal to the mean of the prior.
Predict#
A predict is done for other cause mortality for two cases.
The first case averages over the age interval [ 0.0, 0.9 ] .
The second case averages over the age interval [ 1.1, 100.0 ] .
ode_step_size#
The ode_step_size is
50.0 for this example.
If there were no age_avg_split , a linear approximation
would be used from age 0.0 to age 50.
age_avg_split#
The age average grid is split at age 1.0 ; i.e.,
The age average grid points are 0.0 , 1.0 , 50.0 , and
100.0 .
A piecewise linear function of age is used between these grid points.
(Note everything is constant w.r.t time for this case.)
Source Code#
# true values used to simulate data
omega_0_1 = 1e-1
omega_1_100 = 1e-2
# ------------------------------------------------------------------------
import sys
import os
import copy
test_program = 'example/user/age_avg_split.py'
if sys.argv[0] != test_program or len(sys.argv) != 1 :
usage = 'python3 ' + test_program + '\n'
usage += 'where python3 is the python 3 program on your system\n'
usage += 'and working directory is the dismod_at distribution directory\n'
sys.exit(usage)
print(test_program)
#
# import dismod_at
local_dir = os.getcwd() + '/python'
if( os.path.isdir( local_dir + '/dismod_at' ) ) :
sys.path.insert(0, local_dir)
import dismod_at
#
# change into the build/example/user directory
if not os.path.exists('build/example/user') :
os.makedirs('build/example/user')
os.chdir('build/example/user')
# ------------------------------------------------------------------------
# Note that the a, t values are used for this example
def example_db (file_name) :
#
def fun_omega_parent(a, t) :
if a <= 1.0 :
return ('prior_0_1', 'prior_none', 'prior_none')
else :
return ('prior_1_100', 'prior_none', 'prior_none')
#
def omega_true(a) :
if a < 1.0 :
return omega_0_1
else :
return omega_1_100
#
# ----------------------------------------------------------------------
# age table
age_list = [ 0.0, 0.9, 1.1, 100.0 ]
#
# time table
time_list = [ 1995.0, 2015.0 ]
#
# integrand table
integrand_table = [
{ 'name':'mtother' }
]
#
# node table: world
node_table = [ { 'name':'world', 'parent':'' } ]
#
# weight table:
weight_table = list()
#
# covariate table:
covariate_table = list()
#
# mulcov table
mulcov_table = list()
#
# avgint table
avgint_table = list()
row = {
'integrand': 'mtother',
'node': 'world',
'subgroup': 'world',
'weight': '',
}
row['age_lower'] = 0.0
row['age_upper'] = 0.9
row['time_lower'] = 2000.0
row['time_upper'] = 2000.0
avgint_table.append( copy.copy(row) )
row['age_lower'] = 1.1
row['age_upper'] = 100.0
row['time_lower'] = 2000.0
row['time_upper'] = 2000.0
avgint_table.append( copy.copy(row) )
#
# nslist_dict:
nslist_dict = dict()
#
# data table:
data_table = list()
# ----------------------------------------------------------------------
# prior_table
prior_table = [
{ # prior_none
'name': 'prior_none',
'density': 'uniform',
'mean': 0.0,
},{ # prior_0_1
'name': 'prior_0_1',
'density': 'gaussian',
'mean': omega_0_1,
'std': 1e-1 * omega_0_1
},{ # prior_1_100
'name': 'prior_1_100',
'density': 'gaussian',
'mean': omega_1_100,
'std': 1e-1 * omega_1_100
}
]
# ----------------------------------------------------------------------
# smooth table
#
smooth_table = [
{ # smooth_omega_parent
'name': 'smooth_omega_parent',
'age_id': [0, 1, 2, 3],
'time_id': [0, 1],
'fun': fun_omega_parent
}
]
# ----------------------------------------------------------------------
# rate table
rate_table = [
{
'name': 'omega',
'parent_smooth': 'smooth_omega_parent'
}
]
# ----------------------------------------------------------------------
# option_table
option_table = [
{ 'name':'parent_node_name', 'value':'world' },
{ 'name':'ode_step_size', 'value':'50.0' },
{ 'name':'age_avg_split', 'value':'1.0' },
{ 'name':'random_seed', 'value':'0' },
{ 'name':'rate_case', 'value':'iota_zero_rho_zero' }
]
# ----------------------------------------------------------------------
# subgroup_table
subgroup_table = [ { 'subgroup':'world', 'group':'world' } ]
# ----------------------------------------------------------------------
# create database
dismod_at.create_database(
file_name,
age_list,
time_list,
integrand_table,
node_table,
subgroup_table,
weight_table,
covariate_table,
avgint_table,
data_table,
prior_table,
smooth_table,
nslist_dict,
rate_table,
mulcov_table,
option_table
)
# ----------------------------------------------------------------------
return
# ===========================================================================
file_name = 'example.db'
example_db(file_name)
#
program = '../../devel/dismod_at'
dismod_at.system_command_prc([ program, file_name, 'init'] )
dismod_at.system_command_prc([ program, file_name, 'fit', 'both'] )
dismod_at.system_command_prc([ program, file_name, 'predict', 'fit_var'] )
# -----------------------------------------------------------------------
# connect to database
connection = dismod_at.create_connection(
file_name, new = False, readonly = True
)
# -----------------------------------------------------------------------
# Results for fitting with no noise
var_table = dismod_at.get_table_dict(connection, 'var')
predict_table = dismod_at.get_table_dict(connection, 'predict')
#
parent_node_id = 1
eps = 1e-4
#
# check rates values
tolerance = 1e-10
for row in predict_table :
avgint_id = row['avgint_id']
if avgint_id == 0 :
value_true = omega_0_1
else :
value_true = omega_1_100
value = row['avg_integrand']
assert(abs(1.0 - value / value_true) < tolerance)
# -----------------------------------------------------------------------------
print('age_avg_split.py: OK')
# -----------------------------------------------------------------------------