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user_example
user_re_scale.py
user_re_scale.py
@(@\newcommand{\B}[1]{ {\bf #1} }
\newcommand{\R}[1]{ {\rm #1} }
\newcommand{\W}[1]{ \; #1 \; }@)@This is dismod_at-20221105 documentation: Here is a link to its
current documentation
.
Case Where Re-Scaling is Useful
Source Code
Source Code
import sys
import os
import copy
test_program = 'example/user/re_scale.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 not used for this example
def example_db ( file_name) :
def fun_rate_parent ( a, t) :
return ( 'prior_rate_parent' , 'prior_gauss_zero' , None)
# ----------------------------------------------------------------------
# age table
age_list = [ 0.0 , 50.0 , 100.0 ]
#
# time table
time_list = [ 1995.0 , 2005.0 , 2015.0 ]
#
# integrand table
integrand_table = [
{ 'name' : 'Sincidence' }
]
#
# node table: world -> north_america
# north_america -> (united_states, canada)
node_table = [
{ 'name' : 'world' , 'parent' : '' },
{ 'name' : 'north_america' , 'parent' : 'world' },
{ 'name' : 'united_states' , 'parent' : 'north_america' },
{ 'name' : 'canada' , 'parent' : 'north_america' }
]
#
# weight table:
weight_table = list ()
#
# covariate table: no covriates
covariate_table = list ()
#
# mulcov table
mulcov_table = list ()
#
# nslist_table:
nslist_table = dict ()
#
# avgint_table
avgint_table = list ()
# ----------------------------------------------------------------------
# data table: same order as age_list
data_table = list ()
# values that are the same for all data rows
row = {
'node' : 'canada' ,
'subgroup' : 'world' ,
'density' : 'gaussian' ,
'weight' : '' ,
'hold_out' : False,
'time_lower' : 2000.0 ,
'time_upper' : 2000.0 ,
'integrand' : 'Sincidence' ,
'age_lower' : 0.0
}
# values that change between rows: (one data point for each integrand)
for age_id in range ( len ( age_list) ) :
age = age_list[ age_id]
meas_value = 1e-4 * ( 50.0 + age)
row[ 'meas_value' ] = meas_value
row[ 'meas_std' ] = 1e-4 * ( 50.0 + age_list[ 0 ])
row[ 'age_lower' ] = age
row[ 'age_upper' ] = age
data_table. append ( copy. copy ( row) )
#
# ----------------------------------------------------------------------
# prior_table
prior_table = [
{ # prior_rate_parent
'name' : 'prior_rate_parent' ,
'density' : 'uniform' ,
'lower' : 1e-4 ,
'upper' : 1.0 ,
'mean' : 0.01 ,
},{ # prior_gauss_zero
'name' : 'prior_gauss_zero' ,
'density' : 'gaussian' ,
'mean' : 0.0 ,
'std' : 1e-6 ,
}
]
# ----------------------------------------------------------------------
# smooth table
smooth_table = [
{ # smooth_rate_parent
'name' : 'smooth_rate_parent' ,
'age_id' : range ( len ( age_list) ),
'time_id' : [ 0 ],
'fun' : fun_rate_parent
}
]
# ----------------------------------------------------------------------
# rate table
rate_table = [
{
'name' : 'iota' ,
'parent_smooth' : 'smooth_rate_parent' ,
}
]
# ----------------------------------------------------------------------
# option_table: max_num_iter_fixed will be set later
option_table = [
{ 'name' : 'parent_node_name' , 'value' : 'canada' },
{ 'name' : 'ode_step_size' , 'value' : '10.0' },
{ 'name' : 'random_seed' , 'value' : '0' },
{ 'name' : 'rate_case' , 'value' : 'iota_pos_rho_zero' },
{ 'name' : 'warn_on_stderr' , 'value' : 'false' },
{ 'name' : 'quasi_fixed' , 'value' : 'true' },
{ 'name' : 'derivative_test_fixed' , 'value' : 'first-order' },
{ 'name' : 'print_level_fixed' , 'value' : '0' },
{ 'name' : 'tolerance_fixed' , 'value' : '1e-12' },
{ 'name' : 'derivative_test_random' , 'value' : 'second-order' },
{ 'name' : 'max_num_iter_random' , 'value' : '100' },
{ 'name' : 'print_level_random' , 'value' : '0' },
{ 'name' : 'tolerance_random' , 'value' : '1e-10' }
]
# ----------------------------------------------------------------------
# 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_table,
rate_table,
mulcov_table,
option_table
)
return
# ===========================================================================
# create the database
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, 'set' , 'option' , 'max_num_iter_fixed' , '1'
])
dismod_at. system_command_prc ([ program, file_name, 'fit' , 'both' ])
dismod_at. system_command_prc ([
program, file_name, 'set' , 'scale_var' , 'fit_var'
])
dismod_at. system_command_prc ([
program, file_name, 'set' , 'option' , 'max_num_iter_fixed' , '30'
])
dismod_at. system_command_prc ([
program, file_name, 'set' , 'option' , 'warn_on_stderr' , 'true'
])
dismod_at. system_command_prc ([ program, file_name, 'fit' , 'both' ])
# -----------------------------------------------------------------------
# connect to database
new = False
connection = dismod_at. create_connection ( file_name, new)
#
# get tables
var_table = dismod_at. get_table_dict ( connection, 'var' )
fit_var_table = dismod_at. get_table_dict ( connection, 'fit_var' )
age_table = dismod_at. get_table_dict ( connection, "age" )
log_table = dismod_at. get_table_dict ( connection, "log" )
#
# check that convergence was detected during final fit by making
# sure there are no warnings during the fit
fit_log_id = None
for log_id in range ( len ( log_table) ) :
if log_table[ log_id][ 'message' ] == 'begin fit both' :
fit_log_id = log_id
assert log_table[ fit_log_id + 1 ][ 'message' ] == 'end fit'
#
# rate variables
assert len ( age_table) == 3
iota_optimal = 1e-4 * ( 50.0 + age_table[ 1 ][ 'age' ])
for var_id in range ( len ( var_table) ) :
iota_fit = fit_var_table[ var_id][ 'fit_var_value' ]
assert abs ( iota_fit / iota_optimal - 1.0 ) < 1e-4
# -----------------------------------------------------------------------------
print ( 're_scale.py: OK' )
Input File: example/user/re_scale.py