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user_example
user_continue_fit.py
user_continue_fit.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
.
Continuing a Fit From Where it Left Off
Option Table
Source Code
Option Table
In the option table defined below,
max_num_iter_fixed = 5
.
This fit will terminate when
the maximum number of iterations is reached.
The corresponding warning is suppressed by setting
warn_on_stderr = false
.
The second fit will start where the first left off.
To see this, set
print_level_fixed = 5
(in the option table) and
run this example
.
Source Code
# values used to simulate data
iota_true = 0.01
chi_true = 0.1
n_data = 51
# ------------------------------------------------------------------------
import sys
import os
import copy
test_program = 'example/user/continue_fit.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) :
# note that the a, t values are not used for this case
def fun_iota ( a, t) :
return ( 'prior_iota' , None, None)
def fun_chi ( a, t) :
return ( chi_true, None, None)
# ----------------------------------------------------------------------
# age table:
age_list = [ 0.0 , 5.0 , 15.0 , 35.0 , 50.0 , 75.0 , 90.0 , 100.0 ]
#
# time table:
time_list = [ 1990.0 , 2000.0 , 2010.0 , 2200.0 ]
#
# integrand table:
integrand_table = [
{ 'name' : 'prevalence' }
]
#
# node table:
node_table = [ { 'name' : 'world' , 'parent' : '' } ]
#
# weight table:
weight_table = list ()
#
# covariate table:
covariate_table = list ()
#
# mulcov table:
mulcov_table = list ()
#
# avgint table: empty
avgint_table = list ()
#
# nslist_table:
nslist_table = dict ()
# ----------------------------------------------------------------------
# data table:
data_table = list ()
# values that are the same for all data rows
row = {
'meas_value' : 0.0 , # not used (will be simulated)
'density' : 'gaussian' ,
'weight' : '' ,
'hold_out' : False,
'time_lower' : 2000 .,
'time_upper' : 2000 .,
'subgroup' : 'world' ,
}
# values that change between rows:
for data_id in range ( n_data ) :
fraction = data_id / float ( n_data- 1 )
age = age_list[ 0 ] + ( age_list[- 1 ] - age_list[ 0 ])* fraction
row[ 'age_lower' ] = age
row[ 'age_upper' ] = age
row[ 'node' ] = 'world'
row[ 'integrand' ] = 'prevalence'
row[ 'meas_std' ] = 0.01
data_table. append ( copy. copy ( row) )
#
# ----------------------------------------------------------------------
# prior_table
prior_table = [
{ # prior_iota
'name' : 'prior_iota' ,
'density' : 'uniform' ,
'lower' : iota_true / 10 .,
'upper' : iota_true * 10 .,
'mean' : iota_true * 2.0 ,
}
]
# ----------------------------------------------------------------------
# smooth table
name = 'smooth_iota'
fun = fun_iota
age_id = int ( len ( age_list ) / 2 )
time_id = int ( len ( time_list ) / 2 )
smooth_table = [
{ 'name' : name,
'age_id' :[ age_id],
'time_id' :[ time_id],
'fun' : fun
}
]
name = 'smooth_chi'
fun = fun_chi
age_id = int ( len ( age_list ) / 2 )
time_id = int ( len ( time_list ) / 2 )
smooth_table . append (
{ 'name' : name,
'age_id' :[ age_id],
'time_id' :[ time_id],
'fun' : fun
}
)
# ----------------------------------------------------------------------
# rate table:
rate_table = [
{ 'name' : 'iota' ,
'parent_smooth' : 'smooth_iota' ,
},{ 'name' : 'chi' ,
'parent_smooth' : 'smooth_chi' ,
}
]
# ----------------------------------------------------------------------
# option_table
option_table = [
{ 'name' : 'rate_case' , 'value' : 'iota_pos_rho_zero' },
{ 'name' : 'parent_node_name' , 'value' : 'world' },
{ 'name' : 'ode_step_size' , 'value' : '10.0' },
{ 'name' : 'random_seed' , 'value' : '0' },
{ 'name' : 'warn_on_stderr' , 'value' : 'false' },
{ 'name' : 'quasi_fixed' , 'value' : 'true' },
{ 'name' : 'max_num_iter_fixed' , 'value' : '5' },
{ 'name' : 'print_level_fixed' , 'value' : '0' },
{ 'name' : 'tolerance_fixed' , 'value' : '1e-7' },
{ 'name' : 'max_num_iter_random' , 'value' : '50' },
{ '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
# ===========================================================================
# Run the init command to create the var table
file_name = 'example.db'
example_db ( file_name)
#
program = '../../devel/dismod_at'
dismod_at. system_command_prc ([ program, file_name, 'init' ])
# -----------------------------------------------------------------------
# read database
new = False
connection = dismod_at. create_connection ( file_name, new)
var_table = dismod_at. get_table_dict ( connection, 'var' )
rate_table = dismod_at. get_table_dict ( connection, 'rate' )
# -----------------------------------------------------------------------
# truth table:
tbl_name = 'truth_var'
col_name = [ 'truth_var_value' ]
col_type = [ 'real' ]
row_list = list ()
var_id2true = list ()
for var_id in range ( len ( var_table) ) :
var_info = var_table[ var_id]
truth_var_value = None
var_type = var_info[ 'var_type' ]
assert var_type == 'rate'
rate_id = var_info[ 'rate_id' ]
rate_name = rate_table[ rate_id][ 'rate_name' ]
if rate_name == 'iota' :
value = iota_true
elif rate_name == 'chi' :
value = chi_true
else :
assert False
row_list. append ( [ value ] )
dismod_at. create_table ( connection, tbl_name, col_name, col_type, row_list)
connection. close ()
# -----------------------------------------------------------------------
# Simulate one data set, start at prior mean fit, start at fit results, fit
dismod_at. system_command_prc ([ program, file_name, 'simulate' , '1' ])
dismod_at. system_command_prc ([ program, file_name, 'fit' , 'both' , '0' ])
dismod_at. system_command_prc (
[ program, file_name, 'set' , 'start_var' , 'fit_var' ]
)
dismod_at. system_command_prc ([ program, file_name, 'fit' , 'both' , '0' ])
# -----------------------------------------------------------------------
# check fit results
new = False
connection = dismod_at. create_connection ( file_name, new)
fit_var_table = dismod_at. get_table_dict ( connection, 'fit_var' )
log_table = dismod_at. get_table_dict ( connection, 'log' )
connection. close ()
#
# check that we got one warning
warning_count = 0
for row in log_table :
if row[ 'message_type' ] == 'warning' :
warning_count += 1
assert warning_count in [ 1 , 2 ]
#
max_error = 0.0
for var_id in range ( len ( var_table) ) :
fit_value = fit_var_table[ var_id][ 'fit_var_value' ]
var_row = var_table[ var_id]
rate_id = var_row[ 'rate_id' ]
rate_name = rate_table[ rate_id][ 'rate_name' ]
if rate_name == 'iota' :
ok = abs ( fit_value / iota_true - 1.0 ) < . 05
if not ok :
print ( "iota relative error = " , fit_value / iota_true - 1.0 )
assert abs ( fit_value / iota_true - 1.0 ) < . 05
else :
assert fit_value == chi_true
# -----------------------------------------------------------------------------
print ( 'continue_fit.py: OK' )
# -----------------------------------------------------------------------------
Input File: example/user/continue_fit.py