\(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\W}[1]{ \; #1 \; }\)
user_continue_fit.py#
View page sourceContinuing a Fit From Where it Left Off#
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_dict:
nslist_dict = 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_dict,
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
connection = dismod_at.create_connection(
file_name, new = False, readonly = False
)
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
connection = dismod_at.create_connection(
file_name, new = False, readonly = True
)
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')
# -----------------------------------------------------------------------------