\(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\W}[1]{ \; #1 \; }\)
user_compress.py#
View page sourceUsing Data Interval Compression#
Purpose#
This example shows how to use the compression intervals option.
Integrands#
For this example there are one integrand, Sincidence .
Nodes#
There is only one node called world for this example.
There are no random effects because there are no child nodes.
True Iota#
For this example, the true model incidence rate iota is
def iota_true(age) :
return 0.01 * ( 1 + ((age - 50) / 50)**2 )
Model#
There is only one rate iota and it piecewise linear in age with knots at the age points 0, 50, and 100.
Data#
There is one data point measuring Sincidence with the true value of iota at times 0.0, 50, 100. The corresponding age intervals are [0,0], [0,100], [100,100]. The age interval for the second measurement should be [50,50] and using interval compression with make it so. This is a cooked up example where interval compression makes the solution more accurate. Under normal circumstances, the answer less accurate but faster to compute.
Source Code#
# ------------------------------------------------------------------------
import sys
import os
import csv
import copy
import math
test_program = 'example/user/compress.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)
# ----------------------------------------------------------------------
# age table:
age_list = [ 0.0, 50.0, 100.0 ]
#
# time table:
time_list = [ 1990.0, 2000.0, 2010.0, 2020.0 ]
#
# integrand table:
integrand_table = [
{ 'name':'Sincidence' }
]
#
# 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 points
row = {
'integrand': 'Sincidence',
'hold_out': False,
'density': 'gaussian',
'meas_std': iota_true(0.0) / 10.,
'weight': '',
'time_lower': 2000.,
'time_upper': 2000.,
'node': 'world',
'subgroup': 'world',
}
# Sincidence at age 0.0
row['meas_value'] = iota_true(0.0)
row['age_lower'] = 0.0
row['age_upper'] = 0.0
data_table.append( copy.copy(row) )
#
# average Sincidence between age 0.0 and 100
row['meas_value'] = iota_true(50.0)
row['age_lower'] = 0.0
row['age_upper'] = 100.0
data_table.append( copy.copy(row) )
#
# Sincidence between at age 100
row['meas_value'] = iota_true(100.0)
row['age_lower'] = 100.0
row['age_upper'] = 100.0
data_table.append( copy.copy(row) )
#
# ----------------------------------------------------------------------
# prior_table
prior_table = [
{ # prior_iota
'name': 'prior_iota',
'density': 'uniform',
'lower': iota_true(0) / 10.0,
'upper': iota_true(0) * 10.0,
'mean': iota_true(0) * 2.0,
}
]
# ----------------------------------------------------------------------
# smooth table
name = 'smooth_iota'
fun = fun_iota
smooth_table = [
{ 'name':name,
'age_id':[0, 1, 2], # ages 0, 50, 100
'time_id':[0],
'fun':fun
}
]
# ----------------------------------------------------------------------
# rate table:
rate_table = [
{ 'name': 'iota',
'parent_smooth': 'smooth_iota',
}
]
# ----------------------------------------------------------------------
# option_table
option_table = [
{ 'name':'rate_case', 'value':'iota_pos_rho_zero' },
{ 'name':'parent_node_name', 'value':'world' },
{ 'name':'quasi_fixed', 'value':'false' },
{ 'name':'max_num_iter_fixed', 'value':'50' },
{ 'name':'print_level_fixed', 'value':'0' },
{ 'name':'tolerance_fixed', 'value':'1e-9' },
{ '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
# ===========================================================================
# Create 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, 'fit', 'fixed' ])
# -----------------------------------------------------------------------
# read database
connection = dismod_at.create_connection(
file_name, new = False, readonly = True
)
age_table = dismod_at.get_table_dict(connection, 'age')
var_table = dismod_at.get_table_dict(connection, 'var')
fit_var_table = dismod_at.get_table_dict(connection, 'fit_var')
connection.close()
#
# There are three values for iota in this model
assert len(var_table) == 3
assert len(fit_var_table) == 3
#
# check that this fit is not accurate
for var_id in range( len(var_table) ) :
age_id = var_table[var_id]['age_id']
if age_id == 1 :
age = age_table[age_id]['age']
true_value = iota_true(age)
fit_value = fit_var_table[var_id]['fit_var_value']
rel_error = 1.0 - fit_value/true_value
assert age == 50.0
assert abs(rel_error) > 0.5
# -----------------------------------------------------------------------
# Now compress the age intervals for all the data. This only affects the second
# data points because the others have intervals of size zero.
dismod_at.system_command_prc([
program, file_name, 'set', 'option', 'compress_interval', '100.0 0.0'
])
dismod_at.system_command_prc([ program, file_name, 'fit', 'fixed' ])
#
# read database
connection = dismod_at.create_connection(
file_name, new = False, readonly = True
)
fit_var_table = dismod_at.get_table_dict(connection, 'fit_var')
connection.close()
#
# check that this fit is accurate
for var_id in range( len(var_table) ) :
age_id = var_table[var_id]['age_id']
age = age_table[age_id]['age']
true_value = iota_true(age)
fit_value = fit_var_table[var_id]['fit_var_value']
rel_error = 1.0 - fit_value/true_value
assert abs(rel_error) < 1e-6
# ---------------------------------------------------------------------------
# Now check data.csv for interval compression
os.chdir('../../..')
program = 'python/bin/dismodat.py'
file_name = 'build/example/user/' + file_name
dismod_at.system_command_prc([ program, file_name, 'db2csv' ])
data_file = open('build/example/user/data.csv', 'r')
reader = csv.DictReader(data_file)
for (data_id, row) in enumerate(reader) :
# check flag for age compression
if int(data_id) == 0 :
assert float( row['age_lo'] ) == 0.0
assert float( row['age_up'] ) == 0.0
if int(data_id) == 1 :
assert float( row['age_lo'] ) == 50.0
assert float( row['age_up'] ) == 50.0
if int(data_id) == 2 :
assert float( row['age_lo'] ) == 100.0
assert float( row['age_up'] ) == 100.0
# -----------------------------------------------------------------------------
print('compress.py: OK')
# -----------------------------------------------------------------------------