def prepare_data(db):
# Processes/ methods to be called at the beginning of the pop_synthesis process
dbc = db.cursor()
# Identifying the number of housing units to build the Master Matrix
dbc.execute('select * from housing_pums')
housing_units = dbc.rowcount
ti = time.clock()
# Identifying the control variables for the households, gq's, and persons
hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'hhld')
gq_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'gq')
person_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'person')
# Identifying the number of categories within each control variable for the households, gq's, and persons
hhld_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'hhld', hhld_control_variables))
gq_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'gq', gq_control_variables))
person_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'person', person_control_variables))
print 'Dimensions and Control Variables created in %.4f' %(time.clock()-ti)
ti = time.clock()
update_string = adjusting_pums_joint_distribution.create_update_string(db, hhld_control_variables, hhld_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'hhld', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(db, gq_control_variables, gq_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'gq', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(db, person_control_variables, person_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'person', update_string)
print 'Uniqueid\'s created in %.4f' %(time.clock()-ti)
ti = time.clock()
# Populating the Master Matrix
populated_matrix = psuedo_sparse_matrix.populate_master_matrix(db, 0, housing_units, hhld_dimensions,
gq_dimensions, person_dimensions)
print 'Frequency Matrix Populated in %.4f' %(time.clock()-ti)
ti = time.clock()
# Sparse representation of the Master Matrix
ps_sp_matrix = psuedo_sparse_matrix.psuedo_sparse_matrix(db, populated_matrix, 0)
print 'Psuedo Sparse Representation of the Frequency Matrix created in %.4f' %(time.clock()-ti)
ti = time.clock()
#______________________________________________________________________
#Creating Index Matrix
index_matrix = psuedo_sparse_matrix.generate_index_matrix(db, 0)
print 'Index matrix created in %.4f' %(time.clock()-ti)
ti = time.clock()
dbc.close()
#______________________________________________________________________
# creating synthetic_population tables in MySQL
drawing_households.create_synthetic_attribute_tables(db)
# Total PUMS Sample x composite_type adjustment for hhld
adjusting_pums_joint_distribution.create_joint_dist(db, 'hhld', hhld_control_variables, hhld_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for gq
adjusting_pums_joint_distribution.create_joint_dist(db, 'gq', gq_control_variables, gq_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for person
adjusting_pums_joint_distribution.create_joint_dist(db, 'person', person_control_variables, person_dimensions, 0, 0, 0)
def configure_and_run(index_matrix, p_index_matrix, geoid):
pumano = int(geoid[0])
tract = int(geoid[1])
bg = int(geoid[2])
print '------------------------------------------------------------------'
print 'Geography: PUMA ID- %s, Tract ID- %0.2f, BG ID- %s' \
%(pumano, float(tract)/100, bg)
print '------------------------------------------------------------------'
db = MySQLdb.connect(host = 'localhost', user = '******', passwd = '1234', db = 'ncpopsyn')
dbc = db.cursor()
tii = time.clock()
ti = time.clock()
# Identifying the number of housing units in the disaggregate sample
# Make Sure that the file is sorted by hhid
dbc.execute('select * from housing_pums')
housing_pums = numpy.asarray(dbc.fetchall())[:,1:]
housing_units = dbc.rowcount
# Identifying the control variables for the households, gq's, and persons
hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'hhld')
gq_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'gq')
person_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'person')
# Identifying the number of categories within each control variable for the households, gq's, and persons
hhld_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'hhld', hhld_control_variables))
gq_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'gq', gq_control_variables))
person_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'person', person_control_variables))
#______________________________________________________________________
# Creating the sparse array
dbc.execute('select * from sparse_matrix1_%s' %(0))
sp_matrix = numpy.asarray(dbc.fetchall())
#______________________________________________________________________
# Running IPF for Households
print 'Step 1A: Running IPF procedure for Households... '
hhld_objective_frequency, hhld_estimated_constraint = ipf.ipf_config_run(db, 'hhld', hhld_control_variables, hhld_dimensions, pumano, tract, bg)
print 'IPF procedure for Households completed in %.2f sec \n'%(time.clock()-ti)
ti = time.clock()
# Running IPF for GQ
print 'Step 1B: Running IPF procedure for Gqs... '
gq_objective_frequency, gq_estimated_constraint = ipf.ipf_config_run(db, 'gq', gq_control_variables, gq_dimensions, pumano, tract, bg)
print 'IPF procedure for GQ was completed in %.2f sec \n'%(time.clock()-ti)
ti = time.clock()
# Running IPF for Persons
print 'Step 1C: Running IPF procedure for Persons... '
person_objective_frequency, person_estimated_constraint = ipf.ipf_config_run(db, 'person', person_control_variables, person_dimensions, pumano, tract, bg)
print 'IPF procedure for Persons completed in %.2f sec \n'%(time.clock()-ti)
ti = time.clock()
#______________________________________________________________________
# Creating the weights array
print 'Step 2: Running IPU procedure for obtaining weights that satisfy Household and Person type constraints... '
dbc.execute('select rowno from sparse_matrix1_%s group by rowno'%(0))
result = numpy.asarray(dbc.fetchall())[:,0]
weights = numpy.ones((1,housing_units), dtype = float)[0] * -99
weights[result]=1
#______________________________________________________________________
# Creating the control array
total_constraint = numpy.hstack((hhld_estimated_constraint[:,0], gq_estimated_constraint[:,0], person_estimated_constraint[:,0]))
#______________________________________________________________________
# Running the heuristic algorithm for the required geography
weights, conv_crit_array, wts_array = heuristic_algorithm.heuristic_adjustment(db, 0, index_matrix, weights, total_constraint, sp_matrix)
print 'IPU procedure was completed in %.2f sec\n'%(time.clock()-ti)
ti = time.clock()
#_________________________________________________________________
print 'Step 3: Creating the synthetic households and individuals...'
# creating whole marginal values
hhld_order_dummy = adjusting_pums_joint_distribution.create_aggregation_string(hhld_control_variables)
hhld_frequencies = drawing_households.create_whole_frequencies(db, 'hhld', hhld_order_dummy, pumano, tract, bg)
gq_order_dummy = adjusting_pums_joint_distribution.create_aggregation_string(gq_control_variables)
gq_frequencies = drawing_households.create_whole_frequencies(db, 'gq', gq_order_dummy, pumano, tract, bg)
frequencies = numpy.hstack((hhld_frequencies[:,0], gq_frequencies[:,0]))
#______________________________________________________________________
# Sampling Households and choosing the draw with the best match with with the objective distribution
dbc.execute('select * from person_pums')
person_pums = numpy.asarray(dbc.fetchall())[:,1:]
p_value = 0
max_p = 0
min_chi = 1e10
draw_count = 0
while(p_value < 0.9999 and draw_count < 25):
draw_count = draw_count + 1
synthetic_housing_units = drawing_households.drawing_housing_units(db, frequencies, weights, index_matrix, sp_matrix, 0)
# Creating synthetic hhld, and person attribute tables
synthetic_housing_attributes, synthetic_person_attributes = drawing_households.synthetic_population_properties(db, synthetic_housing_units, p_index_matrix, housing_pums, person_pums)
synth_person_stat, count_person, person_estimated_frequency = drawing_households.checking_against_joint_distribution(person_objective_frequency,
synthetic_person_attributes, person_dimensions,
pumano, tract, bg)
stat = synth_person_stat
dof = count_person - 1
p_value = scipy.stats.stats.chisqprob(stat, dof)
if p_value > max_p or stat < min_chi:
max_p = p_value
max_p_housing_attributes = synthetic_housing_attributes
max_p_person_attributes = synthetic_person_attributes
min_chi = stat
if draw_count >=25:
print 'Max Iterations reached for drawing households with the best draw having a p-value of %.4f' %(max_p)
else:
print 'Population with desirable p-value of %.4f was obtained in %d iterations' %(max_p, draw_count)
drawing_households.storing_synthetic_attributes(db, 'housing', max_p_housing_attributes, pumano, tract, bg)
drawing_households.storing_synthetic_attributes(db, 'person', max_p_person_attributes, pumano, tract, bg)
dbc.execute('select hhtotal from housing_marginals where pumano = %s and tract = %s and bg = %s'%(pumano, tract, bg))
housingtotal = dbc.fetchall()[0][0]
dbc.execute('select sum(gender1 + gender2) from person_marginals where pumano = %s and tract = %s and bg = %s'%(pumano, tract, bg))
persontotal = dbc.fetchall()[0][0]
print 'Number of Synthetic Household - %d, and given Household total from the Census SF - %d' %(sum(max_p_housing_attributes[:,-2]), housingtotal)
print 'Number of Synthetic Persons - %d and given Person total from the Census SF - %d' %(sum(max_p_person_attributes[:,-1]), persontotal)
print 'Synthetic households created for the geography in %.2f\n' %(time.clock()-ti)
db.commit()
dbc.close()
db.close()
def configure_and_run(index_matrix, p_index_matrix, geoid):
pumano = int(geoid[0])
tract = int(geoid[1])
bg = int(geoid[2])
print '------------------------------------------------------------------'
print 'Geography: PUMA ID- %s, Tract ID- %0.2f, BG ID- %s' \
%(pumano, float(tract)/100, bg)
print '------------------------------------------------------------------'
db = MySQLdb.connect(host='localhost',
user='******',
passwd='1234',
db='ncpopsyn')
dbc = db.cursor()
tii = time.clock()
ti = time.clock()
# Identifying the number of housing units in the disaggregate sample
# Make Sure that the file is sorted by hhid
dbc.execute('select * from housing_pums')
housing_pums = numpy.asarray(dbc.fetchall())[:, 1:]
housing_units = dbc.rowcount
# Identifying the control variables for the households, gq's, and persons
hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'hhld')
gq_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'gq')
person_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'person')
# Identifying the number of categories within each control variable for the households, gq's, and persons
hhld_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'hhld', hhld_control_variables))
gq_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'gq', gq_control_variables))
person_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'person', person_control_variables))
#______________________________________________________________________
# Creating the sparse array
dbc.execute('select * from sparse_matrix1_%s' % (0))
sp_matrix = numpy.asarray(dbc.fetchall())
#______________________________________________________________________
# Running IPF for Households
print 'Step 1A: Running IPF procedure for Households... '
hhld_objective_frequency, hhld_estimated_constraint = ipf.ipf_config_run(
db, 'hhld', hhld_control_variables, hhld_dimensions, pumano, tract, bg)
print 'IPF procedure for Households completed in %.2f sec \n' % (
time.clock() - ti)
ti = time.clock()
# Running IPF for GQ
print 'Step 1B: Running IPF procedure for Gqs... '
gq_objective_frequency, gq_estimated_constraint = ipf.ipf_config_run(
db, 'gq', gq_control_variables, gq_dimensions, pumano, tract, bg)
print 'IPF procedure for GQ was completed in %.2f sec \n' % (time.clock() -
ti)
ti = time.clock()
# Running IPF for Persons
print 'Step 1C: Running IPF procedure for Persons... '
person_objective_frequency, person_estimated_constraint = ipf.ipf_config_run(
db, 'person', person_control_variables, person_dimensions, pumano,
tract, bg)
print 'IPF procedure for Persons completed in %.2f sec \n' % (
time.clock() - ti)
ti = time.clock()
#______________________________________________________________________
# Creating the weights array
print 'Step 2: Running IPU procedure for obtaining weights that satisfy Household and Person type constraints... '
dbc.execute('select rowno from sparse_matrix1_%s group by rowno' % (0))
result = numpy.asarray(dbc.fetchall())[:, 0]
weights = numpy.ones((1, housing_units), dtype=float)[0] * -99
weights[result] = 1
#______________________________________________________________________
# Creating the control array
total_constraint = numpy.hstack(
(hhld_estimated_constraint[:, 0], gq_estimated_constraint[:, 0],
person_estimated_constraint[:, 0]))
#______________________________________________________________________
# Running the heuristic algorithm for the required geography
weights, conv_crit_array, wts_array = heuristic_algorithm.heuristic_adjustment(
db, 0, index_matrix, weights, total_constraint, sp_matrix)
print 'IPU procedure was completed in %.2f sec\n' % (time.clock() - ti)
ti = time.clock()
#_________________________________________________________________
print 'Step 3: Creating the synthetic households and individuals...'
# creating whole marginal values
hhld_order_dummy = adjusting_pums_joint_distribution.create_aggregation_string(
hhld_control_variables)
hhld_frequencies = drawing_households.create_whole_frequencies(
db, 'hhld', hhld_order_dummy, pumano, tract, bg)
gq_order_dummy = adjusting_pums_joint_distribution.create_aggregation_string(
gq_control_variables)
gq_frequencies = drawing_households.create_whole_frequencies(
db, 'gq', gq_order_dummy, pumano, tract, bg)
frequencies = numpy.hstack((hhld_frequencies[:, 0], gq_frequencies[:, 0]))
#______________________________________________________________________
# Sampling Households and choosing the draw with the best match with with the objective distribution
dbc.execute('select * from person_pums')
person_pums = numpy.asarray(dbc.fetchall())[:, 1:]
p_value = 0
max_p = 0
min_chi = 1e10
draw_count = 0
while (p_value < 0.9999 and draw_count < 25):
draw_count = draw_count + 1
synthetic_housing_units = drawing_households.drawing_housing_units(
db, frequencies, weights, index_matrix, sp_matrix, 0)
# Creating synthetic hhld, and person attribute tables
synthetic_housing_attributes, synthetic_person_attributes = drawing_households.synthetic_population_properties(
db, synthetic_housing_units, p_index_matrix, housing_pums,
person_pums)
synth_person_stat, count_person, person_estimated_frequency = drawing_households.checking_against_joint_distribution(
person_objective_frequency, synthetic_person_attributes,
person_dimensions, pumano, tract, bg)
stat = synth_person_stat
dof = count_person - 1
p_value = scipy.stats.stats.chisqprob(stat, dof)
if p_value > max_p or stat < min_chi:
max_p = p_value
max_p_housing_attributes = synthetic_housing_attributes
max_p_person_attributes = synthetic_person_attributes
min_chi = stat
if draw_count >= 25:
print 'Max Iterations reached for drawing households with the best draw having a p-value of %.4f' % (
max_p)
else:
print 'Population with desirable p-value of %.4f was obtained in %d iterations' % (
max_p, draw_count)
drawing_households.storing_synthetic_attributes(db, 'housing',
max_p_housing_attributes,
pumano, tract, bg)
drawing_households.storing_synthetic_attributes(db, 'person',
max_p_person_attributes,
pumano, tract, bg)
dbc.execute(
'select hhtotal from housing_marginals where pumano = %s and tract = %s and bg = %s'
% (pumano, tract, bg))
housingtotal = dbc.fetchall()[0][0]
dbc.execute(
'select sum(gender1 + gender2) from person_marginals where pumano = %s and tract = %s and bg = %s'
% (pumano, tract, bg))
persontotal = dbc.fetchall()[0][0]
print 'Number of Synthetic Household - %d, and given Household total from the Census SF - %d' % (
sum(max_p_housing_attributes[:, -2]), housingtotal)
print 'Number of Synthetic Persons - %d and given Person total from the Census SF - %d' % (
sum(max_p_person_attributes[:, -1]), persontotal)
print 'Synthetic households created for the geography in %.2f\n' % (
time.clock() - ti)
db.commit()
dbc.close()
db.close()
def prepare_data(db):
# Processes/ methods to be called at the beginning of the pop_synthesis process
dbc = db.cursor()
# Identifying the number of housing units to build the Master Matrix
dbc.execute('select * from housing_pums')
housing_units = dbc.rowcount
ti = time.clock()
# Identifying the control variables for the households, gq's, and persons
hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'hhld')
gq_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'gq')
person_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'person')
# Identifying the number of categories within each control variable for the households, gq's, and persons
hhld_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'hhld', hhld_control_variables))
gq_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'gq', gq_control_variables))
person_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'person', person_control_variables))
print 'Dimensions and Control Variables created in %.4f' % (time.clock() -
ti)
ti = time.clock()
update_string = adjusting_pums_joint_distribution.create_update_string(
db, hhld_control_variables, hhld_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'hhld', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(
db, gq_control_variables, gq_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'gq', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(
db, person_control_variables, person_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'person',
update_string)
print 'Uniqueid\'s created in %.4f' % (time.clock() - ti)
ti = time.clock()
# Populating the Master Matrix
populated_matrix = psuedo_sparse_matrix.populate_master_matrix(
db, 0, housing_units, hhld_dimensions, gq_dimensions,
person_dimensions)
print 'Frequency Matrix Populated in %.4f' % (time.clock() - ti)
ti = time.clock()
# Sparse representation of the Master Matrix
ps_sp_matrix = psuedo_sparse_matrix.psuedo_sparse_matrix(
db, populated_matrix, 0)
print 'Psuedo Sparse Representation of the Frequency Matrix created in %.4f' % (
time.clock() - ti)
ti = time.clock()
#______________________________________________________________________
#Creating Index Matrix
index_matrix = psuedo_sparse_matrix.generate_index_matrix(db, 0)
print 'Index matrix created in %.4f' % (time.clock() - ti)
ti = time.clock()
dbc.close()
#______________________________________________________________________
# creating synthetic_population tables in MySQL
drawing_households.create_synthetic_attribute_tables(db)
# Total PUMS Sample x composite_type adjustment for hhld
adjusting_pums_joint_distribution.create_joint_dist(
db, 'hhld', hhld_control_variables, hhld_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for gq
adjusting_pums_joint_distribution.create_joint_dist(
db, 'gq', gq_control_variables, gq_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for person
adjusting_pums_joint_distribution.create_joint_dist(
db, 'person', person_control_variables, person_dimensions, 0, 0, 0)
