def ipf_config_run(db, synthesis_type, control_variables, dimensions, pumano, tract, bg): dbc = db.cursor() # Creating objective joint distributions to match the resulting sunthetic populations against adjusting_pums_joint_distribution.create_joint_dist( db, synthesis_type, control_variables, dimensions, pumano, tract, bg) adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type, control_variables, pumano, tract, bg) order_dummy = adjusting_pums_joint_distribution.create_aggregation_string( control_variables) dbc.execute( 'select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s' % (synthesis_type, pumano, tract, bg, order_dummy)) objective_frequency = numpy.asarray(dbc.fetchall()) # Creating the joint distributions corrected for Zero-cell and Zero-marginal problems # Example puma x composite_type adjustment for the synthesis type obtained as a parameter adjusting_pums_joint_distribution.create_joint_dist( db, synthesis_type, control_variables, dimensions, pumano, tract, bg) adjusting_pums_joint_distribution.create_adjusted_frequencies( db, synthesis_type, control_variables, pumano, tract, bg) adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type, control_variables, pumano, tract, bg) dbc.execute( 'select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s' % (synthesis_type, pumano, tract, bg, order_dummy)) estimated_constraint = numpy.asarray(dbc.fetchall()) return objective_frequency, estimated_constraint
def ipf_config_run (db, synthesis_type, control_variables, dimensions, pumano, tract, bg): dbc = db.cursor() # Creating objective joint distributions to match the resulting sunthetic populations against adjusting_pums_joint_distribution.create_joint_dist(db, synthesis_type, control_variables, dimensions, pumano, tract, bg) adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type, control_variables, pumano, tract, bg) order_dummy = adjusting_pums_joint_distribution.create_aggregation_string(control_variables) dbc.execute('select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s'%(synthesis_type, pumano, tract, bg, order_dummy)) objective_frequency = numpy.asarray(dbc.fetchall()) # Creating the joint distributions corrected for Zero-cell and Zero-marginal problems # Example puma x composite_type adjustment for the synthesis type obtained as a parameter adjusting_pums_joint_distribution.create_joint_dist(db, synthesis_type, control_variables, dimensions, pumano, tract, bg) adjusting_pums_joint_distribution.create_adjusted_frequencies(db, synthesis_type, control_variables, pumano, tract, bg) adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type, control_variables, pumano, tract, bg) dbc.execute('select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s'%(synthesis_type, pumano, tract, bg, order_dummy)) estimated_constraint = numpy.asarray(dbc.fetchall()) return objective_frequency, estimated_constraint
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 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)