def main(nid, extract_type_id, code_system_id, launch_set_id): """Collect source metadata.""" print_log_message("Reading disaggregation data for source metadata phase.") # in stata, this was pulled using corrections phase output (step 3) # use data after all garbage codes have been set (step 3 produces ZZZ) # but before any redistribution (including HIV redistribution) df = get_phase_output("disaggregation", nid=nid, extract_type_id=extract_type_id) data_type_id = get_value_from_nid(nid, "data_type_id", extract_type_id) source = get_value_from_nid(nid, "source", extract_type_id) representative_id = get_value_from_nid(nid, "representative_id", extract_type_id) df = run_phase(df, nid, extract_type_id, data_type_id, source, representative_id, code_system_id) print_log_message( "Writing {n} rows of output for launch set {ls}, nid {nid}, extract " "{e}".format(n=len(df), ls=launch_set_id, e=extract_type_id, nid=nid) ) write_phase_output(df, 'sourcemetadata', nid, extract_type_id, launch_set_id)
def main(nid, extract_type_id, launch_set_id): """Run the noise reduction phase.""" # download data from input database print_log_message("Beginning noise reduction phase") data_type_id = get_value_from_nid(nid, 'data_type_id', extract_type_id=extract_type_id) source = get_value_from_nid(nid, 'source', extract_type_id=extract_type_id) model_group = get_value_from_nid(nid, 'model_group', extract_type_id=extract_type_id) malaria_model_group = get_malaria_model_group_from_nid( nid, extract_type_id) df = run_phase(nid, extract_type_id, launch_set_id, data_type_id, source, model_group, malaria_model_group) print_log_message( "Writing {n} rows of output for launch set {ls}, nid {nid}, extract " "{e}".format(n=len(df), ls=launch_set_id, nid=nid, e=extract_type_id)) ids = [ 'age_group_id', 'cause_id', 'extract_type_id', 'location_id', 'year_id', 'site_id', 'sex_id', 'nid' ] df[ids] = df[ids].astype(int) write_phase_output(df, 'noisereduction', nid, extract_type_id, launch_set_id)
def main(nid, extract_type_id, code_system_id, launch_set_id, remove_decimal): """Main method""" start_time = time.time() df = get_claude_data( "disaggregation", nid=nid, extract_type_id=extract_type_id ) data_type_id = get_value_from_nid(nid, 'data_type_id', extract_type_id=extract_type_id) iso3 = get_value_from_nid(nid, 'iso3', extract_type_id=extract_type_id) df = run_pipeline(df, nid, extract_type_id, code_system_id, remove_decimal, data_type_id, iso3) run_time = time.time() - start_time print_log_message("Finished in {} seconds".format(run_time)) write_phase_output( df, "misdiagnosiscorrection", nid, extract_type_id, launch_set_id ) return df
def main(nid, extract_type_id, code_system_id, launch_set_id): cause_set_version_id = CONF.get_id('cause_set_version') location_set_version_id = CONF.get_id('location_set_version') pop_run_id = CONF.get_id('pop_run') env_run_id = CONF.get_id('env_run') # need to use special age/sex distribution for Norway based on National data if get_value_from_nid(nid, 'iso3', extract_type_id=extract_type_id) == 'NOR': distribution_set_version_id = CONF.get_id( 'NOR_distribution_set_version') else: distribution_set_version_id = CONF.get_id('distribution_set_version') # download data from input database df = get_claude_data('formatted', nid=nid, extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) assert len(df) != 0, ("Dataframe is empty." " Are you sure this source is in" "the input database?") # run the pipeline df = run_pipeline(nid, extract_type_id, launch_set_id, df, code_system_id, code_map_version_id, cause_set_version_id, location_set_version_id, pop_run_id, env_run_id, distribution_set_version_id, diagnostic=False) # upload to database write_phase_output(df, 'disaggregation', nid, extract_type_id, launch_set_id)
def run_pipeline(nid, extract_type_id, launch_set_id, df, code_system_id, cause_set_version_id, location_set_version_id, pop_run_id, env_run_id, distribution_set_version_id, diagnostic=False): """Run the full pipeline""" cache_options = { 'force_rerun': False, 'block_rerun': True, 'cache_results': False, 'cache_dir': CONF.get_directory('FILEPATH'), 'verbose': False } location_meta_df = get_current_location_hierarchy( location_set_version_id=location_set_version_id, **cache_options) code_map = get_cause_map(code_system_id=code_system_id, **cache_options) source = get_value_from_nid(nid, "source", extract_type_id) print("Overriding causes when necessary") df = overrides(df, location_meta_df) print("Dropping data out of scope") df = drop_data_out_of_scope(df, location_meta_df, source) if len(df) > 0: # make sure six minor territories are grouped correctly assert_no_six_minor_territories(df) # run mapping print("\nDeaths before MAPPING: {}".format(df.deaths.sum())) Mapper = GBDCauseMapper(cause_set_version_id, code_map) df = Mapper.get_computed_dataframe(df, code_system_id) if diagnostic: write_phase_output(df, 'mapping', nid, extract_type_id, launch_set_id, sub_dirs='diagnostic') print("\nDeaths before AGESEXSPLIT: {}".format(df.deaths.sum())) # run age sex splitting MySplitter = AgeSexSplitter(cause_set_version_id, pop_run_id, distribution_set_version_id, verbose=True, collect_diagnostics=False) df = MySplitter.get_computed_dataframe(df, location_meta_df) if diagnostic: diag_df = MySplitter.get_diagnostic_dataframe() write_phase_output(diag_df, 'agesexsplit', nid, extract_type_id, launch_set_id, sub_dirs='diagnostic') print("\nDeaths before CORRECTIONS: {}".format(df.deaths.sum())) # run restrictions corrections Corrector = RestrictionsCorrector(code_system_id, cause_set_version_id, collect_diagnostics=False, verbose=True) df = Corrector.get_computed_dataframe(df) # calculate cc_code for some sources if source in ['Iran_maternal_surveillance', 'Iran_forensic']: env_meta_df = get_env(env_run_id=env_run_id, **cache_options) df = calculate_cc_code(df, env_meta_df, code_map) print("\nDeaths after adding cc_code: {}".format(df.deaths.sum())) # adjust deaths for New Zealand by maori/non-maori ethnicities if source in ["NZL_MOH_ICD9", "NZL_MOH_ICD10"]: df = correct_maori_non_maori_deaths(df) print("\nDeaths after Maori/non-Maori adjustment: {}".format( df.deaths.sum())) print("\nDeaths at END: {}".format(df.deaths.sum())) return df
def run_phase(df, csvid, nid, extract_type_id, lsvid, pop_run_id, cmvid, launch_set_id, remove_decimal, write_diagnostics=True): read_file_cache_options = { 'block_rerun': True, 'cache_dir': CACHE_DIR, 'force_rerun': False, 'cache_results': False } iso3 = get_value_from_nid(nid, 'iso3', extract_type_id=extract_type_id, location_set_version_id=lsvid) code_system_id = int( get_value_from_nid(nid, 'code_system_id', extract_type_id=extract_type_id)) data_type_id = get_value_from_nid(nid, 'data_type_id', extract_type_id=extract_type_id) cause_map = get_cause_map(code_map_version_id=cmvid, **read_file_cache_options) orig_deaths_sum = int(df['deaths'].sum()) if remove_decimal: print_log_message("Removing decimal from code map") cause_map['value'] = cause_map['value'].apply( lambda x: x.replace(".", "")) if needs_garbage_correction(iso3, data_type_id): print_log_message("Correcting Garbage for {}".format(iso3)) orig_gc_sum = int(df.query('cause_id == 743')['deaths'].sum()) cause_meta_df = get_current_cause_hierarchy(cause_set_version_id=csvid, **read_file_cache_options) age_meta_df = get_ages(**read_file_cache_options) loc_meta_df = get_current_location_hierarchy( location_set_version_id=lsvid, **read_file_cache_options) pop_meta_df = get_pop(pop_run_id=pop_run_id, **read_file_cache_options) hiv_corrector = HIVCorrector(df, iso3, code_system_id, pop_meta_df, cause_meta_df, loc_meta_df, age_meta_df, correct_garbage=True) df = hiv_corrector.get_computed_dataframe() after_gc_sum = int(df.query('cause_id == 743')['deaths'].sum()) after_deaths_sum = int(df['deaths'].sum()) print_log_message(""" Stage [gc deaths / total deaths] Before GC correction [{gco} / {to}] After GC correction [{gca} / {ta}] """.format(gco=orig_gc_sum, to=orig_deaths_sum, gca=after_gc_sum, ta=after_deaths_sum)) df = add_code_metadata(df, ['value', 'code_system_id'], code_map=cause_map, **read_file_cache_options) assert (df['code_system_id'] == code_system_id).all(), "Variable code " \ "system id {} did not agree with all values of df code " \ "system id: \n{}".format( code_system_id, df.loc[df['code_system_id'] != code_system_id]) print_log_message("Formatting data for redistribution") df = format_age_groups(df) # drop observations with 0 deaths df = drop_zero_deaths(df) # merge on redistribution location hierarchy df = add_rd_locations(df, lsvid) # fill in any missing stuff that may have come from rd hierarchy df = fill_missing_df(df, verify_all=True) df = add_split_group_id_column(df) # final check to make sure we have all the necessary columns df = format_columns_for_rd(df, code_system_id) split_groups = list(df.split_group.unique()) parallel = len(split_groups) > 1 print_log_message("Submitting/Running split groups") for split_group in split_groups: # remove intermediate files from previous run delete_split_group_output(nid, extract_type_id, split_group) # save to file split_df = df.loc[df['split_group'] == split_group] write_split_group_input(split_df, nid, extract_type_id, split_group) if parallel: submit_split_group(nid, extract_type_id, split_group, code_system_id, launch_set_id) else: worker_main(nid, extract_type_id, split_group, code_system_id) if parallel: print_log_message("Waiting for splits to complete...") wait('claude_redistributionworker_{}'.format(nid), 30) print_log_message("Done waiting. Appending them together") df = read_append_split_groups(split_groups, nid, extract_type_id, cause_map) print_log_message("Done appending files - {} rows assembled".format( len(df))) df = revert_variables(df) after_deaths_sum = int(df['deaths'].sum()) before_after_text = """ Before GC redistribution: {a} After GC redistribution: {b} """.format(a=orig_deaths_sum, b=after_deaths_sum) diff = abs(orig_deaths_sum - after_deaths_sum) diff_threshold = max(.02 * orig_deaths_sum, 5) if not diff < diff_threshold: raise AssertionError("Deaths not close.\n" + before_after_text) else: print_log_message(before_after_text) return df
def run_phase(df, csvid, nid, extract_type_id, lsvid, pop_run_id, cmvid, launch_set_id, remove_decimal, write_diagnostics=True): """String together processes for redistribution.""" # what to do about caching throughout the phase read_file_cache_options = { 'block_rerun': True, 'cache_dir': CACHE_DIR, 'force_rerun': False, 'cache_results': False } # the iso3 of this data iso3 = get_value_from_nid(nid, 'iso3', extract_type_id=extract_type_id, location_set_version_id=lsvid) # the code system id code_system_id = int( get_value_from_nid(nid, 'code_system_id', extract_type_id=extract_type_id)) # the data type data_type_id = get_value_from_nid(nid, 'data_type_id', extract_type_id=extract_type_id) # cause map cause_map = get_cause_map(code_map_version_id=cmvid, **read_file_cache_options) orig_deaths_sum = int(df['deaths'].sum()) if remove_decimal: print_log_message("Removing decimal from code map") cause_map['value'] = cause_map['value'].apply( lambda x: x.replace(".", "")) if needs_garbage_correction(iso3, data_type_id): print_log_message("Correcting Garbage for {}".format(iso3)) orig_gc_sum = int(df.query('cause_id == 743')['deaths'].sum()) cause_meta_df = get_current_cause_hierarchy(cause_set_version_id=csvid, **read_file_cache_options) # get age group ids age_meta_df = get_ages(**read_file_cache_options) loc_meta_df = get_current_location_hierarchy( location_set_version_id=lsvid, **read_file_cache_options) pop_meta_df = get_pop(pop_run_id=pop_run_id, **read_file_cache_options) # Move garbage to hiv first hiv_corrector = HIVCorrector(df, iso3, code_system_id, pop_meta_df, cause_meta_df, loc_meta_df, age_meta_df, correct_garbage=True) df = hiv_corrector.get_computed_dataframe() after_gc_sum = int(df.query('cause_id == 743')['deaths'].sum()) after_deaths_sum = int(df['deaths'].sum()) print_log_message(""" Stage [gc deaths / total deaths] Before GC correction [{gco} / {to}] After GC correction [{gca} / {ta}] """.format(gco=orig_gc_sum, to=orig_deaths_sum, gca=after_gc_sum, ta=after_deaths_sum)) df = add_code_metadata(df, ['value', 'code_system_id'], code_map=cause_map, **read_file_cache_options) # recognizing that it is weird for code_system_id to come from two places, # make sure they are consistent assert (df['code_system_id'] == code_system_id).all(), "Variable code " \ "system id {} did not agree with all values of df code " \ "system id: \n{}".format( code_system_id, df.loc[df['code_system_id'] != code_system_id]) print_log_message("Formatting data for redistribution") # do we have all the packages we need? # verify_packages(df) # format age groups to match package parameters df = format_age_groups(df) # drop observations with 0 deaths df = drop_zero_deaths(df) # merge on redistribution location hierarchy df = add_rd_locations(df, lsvid) # fill in any missing stuff that may have come from rd hierarchy df = fill_missing_df(df, verify_all=True) # create split groups # NO SPLIT GROUP NEEDED df = add_split_group_id_column(df) # final check to make sure we have all the necessary columns df = format_columns_for_rd(df, code_system_id) split_groups = list(df.split_group.unique()) parallel = len(split_groups) > 1 print_log_message("Submitting/Running split groups") for split_group in split_groups: # remove intermediate files from previous run delete_split_group_output(nid, extract_type_id, split_group) # save to file split_df = df.loc[df['split_group'] == split_group] write_split_group_input(split_df, nid, extract_type_id, split_group) # submit jobs or just run them here if parallel: submit_split_group(nid, extract_type_id, split_group, code_system_id, launch_set_id) else: worker_main(nid, extract_type_id, split_group, code_system_id) if parallel: print_log_message("Waiting for splits to complete...") # wait until all jobs for a given nid have completed # eventually need logic for files not being present wait('claude_redistributionworker_{}'.format(nid), 30) # This seems to be necessary to wait for files print_log_message("Done waiting. Appending them together") # append split groups together df = read_append_split_groups(split_groups, nid, extract_type_id, cause_map) print_log_message("Done appending files - {} rows assembled".format( len(df))) df = revert_variables(df) after_deaths_sum = int(df['deaths'].sum()) before_after_text = """ Before GC redistribution: {a} After GC redistribution: {b} """.format(a=orig_deaths_sum, b=after_deaths_sum) diff = abs(orig_deaths_sum - after_deaths_sum) # bad if change 2% or 5 deaths, whichever is greater # (somewhat arbitrary, just trying to avoid annoying/non-issue failures) diff_threshold = max(.02 * orig_deaths_sum, 5) if not diff < diff_threshold: raise AssertionError("Deaths not close.\n" + before_after_text) else: print_log_message(before_after_text) return df
def run_phase(df, nid, extract_type_id, pop_run_id, cause_set_version_id, location_set_version_id): """Run the full phase, chaining together computational elements.""" cache_dir = CONF.get_directory('FILEPATH') orig_deaths = df['deaths'].sum() standard_cache_options = { 'force_rerun': False, 'block_rerun': True, 'cache_dir': cache_dir, 'cache_results': False } code_system_id = get_value_from_nid(nid, 'code_system_id', extract_type_id=extract_type_id) # this queries the database, maybe should be passed in directly code_system = get_code_system_from_id(code_system_id) source = get_value_from_nid(nid, 'source', extract_type_id=extract_type_id) data_type_id = get_value_from_nid( nid, 'data_type_id', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) # get cause hierarchy cause_meta_df = get_current_cause_hierarchy( cause_set_version_id=cause_set_version_id, **standard_cache_options) is_vr = data_type_id in [9, 10] if not skip_hiv_correction(source) and is_vr: # get location hierarchy loc_meta_df = get_current_location_hierarchy( location_set_version_id=location_set_version_id, **standard_cache_options) # get population pop_meta_df = get_pop(pop_run_id=pop_run_id, **standard_cache_options) # get age metadata age_meta_df = get_ages(**standard_cache_options) # get the country iso3 = get_value_from_nid( nid, 'iso3', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) assert pd.notnull(iso3), "Could not find iso3 for nid {}, " \ "extract_type_id {}".format(nid, extract_type_id) hiv_corrector = HIVCorrector(df, iso3, code_system_id, pop_meta_df, cause_meta_df, loc_meta_df, age_meta_df, correct_garbage=False) print_log_message("Running hiv correction for iso3 {}".format(iso3)) df = hiv_corrector.get_computed_dataframe() if needs_injury_redistribution(source): print_log_message("Correcting injuries") if not 'loc_meta_df' in vars(): # get location hierarchy loc_meta_df = get_current_location_hierarchy( location_set_version_id=location_set_version_id, **standard_cache_options) injury_redistributor = InjuryRedistributor(df, loc_meta_df, cause_meta_df) df = injury_redistributor.get_computed_dataframe() df = combine_with_rd_raw(df, nid, extract_type_id, location_set_version_id) val_cols = ['deaths', 'deaths_raw', 'deaths_corr', 'deaths_rd'] # run china VR rescaling if needs_subnational_rescale(source): china_rescaler = ChinaHospitalUrbanicityRescaler() df = china_rescaler.get_computed_dataframe(df) if needs_strata_collapse(source): # set site id to blank site id and collapse df['site_id'] = 2 group_cols = list(set(df.columns) - set(val_cols)) df = df.groupby(group_cols, as_index=False)[val_cols].sum() if is_vr: # drop if deaths are 0 across all current deaths columns df = df.loc[df[val_cols].sum(axis=1) != 0] # restrict causes based on code system print_log_message("Running bridge mapper") bridge_mapper = BridgeMapper(source, cause_meta_df, code_system) df = bridge_mapper.get_computed_dataframe(df) # run recodes based on expert opinion print_log_message("Enforcing some very hard priors (expert opinion)") expert_opinion_recoder = Recoder(cause_meta_df, source, code_system_id, data_type_id) df = expert_opinion_recoder.get_computed_dataframe(df) end_deaths = df['deaths'].sum() print_log_message("Checking no large loss or gain of deaths") if abs(orig_deaths - end_deaths) >= (.1 * end_deaths): diff = round(abs(orig_deaths - end_deaths), 2) old = round(abs(orig_deaths)) new = round(abs(end_deaths)) raise AssertionError("Change of {} deaths [{}] to [{}]".format( diff, old, new)) return df
def run_phase(df, nid, extract_type_id, env_run_id, pop_run_id, location_set_version_id, cause_set_version_id): cache_dir = CONF.get_directory('db_cache') source = get_value_from_nid( nid, 'source', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) data_type_id = get_value_from_nid( nid, 'data_type_id', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) iso3 = get_value_from_nid(nid, 'iso3', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) standard_cache_options = { 'force_rerun': False, 'block_rerun': True, 'cache_dir': cache_dir, 'cache_results': False } # ************************************************************ # Get cached metadata # ************************************************************ print_log_message("Getting cached db resources") location_hierarchy = get_current_location_hierarchy( location_set_version_id=location_set_version_id, **standard_cache_options) pop_df = get_pop(pop_run_id=pop_run_id, **standard_cache_options) env_df = get_env(env_run_id=env_run_id, **standard_cache_options) age_weight_df = get_age_weights(**standard_cache_options) cause_meta_df = get_current_cause_hierarchy( cause_set_version_id=cause_set_version_id, **standard_cache_options) age_meta_df = get_ages(**standard_cache_options) # ************************************************************ # RAKING # ************************************************************ # Rake if appropriate based on this logic if ((data_type_id in [8, 9, 10] and (source != 'Other_Maternal')) or source in MATERNAL_NR_SOURCES): if source not in NOT_RAKED_SOURCES: print_log_message("Raking sub national estimates") raker = Raker(df, source) df = raker.get_computed_dataframe(location_hierarchy) # for the Other_Maternal source we only rake household surveys elif source == "Other_Maternal": model_groups = get_datasets(nid, extract_type_id, block_rerun=True, force_rerun=False).model_group.unique() assert len(model_groups) == 1 model_group = model_groups[0] if "HH_SURVEYS" in model_group: if model_group == 'MATERNAL-HH_SURVEYS-IND': print_log_message("Raking sub national estimates," \ " applying double raking for India Maternal" ) raker = Raker(df, source, double=True) df = raker.get_computed_dataframe(location_hierarchy) else: print_log_message("Raking sub national estimates") raker = Raker(df, source) df = raker.get_computed_dataframe(location_hierarchy) # ************************************************************ # DROP ZERO SAMPLE SIZE AND RESTRICTED AGE/SEX DATA # ************************************************************ # data with zero sample size is almost certaintly some anomolous result # of a program generating data it shouldn't have, and it shouldn't be # included in codem models. Was probably already dropped, anyway, before # running noise reduction. df = df.query('sample_size != 0') # uploading data before 1980 is a waste of space because neither codem # nor codviz use it df = df.loc[df['year_id'] >= 1980] print_log_message("Enforcing age sex restrictions") # this actually drops data from the dataframe if it violates age/sex # restrictions (e.g. male maternity disorders) df = enforce_asr(df, cause_meta_df, age_meta_df) # ************************************************************ # FIT EACH DRAW TO NON-ZERO FLOOR # ************************************************************ print_log_message("Fitting to non-zero floor...") nonzero_floorer = NonZeroFloorer(df) df = nonzero_floorer.get_computed_dataframe(pop_df, env_df, cause_meta_df) # ************************************************************ # AGE AGGREGATION # ************************************************************ print_log_message("Creating age standardized and all ages groups") age_aggregator = AgeAggregator(df, pop_df, env_df, age_weight_df) df = age_aggregator.get_computed_dataframe() # ************************************************************ # Make CODEm and CoDViz metrics for uncertainty # ************************************************************ # columns that should be present in the phase output final_cols = [ 'age_group_id', 'cause_id', 'cf_corr', 'cf_final', 'cf_raw', 'cf_rd', 'extract_type_id', 'location_id', 'nid', 'sample_size', 'sex_id', 'site_id', 'year_id' ] # Use draws to make metrics for uncertainty to # be used by CODEm and CoDViz # also creates cf_final from mean of draws print_log_message("Making metrics for CODEm and CoDViz") if dataset_has_redistribution_variance(data_type_id, source): df = RedistributionVarianceEstimator.make_codem_codviz_metrics( df, pop_df) final_cols += [ 'cf_final_high_rd', 'cf_final_low_rd', 'variance_rd_log_dr', 'variance_rd_logit_cf' ] # we did this in the old code-- no cfs over 1 nor below 0 for cf_col in ['cf_final', 'cf_rd', 'cf_raw', 'cf_corr']: df.loc[df[cf_col] > 1, cf_col] = 1 df.loc[df[cf_col] < 0, cf_col] = 0 df = df[final_cols] return df
def run_phase(df, nid, extract_type_id, env_run_id, pop_run_id, location_set_version_id, cause_set_version_id): cache_dir = CONF.get_directory('db_cache') source = get_value_from_nid( nid, 'source', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) data_type_id = get_value_from_nid( nid, 'data_type_id', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) standard_cache_options = { 'force_rerun': False, 'block_rerun': True, 'cache_dir': cache_dir, 'cache_results': False } # ************************************************************ # Get cached metadata # ************************************************************ print_log_message("Getting cached db resources") location_hierarchy = get_current_location_hierarchy( location_set_version_id=location_set_version_id, **standard_cache_options) pop_df = get_pop(pop_run_id=pop_run_id, **standard_cache_options) env_df = get_env(env_run_id=env_run_id, **standard_cache_options) age_weight_df = get_age_weights(**standard_cache_options) cause_meta_df = get_current_cause_hierarchy( cause_set_version_id=cause_set_version_id, **standard_cache_options) age_meta_df = get_ages(**standard_cache_options) # ************************************************************ # RAKING # ************************************************************ if ((data_type_id in [8, 9, 10] and (source != "Other_Maternal")) or source in MATERNAL_NR_SOURCES): if source not in NOT_RAKED_SOURCES: print_log_message("Raking sub national estimates") raker = Raker(df, source) df = raker.get_computed_dataframe(location_hierarchy) # for the Other_Maternal source we only rake household surveys elif source == "Other_Maternal": model_groups = get_datasets(nid, extract_type_id, block_rerun=True, force_rerun=False).model_group.unique() assert len(model_groups) == 1 model_group = model_groups[0] if "HH_SURVEYS" in model_group: print_log_message("Raking sub national estimates") raker = Raker(df, source) df = raker.get_computed_dataframe(location_hierarchy) # ************************************************************ # DROP ZERO SAMPLE SIZE AND RESTRICTED AGE/SEX DATA # ************************************************************ df = df.query('sample_size != 0') df = df.loc[df['year_id'] >= 1980] print_log_message("Enforcing age sex restrictions") df = enforce_asr(df, cause_meta_df, age_meta_df) # ************************************************************ # FIT EACH DRAW TO NON-ZERO FLOOR # ************************************************************ print_log_message("Fitting to non-zero floor...") nonzero_floorer = NonZeroFloorer(df) df = nonzero_floorer.get_computed_dataframe(pop_df, env_df, cause_meta_df) # ************************************************************ # AGE AGGREGATION # ************************************************************ print_log_message("Creating age standardized and all ages groups") age_aggregator = AgeAggregator(df, pop_df, env_df, age_weight_df) df = age_aggregator.get_computed_dataframe() # ************************************************************ # Make CODEm and CoDViz metrics for uncertainty # ************************************************************ # columns that should be present in the phase output final_cols = [ 'age_group_id', 'cause_id', 'cf_corr', 'cf_final', 'cf_raw', 'cf_rd', 'extract_type_id', 'location_id', 'nid', 'sample_size', 'sex_id', 'site_id', 'year_id' ] print_log_message("Making metrics for CODEm and CoDViz") if dataset_has_redistribution_variance(data_type_id, source): df = RedistributionVarianceEstimator.make_codem_codviz_metrics( df, pop_df) final_cols += [ 'cf_final_high_rd', 'cf_final_low_rd', 'variance_rd_log_dr', 'variance_rd_logit_cf' ] for cf_col in ['cf_final', 'cf_rd', 'cf_raw', 'cf_corr']: df.loc[df[cf_col] > 1, cf_col] = 1 df.loc[df[cf_col] < 0, cf_col] = 0 df = df[final_cols] return df
def run_phase(df, nid, extract_type_id, pop_run_id, cause_set_version_id, location_set_version_id): """Run the full phase, chaining together computational elements.""" # get filepaths cache_dir = CONF.get_directory('db_cache') orig_deaths = df['deaths'].sum() standard_cache_options = { 'force_rerun': False, 'block_rerun': True, 'cache_dir': cache_dir, 'cache_results': False } code_system_id = get_value_from_nid(nid, 'code_system_id', extract_type_id=extract_type_id) code_system = get_code_system_from_id(code_system_id, **standard_cache_options) source = get_value_from_nid(nid, 'source', extract_type_id=extract_type_id) data_type_id = get_value_from_nid( nid, 'data_type_id', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) # get cause hierarchy cause_meta_df = get_current_cause_hierarchy( cause_set_version_id=cause_set_version_id, **standard_cache_options) is_vr = data_type_id in [9, 10] # run hiv correction on VR, but not Other_Maternal # countries to correct will be further pruned by the master cause # selections csv in the hiv corrector class if not skip_hiv_correction(source) and is_vr: # get location hierarchy loc_meta_df = get_current_location_hierarchy( location_set_version_id=location_set_version_id, **standard_cache_options) # get population pop_meta_df = get_pop(pop_run_id=pop_run_id, **standard_cache_options) # get age metadata age_meta_df = get_ages(**standard_cache_options) # get the country iso3 = get_value_from_nid( nid, 'iso3', extract_type_id=extract_type_id, location_set_version_id=location_set_version_id) assert pd.notnull(iso3), "Could not find iso3 for nid {}, " \ "extract_type_id {}".format(nid, extract_type_id) hiv_corrector = HIVCorrector(df, iso3, code_system_id, pop_meta_df, cause_meta_df, loc_meta_df, age_meta_df, correct_garbage=False) print_log_message("Running hiv correction for iso3 {}".format(iso3)) df = hiv_corrector.get_computed_dataframe() if needs_injury_redistribution(source): print_log_message("Correcting injuries") if not 'loc_meta_df' in vars(): # get location hierarchy loc_meta_df = get_current_location_hierarchy( location_set_version_id=location_set_version_id, **standard_cache_options) injury_redistributor = InjuryRedistributor(df, loc_meta_df, cause_meta_df) df = injury_redistributor.get_computed_dataframe() # apply redistribution of LRI to tb in under 15, non-neonatal ages based # on location/year specific proportions print_log_message( "Applying special redistribution of LRI to TB in under 15") lri_tb_redistributor = LRIRedistributor(df, cause_meta_df) df = lri_tb_redistributor.get_computed_dataframe() # merge in raw and rd here because recodes and bridge mapping should # also apply to the causes that are in previous phases (raw deaths for # secret codes need to be moved up to their parent cause, for example) df = combine_with_rd_raw(df, nid, extract_type_id, location_set_version_id) val_cols = ['deaths', 'deaths_raw', 'deaths_corr', 'deaths_rd'] # run china VR rescaling if needs_subnational_rescale(source): china_rescaler = ChinaHospitalUrbanicityRescaler() df = china_rescaler.get_computed_dataframe(df) if needs_strata_collapse(source): # set site id to blank site id and collapse df['site_id'] = 2 group_cols = list(set(df.columns) - set(val_cols)) df = df.groupby(group_cols, as_index=False)[val_cols].sum() if is_vr: # drop if deaths are 0 across all current deaths columns df = df.loc[df[val_cols].sum(axis=1) != 0] # restrict causes based on code system print_log_message("Running bridge mapper") bridge_mapper = BridgeMapper(source, cause_meta_df, code_system) df = bridge_mapper.get_computed_dataframe(df) # run recodes based on expert opinion print_log_message("Enforcing some very hard priors (expert opinion)") expert_opinion_recoder = Recoder(cause_meta_df, source, code_system_id, data_type_id) df = expert_opinion_recoder.get_computed_dataframe(df) end_deaths = df['deaths'].sum() print_log_message("Checking no large loss or gain of deaths") if abs(orig_deaths - end_deaths) >= (.1 * end_deaths): diff = round(abs(orig_deaths - end_deaths), 2) old = round(abs(orig_deaths)) new = round(abs(end_deaths)) raise AssertionError("Change of {} deaths [{}] to [{}]".format( diff, old, new)) return df