def get_package_code_ids(regression_specification, code_system_id):
"""Returns code_ids for garbage codes in package for given code system"""
package_description = regression_specification[
'package_descriptions'
][code_system_id]
packages = get_package_list(code_system_id)
package_id = packages.loc[
packages['package_description'] == package_description,
'package_id'
]
assert len(package_id) == 1
package_id = package_id.iloc[0]
pkg = json.load(open(
"FILEPATH".format(code_system_id, package_id)))
garbage_codes = list(pkg['garbage_codes'])
code_map = get_cause_map(code_system_id=code_system_id, force_rerun=False)
code_map['value'] = code_map['value'].str.replace(".", "")
is_package_garbage = code_map['value'].isin(garbage_codes)
garbage_code_ids = list(code_map.loc[
is_package_garbage,
'code_id'
].unique())
return garbage_code_ids
def get_code_ids_from_map_ids(self, map_id):
cs_map = get_cause_map(code_map_version_id=self.code_map_version_id,
**self.block_rerun)
pkg_map = get_clean_package_map_for_misdc(
self.code_system_id, remove_decimal=self.remove_decimal)
assert type(map_id) == str
if map_id.startswith('_p_'):
values = pkg_map.loc[pkg_map['map_id'] == map_id, 'value'].values
codes = cs_map.loc[cs_map.value.isin(values), 'code_id'].values
cause_id = 743
assert len(
codes) > 0, "No code_ids matching {} in the cause map".format(
map_id)
else:
codes = cs_map.loc[cs_map.cause_id == int(map_id),
'code_id'].values
cause_id = int(map_id)
if len(codes) == 0:
codes = cs_map.loc[cs_map.cause_id == self.cc_code,
'code_id'].values
cause_id = self.cc_code
code_id = codes[0]
code_dict = {map_id: code_id}
cause_dict = {map_id: cause_id}
return code_dict, cause_dict
def merge_on_scaled(df, move_df, adjust_id, code_system_id):
df = df.merge(move_df[[
'location_id', 'year_id', 'site_id', 'age_group_id', 'sex_id',
'map_id', 'misdiagnosed_scaled'
]],
how='outer')
if len(df.loc[df.cause_id.isnull()]) > 0:
assert all(
df.loc[df.cause_id.isnull(),
'map_id'].values == str(adjust_id)), 'Other missing map_ids'
cs_map = get_cause_map(code_system_id=code_system_id,
force_rerun=False,
block_rerun=True)
possible_codes = cs_map.loc[cs_map.cause_id == adjust_id,
'code_id'].values
use_target = True
if len(possible_codes) == 0:
possible_codes = cs_map.loc[cs_map.cause_id == 919,
'code_id'].values
use_target = False
target_code = possible_codes[0]
df.loc[df.code_id.isnull(), 'code_id'] = target_code
if use_target:
df.loc[df.cause_id.isnull(), 'cause_id'] = adjust_id
else:
df.loc[df.cause_id.isnull(), 'cause_id'] = 919
df['deaths'].fillna(0, inplace=True)
for exravar in ['nid', 'extract_type_id']:
df[exravar].fillna(method='pad', inplace=True)
for idvar in [i for i in list(df) if i.endswith('_id')] + ['nid']:
if df[idvar].dtype == 'float64':
df[idvar] = df[idvar].astype(int)
return df
def format_source(release_date):
# read the raw data and the WHO provided country/year map
df = read_data(release_date)
country_map = get_country_map(release_date)
# subset to just the new loc/years
# also apply location/year restrictions
df = subset_location_years(df, country_map)
# map location information
loc_meta = get_current_location_hierarchy(
location_set_id=CONF.get_id('location_set'),
location_set_version_id=CONF.get_id('location_set_version'),
force_rerun=False,
block_rerun=True)
df = get_gbd_locations(df, country_map, loc_meta)
# replicating age adjustments for WHO data from
df = adjust_WHO_ages(df)
# Limit the dataframe to the columns needed and melt ages wide to long
df = melt_df(df)
# assign age group ids
df = get_age_group_ids(df)
# map code ids and apply special remaps
cause_map = get_cause_map(1, force_rerun=False, block_rerun=True)
df = map_code_id(df, cause_map)
# add manual cols and cleanup
df = cleanup(df)
# apply nids
df = map_nids(df, release_date)
# apply any final special adjustments
df = apply_special_adjustments(df)
# final grouping and finalize formatting
df = df[FINAL_FORMATTED_COLS]
assert df.notnull().values.all()
df = df.groupby(ID_COLS, as_index=False)[VALUE_COL].sum()
# run finalize formatting
locals_present = finalize_formatting(df, SYSTEM_SOURCE, write=WRITE)
nid_meta_df = locals_present['nid_meta_df']
# update nid metadata status
if WRITE:
nid_extracts = nid_meta_df[[
'nid', 'extract_type_id'
]].drop_duplicates().to_records(index=False)
for nid, extract_type_id in nid_extracts:
nid = int(nid)
extract_type_id = int(extract_type_id)
update_nid_metadata_status(nid,
extract_type_id,
is_active=IS_ACTIVE,
is_mort_active=IS_MORT_ACTIVE)
def format_greenland():
grl_14 = pd.read_excel(GRL_PATH_2014)
grl_15 = pd.read_excel(GRL_PATH_2015)
grl_14 = grl_14[['Year', 'Sex', 'ICD-10', 'Age', 'Deaths']]
assert (grl_14.columns.values == grl_15.columns.values).all()
df = pd.concat([grl_14, grl_15])
df = clean_df(df)
df = get_sex_id(df)
df = get_nid(df)
df = get_age_group_id(df)
df = fix_codes(df)
cause_map = get_cause_map(code_system_id=1)
df = map_code_id(df, cause_map)
df = df[FINAL_FORMATTED_COLS]
df = df.groupby(ID_COLS, as_index=False)[VALUE_COL].sum()
system_source = 'Greenland_BoH_ICD10'
finalize_formatting(df, system_source, write=True)
def prep_code_metadata(self):
df = get_cause_map(
self.code_system_id,
**self.standard_cache_options
)
df = df[['code_id', 'value', 'cause_id']]
df = df.rename(columns={'value': 'raw_cause'})
return df
def format_sri_lanka():
#importing raw data from excel file
df = pd.read_excel(path)
#initial cleaning to fix rows/columns imported from excel
df = clean_df(df)
#incoming data has sex data in wide format, following function splits df by sex and manually sets age groups
#function returns initial_total (a float to compare against deaths later to ensure no deaths were lost in process)
df, initial_total = split_sexes(df)
#reshaping df age groups wide to long and assuring no deaths were lost
df = pd.melt(df,
id_vars=['cause_name', 'sex_id', 'value'],
var_name='age',
value_name='deaths')
assert np.allclose(initial_total, df.deaths.sum())
df = get_age_ids(df)
#importing and formatting rdp_frac dataframe to disaggregate tabulated icd10
rdp = pd.read_stata(rdp_path)
rdp = format_rdp_frac(rdp)
#disaggregating tabulated icd10 codes
df = disaggregate(df, rdp)
#mapping code_ids using cause map from engine room
cause_map = get_cause_map(code_system_id=9)
df = map_code_id(df, cause_map)
#addition of manually added columns
#Sri Lanka location id 17
df['location_id'] = 17
#nid 327524
df['nid'] = 327524
#data_type_id 9 (VR)
df['data_type_id'] = 9
#code_system_id 9 (ICD10_tabulated)
df['code_system_id'] = 9
#year: 2013, site: blank, representative_id: 1
df['year_id'] = 2013
df['site'] = ""
df['representative_id'] = 1
#grouping by ID_COLS and assigning system source
df = df[FINAL_FORMATTED_COLS]
df = df.groupby(ID_COLS, as_index=False)[VALUE_COL].sum()
system_source = "ICD10_tabulated"
#df.to_csv('/home/j/temp/mwcunnin/test_lka_out.csv', index = False, encoding = 'utf8')
#run finalize formatting
finalize_formatting(df, system_source, write=True)
def format_sri_lanka():
df = read_and_clean_data()
# incoming data has sex data in wide format, following function splits df by
# sex and manually sets age groups
# function returns initial_total (a float to compare against deaths later to
# ensure no deaths were lost in process)
df, initial_total = split_sexes(df)
# reshaping df age groups wide to long and assuring no deaths were lost
df = pd.melt(df,
id_vars=['cause_name', 'sex_id', 'value', 'year_id'],
var_name='age',
value_name='deaths')
assert np.allclose(initial_total, df.deaths.sum())
df = get_age_ids(df)
# importing and formatting rdp_frac dataframe to disaggregate tabulated icd10
rdp = pd.read_stata(rdp_path)
rdp = format_rdp_frac(rdp)
# disaggregating tabulated icd10 codes
df = disaggregate(df, rdp)
# mapping code_ids using cause map from engine room
cause_map = get_cause_map(code_system_id=9)
df = map_code_id(df, cause_map)
# addition of manually added columns
# Sri Lanka location id 17
df['location_id'] = 17
# nid 327524
df['nid'] = df.year_id.map({2007: 272959, 2013: 327524})
# data_type_id 9 (VR)
df['data_type_id'] = 9
# code_system_id 9 (ICD10_tabulated)
df['code_system_id'] = 9
# site: blank, representative_id: 1
df['site'] = ""
df['representative_id'] = 1
# grouping by ID_COLS and assigning system source
df = df[FINAL_FORMATTED_COLS]
assert df.notnull().values.all()
df[INT_COLS] = df[INT_COLS].astype(int)
df = df.groupby(ID_COLS, as_index=False)[VALUE_COL].sum()
system_source = "ICD10_tabulated"
# run finalize formatting
finalize_formatting(df, system_source, write=WRITE)
return df
def get_computed_dataframe(self, df):
"""Return mapped dataframe."""
# list of all cause columns
raw_cause_cols = MCoDMapper.get_code_columns(df)
df = MCoDMapper.fix_icd_codes(df, raw_cause_cols, self.code_system_id)
print_log_message("Mapping underlying cause/primary diagnosis")
cause_map = get_cause_map(code_map_version_id=self.code_map_version_id,
**self.cache_options)
code_map = MCoDMapper.prep_cause_map(cause_map)
df['cause_mapped'] = df['cause'].map(code_map)
print_log_message(
"Trimming ICD codes and remapping underlying cause/primary diagnosis"
)
df = MCoDMapper.trim_and_remap(df, {'cause': 'cause_mapped'}, code_map,
self.code_system_id)
report_if_merge_fail(df, 'cause_mapped', 'cause')
# merge on the cause_id for the underlying cause
df = df.rename(columns={'cause_mapped': 'code_id'})
df['code_id'] = df['code_id'].astype(int)
df = add_code_metadata(df,
'cause_id',
code_map_version_id=self.code_map_version_id,
**self.cache_options)
report_if_merge_fail(df, 'cause_id', 'code_id')
print_log_message("Mapping chain causes")
# get the special intermediate cause map
int_cause_map = self.prep_int_cause_map()
df = MCoDMapper.map_cause_codes(df, int_cause_map, self.int_cause)
print_log_message("Trimming ICD codes and remapping chain causes")
int_cause_cols = [x for x in df.columns if self.int_cause in x]
int_cause_col_dict = MCoDMapper.prep_raw_mapped_cause_dictionary(
raw_cause_cols, int_cause_cols)
df = MCoDMapper.trim_and_remap(df, int_cause_col_dict, int_cause_map,
self.code_system_id)
print_log_message(
"Identifying rows with intermediate cause of interest")
df = self.capture_int_cause(df, int_cause_cols)
if not self.drop_p2:
df = self.set_part2_flag(df)
return df
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
