Author: Pascal, pascal@bayesimpact.org
Date: 2019-10-23
In October 2019 a new version of the ROME was released. I want to investigate what changed and whether we need to do anything about it.
You might not be able to reproduce this notebook, mostly because it requires to have the two versions of the ROME in your data/rome/csv folder which happens only just before we switch to v341. You will have to trust me on the results ;-)
Skip the run test because it requires older versions of the ROME.
In [1]:
import collections
import glob
import os
from os import path
import matplotlib_venn
import pandas as pd
rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')
OLD_VERSION = '339'
NEW_VERSION = '341'
old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(NEW_VERSION)))
First let's check if there are new or deleted files (only matching by file names).
In [2]:
new_files = new_version_files - frozenset(f.replace(OLD_VERSION, NEW_VERSION) for f in old_version_files)
deleted_files = old_version_files - frozenset(f.replace(NEW_VERSION, OLD_VERSION) for f in new_version_files)
print('{:d} new files'.format(len(new_files)))
print('{:d} deleted files'.format(len(deleted_files)))
Let's find this deleted file:
In [3]:
list(deleted_files)
Out[3]:
OK, not too bad: this is a file we've never used. We'll still check its content to make sure:
In [4]:
en_tete_regroupement = pd.read_csv(list(deleted_files)[0])
display(en_tete_regroupement.head())
print(f'{len(en_tete_regroupement)} rows')
It looks like a header for some kinds of activities. Not that big a deal.
Now let's set up a dataset that, for each table, links both the old and the new file together.
In [5]:
# Load all ROME datasets for the two versions we compare.
VersionedDataset = collections.namedtuple('VersionedDataset', ['basename', 'old', 'new'])
def read_csv(filename):
try:
return pd.read_csv(filename)
except pd.errors.ParserError:
display(f'While parsing: {filename}')
raise
rome_data = [VersionedDataset(
basename=path.basename(f),
old=read_csv(f.replace(NEW_VERSION, OLD_VERSION)),
new=read_csv(f))
for f in sorted(new_version_files)]
def find_rome_dataset_by_name(data, partial_name):
for dataset in data:
if 'unix_{}_v{}_utf8.csv'.format(partial_name, NEW_VERSION) == dataset.basename:
return dataset
raise ValueError('No dataset named {}, the list is\n{}'.format(partial_name, [d.basename for d in data]))
Let's make sure the structure hasn't changed:
In [6]:
for dataset in rome_data:
if set(dataset.old.columns) != set(dataset.new.columns):
print('Columns of {} have changed.'.format(dataset.basename))
OK, let's check what's new in there:
In [7]:
jobs = find_rome_dataset_by_name(rome_data, 'referentiel_appellation')
print(f'New columns: {set(jobs.old.columns) - set(jobs.new.columns)}')
print(f'Old columns: {set(jobs.new.columns) - set(jobs.old.columns)}')
Ouch, it seems they have decided to rename one column. Lucky us we never used it.
Now let's see for each file if there are more or less rows.
In [8]:
same_row_count_files = 0
for dataset in rome_data:
diff = len(dataset.new.index) - len(dataset.old.index)
if diff > 0:
print('{:d}/{:d} values added in {}'.format(
diff, len(dataset.new.index), dataset.basename))
elif diff < 0:
print('{:d}/{:d} values removed in {}'.format(
-diff, len(dataset.old.index), dataset.basename))
else:
same_row_count_files += 1
print('{:d}/{:d} files with the same number of rows'.format(
same_row_count_files, len(rome_data)))
There are some minor changes in many files, but based on my knowledge of ROME, none from the main files.
The most interesting ones are in referentiel_appellation, item, and liens_rome_referentiels, so let's see more precisely.
In [9]:
jobs = find_rome_dataset_by_name(rome_data, 'referentiel_appellation')
new_jobs = set(jobs.new.code_ogr) - set(jobs.old.code_ogr)
obsolete_jobs = set(jobs.old.code_ogr) - set(jobs.new.code_ogr)
stable_jobs = set(jobs.new.code_ogr) & set(jobs.old.code_ogr)
matplotlib_venn.venn2((len(obsolete_jobs), len(new_jobs), len(stable_jobs)), (OLD_VERSION, NEW_VERSION));
Alright, so the only change seems to be 1 new job added. Let's take a look (only showing interesting fields):
In [10]:
pd.options.display.max_colwidth = 2000
jobs.new[jobs.new.code_ogr.isin(new_jobs)][['code_ogr', 'libelle_appellation_long', 'code_rome']]
Out[10]:
That's indeed a new job related to digitalization of the construction industry.
OK, let's check at the changes in items:
In [11]:
items = find_rome_dataset_by_name(rome_data, 'item')
new_items = set(items.new.code_ogr) - set(items.old.code_ogr)
obsolete_items = set(items.old.code_ogr) - set(items.new.code_ogr)
stable_items = set(items.new.code_ogr) & set(items.old.code_ogr)
matplotlib_venn.venn2((len(obsolete_items), len(new_items), len(stable_items)), (OLD_VERSION, NEW_VERSION));
As anticipated it is a very minor change (hard to see it visually): there are 17 new ones have been created. Let's have a look at them.
In [12]:
items.new[items.new.code_ogr.isin(new_items)].head()
Out[12]:
The new ones seem legit to me.
The changes in liens_rome_referentiels include changes for those items, so let's only check the changes not related to those.
In [13]:
links = find_rome_dataset_by_name(rome_data, 'liens_rome_referentiels')
old_links_on_stable_items = links.old[links.old.code_ogr.isin(stable_items)]
new_links_on_stable_items = links.new[links.new.code_ogr.isin(stable_items)]
old = old_links_on_stable_items[['code_rome', 'code_ogr']]
new = new_links_on_stable_items[['code_rome', 'code_ogr']]
links_merged = old.merge(new, how='outer', indicator=True)
links_merged['_diff'] = links_merged._merge.map({'left_only': 'removed', 'right_only': 'added'})
links_merged._diff.value_counts()
Out[13]:
So in addition to the added items, there are few fixes. Let's have a look at them:
In [14]:
job_group_names = find_rome_dataset_by_name(rome_data, 'referentiel_code_rome').new.set_index('code_rome').libelle_rome
item_names = items.new.set_index('code_ogr').libelle.drop_duplicates()
links_merged['job_group_name'] = links_merged.code_rome.map(job_group_names)
links_merged['item_name'] = links_merged.code_ogr.map(item_names)
display(links_merged[links_merged._diff == 'removed'].dropna().head(5))
links_merged[links_merged._diff == 'added'].dropna().head(5)
Out[14]:
Those fixes make sense (not sure why they were not done before, but let's not complain: it is fixed now).
That's all the changes we wanted to check (no change in referentiel_code_rome).
The new version of ROME, v341, introduces very minor changes which reflect quite well what they wrote in their changelog.
Technically they were some format changes (some of them were discovered while preparing this notebook):
csv folder.en_tete_regroupement containing phrasing headers that we've never used has been removedThe transition should be transparent with a very small advantage over the old version.