Author: Pascal, pascal@bayesimpact.org
Date: 2017-11-08
In November 2017 a new version of the ROME was realeased. 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 v333. 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 = '332'
NEW_VERSION = '333'
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)))
So we have the same set of files in both versions: good start.
Now let's set up a dataset that, for each table, links both the old and the new file together.
In [3]:
# Load all ROME datasets for the two versions we compare.
VersionedDataset = collections.namedtuple('VersionedDataset', ['basename', 'old', 'new'])
rome_data = [VersionedDataset(
basename=path.basename(f),
old=pd.read_csv(f.replace(NEW_VERSION, OLD_VERSION)),
new=pd.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 [4]:
for dataset in rome_data:
if set(dataset.old.columns) != set(dataset.new.columns):
print('Columns of {} have changed.'.format(dataset.basename))
All files have the same columns as before: still good.
Now let's see for each file if there are more or less rows.
In [5]:
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 [6]:
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 15 new jobs added. Let's take a look (only showing interesting fields):
In [7]:
pd.options.display.max_colwidth = 2000
jobs.new[jobs.new.code_ogr.isin(new_jobs)][['code_ogr', 'libelle_appellation_long', 'code_rome']]
Out[7]:
They mostly seem related to the digital industry, e.g. we finally have a job for John, our UX Designer. But there are also few others.
OK, let's check at the changes in items:
In [8]:
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): some items are now obsolete and new ones have been created. Let's have a look.
In [9]:
items.old[items.old.code_ogr.isin(obsolete_items)].tail()
Out[9]:
In [10]:
items.new[items.new.code_ogr.isin(new_items)].head()
Out[10]:
Those entries look legitimate.
The changes in liens_rome_referentiels include changes for those items, so let's only check the changes not related to those.
In [11]:
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[11]:
So in addition to the added and remove items, there are 48 fixes. Let's have a look:
In [12]:
job_group_names = find_rome_dataset_by_name(rome_data, 'referentiel_code_rome').old.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)
links_merged.dropna().head(10)
Out[12]:
Those fixes make sense (not sure why they were not done before, but let's not complain: it is fixed now).
The new version of ROME, v333, introduces very minor changes which reflect quite well what they wrote in their changelog. The transition should be transparent with a very small advantage over the old version.