def score_to_hundred(self, project: scoring_base.ScoringProject) -> float:
"""Compute a score for the given ScoringProject."""
area_type = project.details.area_type
if not area_type:
raise scoring_base.NotEnoughDataException()
if not project.imt_proto().yearly_avg_offers_per_10_candidates:
raise scoring_base.NotEnoughDataException()
num_better_departements = project.local_diagnosis(
).num_less_stressful_departements
# User is already in one of the top 3 départements.
if num_better_departements < 3:
raise scoring_base.NotEnoughDataException()
# Give a score centered around 50 corresponding to the user's mobility.
# The worse the current département is, the more extreme we set the
# score.
score_range = min(num_better_departements * 1.5, 100) / 2
if area_type >= geo_pb2.COUNTRY:
return 50 + score_range
if area_type >= geo_pb2.REGION:
return 50
if area_type >= geo_pb2.DEPARTEMENT:
return 50 - score_range / 2
return 50 - score_range
def get_local_jobbing(self, project: scoring_base.ScoringProject) \
-> reorient_jobbing_pb2.JobbingReorientJobs:
"""Get the jobbing opportunities for the departement."""
recommended_jobs = reorient_jobbing_pb2.JobbingReorientJobs()
departement_id = project.details.city.departement_id
gender = project.user_profile.gender
top_unqualified_jobs = self.list_reorient_jobbing_jobs(project)
current_job_market_score = project.imt_proto(
).yearly_avg_offers_per_10_candidates
if departement_id in top_unqualified_jobs:
for job in top_unqualified_jobs[
departement_id].departement_job_stats.jobs:
if current_job_market_score:
if job.market_score / current_job_market_score < 1 and \
not project.features_enabled.all_modules:
break
offers_gain = (
job.market_score / current_job_market_score - 1) * 100
else:
if job.market_score < 8:
# User is in an unknown market, we only show jobbing jobs that have at least
# some job offers.
break
offers_gain = 0
recommended_jobs.reorient_jobbing_jobs.add(
name=project.translate_string(
french.genderize_job(job, gender)),
offers_percent_gain=offers_gain)
return recommended_jobs
def _find_best_departements(unused_: Any, project: scoring_base.ScoringProject) \
-> list[project_pb2.DepartementScore]:
"""Find which are the best departement to relocate for a given job group."""
own_departement_offers = project.imt_proto(
).yearly_avg_offers_per_10_candidates
# If we do not have data about our own departement, we choose not to say anything.
if not own_departement_offers:
return []
best_departements = project.job_group_info().departement_scores
result: list[project_pb2.DepartementScore] = []
for dep in itertools.islice(best_departements, 10):
if dep.local_stats.imt.yearly_avg_offers_per_10_candidates <= own_departement_offers:
return result
offer_ratio = \
dep.local_stats.imt.yearly_avg_offers_per_10_candidates / own_departement_offers
result.append(
project_pb2.DepartementScore(name=project.translate_string(
geo.get_departement_name(project.database,
dep.departement_id)),
offer_ratio=offer_ratio))
return result
def get_local_jobbing(self, project: scoring_base.ScoringProject) \
-> reorient_jobbing_pb2.JobbingReorientJobs:
"""Get the jobbing opportunities for the departement."""
recommended_jobs = reorient_jobbing_pb2.JobbingReorientJobs()
departement_id = project.details.city.departement_id
gender = project.user_profile.gender
top_unqualified_jobs = self.list_reorient_jobbing_jobs(project)
current_job_market_score = project.imt_proto().yearly_avg_offers_per_10_candidates
if current_job_market_score and departement_id in top_unqualified_jobs:
for job in top_unqualified_jobs[departement_id].departement_job_stats.jobs:
if job.market_score / current_job_market_score < 1:
break
offers_gain = (job.market_score / current_job_market_score - 1) * 100
recommended_jobs.reorient_jobbing_jobs.add(
name=french.genderize_job(job, gender),
offers_percent_gain=offers_gain)
return recommended_jobs
