def _score_and_explain_after_filters(project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""A helper function to give a score and an explanation for all advices in the module,
once some prerequisite filters have been met.
"""
if project.user_profile.has_car_driving_license != project_pb2.FALSE:
return scoring_base.NULL_EXPLAINED_SCORE
reasons = []
license_required = next(
(license.percent_required
for license in project.job_group_info().requirements.driving_licenses
if license.driving_license == job_pb2.CAR), 0)
if license_required:
reasons.append(
project.translate_string(
'le permis est important dans votre métier'))
score_modifier = 0
if _license_helps_mobility(project.details):
reasons.append(
project.translate_string('le permis augmenterait votre mobilité'))
score_modifier = 1
if not reasons:
return scoring_base.NULL_EXPLAINED_SCORE
score = min(
3,
score_modifier + (
# Example at 80% is civil engineer F1106.
3 if license_required > 80 else
# Example at 67% is translator E1108.
2 if license_required > 67 else
# Example at 50% is chiropractor J1408.
1 if license_required > 50 else 0))
return scoring_base.ExplainedScore(score, reasons)
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
reasons: List[str] = []
# For now we just match for people willing to move to the whole country.
# There might be cases where we should be able to recommend to people who want to move to
# their own region, but it would add complexity to find them.
is_not_ready_to_move = project.details.area_type < geo_pb2.COUNTRY
is_not_single = project.user_profile.family_situation != user_pb2.SINGLE
has_advanced_degree = project.user_profile.highest_degree >= job_pb2.LICENCE_MAITRISE
is_not_young = project.get_user_age() > 30
looks_only_for_cdi = project.details.employment_types == [job_pb2.CDI]
if (is_not_ready_to_move or is_not_young or is_not_single
or has_advanced_degree or looks_only_for_cdi):
return scoring_base.NULL_EXPLAINED_SCORE
reasons.append(
project.translate_string(
'vous nous avez dit être prêt%eFeminine à déménager'))
reasons.append(
project.translate_string('vous êtes disponible familialement'))
if len(self._get_seasonal_departements(project).departement_stats) > 1:
reasons.append(
project.translate_string(
"il y a plus d'offres saisonnières par habitants dans d'autres villes"
))
return scoring_base.ExplainedScore(2, reasons)
return scoring_base.NULL_EXPLAINED_SCORE
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute the score for a given project and explains it.
Requirements are:
- being between 16 and 30 y.o if having a handicap or between 16 and 25 otherwise
- having low or no experience (intern maximum)
"""
age = project.get_user_age()
seniority = project.details.seniority
reasons: List[str] = []
if age < 16 or seniority > project_pb2.INTERN:
return scoring_base.NULL_EXPLAINED_SCORE
if project.user_profile.has_handicap and age <= 30:
reasons = [
project.translate_string('vous avez entre 16 et 30 ans')
]
if age <= 25:
reasons = [
project.translate_string('vous avez entre 16 et 25 ans')
]
if not reasons:
return scoring_base.NULL_EXPLAINED_SCORE
return scoring_base.ExplainedScore(2, reasons)
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
close_jobs = self.get_close_jobs(project)
search_since_nb_months = round(project.get_search_length_now())
score_modifier = 0
reasons: List[str] = []
if len(close_jobs.close_jobs) + len(close_jobs.evolution_jobs) < 2:
return scoring_base.NULL_EXPLAINED_SCORE
# TODO(cyrille): Make this more robust.
force_in_stuck_market = None
# TODO(cyrille): Rather use market_stress to avoid depending on diagnostic to be computed.
if project.details.diagnostic.category_id == 'stuck-market':
force_in_stuck_market = scoring_base.ExplainedScore(1, reasons)
if project.get_user_age() >= 45:
return force_in_stuck_market or scoring_base.NULL_EXPLAINED_SCORE
if project.details.passionate_level >= project_pb2.PASSIONATING_JOB:
score_modifier = -1
else:
reasons.append(project.translate_string(
"vous n'êtes pas trop attaché à votre métier"))
if project.details.job_search_has_not_started or search_since_nb_months <= 1:
return scoring_base.ExplainedScore(2 + score_modifier, reasons)
reasons = [
project.translate_string('vous cherchez depuis {} mois')
.format(search_since_nb_months)]
if search_since_nb_months >= 12:
return scoring_base.ExplainedScore(3, reasons)
if search_since_nb_months >= 9:
return scoring_base.ExplainedScore(2, reasons)
if search_since_nb_months >= 6:
return scoring_base.ExplainedScore(1, reasons)
return force_in_stuck_market or scoring_base.NULL_EXPLAINED_SCORE
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
if (self._num_interviews_increase(project) >= 2 and
project.details.job_search_length_months <= 6):
return scoring_base.ExplainedScore(3, [project.translate_string(
"nous pensons qu'avec votre profil vous pourriez "
"décrocher plus d'entretiens")])
if project.details.diagnostic.category_id == 'bravo' and \
user_pb2.RESUME in project.user_profile.frustrations:
return scoring_base.ExplainedScore(1, [project.translate_string(
'vous nous avez dit avoir du mal à rédiger votre CV')])
return scoring_base.NULL_EXPLAINED_SCORE
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
if project.details.weekly_applications_estimate <= project_pb2.LESS_THAN_2 or \
project.details.job_search_length_months < 2:
return scoring_base.ExplainedScore(3, [project.translate_string(
'vous nous avez dit que vous en êtes au début de '
'vos candidatures')])
if project.details.diagnostic.category_id == 'bravo' and \
user_pb2.RESUME in project.user_profile.frustrations:
return scoring_base.ExplainedScore(1, [project.translate_string(
'vous nous avez dit avoir du mal à rédiger votre CV')])
return scoring_base.NULL_EXPLAINED_SCORE
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_expanded_card_data(self, project: scoring_base.ScoringProject) \
-> network_pb2.ContactLeads:
"""Retrieve data for the expanded card."""
contact_leads = self._list_contact_leads(project)
sorted_leads = sorted(contact_leads,
key=lambda l: (-len(l.filters), random.random()))
return network_pb2.ContactLeads(leads=[
network_pb2.ContactLead(
name=project.populate_template(
project.translate_string(template.name)),
email_example=project.populate_template(
project.translate_string(template.email_template)),
contact_tip=project.translate_string(template.contact_tip))
for template in sorted_leads
])
def _convert_to_reorient_jobs(
self, database: mongo.NoPiiMongoDatabase,
reorient_jobs: Iterable[job_pb2.RelatedJobGroup],
market_score_source: float, project: scoring_base.ScoringProject
) -> Iterator[reorient_jobbing_pb2.ReorientJob]:
for job in reorient_jobs:
# Here the market score improvement
# (job that the user is searching for vs recommended job)
# is overly simplified as offers gain.
# TODO(sil): Find a way to explain the market score improvement to the user.
# TODO(cyrille): Replace offers_percent_gain by stress_percent_loss to simplify
# client-side computations.
offers_gain = 100 * (
job.local_stats.imt.yearly_avg_offers_per_10_candidates /
market_score_source - 1)
job_group_info = jobs.get_group_proto(database,
job.job_group.rome_id,
project.user_profile.locale)
is_diploma_required = False
if job_group_info:
is_diploma_required = job_group_info.is_diploma_strictly_required
yield reorient_jobbing_pb2.ReorientJob(
name=job_group_info and job_group_info.name
or project.translate_string(job.job_group.name),
offers_percent_gain=offers_gain,
is_diploma_strictly_required=is_diploma_required)
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
local_jobbing = self.get_local_jobbing(project)
if len(local_jobbing.reorient_jobbing_jobs) < 2:
return scoring_base.NULL_EXPLAINED_SCORE
score_modifier = 0
reasons: List[str] = []
if project.details.passionate_level == project_pb2.LIFE_GOAL_JOB:
score_modifier = -2
if project.job_group_info().growth_2012_2022 < .1:
score_modifier = -1
if score_modifier >= 0:
reasons.append(project.translate_string(
'votre métier ne vous tient pas trop à cœur'))
if project.user_profile.highest_degree <= job_pb2.CAP_BEP:
return scoring_base.ExplainedScore(3 + score_modifier, reasons)
if project.user_profile.highest_degree <= job_pb2.BAC_BACPRO:
return scoring_base.ExplainedScore(max(2 + score_modifier, 1), reasons)
if project.user_profile.highest_degree <= job_pb2.BTS_DUT_DEUG:
return scoring_base.ExplainedScore(1, reasons)
return scoring_base.NULL_EXPLAINED_SCORE
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 score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
application_modes = project.job_group_info().application_modes.values()
first_modes = set(fap_modes.modes[0].mode
for fap_modes in application_modes)
first_modes.discard(job_pb2.UNDEFINED_APPLICATION_MODE)
if first_modes == {job_pb2.PERSONAL_OR_PROFESSIONAL_CONTACTS}:
return scoring_base.ExplainedScore(2, [
project.translate_string(
'les embauches se font surtout par les contacts personnels ou professionnels dans'
' votre métier')
])
return scoring_base.ExplainedScore(1, [
project.translate_string(
"c'est un bon moyen d'étendre votre réseau")
])
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
frustration_reasons = list(self._get_frustrations_reasons(project))
its_easy = project.translate_string(
"c'est plus facile à faire qu'on peut le croire")
if frustration_reasons or project.get_search_length_now() > 3:
return scoring_base.ExplainedScore(
2, frustration_reasons or [its_easy])
return scoring_base.ExplainedScore(1, [its_easy])
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
application_modes = project.job_group_info().application_modes.values()
first_modes = set(fap_modes.modes[0].mode
for fap_modes in application_modes
if len(fap_modes.modes))
if job_pb2.SPONTANEOUS_APPLICATION in first_modes:
return scoring_base.ExplainedScore(3, [
project.translate_string(
"c'est le canal de recrutement n°1 pour votre métier")
])
# In the category missing-diploma, we always have the alternance strategy which requires
# spontaneous application data.
if project.details.diagnostic.category_id == 'missing-diploma':
return scoring_base.ExplainedScore(2, [
project.translate_string(
"c'est le meilleur moyen de trouver un contrat en alternance"
)
])
second_modes = set(fap_modes.modes[1].mode
for fap_modes in application_modes
if len(fap_modes.modes) > 1)
if job_pb2.SPONTANEOUS_APPLICATION in second_modes:
return scoring_base.ExplainedScore(2, [
project.translate_string(
"c'est un des meilleurs canaux de recrutement pour votre métier"
)
])
if project.details.diagnostic.category_id == 'bravo' and \
user_pb2.NO_OFFERS in project.user_profile.frustrations:
return scoring_base.ExplainedScore(2, [
project.translate_string(
"vous nous avez dit ne pas trouver assez d'offres.")
])
return scoring_base.NULL_EXPLAINED_SCORE
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
# This job group has jobs that are too different to consider them as a
# small change.
# TODO(pascal): Check for other such job groups and move the config to
# a class property.
if project.details.target_job.job_group.rome_id == 'K2401':
return scoring_base.NULL_EXPLAINED_SCORE
specific_jobs = project.requirements().specific_jobs
if not specific_jobs or specific_jobs[0].code_ogr == project.details.target_job.code_ogr:
return scoring_base.NULL_EXPLAINED_SCORE
try:
target_job_percentage = next(
j.percent_suggested for j in specific_jobs
if j.code_ogr == project.details.target_job.code_ogr)
except StopIteration:
target_job_percentage = 0
has_way_better_job = target_job_percentage + 30 < specific_jobs[0].percent_suggested
has_better_job = target_job_percentage + 5 < specific_jobs[0].percent_suggested
is_looking_for_new_job = project.details.kind == project_pb2.REORIENTATION
reasons = []
if has_way_better_job:
reasons.append(project.translate_string(
"il y a beaucoup plus d'offres dans des métiers proches"))
elif (project.get_search_length_at_creation() > 6 and has_better_job):
reasons.append(project.translate_string(
"il y a plus d'offres dans des métiers proches"))
if is_looking_for_new_job:
reasons.append(project.translate_string(
'vous nous avez dit vouloir vous reconvertir'))
if reasons:
return scoring_base.ExplainedScore(3, reasons)
return scoring_base.ExplainedScore(2, [project.translate_string(
"il y a un bon nombre d'offres dans des métiers proches")])
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
if project.details.previous_job_similarity != project_pb2.NEVER_DONE or \
project.get_user_age() > 25:
return scoring_base.NULL_EXPLAINED_SCORE
explanations: List[str] = []
score: float = 2
if project.details.network_estimate <= 2:
explanations.append(
project.translate_string(
'ça vous aide à développer votre réseau'))
score += .5
if project.details.passionate_level >= project_pb2.PASSIONATING_JOB:
explanations.append(
project.translate_string('ça montre votre motivation'))
score += .5
return scoring_base.ExplainedScore(score, explanations)
def _get_frustrations_reasons(self, project: scoring_base.ScoringProject) \
-> Set[str]:
discrimination_reason = project.translate_string(
'vous nous avez dit que les employeurs ne '
'vous donnent pas votre chance')
relevant_frustrations = {
user_pb2.AGE_DISCRIMINATION:
discrimination_reason,
user_pb2.ATYPIC_PROFILE:
discrimination_reason,
user_pb2.NO_OFFERS:
project.translate_string(
"vous nous avez dit ne pas trouver d'offres correspondant "
'à vos critères'),
user_pb2.SEX_DISCRIMINATION:
discrimination_reason,
}
frustration_reasons = {
relevant_frustrations[frustration]
for frustration in project.user_profile.frustrations
if frustration in relevant_frustrations
}
return frustration_reasons
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
"""Compute a score for the given ScoringProject."""
relevant_salons = self._get_relevant_salons(project)
if not relevant_salons:
return scoring_base.NULL_EXPLAINED_SCORE
reasons = []
# TODO(cyrille): Refine this depending on salons' locations.
if project.details.area_type >= geo_pb2.COUNTRY:
reasons.append(
project.translate_string('vous êtes mobile partout en France'))
elif any(salon.HasField('location') for salon in relevant_salons):
reasons.append(
project.translate_string(
'certains salons concernent votre zone géographique'))
if any(salon.job_group_ids for salon in relevant_salons):
reasons.append(
f'des entreprises {project.job_group_info().in_domain} recherchent du monde'
)
return scoring_base.ExplainedScore(1, reasons)
def score_and_explain(self, project: scoring_base.ScoringProject) \
-> scoring_base.ExplainedScore:
if project.details.diagnostic.category_id == 'enhance-methods-to-interview':
return scoring_base.ExplainedScore(3, [])
reasons = [project.translate_string(
"vous nous avez dit avoir passé beaucoup d'entretiens sans succès")]
if project.details.total_interview_count < 0:
num_interviews = 0
elif project.details.total_interview_count > 0:
num_interviews = project.details.total_interview_count
else:
num_interviews = _NUM_INTERVIEWS.get(project.details.total_interviews_estimate, 0)
num_monthly_interviews = num_interviews / (project.details.job_search_length_months or 1)
if num_monthly_interviews > _max_monthly_interviews(project):
return scoring_base.ExplainedScore(3, reasons)
# Whatever the number of month of search, trigger 3 if the user did more than 5 interviews:
if num_interviews >= _NUM_INTERVIEWS[project_pb2.DECENT_AMOUNT]:
return scoring_base.ExplainedScore(3, reasons)
if project.details.diagnostic.category_id == 'bravo':
return scoring_base.ExplainedScore(1, [])
return scoring_base.NULL_EXPLAINED_SCORE
