feat: personalize scoring and add skills radar
This commit is contained in:
Jens
@@ -0,0 +1,252 @@
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from __future__ import annotations
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from dataclasses import dataclass
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from django.db.models import CharField, Exists, OuterRef, Q, QuerySet, Subquery
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from django.utils.text import slugify
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from apps.jobs.models import JobPosting, JobSourceAlias, ScoreRun
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from apps.profiles.models import SearchProfile
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from apps.profiles.taxonomy import SKILL_CHOICES
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from apps.sources.models import Source
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from .normalization import normalize_token
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from .relevance import it_relevance_query
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from .skill_terms import canonical_skill_key, skill_is_present
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@dataclass(frozen=True)
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class SkillDefinition:
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key: str
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label: str
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category: str
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category_key: str
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@dataclass(frozen=True)
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class SkillJobExample:
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id: str
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title: str
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employer: str
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@dataclass(frozen=True)
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class SkillDemandItem:
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key: str
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label: str
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category: str
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category_key: str
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vacancy_count: int
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share_percent: int
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explicit_count: int
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inferred_count: int
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covered: bool
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learning_focus: str
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search_term: str
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examples: tuple[SkillJobExample, ...]
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@dataclass(frozen=True)
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class SkillDemandReport:
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total_jobs: int
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jobs_with_signals: int
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signal_count: int
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covered_count: int
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gap_count: int
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coverage_percent: int
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items: tuple[SkillDemandItem, ...]
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categories: tuple[tuple[str, str], ...]
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selected_category: str
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selected_coverage: str
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def skill_catalog() -> tuple[SkillDefinition, ...]:
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return tuple(
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SkillDefinition(
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key=normalize_token(value),
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label=label,
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category=category,
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category_key=slugify(category),
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)
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for category, choices in SKILL_CHOICES
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for value, label in choices
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)
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def relevant_jobs_for_profile(profile: SearchProfile) -> QuerySet[JobPosting]:
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latest_score = ScoreRun.objects.filter(
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job=OuterRef("pk"),
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profile=profile,
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profile_version=profile.version,
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).order_by("-created_at")
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source_aliases = JobSourceAlias.objects.filter(job=OuterRef("pk"))
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active_source_aliases = source_aliases.filter(source__status=Source.Status.ACTIVE)
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return (
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JobPosting.objects.select_related("employer")
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.filter(status=JobPosting.Status.ACTIVE)
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.filter(it_relevance_query())
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.annotate(
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profile_recommendation=Subquery(
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latest_score.values("recommendation")[:1], output_field=CharField()
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),
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has_source_alias=Exists(source_aliases),
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has_active_source_alias=Exists(active_source_aliases),
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)
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.filter(
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profile_recommendation__in=(
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ScoreRun.Recommendation.STRONG,
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ScoreRun.Recommendation.POSSIBLE,
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ScoreRun.Recommendation.WEAK,
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)
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)
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.filter(Q(has_source_alias=False) | Q(has_active_source_alias=True))
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.order_by("-first_seen")
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)
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def _structured_skill_text(job: JobPosting) -> str:
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return normalize_token(
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" ".join(str(value) for value in [*job.skills_required, *job.skills_preferred])
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)
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def _inferred_skill_text(job: JobPosting) -> str:
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requirements = " ".join(str(value) for value in job.requirements)
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return normalize_token(f"{job.original_title} {requirements} {job.description_text}")
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def _learning_focus(definition: SkillDefinition) -> str:
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focuses = {
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"Microsoft & endpoint": (
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"Oefen een praktische beheer- of migratiecase en leg de relevante "
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"Microsoft Learn-modules vast."
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),
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"Netwerk & security": (
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"Bouw een kleine labcase rond configuratie, troubleshooting en beveiligde toegang."
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),
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"Back-up & monitoring": (
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"Oefen detectie, herstel en rapportering met een reproduceerbare homelabcase."
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),
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"Virtualisatie & platform": (
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"Maak een deployment- of platformlab en documenteer beschikbaarheid en herstel."
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),
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"IT-servicemanagement": (
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"Koppel de methodiek aan een concrete incident-, problem- of changecase."
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),
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"Digitale werkplek & adoptie": (
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"Werk een kleine governance- of adoptiecase uit met meetbare gebruikersimpact."
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),
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"Automation & scripting": (
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"Automatiseer één herkenbare beheertaak en publiceer een veilig voorbeeldscript."
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),
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"VoIP & telefonie": "Simuleer configuratie en troubleshooting in een kleine telefoniecase.",
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"Field service & uitvoering": (
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"Documenteer een end-to-end interventie: diagnose, oplossing en overdracht."
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),
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}
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return focuses.get(
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definition.category,
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"Maak een kleine praktijkcase en leg vast welke vacature-eis je ermee kunt aantonen.",
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)
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def _profile_skill_keys(profile: SearchProfile, catalog: tuple[SkillDefinition, ...]) -> set[str]:
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catalog_keys = {definition.key for definition in catalog}
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return {
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canonical
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for value in profile.desired_skills
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if (canonical := canonical_skill_key(str(value), catalog_keys)) is not None
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}
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def build_skill_demand_report(
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profile: SearchProfile,
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*,
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category: str = "all",
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coverage: str = "all",
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jobs: QuerySet[JobPosting] | list[JobPosting] | None = None,
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) -> SkillDemandReport:
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catalog = skill_catalog()
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valid_categories = {definition.category_key for definition in catalog}
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selected_category = category if category in valid_categories else "all"
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selected_coverage = coverage if coverage in {"all", "gap", "covered"} else "all"
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job_list = list(relevant_jobs_for_profile(profile) if jobs is None else jobs)
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profile_skills = _profile_skill_keys(profile, catalog)
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items: list[SkillDemandItem] = []
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jobs_with_signals: set[str] = set()
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for definition in catalog:
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examples: list[SkillJobExample] = []
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explicit_count = 0
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inferred_count = 0
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for job in job_list:
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explicit = skill_is_present(definition.key, _structured_skill_text(job))
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inferred = skill_is_present(definition.key, _inferred_skill_text(job))
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if not explicit and not inferred:
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continue
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if explicit:
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explicit_count += 1
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else:
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inferred_count += 1
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jobs_with_signals.add(str(job.pk))
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if len(examples) < 3:
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examples.append(
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SkillJobExample(
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id=str(job.pk),
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title=job.original_title,
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employer=job.employer_name,
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)
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)
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vacancy_count = explicit_count + inferred_count
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if vacancy_count == 0:
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continue
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items.append(
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SkillDemandItem(
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key=definition.key,
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label=definition.label,
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category=definition.category,
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category_key=definition.category_key,
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vacancy_count=vacancy_count,
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share_percent=round(vacancy_count / len(job_list) * 100) if job_list else 0,
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explicit_count=explicit_count,
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inferred_count=inferred_count,
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covered=definition.key in profile_skills,
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learning_focus=_learning_focus(definition),
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search_term=definition.key,
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examples=tuple(examples),
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)
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)
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ranked = sorted(
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items,
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key=lambda item: (-item.vacancy_count, -item.explicit_count, item.label.casefold()),
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)
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covered_count = sum(item.covered for item in ranked)
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gap_count = len(ranked) - covered_count
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filtered = tuple(
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item
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for item in ranked
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if (selected_category == "all" or item.category_key == selected_category)
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and (
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selected_coverage == "all"
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or (selected_coverage == "covered" and item.covered)
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or (selected_coverage == "gap" and not item.covered)
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)
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)
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categories = tuple(
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(slugify(category_label), category_label)
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for category_label, _choices in SKILL_CHOICES
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if any(item.category == category_label for item in ranked)
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)
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return SkillDemandReport(
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total_jobs=len(job_list),
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jobs_with_signals=len(jobs_with_signals),
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signal_count=len(ranked),
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covered_count=covered_count,
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gap_count=gap_count,
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coverage_percent=round(covered_count / len(ranked) * 100) if ranked else 0,
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items=filtered,
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categories=categories,
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selected_category=selected_category,
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selected_coverage=selected_coverage,
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)
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