You've already forked RekomenciBackend
Merge branch 'main' of gitlab.prodcontest.com:team-39/backend
* 'main' of gitlab.prodcontest.com:team-39/backend: fix(): reference real containerfiles in docker compose feat(): salary prediction
This commit is contained in:
+1
-1
@@ -64,7 +64,7 @@ services:
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ml:
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build:
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context: .
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dockerfile: Containerfile.ml
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dockerfile: Containerfile
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target: ml
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tags:
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- template-project-ml:latest
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@@ -0,0 +1,208 @@
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from collections import defaultdict
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from decimal import Decimal
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from operator import itemgetter
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from Levenshtein import ratio
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from template_project.application.common.data_structure import to_data_structure
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from template_project.application.common.interactor import to_interactor
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from template_project.application.resume.entity import ResumeId
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@to_data_structure
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class VacancyInput:
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vacancy_id: str
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from_salary: Decimal
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to_salary: Decimal
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key_skills: list[str]
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resume_similarity: float
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@to_data_structure
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class PredictSalaryRequest:
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resume_id: ResumeId
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key_skills: list[str]
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vacancies: list[VacancyInput]
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@to_data_structure
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class PredictSalaryResponse:
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salary_from: Decimal
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salary_to: Decimal
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recommended_skills: list[str]
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@to_interactor
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class PredictSalaryInteractor:
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async def execute(self, request: PredictSalaryRequest) -> PredictSalaryResponse:
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salary_from, salary_to = self._predict_salary(request.vacancies, request.key_skills)
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recommended_skills = self._recommend_skills(request.vacancies, request.key_skills)
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return PredictSalaryResponse(
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salary_from=salary_from,
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salary_to=salary_to,
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recommended_skills=recommended_skills,
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)
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def _predict_salary(self, vacancies: list[VacancyInput], resume_skills: list[str]) -> tuple[Decimal, Decimal]:
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if not vacancies:
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return Decimal(50000), Decimal(80000)
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vacancy_weights: list[float] = []
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for vacancy in vacancies:
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skills_similarity = self._calculate_skills_similarity(resume_skills, vacancy.key_skills)
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vacancy_weight = 0.8 * vacancy.resume_similarity + 0.2 * skills_similarity
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vacancy_weights.append(vacancy_weight)
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total_weight = sum(vacancy_weights)
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if total_weight == 0:
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return Decimal(50000), Decimal(80000)
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weighted_from_sum = Decimal(0)
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weighted_to_sum = Decimal(0)
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for vacancy, weight in zip(vacancies, vacancy_weights, strict=False):
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weighted_from_sum += vacancy.from_salary * Decimal(str(weight))
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weighted_to_sum += vacancy.to_salary * Decimal(str(weight))
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predicted_from = weighted_from_sum / Decimal(str(total_weight))
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predicted_to = weighted_to_sum / Decimal(str(total_weight))
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return predicted_from.quantize(Decimal("0.01")), predicted_to.quantize(Decimal("0.01"))
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def _recommend_skills(
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self,
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vacancies: list[VacancyInput],
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resume_skills: list[str],
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) -> list[str]:
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if not vacancies:
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return []
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skill_salaries, skill_frequencies = self._collect_skill_statistics(vacancies)
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filtered_skills = self._filter_skills_by_frequency(skill_frequencies, min_frequency=3)
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candidate_skills = self._filter_skills_by_resume_similarity(filtered_skills, resume_skills)
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if not candidate_skills:
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return []
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skill_scores = self._calculate_skill_scores(candidate_skills, skill_salaries, skill_frequencies)
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return self._get_top_skills(skill_scores, top_n=3)
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def _collect_skill_statistics(
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self, vacancies: list[VacancyInput]
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) -> tuple[dict[str, list[Decimal]], dict[str, int]]:
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skill_salaries: dict[str, list[Decimal]] = defaultdict(list)
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skill_frequencies: dict[str, int] = defaultdict(int)
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for vacancy in vacancies:
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avg_salary = (vacancy.from_salary + vacancy.to_salary) / Decimal(2)
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for skill in vacancy.key_skills:
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normalized_skill = skill.lower().strip()
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skill_salaries[normalized_skill].append(avg_salary)
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skill_frequencies[normalized_skill] += 1
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return skill_salaries, skill_frequencies
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def _filter_skills_by_frequency(
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self,
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skill_frequencies: dict[str, int],
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min_frequency: int = 3,
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) -> set[str]:
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return {skill for skill, frequency in skill_frequencies.items() if frequency >= min_frequency}
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def _filter_skills_by_resume_similarity(
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self,
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skills: set[str],
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resume_skills: list[str],
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) -> list[str]:
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resume_skills_normalized = {skill.lower().strip() for skill in resume_skills}
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candidate_skills: list[str] = []
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for skill in skills:
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is_already_in_resume = any(
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self._is_skill_similar(skill, resume_skill) for resume_skill in resume_skills_normalized
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)
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if not is_already_in_resume:
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candidate_skills.append(skill)
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return candidate_skills
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def _calculate_skill_scores(
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self,
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candidate_skills: list[str],
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skill_salaries: dict[str, list[Decimal]],
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skill_frequencies: dict[str, int],
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) -> list[tuple[str, float]]:
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skill_avg_salaries: dict[str, Decimal] = {
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skill: sum(salaries) / Decimal(str(len(salaries)))
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for skill, salaries in skill_salaries.items()
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if skill in candidate_skills
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}
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frequencies = [skill_frequencies[skill] for skill in candidate_skills]
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avg_salaries = [float(skill_avg_salaries[skill]) for skill in candidate_skills]
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min_freq = min(frequencies)
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max_freq = max(frequencies)
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min_salary = min(avg_salaries)
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max_salary = max(avg_salaries)
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skill_scores: list[tuple[str, float]] = []
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for skill in candidate_skills:
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normalized_freq = self._normalize(float(skill_frequencies[skill]), min_freq, max_freq)
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normalized_salary = self._normalize(float(skill_avg_salaries[skill]), min_salary, max_salary)
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score = normalized_freq + normalized_salary
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skill_scores.append((skill, score))
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return skill_scores
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def _get_top_skills(self, skill_scores: list[tuple[str, float]], top_n: int = 3) -> list[str]:
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skill_scores.sort(key=itemgetter(1), reverse=True)
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return [skill for skill, _ in skill_scores[:top_n]]
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def _normalize(self, value: float, min_val: float, max_val: float) -> float:
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if max_val == min_val:
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return 0.0
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return (value - min_val) / (max_val - min_val)
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def _is_skill_similar(self, skill1: str, skill2: str, threshold: float = 0.7) -> bool:
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return ratio(skill1.lower().strip(), skill2.lower().strip()) >= threshold
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def _calculate_skills_similarity(self, resume_skills: list[str], vacancy_skills: list[str]) -> float:
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if not resume_skills or not vacancy_skills:
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return 0.0
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resume_skills_normalized = {skill.lower().strip() for skill in resume_skills}
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vacancy_skills_normalized = {skill.lower().strip() for skill in vacancy_skills}
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matched_resume_skills = set()
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matched_vacancy_skills = set()
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for resume_skill in resume_skills_normalized:
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best_match_ratio = 0.0
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best_match_skill = None
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for vacancy_skill in vacancy_skills_normalized:
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if vacancy_skill in matched_vacancy_skills:
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continue
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similarity_ratio = ratio(resume_skill, vacancy_skill)
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if similarity_ratio > best_match_ratio:
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best_match_ratio = similarity_ratio
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best_match_skill = vacancy_skill
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if best_match_ratio >= 0.7 and best_match_skill is not None:
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matched_resume_skills.add(resume_skill)
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matched_vacancy_skills.add(best_match_skill)
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intersection_size = len(matched_resume_skills)
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union_size = len(resume_skills_normalized | vacancy_skills_normalized)
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if union_size == 0:
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return 0.0
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return intersection_size / union_size
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@@ -14,7 +14,7 @@ from fastapi.middleware.cors import CORSMiddleware
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from template_project.ml.configuration import load_configuration
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from template_project.ml.ioc.make import make_ioc
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from template_project.ml.routes import embedding, healthcheck, predict
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from template_project.ml.routes import embed, healthcheck, predict
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LOG_CONFIG: Final = {
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"version": 1,
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@@ -55,7 +55,7 @@ def make_asgi_application(
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allow_headers=["*"],
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)
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app.include_router(healthcheck.router)
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app.include_router(embedding.router)
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app.include_router(embed.router)
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app.include_router(predict.router)
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setup_dishka(container=ioc, app=app)
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@@ -1,38 +0,0 @@
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from decimal import Decimal
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from template_project.application.common.data_structure import to_data_structure
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from template_project.application.common.interactor import to_interactor
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from template_project.application.resume.entity import ResumeId
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@to_data_structure
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class VacancyInput:
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vacancy_id: str
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from_salary: Decimal
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to_salary: Decimal
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key_skills: list[str]
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resume_similarity: float
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@to_data_structure
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class PredictSalaryRequest:
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resume_id: ResumeId
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key_skills: list[str]
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vacancies: list[VacancyInput]
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@to_data_structure
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class PredictSalaryResponse:
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salary_from: Decimal
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salary_to: Decimal
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recommended_skills: list[str]
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@to_interactor
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class PredictSalaryInteractor:
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async def execute(self, request: PredictSalaryRequest) -> PredictSalaryResponse:
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return PredictSalaryResponse(
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salary_from=Decimal(50000),
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salary_to=Decimal(80000),
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recommended_skills=["python", "django", "postgresql"],
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)
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@@ -1,6 +1,6 @@
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from dishka import BaseScope, Provider, Scope, provide_all
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from template_project.ml.interactors.predict_salary import PredictSalaryInteractor
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from template_project.application.resume.interactors.predict_salary import PredictSalaryInteractor
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class InteractorProvider(Provider):
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@@ -6,7 +6,7 @@ from fastapi import APIRouter
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from pydantic import BaseModel, Field
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from template_project.application.resume.entity import ResumeId
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from template_project.ml.interactors.predict_salary import (
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from template_project.application.resume.interactors.predict_salary import (
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PredictSalaryInteractor,
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PredictSalaryRequest,
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VacancyInput,
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@@ -82,14 +82,17 @@ class PredictSalaryResponseModel(BaseModel):
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@router.post(
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"/predict_salary",
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summary="Predict salary",
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"/predict",
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summary="Predict salary and recommend skills",
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description="Predict salary range and recommend skills based on resume and relevant vacancies",
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responses={
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200: {"description": "Salary prediction generated successfully", "model": PredictSalaryResponseModel},
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200: {
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"description": "Salary prediction and skills recommendation generated successfully",
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"model": PredictSalaryResponseModel,
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},
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},
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)
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async def predict_salary(
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async def predict(
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request: PredictSalaryRequestModel,
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interactor: FromDishka[PredictSalaryInteractor],
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||||
) -> PredictSalaryResponseModel:
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|
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@@ -1438,29 +1438,14 @@ wheels = [
|
||||
|
||||
[[package]]
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||||
name = "numpy"
|
||||
version = "2.1.2"
|
||||
version = "1.26.3"
|
||||
source = { registry = "https://download.pytorch.org/whl/cpu" }
|
||||
wheels = [
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d7bf0a4f9f15b32b5ba53147369e94296f5fffb783db5aacc1be15b4bf72f43b" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b1d0fcae4f0949f215d4632be684a539859b295e2d0cb14f78ec231915d644db" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:f751ed0a2f250541e19dfca9f1eafa31a392c71c832b6bb9e113b10d050cb0f1" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:bd33f82e95ba7ad632bc57837ee99dba3d7e006536200c4e9124089e1bf42426" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1b8cde4f11f0a975d1fd59373b32e2f5a562ade7cde4f85b7137f3de8fbb29a0" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d95f286b8244b3649b477ac066c6906fbb2905f8ac19b170e2175d3d799f4df" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-win_amd64.whl", hash = "sha256:456e3b11cb79ac9946c822a56346ec80275eaf2950314b249b512896c0d2505e" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a84498e0d0a1174f2b3ed769b67b656aa5460c92c9554039e11f20a05650f00d" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4d6ec0d4222e8ffdab1744da2560f07856421b367928026fb540e1945f2eeeaf" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:259ec80d54999cc34cd1eb8ded513cb053c3bf4829152a2e00de2371bd406f5e" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:675c741d4739af2dc20cd6c6a5c4b7355c728167845e3c6b0e824e4e5d36a6c3" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05b2d4e667895cc55e3ff2b56077e4c8a5604361fc21a042845ea3ad67465aa8" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:43cca367bf94a14aca50b89e9bc2061683116cfe864e56740e083392f533ce7a" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-win_amd64.whl", hash = "sha256:f2ded8d9b6f68cc26f8425eda5d3877b47343e68ca23d0d0846f4d312ecaa445" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2ffef621c14ebb0188a8633348504a35c13680d6da93ab5cb86f4e54b7e922b5" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:ad369ed238b1959dfbade9018a740fb9392c5ac4f9b5173f420bd4f37ba1f7a0" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d82075752f40c0ddf57e6e02673a17f6cb0f8eb3f587f63ca1eaab5594da5b17" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:1600068c262af1ca9580a527d43dc9d959b0b1d8e56f8a05d830eea39b7c8af6" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a26ae94658d3ba3781d5e103ac07a876b3e9b29db53f68ed7df432fd033358a8" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13311c2db4c5f7609b462bc0f43d3c465424d25c626d95040f073e30f7570e35" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:a7081fd19a6d573e1a05e600c82a1c421011db7935ed0d5c483e9dd96b99cf13" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:12c70ac274b32bc00c7f61b515126c9205323703abb99cd41836e8125ea0043e" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7f784e13e598e9594750b2ef6729bcd5a47f6cfe4a12cca13def35e06d8163e3" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5f24750ef94d56ce6e33e4019a8a4d68cfdb1ef661a52cdaee628a56d2437419" },
|
||||
{ url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-win_amd64.whl", hash = "sha256:da4b0c6c699a0ad73c810736303f7fbae483bcb012e38d7eb06a5e3b432c981b" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
Reference in New Issue
Block a user