Spaces:
Sleeping
Sleeping
| """Behavioral recruitability features from the 23 Redrob signals.""" | |
| from __future__ import annotations | |
| from datetime import datetime, timezone | |
| import numpy as np | |
| import pandas as pd | |
| def _to_dt(s): | |
| if not s: | |
| return None | |
| try: | |
| return datetime.strptime(str(s)[:10], "%Y-%m-%d") | |
| except Exception: | |
| return None | |
| def recruitability_features(df: pd.DataFrame) -> pd.DataFrame: | |
| """Compute a 0..1 recruitability composite plus sub-features.""" | |
| n = len(df) | |
| sig_col = "signals" | |
| if sig_col not in df.columns: | |
| return pd.DataFrame({ | |
| "recruit_open_to_work": np.zeros(n), | |
| "recruit_response_rate": np.zeros(n), | |
| "recruit_verified": np.zeros(n), | |
| "recruit_completeness": np.zeros(n), | |
| "recruit_recency": np.zeros(n), | |
| "recruit_notice_ok": np.zeros(n), | |
| "recruit_recruiter_saves": np.zeros(n), | |
| "recruit_interview_completion": np.zeros(n), | |
| "recruit_offer_acceptance": np.zeros(n), | |
| "recruit_github": np.zeros(n), | |
| "recruitability": np.zeros(n), | |
| }) | |
| def get(row, key, default=0): | |
| d = row.get(sig_col) | |
| if d is None: | |
| d = {} | |
| elif hasattr(d, "tolist"): | |
| d = d.tolist() if d else {} | |
| if not isinstance(d, dict): | |
| return default | |
| v = d.get(key, default) | |
| return v if v is not None else default | |
| open_to_work = np.array([float(get(r, "open_to_work_flag", 0)) for _, r in df.iterrows()]) | |
| response_rate = np.array([float(get(r, "recruiter_response_rate", 0.0)) for _, r in df.iterrows()]) | |
| verified_email = np.array([float(get(r, "verified_email", 0)) for _, r in df.iterrows()]) | |
| verified_phone = np.array([float(get(r, "verified_phone", 0)) for _, r in df.iterrows()]) | |
| linkedin = np.array([float(get(r, "linkedin_connected", 0)) for _, r in df.iterrows()]) | |
| completeness = np.array([float(get(r, "profile_completeness_score", 0.0)) for _, r in df.iterrows()]) / 100.0 | |
| notice = np.array([float(get(r, "notice_period_days", 90)) for _, r in df.iterrows()]) | |
| saves = np.array([float(get(r, "saved_by_recruiters_30d", 0)) for _, r in df.iterrows()]) | |
| interview_completion = np.array([float(get(r, "interview_completion_rate", 0.0)) for _, r in df.iterrows()]) | |
| offer_acceptance = np.array([float(get(r, "offer_acceptance_rate", -1.0)) for _, r in df.iterrows()]) | |
| github = np.array([float(get(r, "github_activity_score", -1.0)) for _, r in df.iterrows()]) | |
| # Recency: anchored to fixed reference date for determinism. | |
| # Reference = 2026-06-01 (near dataset creation date). | |
| # 90-day half-life exponential decay. | |
| from datetime import datetime as _dt | |
| _REF_DATE = _dt(2026, 6, 1) | |
| recency = np.zeros(n) | |
| for i, (_, r) in enumerate(df.iterrows()): | |
| d = _to_dt(get(r, "last_active_date", None)) | |
| if d is None: | |
| recency[i] = 0.0 | |
| continue | |
| days = (_REF_DATE - d).days | |
| import math | |
| recency[i] = max(0.0, math.exp(-max(0, days) / 90.0)) | |
| # Notice period | |
| notice_ok = np.where(notice <= 30, 1.0, np.maximum(0.0, 1.0 - (notice - 30) / 150.0)) | |
| recruiter_saves = np.minimum(saves / 20.0, 1.0) | |
| offer_acc = np.where(offer_acceptance < 0, 0.3, offer_acceptance) | |
| offer_acc = np.clip(offer_acc, 0.0, 1.0) | |
| github_norm = np.where(github < 0, 0.0, github / 100.0) | |
| verified = (verified_email + verified_phone + linkedin) / 3.0 | |
| composite = ( | |
| 0.18 * open_to_work | |
| + 0.10 * verified | |
| + 0.10 * response_rate | |
| + 0.06 * interview_completion | |
| + 0.06 * offer_acc | |
| + 0.10 * completeness | |
| + 0.08 * recency | |
| + 0.10 * notice_ok | |
| + 0.10 * recruiter_saves | |
| + 0.06 * github_norm | |
| + 0.06 * open_to_work * verified # bonus: open AND verified | |
| ) | |
| composite = np.clip(composite, 0.0, 1.0) | |
| return pd.DataFrame({ | |
| "recruit_open_to_work": open_to_work, | |
| "recruit_response_rate": response_rate, | |
| "recruit_verified": verified, | |
| "recruit_completeness": completeness, | |
| "recruit_recency": recency, | |
| "recruit_notice_ok": notice_ok, | |
| "recruit_recruiter_saves": recruiter_saves, | |
| "recruit_interview_completion": interview_completion, | |
| "recruit_offer_acceptance": offer_acc, | |
| "recruit_github": github_norm, | |
| "recruitability": composite, | |
| }) | |