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1aebf33 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | """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,
})
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