Add gender and nationality columns to admission data pipeline

cli/main.py

@@ -657,6 +657,17 @@ def cmd_stats(args: argparse.Namespace) -> None:
         table.add_row(prog_id, str(acc), str(rej), str(wl), str(total), rate)
 
     console.print(table)
+
+    # Gender & nationality summary
+    gender = summary.get("gender_dist", {})
+    nat = summary.get("nationality_dist", {})
+    if gender or nat:
+        gender_str = f"M:{gender.get('M', 0)} F:{gender.get('F', 0)}"
+        nat_parts = [f"{k}:{v}" for k, v in sorted(nat.items(), key=lambda x: -x[1])]
+        console.print(
+            f"  [bold]Demographics:[/bold]  Gender: {gender_str}  |  "
+            f"Nationality: {', '.join(nat_parts)}"
+        )
     console.print()
 
     # GPA distribution

core/admission_data.py

@@ -6,8 +6,9 @@
 
 CSV schema
 ----------
-id, bg_type, gpa, gpa_scale, gre, toefl, major, intern_desc,
-has_paper, has_research, courses_note, program, result, season, source
+id, gender, bg_type, nationality, gpa, gpa_scale, gre, toefl, major,
+intern_desc, has_paper, has_research, courses_note, program, result,
+season, source
 """
 
 from __future__ import annotations
@@ -165,6 +166,60 @@ def classify_background(bg_type: str) -> int:
     return 4  # default
 
 
+# ---------------------------------------------------------------------------
+# Nationality classification
+# ---------------------------------------------------------------------------
+
+# Canonical nationality values
+NATIONALITY_DOMESTIC = "domestic"  # US citizen / permanent resident
+NATIONALITY_CHINA = "china"  # Chinese mainland
+NATIONALITY_HK_TW = "hk_tw"  # Hong Kong, Macau, Taiwan
+NATIONALITY_OTHER_INTL = "other_intl"  # Other international
+
+_NATIONALITY_MAP: dict[str, str] = {
+    "美籍": NATIONALITY_DOMESTIC,
+    "美国": NATIONALITY_DOMESTIC,
+    "us": NATIONALITY_DOMESTIC,
+    "domestic": NATIONALITY_DOMESTIC,
+    "greencard": NATIONALITY_DOMESTIC,
+    "绿卡": NATIONALITY_DOMESTIC,
+    "pr": NATIONALITY_DOMESTIC,
+    "中国大陆": NATIONALITY_CHINA,
+    "中国": NATIONALITY_CHINA,
+    "大陆": NATIONALITY_CHINA,
+    "china": NATIONALITY_CHINA,
+    "mainland": NATIONALITY_CHINA,
+    "港澳台": NATIONALITY_HK_TW,
+    "香港": NATIONALITY_HK_TW,
+    "台湾": NATIONALITY_HK_TW,
+    "澳门": NATIONALITY_HK_TW,
+    "hk": NATIONALITY_HK_TW,
+    "taiwan": NATIONALITY_HK_TW,
+}
+
+
+def classify_nationality(nationality: str) -> str:
+    """Map a nationality string to a canonical value.
+
+    Returns one of: 'domestic', 'china', 'hk_tw', 'other_intl'.
+    Empty/unknown values return 'china' (most common in MFE applicant pool).
+    """
+    val = nationality.strip().lower().replace(" ", "")
+    if not val or val in ("不明", "n/a", "unknown"):
+        return NATIONALITY_CHINA  # default for MFE applicant pool
+
+    # Exact match
+    if val in _NATIONALITY_MAP:
+        return _NATIONALITY_MAP[val]
+
+    # Partial match
+    for key, canonical in _NATIONALITY_MAP.items():
+        if key in val or val in key:
+            return canonical
+
+    return NATIONALITY_OTHER_INTL
+
+
 # ---------------------------------------------------------------------------
 # Intern strength scoring
 # ---------------------------------------------------------------------------
@@ -230,8 +285,11 @@ class AdmissionRecord:
     """A single real applicant data point with normalized fields."""
 
     id: str = ""
+    gender: str = ""  # M / F / empty
     bg_type: str = ""
     bg_tier: int = 4  # 1-5, computed from bg_type
+    nationality: str = ""  # raw value
+    nationality_canonical: str = ""  # domestic / china / hk_tw / other_intl
     gpa_raw: float = 0.0
     gpa_scale: float = 4.0
     gpa_normalized: float = 0.0  # on 4.0 scale
@@ -266,6 +324,8 @@ class ProgramStats:
     avg_intern_score_accepted: float = 0.0
     paper_rate_accepted: float = 0.0
     research_rate_accepted: float = 0.0
+    female_rate_accepted: float = 0.0  # fraction of female among accepted
+    nationality_dist_accepted: dict[str, int] = field(default_factory=dict)
 
     # Rejected applicant stats
     avg_gpa_rejected: float = 0.0
@@ -348,11 +408,15 @@ def load_admission_csv(path: str | Path) -> list[AdmissionRecord]:
                 gpa_scale = 4.0
 
             bg_type = row.get("bg_type", "").strip()
+            nationality_raw = row.get("nationality", "").strip()
 
             rec = AdmissionRecord(
                 id=row.get("id", "").strip(),
+                gender=row.get("gender", "").strip().upper(),
                 bg_type=bg_type,
                 bg_tier=classify_background(bg_type),
+                nationality=nationality_raw,
+                nationality_canonical=classify_nationality(nationality_raw),
                 gpa_raw=gpa_raw,
                 gpa_scale=gpa_scale,
                 gpa_normalized=normalize_gpa(gpa_raw, gpa_scale),
@@ -454,6 +518,19 @@ def compute_program_stats(
             if research_known
             else 0.0
         )
+        # Gender stats
+        gendered = [r for r in accepted if r.gender in ("M", "F")]
+        stats.female_rate_accepted = (
+            sum(1 for r in gendered if r.gender == "F") / len(gendered)
+            if gendered
+            else 0.0
+        )
+        # Nationality distribution
+        nat_dist: dict[str, int] = {}
+        for r in accepted:
+            nat = r.nationality_canonical or "unknown"
+            nat_dist[nat] = nat_dist.get(nat, 0) + 1
+        stats.nationality_dist_accepted = nat_dist
 
     # Rejected stats
     if rejected:
@@ -530,6 +607,22 @@ def _effect_size(acc_vals: list[float], rej_vals: list[float]) -> float:
         [1.0 if r.has_research else 0.0 for r in rejected if r.has_research is not None],
     )
 
+    # Gender (female = 1, male = 0)
+    acc_gender = [1.0 if r.gender == "F" else 0.0 for r in accepted if r.gender in ("M", "F")]
+    rej_gender = [1.0 if r.gender == "F" else 0.0 for r in rejected if r.gender in ("M", "F")]
+    features["gender_f"] = _effect_size(acc_gender, rej_gender)
+
+    # Nationality (domestic = 1, international = 0)
+    acc_nat = [
+        1.0 if r.nationality_canonical == "domestic" else 0.0
+        for r in accepted if r.nationality_canonical
+    ]
+    rej_nat = [
+        1.0 if r.nationality_canonical == "domestic" else 0.0
+        for r in rejected if r.nationality_canonical
+    ]
+    features["domestic"] = _effect_size(acc_nat, rej_nat)
+
     return features
 
 
@@ -564,6 +657,18 @@ def summarize_records(records: list[AdmissionRecord]) -> dict[str, Any]:
     seasons = sorted({r.season for r in records if r.season})
     sources = sorted({r.source for r in records if r.source})
 
+    # Gender breakdown
+    gendered = [r for r in records if r.gender in ("M", "F")]
+    gender_dist = {"M": 0, "F": 0}
+    for r in gendered:
+        gender_dist[r.gender] += 1
+
+    # Nationality breakdown
+    nat_dist: dict[str, int] = {}
+    for r in records:
+        nat = r.nationality_canonical or "unknown"
+        nat_dist[nat] = nat_dist.get(nat, 0) + 1
+
     return {
         "total_records": len(records),
         "unique_applicants": len({r.id for r in records}),
@@ -572,4 +677,6 @@ def summarize_records(records: list[AdmissionRecord]) -> dict[str, Any]:
         "sources": sources,
         "avg_gpa_normalized": _safe_avg([r.gpa_normalized for r in records]),
         "gre_available": sum(1 for r in records if r.gre is not None),
+        "gender_dist": gender_dist,
+        "nationality_dist": nat_dist,
     }

core/calibrator.py

@@ -209,28 +209,28 @@ def predict_outcome(
     score = 0.0
     max_score = 0.0
 
-    # GPA component (40%)
-    weight_gpa = 0.4
+    # GPA component (35%)
+    weight_gpa = 0.35
     max_score += weight_gpa
     if threshold.gpa_target > 0:
         gpa_ratio = record.gpa_normalized / threshold.gpa_target
         score += weight_gpa * min(1.0, gpa_ratio)
 
-    # Background tier (25%)
-    weight_bg = 0.25
+    # Background tier (20%)
+    weight_bg = 0.20
     max_score += weight_bg
     if threshold.max_bg_tier_accepted > 0:
         bg_ratio = 1.0 - (record.bg_tier - 1) / 4.0  # tier 1=1.0, tier 5=0.0
         score += weight_bg * max(0.0, bg_ratio)
 
-    # Intern score (20%)
-    weight_intern = 0.2
+    # Intern score (18%)
+    weight_intern = 0.18
     max_score += weight_intern
     if record.intern_score > 0:
         score += weight_intern * min(1.0, record.intern_score / 8.0)
 
-    # Research/paper bonus (15%)
-    weight_research = 0.15
+    # Research/paper bonus (12%)
+    weight_research = 0.12
     max_score += weight_research
     bonus = 0.0
     if record.has_paper:
@@ -239,6 +239,29 @@ def predict_outcome(
         bonus += 0.5
     score += weight_research * bonus
 
+    # Gender diversity bonus (7%)
+    # MFE programs skew heavily male; female applicants may benefit
+    weight_gender = 0.07
+    max_score += weight_gender
+    if record.gender == "F":
+        score += weight_gender * 1.0
+    elif record.gender == "M":
+        score += weight_gender * 0.4  # baseline, no penalty
+
+    # Nationality / domestic advantage (8%)
+    # Domestic applicants (US citizens/PR) have slight advantage
+    weight_nat = 0.08
+    max_score += weight_nat
+    nat = record.nationality_canonical
+    if nat == "domestic":
+        score += weight_nat * 1.0
+    elif nat == "hk_tw":
+        score += weight_nat * 0.6
+    elif nat == "china":
+        score += weight_nat * 0.4  # largest applicant pool, most competitive
+    else:
+        score += weight_nat * 0.5
+
     # Classify based on score ratio
     ratio = score / max_score if max_score > 0 else 0.0
 

data/admissions/sample.csv

@@ -1,31 +1,31 @@
-id,bg_type,gpa,gpa_scale,gre,toefl,major,intern_desc,has_paper,has_research,courses_note,program,result,season,source
-1,两财一贸(211),91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,baruch-mfe,accepted,2025Fall,quantnet
-2,两财一贸(211),91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,princeton-mfin,rejected,2025Fall,quantnet
-3,两财一贸(211),91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,cmu-mscf,accepted,2025Fall,quantnet
-4,两财一贸(211),91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,mit-mfin,rejected,2025Fall,quantnet
-5,两财一贸(211),91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,uchicago-msfm,accepted,2025Fall,quantnet
-6,两财一贸(211),91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,gatech-qcf,accepted,2025Fall,quantnet
-7,两财一贸(211),91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,nus-qf,accepted,2025Fall,quantnet
-8,985,3.8,4,332,112,数学,2段量化实习+1段券商研究,是,是,实分析+随机过程+C++,baruch-mfe,accepted,2025Fall,chasedream
-9,985,3.8,4,332,112,数学,2段量化实习+1段券商研究,是,是,实分析+随机过程+C++,princeton-mfin,rejected,2025Fall,chasedream
-10,985,3.8,4,332,112,数学,2段量化实习+1段券商研究,是,是,实分析+随机过程+C++,cmu-mscf,accepted,2025Fall,chasedream
-11,海本(Top30),3.92,4,335,,,2段顶级量化+1段投行,是,否,数学+CS双专业,baruch-mfe,accepted,2025Fall,linkedin
-12,海本(Top30),3.92,4,335,,,2段顶级量化+1段投行,是,否,数学+CS双专业,princeton-mfin,accepted,2025Fall,linkedin
-13,海本(Top30),3.92,4,335,,,2段顶级量化+1段投行,是,否,数学+CS双专业,cmu-mscf,accepted,2025Fall,linkedin
-14,985,3.5,4,325,105,金融,1段银行实习,否,否,,columbia-mafn,rejected,2025Fall,chasedream
-15,985,3.5,4,325,105,金融,1段银行实习,否,否,,gatech-qcf,accepted,2025Fall,chasedream
-16,211,3.6,4,328,108,统计,2段数据分析实习,否,是,时间序列+回归分析,nyu-mfe,rejected,2025Fall,quantnet
-17,211,3.6,4,328,108,统计,2段数据分析实习,否,是,时间序列+回归分析,rutgers-msmf,accepted,2025Fall,quantnet
-18,211,3.6,4,328,108,统计,2段数据分析实习,否,是,时间序列+回归分析,fordham-msqf,accepted,2025Fall,quantnet
-19,985,87,100,330,115,金工,3段量化+1段投行,是,是,随机微积分+实分析+ML,baruch-mfe,accepted,2025Fall,offershow
-20,985,87,100,330,115,金工,3段量化+1段投行,是,是,随机微积分+实分析+ML,nyu-mfe,accepted,2025Fall,offershow
-21,海本(Top50),3.7,4,329,,,1段量化实习+1段fintech,否,否,CS+数学辅修,columbia-mafn,accepted,2025Fall,offershow
-22,海本(Top50),3.7,4,329,,,1段量化实习+1段fintech,否,否,CS+数学辅修,mit-mfin,rejected,2025Fall,offershow
-23,双非一本,3.8,4,326,102,应用数学,1段量化实习,否,否,概率论+线代+微积分,rutgers-msmf,accepted,2025Fall,chasedream
-24,双非一本,3.8,4,326,102,应用数学,1段量化实习,否,否,概率论+线代+微积分,baruch-mfe,rejected,2025Fall,chasedream
-25,985,3.9,4,333,118,计算机,3段量化实习+kaggle金牌,是,是,ML+深度学习+C++,baruch-mfe,accepted,2025Fall,quantnet
-26,985,3.9,4,333,118,计算机,3段量化实习+kaggle金牌,是,是,ML+深度学习+C++,cmu-mscf,accepted,2025Fall,quantnet
-27,985,3.9,4,333,118,计算机,3段量化实习+kaggle金牌,是,是,ML+深度学习+C++,princeton-mfin,waitlisted,2025Fall,quantnet
-28,海本(Top10),3.95,4.3,337,,,数学+金融双专业,2段顶级投行+1段对冲基金,是,是,实分析+泛函+随机微积分,princeton-mfin,accepted,2025Fall,linkedin
-29,海本(Top10),3.95,4.3,337,,,数学+金融双专业,2段顶级投行+1段对冲基金,是,是,实分析+泛函+随机微积分,baruch-mfe,accepted,2025Fall,linkedin
-30,海本(Top10),3.95,4.3,337,,,数学+金融双专业,2段顶级投行+1段对冲基金,是,是,实分析+泛函+随机微积分,mit-mfin,accepted,2025Fall,linkedin
+id,gender,bg_type,nationality,gpa,gpa_scale,gre,toefl,major,intern_desc,has_paper,has_research,courses_note,program,result,season,source
+1,M,两财一贸(211),中国大陆,91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,baruch-mfe,accepted,2025Fall,quantnet
+2,M,两财一贸(211),中国大陆,91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,princeton-mfin,rejected,2025Fall,quantnet
+3,M,两财一贸(211),中国大陆,91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,cmu-mscf,accepted,2025Fall,quantnet
+4,M,两财一贸(211),中国大陆,91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,mit-mfin,rejected,2025Fall,quantnet
+5,M,两财一贸(211),中国大陆,91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,uchicago-msfm,accepted,2025Fall,quantnet
+6,M,两财一贸(211),中国大陆,91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,gatech-qcf,accepted,2025Fall,quantnet
+7,M,两财一贸(211),中国大陆,91.8,100,331,110+,金工,3段量化私募QR(含top百亿)+三中一华金工组,不明,不明,,nus-qf,accepted,2025Fall,quantnet
+8,M,985,中国大陆,3.8,4,332,112,数学,2段量化实习+1段券商研究,是,是,实分析+随机过程+C++,baruch-mfe,accepted,2025Fall,chasedream
+9,M,985,中国大陆,3.8,4,332,112,数学,2段量化实习+1段券商研究,是,是,实分析+随机过程+C++,princeton-mfin,rejected,2025Fall,chasedream
+10,M,985,中国大陆,3.8,4,332,112,数学,2段量化实习+1段券商研究,是,是,实分析+随机过程+C++,cmu-mscf,accepted,2025Fall,chasedream
+11,F,海本(Top30),美籍,3.92,4,335,,,2段顶级量化+1段投行,是,否,数学+CS双专业,baruch-mfe,accepted,2025Fall,linkedin
+12,F,海本(Top30),美籍,3.92,4,335,,,2段顶级量化+1段投行,是,否,数学+CS双专业,princeton-mfin,accepted,2025Fall,linkedin
+13,F,海本(Top30),美籍,3.92,4,335,,,2段顶级量化+1段投行,是,否,数学+CS双专业,cmu-mscf,accepted,2025Fall,linkedin
+14,M,985,中国大陆,3.5,4,325,105,金融,1段银行实习,否,否,,columbia-mafn,rejected,2025Fall,chasedream
+15,M,985,中国大陆,3.5,4,325,105,金融,1段银行实习,否,否,,gatech-qcf,accepted,2025Fall,chasedream
+16,F,211,中国大陆,3.6,4,328,108,统计,2段数据分析实习,否,是,时间序列+回归分析,nyu-mfe,rejected,2025Fall,quantnet
+17,F,211,中国大陆,3.6,4,328,108,统计,2段数据分析实习,否,是,时间序列+回归分析,rutgers-msmf,accepted,2025Fall,quantnet
+18,F,211,中国大陆,3.6,4,328,108,统计,2段数据分析实习,否,是,时间序列+回归分析,fordham-msqf,accepted,2025Fall,quantnet
+19,M,985,中国大陆,87,100,330,115,金工,3段量化+1段投行,是,是,随机微积分+实分析+ML,baruch-mfe,accepted,2025Fall,offershow
+20,M,985,中国大陆,87,100,330,115,金工,3段量化+1段投行,是,是,随机微积分+实分析+ML,nyu-mfe,accepted,2025Fall,offershow
+21,M,海本(Top50),港澳台,3.7,4,329,,,1段量化实习+1段fintech,否,否,CS+数学辅修,columbia-mafn,accepted,2025Fall,offershow
+22,M,海本(Top50),港澳台,3.7,4,329,,,1段量化实习+1段fintech,否,否,CS+数学辅修,mit-mfin,rejected,2025Fall,offershow
+23,M,双非一本,中国大陆,3.8,4,326,102,应用数学,1段量化实习,否,否,概率论+线代+微积分,rutgers-msmf,accepted,2025Fall,chasedream
+24,M,双非一本,中国大陆,3.8,4,326,102,应用数学,1段量化实习,否,否,概率论+线代+微积分,baruch-mfe,rejected,2025Fall,chasedream
+25,M,985,中国大陆,3.9,4,333,118,计算机,3段量化实习+kaggle金牌,是,是,ML+深度学习+C++,baruch-mfe,accepted,2025Fall,quantnet
+26,M,985,中国大陆,3.9,4,333,118,计算机,3段量化实习+kaggle金牌,是,是,ML+深度学习+C++,cmu-mscf,accepted,2025Fall,quantnet
+27,M,985,中国大陆,3.9,4,333,118,计算机,3段量化实习+kaggle金牌,是,是,ML+深度学习+C++,princeton-mfin,waitlisted,2025Fall,quantnet
+28,F,海本(Top10),美籍,3.95,4.3,337,,,数学+金融双专业,2段顶级投行+1段对冲基金,是,是,实分析+泛函+随机微积分,princeton-mfin,accepted,2025Fall,linkedin
+29,F,海本(Top10),美籍,3.95,4.3,337,,,数学+金融双专业,2段顶级投行+1段对冲基金,是,是,实分析+泛函+随机微积分,baruch-mfe,accepted,2025Fall,linkedin
+30,F,海本(Top10),美籍,3.95,4.3,337,,,数学+金融双专业,2段顶级投行+1段对冲基金,是,是,实分析+泛函+随机微积分,mit-mfin,accepted,2025Fall,linkedin

data/admissions/template.csv

@@ -1 +1 @@
-id,bg_type,gpa,gpa_scale,gre,toefl,major,intern_desc,has_paper,has_research,courses_note,program,result,season,source
+id,gender,bg_type,nationality,gpa,gpa_scale,gre,toefl,major,intern_desc,has_paper,has_research,courses_note,program,result,season,source