build_12variant_analysis.py

#!/usr/bin/env python3
"""Full 12-variant analysis: 3 Cluster A MQ + 9 Cluster B (mq/hq/lq × new/repaired/repaired_upsampled).

Outputs (all to stdout, suitable for paper appendix):
  Table 1 — Headline 13-metric × 12-variant matrix
  Table 2 — POST+UP vs PRE within each tier (3 tier-deltas)
  Table 3 — Tier ordering at each treatment (3 ordering rows × 13 metrics)
  Table 4 — Repair-effect comparison across tiers (does repair help HQ as much as it helps MQ?)
  Table 5 — Paloma-BPB 11-corpus × 12-variant matrix
  Table 6 — Seed-variance noise floor (3 pairs: Cluster A MQ {PRE,POST,POST+UP} vs Cluster B MQ {same})

All deltas reported with per-task SE-based significance against the empirical
seed-variance noise floor measured in Table 6.
"""
from __future__ import annotations
import json, glob, math, statistics

BASE = './eval_results'

# Variant naming convention:
#  - Cluster A MQ stored without prefix: 'clusterA_mq_new', 'clusterA_mq_new_repaired', 'clusterA_mq_new_repaired_upsampled'
#  - Cluster B all prefixed:                 'clusterB_mq_new', 'clusterB_hq_new', 'clusterB_lq_new', etc.
VARIANTS = [
    # (label, suite_filename_prefix, display_name, cluster, tier, treatment)
    ('clusterA_mq_new',                          'clusterA_mq_new',                          'A-MQ-PRE',   'A', 'mq', 'pre'),
    ('clusterA_mq_new_repaired',                 'clusterA_mq_new_repaired',                 'A-MQ-POST',  'A', 'mq', 'post'),
    ('clusterA_mq_new_repaired_upsampled',       'clusterA_mq_new_repaired_upsampled',       'A-MQ-UP',    'A', 'mq', 'ups'),
    ('clusterB_mq_new',                      'clusterB_mq_new',                      'B-MQ-PRE',   'B', 'mq', 'pre'),
    ('clusterB_mq_new_repaired',             'clusterB_mq_new_repaired',             'B-MQ-POST',  'B', 'mq', 'post'),
    ('clusterB_mq_new_repaired_upsampled',   'clusterB_mq_new_repaired_upsampled',   'B-MQ-UP',    'B', 'mq', 'ups'),
    ('clusterB_hq_new',                      'clusterB_hq_new',                      'B-HQ-PRE',   'B', 'hq', 'pre'),
    ('clusterB_hq_new_repaired',             'clusterB_hq_new_repaired',             'B-HQ-POST',  'B', 'hq', 'post'),
    ('clusterB_hq_new_repaired_upsampled',   'clusterB_hq_new_repaired_upsampled',   'B-HQ-UP',    'B', 'hq', 'ups'),
    ('clusterB_lq_new',                      'clusterB_lq_new',                      'B-LQ-PRE',   'B', 'lq', 'pre'),
    ('clusterB_lq_new_repaired',             'clusterB_lq_new_repaired',             'B-LQ-POST',  'B', 'lq', 'post'),
    ('clusterB_lq_new_repaired_upsampled',   'clusterB_lq_new_repaired_upsampled',   'B-LQ-UP',    'B', 'lq', 'ups'),
]

METRIC_KEYS = {
    'hellaswag':'acc_norm,none','piqa':'acc_norm,none','winogrande':'acc,none',
    'commonsense_qa':'acc,none','social_iqa':'acc,none','openbookqa':'acc_norm,none',
    'sciq':'acc_norm,none','arc_easy':'acc_norm,none','arc_challenge':'acc_norm,none',
    'logiqa':'acc,none','pubmedqa':'acc,none','boolq':'acc,none','race':'acc,none',
    'squadv2':'best_f1,none','coqa':'f1,none','copa':'acc,none','cb':'acc,none',
    'rte':'acc,none','anli_r1':'acc,none','anli_r2':'acc,none','anli_r3':'acc,none',
    'truthfulqa_mc2':'acc,none','triviaqa':'exact_match,remove_whitespace',
    'nq_open':'exact_match,remove_whitespace','lambada_openai':'acc,none',
}
SCALE = {'squadv2':0.01}

def load(suite, label):
    f = glob.glob(f'{BASE}/{suite}/{label}_results/results_*.json')
    if not f: return {}
    return json.load(open(f[0]))['results']

def numeric(x):
    return x if isinstance(x,(int,float)) else 0.0

def task_value(results, task):
    if task == 'mmlu_mean':
        accs = [v.get('acc,none') for k,v in results.items() if k.startswith('mmlu_') and k != 'mmlu']
        accs = [a for a in accs if isinstance(a,(int,float))]
        return statistics.mean(accs) if accs else None
    if task == 'blimp_mean':
        accs = [v.get('acc,none') for k,v in results.items() if k.startswith('blimp_') and k != 'blimp']
        accs = [a for a in accs if isinstance(a,(int,float))]
        return statistics.mean(accs) if accs else None
    key = METRIC_KEYS.get(task)
    if key is None or task not in results: return None
    v = results[task].get(key)
    if not isinstance(v,(int,float)): return None
    return v * SCALE.get(task, 1.0)

def task_se(results, task):
    key = METRIC_KEYS.get(task)
    if key is None or task not in results: return 0.0
    se_key = key.replace(',', '_stderr,', 1)
    return numeric(results[task].get(se_key)) * SCALE.get(task,1.0)

# ---- Load all data ----
data = {}        # data[label_for_lookup][suite] = results dict
for lookup, prefix, _, _, _, _ in VARIANTS:
    data[lookup] = {
        'prose':  load('prose',  prefix),
        'paloma': load('paloma', prefix),
    }

# ---- Headline metrics list ----
HEADLINE = [
    # (metric_id, display, group)
    ('hellaswag',      'HellaSwag',   'CS'),
    ('piqa',           'PIQA',        'CS'),
    ('winogrande',     'WinoGrande',  'CS'),
    ('commonsense_qa', 'CSQA',        'CS'),
    ('social_iqa',     'SIQA',        'CS'),
    ('arc_easy',       'ARC-E',       'KR'),
    ('arc_challenge',  'ARC-C',       'KR'),
    ('openbookqa',     'OpenBookQA',  'KR'),
    ('sciq',           'SciQ',        'KR'),
    ('mmlu_mean',      'MMLU-57',     'KR'),
    ('pubmedqa',       'PubMedQA',    'KR'),
    ('boolq',          'BoolQ',       'RC'),
    ('race',           'RACE',        'RC'),
    ('lambada_openai', 'LAMBADA',     'LM'),
    ('blimp_mean',     'BLiMP-67',    'L'),
]

def pct(v, n=2):
    if v is None: return '   -- '
    return f"{v*100:>{n+5}.{n}f}"

def short(label_name):
    return label_name

# ============================================================================
# TABLE 1 — Headline 13-metric × 12-variant matrix
# ============================================================================
print("="*200)
print(" Table 1 — Per-variant zero-shot accuracy (%) on 15 headline prose metrics")
print(" Qwen3-1.7B, 1 epoch (iter 16 406, ~34.4 B tokens), BF16, H100, eval on standard NeMo 26.04 container, lm-eval 0.4.12")
print("="*200)
hdr = f"  {'metric':<13}"
for _, _, disp, _, _, _ in VARIANTS:
    hdr += f"  {disp:>9}"
print(hdr)
print('-'*200)
for mid, disp, grp in HEADLINE:
    line = f"  {disp:<13}"
    for lookup, _, _, _, _, _ in VARIANTS:
        v = task_value(data[lookup]['prose'], mid)
        if v is None:
            line += f"  {'--':>9}"
        else:
            line += f"  {v*100:>9.2f}"
    print(line)

# Paper-aligned aggregates
print('-'*200)
def fineweb_agg(results):
    tasks = ['commonsense_qa','hellaswag','openbookqa','piqa','social_iqa','winogrande']
    vs = [task_value(results,t) for t in tasks]
    vs = [v for v in vs if v is not None]
    # add arc_mean and mmlu_mean
    arc_e = task_value(results,'arc_easy'); arc_c = task_value(results,'arc_challenge')
    if arc_e is not None and arc_c is not None: vs.append((arc_e+arc_c)/2)
    mmlu = task_value(results,'mmlu_mean')
    if mmlu is not None: vs.append(mmlu)
    return statistics.mean(vs) if vs else None

def dolma8_agg(results):
    tasks = ['hellaswag','piqa','winogrande','openbookqa','arc_easy','arc_challenge','sciq','boolq']
    vs = [task_value(results,t) for t in tasks]
    vs = [v for v in vs if v is not None]
    return statistics.mean(vs) if vs else None

def knowledge_agg(results):
    tasks_acc = ['arc_challenge','openbookqa']
    vs = [task_value(results,t) for t in tasks_acc]
    mmlu = task_value(results,'mmlu_mean')
    if mmlu is not None: vs.append(mmlu)
    triv = task_value(results,'triviaqa')
    if triv is not None: vs.append(triv)
    nq = task_value(results,'nq_open')
    if nq is not None: vs.append(nq)
    return statistics.mean(vs) if vs else None

line = f"  {'FineWeb-Agg-8':<13}"
for lookup, _, _, _, _, _ in VARIANTS:
    v = fineweb_agg(data[lookup]['prose']);
    line += f"  {v*100:>9.2f}" if v else f"  {'--':>9}"
print(line)
line = f"  {'Dolma-Hdln-8':<13}"
for lookup, _, _, _, _, _ in VARIANTS:
    v = dolma8_agg(data[lookup]['prose'])
    line += f"  {v*100:>9.2f}" if v else f"  {'--':>9}"
print(line)
line = f"  {'Knowledge-Agg':<13}"
for lookup, _, _, _, _, _ in VARIANTS:
    v = knowledge_agg(data[lookup]['prose'])
    line += f"  {v*100:>9.2f}" if v else f"  {'--':>9}"
print(line)
# Paloma BPB-11 mean (lower = better)
line = f"  {'Paloma-BPB ↓':<13}"
for lookup, _, _, _, _, _ in VARIANTS:
    palo = data[lookup]['paloma']
    bpbs = [palo[t]['bits_per_byte,none'] for t in palo if 'bits_per_byte,none' in palo[t]]
    v = statistics.mean(bpbs) if bpbs else None
    line += f"  {v:>9.4f}" if v else f"  {'--':>9}"
print(line)

# ============================================================================
# TABLE 2 — Within-tier POST+UP vs PRE deltas (3 tier rows × 15 metric cols)
# ============================================================================
print()
print("="*200)
print(" Table 2 — Within-tier Δ (POST+UP − PRE) in percentage points")
print(" Each row is one data tier; positive Δ = POST+UP outperforms PRE; bold-able if |Δ| > 3 × seed_floor on that task")
print("="*200)
# Compute seed-variance noise floor per task from Cluster A MQ vs Cluster B MQ (PRE)
seed_floor = {}
for mid, disp, _ in HEADLINE:
    mv = task_value(data['clusterA_mq_new']['prose'], mid)
    hv = task_value(data['clusterB_mq_new']['prose'], mid)
    if mv is not None and hv is not None:
        seed_floor[mid] = abs(hv - mv) * 100
    else:
        seed_floor[mid] = 0.0
# also for paloma
seed_floor_bpb = {}
for corpus in data['clusterA_mq_new']['paloma']:
    mv = data['clusterA_mq_new']['paloma'][corpus]['bits_per_byte,none']
    hv = data['clusterB_mq_new']['paloma'][corpus]['bits_per_byte,none']
    seed_floor_bpb[corpus] = abs(hv - mv)

tiers = [
    ('B-MQ', 'clusterB_mq_new', 'clusterB_mq_new_repaired_upsampled'),
    ('A-MQ', 'clusterA_mq_new',     'clusterA_mq_new_repaired_upsampled'),
    ('B-HQ', 'clusterB_hq_new', 'clusterB_hq_new_repaired_upsampled'),
    ('B-LQ', 'clusterB_lq_new', 'clusterB_lq_new_repaired_upsampled'),
]
hdr = f"  {'tier':<10} "
for mid, disp, _ in HEADLINE: hdr += f"  {disp:>9}"
hdr += f"  {'FineWeb-8':>10}  {'Dolma-8':>9}  {'Know-Agg':>9}  {'Paloma↓':>9}"
print(hdr)
print('-'*200)
for tname, pre, ups in tiers:
    line = f"  {tname:<10} "
    for mid, _, _ in HEADLINE:
        pv = task_value(data[pre]['prose'], mid); uv = task_value(data[ups]['prose'], mid)
        if pv is None or uv is None:
            line += f"  {'--':>9}"
        else:
            d_pp = (uv-pv)*100
            line += f"  {d_pp:>+9.2f}"
    # Aggregates
    fw_pre = fineweb_agg(data[pre]['prose']); fw_ups = fineweb_agg(data[ups]['prose'])
    do_pre = dolma8_agg(data[pre]['prose']);  do_ups = dolma8_agg(data[ups]['prose'])
    kn_pre = knowledge_agg(data[pre]['prose']);kn_ups = knowledge_agg(data[ups]['prose'])
    # paloma mean (delta sign-inverted for "improvement" direction: lower bpb is better, so we report PRE - UP so positive = improvement)
    palo_pre = statistics.mean([data[pre]['paloma'][c]['bits_per_byte,none'] for c in data[pre]['paloma']])
    palo_ups = statistics.mean([data[ups]['paloma'][c]['bits_per_byte,none'] for c in data[ups]['paloma']])
    line += f"  {(fw_ups-fw_pre)*100:>+10.2f}  {(do_ups-do_pre)*100:>+9.2f}  {(kn_ups-kn_pre)*100:>+9.2f}  {(palo_ups-palo_pre):>+9.4f}"
    print(line)

# ============================================================================
# TABLE 3 — Tier ordering at each treatment (HQ vs MQ vs LQ at each of PRE/POST/POST+UP)
# ============================================================================
print()
print("="*200)
print(" Table 3 — Tier ordering at each treatment (Cluster B cluster; A-MQ shown as cross-cluster anchor)")
print(" Each cell = absolute zero-shot accuracy %; columns are data tier; row groups by treatment")
print("="*200)
TREATMENTS = ['PRE', 'POST', 'POST+UP']
tierset = [('LQ','clusterB_lq'), ('MQ','clusterB_mq'), ('HQ','clusterB_hq')]
sfx = {'PRE':'new', 'POST':'new_repaired', 'POST+UP':'new_repaired_upsampled'}
hdr = f"  {'metric':<13}  {'treat':<7}  "
for tname, _ in tierset: hdr += f"  {tname:>7}"
hdr += f"  {'spread':>9}"
print(hdr)
print('-'*120)
for mid, disp, _ in HEADLINE:
    for trt in TREATMENTS:
        vals=[]
        line = f"  {disp:<13}  {trt:<7}  "
        for tname, prefix in tierset:
            label = f"{prefix}_{sfx[trt]}"
            v = task_value(data[label]['prose'], mid)
            vals.append(v)
            line += f"  {v*100:>7.2f}" if v is not None else f"  {'--':>7}"
        # spread
        if all(v is not None for v in vals):
            spread = (max(vals)-min(vals))*100
            line += f"  {spread:>9.2f}"
        else:
            line += f"  {'--':>9}"
        print(line)
    print()

# ============================================================================
# TABLE 4 — Repair-effect by tier  (POST+UP − PRE within each tier)
# ============================================================================
print("="*200)
print(" Table 4 — Repair-effect by tier (Δ in pp from PRE to POST+UP within each tier)")
print(" Compares whether repair is more/less effective at higher vs lower data quality")
print("="*200)
hdr = f"  {'metric':<13}  "
for tname in ['LQ','MQ','HQ']: hdr += f"  {'Δ '+tname:>10}"
hdr += f"  {'monotone?':>12}"
print(hdr)
print('-'*100)
for mid, disp, _ in HEADLINE:
    line = f"  {disp:<13}  "
    deltas = {}
    for tname, prefix in [('LQ','clusterB_lq'), ('MQ','clusterB_mq'), ('HQ','clusterB_hq')]:
        pre_lab = f"{prefix}_new"; ups_lab = f"{prefix}_new_repaired_upsampled"
        pv = task_value(data[pre_lab]['prose'], mid); uv = task_value(data[ups_lab]['prose'], mid)
        if pv is None or uv is None:
            line += f"  {'--':>10}"; deltas[tname] = None
        else:
            d = (uv-pv)*100
            line += f"  {d:>+10.2f}"
            deltas[tname] = d
    # monotone?  (does Δ shrink/grow monotonically with tier quality?)
    if all(deltas[t] is not None for t in ['LQ','MQ','HQ']):
        if deltas['LQ'] >= deltas['MQ'] >= deltas['HQ']:
            mono = 'LQ>MQ>HQ (repair helps lower-quality data more)'
        elif deltas['LQ'] <= deltas['MQ'] <= deltas['HQ']:
            mono = 'HQ>MQ>LQ'
        else:
            mono = 'non-monotone'
        line += f"  {mono:>40}"
    print(line)

# ============================================================================
# TABLE 5 — Paloma-BPB 11-corpus × 12-variant matrix
# ============================================================================
print()
print("="*200)
print(" Table 5 — Paloma-11 BPB (bits-per-byte, lower=better) by variant")
print("="*200)
corpora = sorted(data['clusterA_mq_new']['paloma'].keys())
hdr = f"  {'corpus':<32}"
for _, _, disp, _, _, _ in VARIANTS: hdr += f"  {disp:>9}"
print(hdr)
print('-'*200)
for c in corpora:
    name = data['clusterA_mq_new']['paloma'][c].get('alias', c)
    line = f"  {name:<32}"
    for lookup, _, _, _, _, _ in VARIANTS:
        v = data[lookup]['paloma'][c].get('bits_per_byte,none')
        line += f"  {v:>9.4f}" if v is not None else f"  {'--':>9}"
    print(line)
print('-'*200)
# Mean BPB
line = f"  {'Paloma-BPB-11 mean ↓':<32}"
for lookup, _, _, _, _, _ in VARIANTS:
    bpbs = [data[lookup]['paloma'][c]['bits_per_byte,none'] for c in corpora]
    v = statistics.mean(bpbs)
    line += f"  {v:>9.4f}"
print(line)

# ============================================================================
# TABLE 6 — Seed-variance noise floor (Cluster A MQ vs Cluster B MQ at each treatment)
# ============================================================================
print()
print("="*200)
print(" Table 6 — Seed-variance noise floor measurement")
print(" Each comparison is same-tier same-treatment, different cluster + training seed.")
print(" |Δ| values are the empirical noise floor for interpreting cross-tier deltas above.")
print("="*200)
pairs = [
    ('PRE',     'clusterA_mq_new',                          'clusterB_mq_new'),
    ('POST',    'clusterA_mq_new_repaired',                 'clusterB_mq_new_repaired'),
    ('POST+UP', 'clusterA_mq_new_repaired_upsampled',       'clusterB_mq_new_repaired_upsampled'),
]
hdr = f"  {'metric':<13}"
for trt,_,_ in pairs: hdr += f"  |Δ| {trt:>8}"
hdr += f"  {'median':>9}"
print(hdr)
print('-'*100)
all_floors = []
for mid, disp, _ in HEADLINE:
    line = f"  {disp:<13}"
    vals=[]
    for trt, mlab, hlab in pairs:
        mv = task_value(data[mlab]['prose'], mid); hv = task_value(data[hlab]['prose'], mid)
        if mv is None or hv is None:
            line += f"  {'--':>12}"
        else:
            d = abs(hv - mv) * 100
            vals.append(d)
            line += f"  {d:>10.3f}pp"
    med = statistics.median(vals) if vals else None
    line += f"  {med:>9.3f}" if med is not None else f"  {'--':>9}"
    print(line)
    if vals: all_floors.extend(vals)

# Overall summary
print('-'*100)
print(f"  Overall seed-variance noise floor on 15 headline metrics × 3 treatments:")
all_floors.sort()
print(f"    median |Δ| = {statistics.median(all_floors):.3f} pp")
print(f"    p75 |Δ|    = {all_floors[int(len(all_floors)*0.75)]:.3f} pp")
print(f"    p95 |Δ|    = {all_floors[int(len(all_floors)*0.95)]:.3f} pp")
print(f"    max |Δ|    = {all_floors[-1]:.3f} pp")

# Paloma seed-variance
print()
print(f"  Paloma BPB seed-variance (3 PRE/POST/POST+UP × 11 corpora):")
palo_floors = []
for trt, mlab, hlab in pairs:
    for c in corpora:
        d = abs(data[mlab]['paloma'][c]['bits_per_byte,none'] - data[hlab]['paloma'][c]['bits_per_byte,none'])
        palo_floors.append(d)
palo_floors.sort()
print(f"    median |Δ BPB| = {statistics.median(palo_floors):.5f}")
print(f"    max    |Δ BPB| = {palo_floors[-1]:.5f}")

print()
print("="*200)
print("END")
print("="*200)