make_paper_figures.py

#!/usr/bin/env python3
"""Generate Figures 1 and 2 for the EMNLP 2026 paper.

Figure 1 — Repair effect (POST+UP − PRE) by tier on five robust benchmarks.
Figure 2 — Per-corpus Paloma BPB delta under repair (heatmap).

Outputs PDF (vector, for LaTeX inclusion) and PNG (for previewing).
"""
from __future__ import annotations
import json
import glob
import statistics
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.colors import LinearSegmentedColormap, TwoSlopeNorm
import numpy as np

BASE = './eval_results'
OUTDIR = './figures'

import os
os.makedirs(OUTDIR, exist_ok=True)


def load(suite, label):
    f = glob.glob(f'{BASE}/{suite}/{label}_results/results_*.json')
    return json.load(open(f[0]))['results'] if f else {}


# Variant lookup → anonymized display label (cluster A = Cluster A, cluster B = Cluster B)
PAIRS = {
    'LQ-A':  None,                                                   # no Cluster A LQ runs
    'LQ-B':  ('clusterB_lq_new', 'clusterB_lq_new_repaired_upsampled'),
    'MQ-A':  ('clusterA_mq_new',     'clusterA_mq_new_repaired_upsampled'),
    'MQ-B':  ('clusterB_mq_new', 'clusterB_mq_new_repaired_upsampled'),
    'HQ-A':  None,
    'HQ-B':  ('clusterB_hq_new', 'clusterB_hq_new_repaired_upsampled'),
}


def task_value(results, task, key=None):
    """Get accuracy in [0,1] for a task. Handles mmlu_/blimp_ aggregation."""
    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
    if key is None:
        key_map = {
            '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',
            'boolq': 'acc,none', 'race': 'acc,none', 'lambada_openai': 'acc,none',
        }
        key = key_map.get(task)
    if key is None or task not in results:
        return None
    v = results[task].get(key)
    return v if isinstance(v, (int, float)) else None


# ============================================================================
# FIGURE 1 — Repair effect by tier on five robust benchmarks
# ============================================================================
print("[fig1] computing repair-effect deltas by tier ...")

# 5 benchmarks (the S/N >= 4× headline set)
BENCHMARKS = [
    ('lambada_openai', 'LAMBADA-OpenAI'),
    ('boolq',          'BoolQ'),
    ('race',           'RACE'),
    ('hellaswag',      'HellaSwag'),
    ('openbookqa',     'OpenBookQA'),
]

# 4 tier × cluster bars per benchmark, in the visual order LQ → MQ-A → MQ-B → HQ
BAR_KEYS = [('LQ-B', 'LQ'), ('MQ-A', 'MQ-A'), ('MQ-B', 'MQ-B'), ('HQ-B', 'HQ')]
SIGMA_SEED = 0.6  # per-task noise floor in pp (median from Table 2)

# Load all data once
RAW = {}
for k, pair in PAIRS.items():
    if pair is None:
        continue
    pre_lab, ups_lab = pair
    RAW[k] = {
        'pre':  load('prose', pre_lab),
        'ups':  load('prose', ups_lab),
    }

fig, axes = plt.subplots(1, 5, figsize=(14, 3.4), sharey=False)
colors_per_tier = {'LQ-B':'#c0392b', 'MQ-A':'#f39c12', 'MQ-B':'#27ae60', 'HQ-B':'#2980b9'}
tier_labels = {'LQ-B':'LQ', 'MQ-A':'MQ-A', 'MQ-B':'MQ-B', 'HQ-B':'HQ'}

for ax, (task_id, task_disp) in zip(axes, BENCHMARKS):
    deltas = []
    bar_colors = []
    bar_labels = []
    for k, lab in BAR_KEYS:
        if k not in RAW:
            deltas.append(0.0); bar_colors.append('#cccccc'); bar_labels.append(lab); continue
        pv = task_value(RAW[k]['pre'], task_id)
        uv = task_value(RAW[k]['ups'], task_id)
        if pv is None or uv is None:
            deltas.append(0.0)
        else:
            deltas.append((uv - pv) * 100)
        bar_colors.append(colors_per_tier[k])
        bar_labels.append(tier_labels[k])

    x = np.arange(len(deltas))
    bars = ax.bar(x, deltas, color=bar_colors, edgecolor='black', linewidth=0.4, width=0.7)
    ax.errorbar(x, deltas, yerr=SIGMA_SEED, fmt='none', ecolor='black', lw=0.7, capsize=2)
    # ±3σ significance lines
    ax.axhline(y= 3*SIGMA_SEED, ls='--', lw=0.5, color='gray')
    ax.axhline(y=-3*SIGMA_SEED, ls='--', lw=0.5, color='gray')
    ax.axhline(y=0, color='black', lw=0.5)
    ax.set_xticks(x); ax.set_xticklabels(bar_labels, fontsize=8)
    ax.set_title(task_disp, fontsize=10)
    ax.tick_params(axis='y', labelsize=8)
    # annotate bars with delta values
    for xi, di in zip(x, deltas):
        ax.text(xi, di + (0.15 if di >= 0 else -0.45), f'{di:+.1f}',
                ha='center', va='bottom' if di >= 0 else 'top', fontsize=7)
    ax.set_ylim(min(min(deltas) - 1.0, -4.0), max(max(deltas) + 1.0, 6.0))

axes[0].set_ylabel('Δ accuracy (POST+UP − PRE), pp', fontsize=9)
fig.suptitle('Repair effect by tier on the five robust benchmarks (S/N ≥ 4×)',
             fontsize=11, y=1.00)
# Add legend below figures
proxies = [mpatches.Patch(color=c, label=tier_labels[k]) for k, c in colors_per_tier.items()]
proxies.append(mpatches.Patch(color='gray', alpha=0.3, label=f'±3σ noise (={3*SIGMA_SEED:.1f} pp)'))
fig.legend(handles=proxies, loc='lower center', ncol=5, fontsize=8, frameon=False,
           bbox_to_anchor=(0.5, -0.05))
plt.tight_layout()
plt.subplots_adjust(bottom=0.15)
plt.savefig(f'{OUTDIR}/fig1_repair_by_tier.pdf', bbox_inches='tight', dpi=300)
plt.savefig(f'{OUTDIR}/fig1_repair_by_tier.png', bbox_inches='tight', dpi=200)
print(f"[fig1] saved to {OUTDIR}/fig1_repair_by_tier.{{pdf,png}}")
plt.close()


# ============================================================================
# FIGURE 2 — Paloma per-corpus BPB delta heatmap
# ============================================================================
print("[fig2] computing Paloma per-corpus deltas ...")

ROWS = [
    ('LQ-B', 'LQ',   'clusterB_lq_new', 'clusterB_lq_new_repaired_upsampled'),
    ('MQ-A', 'MQ-A', 'clusterA_mq_new',     'clusterA_mq_new_repaired_upsampled'),
    ('MQ-B', 'MQ-B', 'clusterB_mq_new', 'clusterB_mq_new_repaired_upsampled'),
    ('HQ-B', 'HQ',   'clusterB_hq_new', 'clusterB_hq_new_repaired_upsampled'),
]
# Web → mixed → literary column ordering
COL_ORDER = [
    ('paloma_c4_100_domains',     'C4-100',  'web'),
    ('paloma_c4_en',              'C4-en',   'web'),
    ('paloma_falcon-refinedweb',  'Falcon',  'web'),
    ('paloma_mc4',                'mC4',     'web'),
    ('paloma_m2d2_s2orc_unsplit', 'S2ORC',   'mixed'),
    ('paloma_m2d2_wikipedia_unsplit', 'M2D2-Wiki', 'mixed'),
    ('paloma_dolma_100_subreddits', 'Subreddit', 'mixed'),
    ('paloma_dolma-v1_5',         'Dolma',   'lit'),
    ('paloma_wikitext_103',       'WikiText','lit'),
    ('paloma_redpajama',          'RedPajama','lit'),
    ('paloma_ptb',                'PTB',     'lit'),
]

# Compute delta matrix
matrix = np.zeros((len(ROWS), len(COL_ORDER)))
for i, (key, _label, pre_lab, ups_lab) in enumerate(ROWS):
    pre_palo = load('paloma', pre_lab)
    ups_palo = load('paloma', ups_lab)
    for j, (corpus, _name, _kind) in enumerate(COL_ORDER):
        p = pre_palo[corpus]['bits_per_byte,none']
        u = ups_palo[corpus]['bits_per_byte,none']
        matrix[i, j] = u - p

fig2, ax2 = plt.subplots(figsize=(9.5, 3.0))
# Diverging colormap: red for positive (regression), blue for negative (improvement)
vmax = max(abs(matrix.min()), abs(matrix.max()))
cmap = plt.get_cmap('RdBu_r')
norm = TwoSlopeNorm(vmin=-vmax, vcenter=0.0, vmax=vmax)
im = ax2.imshow(matrix, cmap=cmap, norm=norm, aspect='auto')

# Tick labels
ax2.set_xticks(range(len(COL_ORDER)))
ax2.set_xticklabels([n[1] for n in COL_ORDER], rotation=25, ha='right', fontsize=8)
ax2.set_yticks(range(len(ROWS)))
ax2.set_yticklabels([r[1] for r in ROWS], fontsize=9)

# Annotate cells
for i in range(matrix.shape[0]):
    for j in range(matrix.shape[1]):
        v = matrix[i, j]
        col = 'white' if abs(v) > 0.06 else 'black'
        ax2.text(j, i, f'{v:+.3f}', ha='center', va='center', fontsize=7, color=col)

# Vertical dividers between web/mixed/literary blocks
boundary_indices = []
kinds = [c[2] for c in COL_ORDER]
for j in range(1, len(kinds)):
    if kinds[j] != kinds[j-1]:
        boundary_indices.append(j - 0.5)
for x in boundary_indices:
    ax2.axvline(x=x, color='black', lw=1.2)

# Region annotations under x-axis
kind_labels = {'web':'WEB (improvement)', 'mixed':'NEUTRAL', 'lit':'LITERARY (regression)'}
# Find midpoints of each block
from itertools import groupby
groups = []
idx = 0
for kind, group in groupby(kinds):
    g = list(group)
    groups.append((kind, idx, idx + len(g) - 1))
    idx += len(g)
for kind, lo, hi in groups:
    mid = (lo + hi) / 2
    ax2.text(mid, len(ROWS) + 0.15, kind_labels[kind],
             ha='center', va='top', fontsize=8, style='italic',
             transform=ax2.transData)

# Colorbar
cbar = fig2.colorbar(im, ax=ax2, fraction=0.025, pad=0.02)
cbar.set_label('Δ BPB (POST+UP − PRE)', fontsize=8)
cbar.ax.tick_params(labelsize=7)

ax2.set_title('Paloma-11 per-corpus repair Δ — universal web/literary split across tiers',
              fontsize=10, pad=10)
plt.tight_layout()
plt.subplots_adjust(bottom=0.30, right=0.92)
plt.savefig(f'{OUTDIR}/fig2_paloma_split.pdf', bbox_inches='tight', dpi=300)
plt.savefig(f'{OUTDIR}/fig2_paloma_split.png', bbox_inches='tight', dpi=200)
print(f"[fig2] saved to {OUTDIR}/fig2_paloma_split.{{pdf,png}}")
plt.close()

print("Done.")