#!/usr/bin/env python3 """ SVS Test 6: Pairwise Discrimination Matrix ============================================= Trains classifiers on ALL 6 pairwise content comparisons. Valid Visual Subspace Generic Geometry Visual vs Gibberish HIGH HIGH Visual vs Factual HIGH HIGH Visual vs Math HIGH HIGH Factual vs Gibberish LOW HIGH ← key Factual vs Math LOW HIGH ← key Math vs Gibberish LOW HIGH ← key If ALL pairs are ~100% → generic geometry (invalid subspace) If ONLY visual-vs-others → valid visual subspace If ALL pairs ~50% → no information at all CPU only. Loads existing scaled gibberish data. Setup: !pip install -q scikit-learn scipy """ import json import numpy as np from pathlib import Path from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import cross_val_score, StratifiedKFold from sklearn.preprocessing import StandardScaler from google.colab import drive drive.mount("/content/drive", force_remount=False) print("=" * 65) print("SVS Test 6: Pairwise Discrimination Matrix") print("=" * 65) # ---- Load data ---- CHECKPOINT = Path("/content/drive/MyDrive/topohd_scaled_gib/gpu_checkpoint.json") assert CHECKPOINT.exists(), "Run scaled_gibberish_gpu.py first!" with open(CHECKPOINT) as f: raw = json.load(f) TARGET_LAYERS = [8, 16, 24, 32] methods = set() for key in raw: if key.startswith("_"): continue parts = key.split("|") if len(parts) == 3: methods.add(parts[1]) methods.discard("random") methods = sorted(methods) feature_names = [f"{m}_L{l}" for m in methods for l in TARGET_LAYERS] N_FEATURES = len(feature_names) TYPES = ["visual", "factual", "math", "gibberish"] # Build feature matrices for each type type_data = {} for ptype in TYPES: n = raw.get(f"_progress_{ptype}", 0) X = np.zeros((n, N_FEATURES)) for fi, (m, l) in enumerate([(m, l) for m in methods for l in TARGET_LAYERS]): vals = raw.get(f"{ptype}|{m}|{l}", []) for i in range(min(n, len(vals))): X[i, fi] = vals[i] valid = X.sum(axis=1) != 0 type_data[ptype] = X[valid] print(f" {ptype}: {type_data[ptype].shape[0]} samples, {N_FEATURES} features") # ---- Pairwise classification ---- print(f"\n Pairwise Discrimination (Gradient Boosted Trees, 10-fold CV)") print(f" {'Pair':<30} {'Accuracy':>10} {'Std':>8} {'AUROC':>8}") print(f" {'-'*56}") cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42) scaler = StandardScaler() pair_results = {} for i, t1 in enumerate(TYPES): for t2 in TYPES[i+1:]: X1 = type_data[t1] X2 = type_data[t2] N = min(len(X1), len(X2)) X = np.vstack([X1[:N], X2[:N]]) y = np.array([1]*N + [0]*N) X_s = scaler.fit_transform(X) clf = GradientBoostingClassifier(n_estimators=200, max_depth=4, random_state=42) acc = cross_val_score(clf, X_s, y, cv=cv, scoring='accuracy') auc = cross_val_score(clf, X_s, y, cv=cv, scoring='roc_auc') pair_name = f"{t1} vs {t2}" pair_results[pair_name] = { "accuracy": float(acc.mean()), "std": float(acc.std()), "auroc": float(auc.mean()), } involves_visual = "visual" in pair_name marker = " ← visual" if involves_visual else " ← non-visual" print(f" {pair_name:<30} {acc.mean()*100:>9.1f}% {acc.std()*100:>7.1f}% " f"{auc.mean():>7.3f}{marker}") # ---- Diagnosis ---- print(f"\n{'='*65}") print("DIAGNOSIS") print(f"{'='*65}") visual_pairs = [v for k, v in pair_results.items() if "visual" in k] nonvis_pairs = [v for k, v in pair_results.items() if "visual" not in k] mean_vis_acc = np.mean([p["accuracy"] for p in visual_pairs]) mean_nonvis_acc = np.mean([p["accuracy"] for p in nonvis_pairs]) print(f"\n Visual-involving pairs: mean accuracy = {mean_vis_acc*100:.1f}%") print(f" Non-visual pairs: mean accuracy = {mean_nonvis_acc*100:.1f}%") if mean_vis_acc > 0.90 and mean_nonvis_acc > 0.90: print(f""" >>> GENERIC GEOMETRY (invalid subspace) <<< ALL content type pairs are separable ({mean_vis_acc*100:.0f}% / {mean_nonvis_acc*100:.0f}%). The subspace projections encode general linguistic features (tokenization, vocabulary, sentence structure), not specifically visual content. A valid visual subspace would distinguish visual from non-visual inputs WITHOUT distinguishing non-visual types from each other. Diagnostic: FAIL — projections are content-type-generic, not visually-specific. """) elif mean_vis_acc > 0.80 and mean_nonvis_acc < 0.60: print(f""" >>> VISUAL-SPECIFIC SUBSPACE (potentially valid) <<< Visual pairs are separable ({mean_vis_acc*100:.0f}%) but non-visual pairs are not ({mean_nonvis_acc*100:.0f}%). The projections encode visual-specific information. Diagnostic: PASS — projections discriminate visual content specifically. """) elif mean_vis_acc < 0.60 and mean_nonvis_acc < 0.60: print(f""" >>> NO DISCRIMINATIVE INFORMATION <<< No pair is separable. Projections encode nothing about content. Diagnostic: FAIL — projections are content-blind. """) else: print(f""" >>> MIXED RESULT <<< Visual pairs: {mean_vis_acc*100:.1f}%, Non-visual: {mean_nonvis_acc*100:.1f}% Partial content information exists but pattern is unclear. """) # ---- Summary table for paper ---- print(f" PAPER TABLE:") print(f" {'':>20}", end="") for t2 in TYPES[1:]: print(f" {t2:>12}", end="") print() for i, t1 in enumerate(TYPES[:-1]): print(f" {t1:>20}", end="") for t2 in TYPES[i+1:]: pair = f"{t1} vs {t2}" if pair in pair_results: print(f" {pair_results[pair]['accuracy']*100:>11.1f}%", end="") else: print(f" {'':>12}", end="") # Pad remaining columns for _ in range(i): print(f" {'':>12}", end="") print() # Save OUT = Path("/content/drive/MyDrive/topohd_classification") OUT.mkdir(exist_ok=True, parents=True) with open(OUT / "pairwise_discrimination.json", "w") as f: json.dump(pair_results, f, indent=2) print(f"\n Saved to {OUT}/")