#!/usr/bin/env python3
"""
Distributional Match Principle: Quantitative Validation
=========================================================
CPU only. Loads saved bases from prior experiments.
Computes subspace similarity between each basis and image-token PCA.
Correlates with VGCD hallucination reduction.

If correlation is strong (r > 0.9): DMP is quantitatively validated.
"""

import json
import numpy as np
from pathlib import Path
from scipy import stats as sp

from google.colab import drive
drive.mount("/content/drive", force_remount=False)

print("=" * 65)
print("Distributional Match Principle: Quantitative Validation")
print("=" * 65)

# ---- Load bases from multiple sources ----
print("\n[1/3]  Loading bases ...")

SEARCH_PATHS = {
    "makebreak": Path("/content/drive/MyDrive/topohd_makebreak"),
    "corrected": Path("/content/drive/MyDrive/topohd_corrected"),
    "vicuna_vgcd": Path("/content/drive/MyDrive/topohd_vicuna_vgcd"),
    "illusion": Path("/content/drive/MyDrive/topohd_illusion"),
    "vista_nullu": Path("/content/drive/MyDrive/topohd_vista_nullu"),
    "scaled_makebreak": Path("/content/drive/MyDrive/topohd_scaled_makebreak"),
}

bases = {}

# Try corrected_subspace first (has image, backbone, random)
cp = SEARCH_PATHS["corrected"] / "all_bases.npz"
if cp.exists():
    d = np.load(cp)
    if "image_basis" in d: bases["image_pca"] = d["image_basis"]
    if "backbone_basis" in d: bases["backbone_pca"] = d["backbone_basis"]
    if "random_basis" in d: bases["random"] = d["random_basis"]
    print(f"  Loaded from corrected: {[k for k in ['image_pca','backbone_pca','random'] if k in bases]}")

# Try makebreak (has visual, random, maybe text)
for mp in [SEARCH_PATHS["makebreak"] / "bases.npz",
           SEARCH_PATHS["scaled_makebreak"] / "bases.npz"]:
    if mp.exists():
        d = np.load(mp)
        if "visual" in d and "image_pca" not in bases:
            bases["image_pca"] = d["visual"]
        if "random" in d and "random" not in bases:
            bases["random"] = d["random"]
        print(f"  Loaded from {mp.parent.name}: {list(d.files)}")

# Try illusion (has img PCA and txt PCA)
ip = SEARCH_PATHS["illusion"] / "subspaces.npz"
if ip.exists():
    d = np.load(ip)
    # These are per-layer; take layer 16 if available
    for key in d.files:
        if key.startswith("img_16"):
            bases["image_pca_L16"] = d[key]
        elif key.startswith("txt_16"):
            bases["text_pca_L16"] = d[key]
        elif key.startswith("img_") and "image_pca" not in bases:
            bases["image_pca"] = d[key]
        elif key.startswith("txt_") and "text_pca" not in bases:
            bases["text_pca"] = d[key]
    print(f"  Loaded from illusion: {[k for k in bases if 'L16' in k or k in ['image_pca','text_pca']]}")

# Try vicuna_vgcd (has vicuna PCA)
vp = SEARCH_PATHS["vicuna_vgcd"] / "vicuna_pca_basis.npy"
if vp.exists():
    bases["backbone_pca"] = np.load(vp)
    print(f"  Loaded backbone PCA from vicuna_vgcd: {bases['backbone_pca'].shape}")

print(f"\n  Available bases: {list(bases.keys())}")

# Use image_pca as the reference
ref_key = "image_pca"
if ref_key not in bases and "image_pca_L16" in bases:
    ref_key = "image_pca_L16"
assert ref_key in bases, f"No image PCA basis found! Available: {list(bases.keys())}"
ref_basis = bases[ref_key]
print(f"  Reference basis: {ref_key}, shape {ref_basis.shape}")

# ---- Compute subspace similarities ----
print(f"\n[2/3]  Computing subspace similarities ...")

def subspace_similarity(A, B):
    """Compute similarity between two subspaces.
    Returns multiple measures."""
    # Ensure same number of directions
    k = min(A.shape[0], B.shape[0])
    A, B = A[:k], B[:k]

    # 1. Mean absolute cosine: for each dir in A, find best match in B
    cos_matrix = np.abs(A @ B.T)  # (k, k)
    best_match_A = cos_matrix.max(axis=1).mean()  # avg best match for A dirs
    best_match_B = cos_matrix.max(axis=0).mean()  # avg best match for B dirs

    # 2. Projection Frobenius: ||A @ B^T||_F^2 / k
    proj_frob = np.sum(cos_matrix**2) / k

    # 3. Mean cosine (all pairs)
    mean_cos = cos_matrix.mean()

    # 4. Top-1 principal angle cosine
    U, S, Vt = np.linalg.svd(A @ B.T)
    top_cos = S[0]  # cosine of first principal angle

    return dict(
        best_match_mean=(best_match_A + best_match_B) / 2,
        proj_frobenius=proj_frob,
        mean_cosine=mean_cos,
        top_principal_cos=top_cos,
    )

# VGCD results from makebreak (alpha=1.5)
VGCD_RESULTS = {
    "image_pca": -5.0,      # 57.5% vs 62.5% baseline
    "text_pca": -2.0,       # 60.5%
    "backbone_pca": +3.5,   # 66.0%
    "random": +5.0,         # 67.5%
}

# Compute similarities
print(f"\n  {'Basis':<20} {'BestMatch':>10} {'ProjFrob':>10} {'MeanCos':>10} "
      f"{'TopPC':>10} {'VGCD Δpp':>10}")
print(f"  {'-'*70}")

sim_data = []
for bname, basis in bases.items():
    if bname == ref_key: continue
    if bname.endswith("_L16"): continue  # skip duplicates

    # Map to VGCD results
    vgcd_key = bname
    if vgcd_key not in VGCD_RESULTS:
        # Try without suffix
        for vk in VGCD_RESULTS:
            if vk in bname:
                vgcd_key = vk
                break

    if vgcd_key not in VGCD_RESULTS:
        print(f"  {bname:<20} (no VGCD data, skipping)")
        continue

    sim = subspace_similarity(ref_basis, basis)
    vgcd_delta = VGCD_RESULTS[vgcd_key]

    print(f"  {bname:<20} {sim['best_match_mean']:>10.4f} "
          f"{sim['proj_frobenius']:>10.4f} {sim['mean_cosine']:>10.4f} "
          f"{sim['top_principal_cos']:>10.4f} {vgcd_delta:>+10.1f}")

    sim_data.append(dict(name=bname, vgcd=vgcd_delta, **sim))

# Add self-similarity for image_pca
self_sim = subspace_similarity(ref_basis, ref_basis)
sim_data.append(dict(name=ref_key, vgcd=VGCD_RESULTS.get("image_pca", -5.0),
                      **self_sim))
print(f"  {ref_key:<20} {self_sim['best_match_mean']:>10.4f} "
      f"{self_sim['proj_frobenius']:>10.4f} {self_sim['mean_cosine']:>10.4f} "
      f"{self_sim['top_principal_cos']:>10.4f} {VGCD_RESULTS.get('image_pca', -5.0):>+10.1f}")

# ---- Correlation analysis ----
print(f"\n[3/3]  Correlation: Distributional Match vs VGCD Effectiveness")
print("=" * 65)

if len(sim_data) < 3:
    print("  Not enough data points for correlation.")
else:
    vgcd_vals = np.array([d["vgcd"] for d in sim_data])

    for metric in ["best_match_mean", "proj_frobenius", "mean_cosine", "top_principal_cos"]:
        sim_vals = np.array([d[metric] for d in sim_data])

        # Pearson correlation
        r, p = sp.pearsonr(sim_vals, vgcd_vals)
        # Spearman rank correlation
        rho, p_rho = sp.spearmanr(sim_vals, vgcd_vals)

        print(f"\n  {metric}:")
        print(f"    Pearson  r = {r:+.4f}  (p = {p:.4f})")
        print(f"    Spearman ρ = {rho:+.4f}  (p = {p_rho:.4f})")

        if abs(r) > 0.9:
            print(f"    >>> STRONG correlation: higher similarity → lower hallucination")
        elif abs(r) > 0.7:
            print(f"    >> Moderate correlation")
        else:
            print(f"    > Weak correlation")

    # Print the data points for the figure
    print(f"\n  Data points for Figure (best_match_mean vs VGCD):")
    for d in sorted(sim_data, key=lambda x: x["best_match_mean"], reverse=True):
        print(f"    {d['name']:<20} similarity={d['best_match_mean']:.4f}  "
              f"VGCD={d['vgcd']:+.1f}pp")

# ---- Verdict ----
print(f"\n{'='*65}")
print("VERDICT: Distributional Match Principle")
print(f"{'='*65}")

if len(sim_data) >= 3:
    r_best, _ = sp.pearsonr(
        [d["best_match_mean"] for d in sim_data],
        [d["vgcd"] for d in sim_data])

    if r_best < -0.85:  # negative: higher similarity → more negative VGCD (less halluc)
        print(f"\n  >>> DMP VALIDATED (r = {r_best:.3f}) <<<")
        print(f"  Subspace similarity to image-token PCA strongly predicts")
        print(f"  VGCD hallucination reduction. The closer the basis matches")
        print(f"  the image-token distribution, the better the steering.")
        print(f"\n  This resolves the paradox:")
        print(f"    - Directions fail content-specificity (gibberish test)")
        print(f"    - But succeed at steering when distribution-matched")
        print(f"    - Content ≠ distribution: the mechanism is geometric,")
        print(f"      not semantic")
        print(f"\n  DMP provides a principled selection criterion:")
        print(f"    'Use PCA of the target distribution, not a content-specific basis.'")
    elif r_best < -0.7:
        print(f"\n  >> DMP PARTIALLY SUPPORTED (r = {r_best:.3f})")
        print(f"  Moderate correlation between similarity and effectiveness.")
    else:
        print(f"\n  > DMP NOT SUPPORTED (r = {r_best:.3f})")
        print(f"  Distributional match does not predict steering effectiveness.")

# Save
results = {
    "sim_data": [{k: float(v) if isinstance(v, (float, np.floating)) else v
                   for k, v in d.items()} for d in sim_data],
}
OUT = Path("/content/drive/MyDrive/topohd_dmp")
OUT.mkdir(exist_ok=True, parents=True)
with open(OUT / "dmp_results.json", "w") as f:
    json.dump(results, f, indent=2)
print(f"\n  Saved to {OUT}/")