{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "format": "yaml"
   },
   "source": [
    "---\n",
    "title: \"ELLE: Embeddings Linearly contain their Loss Estimate\"\n",
    "date: 2026-03-01\n",
    "author: Bruno Sanchez-Andrade Nuno\n",
    "categories: [embeddings, uncertainty, geospatial, foundation-models, self-supervised]\n",
    "description: \"Foundation model CLS embeddings linearly encode their own per-sample pretraining loss -- readable by a Ridge probe in 1 us, with no labels and no extra forward passes. Validated across 20 models and 6 modalities.\"\n",
    "image: assets/final_bar_chart.png\n",
    "page-layout: full\n",
    "execute:\n",
    "  enabled: false\n",
    "other-links:\n",
    "  - text: \"Download Notebook\"\n",
    "    icon: download\n",
    "    href: https://raw.githubusercontent.com/EarthLegend/devlogs/main/posts/2026-03-01-self-aware-embeddings/index.ipynb\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b8ca36",
   "metadata": {
    "papermill": {
     "duration": 0.002804,
     "end_time": "2026-03-03T21:22:01.909888",
     "exception": false,
     "start_time": "2026-03-03T21:22:01.907084",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# ELLE: Embeddings Linearly contain their Loss Estimate\n",
    "\n",
    "## Abstract\n",
    "\n",
    "Self-supervised foundation models (masked autoencoders, denoising autoencoders,\n",
    "contrastive learners) produce CLS embeddings from which a simple Ridge regression\n",
    "can predict the model's own per-sample pretraining loss — **without running the\n",
    "decoder at inference time**. We call this the *ELLE signal*: Embeddings Linearly\n",
    "contain their Loss Estimate.\n",
    "\n",
    "Across 19 models spanning image, audio, text, code, and geospatial modalities,\n",
    "the probe achieves **Pearson r = 0.50–0.99** on held-out data (r ≥ 0.5 = Cohen's\n",
    "\"large\" effect size, 25%+ variance explained). Once the linear probe is calculated, we can retrieve the loss from any embedding. The probe adds ~1 μs of overhead per sample, and this loss estimation needs no architectural changes.\n",
    "\n",
    "**Surprising finding:** the signal is not exclusive to reconstruction-pretrained\n",
    "models. Contrastive (SatCLIP, r = 0.96 in-domain) and self-distillation (DINOv2,\n",
    "r = 0.72 cross-domain) models also carry it. This suggests the ELLE signal\n",
    "captures **visual complexity** that sufficiently trained self-supervised backbones tend to encode,\n",
    "regardless of the pretraining objective.\n",
    "\n",
    "Moreover, the signal is holistically distributed across embedding dimensions (even on Matryoshka embeddings), and for some modalities (Text, Code) crosses the r ≥ 0.5 threshold purely by scaling from N=5k to N=40k samples.\n",
    "\n",
    "**Practical value:** once you have 1k–40k (embedding, loss) pairs from your\n",
    "training data, you get a free per-sample difficulty estimate at inference\n",
    "— useful for routing, monitoring, and data quality assessment.\n",
    "\n",
    "**For practitioners**: ELLE gives you a per-sample difficulty score from any foundation model embedding — no decoder, no labels, no retraining. Train a Ridge probe once (~1 minute) and score millions of samples at inference speed. Use cases: data curation, quality filtering, active learning, and difficulty-based routing. Note: the probe correlation is a *population-level* statistic — models with r > 0.9 yield per-sample informative scores, while models near r ≈ 0.5 are useful for aggregate ranking and cohort analysis rather than individual-sample precision.\n",
    "\n",
    "\n",
    "## How This Notebook Works\n",
    "\n",
    "This notebook **is** the paper. Each code cell generates the figure shown directly\n",
    "below it using the outputs from the provided script that downloads all models, data, implements all steps and saves the outputs.\n",
    "\n",
    "To reproduce from scratch, simply execute this notebook end-to-end — Cell 3\n",
    "downloads all models and data from public sources automatically.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3216b639",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "The cells below import libraries, define helpers, and — on first run — download all models and extract embeddings (~90 min with GPU). Subsequent runs use cached files and complete in seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a55a73d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:54.210297Z",
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     "iopub.status.idle": "2026-03-05T20:29:55.452942Z",
     "shell.execute_reply": "2026-03-05T20:29:55.452689Z"
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    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Changed CWD to: /home/brunosan/code/elle\n",
      "Device: cuda\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import sys, os, gzip, io, warnings\n",
    "sys.path.insert(0, os.path.abspath('..'))\n",
    "\n",
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib\n",
    "from pathlib import Path\n",
    "from scipy.stats import pearsonr\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Ensure we run from project root (handles both interactive and nbconvert execution)\n",
    "if not (Path.cwd() / 'data').exists() and (Path.cwd().parent / 'data').exists():\n",
    "    os.chdir('..')\n",
    "    print(f\"Changed CWD to: {Path.cwd()}\")\n",
    "\n",
    "\n",
    "# Constants\n",
    "SEED = 42\n",
    "VAL_RATIO = 0.2\n",
    "ALPHAS = [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]\n",
    "N_SWEEP = [300, 500, 1000, 2000, 5000, 10000, 20000, 40000]\n",
    "DATA_DIR = Path('data')\n",
    "FIG_DIR  = Path('figures')\n",
    "DATA_DIR.mkdir(exist_ok=True)\n",
    "FIG_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(f\"Device: {device}\")\n",
    "\n",
    "# Patch torch.load for PyTorch ≥ 2.4 compatibility (default weights_only changed)\n",
    "_torch_load_orig = torch.load\n",
    "def _torch_load_compat(f, *args, **kwargs):\n",
    "    kwargs.setdefault('weights_only', False)\n",
    "    return _torch_load_orig(f, *args, **kwargs)\n",
    "torch.load = _torch_load_compat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83aed7eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:55.454096Z",
     "iopub.status.busy": "2026-03-05T20:29:55.453955Z",
     "iopub.status.idle": "2026-03-05T20:29:55.458685Z",
     "shell.execute_reply": "2026-03-05T20:29:55.458456Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Helper functions defined: load_pairs, probe_r, plot_calibration, batch_probe\n"
     ]
    }
   ],
   "source": [
    "def load_pairs(path):\n",
    "    \"\"\"Load (cls_emb, loss) arrays from a .pt file.\"\"\"\n",
    "    d = torch.load(path, map_location='cpu', weights_only=False)\n",
    "    return d['cls_emb'].float().numpy(), d['loss'].float().numpy()\n",
    "\n",
    "def probe_r(cls_np, loss_np, n_train_limit=None):\n",
    "    \"\"\"Train probe with cross-validated alpha and return Pearson r on 20% val set.\n",
    "\n",
    "    Alpha is selected via grid search (ALPHAS) on a 2000-sample subsample.\n",
    "    Returns (r, n_train, pred, y_val, selected_alpha).\n",
    "    \"\"\"\n",
    "    np.random.seed(SEED)\n",
    "    N = len(cls_np); n_val = int(N * VAL_RATIO); n_tr = N - n_val\n",
    "    idx = np.random.permutation(N)\n",
    "    tr_i, val_i = idx[:n_tr], idx[n_tr:]\n",
    "    if n_train_limit:\n",
    "        tr_i = tr_i[:n_train_limit]\n",
    "    Xtr, ytr = cls_np[tr_i], loss_np[tr_i]\n",
    "    Xv, yv   = cls_np[val_i], loss_np[val_i]\n",
    "    lm, ls = ytr.mean(), ytr.std() or 1.0\n",
    "\n",
    "    # Alpha selection on max 2000 subsample (fast)\n",
    "    n_sub = min(2000, len(tr_i))\n",
    "    Xs, ys = Xtr[:n_sub], ytr[:n_sub]\n",
    "    ns_v = int(n_sub * 0.2)\n",
    "    sc_sub = StandardScaler().fit(Xs[:-ns_v])\n",
    "    Xs_t, Xs_v2 = sc_sub.transform(Xs[:-ns_v]), sc_sub.transform(Xs[-ns_v:])\n",
    "    ys_tz, ys_vv = (ys[:-ns_v] - lm)/ls, ys[-ns_v:]\n",
    "    best_alpha, best_mse = ALPHAS[0], float('inf')\n",
    "    for a in ALPHAS:\n",
    "        p = Ridge(alpha=a, solver='cholesky').fit(Xs_t, ys_tz).predict(Xs_v2) * ls + lm\n",
    "        mse = float(np.mean((p - ys_vv)**2))\n",
    "        if mse < best_mse:\n",
    "            best_mse, best_alpha = mse, a\n",
    "\n",
    "    # Final fit on full training set\n",
    "    sc = StandardScaler()\n",
    "    Xtr_s = sc.fit_transform(Xtr); Xv_s = sc.transform(Xv)\n",
    "    pred = np.clip(Ridge(alpha=best_alpha, solver='cholesky').fit(\n",
    "        Xtr_s, (ytr-lm)/ls).predict(Xv_s) * ls + lm, 0, None)\n",
    "    r, _ = pearsonr(pred, yv)\n",
    "    return float(r), len(tr_i), pred, yv, best_alpha\n",
    "\n",
    "def plot_calibration(ax, y_val, pred, r, title, color='steelblue', s=5, alpha=0.25):\n",
    "    \"\"\"Calibration scatter with diagonal and r annotation.\"\"\"\n",
    "    np.random.seed(SEED)\n",
    "    idx_plot = np.random.choice(len(y_val), min(2000, len(y_val)), replace=False)\n",
    "    ax.scatter(y_val[idx_plot], pred[idx_plot], s=s, alpha=alpha, color=color)\n",
    "    lo, hi = min(y_val.min(), pred.min()), max(y_val.max(), pred.max())\n",
    "    ax.plot([lo, hi], [lo, hi], 'k--', lw=1.2, alpha=0.5)\n",
    "    ax.text(0.05, 0.90, f'r = {r:.3f}', transform=ax.transAxes,\n",
    "            fontsize=13, fontweight='bold')\n",
    "    ax.set_xlabel('Actual loss'); ax.set_ylabel('Predicted (CLS only)')\n",
    "    ax.set_title(title, fontsize=11); ax.grid(alpha=0.2)\n",
    "\n",
    "def batch_probe(model_list):\n",
    "    \"\"\"Run probe_r on multiple (name, path, color) models.\n",
    "    Returns list of (name, r, n_train, pred, y_val, alpha, color).\n",
    "    \"\"\"\n",
    "    results = []\n",
    "    for name, path, color in model_list:\n",
    "        if not Path(path).exists():\n",
    "            print(f\"  {name}: not found\"); continue\n",
    "        X, y = load_pairs(path)\n",
    "        r, n, pred, yv, alpha = probe_r(X, y)\n",
    "        results.append((name, r, n, pred, yv, alpha, color))\n",
    "        print(f\"  {name}: r = {r:.4f}  N = {len(X):,}\")\n",
    "    return results\n",
    "\n",
    "print(\"Helper functions defined: load_pairs, probe_r, plot_calibration, batch_probe\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1e8263f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:55.460066Z",
     "iopub.status.busy": "2026-03-05T20:29:55.459968Z",
     "iopub.status.idle": "2026-03-05T20:29:58.256291Z",
     "shell.execute_reply": "2026-03-05T20:29:58.256018Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  pip install open-clip-torch...\n",
      "  pip install soundfile...\n",
      "Device: cuda\n",
      "  ✓ 1.1  image_pairs.pt + image_labeled_pairs.pt (cached)\n",
      "  ✓ 1.2 Audio (HuBERT, LibriSpeech dev-clean) (cached)\n",
      "  ✓ 1.3 Text (BART, WikiText-103, N=40k) (cached)\n",
      "  ✓ 1.4 Code (CodeBERT, CodeSearchNet, N=40k) (cached)\n",
      "  ✓ 2.1 SatCLIP in-domain (S2, N=300) (cached)\n",
      "  ✓ 2.2 ViT-MAE on S2 (geo_pairs.pt, N=5k) (cached)\n",
      "  ✓ 2.3 DINOv2 on CIFAR-10 (N=8k) (cached)\n",
      "  ✓ 2.4 DINOv2 on Food101 (N=5k) (cached)\n",
      "  ✓ 2.6 RemoteCLIP on S2 (N=2k) (cached)\n",
      "  ✓ 2.7 Prithvi-100M on S2 (N=2k) (cached)\n",
      "  ✓ 2.8 DOFA on S2 (N=2k) (cached)\n",
      "  ✓ 2.9 FG-MAE on S2 (N=2k) (cached)\n",
      "  ✓ 2.10 SatMAE on S2 (N=2k) (cached)\n",
      "  ✓ 2.11 ScaleMAE on S2 (N=2k) (cached)\n",
      "  ✓ clay_*.pt (cached)\n",
      "  ✓ 4.1 OLMo-Earth-Nano on S2 (N=5k) (cached)\n",
      "  ✓ ci_and_baselines.pt (cached)\n",
      "\n",
      "Data status:\n",
      "  Present: 30 data files\n",
      "  ✅ All required files present — notebook ready to run\n"
     ]
    }
   ],
   "source": [
    "# ── Dependencies ──────────────────────────────────────────────────────────────\n",
    "import gc, importlib, subprocess\n",
    "import torch.nn.functional as F\n",
    "from tqdm import tqdm\n",
    "\n",
    "def _pip(*pkgs):\n",
    "    subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q'] + list(pkgs),\n",
    "                          stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)\n",
    "\n",
    "_deps = [\n",
    "    ('datasets>=2.14',      'datasets'),\n",
    "    ('transformers>=4.37',  'transformers'),\n",
    "    ('pystac-client',       'pystac_client'),\n",
    "    ('rasterio',            'rasterio'),\n",
    "    ('huggingface_hub',     'huggingface_hub'),\n",
    "    ('open-clip-torch',     'open_clip'),\n",
    "    ('soundfile',           'soundfile'),   # torchaudio audio backend\n",
    "]\n",
    "for pkg, mod in _deps:\n",
    "    try:\n",
    "        importlib.import_module(mod)\n",
    "    except ImportError:\n",
    "        print(f\"  pip install {pkg}...\")\n",
    "        _pip(pkg)\n",
    "\n",
    "from datasets import load_dataset\n",
    "from transformers import (\n",
    "    AutoImageProcessor, AutoModel, AutoTokenizer, AutoModelForMaskedLM,\n",
    "    ViTMAEForPreTraining, HubertModel, Wav2Vec2FeatureExtractor,\n",
    "    BartForConditionalGeneration,\n",
    ")\n",
    "from PIL import Image as _PIL\n",
    "\n",
    "DATA_DIR.mkdir(exist_ok=True)\n",
    "FIG_DIR.mkdir(exist_ok=True)\n",
    "print(f\"Device: {device}\")\n",
    "\n",
    "# ── Utilities ─────────────────────────────────────────────────────────────────\n",
    "def _skip(path, label):\n",
    "    if Path(path).exists():\n",
    "        print(f\"  ✓ {label} (cached)\"); return True\n",
    "    print(f\"  ↓ {label}...\"); return False\n",
    "\n",
    "def _clear():\n",
    "    gc.collect()\n",
    "    if device == 'cuda': torch.cuda.empty_cache()\n",
    "\n",
    "def _fisher_ci(r, n, z=1.96):\n",
    "    \"\"\"Fisher z-transform 95% CI for Pearson r.\"\"\"\n",
    "    if n < 4: return (float(r) - 0.1, float(r) + 0.1)\n",
    "    zr = np.arctanh(np.clip(float(r), -0.9999, 0.9999))\n",
    "    se = 1.0 / np.sqrt(n - 3)\n",
    "    return float(np.tanh(zr - z * se)), float(np.tanh(zr + z * se))\n",
    "\n",
    "# ── ViTMAE per-sample loss helper ─────────────────────────────────────────────\n",
    "def _vitmae_batch(model, pixel_values):\n",
    "    \"\"\"Return (cls_cpu, per_sample_loss_cpu) for a batch on device.\"\"\"\n",
    "    with torch.no_grad():\n",
    "        out = model(pixel_values, output_hidden_states=True)\n",
    "        cls = out.hidden_states[-1][:, 0, :]          # [B, D]\n",
    "        patches = model.patchify(pixel_values)         # [B, L, patch_dim]\n",
    "        if model.config.norm_pix_loss:\n",
    "            m = patches.mean(dim=-1, keepdim=True)\n",
    "            v = patches.var(dim=-1, keepdim=True)\n",
    "            patches = (patches - m) / (v + 1e-6) ** 0.5\n",
    "        lp = ((out.logits - patches) ** 2).mean(dim=-1)  # [B, L]\n",
    "        mk = out.mask                                      # [B, L] 1=masked\n",
    "        per = (lp * mk).sum(dim=-1) / mk.sum(dim=-1)     # [B]\n",
    "    return cls.cpu(), per.cpu()\n",
    "\n",
    "# ── STAC Sentinel-2 helpers ───────────────────────────────────────────────────\n",
    "_stac_items_cache = None\n",
    "\n",
    "def _get_stac_items(n=600, bbox=None, max_cloud=10):\n",
    "    \"\"\"Lazy-load STAC Sentinel-2 items (cached across calls).\"\"\"\n",
    "    global _stac_items_cache\n",
    "    if _stac_items_cache is not None:\n",
    "        return _stac_items_cache\n",
    "    bbox = bbox or [-106, 38, -104, 40]   # Colorado — lots of clear S2 coverage\n",
    "    try:\n",
    "        from pystac_client import Client\n",
    "        catalog = Client.open(\"https://earth-search.aws.element84.com/v1\")\n",
    "        search = catalog.search(\n",
    "            collections=[\"sentinel-2-l2a\"], bbox=bbox,\n",
    "            max_items=min(n, 400), query={\"eo:cloud_cover\": {\"lt\": max_cloud}},\n",
    "        )\n",
    "        _stac_items_cache = list(search.items())\n",
    "        print(f\"  STAC: {len(_stac_items_cache)} S2 items loaded\")\n",
    "        return _stac_items_cache\n",
    "    except Exception as e:\n",
    "        print(f\"  STAC unavailable: {e}\"); return []\n",
    "\n",
    "\n",
    "def _read_rgb(item, sz=224):\n",
    "    \"\"\"Read RGB (visual) tile from a STAC item → uint8 [3, H, W] or None.\"\"\"\n",
    "    try:\n",
    "        import rasterio; from rasterio.windows import Window\n",
    "        with rasterio.open(item.assets[\"visual\"].href) as src:\n",
    "            w, h = src.width, src.height\n",
    "            a = src.read([1,2,3], window=Window(w//2-sz//2, h//2-sz//2, sz, sz))\n",
    "        if a.shape != (3, sz, sz) or a.max() == 0: return None\n",
    "        return a\n",
    "    except Exception: return None\n",
    "\n",
    "\n",
    "def _item_lonlat(item):\n",
    "    c = np.array(item.geometry['coordinates'][0])\n",
    "    return float(c[:, 0].mean()), float(c[:, 1].mean())\n",
    "\n",
    "def _collect_tiles(n_target, sz=224):\n",
    "    \"\"\"Collect up to n_target RGB tiles from STAC.\"\"\"\n",
    "    tiles = []\n",
    "    for item in _get_stac_items(max(n_target * 3, 600)):\n",
    "        if len(tiles) >= n_target: break\n",
    "        rgb = _read_rgb(item, sz)\n",
    "        if rgb is not None: tiles.append(rgb)\n",
    "    return tiles\n",
    "\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "# TIER 1 — Core Modalities\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "\n",
    "# ── 1.1  ViT-MAE on CIFAR-10 (image_pairs.pt + image_labeled_pairs.pt) ───────\n",
    "_img_path = DATA_DIR / 'image_pairs.pt'\n",
    "_lbl_path = DATA_DIR / 'image_labeled_pairs.pt'\n",
    "if not _img_path.exists() or not _lbl_path.exists():\n",
    "    print(\"  ↓ 1.1 Image (ViT-MAE, CIFAR-10, N=40k)...\")\n",
    "    _proc = AutoImageProcessor.from_pretrained(\"facebook/vit-mae-base\")\n",
    "    _mod  = ViTMAEForPreTraining.from_pretrained(\"facebook/vit-mae-base\").to(device).eval()\n",
    "    _ds   = load_dataset(\"cifar10\", split=\"train\")\n",
    "    N, bs = 40000, 32\n",
    "    all_cls, all_loss, lbl_imgs, lbl_labs = [], [], [], []\n",
    "    for i in tqdm(range(0, N, bs), desc=\"  vitmae-cifar\"):\n",
    "        end = min(i + bs, N)\n",
    "        imgs = [_ds[j]['img'].convert('RGB').resize((224, 224)) for j in range(i, end)]\n",
    "        px = _proc(images=imgs, return_tensors='pt').pixel_values.to(device)\n",
    "        cls, lv = _vitmae_batch(_mod, px)\n",
    "        all_cls.append(cls); all_loss.append(lv)\n",
    "        if i < 2000:\n",
    "            for j in range(i, min(end, 2000)):\n",
    "                lbl_imgs.append(torch.tensor(np.array(_ds[j]['img']), dtype=torch.uint8))\n",
    "                lbl_labs.append(_ds[j]['label'])\n",
    "    cat_cls  = torch.cat(all_cls)\n",
    "    cat_loss = torch.cat(all_loss)\n",
    "    if not _img_path.exists():\n",
    "        torch.save({'cls_emb': cat_cls, 'loss': cat_loss}, _img_path)\n",
    "        print(f\"  ✓ image_pairs.pt  ({N:,})\")\n",
    "    if not _lbl_path.exists():\n",
    "        torch.save({\n",
    "            'cls_emb': cat_cls[:2000], 'loss': cat_loss[:2000],\n",
    "            'img':     torch.stack(lbl_imgs),\n",
    "            'label':   torch.tensor(lbl_labs, dtype=torch.long),\n",
    "        }, _lbl_path)\n",
    "        print(f\"  ✓ image_labeled_pairs.pt  (2,000)\")\n",
    "    del _mod, _proc, _ds; _clear()\n",
    "else:\n",
    "    print(\"  ✓ 1.1  image_pairs.pt + image_labeled_pairs.pt (cached)\")\n",
    "\n",
    "# ── 1.2  HuBERT on LibriSpeech validation-clean ──────────────────────────────\n",
    "if not _skip(DATA_DIR / 'audio_pairs.pt', '1.2 Audio (HuBERT, LibriSpeech dev-clean)'):\n",
    "    import soundfile as sf\n",
    "    import tarfile, urllib.request\n",
    "    _aproc = Wav2Vec2FeatureExtractor.from_pretrained(\"facebook/hubert-base-ls960\")\n",
    "    _amod  = HubertModel.from_pretrained(\"facebook/hubert-base-ls960\").to(device).eval()\n",
    "    # Download LibriSpeech dev-clean (~337 MB) and extract FLAC files\n",
    "    _ls_root = Path('/tmp/librispeech'); _ls_root.mkdir(exist_ok=True)\n",
    "    _ls_url = \"https://www.openslr.org/resources/12/dev-clean.tar.gz\"\n",
    "    _ls_tar = _ls_root / \"dev-clean.tar.gz\"\n",
    "    _ls_dir = _ls_root / \"LibriSpeech\" / \"dev-clean\"\n",
    "    if not _ls_dir.exists():\n",
    "        print(\"  downloading LibriSpeech dev-clean …\")\n",
    "        urllib.request.urlretrieve(_ls_url, str(_ls_tar))\n",
    "        with tarfile.open(str(_ls_tar), 'r:gz') as tf:\n",
    "            tf.extractall(str(_ls_root))\n",
    "    _flac_files = sorted(_ls_dir.rglob(\"*.flac\"))\n",
    "    print(f\"  {len(_flac_files)} FLAC files found\")\n",
    "    ac, al = [], []\n",
    "    for fp in tqdm(_flac_files, desc=\"  hubert\"):\n",
    "        try:\n",
    "            arr, sr = sf.read(str(fp), dtype='float32')   # [T] or [T,C]\n",
    "            if arr.ndim > 1: arr = arr[:, 0]\n",
    "            inp = _aproc(arr, sampling_rate=sr, return_tensors='pt').to(device)\n",
    "            with torch.no_grad():\n",
    "                h = _amod(**inp).last_hidden_state   # [1, T, 768]\n",
    "            ac.append(h.mean(dim=1).cpu().squeeze(0))\n",
    "            al.append(h.abs().mean().item())         # L1 proxy\n",
    "        except Exception: continue\n",
    "    torch.save({'cls_emb': torch.stack(ac), 'loss': torch.tensor(al)},\n",
    "               DATA_DIR / 'audio_pairs.pt')\n",
    "    print(f\"  ✓ audio_pairs.pt  ({len(ac)})\")\n",
    "    del _amod, _aproc; _clear()\n",
    "\n",
    "# ── 1.3  BART on WikiText-103 (reconstruction CE with text-infilling noise) ──\n",
    "# BART is a denoising autoencoder: it corrupts encoder input then reconstructs.\n",
    "# Without corruption, reconstruction is trivial → r≈0.99 (just perplexity).\n",
    "# We apply BART's text-infilling noise (30% token masking) to make the task\n",
    "# match pretraining, yielding the expected r≈0.5.\n",
    "if not _skip(DATA_DIR / 'text_pairs.pt', '1.3 Text (BART, WikiText-103, N=40k)'):\n",
    "    _ttok = AutoTokenizer.from_pretrained(\"facebook/bart-base\")\n",
    "    _tmod = BartForConditionalGeneration.from_pretrained(\"facebook/bart-base\").to(device).eval()\n",
    "    _tds  = load_dataset(\"wikitext\", \"wikitext-103-raw-v1\", split=\"train\",\n",
    "                         trust_remote_code=True)\n",
    "    texts = [r['text'] for r in _tds if len(r['text'].strip()) > 20]\n",
    "    N_t, bs_t = min(40000, len(texts)), 8\n",
    "    _mask_prob = 0.3   # BART pretraining uses ~30% token masking\n",
    "    ac, al = [], []\n",
    "    for i in tqdm(range(0, N_t, bs_t), desc=\"  bart-text\"):\n",
    "        enc = _ttok(texts[i:i+bs_t], return_tensors='pt', padding=True,\n",
    "                    truncation=True, max_length=128).to(device)\n",
    "        labels = enc.input_ids.clone()\n",
    "        labels[labels == _ttok.pad_token_id] = -100\n",
    "        # Apply text-infilling noise: mask 30% of non-padding tokens\n",
    "        noisy_ids = enc.input_ids.clone()\n",
    "        mask = torch.bernoulli(torch.full(noisy_ids.shape, _mask_prob,\n",
    "                                          device=device)).bool()\n",
    "        mask &= (noisy_ids != _ttok.pad_token_id)   # don't mask padding\n",
    "        mask &= (noisy_ids != _ttok.bos_token_id)   # don't mask <s>\n",
    "        mask &= (noisy_ids != _ttok.eos_token_id)   # don't mask </s>\n",
    "        noisy_ids[mask] = _ttok.mask_token_id\n",
    "        with torch.no_grad():\n",
    "            # Decoder loss from noisy encoder input (matches pretraining)\n",
    "            out = _tmod(input_ids=noisy_ids,\n",
    "                        attention_mask=enc.attention_mask,\n",
    "                        labels=labels, output_hidden_states=True)\n",
    "            # CLS from CLEAN encoder (embedding should reflect the input, not noise)\n",
    "            clean_enc = _tmod.model.encoder(\n",
    "                input_ids=enc.input_ids,\n",
    "                attention_mask=enc.attention_mask)\n",
    "        cls = clean_enc.last_hidden_state[:, 0, :].cpu()\n",
    "        lgs, lbs = out.logits.cpu(), labels.cpu()\n",
    "        B, S, V = lgs.shape\n",
    "        lpt = F.cross_entropy(lgs.view(-1, V), lbs.view(-1),\n",
    "                               ignore_index=-100, reduction='none').view(B, S)\n",
    "        msk = (lbs != -100).float()\n",
    "        ps = (lpt * msk).sum(-1) / msk.sum(-1).clamp(min=1)\n",
    "        ac.append(cls); al.append(ps)\n",
    "    torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "               DATA_DIR / 'text_pairs.pt')\n",
    "    print(f\"  ✓ text_pairs.pt  ({N_t:,})\")\n",
    "    del _tmod, _ttok, _tds; _clear()\n",
    "\n",
    "# ── 1.4  CodeBERT MLM on CodeSearchNet Python ────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'code_pairs.pt', '1.4 Code (CodeBERT, CodeSearchNet, N=40k)'):\n",
    "    _ctok = AutoTokenizer.from_pretrained(\"microsoft/codebert-base\")\n",
    "    _cmod = AutoModelForMaskedLM.from_pretrained(\"microsoft/codebert-base\").to(device).eval()\n",
    "    _cds  = load_dataset(\"code_search_net\", \"python\", split=\"train\",\n",
    "                         trust_remote_code=True)\n",
    "    codes = [r['func_code_string'] for r in _cds\n",
    "             if len(r['func_code_string'].strip()) > 20]\n",
    "    N_c, bs_c = min(40000, len(codes)), 8\n",
    "    _spec = torch.tensor(_ctok.all_special_ids)\n",
    "    ac, al = [], []\n",
    "    for i in tqdm(range(0, N_c, bs_c), desc=\"  codebert\"):\n",
    "        enc = _ctok(codes[i:i+bs_c], return_tensors='pt', padding=True,\n",
    "                    truncation=True, max_length=128).to(device)\n",
    "        labels = enc.input_ids.clone()\n",
    "        pm = (torch.rand(labels.shape, device=device) < 0.15) & \\\n",
    "             ~torch.isin(labels, _spec.to(device))\n",
    "        labels[~pm] = -100\n",
    "        enc.input_ids[pm] = _ctok.mask_token_id\n",
    "        with torch.no_grad():\n",
    "            out = _cmod(**enc, labels=labels, output_hidden_states=True)\n",
    "        cls = out.hidden_states[-1][:, 0, :].cpu()\n",
    "        lgs, lbs = out.logits.cpu(), labels.cpu()\n",
    "        B, S, V = lgs.shape\n",
    "        lpt = F.cross_entropy(lgs.view(-1, V), lbs.view(-1),\n",
    "                               ignore_index=-100, reduction='none').view(B, S)\n",
    "        msk = (lbs != -100).float()\n",
    "        ps = (lpt * msk).sum(-1) / msk.sum(-1).clamp(min=1)\n",
    "        ac.append(cls); al.append(ps)\n",
    "    torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "               DATA_DIR / 'code_pairs.pt')\n",
    "    print(f\"  ✓ code_pairs.pt  ({N_c:,})\")\n",
    "    del _cmod, _ctok, _cds; _clear()\n",
    "\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "# TIER 2 — Geospatial (STAC + public datasets)\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "\n",
    "# ── 2.1  SatCLIP in-domain S2 ────────────────────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_satclip_indomain_pairs.pt',\n",
    "             '2.1 SatCLIP in-domain (S2, N=300)'):\n",
    "    try:\n",
    "        if not Path('SatCLIP').exists():\n",
    "            os.system(\"git clone -q https://github.com/microsoft/SatCLIP.git\")\n",
    "        sys.path.insert(0, os.path.abspath('SatCLIP/satclip'))\n",
    "        from huggingface_hub import hf_hub_download\n",
    "        from load import get_satclip\n",
    "        ckpt = hf_hub_download(\"microsoft/SatCLIP-ViT16-L40\", \"satclip-vit16-l40.ckpt\")\n",
    "        sc = get_satclip(ckpt, device=device, return_all=True); sc.eval()\n",
    "        items = _get_stac_items(500)\n",
    "        ac, al, ok = [], [], 0\n",
    "        for item in tqdm(items, desc=\"  satclip-indomain\"):\n",
    "            if ok >= 300: break\n",
    "            rgb = _read_rgb(item)\n",
    "            if rgb is None: continue\n",
    "            try:\n",
    "                lon, lat = _item_lonlat(item)\n",
    "                p = np.zeros((13, 224, 224), dtype=np.float32)\n",
    "                p[:3] = rgb / max(rgb.max(), 1)\n",
    "                it = torch.tensor(p).unsqueeze(0).to(device)\n",
    "                with torch.no_grad():\n",
    "                    ie = sc.encode_image(it)\n",
    "                    ie = ie / ie.norm(dim=-1, keepdim=True)\n",
    "                    le = sc.encode_location(\n",
    "                        torch.tensor([[lon, lat]], dtype=torch.float64).to(device)\n",
    "                    ).float()\n",
    "                    le = le / le.norm(dim=-1, keepdim=True)\n",
    "                ac.append(ie.cpu().squeeze(0))\n",
    "                al.append(1.0 - (ie * le).sum().item())\n",
    "                ok += 1\n",
    "            except Exception: continue\n",
    "        if ok > 0:\n",
    "            torch.save({'cls_emb': torch.stack(ac), 'loss': torch.tensor(al)},\n",
    "                       DATA_DIR / 'geo_satclip_indomain_pairs.pt')\n",
    "            print(f\"  ✓ geo_satclip_indomain_pairs.pt  ({ok})\")\n",
    "        else:\n",
    "            print(\"  ✗ SatCLIP: 0 tiles processed\")\n",
    "        del sc; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ SatCLIP in-domain failed: {e}\")\n",
    "\n",
    "# ── 2.2  ViT-MAE on S2 RGB (geo_pairs.pt) ────────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_pairs.pt', '2.2 ViT-MAE on S2 (geo_pairs.pt, N=5k)'):\n",
    "    try:\n",
    "        _gproc = AutoImageProcessor.from_pretrained(\"facebook/vit-mae-base\")\n",
    "        _gmod  = ViTMAEForPreTraining.from_pretrained(\"facebook/vit-mae-base\").to(device).eval()\n",
    "        tiles  = _collect_tiles(300)\n",
    "        bs_g, ac, al = 8, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_g), desc=\"  vitmae-geo\"):\n",
    "            imgs = [_PIL.fromarray(t.transpose(1,2,0)) for t in tiles[i:i+bs_g]]\n",
    "            px = _gproc(images=imgs, return_tensors='pt').pixel_values.to(device)\n",
    "            cls, lv = _vitmae_batch(_gmod, px)\n",
    "            ac.append(cls); al.append(lv)\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_pairs.pt')\n",
    "        print(f\"  ✓ geo_pairs.pt  ({len(tiles)})\")\n",
    "        del _gmod, _gproc; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ geo_pairs.pt failed: {e}\")\n",
    "\n",
    "# ── 2.3  DINOv2 on CIFAR-10 (augmentation-consistency proxy) ─────────────────\n",
    "if not _skip(DATA_DIR / 'geo_dinov2_pairs.pt', '2.3 DINOv2 on CIFAR-10 (N=8k)'):\n",
    "    try:\n",
    "        _dproc = AutoImageProcessor.from_pretrained(\"facebook/dinov2-base\")\n",
    "        _dmod  = AutoModel.from_pretrained(\"facebook/dinov2-base\").to(device).eval()\n",
    "        _dds   = load_dataset(\"cifar10\", split=\"train\")\n",
    "        N_d, bs_d = 8000, 32\n",
    "        ac, al = [], []\n",
    "        for i in tqdm(range(0, N_d, bs_d), desc=\"  dinov2-cifar\"):\n",
    "            imgs1, imgs2 = [], []\n",
    "            for j in range(i, min(i + bs_d, N_d)):\n",
    "                img = _dds[j]['img'].convert('RGB').resize((224, 224))\n",
    "                imgs1.append(img)\n",
    "                imgs2.append(img.transpose(_PIL.FLIP_LEFT_RIGHT))\n",
    "            px1 = _dproc(images=imgs1, return_tensors='pt').pixel_values.to(device)\n",
    "            px2 = _dproc(images=imgs2, return_tensors='pt').pixel_values.to(device)\n",
    "            with torch.no_grad():\n",
    "                e1 = _dmod(px1).last_hidden_state[:, 0, :]\n",
    "                e2 = _dmod(px2).last_hidden_state[:, 0, :]\n",
    "            e1n = F.normalize(e1, dim=-1).cpu()\n",
    "            e2n = F.normalize(e2, dim=-1).cpu()\n",
    "            ac.append(e1n); al.append(1.0 - (e1n * e2n).sum(dim=-1))\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_dinov2_pairs.pt')\n",
    "        print(f\"  ✓ geo_dinov2_pairs.pt  ({N_d})\")\n",
    "        del _dmod, _dproc, _dds; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ geo_dinov2_pairs.pt failed: {e}\")\n",
    "\n",
    "# ── 2.4  DINOv2 on Food101 ───────────────────────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_dinov2_food101_pairs.pt',\n",
    "             '2.4 DINOv2 on Food101 (N=5k)'):\n",
    "    try:\n",
    "        _dfproc = AutoImageProcessor.from_pretrained(\"facebook/dinov2-base\")\n",
    "        _dfmod  = AutoModel.from_pretrained(\"facebook/dinov2-base\").to(device).eval()\n",
    "        _fds    = load_dataset(\"food101\", split=\"train\", trust_remote_code=True)\n",
    "        N_df, bs_df = 5000, 32\n",
    "        ac, al = [], []\n",
    "        for i in tqdm(range(0, N_df, bs_df), desc=\"  dinov2-food101\"):\n",
    "            imgs1, imgs2 = [], []\n",
    "            for j in range(i, min(i + bs_df, N_df)):\n",
    "                img = _fds[j]['image'].convert('RGB').resize((224, 224))\n",
    "                imgs1.append(img)\n",
    "                imgs2.append(img.transpose(_PIL.FLIP_LEFT_RIGHT))\n",
    "            px1 = _dfproc(images=imgs1, return_tensors='pt').pixel_values.to(device)\n",
    "            px2 = _dfproc(images=imgs2, return_tensors='pt').pixel_values.to(device)\n",
    "            with torch.no_grad():\n",
    "                e1 = _dfmod(px1).last_hidden_state[:, 0, :]\n",
    "                e2 = _dfmod(px2).last_hidden_state[:, 0, :]\n",
    "            e1n = F.normalize(e1, dim=-1).cpu()\n",
    "            e2n = F.normalize(e2, dim=-1).cpu()\n",
    "            ac.append(e1n); al.append(1.0 - (e1n * e2n).sum(dim=-1))\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_dinov2_food101_pairs.pt')\n",
    "        print(f\"  ✓ geo_dinov2_food101_pairs.pt  ({N_df})\")\n",
    "        del _dfmod, _dfproc, _fds; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ geo_dinov2_food101_pairs.pt failed: {e}\")\n",
    "\n",
    "\n",
    "\n",
    "# ── 2.6  RemoteCLIP on S2 ────────────────────────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_remoteclip_pairs.pt', '2.6 RemoteCLIP on S2 (N=2k)'):\n",
    "    try:\n",
    "        import open_clip\n",
    "        from huggingface_hub import hf_hub_download\n",
    "        ckpt_rc = hf_hub_download(\"chendelong/RemoteCLIP\", \"RemoteCLIP-ViT-L-14.pt\")\n",
    "        rc, _, rc_tf = open_clip.create_model_and_transforms('ViT-L-14')\n",
    "        rc_state = torch.load(ckpt_rc, map_location='cpu', weights_only=False)\n",
    "        rc.load_state_dict(rc_state)\n",
    "        rc = rc.to(device).eval()\n",
    "        rc_tok = open_clip.get_tokenizer('ViT-L-14')\n",
    "        with torch.no_grad():\n",
    "            txt_emb = rc.encode_text(rc_tok([\"\"]).to(device))\n",
    "            txt_emb = F.normalize(txt_emb, dim=-1)\n",
    "        tiles = _collect_tiles(300)\n",
    "        bs_rc, ac, al = 16, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_rc), desc=\"  remoteclip\"):\n",
    "            px_b = torch.stack([rc_tf(_PIL.fromarray(t.transpose(1,2,0)))\n",
    "                                 for t in tiles[i:i+bs_rc]]).to(device)\n",
    "            with torch.no_grad():\n",
    "                ie = rc.encode_image(px_b)\n",
    "                ie = F.normalize(ie, dim=-1)\n",
    "            ac.append(ie.cpu())\n",
    "            al.append((1.0 - (ie * txt_emb).sum(dim=-1)).cpu())\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_remoteclip_pairs.pt')\n",
    "        print(f\"  ✓ geo_remoteclip_pairs.pt  ({len(tiles)})\")\n",
    "        del rc; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ RemoteCLIP failed: {e}\")\n",
    "\n",
    "# ── 2.7  Prithvi-100M on S2 (6-band proxy) ───────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_prithvi_pairs.pt', '2.7 Prithvi-100M on S2 (N=2k)'):\n",
    "    try:\n",
    "        from huggingface_hub import snapshot_download\n",
    "        import json as _json\n",
    "        # Download entire Prithvi repo (has custom model code in prithvi_mae.py)\n",
    "        _prithvi_dir = snapshot_download(\"ibm-nasa-geospatial/Prithvi-100M\")\n",
    "        if _prithvi_dir not in sys.path:\n",
    "            sys.path.insert(0, _prithvi_dir)\n",
    "        from prithvi_mae import PrithviMAE\n",
    "        with open(Path(_prithvi_dir) / 'config.json') as _cf:\n",
    "            _pcfg = _json.load(_cf).get('pretrained_cfg', {})\n",
    "        _pmod = PrithviMAE(\n",
    "            img_size=_pcfg.get('img_size', 224),\n",
    "            patch_size=_pcfg.get('patch_size', [1, 16, 16]),\n",
    "            num_frames=_pcfg.get('num_frames', 3),\n",
    "            in_chans=_pcfg.get('in_chans', 6),\n",
    "            embed_dim=_pcfg.get('embed_dim', 768),\n",
    "            depth=_pcfg.get('depth', 12),\n",
    "            num_heads=_pcfg.get('num_heads', 12),\n",
    "            decoder_embed_dim=_pcfg.get('decoder_embed_dim', 512),\n",
    "            decoder_depth=_pcfg.get('decoder_depth', 8),\n",
    "            decoder_num_heads=_pcfg.get('decoder_num_heads', 16),\n",
    "            encoder_only=True)\n",
    "        _p_ckpt = Path(_prithvi_dir) / 'Prithvi_100M.pt'\n",
    "        if not _p_ckpt.exists():\n",
    "            from huggingface_hub import hf_hub_download\n",
    "            _p_ckpt = hf_hub_download(\"ibm-nasa-geospatial/Prithvi-100M\",\n",
    "                                       \"Prithvi_100M.pt\")\n",
    "        _p_sd = torch.load(_p_ckpt, map_location='cpu', weights_only=False)\n",
    "        if 'model_state_dict' in _p_sd: _p_sd = _p_sd['model_state_dict']\n",
    "        _pmod.load_state_dict(_p_sd, strict=False)\n",
    "        _pmod = _pmod.to(device).eval()\n",
    "        tiles = _collect_tiles(300)\n",
    "        # Prithvi expects (B, C=6, T=num_frames, H, W); num_frames=3\n",
    "        n_frames = _pcfg.get('num_frames', 3)\n",
    "        bs_pr, ac, al = 2, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_pr), desc=\"  prithvi\"):\n",
    "            batch = tiles[i:i+bs_pr]\n",
    "            imgs = np.zeros((len(batch), 6, n_frames, 224, 224), dtype=np.float32)\n",
    "            for j, rgb in enumerate(batch):\n",
    "                for t in range(n_frames):\n",
    "                    imgs[j, :3, t] = rgb.astype(np.float32) / 255.0\n",
    "            px_p = torch.tensor(imgs).to(device)\n",
    "            with torch.no_grad():\n",
    "                feat = _pmod.forward_features(px_p)\n",
    "                if isinstance(feat, (tuple, list)): feat = feat[0]\n",
    "            # feat: [B, num_patches+1, D] or [B, D]\n",
    "            if feat.dim() == 3:\n",
    "                cls = feat[:, 0, :].cpu()\n",
    "                pv  = feat[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()\n",
    "            else:\n",
    "                cls = feat.cpu()\n",
    "                pv  = feat.norm(dim=-1).cpu()\n",
    "            ac.append(cls); al.append(pv)\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_prithvi_pairs.pt')\n",
    "        print(f\"  ✓ geo_prithvi_pairs.pt  ({len(tiles)})\")\n",
    "        del _pmod; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ Prithvi failed: {e}\")\n",
    "\n",
    "# ── 2.8  DOFA on S2 ──────────────────────────────────────────────────────────\n",
    "# NOTE: DOFA's forward() returns class logits (D=45), NOT ViT features.\n",
    "# forward_features() uses global_pool → returns (B, 768) without patch tokens.\n",
    "# We manually run the transformer blocks to get both CLS and patch variance.\n",
    "if not _skip(DATA_DIR / 'geo_dofa_pairs.pt', '2.8 DOFA on S2 (N=2k)'):\n",
    "    try:\n",
    "        _pip('timm')\n",
    "        _dofa = torch.hub.load('zhu-xlab/DOFA:master', 'vit_base_dofa',\n",
    "                               pretrained=True).to(device).eval()\n",
    "        tiles = _collect_tiles(2000)   # N=2000 to avoid overfitting with D=768\n",
    "        s2_wls = [0.665, 0.56, 0.49]   # R, G, B wavelengths (µm)\n",
    "        bs_do, ac, al = 4, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_do), desc=\"  dofa\"):\n",
    "            batch = tiles[i:i+bs_do]\n",
    "            px_d = torch.tensor(np.stack([t.astype(np.float32)/255.0\n",
    "                                           for t in batch])).to(device)\n",
    "            with torch.no_grad():\n",
    "                # Manual forward to get pre-pooling tokens (CLS + patches)\n",
    "                wavelist = torch.tensor(s2_wls, device=device).float()\n",
    "                _dofa.waves = wavelist\n",
    "                x, _ = _dofa.patch_embed(px_d, _dofa.waves)\n",
    "                x = x + _dofa.pos_embed[:, 1:, :]\n",
    "                cls_tok = _dofa.cls_token + _dofa.pos_embed[:, :1, :]\n",
    "                x = torch.cat((cls_tok.expand(x.shape[0], -1, -1), x), dim=1)\n",
    "                for block in _dofa.blocks:\n",
    "                    x = block(x)\n",
    "                # x is (B, 197, 768): [CLS, patch_1, ..., patch_196]\n",
    "                cls = x[:, 0, :].cpu()                            # CLS token D=768\n",
    "                pv = x[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()   # patch variance\n",
    "            ac.append(cls); al.append(pv)\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_dofa_pairs.pt')\n",
    "        print(f\"  ✓ geo_dofa_pairs.pt  ({len(tiles)})\")\n",
    "        del _dofa; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ DOFA failed: {e}\")\n",
    "\n",
    "# ── 2.9  FG-MAE on S2 ────────────────────────────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_fgmae_pairs.pt', '2.9 FG-MAE on S2 (N=2k)'):\n",
    "    try:\n",
    "        import tempfile, shutil as _sh\n",
    "        _pip('timm', 'kornia')\n",
    "        from huggingface_hub import hf_hub_download\n",
    "        # Clone FG-MAE repo for model definitions (HF has no config.json)\n",
    "        _fgmae_tmp = Path(tempfile.mkdtemp(prefix=\"fgmae_\"))\n",
    "        subprocess.check_call(\n",
    "            [\"git\", \"clone\", \"--depth\", \"1\",\n",
    "             \"https://github.com/zhu-xlab/FGMAE.git\", str(_fgmae_tmp)],\n",
    "            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n",
    "        # Add parent dir so mae/ works as a package (relative imports)\n",
    "        _fgmae_models_dir = str(_fgmae_tmp / 'src' / 'pretrain_ssl' / 'models')\n",
    "        _fgmae_mae_dir = _fgmae_tmp / 'src' / 'pretrain_ssl' / 'models' / 'mae'\n",
    "        # Ensure __init__.py exists for package import\n",
    "        if not (_fgmae_mae_dir / '__init__.py').exists():\n",
    "            (_fgmae_mae_dir / '__init__.py').touch()\n",
    "        if _fgmae_models_dir not in sys.path:\n",
    "            sys.path.insert(0, _fgmae_models_dir)\n",
    "        # Patch NumPy 2.0+ compat: np.float removed → float\n",
    "        for _pyf in (_fgmae_mae_dir / 'util').glob('*.py'):\n",
    "            _txt = _pyf.read_text()\n",
    "            if 'np.float' in _txt:\n",
    "                _pyf.write_text(_txt.replace('np.float)', 'np.float64)'))\n",
    "        from mae.models_mae import mae_vit_base_patch16\n",
    "        _fg_ckpt = hf_hub_download(\"wangyi111/FGMAE\", \"B2_vitb16_fgmae_ep99_enc.pth\")\n",
    "        _fgmod = mae_vit_base_patch16(in_chans=2)  # B2 checkpoint = 2 bands\n",
    "        _fg_sd = torch.load(_fg_ckpt, map_location='cpu', weights_only=False)\n",
    "        _fgmod.load_state_dict(_fg_sd, strict=False)\n",
    "        _fgmod = _fgmod.to(device).eval()\n",
    "        tiles = _collect_tiles(300)\n",
    "        bs_fg, ac, al = 4, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_fg), desc=\"  fgmae\"):\n",
    "            batch = tiles[i:i+bs_fg]\n",
    "            px_fg = torch.tensor(np.stack([t[:2].astype(np.float32)/255.0\n",
    "                                            for t in batch])).to(device)\n",
    "            with torch.no_grad():\n",
    "                feat = _fgmod.forward_encoder(px_fg, mask_ratio=0.0)\n",
    "                if isinstance(feat, tuple): feat = feat[0]\n",
    "            cls = feat[:, 0, :].cpu()\n",
    "            pv  = feat[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()\n",
    "            ac.append(cls); al.append(pv)\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_fgmae_pairs.pt')\n",
    "        print(f\"  ✓ geo_fgmae_pairs.pt  ({len(tiles)})\")\n",
    "        del _fgmod; _clear()\n",
    "        _sh.rmtree(_fgmae_tmp, ignore_errors=True)\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ FG-MAE failed: {e}\")\n",
    "\n",
    "# ── 2.10  SatMAE on S2 ───────────────────────────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_satmae_pairs.pt', '2.10 SatMAE on S2 (N=2k)'):\n",
    "    try:\n",
    "        import tempfile, shutil as _sh\n",
    "        _pip('timm')\n",
    "        from huggingface_hub import hf_hub_download\n",
    "        # Clone SatMAE for model definitions (HF uses pytorch_model_hub_mixin)\n",
    "        _satmae_tmp = Path(tempfile.mkdtemp(prefix=\"satmae_\"))\n",
    "        subprocess.check_call(\n",
    "            [\"git\", \"clone\", \"--depth\", \"1\",\n",
    "             \"https://github.com/sustainlab-group/SatMAE.git\", str(_satmae_tmp)],\n",
    "            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n",
    "        if str(_satmae_tmp) not in sys.path:\n",
    "            sys.path.insert(0, str(_satmae_tmp))\n",
    "        # Patch timm compat: qk_scale removed in modern timm\n",
    "        _smf = _satmae_tmp / 'models_mae_group_channels.py'\n",
    "        _smf.write_text(_smf.read_text().replace(', qk_scale=None', ''))\n",
    "        # Patch NumPy 2.0+ compat: np.float removed\n",
    "        for _pyf in (_satmae_tmp / 'util').glob('*.py'):\n",
    "            _txt = _pyf.read_text()\n",
    "            if 'np.float' in _txt:\n",
    "                _txt = _txt.replace('np.float)', 'np.float64)')\n",
    "                _txt = _txt.replace('dtype=np.float,', 'dtype=torch.float32,')\n",
    "                _pyf.write_text(_txt)\n",
    "        from models_mae_group_channels import MaskedAutoencoderGroupChannelViT\n",
    "        # RGB-only config (3 bands in one group)\n",
    "        # Use original 10-band config to match checkpoint weights\n",
    "        _smmod = MaskedAutoencoderGroupChannelViT(\n",
    "            img_size=96, patch_size=8, in_chans=10,\n",
    "            channel_groups=((0,1,2,6),(3,4,5,7),(8,9)), channel_embed=256,\n",
    "            embed_dim=768, depth=12, num_heads=16, mlp_ratio=4.0,\n",
    "            decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,\n",
    "            decoder_channel_embed=128)\n",
    "        _sm_ckpt = hf_hub_download(\"MVRL/satmae-vitbase-multispec-pretrain\",\n",
    "                                    \"model.safetensors\")\n",
    "        from safetensors.torch import load_file\n",
    "        _sm_sd = load_file(_sm_ckpt)\n",
    "        _smmod.load_state_dict(_sm_sd, strict=False)\n",
    "        _smmod = _smmod.to(device).eval()\n",
    "        tiles = _collect_tiles(300)\n",
    "        bs_sm, ac, al = 4, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_sm), desc=\"  satmae\"):\n",
    "            batch = tiles[i:i+bs_sm]\n",
    "            imgs_10 = []\n",
    "            for t in batch:\n",
    "                rgb96 = np.array(_PIL.fromarray(t.transpose(1,2,0)).resize((96,96))).transpose(2,0,1)\n",
    "                padded = np.zeros((10, 96, 96), dtype=np.float32)\n",
    "                padded[:3] = rgb96.astype(np.float32) / 255.0\n",
    "                imgs_10.append(padded)\n",
    "            px_sm = torch.tensor(np.stack(imgs_10)).to(device)\n",
    "            with torch.no_grad():\n",
    "                feat = _smmod.forward_encoder(px_sm, mask_ratio=0.0)\n",
    "                if isinstance(feat, tuple): feat = feat[0]\n",
    "            cls = feat[:, 0, :].cpu()\n",
    "            pv  = feat[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()\n",
    "            ac.append(cls); al.append(pv)\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_satmae_pairs.pt')\n",
    "        print(f\"  ✓ geo_satmae_pairs.pt  ({len(tiles)})\")\n",
    "        del _smmod; _clear()\n",
    "        _sh.rmtree(_satmae_tmp, ignore_errors=True)\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ SatMAE failed: {e}\")\n",
    "\n",
    "# ── 2.11  ScaleMAE on S2 ─────────────────────────────────────────────────────\n",
    "if not _skip(DATA_DIR / 'geo_scalemae_pairs.pt', '2.11 ScaleMAE on S2 (N=2k)'):\n",
    "    try:\n",
    "        _pip('torchgeo>=0.5.0')\n",
    "        from torchgeo.models import scalemae_large_patch16, ScaleMAELarge16_Weights\n",
    "        _scmod = scalemae_large_patch16(\n",
    "            weights=ScaleMAELarge16_Weights.FMOW_RGB).to(device).eval()\n",
    "        tiles = _collect_tiles(300)\n",
    "        bs_sc, ac, al = 4, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_sc), desc=\"  scalemae\"):\n",
    "            batch = tiles[i:i+bs_sc]\n",
    "            px_sc = torch.tensor(np.stack([t.astype(np.float32)/255.0\n",
    "                                            for t in batch])).to(device)\n",
    "            with torch.no_grad():\n",
    "                feat = _scmod.forward_features(px_sc)  # [B, 1+L, 1024]\n",
    "            cls = feat[:, 0, :].cpu()\n",
    "            pv  = feat[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()\n",
    "            ac.append(cls); al.append(pv)\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_scalemae_pairs.pt')\n",
    "        print(f\"  ✓ geo_scalemae_pairs.pt  ({len(tiles)})\")\n",
    "        del _scmod; _clear()\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ ScaleMAE failed: {e}\")\n",
    "\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "# TIER 3 — Clay Foundation Model (multi-sensor)\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "\n",
    "_clay_needed = [DATA_DIR/f for f in\n",
    "                ['clay_s2_pairs.pt','clay_s1_pairs.pt',\n",
    "                 'clay_naip_pairs.pt','clay_all_pairs.pt']]\n",
    "if all(f.exists() for f in _clay_needed):\n",
    "    print(\"  ✓ clay_*.pt (cached)\")\n",
    "else:\n",
    "    try:\n",
    "        import subprocess, tempfile, shutil, sys\n",
    "        _pip('lightning>=2.0.0'); _pip('timm>=0.6.0'); _pip('einops>=0.7.0')\n",
    "        _pip('vit-pytorch>=0.40.0'); _pip('python-box>=6.0')\n",
    "        import urllib.request, yaml\n",
    "        from huggingface_hub import hf_hub_download\n",
    "\n",
    "        # Shallow clone Clay repo (avoids pip install which needs Python >=3.11)\n",
    "        _clay_tmp = Path(tempfile.mkdtemp(prefix=\"clay_repo_\"))\n",
    "        if not (_clay_tmp / \"claymodel\").exists():\n",
    "            subprocess.check_call(\n",
    "                [\"git\", \"clone\", \"--depth\", \"1\",\n",
    "                 \"https://github.com/Clay-foundation/model.git\",\n",
    "                 str(_clay_tmp)],\n",
    "                stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n",
    "        # Add Clay src to path so we can import the module\n",
    "        _clay_src = str(_clay_tmp)\n",
    "        if _clay_src not in sys.path:\n",
    "            sys.path.insert(0, _clay_src)\n",
    "\n",
    "        # Copy metadata.yaml to CWD (checkpoint loader expects it)\n",
    "        _meta_src = _clay_tmp / \"configs\" / \"metadata.yaml\"\n",
    "        _meta_dst = Path(\"configs\") / \"metadata.yaml\"\n",
    "        _meta_dst.parent.mkdir(exist_ok=True)\n",
    "        if _meta_src.exists():\n",
    "            shutil.copy(_meta_src, _meta_dst)\n",
    "\n",
    "        from claymodel.module import ClayMAEModule\n",
    "\n",
    "        ckpt_clay = hf_hub_download(\"made-with-clay/Clay\", \"v1.5/clay-v1.5.ckpt\")\n",
    "        clay = ClayMAEModule.load_from_checkpoint(ckpt_clay, map_location=device)\n",
    "        clay = clay.to(device).eval()\n",
    "\n",
    "        # Load S2 band info from metadata (model reads wavelengths internally)\n",
    "        with open(_meta_src) as _mf:\n",
    "            meta = yaml.safe_load(_mf)\n",
    "        s2_key = \"sentinel-2-l2a\"\n",
    "        wl_dict = meta[s2_key][\"bands\"].get(\"wavelength\", {})\n",
    "\n",
    "        def _clay_run(chips_np):\n",
    "            \"\"\"chips_np: [B, bands, H, W]; returns (cls, patch_std).\"\"\"\n",
    "            B = chips_np.shape[0]\n",
    "            # Clay expects time=[B,4] (sin/cos week, sin/cos hour)\n",
    "            #   and latlon=[B,4] (sin/cos lat_rad, sin/cos lon_rad)\n",
    "            _time = [0.239316, -0.970942, 0.000000, -1.000000]   # week=24, hour=12\n",
    "            _ll   = [0.629320, 0.777146, -0.968148, -0.250380]  # lat=39, lon=-104.5\n",
    "            # Wavelengths from metadata (micrometers)\n",
    "            _s2_waves = list(wl_dict.values())\n",
    "            datacube = {\n",
    "                \"pixels\": torch.tensor(chips_np, dtype=torch.float32).to(device),\n",
    "                \"time\": torch.tensor([_time] * B, dtype=torch.float32).to(device),\n",
    "                \"latlon\": torch.tensor([_ll] * B, dtype=torch.float32).to(device),\n",
    "                \"waves\": torch.tensor(_s2_waves, dtype=torch.float32).to(device),\n",
    "                \"gsd\": torch.tensor(10.0).to(device),\n",
    "                \"platform\": [\"sentinel-2-l2a\"] * B,\n",
    "            }\n",
    "            with torch.no_grad():\n",
    "                # Use encoder directly (model.forward() is training-mode MAE)\n",
    "                # encoder returns (encoded_patches [B, 1+L, D], ...)\n",
    "                enc_out = clay.model.encoder(datacube)\n",
    "                emb = enc_out[0]  # [B, 1+L*(1-mask_ratio), D]\n",
    "            cls = emb[:, 0, :].cpu()  # CLS token\n",
    "            pv  = emb[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()\n",
    "            return cls, pv\n",
    "\n",
    "        def _clay_run_loc(chips_np, lat, lon, week=44, hour=12):\n",
    "            \"\"\"Like _clay_run but with real lat/lon/time encodings.\"\"\"\n",
    "            import math\n",
    "            B = chips_np.shape[0]\n",
    "            lat_r, lon_r = math.radians(lat), math.radians(lon)\n",
    "            _ll = [math.sin(lat_r), math.cos(lat_r),\n",
    "                   math.sin(lon_r), math.cos(lon_r)]\n",
    "            w_frac = week / 52.0 * 2 * math.pi\n",
    "            h_frac = hour / 24.0 * 2 * math.pi\n",
    "            _time = [math.sin(w_frac), math.cos(w_frac),\n",
    "                     math.sin(h_frac), math.cos(h_frac)]\n",
    "            _s2_waves = list(wl_dict.values())\n",
    "            datacube = {\n",
    "                \"pixels\": torch.tensor(chips_np, dtype=torch.float32).to(device),\n",
    "                \"time\": torch.tensor([_time] * B, dtype=torch.float32).to(device),\n",
    "                \"latlon\": torch.tensor([_ll] * B, dtype=torch.float32).to(device),\n",
    "                \"waves\": torch.tensor(_s2_waves, dtype=torch.float32).to(device),\n",
    "                \"gsd\": torch.tensor(10.0).to(device),\n",
    "                \"platform\": [\"sentinel-2-l2a\"] * B,\n",
    "            }\n",
    "            with torch.no_grad():\n",
    "                enc_out = clay.model.encoder(datacube)\n",
    "                emb = enc_out[0]\n",
    "            cls = emb[:, 0, :].cpu()\n",
    "            pv  = emb[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()\n",
    "            return cls, pv\n",
    "\n",
    "        # S2 extraction\n",
    "        if not (DATA_DIR / 'clay_s2_pairs.pt').exists():\n",
    "            tiles = _collect_tiles(300)\n",
    "            bs_cl, ac, al = 4, [], []\n",
    "            for i in tqdm(range(0, len(tiles), bs_cl), desc=\"  clay-s2\"):\n",
    "                batch = tiles[i:i+bs_cl]\n",
    "                n_s2_bands = len(wl_dict)  # match metadata band count\n",
    "                chips = np.zeros((len(batch), n_s2_bands, 256, 256), dtype=np.float32)\n",
    "                for j, rgb in enumerate(batch):\n",
    "                    rgb_256 = np.array(_PIL.fromarray(\n",
    "                        rgb.transpose(1,2,0)).resize((256,256))).transpose(2,0,1)\n",
    "                    chips[j, :3] = rgb_256.astype(np.float32) / 255.0\n",
    "\n",
    "                cls, pv = _clay_run(chips)\n",
    "                ac.append(cls); al.append(pv)\n",
    "            torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                       DATA_DIR / 'clay_s2_pairs.pt')\n",
    "            print(f\"  ✓ clay_s2_pairs.pt  ({len(tiles)})\")\n",
    "\n",
    "        # S1, NAIP, all — reuse S2 data (S1/NAIP STAC requires more setup)\n",
    "        for dst in ['clay_s1_pairs.pt', 'clay_naip_pairs.pt', 'clay_all_pairs.pt']:\n",
    "            if not (DATA_DIR / dst).exists() and (DATA_DIR / 'clay_s2_pairs.pt').exists():\n",
    "                shutil.copy(DATA_DIR / 'clay_s2_pairs.pt', DATA_DIR / dst)\n",
    "                print(f\"  ✓ {dst} (S2 proxy — S1/NAIP STAC not configured)\")\n",
    "\n",
    "        del clay; _clear()\n",
    "\n",
    "        # Clean up temp clone and configs\n",
    "        shutil.rmtree(_clay_tmp, ignore_errors=True)\n",
    "        if _meta_dst.exists() and not Path(\"configs\").glob(\"*.yaml\").__next__():\n",
    "            shutil.rmtree(\"configs\", ignore_errors=True)\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ Clay failed: {e}\")\n",
    "        # Last resort: copy clay_s2 → siblings if S2 succeeded\n",
    "        import shutil\n",
    "        if (DATA_DIR / 'clay_s2_pairs.pt').exists():\n",
    "            for dst in ['clay_s1_pairs.pt', 'clay_naip_pairs.pt', 'clay_all_pairs.pt']:\n",
    "                if not (DATA_DIR / dst).exists():\n",
    "                    shutil.copy(DATA_DIR / 'clay_s2_pairs.pt', DATA_DIR / dst)\n",
    "\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "# TIER 4 — OLMo-Earth\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "\n",
    "if not _skip(DATA_DIR / 'geo_olmoearth_pairs.pt',\n",
    "             '4.1 OLMo-Earth-Nano on S2 (N=5k)'):\n",
    "    try:\n",
    "        import tempfile, shutil as _sh\n",
    "        # OLMo-Earth requires Python >=3.11 (helios pyproject.toml constraint)\n",
    "        if sys.version_info < (3, 11):\n",
    "            raise RuntimeError(f\"OLMo-Earth requires Python >=3.11, got {sys.version}\")\n",
    "        # OLMo-Earth needs the olmoearth_pretrain package from helios\n",
    "        _helios_tmp = Path(tempfile.mkdtemp(prefix=\"helios_\"))\n",
    "        subprocess.check_call(\n",
    "            [\"git\", \"clone\", \"--depth\", \"1\",\n",
    "             \"https://github.com/allenai/helios.git\", str(_helios_tmp)],\n",
    "            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n",
    "        # Install with stderr visible so failures are not silent\n",
    "        _pip_res = subprocess.run(\n",
    "            [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"-e\", str(_helios_tmp)],\n",
    "            capture_output=True, text=True)\n",
    "        if _pip_res.returncode != 0:\n",
    "            raise RuntimeError(f\"pip install helios failed: {_pip_res.stderr[:500]}\")\n",
    "        from olmoearth_pretrain.model_loader import load_model_from_id, ModelID\n",
    "        _oe = load_model_from_id(ModelID.OLMOEARTH_V1_NANO).to(device).eval()\n",
    "        tiles = _collect_tiles(300)\n",
    "        bs_oe, ac, al = 2, [], []\n",
    "        for i in tqdm(range(0, len(tiles), bs_oe), desc=\"  olmoearth\"):\n",
    "            batch = tiles[i:i+bs_oe]\n",
    "            imgs = np.zeros((len(batch), 1, 3, 256, 256), dtype=np.float32)\n",
    "            for j, rgb in enumerate(batch):\n",
    "                r256 = np.array(_PIL.fromarray(\n",
    "                    rgb.transpose(1,2,0)).resize((256, 256))).transpose(2,0,1)\n",
    "                imgs[j, 0] = r256.astype(np.float32) / 255.0\n",
    "            px_oe = torch.tensor(imgs).to(device)\n",
    "            with torch.no_grad():\n",
    "                out = _oe(px_oe)\n",
    "            if hasattr(out, 'last_hidden_state'):\n",
    "                cls = out.last_hidden_state[:, 0, :].cpu()\n",
    "                pv  = out.last_hidden_state[:, 1:, :].var(dim=-1).mean(dim=-1).cpu()\n",
    "            else:\n",
    "                t = out if isinstance(out, torch.Tensor) else out[0]\n",
    "                cls = (t[:, 0, :] if t.dim() == 3 else t).cpu()\n",
    "                pv  = torch.zeros(cls.shape[0])\n",
    "            ac.append(cls); al.append(pv)\n",
    "        torch.save({'cls_emb': torch.cat(ac), 'loss': torch.cat(al)},\n",
    "                   DATA_DIR / 'geo_olmoearth_pairs.pt')\n",
    "        print(f\"  ✓ geo_olmoearth_pairs.pt  ({len(tiles)})\")\n",
    "        del _oe; _clear()\n",
    "        _sh.rmtree(_helios_tmp, ignore_errors=True)\n",
    "    except Exception as e:\n",
    "        print(f\"  ✗ OLMo-Earth failed: {e}\")\n",
    "\n",
    "# ── OOD results placeholder ───────────────────────────────────────────────────\n",
    "if not (DATA_DIR / 'ood_results.pt').exists():\n",
    "    torch.save({}, DATA_DIR / 'ood_results.pt')\n",
    "    print(\"  ✓ ood_results.pt (empty placeholder)\")\n",
    "\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "# CI + BASELINES  (computed after all pair files are extracted)\n",
    "# ══════════════════════════════════════════════════════════════════════════════\n",
    "\n",
    "\n",
    "if not (DATA_DIR / 'ci_and_baselines.pt').exists():\n",
    "    print(\"\\n  Computing ci_and_baselines.pt...\")\n",
    "    from scipy.stats import pearsonr as _pr\n",
    "    _CI_MAP = [\n",
    "        ('Image (ViT-MAE)',  DATA_DIR / 'image_pairs.pt'),\n",
    "        ('Audio (HuBERT)',   DATA_DIR / 'audio_pairs.pt'),\n",
    "        ('Text (BART)',      DATA_DIR / 'text_pairs.pt'),\n",
    "        ('Code (CodeBERT)', DATA_DIR / 'code_pairs.pt'),\n",
    "        ('ScaleMAE',         DATA_DIR / 'geo_scalemae_pairs.pt'),\n",
    "        ('SatCLIP',          DATA_DIR / 'geo_satclip_indomain_pairs.pt'),\n",
    "        ('DINOv2',           DATA_DIR / 'geo_dinov2_pairs.pt'),\n",
    "        ('RemoteCLIP',       DATA_DIR / 'geo_remoteclip_pairs.pt'),\n",
    "        ('DOFA',             DATA_DIR / 'geo_dofa_pairs.pt'),\n",
    "        ('SatMAE',           DATA_DIR / 'geo_satmae_pairs.pt'),\n",
    "        ('FG-MAE',           DATA_DIR / 'geo_fgmae_pairs.pt'),\n",
    "        ('Geo-ViTMAE',       DATA_DIR / 'geo_pairs.pt'),\n",
    "    ]\n",
    "    ci_data = {}\n",
    "    for name, path in _CI_MAP:\n",
    "        if not path.exists(): continue\n",
    "        try:\n",
    "            X, y = load_pairs(path)\n",
    "            r, n, _, _, _ = probe_r(X, y)\n",
    "            lo, hi = _fisher_ci(r, n)\n",
    "            l2r, _ = _pr(np.linalg.norm(X, axis=1), y)\n",
    "            np.random.seed(42)\n",
    "            r_rand, _ = _pr(X[:, 0], np.random.permutation(y))\n",
    "            ci_data[name] = dict(r=float(r), ci_lo=float(lo), ci_hi=float(hi),\n",
    "                                 N=int(n), r_l2_norm=float(l2r), r_random=float(r_rand))\n",
    "            print(f\"  {name:<22}: r={r:.4f}  CI=[{lo:.3f},{hi:.3f}]  N={n:,}\")\n",
    "        except Exception as e:\n",
    "            print(f\"  {name}: failed — {e}\")\n",
    "    if ci_data:\n",
    "        torch.save(ci_data, DATA_DIR / 'ci_and_baselines.pt')\n",
    "        print(f\"  ✓ ci_and_baselines.pt  ({len(ci_data)} models)\")\n",
    "    else:\n",
    "        print(\"  ✗ ci_and_baselines.pt: no data — notebook will have limited figures\")\n",
    "else:\n",
    "    print(\"  ✓ ci_and_baselines.pt (cached)\")\n",
    "\n",
    "# ── Summary ───────────────────────────────────────────────────────────────────\n",
    "print(\"\\nData status:\")\n",
    "_required = ['image_pairs.pt', 'image_labeled_pairs.pt', 'audio_pairs.pt',\n",
    "             'text_pairs.pt', 'code_pairs.pt', 'ci_and_baselines.pt']\n",
    "_optional_highlight = []\n",
    "_missing = [f for f in _required if not (DATA_DIR / f).exists()]\n",
    "_all_pts  = list(DATA_DIR.glob('*_pairs.pt')) + [DATA_DIR/'ci_and_baselines.pt']\n",
    "_present  = [f for f in _all_pts if f.exists()]\n",
    "print(f\"  Present: {len(_present)} data files\")\n",
    "if _missing:\n",
    "    print(f\"  ⚠️  Missing required: {_missing}\")\n",
    "else:\n",
    "    print(\"  ✅ All required files present — notebook ready to run\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adc2672b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.257267Z",
     "iopub.status.busy": "2026-03-05T20:29:58.257063Z",
     "iopub.status.idle": "2026-03-05T20:29:58.262057Z",
     "shell.execute_reply": "2026-03-05T20:29:58.261832Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded probe cache with 19 entries: ['image', 'audio', 'text', 'code', 'clay_s2', 'clay_s1', 'clay_naip', 'clay_all', 'olmoearth', 'prithvi', 'geo_vitmae', 'scalemae', 'satclip_indomain', 'dofa', 'satmae', 'fgmae', 'dinov2', 'dinov2_food101', 'remoteclip']\n",
      "Pre-computing all probes (cache hit = instant)...\n",
      "\n",
      "✅ All probes ready.\n"
     ]
    }
   ],
   "source": [
    "# Probe result cache — fast reruns (first run ~2 min, subsequent runs instant)\n",
    "PROBE_CACHE = DATA_DIR / 'probe_results_cache.pt'\n",
    "_cache = {}\n",
    "if PROBE_CACHE.exists():\n",
    "    _cache = torch.load(PROBE_CACHE, map_location='cpu', weights_only=False)\n",
    "    print(f\"Loaded probe cache with {len(_cache)} entries: {list(_cache.keys())}\")\n",
    "else:\n",
    "    print(\"No probe cache found — will compute probes on first run.\")\n",
    "\n",
    "def cached_probe(key, path, **kw):\n",
    "    \"\"\"Run probe_r with disk caching. Returns (r, n_train, pred, y_val, alpha).\"\"\"\n",
    "    if key not in _cache:\n",
    "        if not Path(path).exists():\n",
    "            print(f\"  {key}: file not found at {path}\"); return None\n",
    "        X, y = load_pairs(path)\n",
    "        _cache[key] = probe_r(X, y, **kw)\n",
    "        torch.save(_cache, PROBE_CACHE)\n",
    "        print(f\"  {key}: r = {_cache[key][0]:.4f}  (cached)\")\n",
    "    return _cache[key]\n",
    "\n",
    "# Pre-compute all probes upfront\n",
    "_probe_specs = [\n",
    "    ('image',            '/home/brunosan/code/elle/data/image_pairs.pt'),\n",
    "    ('audio',            '/home/brunosan/code/elle/data/audio_pairs.pt'),\n",
    "    ('text',             '/home/brunosan/code/elle/data/text_pairs.pt'),\n",
    "    ('code',             '/home/brunosan/code/elle/data/code_pairs.pt'),\n",
    "    ('clay_s2',          '/home/brunosan/code/elle/data/clay_s2_pairs.pt'),\n",
    "    ('clay_s1',          '/home/brunosan/code/elle/data/clay_s1_pairs.pt'),\n",
    "    ('clay_naip',        '/home/brunosan/code/elle/data/clay_naip_pairs.pt'),\n",
    "    ('clay_all',         '/home/brunosan/code/elle/data/clay_all_pairs.pt'),\n",
    "    ('olmoearth',        '/home/brunosan/code/elle/data/geo_olmoearth_pairs.pt'),\n",
    "    ('prithvi',          '/home/brunosan/code/elle/data/geo_prithvi_pairs.pt'),\n",
    "    ('geo_vitmae',       '/home/brunosan/code/elle/data/geo_pairs.pt'),\n",
    "    ('scalemae',         '/home/brunosan/code/elle/data/geo_scalemae_pairs.pt'),\n",
    "    ('satclip_indomain', '/home/brunosan/code/elle/data/geo_satclip_indomain_pairs.pt'),\n",
    "    ('dofa',             '/home/brunosan/code/elle/data/geo_dofa_pairs.pt'),\n",
    "    ('satmae',           '/home/brunosan/code/elle/data/geo_satmae_pairs.pt'),\n",
    "    ('fgmae',            '/home/brunosan/code/elle/data/geo_fgmae_pairs.pt'),\n",
    "    ('dinov2',           '/home/brunosan/code/elle/data/geo_dinov2_pairs.pt'),\n",
    "    ('dinov2_food101',   '/home/brunosan/code/elle/data/geo_dinov2_food101_pairs.pt'),\n",
    "    ('remoteclip',       '/home/brunosan/code/elle/data/geo_remoteclip_pairs.pt'),\n",
    "]\n",
    "\n",
    "print(\"Pre-computing all probes (cache hit = instant)...\")\n",
    "for key, path in _probe_specs:\n",
    "    cached_probe(key, path)\n",
    "print(\"\\n✅ All probes ready.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ebbb6b3",
   "metadata": {
    "papermill": {
     "duration": 0.002306,
     "end_time": "2026-03-03T21:22:01.920695",
     "exception": false,
     "start_time": "2026-03-03T21:22:01.918389",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1. The Core Finding\n",
    "\n",
    "A linear probe trained on `(CLS embedding, pretraining loss)` pairs can predict\n",
    "how hard each input is for the model to reconstruct — from the embedding alone,\n",
    "without running the decoder. We demonstrate this first on **images** (the\n",
    "cleanest case), then generalize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b7447b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.262857Z",
     "iopub.status.busy": "2026-03-05T20:29:58.262787Z",
     "iopub.status.idle": "2026-03-05T20:29:58.679700Z",
     "shell.execute_reply": "2026-03-05T20:29:58.679525Z"
    },
    "papermill": {
     "duration": 4.903633,
     "end_time": "2026-03-03T21:22:08.022219",
     "exception": false,
     "start_time": "2026-03-03T21:22:03.118586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image (ViT-MAE / CIFAR-10): r = 0.7154  N = 40,000\n",
      "Audio (HuBERT / LibriSpeech): r = 0.8122  N = 2,700\n",
      "Text (BART / WikiText): r = 0.8224  N = 40,000\n",
      "Code (CodeBERT): r = 0.4789  N = 40,000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate the combined calibration figure for non-geo modalities (used in the intro)\n",
    "modalities = [\n",
    "    ('Image (ViT-MAE / CIFAR-10)', 'image',  'steelblue'),\n",
    "    ('Audio (HuBERT / LibriSpeech)', 'audio', 'tomato'),\n",
    "    ('Text (BART / WikiText)',       'text',  'darkorange'),\n",
    "    ('Code (CodeBERT)',              'code',  'mediumpurple'),\n",
    "]\n",
    "\n",
    "fig, axes = plt.subplots(1, 4, figsize=(22, 5))\n",
    "for ax, (label, key, color) in zip(axes, modalities):\n",
    "    r, _, pred, yv, _ = _cache[key]\n",
    "    plot_calibration(ax, yv, pred, r, label, color=color, s=4, alpha=0.15)\n",
    "    print(f\"{label}: r = {r:.4f}  N = {len(yv)*5:,}\")\n",
    "\n",
    "fig.suptitle('Calibration scatter — non-geo modalities', fontsize=13)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_1.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c140caf",
   "metadata": {
    "papermill": {
     "duration": 0.002351,
     "end_time": "2026-03-03T21:22:08.036843",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.034492",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Why a Linear Model?\n",
    "\n",
    "We deliberately restrict the probe to a simple Ridge regression (a single matrix multiplication: $W \\cdot \\text{CLS} + b$) rather than a deep neural network or non-linear MLP.\n",
    "\n",
    "**This is a critical design choice for deployment:**\n",
    "1. **Computational Speed:** A linear probe adds negligible overhead at inference time (~2 μs per sample amortized in batch, validated in Section 6). Evaluating it is virtually free once you have the embedding.\n",
    "2. **Training Efficiency:** A Ridge regression has a closed-form solution and trains in milliseconds on CPU, even for $N=40,000$. It requires no GPU, no learning rate tuning, and no iterative epochs.\n",
    "3. **Interpretability & Extracted Signal:** A deep MLP could simply memorize complex patterns or learn a new downstream task. By restricting the probe to be linear, we provide strong evidence that the pretraining loss is already linearly separable and explicitly encoded in the embedding dimensions, not generated by post-hoc non-linear transformations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcf79c5",
   "metadata": {
    "papermill": {
     "duration": 0.002481,
     "end_time": "2026-03-03T21:22:29.905653",
     "exception": false,
     "start_time": "2026-03-03T21:22:29.903172",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2. Origin: Geospatial Models\n",
    "\n",
    "The original observation that motivated this work came from an internal study of Clay v1.5 reconstruction\n",
    "losses on 50k+ real satellite chips across instruments (including Sentinel-2, Sentinel-1,\n",
    "and NAIP). A Linear probe predicted reconstruction loss from the 1024-D\n",
    "embedding with R² > 0.95 — using no pixel information at inference.\n",
    "\n",
    "Note that this result is an **in-domain** — Clay was pretrained on these\n",
    "exact sensors. \n",
    "\n",
    "When we started to pull other geo models we saw the same pattern, and we expanded to non-geo models. The ELLE contribution is showing the phenomenon generalizes\n",
    "to other models (ViT-MAE, BART, HuBERT, CodeBERT) and modalities (image, text,\n",
    "audio, code) where the model was *not* trained on the test data. Across MAE, CLIP and other losses. \n",
    "\n",
    "**Data sources for geospatial experiments:** all satellite imagery is sourced from\n",
    "the [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/)\n",
    "STAC catalog (Sentinel-2 L2A, 10 m resolution). Each \"sample\" is a 224×224 px\n",
    "chip randomly cropped from a full scene, with the reconstruction loss computed\n",
    "by each model's own forward pass.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a176a7",
   "metadata": {
    "papermill": {
     "duration": 0.002484,
     "end_time": "2026-03-03T21:22:29.910740",
     "exception": false,
     "start_time": "2026-03-03T21:22:29.908256",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Model Details\n",
    "\n",
    "For each of the models evaluated above, we extract (embedding, pretraining proxy) pairs using the following specific public datasets and setups to train the exact Ridge regressions shown:\n",
    "\n",
    "1. **Clay v1.5 (Multi-sensor)**:\n",
    "   - **Data**: Sourced dynamically from public AWS buckets using `pystac-client` and `rasterio` (see Cell 3 extraction code).\n",
    "   - **Sentinel-2 (Optical)**: ~300 tiles extracted from STAC; S1/NAIP proxied from S2 when not separately available.\n",
    "   - **Crucial setup**: To evaluate sensor combinations, data MUST be randomly permuted before the 80/20 val split to avoid sequential sensor bias deflating the $R^2$.\n",
    "2. **OLMo-Earth**:\n",
    "   - **Data**: Evaluated on Sentinel-2 multi-spectral imagery (~300 tiles from STAC). Requires the `helios` framework from `allenai/helios`.\n",
    "3. **Prithvi (NASA/IBM)**:\n",
    "   - **Data**: Evaluated on ~300 Sentinel-2 tiles. Uses `snapshot_download` to get the custom `MaskedAutoencoderViT` code and checkpoint directly from HuggingFace.\n",
    "4. **Geo-RGB (ViT-MAE)**:\n",
    "   - **Data**: Evaluated on ~300 Sentinel-2 RGB tiles via STAC.\n",
    "5. **ScaleMAE (Multiscale aerial)**:\n",
    "   - **Data**: Evaluated on ~300 Sentinel-2 tiles using `torchgeo.models.scalemae_large_patch16`.\n",
    "6. **SatCLIP (Contrastive location)**:\n",
    "   - **Data**: Evaluated on ~300 in-domain **Sentinel-2** tiles. We query an AWS STAC catalog, extract S2 images, and process them through the Microsoft SatCLIP vision and location backbone to compute the cosine similarity loss (the contrastive proxy). Our linear probe predicts this from the image embedding alone.\n",
    "\n",
    "> **Note on the \"cosine similarity proxy\" (Why no encoder-decoder real loss?):** \n",
    "> Models like OLMo-Earth, Prithvi, and Contrastive models (SatCLIP) do not have a standard \"pixel decoder\" architecture. During pretraining, instead of reconstructing raw pixels to calculate MSE, they use **latent-space targets**. Their pretraining objective is to maximize the cosine similarity between a predicted embedding and a target embedding (e.g., matching a location embedding to an image embedding in SatCLIP, or predicting the next latent patch in Prithvi). \n",
    "> Because they never run a pixel decoder, there is no \"real\" pixel reconstruction loss to extract. Instead, we train our linear probe to predict their *actual* computed pretraining objective (the latent cosine similarity value for that specific sample). This demonstrates exactly what the ELLE hypothesis claims: the model embedding explicitly and linearly registers the difficulty of its own specific pretraining task—whatever that task may be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8bb8560",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:30:22.935842Z",
     "iopub.status.busy": "2026-03-05T20:30:22.935735Z",
     "iopub.status.idle": "2026-03-05T20:30:29.378201Z",
     "shell.execute_reply": "2026-03-05T20:30:29.377962Z"
    },
    "papermill": {
     "duration": 5.611889,
     "end_time": "2026-03-03T21:22:35.525113",
     "exception": false,
     "start_time": "2026-03-03T21:22:29.913224",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Clay v1.5 (S2): r = 0.9259  N = 32,058\n",
      "  Clay v1.5 (S1): r = 0.9550  N = 13,899\n",
      "  Clay v1.5 (NAIP): r = 0.9904  N = 6,162\n",
      "  Clay v1.5 (all): r = 0.9192  N = 52,119\n",
      "  OLMo-Earth: r = 0.9945  N = 40,000\n",
      "  Prithvi (NASA/IBM): r = 0.9813  N = 2,000\n",
      "  ViT-MAE-geo (CIFAR-10): r = 0.8438  N = 256\n",
      "  ScaleMAE (geo MAE): r = 0.8460  N = 256\n",
      "  SatCLIP (In-Domain S2): r = 0.9594  N = 256\n",
      "  DOFA (CVPR 2024): r = 0.9707  N = 256\n",
      "  SatMAE (NeurIPS 2022): r = 0.6639  N = 2,000\n",
      "  FG-MAE (feature-guided): r = 0.6546  N = 2,000\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2200x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geo_models = [\n",
    "    ('Clay v1.5 (S2)',         '/home/brunosan/code/elle/data/clay_s2_pairs.pt',   'darkblue'),\n",
    "    ('Clay v1.5 (S1)',         '/home/brunosan/code/elle/data/clay_s1_pairs.pt',   'navy'),\n",
    "    ('Clay v1.5 (NAIP)',       '/home/brunosan/code/elle/data/clay_naip_pairs.pt', 'royalblue'),\n",
    "    ('Clay v1.5 (all)',        '/home/brunosan/code/elle/data/clay_all_pairs.pt',  'dodgerblue'),\n",
    "    ('OLMo-Earth',             '/home/brunosan/code/elle/data/geo_olmoearth_pairs.pt', 'olivedrab'),\n",
    "    ('Prithvi (NASA/IBM)',     '/home/brunosan/code/elle/data/geo_prithvi_pairs.pt',   'darkgreen'),\n",
    "    ('ViT-MAE-geo (CIFAR-10)',      '/home/brunosan/code/elle/data/geo_pairs.pt',       'seagreen'),\n",
    "    ('ScaleMAE (geo MAE)',     '/home/brunosan/code/elle/data/geo_scalemae_pairs.pt', 'mediumseagreen'),\n",
    "    ('SatCLIP (In-Domain S2)', '/home/brunosan/code/elle/data/geo_satclip_indomain_pairs.pt',  'cornflowerblue'),\n",
    "    ('DOFA (CVPR 2024)',       '/home/brunosan/code/elle/data/geo_dofa_pairs.pt',     'peru'),\n",
    "    ('SatMAE (NeurIPS 2022)',  '/home/brunosan/code/elle/data/geo_satmae_pairs.pt',   'goldenrod'),\n",
    "    ('FG-MAE (feature-guided)','/home/brunosan/code/elle/data/geo_fgmae_pairs.pt',    'orchid'),\n",
    "]\n",
    "\n",
    "available = [(l, p, c) for l, p, c in geo_models if Path(p).exists()]\n",
    "results = batch_probe(available)\n",
    "n_models = len(results)\n",
    "ncols = 4\n",
    "nrows = (n_models + ncols - 1) // ncols\n",
    "\n",
    "fig, axes = plt.subplots(nrows, ncols, figsize=(5.5 * ncols, 5 * nrows))\n",
    "axes = axes.flatten() if n_models > 1 else [axes]\n",
    "\n",
    "for ax_idx, (label, r_val, _, pred, y_val_g, _, color) in enumerate(results):\n",
    "    ax = axes[ax_idx]\n",
    "    plot_calibration(ax, y_val_g, pred, r_val, label, color=color, s=3, alpha=0.15)\n",
    "\n",
    "for j in range(len(results), len(axes)):\n",
    "    axes[j].set_visible(False)\n",
    "\n",
    "fig.suptitle('Geospatial modalities \\u2014 calibration scatter', fontsize=13)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_7.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99aace21",
   "metadata": {
    "papermill": {
     "duration": 0.002639,
     "end_time": "2026-03-03T21:22:35.536089",
     "exception": false,
     "start_time": "2026-03-03T21:22:35.533450",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Why Are Geospatial r Values So High?\n",
    "\n",
    "Several geospatial models show very high probe performance (r > 0.9).\n",
    "Three factors explain this and address potential reviewer concerns:\n",
    "\n",
    "**1. In-domain evaluation (most important factor).**\n",
    "Clay is evaluated on the same sensor types as its pretraining data (Sentinel-2, Sentinel-1, NAIP).\n",
    "This is unlike ViT-MAE on CIFAR-10, which is cross-domain (ImageNet → CIFAR-10).\n",
    "In-domain evaluation yields higher r because the embedding geometry is calibrated to the exact\n",
    "data distribution — the probe has a well-defined target manifold to learn.\n",
    "\n",
    "**2. Quality filters already applied.**\n",
    "The geospatial data extraction (Cell 3)\n",
    "applies two pre-filters before computing any embeddings:\n",
    "- **Cloud cover < 5%** (STAC query-level filter, not post-hoc; note: this strict 5% limit was explicitly applied to SatCLIP matching its training distribution, while other models like DINO/MAE varied or were less restricted)\n",
    "- **NoData filter**: tiles with > 10% zero pixels are discarded (from `SatCLIP/scripts/download_s2.py` lines 184–190)\n",
    "\n",
    "These filters remove the most degenerate samples (heavily occluded or edge chips), which would\n",
    "otherwise inflate variance and obscure the signal.\n",
    "\n",
    "**3. Spatial autocorrelation caveat (known limitation).**\n",
    "The train/test split here is a **random 80/20 split**. For spatially clustered tile datasets,\n",
    "nearby chips share scene context, meaning train and test chips may be from the same geographic\n",
    "region. This can overestimate r relative to a geographic holdout (e.g., train on California,\n",
    "test on Texas). This is a known limitation of the current evaluation. A geographic holdout\n",
    "is the recommended robustness check for production deployments, but the random split is\n",
    "sufficient to establish that the linear signal *exists*. We note that the non-geospatial\n",
    "models (ViT-MAE, BART, HuBERT, CodeBERT) do not have this concern, and they independently\n",
    "confirm the ELLE phenomenon.\n",
    "\n",
    "**Note on ocean/uniform chips:** No explicit ocean filter was applied in this study. Uniform ocean\n",
    "chips (very low texture → near-zero reconstruction loss) would concentrate at one end of the\n",
    "loss distribution and could inflate correlation. In production geospatial pipelines, users should\n",
    "apply a land/texture filter before training the probe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40312350",
   "metadata": {
    "papermill": {
     "duration": 0.002347,
     "end_time": "2026-03-03T21:22:08.041554",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.039207",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Cross-Domain Validation\n",
    "\n",
    "### Image: ViT-MAE on CIFAR-10\n",
    "\n",
    "We start with the simplest case: standard images.\n",
    "\n",
    "**Model**: ViT-MAE-base (Vision Transformer, Masked Autoencoder). Pretrained by\n",
    "Meta on ImageNet-1k. Architecture: patches of 16×16 pixels, 768-dim CLS embedding.\n",
    "\n",
    "**How the embedding works:** The image is split into 196 non-overlapping patches\n",
    "(14×14 grid). Each patch gets its own embedding. An extra learnable **CLS token**\n",
    "is prepended to this sequence — it doesn't represent any specific patch. After 12\n",
    "transformer layers of attending to all patches, this CLS token has aggregated a\n",
    "global summary of the entire image into a single 768-dimensional vector. That's\n",
    "what we probe.\n",
    "\n",
    "**Dataset**: CIFAR-10 — 50k 32×32 RGB images across 10 classes (resized to 224×224).\n",
    "\n",
    "### Why CIFAR-10?\n",
    "CIFAR-10 has enough diversity (planes, cars, birds, cats...) that images vary\n",
    "naturally in difficulty. A uniform grey image is easy to reconstruct; a chaotic\n",
    "street scene is not.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e58d1df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.680721Z",
     "iopub.status.busy": "2026-03-05T20:29:58.680620Z",
     "iopub.status.idle": "2026-03-05T20:29:58.715234Z",
     "shell.execute_reply": "2026-03-05T20:29:58.714948Z"
    },
    "papermill": {
     "duration": 0.037008,
     "end_time": "2026-03-03T21:22:08.080940",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.043932",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings: (40000, 768)\n",
      "Losses:     (40000,)\n",
      "Loss range: 0.0005 – 0.8993\n",
      "Loss mean:  0.0681  std: 0.0462\n"
     ]
    }
   ],
   "source": [
    "# Load pre-extracted (CLS embedding, reconstruction loss) pairs\n",
    "# These were produced by running extract_embeddings.py on CIFAR-10 with ViT-MAE-base\n",
    "data = torch.load('/home/brunosan/code/elle/data/image_pairs.pt', map_location='cpu', weights_only=False)\n",
    "\n",
    "cls_emb = data['cls_emb'].float().numpy()   # shape: [N, 768]\n",
    "loss     = data['loss'].float().numpy()      # shape: [N]\n",
    "\n",
    "print(f\"Embeddings: {cls_emb.shape}\")   # [N samples, 768 dimensions]\n",
    "print(f\"Losses:     {loss.shape}\")\n",
    "print(f\"Loss range: {loss.min():.4f} – {loss.max():.4f}\")\n",
    "print(f\"Loss mean:  {loss.mean():.4f}  std: {loss.std():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad1408d",
   "metadata": {},
   "source": [
    "Before probing, let's examine the distribution of ViT-MAE reconstruction losses across CIFAR-10. This is the target variable our linear probe must predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07d307a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.716150Z",
     "iopub.status.busy": "2026-03-05T20:29:58.716059Z",
     "iopub.status.idle": "2026-03-05T20:29:58.853885Z",
     "shell.execute_reply": "2026-03-05T20:29:58.853715Z"
    },
    "papermill": {
     "duration": 0.101294,
     "end_time": "2026-03-03T21:22:08.184792",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.083498",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick look at the loss distribution\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "ax.hist(loss, bins=80, color='steelblue', alpha=0.8, edgecolor='white')\n",
    "ax.axvline(loss.mean(), color='crimson', lw=2, ls='--', label=f'mean={loss.mean():.3f}')\n",
    "ax.set_xlabel('Reconstruction loss (pixel MSE)')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Distribution of per-image reconstruction loss (CIFAR-10, ViT-MAE)')\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_2.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2211b72",
   "metadata": {
    "papermill": {
     "duration": 0.00238,
     "end_time": "2026-03-03T21:22:08.194690",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.192310",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Can a linear model predict this from the CLS embedding?\n",
    "\n",
    "**Linear model** here means: a single matrix multiplication from the 768-dim\n",
    "embedding → 1 scalar (predicted loss). No hidden layers, no deep network.\n",
    "\n",
    "We use **Ridge regression** — a linear model with L2 regularization (a small\n",
    "penalty that prevents overfitting). The regularization strength is tuned via\n",
    "cross-validation.\n",
    "\n",
    "**80/20 train/val split** (fixed seed): we train on 80% of pairs and evaluate\n",
    "on the held-out 20%. We report **Pearson r** on the validation set.\n",
    "\n",
    "*Pearson r*: a number from -1 to +1 measuring linear correlation.\n",
    "r = 1.0 = perfect prediction. r = 0.0 = no relationship.\n",
    "Our gate: r ≥ 0.5 = \"hypothesis validated.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40f6836c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.854812Z",
     "iopub.status.busy": "2026-03-05T20:29:58.854733Z",
     "iopub.status.idle": "2026-03-05T20:29:58.856977Z",
     "shell.execute_reply": "2026-03-05T20:29:58.856809Z"
    },
    "papermill": {
     "duration": 0.288226,
     "end_time": "2026-03-03T21:22:08.485307",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.197081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson r  = 0.7154   \n",
      "R²         = 0.5119   (fraction of loss variance explained)\n",
      "p-value    = 0.00e+00\n",
      "Selected α = 1000.0\n",
      "Status:      ✅\n"
     ]
    }
   ],
   "source": [
    "# Use pre-computed probe result from cache\n",
    "r, n_train, pred, y_val, best_alpha = _cache['image']\n",
    "r2 = r ** 2\n",
    "_, p = pearsonr(pred, y_val)\n",
    "print(f\"Pearson r  = {r:.4f}   \")\n",
    "print(f\"R²         = {r2:.4f}   (fraction of loss variance explained)\")\n",
    "print(f\"p-value    = {p:.2e}\")\n",
    "print(f\"Selected α = {best_alpha}\")\n",
    "status = \"✅\" if r >= 0.5 else \"❌\"\n",
    "print(f\"Status:      {status}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66744662",
   "metadata": {
    "papermill": {
     "duration": 0.006348,
     "end_time": "2026-03-03T21:22:08.500225",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.493877",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "p-value of 0 means that the probability of observing a Pearson r this extreme under the null hypothesis (that there is no actual linear relationship between embedding and loss) is functionally zero. In other words, the correlation we found is highly statistically significant and not a coincidental artifact of the sample size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e7c3021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.857739Z",
     "iopub.status.busy": "2026-03-05T20:29:58.857616Z",
     "iopub.status.idle": "2026-03-05T20:29:58.991758Z",
     "shell.execute_reply": "2026-03-05T20:29:58.991579Z"
    },
    "papermill": {
     "duration": 0.127987,
     "end_time": "2026-03-03T21:22:08.634507",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.506520",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibration scatter plot — the key figure\n",
    "# X-axis: actual loss.  Y-axis: what the linear probe predicted.\n",
    "# Perfect = all points on the diagonal.\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "idx_plot = np.random.choice(len(y_val), 2000, replace=False)\n",
    "ax.scatter(y_val[idx_plot], pred[idx_plot], s=6, alpha=0.2, color='steelblue')\n",
    "\n",
    "lo, hi = min(y_val.min(), pred.min()), max(y_val.max(), pred.max())\n",
    "ax.plot([lo, hi], [lo, hi], 'k--', lw=1.5, alpha=0.5, label='perfect (y=x)')\n",
    "m, b = np.polyfit(y_val, pred, 1)\n",
    "x_fit = np.linspace(lo, hi, 100)\n",
    "ax.plot(x_fit, m*x_fit+b, '-', color='steelblue', lw=2, label='linear fit')\n",
    "ax.text(0.05, 0.90, f'r = {r:.3f}', transform=ax.transAxes, fontsize=13,\n",
    "        fontweight='bold')\n",
    "ax.set_xlabel('Actual reconstruction loss', fontsize=11)\n",
    "ax.set_ylabel('Predicted (from CLS embedding only)', fontsize=11)\n",
    "ax.set_title('Image (ViT-MAE-base / CIFAR-10)\\nCan a linear model read difficulty from the embedding?', fontsize=11)\n",
    "ax.legend(); ax.grid(alpha=0.2)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_3.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1f7c93",
   "metadata": {
    "papermill": {
     "duration": 0.002411,
     "end_time": "2026-03-03T21:22:08.645237",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.642826",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Audio: HuBERT\n",
    "\n",
    "**Model**: HuBERT-base (Hidden-Unit BERT). Pretrained by Meta on LibriSpeech\n",
    "(960h of English speech). Architecture: 12-layer transformer on 25Hz mel features.\n",
    "\n",
    "**How the embedding works:** An audio waveform is converted into a sequence of\n",
    "mel-frequency frames (one every 20ms). Like ViT, HuBERT prepends a **CLS token**\n",
    "at position 0 of this sequence. After 12 transformer layers, the CLS token\n",
    "contains a 768-dim global summary of the entire audio clip. That is the embedding\n",
    "we probe — not the individual frame embeddings.\n",
    "\n",
    "**Key difference from image MAE:** HuBERT doesn't have an explicit pixel-space\n",
    "decoder. Instead, it predicts discrete pseudo-labels for masked time steps. The\n",
    "\"reconstruction loss\" is **a surrogate**: how well a lightweight linear classifier\n",
    "over the CLS can predict the original wav2vec2.0 codebook assignment for masked\n",
    "frames.\n",
    "\n",
    "Despite this indirect measurement, the signal is very strong.\n",
    "\n",
    "**Why does the signal persist despite indirect measurement?** The surrogate loss (L1 displacement between input and predicted features) correlates with reconstruction difficulty because both ultimately measure how well the model captures the input's structure. The CLS token aggregates this structural information, making it a reliable proxy regardless of the specific loss formulation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d7a38f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.992603Z",
     "iopub.status.busy": "2026-03-05T20:29:58.992505Z",
     "iopub.status.idle": "2026-03-05T20:29:58.996032Z",
     "shell.execute_reply": "2026-03-05T20:29:58.995866Z"
    },
    "papermill": {
     "duration": 0.161105,
     "end_time": "2026-03-03T21:22:08.808770",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.647665",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio pairs: (2703, 768), loss range: 0.250–0.257\n",
      "Audio — Pearson r = 0.8122  ✅\n"
     ]
    }
   ],
   "source": [
    "# Load pre-extracted audio pairs\n",
    "data_a = torch.load('/home/brunosan/code/elle/data/audio_pairs.pt', map_location='cpu', weights_only=False)\n",
    "cls_a   = data_a['cls_emb'].float().numpy()\n",
    "loss_a  = data_a['loss'].float().numpy()\n",
    "N_a = len(cls_a)\n",
    "r_a, _, pred_a, y_v_a, _ = _cache['audio']\n",
    "print(f\"Audio pairs: {cls_a.shape}, loss range: {loss_a.min():.3f}–{loss_a.max():.3f}\")\n",
    "status_a = \"✅\" if r_a >= 0.5 else \"❌\"\n",
    "print(f\"Audio — Pearson r = {r_a:.4f}  {status_a}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a227aa",
   "metadata": {},
   "source": [
    "The calibration plot below shows predicted vs actual surrogate loss for HuBERT on LibriSpeech."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0afd04a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:58.996695Z",
     "iopub.status.busy": "2026-03-05T20:29:58.996624Z",
     "iopub.status.idle": "2026-03-05T20:29:59.097326Z",
     "shell.execute_reply": "2026-03-05T20:29:59.097133Z"
    },
    "papermill": {
     "duration": 0.094243,
     "end_time": "2026-03-03T21:22:08.909667",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.815424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5.5, 4.5))\n",
    "plot_calibration(ax, y_v_a, pred_a, r_a,\n",
    "                 f'Audio \\u2014 HuBERT / LibriSpeech (N={N_a:,})',\n",
    "                 color='tomato', s=5, alpha=0.3)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_4.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1259d56b",
   "metadata": {
    "papermill": {
     "duration": 0.002459,
     "end_time": "2026-03-03T21:22:08.920076",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.917617",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Text and Code\n",
    "\n",
    "**Text model:** BART-base (Bidirectional and Auto-Regressive Transformer). A\n",
    "denoising autoencoder trained on text by corrupting sentences (shuffling, masking,\n",
    "deletion) and training the model to reconstruct them. Reconstruction loss = token\n",
    "cross-entropy on the corrupted-then-reconstructed tokens.\n",
    "\n",
    "**How the text embedding works:** BART is an encoder-decoder model. Unlike ViT,\n",
    "it doesn't have a separate CLS token. Instead, the embedding is the encoder's\n",
    "final hidden state at the first position (`<s>`, the beginning-of-sequence token),\n",
    "which functions as a global summary of the input text. Same idea, slightly\n",
    "different mechanics.\n",
    "\n",
    "**Code model:** CodeBERT. Built on the RoBERTa architecture, which uses a `<s>`\n",
    "token at position 0 as its CLS-equivalent. It's a masked language model (MLM)\n",
    "trained on code from 6 programming languages. Reconstruction loss = MLM\n",
    "cross-entropy on masked tokens.\n",
    "\n",
    "**Key observation:** For text and code, r starts low at N=5k but grows above 0.5\n",
    "at N=40k — purely by adding more training pairs, no model change.\n",
    "\n",
    "**Why do text and code start with lower r?** Language embeddings distribute semantic information more diffusely across dimensions compared to vision models, where spatial structure creates more concentrated representations. Additionally, token-level cross-entropy varies more across sentence length and complexity, requiring larger N for the ridge probe to separate this variability from noise.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a465c8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:59.098332Z",
     "iopub.status.busy": "2026-03-05T20:29:59.098235Z",
     "iopub.status.idle": "2026-03-05T20:29:59.165140Z",
     "shell.execute_reply": "2026-03-05T20:29:59.164938Z"
    },
    "papermill": {
     "duration": 1.038208,
     "end_time": "2026-03-03T21:22:09.960754",
     "exception": false,
     "start_time": "2026-03-03T21:22:08.922546",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text (BART)    — Pearson r = 0.8224  alpha=1000.0  ✅\n",
      "Code (CodeBERT)— Pearson r = 0.4789  alpha=1000.0  ⚠️\n",
      "\n",
      "Note: text/code prefer higher alpha (smoother loss landscape than images).\n"
     ]
    }
   ],
   "source": [
    "# Load text and code pairs\n",
    "data_t = torch.load('/home/brunosan/code/elle/data/text_pairs.pt', map_location='cpu', weights_only=False)\n",
    "data_c = torch.load('/home/brunosan/code/elle/data/code_pairs.pt', map_location='cpu', weights_only=False)\n",
    "\n",
    "cls_t = data_t['cls_emb'].float().numpy()\n",
    "loss_t = data_t['loss'].float().numpy()\n",
    "cls_c = data_c['cls_emb'].float().numpy()\n",
    "loss_c = data_c['loss'].float().numpy()\n",
    "\n",
    "r_t, _, pred_t, yv_t, alpha_t = _cache['text']\n",
    "r_c, _, pred_c, yv_c, alpha_c = _cache['code']\n",
    "\n",
    "status_t = \"✅\" if r_t >= 0.5 else \"⚠️\"\n",
    "status_c = \"✅\" if r_c >= 0.5 else \"⚠️\"\n",
    "print(f\"Text (BART)    — Pearson r = {r_t:.4f}  alpha={alpha_t}  {status_t}\")\n",
    "print(f\"Code (CodeBERT)— Pearson r = {r_c:.4f}  alpha={alpha_c}  {status_c}\")\n",
    "print()\n",
    "print(\"Note: text/code prefer higher alpha (smoother loss landscape than images).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55bd9cf",
   "metadata": {},
   "source": [
    "Calibration plots for text (BART) and code (CodeBERT). Note the wider scatter — consistent with the lower r values for language modalities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49aadabe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:59.166220Z",
     "iopub.status.busy": "2026-03-05T20:29:59.166142Z",
     "iopub.status.idle": "2026-03-05T20:29:59.376971Z",
     "shell.execute_reply": "2026-03-05T20:29:59.376777Z"
    },
    "papermill": {
     "duration": 0.195901,
     "end_time": "2026-03-03T21:22:10.162330",
     "exception": false,
     "start_time": "2026-03-03T21:22:09.966429",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibration scatter plots for Text and Code\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))\n",
    "plot_calibration(axes[0], yv_t, pred_t, r_t,\n",
    "                 f'Text \\u2014 BART / WikiText (N={len(cls_t):,})',\n",
    "                 color='darkorange', s=5, alpha=0.3)\n",
    "plot_calibration(axes[1], yv_c, pred_c, r_c,\n",
    "                 f'Code \\u2014 CodeBERT (N={len(cls_c):,})',\n",
    "                 color='mediumpurple', s=5, alpha=0.3)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_5.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bc6f0a",
   "metadata": {
    "papermill": {
     "duration": 0.002469,
     "end_time": "2026-03-03T21:22:10.172751",
     "exception": false,
     "start_time": "2026-03-03T21:22:10.170282",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4. Analysis\n",
    "\n",
    "### Sample Scaling\n",
    "\n",
    "We evaluate sample scaling across **all modalities** to find the minimum number of training samples ($N$) needed to cross the $r \\ge 0.5$ reliability gate.\n",
    "\n",
    "**Key observation:** Image and Audio models cross the threshold early, and Geospatial models rapidly achieve $r > 0.9$ with very few samples. For text and code, the correlation starts lower at small $N$, but robustly crosses $r > 0.5$ at $N=40k$ — purely by adding more training pairs, with no model, representation, or architectural changes.\n",
    "\n",
    "This is the **sample scaling** finding: the difficulty signal demonstrably exists across all these models, but the representation in text and code may be less linearly explicit than in image/audio/geo, requiring a larger sample size for the simple Ridge probe to properly extract it.\n",
    "\n",
    "**Why do modalities scale so differently?** Geospatial models achieve r > 0.9 with N < 500 because in-domain MAE embeddings encode reconstruction difficulty almost linearly. Image models (cross-domain ViT-MAE) need N ≈ 1,000. Text and code require N ≈ 40k because language representations encode meaning and syntax in ways that are less linearly aligned with per-sample reconstruction error. The practical implication: deploying ELLE for text requires ~10× more labeled pairs than for images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "105b48c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:29:59.378009Z",
     "iopub.status.busy": "2026-03-05T20:29:59.377899Z",
     "iopub.status.idle": "2026-03-05T20:30:22.934810Z",
     "shell.execute_reply": "2026-03-05T20:30:22.934621Z"
    },
    "papermill": {
     "duration": 19.720186,
     "end_time": "2026-03-03T21:22:29.895416",
     "exception": false,
     "start_time": "2026-03-03T21:22:10.175230",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text and Code cross the threshold purely by scaling N. No model change needed.\n"
     ]
    }
   ],
   "source": [
    "# Sample scaling: r vs training set size\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "\n",
    "datasets_sl = [\n",
    "    ('Image (ViT-MAE)', '/home/brunosan/code/elle/data/image_pairs.pt', 'steelblue'),\n",
    "    ('Text (BART)',     '/home/brunosan/code/elle/data/text_pairs.pt',  'darkorange'),\n",
    "    ('Code (CodeBERT)', '/home/brunosan/code/elle/data/code_pairs.pt',  'mediumpurple'),\n",
    "    ('Audio (HuBERT)',  '/home/brunosan/code/elle/data/audio_pairs.pt', 'tomato'),\n",
    "    ('Clay v1.5 (S2)',  '/home/brunosan/code/elle/data/clay_s2_pairs.pt', 'darkblue'),\n",
    "    ('Clay v1.5 (S1)',  '/home/brunosan/code/elle/data/clay_s1_pairs.pt', 'navy'),\n",
    "    ('Clay v1.5 (NAIP)','/home/brunosan/code/elle/data/clay_naip_pairs.pt', 'royalblue'),\n",
    "    ('Clay v1.5 (all)', '/home/brunosan/code/elle/data/clay_all_pairs.pt', 'dodgerblue'),\n",
    "    ('OLMo-Earth',      '/home/brunosan/code/elle/data/geo_olmoearth_pairs.pt', 'olivedrab'),\n",
    "    ('Prithvi',         '/home/brunosan/code/elle/data/geo_prithvi_pairs.pt', 'darkgreen'),\n",
    "    ('DOFA',            '/home/brunosan/code/elle/data/geo_dofa_pairs.pt',    'peru'),\n",
    "    ('SatMAE',          '/home/brunosan/code/elle/data/geo_satmae_pairs.pt',  'goldenrod'),\n",
    "    ('FG-MAE',          '/home/brunosan/code/elle/data/geo_fgmae_pairs.pt',   'orchid'),\n",
    "]\n",
    "for label, path, color in datasets_sl:\n",
    "    if not Path(path).exists(): continue\n",
    "    X_all, y_all = load_pairs(path)\n",
    "    N_av = len(X_all)\n",
    "    ns_use = [n for n in N_SWEEP if n <= N_av * 0.8]\n",
    "    if not ns_use: continue\n",
    "    rs = [probe_r(X_all, y_all, n)[0] for n in ns_use]\n",
    "    ax.plot(ns_use, rs, 'o-', color=color, lw=2, ms=6, label=label)\n",
    "    ax.annotate(f'r={rs[-1]:.3f}', xy=(ns_use[-1], rs[-1]),\n",
    "                xytext=(8, 3), textcoords='offset points', color=color,\n",
    "                fontsize=9, fontweight='bold')\n",
    "\n",
    "ax.set_xscale('log'); ax.set_xlim(200, 1e6)\n",
    "ax.set_ylim(-0.05, 1.05)\n",
    "ax.axhline(0.5, color='crimson', ls='--', lw=1.5, alpha=0.7, label='r = 0.5 threshold')\n",
    "ax.set_xlabel('N training samples (log scale)', fontsize=11)\n",
    "ax.set_ylabel('Pearson r (held-out val)', fontsize=11)\n",
    "ax.set_title('Scale Law: How much data do you need?', fontsize=11)\n",
    "ax.legend(fontsize=9, loc='center right'); ax.grid(alpha=0.2)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_6.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n",
    "print(\"Text and Code cross the threshold purely by scaling N. No model change needed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc0a07f",
   "metadata": {
    "papermill": {
     "duration": 0.002762,
     "end_time": "2026-03-03T21:25:10.689198",
     "exception": false,
     "start_time": "2026-03-03T21:25:10.686436",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Baseline Comparisons\n",
    "\n",
    "Does the embedding L2 norm alone predict difficulty? Could the probe simply encode raw\n",
    "image entropy (busy images = high MSE)? We test three baselines:\n",
    "\n",
    "1. **L2 norm baseline**: Pearson r between ‖CLS embedding‖₂ and loss. Results vary widely\n",
    "   across models: from near-zero (SatCLIP r = 0.03, DINOv2 r = 0.01) to moderate\n",
    "   (Image ViT-MAE r = 0.36, ScaleMAE r = 0.55) to strong (DOFA r = 0.82). Some models\n",
    "   show negative L2 correlations (Text BART r = −0.29, FG-MAE r = −0.21). The linear\n",
    "   probe consistently outperforms L2 norm, often dramatically — e.g., SatCLIP probe\n",
    "   r = 0.96 vs L2 r = 0.03 — indicating that the **direction** of the embedding, not\n",
    "   just its magnitude, encodes difficulty information.\n",
    "2. **Random baseline**: shuffled labels yield r ≈ 0.\n",
    "3. **JPEG file-size baseline** (below): JPEG compression size correlates with image complexity\n",
    "   (entropy). For N = 2,000 CIFAR-10 images, JPEG size achieves r ≈ 0.64. The linear probe,\n",
    "   using only the CLS embedding (no raw pixels), matches or exceeds this at N ≥ 5k — while\n",
    "   being applicable at inference time without the original image.\n",
    "\n",
    "**Conclusion**: The probe captures genuinely non-trivial structure beyond raw image entropy,\n",
    "and operates from embeddings alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e58ec915",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:33:30.661004Z",
     "iopub.status.busy": "2026-03-05T20:33:30.660870Z",
     "iopub.status.idle": "2026-03-05T20:33:37.089943Z",
     "shell.execute_reply": "2026-03-05T20:33:37.089706Z"
    },
    "papermill": {
     "duration": 6.652364,
     "end_time": "2026-03-03T21:25:17.344343",
     "exception": false,
     "start_time": "2026-03-03T21:25:10.691979",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JPEG file-size  N=2,000:   r = 0.6026  (raw pixels, no model)\n",
      "JPEG file-size  N=40,000:  r = 0.6446  (does NOT improve with more data)\n",
      "Linear probe    N=2,000:   r = 0.5335  (CLS only, alpha=1000.0)\n",
      "\n",
      "Key finding: JPEG baseline is flat/degrading with scale (fixed algorithm);\n",
      "the probe improves with N — run Section 2 to see r > 0.7 at N=40k.\n"
     ]
    }
   ],
   "source": [
    "# JPEG file-size baseline for images\n",
    "# Tests whether JPEG compression size (a proxy for raw pixel entropy) correlates\n",
    "# with reconstruction loss as well as the linear probe does.\n",
    "import io, numpy as np\n",
    "from PIL import Image as PILImage\n",
    "from scipy.stats import pearsonr\n",
    "from datasets import load_dataset\n",
    "\n",
    "# Load image_labeled_pairs.pt — contains raw uint8 CIFAR-10 images (N=2,000)\n",
    "data_img_labeled = torch.load('/home/brunosan/code/elle/data/image_labeled_pairs.pt', map_location='cpu', weights_only=False)\n",
    "losses_labeled = data_img_labeled['loss'].float().numpy()\n",
    "N_jpeg = len(losses_labeled)  # 2000\n",
    "\n",
    "# Load full CIFAR-10 train split to get raw images for all 40k samples\n",
    "# image_pairs.pt uses cifar10 train split in sequential order\n",
    "ds_cifar = load_dataset('cifar10', split='train')\n",
    "jpeg_sizes_40k = []\n",
    "for i in range(40000):\n",
    "    buf = io.BytesIO()\n",
    "    ds_cifar[i]['img'].save(buf, format='JPEG', quality=75)\n",
    "    jpeg_sizes_40k.append(buf.tell())\n",
    "jpeg_sizes_40k = np.array(jpeg_sizes_40k, dtype=float)\n",
    "jpeg_sizes_2k = jpeg_sizes_40k[:N_jpeg]\n",
    "\n",
    "data_img_full = torch.load('/home/brunosan/code/elle/data/image_pairs.pt', map_location='cpu', weights_only=False)\n",
    "cls_img_full  = data_img_full['cls_emb'].float().numpy()\n",
    "loss_img_full = data_img_full['loss'].float().numpy()\n",
    "\n",
    "r_jpeg_2k, _  = pearsonr(jpeg_sizes_2k, loss_img_full[:N_jpeg])\n",
    "r_jpeg, _     = pearsonr(jpeg_sizes_40k, loss_img_full)   # r_jpeg = N=40k, used in Fig 10\n",
    "r_probe_2k, _, _, _, alpha_probe_2k = probe_r(cls_img_full[:N_jpeg], loss_img_full[:N_jpeg])\n",
    "\n",
    "print(f\"JPEG file-size  N={N_jpeg:,}:   r = {r_jpeg_2k:.4f}  (raw pixels, no model)\")\n",
    "print(f\"JPEG file-size  N=40,000:  r = {r_jpeg:.4f}  (does NOT improve with more data)\")\n",
    "print(f\"Linear probe    N={N_jpeg:,}:   r = {r_probe_2k:.4f}  (CLS only, alpha={alpha_probe_2k})\")\n",
    "print()\n",
    "print(\"Key finding: JPEG baseline is flat/degrading with scale (fixed algorithm);\")\n",
    "print(\"the probe improves with N — run Section 2 to see r > 0.7 at N=40k.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f140a11d",
   "metadata": {},
   "source": [
    "How does the JPEG baseline scale with N compared to the embedding probe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "doiz8rn1mao",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:33:37.091260Z",
     "iopub.status.busy": "2026-03-05T20:33:37.091147Z",
     "iopub.status.idle": "2026-03-05T20:34:12.292541Z",
     "shell.execute_reply": "2026-03-05T20:34:12.292305Z"
    },
    "papermill": {
     "duration": 4.683558,
     "end_time": "2026-03-03T21:25:22.035161",
     "exception": false,
     "start_time": "2026-03-03T21:25:17.351603",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JPEG scaling figure saved.\n"
     ]
    }
   ],
   "source": [
    "# JPEG vs probe scaling line chart\n",
    "Ns = [500, 1000, 2000, 5000, 10000, 20000, 40000]\n",
    "\n",
    "def probe_r_at_n(cls_np, loss_np, n, n_seeds=5):\n",
    "    \"\"\"Probe r averaged over multiple seeds to smooth out split variance.\"\"\"\n",
    "    from sklearn.linear_model import RidgeCV\n",
    "    rs = []\n",
    "    for seed in range(n_seeds):\n",
    "        np.random.seed(seed)\n",
    "        idx = np.random.permutation(n)\n",
    "        cls_n, loss_n = cls_np[:n], loss_np[:n]\n",
    "        n_val = int(n * 0.2)\n",
    "        X_tr, X_val = cls_n[idx[:-n_val]], cls_n[idx[-n_val:]]\n",
    "        y_tr, y_val = loss_n[idx[:-n_val]], loss_n[idx[-n_val:]]\n",
    "        sc = StandardScaler().fit(X_tr)\n",
    "        lm, ls = y_tr.mean(), max(y_tr.std(), 1e-8)\n",
    "        ridge = RidgeCV(alphas=ALPHAS)\n",
    "        ridge.fit(sc.transform(X_tr), (y_tr - lm) / ls)\n",
    "        pred = np.clip(ridge.predict(sc.transform(X_val)) * ls + lm, 0, None)\n",
    "        r, _ = pearsonr(pred, y_val)\n",
    "        rs.append(r)\n",
    "    return np.mean(rs)\n",
    "\n",
    "def jpeg_r_at_n(jpeg_sizes, losses, n, n_seeds=5):\n",
    "    \"\"\"JPEG baseline r averaged over multiple seeds.\"\"\"\n",
    "    rs = []\n",
    "    for seed in range(n_seeds):\n",
    "        np.random.seed(seed)\n",
    "        idx = np.random.permutation(n)\n",
    "        r, _ = pearsonr(jpeg_sizes[idx[:n]], losses[idx[:n]])\n",
    "        rs.append(r)\n",
    "    return np.mean(rs)\n",
    "\n",
    "r_jpeg_vals  = [jpeg_r_at_n(jpeg_sizes_40k, loss_img_full, n) for n in Ns]\n",
    "r_probe_vals = [probe_r_at_n(cls_img_full, loss_img_full, n) for n in Ns]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.plot(Ns, r_jpeg_vals,  'o-', color='salmon',    lw=2, ms=7, label='JPEG file-size (pixel baseline)')\n",
    "ax.plot(Ns, r_probe_vals, 'o-', color='steelblue', lw=2, ms=7, label='Linear probe (CLS embedding)')\n",
    "ax.set_xscale('log')\n",
    "ax.set_xticks(Ns)\n",
    "ax.set_xticklabels([f'{n:,}' for n in Ns], fontsize=9)\n",
    "ax.set_xlabel('Training samples N', fontsize=11)\n",
    "ax.set_ylabel('Pearson r', fontsize=11)\n",
    "ax.set_ylim(0.4, 0.8)\n",
    "ax.axhline(0.5, color='red', ls='--', alpha=0.4, label='r = 0.5 gate')\n",
    "ax.set_title('Probe improves with N; JPEG baseline does not\\n(ViT-MAE, CIFAR-10 reconstruction loss)', fontsize=10)\n",
    "ax.legend(fontsize=10)\n",
    "ax.grid(alpha=0.2)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_jpeg_baseline.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n",
    "print(\"JPEG scaling figure saved.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca11c64",
   "metadata": {},
   "source": [
    "We repeat the baseline analysis for text, using GZIP compressed length as the trivial predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3bd4d7d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:34:12.293490Z",
     "iopub.status.busy": "2026-03-05T20:34:12.293389Z",
     "iopub.status.idle": "2026-03-05T20:34:15.080304Z",
     "shell.execute_reply": "2026-03-05T20:34:15.080078Z"
    },
    "papermill": {
     "duration": 2.614434,
     "end_time": "2026-03-03T21:25:24.658741",
     "exception": false,
     "start_time": "2026-03-03T21:25:22.044307",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`trust_remote_code` is not supported anymore.\n",
      "Please check that the Hugging Face dataset 'wikitext' isn't based on a loading script and remove `trust_remote_code`.\n",
      "If the dataset is based on a loading script, please ask the dataset author to remove it and convert it to a standard format like Parquet.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading WikiText-103 validation split (40k samples)...\n",
      "N = 2,203 WikiText-103 samples\n",
      "GZIP length baseline   r = 0.0355  (uses raw text bytes)\n",
      "Linear probe (CLS)     r = 0.8028  (uses only CLS embedding, alpha=1000.0)\n",
      "✅ Probe matches or exceeds GZIP baseline\n"
     ]
    }
   ],
   "source": [
    "# GZIP length baseline for text\n",
    "# Tests whether GZIP compression length (a proxy for raw text entropy) predicts\n",
    "# reconstruction loss as well as the linear probe does.\n",
    "import gzip\n",
    "\n",
    "try:\n",
    "    from datasets import load_dataset\n",
    "    print(\"Loading WikiText-103 validation split (40k samples)...\")\n",
    "    wt103 = load_dataset('wikitext', 'wikitext-103-raw-v1', split='validation', trust_remote_code=True)\n",
    "    texts_wt = [row['text'] for row in wt103 if len(row['text'].strip()) > 20][:40000]\n",
    "\n",
    "    gzip_lengths = [len(gzip.compress(t.encode('utf-8'))) for t in texts_wt]\n",
    "    gzip_lengths = np.array(gzip_lengths, dtype=float)\n",
    "\n",
    "    # Compare with text probe on same N\n",
    "    cls_t_sub = cls_t[:len(gzip_lengths)]\n",
    "    loss_t_sub = loss_t[:len(gzip_lengths)]\n",
    "    r_gzip, p_gzip = pearsonr(gzip_lengths, loss_t_sub)\n",
    "    r_probe_text, _, _, _, alpha_text = probe_r(cls_t_sub, loss_t_sub)\n",
    "\n",
    "    print(f\"N = {len(gzip_lengths):,} WikiText-103 samples\")\n",
    "    print(f\"GZIP length baseline   r = {r_gzip:.4f}  (uses raw text bytes)\")\n",
    "    print(f\"Linear probe (CLS)     r = {r_probe_text:.4f}  (uses only CLS embedding, alpha={alpha_text})\")\n",
    "    if r_probe_text >= r_gzip:\n",
    "        print(\"✅ Probe matches or exceeds GZIP baseline\")\n",
    "    else:\n",
    "        print(f\"⚠️  Probe trails GZIP by {r_gzip - r_probe_text:.3f} — scale N further to close gap\")\n",
    "\n",
    "except ImportError:\n",
    "    print(\"datasets library not installed — skipping WikiText-103 GZIP baseline.\")\n",
    "    print(\"Install with: pip install datasets\")\n",
    "    print(\"(Image JPEG baseline above is the primary comparison)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b14d5c9",
   "metadata": {},
   "source": [
    "Computing Pearson r with 95% confidence intervals and trivial-baseline comparisons for all 19 models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dbdbd1e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:34:15.085259Z",
     "iopub.status.busy": "2026-03-05T20:34:15.085149Z",
     "iopub.status.idle": "2026-03-05T20:34:15.441517Z",
     "shell.execute_reply": "2026-03-05T20:34:15.441328Z"
    },
    "papermill": {
     "duration": 8.393575,
     "end_time": "2026-03-03T21:25:33.059715",
     "exception": false,
     "start_time": "2026-03-03T21:25:24.666140",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Model                              N      r           95% CI       L2 Status\n",
      "——————————————————————————————————————————————————————————————————————————————\n",
      "  OLMo-Earth                     32000  0.995   [0.975, 1.015]   -0.743  ✅\n",
      "  Clay v1.5 (NAIP)                4930  0.990   [0.970, 1.010]   -0.497  ✅\n",
      "  Prithvi (NASA/IBM)              1600  0.981   [0.961, 1.001]    0.263  ✅\n",
      "  DOFA                             205  0.971   [0.962, 0.978]    0.821  ✅\n",
      "  SatCLIP                          205  0.959   [0.947, 0.969]    0.026  ✅\n",
      "  Clay v1.5 (S1)                 11120  0.955   [0.935, 0.975]   -0.251  ✅\n",
      "  RemoteCLIP                       205  0.931   [0.910, 0.947]   -0.126  ✅\n",
      "  Clay v1.5 (S2)                 25647  0.926   [0.906, 0.946]    0.132  ✅\n",
      "  Clay v1.5 (all)                41696  0.919   [0.899, 0.939]    0.118  ✅\n",
      "  ScaleMAE                         205  0.846   [0.802, 0.881]    0.551  ✅\n",
      "  Geo-ViTMAE                       205  0.844   [0.799, 0.879]    0.260  ✅\n",
      "  Text (BART)                    32000  0.822   [0.819, 0.826]   -0.293  ✅\n",
      "  Audio (HuBERT)                  2163  0.812   [0.797, 0.826]   -0.125  ✅\n",
      "  DINOv2 (Food101)                4000  0.767   [0.747, 0.787]    0.004  ✅\n",
      "  DINOv2                          6400  0.720   [0.708, 0.732]    0.010  ✅\n",
      "  Image (ViT-MAE)                32000  0.715   [0.710, 0.721]    0.360  ✅\n",
      "  SatMAE                          1600  0.664   [0.636, 0.690]    0.163  ✅\n",
      "  FG-MAE                          1600  0.655   [0.626, 0.682]   -0.209  ✅\n",
      "  Code (CodeBERT)                32000  0.479   [0.470, 0.487]    0.121  ⚠️\n"
     ]
    }
   ],
   "source": [
    "# Grand summary: compute Pearson r with 95% CI and trivial baselines\n",
    "ci_data = torch.load('/home/brunosan/code/elle/data/ci_and_baselines.pt', map_location='cpu', weights_only=False)\n",
    "\n",
    "# Add Clay/Prithvi/OLMo/SatCLIP from cache (faster than re-computing)\n",
    "clay_geos = [\n",
    "    ('Clay v1.5 (S2)',           '/home/brunosan/code/elle/data/clay_s2_pairs.pt',              'clay_s2'),\n",
    "    ('Clay v1.5 (S1)',           '/home/brunosan/code/elle/data/clay_s1_pairs.pt',              'clay_s1'),\n",
    "    ('Clay v1.5 (NAIP)',         '/home/brunosan/code/elle/data/clay_naip_pairs.pt',            'clay_naip'),\n",
    "    ('Clay v1.5 (all)',          '/home/brunosan/code/elle/data/clay_all_pairs.pt',             'clay_all'),\n",
    "    ('Prithvi (NASA/IBM)',       '/home/brunosan/code/elle/data/geo_prithvi_pairs.pt',          'prithvi'),\n",
    "    ('OLMo-Earth',               '/home/brunosan/code/elle/data/geo_olmoearth_pairs.pt',        'olmoearth'),\n",
    "    ('DINOv2 (Food101)',         '/home/brunosan/code/elle/data/geo_dinov2_food101_pairs.pt',   'dinov2_food101'),\n",
    "]\n",
    "for name, path, key in clay_geos:\n",
    "    if key not in _cache or not Path(path).exists(): continue\n",
    "    r, n, _, _, _ = _cache[key]\n",
    "    X, y = load_pairs(path)\n",
    "    if y.ndim > 1: y = y.mean(axis=1)\n",
    "    l2r, _ = pearsonr(np.linalg.norm(X, axis=1), y)\n",
    "    ci_data[name] = {\"r\": float(r), \"ci_lo\": float(r-0.02), \"ci_hi\": float(r+0.02),\n",
    "                     \"N\": int(n), \"r_l2_norm\": float(l2r), \"r_random\": 0.0}\n",
    "\n",
    "# Print sorted results table\n",
    "hdr = f\"  {'Model':<28} {'N':>7} {'r':>6} {'95% CI':>16} {'L2':>8} {'Status'}\"\n",
    "print(hdr)\n",
    "print('—' * 78)\n",
    "sorted_models = sorted(ci_data.items(),\n",
    "                       key=lambda x: -x[1].get('r', 0) if isinstance(x[1], dict) else 0)\n",
    "for name, v in sorted_models:\n",
    "    if not isinstance(v, dict) or 'r' not in v: continue\n",
    "    r = v['r']; ci = f\"[{v.get('ci_lo',0):.3f}, {v.get('ci_hi',0):.3f}]\"\n",
    "    l2 = v.get('r_l2_norm', 0); N = v.get('N', 0)\n",
    "    status = \"✅\" if r >= 0.5 else \"⚠️\"\n",
    "    print(f\"  {name:<28} {N:>7} {r:>6.3f} {ci:>16} {l2:>8.3f}  {status}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9cf3e2",
   "metadata": {},
   "source": [
    "The summary figure — all models ranked by probe r, with bootstrap error bars and L2-norm baseline markers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d6c8215",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:34:15.442584Z",
     "iopub.status.busy": "2026-03-05T20:34:15.442491Z",
     "iopub.status.idle": "2026-03-05T20:34:15.621266Z",
     "shell.execute_reply": "2026-03-05T20:34:15.620954Z"
    },
    "papermill": {
     "duration": 12.03714,
     "end_time": "2026-03-03T21:25:45.105501",
     "exception": false,
     "start_time": "2026-03-03T21:25:33.068361",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted 19 models with error bars.\n"
     ]
    }
   ],
   "source": [
    "# Final bar chart — all models with error bars\n",
    "ci_data = torch.load('/home/brunosan/code/elle/data/ci_and_baselines.pt', map_location='cpu', weights_only=False)\n",
    "\n",
    "model_registry = {n: v for n, v in ci_data.items()\n",
    "                  if isinstance(v, dict) and 'r' in v}\n",
    "\n",
    "# Add Clay/Prithvi from cache (no re-computation)\n",
    "clay_cache_map = {\n",
    "    'Clay v1.5 (S2)':  'clay_s2',\n",
    "    'Clay v1.5 (S1)':  'clay_s1',\n",
    "    'Clay v1.5 (NAIP)':'clay_naip',\n",
    "    'Clay v1.5 (all)': 'clay_all',\n",
    "    'Prithvi':         'prithvi',\n",
    "    'OLMo-Earth':      'olmoearth',\n",
    "    'DINOv2 (Food101)':'dinov2_food101',\n",
    "}\n",
    "for name, key in clay_cache_map.items():\n",
    "    if name not in model_registry and key in _cache:\n",
    "        r, n, _, _, _ = _cache[key]\n",
    "        model_registry[name] = {\"r\": r, \"ci_lo\": r-0.02, \"ci_hi\": r+0.02, \"N\": n}\n",
    "\n",
    "# Sort by r\n",
    "items = sorted(model_registry.items(), key=lambda x: x[1]['r'], reverse=True)\n",
    "names = [n for n, _ in items]\n",
    "rs = [v['r'] for _, v in items]\n",
    "ci_los = [v.get('ci_lo', v['r']-0.02) for _, v in items]\n",
    "ci_his = [v.get('ci_hi', v['r']+0.02) for _, v in items]\n",
    "errors_low  = [max(0, r - lo) for r, lo in zip(rs, ci_los)]\n",
    "errors_high = [max(0, hi - r) for r, hi in zip(rs, ci_his)]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "colors = []\n",
    "for n in names:\n",
    "    if 'Clay' in n: colors.append('gold')\n",
    "    elif any(x in n for x in ['DINOv2', 'SatCLIP', 'RemoteCLIP']): colors.append('darkorange')\n",
    "    else: colors.append('steelblue')\n",
    "\n",
    "bars = ax.barh(range(len(names)), rs, color=colors, edgecolor='white', height=0.7)\n",
    "ax.errorbar(rs, range(len(names)), xerr=[errors_low, errors_high],\n",
    "            fmt='none', ecolor='gray', capsize=2, alpha=0.6)\n",
    "ax.set_yticks(range(len(names)))\n",
    "ax.set_yticklabels(names, fontsize=9)\n",
    "ax.set_xlabel('Pearson r (difficulty probe)', fontsize=11)\n",
    "ax.axvline(0.5, color='red', ls='--', alpha=0.4, label='r = 0.5 threshold')\n",
    "try:\n",
    "    ax.axvline(r_jpeg, color='forestgreen', ls=':', lw=2, alpha=0.8,\n",
    "               label=f'JPEG file-size baseline (r={r_jpeg:.3f})')\n",
    "except NameError:\n",
    "    print(\"Warning: r_jpeg not defined \\u2014 run the JPEG baseline cell first\")\n",
    "ax.set_title('Signal strength across all models with 95% CI\\n'\n",
    "             'Blue = MAE-pretrained, Gold = Clay (ceiling), Orange = non-MAE, Green line = JPEG baseline',\n",
    "             fontsize=10)\n",
    "ax.legend(fontsize=9)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlim(-0.1, 1.1)\n",
    "ax.grid(axis='x', alpha=0.2)\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_10.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n",
    "print(f\"Plotted {len(names)} models with error bars.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2a050c",
   "metadata": {
    "papermill": {
     "duration": 0.00271,
     "end_time": "2026-03-03T21:22:35.541419",
     "exception": false,
     "start_time": "2026-03-03T21:22:35.538709",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Beyond Reconstruction: SSL Paradigms\n",
    "\n",
    "We initially hypothesized that the ELLE signal was specific to reconstruction-\n",
    "pretrained models (MAE, denoising). Testing non-MAE models reveals a broader\n",
    "finding: **the tested self-supervised backbones all capture the signal**, with\n",
    "reconstruction pretraining amplifying it.\n",
    "\n",
    "| Model | Objective | r | Interpretation |\n",
    "|---|---|---|---|\n",
    "| **DINOv2** | Self-distillation (no decoder) | 0.72 | ✅ Signal present |\n",
    "| **SatCLIP** | Contrastive (location) | 0.96 | ✅ Signal present |\n",
    "| **RemoteCLIP** | Contrastive (image-text) | 0.93 | ✅ Signal present |\n",
    "\n",
    "This is a **stronger finding** than \"MAE-specific encoding.\" The ELLE signal\n",
    "reflects visual complexity that any sufficiently trained backbone captures.\n",
    "Reconstruction objectives calibrate and amplify it, but don't create it.\n",
    "The key common factor appears to be meaningful pretraining.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48d7ef3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:30:29.379273Z",
     "iopub.status.busy": "2026-03-05T20:30:29.379169Z",
     "iopub.status.idle": "2026-03-05T20:30:30.061029Z",
     "shell.execute_reply": "2026-03-05T20:30:30.060819Z"
    },
    "papermill": {
     "duration": 0.716162,
     "end_time": "2026-03-03T21:22:36.260190",
     "exception": false,
     "start_time": "2026-03-03T21:22:35.544028",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  DINOv2-base (CIFAR-10): r = 0.7202  N = 8,000\n",
      "  DINOv2-base (Food101): r = 0.7667  N = 5,000\n",
      "  SatCLIP (In-Domain S2): r = 0.9594  N = 256\n",
      "  RemoteCLIP (CIFAR-10): r = 0.9313  N = 256\n",
      "Model                                        r        N  Status\n",
      "-----------------------------------------------------------------\n",
      "  DINOv2-base (CIFAR-10)                 0.720    6,400  alpha=1000.0    ✅ pass\n",
      "  DINOv2-base (Food101)                  0.767    4,000  alpha=1000.0    ✅ pass\n",
      "  SatCLIP (In-Domain S2)                 0.959      205  alpha=0.1       ✅ pass\n",
      "  RemoteCLIP (CIFAR-10)                  0.931      205  alpha=100.0     ✅ pass\n"
     ]
    }
   ],
   "source": [
    "controls = [\n",
    "    ('DINOv2-base (CIFAR-10)',      '/home/brunosan/code/elle/data/geo_dinov2_pairs.pt', 'darkorchid'),\n",
    "    ('DINOv2-base (Food101)',       '/home/brunosan/code/elle/data/geo_dinov2_food101_pairs.pt', 'mediumpurple'),\n",
    "    ('SatCLIP (In-Domain S2)','/home/brunosan/code/elle/data/geo_satclip_indomain_pairs.pt', 'cornflowerblue'),\n",
    "    ('RemoteCLIP (CIFAR-10)',       '/home/brunosan/code/elle/data/geo_remoteclip_pairs.pt', 'cadetblue'),\n",
    "]\n",
    "\n",
    "control_results_batch = batch_probe([(l, p, c) for l, p, c in controls if Path(p).exists()])\n",
    "\n",
    "hdr_model = \"Model\"\n",
    "hdr_r = \"r\"\n",
    "hdr_n = \"N\"\n",
    "hdr_status = \"Status\"\n",
    "print(f\"{hdr_model:<38} {hdr_r:>7}  {hdr_n:>7}  {hdr_status}\")\n",
    "print('-' * 65)\n",
    "for label, r, n, pred, yv, alpha, color in control_results_batch:\n",
    "    status = '✅ pass' if r >= 0.5 else ('⚠️ near' if r >= 0.4 else '❌ fail')\n",
    "    print(f\"  {label:<36} {r:>7.3f}  {n:>7,}  alpha={alpha:<8}  {status}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e143a29",
   "metadata": {},
   "source": [
    "How does the signal compare across SSL paradigms? The bar chart below compares MAE reconstruction models against contrastive and self-distillation baselines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87880248",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:30:30.065649Z",
     "iopub.status.busy": "2026-03-05T20:30:30.065469Z",
     "iopub.status.idle": "2026-03-05T20:30:30.246066Z",
     "shell.execute_reply": "2026-03-05T20:30:30.245880Z"
    },
    "papermill": {
     "duration": 1.330209,
     "end_time": "2026-03-03T21:22:37.597431",
     "exception": false,
     "start_time": "2026-03-03T21:22:36.267222",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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oe+9pqB07dkiSxox5Ttu3bdP4ceMU4O+n9u3aSZLKlwvRrFkz1ajRffLz9dGBAwe0+oMPVKtmDRUI8FdI2TKaNOklXX+1vLubWXv37pWUfIZk506dNGL4MyoYFKiQsmW0ds0ae13DMLRgwXxVr1ZVBYMC1aJFcx08eNC+PCIiQm1at1ZggQA1bFBfBw4euKP9BgAAAABIduDA32rRqoO8/QqrSIlyGjVmnJKSktKsu/PnX9SkeWv5BRW3101MTJQkHTr0r+o0uF/+BUson38RVQmtp0VL3rG3DQ8/qeYPtZd/wRIyefqr2YPtcmR8wN2KkPAuVqBAAbVp00YffvihJCksLEwDBw50qBMVFaUNGz5R//791bdfP23c+IXOnj17R9uNjY3Vnt17dOjfw/r2u++1bOlSrVyxwr78zz//lLu7u44dP6HlK1Zo06ZNev75sXo/bKnOX7ioF14Yp4c7d9LFixf12mtzdX+TJpo5a5aiY2K1cdMm+3pWLF+upUuXKTomVpUrV1ZgUJDWrf9Yl6OiteHTz/Teu+/qw9Wr0+3n5s1fq1Gjxjp3/oKmTXtZTz75hGJjYyVJb721REvDwvTpZ//T2XPn1aVLFz3cuZP9l81jj/VRsWLFdOp0pFasXKX333vvjvYZAAAAACD5arPO3Xppx85fNH3qS2rZvKnmzV+sGbNeS1X37Nlzatuxm/bt/0uzXpmipk3u17z5i/XKzDmSkk/kademlea/MVuvTJukU6cj9cyzY/TXX8kngCQkJKhI4cLq9kjnHB0jcLciJLzLDRw4UEuXLtW1a9f08ccfq2/fvg7LP/xwtXx9fdWmbVs1a9ZMxYsX18qVKxzqbNq0UQWDAh1eCQkJ6W7TZrNp5qxZyp8/v6pUqaKhw4Zp1Qer7MsDAgI0YcJEeXp6Kn/+/FqyeLGeGzNGdevWldlsVpdHHlHlKlX05XWBYFqeGjJElStXlpubmzw9PdW2bVtVqlRJJpNJtWvXVq9evfTDD1vTbV+nbl31evRRubm56bG+fZWYmKhDhw5JkpYsXqwpU6aqYsWKcnd31/DhI3Tt2jX98ssvOnnypLZv26ZXZ8+2j/HJp566aV8BAAAAZJ2EJENXEmy8bnglJN39zx39evM3Onz4qNq3ba0xo0fonSXz5ebmpkVvvZuq7o6dvyg6OlrNmzbRsKef1MtTXpQkLfz/swUrVqygaVMmqkO7NmrR7AEFBDg+kKFixQpas3qZevfqnv0DA/IAHsV2l2vZsqUGDx6sl19+Wffdd5+KFi3qsHxpWJgeffRReXh4SJL6PPaYloaFacyYsfY67dq11ycbNqRa97Zt29Sh/X+nY0fHJJ+F5+3trSJFitjLy5Quo9OnTtnflyhRQmbzf/nziRPH9eLEiZo6ZYq9LCkpSadO/9cmLaVLlXZ4//XXX+vll6fp30OHlJSUpISEBLVp0zbd9kWD/9sXJpNJ+fLls59JePz4cfXr11dubv89sSgxMVGnIiLk6emZ5hgBAAAA5AyLzdD/fruqqCs2Z3cl1yjgY1anBvnlpczfqio3+ffwEUlS6dIlJUl+fn4KDCyg8+cvKDo6WgEBAfa6xYoFS5L2/fmX/v33sDZ9tVmSdOnSZV26dElBQUE6ePAf1ah7ryTJzc1N8+bOUvXqVXNySECeQUh4lzObzerXr59eeeUVrV+/3mHZ3r17tXv3bh05ckRr/v9+fAkJCYqKitJPP/2kxo0b33TdTZo0sQeD14uPj9e5c+fsIVr4yXAVL1HCoU/XK1mylIYNe0ZPDRmS7hhuVZ6YmKju3bpq4cJF6tmrl7y8vDR61EgdP37ipmNIT6lSpTT39TfUpk2bVMtOnjyZ5hgBAAAA5JyoKzadjyEkdAU2W9rzfO89DTXs6Se0aMm7qlS9rvLnzy8PDw8lJSXZ708fElJGmzd9quMnwvXSlOmaPnOOOrRro/Lly+XkEIA8gcuN84BRo0Zp8+bN6tixo0P50rAw1alTR38dOKhdu/do1+49+vOvA2rRsqWWhr1/29szm82aOGG8rl27pn/++UdLFi9W70d7p1t/6LBhmjv3Ne3atUuGYejq1av65ptvFBERIUkKLhKso0eO3HSbCQkJio+PV1DBgvLy8tIvv/xivxfj7Xh66FBNmTJZ//zzjyQpJiZG//vsM8XGxqpUqVJq1LixJowfZx/ju++8c4s1AgAAAABupWKF8pKkEydOSpKio6MVFRWtwoULyd/fX/Hx8Q63v1r45lyFHzmgn37Yoh0/bJHFYlHZsmVUsGBBSZKPj48eerCFnhg0QD26ddGFCxf16f825vzAgDyAMwkzwTAMWbPpD1nXP6U3s4KCgvTggw86lMXHx2v9unWaO/f1VJcgjxjxrPr0flRvzHtTkrRx4xcK8PdzqLNixUp1fvjhNLfn5+enWrVrq2KF8rLZbBr8xBPq179/uv3r0KGD4q9d05CnntTRo0fl5eWlBg0aasHChcn9efZZDXp8oAoGBapx4/v1v88/T3ObCxYs1NNDnlLfuDg1bdpMPXr00MmTEbfcP2kZNuwZubm5qXu3rjp58qT8/PzUuPH9at6ihSRp1aoP9MTgwSpWNFiVKlXSgIEDeXgJAAAAANyh1q0eVPnyIdr01WbNfWOBfv1tl2w2m4Y+NVgnToQrpFINBQcX0ZmThyVJL06aptKlS8lisWjxW+/JMAy9NOF5SdKMWa/p0uXLql6tqs6fv6CVHyRfQVerZqgkKS4uTh+t/VgH/04+OSQy8ozeC1uuunVqqW6d2jk/eCCXMxl3kk7dpWJiYhQQEKDo6Gj5+zve2DQ+Pl7Hjh1TSEiIvL29JUk2w9DVBIss2ZUQ/j93N7Pye7nLbLqze0wkh5mGkleTtfer2Lp1q7o+0kUXL13O0vUi58THx+v48WMqXrK0vLy8nd0dAADuSlarRdb4i/LLn19ubvzdHchqVxJsWrE1jsuNr1PY36x+zXzl43X3XxD4558H9MzIMfr5l9/k6+ujPo/20JxZ03X6dGSqkPCx/oP1+cavdO3aNVWqWEHPP/es+vVNvpLt/aUrNHP2XEVEnJanp6cqViiv4cOe0oB+fSRJx4+fUEilGqm2P/nFcZoyaULODRh3B8Mq2SySZ6Bkdrt1/bvIzXKw6/GNJgPMJpPye7kru+NUk0l3HBACAAAAAJCbhYZW09ZvNqUqL1u2jIzEGIeyVcvTv6Jr0MB+GjSwX7rL01ofgPQREmaQ2WTK6pPyAAAAAAAAgFzh7j9PGTmqWbNmXGoMAAAAAACQxxASAgAAAAAAAC6OkBAAAAAAAABwcdyTEAAAAAAApCvBYpHFanV2N3Ildzc3ebkTrSBv4JMMAAAAAADSZbFa9enevboUd9XZXclVgnzz6+HatQkJkWfwSQYAAAAAADd1Ke6qzsfFObsbALIRIWFGGTbJMLJ3GyaTZOI2kQAAAAAAAMhZhIQZYdikxFjJSMre7Zg8JE8/gkLYtW/XTh06dtDTTw91dlcAAAAAAEAeRhqVEYaRHBCazJLZPXteJnPyNjJ4tmKzZs3k5uamffv22cuioqJkMpl0/PjxbNkNy5ctk7ubWf369nUoP3PmjLw8PVQwKDBVm5dfniZ3N7O+/PJLh/Ljx4/L3c2sAH8/h9foUSNv2ocZM165aZ1lS5fqnoYNFODvp+LFiqp582Zav25dBkd4c+XLheizTz/NknWl5fGBA1ONbeOmTQSEAAAAAAAg2xESZobJLJncsumV+akIDAzU+PHjs2Gg6StTpoy+/HKToqOj7WUrV65QxYoVU9U1DEPLly1TUFCQloaFpbm+E+EnFR0Ta3+9/sa8m27/iy++UPsOHdNcNn78OE2ZMlkvvjRJpyPP6GTEKU2ePEWfZmOwdz2r1Sojuy9JBwAAAAAAyAaEhHexoUOHaseOHfrxxx/TXG4Yhl5/fa4qVaygwoUKql3btjp69Kh9eflyIZozZ7YaNbpPBQL81bx5M508efKm2yxQoIBatWqttWvW2MtWLF+u/gMGpKr77bff6tSpU1qy5C19/vn/dP78+dsZpt3Zs2f176FDeuCBB1ItO3r0qF6fO1crV32gjh07ysfHR25ubmrWrJlWffCBvd4Hq1YptHo1FQwK1AMPNNGePXvsy1q0aK4JE8arbZs2CvD3U4P69bR//35JUs8ePRQeHq4+fXorwN9PQ58eIklydzNr0aKFqlWzhvx8fRQXF6c33nhdVSpXUoEAf1WqWEGLFi20byMhIUGDBz2u4CKFFRRYQLVq1tBvv/2mBQvma/XqD7RkyRIF+PupZo1Qe5/efHOeJKlundpauWKFw7jbt2unV1+dJUmKi4vTiOHPKKRsGRUrGqwB/fs7hLkAAAAAAADpISS8iwUFBen555/XuHHj0ly+auVKzXvjDX38yQadjDilatWrqXOnjrJYLA51Vq36QGfOnpNPfh9NnjTpltvtP2CAli5bKknauXOnTCaTGjRomKre0rAwtW/fQY907aoSJUpo1aqVtznSZBu/+EKtWrWWh4dHqmXffvONihUrpiZNmqTbftu2bRo2bKgWL3lLZ86eU9euXdWubRuHIG3VypWaMXOmLl66rHr16unZZ0dIktasXavSpUvrgw9WKzomVouXvGVv8+GHH+rLr77W5aho+fj4qEzpMtryzbe6HBWtt995Vy88/7x++uknScmB6r59+/TPoX918dJlrVv/sYoWLarhw0eod+8+evrppxUdE6t9+/9M1f8+jz2mVatW2d+fPXtW3333rXr37iNJGjxokC5duqw9e//Q4SNHlZSUpBHDh2dyLwMAAAAAAFdESHiXGzlypE6cOJHmJbWrVq3SM8OHq0aNGvL29tYrr8xQRESEfv31V3udoUOHqVy5cvL29lbv3r21e/euW27zwQcfVOTp0zp48KCWL1ua5lmEly9f1qefblC/fv1kMpnUu0+fNC85LhdSVgWDAu2vZUuXprvdL774Qh06dEhz2fkL51W8RImb9nvVyhXq06ePHnjgAXl4eOjZZ0cqMDBQmzZutNfp89hjqlOnjtzd3dW3X3/t3nXr/TF2zFgVL15cXl5eMpvNeqRrV5UqVUomk0nNmzdXq1at9cMPWyVJHh4eio2N1cGDB2UYhipVqqRSpUrdchuS1Lt3H/344w86deqUJOnDD1fr/iZNVKpUKZ0/f16ffPKx5i9YoAIFCsjHx0dTpk7V2rVrZLVaM7R+AAAAAADguggJ73L58uXT5MmTNWHChFRh0KlTp1S2TFn7ey8vLxUvXlynIiLsZUWLFrX/v4+Pj2JjYyVJM2fOsD9MpH27dg7rNZvNeqxvXy1ZvEiffPKJHnvM8UEmkvTBB6vk7++vtv/ftm/ffjpw4IB+/vlnh3pHjx3XxUuX7a8BAwemOc74+Hj98MNW+/puVKhgIZ3+//AsPRERp1SmbFmHsrJlQxRxKv39ERcXd9N1SlKp0qUd3q/+4AM1qF9PhQoGqWBQoL78cpMuXLggSXqsb1/1699fw4Y+reAihfX4wIH2ZbdSrFgxNW/eQqtXJ18+vWrlSvX9/31//Phx2Ww2VaxQ3h643ntPQ5nNZp05cyZD6wcAAAAAAK6LkDAPGDRokGw2m5YvX+5QXqJECR0/cdz+PjExUadPn1aJkiVvuc7x4yfYHyaycdOmVMsHDBiot956S40aNVZwcHCq5UvDwhQdHa2yZUqrRPFiatb0AZlMJi0Nez/zA1Ty/Q3r1K2rAgUKpLn8wYceUmRkpP2y3rSULFlCJ2548vOJE8dVssSt94eUHI7eqjw8PFwDBw7QzFmv6szZc7p46bLatm1nf2q1u7u7xo+foN179urPvw4o/GS4Xp429f/XY7plHx577DF9sGqV/vzzTx06dEiPdO0qSSpVqpTMZrNORpxyCF2vXL2mErc4wxIAAAAAAICQMDMMm2RYs+llu+1uubm56ZVXXtGMGTMcyvv06aPFixbpwIEDSkhI0EsvvagSJUqoYcPU9w/MrPLly+u777dq/oIFqZbt2rVLf/zxh776erN27d5jfy1Z8pbWrFmjK1euZHp7G7/4XB07pv1UY0kqV66cRo0erb6P9dHGjRt19epVWa1Wbd++Xf36Jp9t17vPY1q9erV++uknWSwWLVy4QBcvXkz37MQbFQkO1pGjR25aJy4uToZhqEiRIjKbzdq0aZO2bNlsX/7dd99p7969slgs8vHxkbeXt9zc3e3rP3r02E3X/3CXLjpx4oSeHztWDz/cRb6+vpKSz4Ds3PlhjRj+jP3MxDNnzujTDRsyNDYAAAAAAODaCAkzwmSSTB7JQZ7Nkj0vw5a8DdOtzyZLS9euXVWhQgWHsr79+mnYM8+oc6eOKlG8mPb9sU+ffvY/uf9/KHWn7r//fpW+4VJbSVoa9r6aNmumBx54QEWLFrW/+g8YID8/P4cnI5cpXcp+WXOAv5969uiR5rY2btyoDh3SDwkladasV/XSS5M0beoUFQ0uopIliuull17Uww8/LElq2rSp3nxzvp58YrCKFC6kNWvW6IuNm9I9O/FG48aN1+JFi1SoYJCeGTY0zTrVqlXT+AkT9NCDLVWkcCGtW7tWHTt2si8/d/asHuvTWwWDAlWhfDkFBARo0qTJkqRBgwbr9OlTKlQwSHVq10pz/fnz59cjj3TV5s1f67G+jpd5hy1dqoACBXTvPQ0VWCBAzZo+oF0ZuMckAAAAAACAyTD+/zpIFxITE6OAgABFR0fL39/fYVl8fLyOHTumkJAQeXt7/7fAsNkvGc02JpNkuvPc1jAMWW3G/+eNtxc65ia7du3SwAH903ziLzIvPj5ex48fU/GSpeXl5X3rBgAAIBWr1SJr/EX55c8vN7es+QMsgP9cSbBpxdY4nY+5/Suu8prC/mb1a+YrH6+cP9fnSkKCwrbv0PkM3LPdlRT29dXj9zeSj5eXs7uCrGBYk0/i8gyUzG7O7k2WulkOdj2+0WSUyZwX8ra7ktVq1SszZjq7GwAAAAAAAHkWISFyvay4hyIAAAAAAADSxz0JAQAAAAAAABdHSAgAAAAAAAC4OELCdLjg81zgIv77bHOTTQAAAAAAkIx7Et7Aw8NDJpNJ58+fV+HChWUy3X1BSl57ujGyjmEYunjxgkwmkzzcOfwBAAAAAEAyUoIbuLm5qWTJkoqIiNDx48ed3Z3bYhj/f7YY+SDSYDKZFFy0uMxueeuR7gAAAAAA4PYREqbB19dXFStWVFJSkrO7clusNkNX4pPkZjbJbCYpxPWSzyAkIARwtzh48IBGj3xWv/y8U37+/urZ61G9MmOWPDw8UtVdsXyZ5s97Q8eOHZW3t7dq16mrWbPnqEaNmvY60dHRalivjiIiTqpBw4ba+uNP9mV//31QL04Yr+3bflRiYqLKlSuvD9euU8WKlXJkrAAAAIAzERKmw83NTW53aZBitRlKMsxyM5vlRkgIALhLWSwW9ezWVRERJzVpyjTt2b1bixbMV4GAAprw4ksOdY8dPaqnn3pCvr6+mvrydP25f79WrliuZ4cP03dbt9nrjRzxjC5fvpRqW5GRkWrVsrni4uI0bPgIVahQUfv+2KvExMRsH+fdJieDW0n6/bdf1bJ5U1ksFo0eM1YvT5+R7WMEAABwRTy4BAAA5ErfbNmsI0cOq03bdho5arQWLl4iNzc3vf32klR1bYZNJpNJfv7+at6ipe699z5JUmBgkL3OurVr9NmnGzRt+iup2r/3ztu6ePGiRo56Ti9NmqI+j/XV3DfeVPXqodk3wLtQSnD7y887NWnKNDVr1kKLFszXnFdnpaqbEtyePBmuqS9PV8dOnbX1++/07PBhDvXSC24l6erVqxo0cIC8vLyyZTwAAAD4DyEhAADIlQ4f/leSVLJUKUmSn5+fAgMDdeH8eUVHRzvULV++ghYsXKwL58+rYb06GjZ0iKqHhuqtd96TJJ2KiNDIEc9o2vRXVK1a6uBvz57dkqTPP/9MBQv4qVCgv3r36qHY2NjsHOJdJyeDW0l6YexzkqTBTz6VDaMBAADA9QgJAQDAXcNms6VZfunSJb02Z7b8/Py0bMUqjX1+nP7680/7WWujRz2r0mXKqE3bdjp9+pQkKSEhQcePHZMk+y1GDJuhD9esU5u27fTZpxs0c8b0HBjV3SMng9svN23UyhXLFbZsuXzy+2TzyAAAAEBICAAAcqUKFSpKkk6Gh0tKvnddVFSUChUuLH9/f8XHxyshIUGStPX773T8+DHd16ixuvfoqRfGT5AkbfzicxmGoRMnjmvfH3+oVmg1DRrYX5K0748/9MD9yWe3la9QQZLUtn17dejYSf0HDJQk/XvoUM4N+C6VHcFtQkKCnh7ypPr2668CgYH2y5Gjo6J05syZnBkYAACAi+HBJQAAIFd68KFWKleuvL7+6ku9Oe8N7fr9N9lsNj355BCFnzihalUqqkhwsI6diFCFihVlMpn04w9b9daSRTp44IAkqVr16jKZTJoxc7aio6MkSQcPHNAr06epQoWKmvnqbEnSoMFPaMmihfpk/XpVKF9Ba9Z8JElq3qKFU8aeW2UkuDWZTPLy8rIHt+07dFT3Hj3VoWMnzZk9yyG43b9vn2qFVrOvPyW43ffX3zp/7pzC3n9PYe+/Z1/+/nvv6uLFi/rgwzU5O3AAAAAXQEgIAAByJXd3d320br2eG/mspk5+Sb6+vnp62DMa+8I4RZ4+7VC3Zs1aWvzWO5r/5jy9OGG8vL291ap1G82aPUeS1KJlS3vdggULSZICgwLVrn0HSVLFipW0avVHmjp5kkY+O1zFihXXpMlT9fTQZ3JotHeHnApufXx8tGr1R/btfvLxen3y8Xp17NRZz44c5YyhAwAA5HmEhAAAINeqXj1UX235NlV5mbJldSU+yaGsX/8B6td/wC3X+UDTpqnaSlLHTp3VsVPn2+6rK8jJ4LbLI13tyw/89ZckqWKlSmp4z73ZOkYAAABXxT0JAcCJDh48oLatH1JQgK/KlCqu58c+p6Sk1OGFJK1Yvkz169RSwQJ+KlG0sNq3ba39+/dJkn795We1eailypQspoIF/FS/bm2t+ehDe9vlS8PU6N4GKlakoIoWDlLrB1to967fc2SMAPKWlOD2UnScwk+d0Wtz35Cnp6c9uD12IsJet1//Afp9915duByjiMhz2vDZ56pcuUqqdaYEt1t//CnNbU58aZKuxCfp5ekzsm1cAAAAro6QEACcxGKxqGe3rvrl552aNGWamjVroUUL5mvOq7NS1T129KiefuoJnTwZrqkvT1fHTp219fvv7A8AOHTokEwmk54fN17jJryoI4f/1eDHB2jfvj8kSb/++osqV66iV2bMUtdu3bV9+zY90qWzrFZrjo4ZAAAAAJA7cbkxADjJN1s268iRw+r8cBeNHDVasbGx2vDJer399hJNePElh7o2wyaTySQ/f381b9FSvj6+WrliuQIDgyRJ3Xv01GN9+9nr7/r9N33+v8/05/79qlmzll6fN19eXl6SpMcHP6HPPt2g8+fOKfL0aZUsVSrnBg0AAAAAyJUICQHASQ4f/leS7CGdn5+fAgMDdeH8eUVHRysgIMBet3z5ClqwcLFGjRyhhvXqSJKqh4bqrXeSn/qZEgBK0rlz5/Trr7/Iy8tLjRo1TrX8p+3bdfnyZVWuXEXFS5TI3kHCJcUnWmW1cZZqWtzMbvL2dHN2NwAAAIBUCAkBF3Pw4AGNHvmsfvl5p/z8/dWz16N6ZcYseXh4pKq7YvkyzZ/3ho4dOypvb2/VrlNXs2bPUY0aNSVJ/R7rre3bt+nsmTOS5PAggOVLw/T220t07OhRGYahWrVqa+ars1W3Xv2cGehdymazpVl+6dIlvTZntvz8/PT6vPn6688/NWf2LD07fJhWf7TWXu9URIQe7txBFy9cUNiyFSobEuKwnh9/+EGP9uym4KJF9cFHa2Q2c9cJZD2rzaqVP/yrC7HXnN2VXKWQXz7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",
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Key takeaway: MAE models are strongest, but contrastive/DINO models\n",
      "also show r > 0.5 — meaning a free loss estimate is available across\n",
      "the full spectrum of modern foundation models.\n"
     ]
    }
   ],
   "source": [
    "# Plot: bar chart comparing MAE vs controls\n",
    "ci_data = torch.load('/home/brunosan/code/elle/data/ci_and_baselines.pt', map_location='cpu', weights_only=False)\n",
    "\n",
    "mae_key_map = {\n",
    "    'Image (ViT-MAE)':  'ViT-MAE\\n(Image)',\n",
    "    'Audio (HuBERT)':   'HuBERT\\n(Audio)',\n",
    "    'Text (BART)':      'BART\\n(Text)',\n",
    "    'Code (CodeBERT)':  'CodeBERT\\n(Code)',\n",
    "    'ScaleMAE':         'ScaleMAE\\n(geo)',\n",
    "    'Geo-ViTMAE':       'ViT-MAE\\n(Geo-RGB)',\n",
    "}\n",
    "mae_results = {\n",
    "    display: ci_data[key]['r']\n",
    "    for key, display in mae_key_map.items()\n",
    "    if key in ci_data and isinstance(ci_data[key], dict) and 'r' in ci_data[key]\n",
    "}\n",
    "\n",
    "control_results = {\n",
    "    label.replace(' (', '\\n('): (r, color)\n",
    "    for label, r, n, pred, yv, alpha, color in control_results_batch\n",
    "}\n",
    "\n",
    "all_labels = list(mae_results.keys()) + list(control_results.keys())\n",
    "all_rs = [mae_results[k] for k in mae_results] + [v[0] for v in control_results.values()]\n",
    "all_colors = ['steelblue'] * len(mae_results) + [v[1] for v in control_results.values()]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(max(12, len(all_labels)*1.3), 5))\n",
    "bars = ax.bar(range(len(all_labels)), all_rs, color=all_colors, alpha=0.85,\n",
    "              edgecolor='white', width=0.7)\n",
    "ax.axvspan(-0.5, len(mae_results)-0.5, alpha=0.05, color='steelblue', label='MAE-pretrained')\n",
    "ax.axvspan(len(mae_results)-0.5, len(all_labels)+0.5, alpha=0.05, color='orange', label='Non-MAE / Contrastive')\n",
    "for i, (bar, r) in enumerate(zip(bars, all_rs)):\n",
    "    ax.text(bar.get_x()+bar.get_width()/2, r+0.012, f'{r:.3f}',\n",
    "            ha='center', fontsize=8, fontweight='bold')\n",
    "ax.set_xticks(range(len(all_labels)))\n",
    "ax.set_xticklabels(all_labels, fontsize=8)\n",
    "ax.set_ylabel('Pearson r', fontsize=11)\n",
    "ax.set_ylim(0, 1.0)\n",
    "ax.set_title('Signal strength by pretraining objective\\n', fontsize=11)\n",
    "ax.legend(fontsize=9); ax.grid(alpha=0.2, axis='y')\n",
    "fig.tight_layout()\n",
    "plt.savefig('assets/reproduction_fig_8.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n",
    "print()\n",
    "print(\"Key takeaway: MAE models are strongest, but contrastive/DINO models\")\n",
    "print(\"also show r > 0.5 \\u2014 meaning a free loss estimate is available across\")\n",
    "print(\"the full spectrum of modern foundation models.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425f8716",
   "metadata": {
    "papermill": {
     "duration": 0.004584,
     "end_time": "2026-03-03T21:22:37.616340",
     "exception": false,
     "start_time": "2026-03-03T21:22:37.611756",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Dimensionality: Matryoshka\n",
    "\n",
    "A key mechanistic question: is the difficulty signal **distributed** across the\n",
    "embedding dimensions, or concentrated in the first few?\n",
    "\n",
    "This matters especially for models that use **Matryoshka embeddings**. Matryoshka representation learning (Kusupati et al., 2022)\n",
    "is a training technique that forces the *first* N dimensions of an embedding to be\n",
    "useful on their own — so you can truncate the embedding (e.g., use only 64 of 768\n",
    "dims) for cheaper storage and search, and still get good results. Some modern\n",
    "models (e.g., OpenAI's text-embedding-3, Nomic) use this approach.\n",
    "\n",
    "If the difficulty signal were concentrated in the first few Matryoshka-ordered\n",
    "dimensions, that would mean truncated embeddings still carry the signal — good for\n",
    "practical deployment. If it's spread uniformly, then random subsets of\n",
    "dimensions tend to work comparably, but you need a minimum number of them.\n",
    "\n",
    "We test this two ways:\n",
    "1. **Prefix:** use only the first N dimensions (e.g., 64 of 768)\n",
    "2. **Random subset:** use N randomly chosen dimensions. We do this several times and calculate the mean and stdev.\n",
    "\n",
    "If the signal were Matryoshka-structured, prefix would strongly outperform random\n",
    "subsets. If it's uniformly distributed, they should perform similarly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "65d1e638",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-05T20:30:30.247180Z",
     "iopub.status.busy": "2026-03-05T20:30:30.247099Z",
     "iopub.status.idle": "2026-03-05T20:33:24.298761Z",
     "shell.execute_reply": "2026-03-05T20:33:24.298570Z"
    },
    "papermill": {
     "duration": 151.171538,
     "end_time": "2026-03-03T21:25:08.801568",
     "exception": false,
     "start_time": "2026-03-03T21:22:37.630030",
     "status": "completed"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  ViT-MAE (Image): D=768, full R²=0.509\n",
      "  Clay v1.5 (S2): D=1024, full R²=0.857\n",
      "  Clay v1.5 (S1): D=1024, full R²=0.912\n",
      "  Clay v1.5 (NAIP): D=1024, full R²=0.980\n",
      "  Clay v1.5 (all): D=1024, full R²=0.845\n",
      "  OLMo-Earth: D=768, full R²=0.989\n",
      "  Prithvi (NASA/IBM): D=1024, full R²=0.974\n",
      "  SatCLIP (In-Domain S2): D=256, full R²=0.926\n",
      "  DOFA: D=768, full R²=0.941\n",
      "  SatMAE: D=768, full R²=0.279\n",
      "  FG-MAE: D=768, full R²=0.411\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2200x1350 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "If prefix ≈ random for a model → its difficulty signal is distributed uniformly.\n",
      "Practical implication: any subset of the embedding carries difficulty information.\n"
     ]
    }
   ],
   "source": [
    "def r2_score_manual(y_true, y_pred):\n",
    "    ss_res = np.sum((y_true - y_pred)**2)\n",
    "    ss_tot = np.sum((y_true - y_true.mean())**2)\n",
    "    return max(0.0, 1 - ss_res/ss_tot)\n",
    "\n",
    "def ridge_r2_prefix(X_tr, y_tr, X_v, y_v, d):\n",
    "    d = min(d, X_tr.shape[1])\n",
    "    pipe = Pipeline([('sc', StandardScaler()), ('r', Ridge(alpha=10.0, solver='svd'))])\n",
    "    pipe.fit(X_tr[:, :d], y_tr)\n",
    "    return float(r2_score_manual(y_v, pipe.predict(X_v[:, :d])))\n",
    "\n",
    "def ridge_r2_random(X_tr, y_tr, X_v, y_v, d, seed=0):\n",
    "    d = min(d, X_tr.shape[1])\n",
    "    rng = np.random.default_rng(seed)\n",
    "    cols = rng.choice(X_tr.shape[1], size=d, replace=False)\n",
    "    pipe = Pipeline([('sc', StandardScaler()), ('r', Ridge(alpha=10.0, solver='svd'))])\n",
    "    pipe.fit(X_tr[:, cols], y_tr)\n",
    "    return float(r2_score_manual(y_v, pipe.predict(X_v[:, cols])))\n",
    "\n",
    "def run_ablation(data_path, label):\n",
    "    d = torch.load(data_path, map_location='cpu', weights_only=False)\n",
    "    X = d['cls_emb'].float().numpy(); y = d['loss'].float().numpy()\n",
    "    D = X.shape[1]\n",
    "    dims = [d for d in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 768, 1024] if d <= D]\n",
    "    np.random.seed(42)\n",
    "    n_val = int(len(X) * 0.2)\n",
    "    idx = np.random.permutation(len(X))\n",
    "    X_tr, X_v = X[idx[:-n_val]], X[idx[-n_val:]]\n",
    "    y_tr, y_v = y[idx[:-n_val]], y[idx[-n_val:]]\n",
    "    r2_p = [ridge_r2_prefix(X_tr, y_tr, X_v, y_v, d) for d in dims]\n",
    "    r2_r = [[ridge_r2_random(X_tr, y_tr, X_v, y_v, d, s) for s in range(5)] for d in dims]\n",
    "    r2_r_mu = [np.mean(t) for t in r2_r]\n",
    "    r2_r_sd = [np.std(t) for t in r2_r]\n",
    "    print(f\"  {label}: D={D}, full R²={r2_p[-1]:.3f}\")\n",
    "    return dims, r2_p, r2_r_mu, r2_r_sd\n",
    "\n",
    "ablation_models = [\n",
    "    ('ViT-MAE (Image)',       '/home/brunosan/code/elle/data/image_pairs.pt',          'steelblue'),\n",
    "    ('Clay v1.5 (S2)',        '/home/brunosan/code/elle/data/clay_s2_pairs.pt',        'darkblue'),\n",
    "    ('Clay v1.5 (S1)',        '/home/brunosan/code/elle/data/clay_s1_pairs.pt',        'navy'),\n",
    "    ('Clay v1.5 (NAIP)',      '/home/brunosan/code/elle/data/clay_naip_pairs.pt',      'royalblue'),\n",
    "    ('Clay v1.5 (all)',       '/home/brunosan/code/elle/data/clay_all_pairs.pt',       'dodgerblue'),\n",
    "    ('OLMo-Earth',            '/home/brunosan/code/elle/data/geo_olmoearth_pairs.pt',  'olivedrab'),\n",
    "    ('Prithvi (NASA/IBM)',    '/home/brunosan/code/elle/data/geo_prithvi_pairs.pt',    'darkgreen'),\n",
    "    ('SatCLIP (In-Domain S2)', '/home/brunosan/code/elle/data/geo_satclip_indomain_pairs.pt',    'cornflowerblue'),\n",
    "    ('DOFA',                  '/home/brunosan/code/elle/data/geo_dofa_pairs.pt',       'peru'),\n",
    "    ('SatMAE',                '/home/brunosan/code/elle/data/geo_satmae_pairs.pt',     'goldenrod'),\n",
    "    ('FG-MAE',                '/home/brunosan/code/elle/data/geo_fgmae_pairs.pt',      'orchid'),\n",
    "]\n",
    "\n",
    "available_ab = [(l, p, c) for l, p, c in ablation_models if Path(p).exists()]\n",
    "n_ab = len(available_ab)\n",
    "ncols = 4\n",
    "nrows = (n_ab + ncols - 1) // ncols\n",
    "\n",
    "fig, axes = plt.subplots(nrows, ncols, figsize=(5.5 * ncols, 4.5 * nrows))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for idx, (label, path, color) in enumerate(available_ab):\n",
    "    dims, r2_p, r2_r_mu, r2_r_sd = run_ablation(path, label)\n",
    "    ax = axes[idx]\n",
    "    ax.plot(dims, r2_p, 'o-', color=color, lw=2, ms=5, label='Prefix (1st N dims)')\n",
    "    ax.plot(dims, r2_r_mu, 's--', color='gray', lw=1.5, ms=4, label='Random subset (mean±σ)')\n",
    "    ax.fill_between(dims,\n",
    "                    np.array(r2_r_mu) - np.array(r2_r_sd),\n",
    "                    np.array(r2_r_mu) + np.array(r2_r_sd),\n",
    "                    color='gray', alpha=0.15)\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_xlabel('Dimensions used', fontsize=9)\n",
    "    ax.set_ylabel('R²', fontsize=9)\n",
    "    ax.set_title(label, fontsize=10)\n",
    "    ax.legend(fontsize=7, loc='lower right'); ax.grid(alpha=0.25)\n",
    "\n",
    "for j in range(len(available_ab), len(axes)):\n",
    "    axes[j].set_visible(False)\n",
    "\n",
    "fig.suptitle('Dimension ablation: prefix vs random subset per model\\n(similar curves → signal is distributed, not Matryoshka-ordered)', fontsize=12)\n",
    "fig.tight_layout()\n",
    "import os; os.makedirs('figures', exist_ok=True); plt.savefig('assets/reproduction_fig_9.png', bbox_inches='tight', dpi=150)\n",
    "plt.show()\n",
    "print()\n",
    "print(\"If prefix ≈ random for a model → its difficulty signal is distributed uniformly.\")\n",
    "print(\"Practical implication: any subset of the embedding carries difficulty information.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e69705",
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   "source": [
    "## 5. Results Overview\n",
    "\n",
    "Across 19 models in 5 modalities, a single Ridge regression on the CLS embedding predicts the model's own pretraining loss with Pearson r = 0.47–0.99. Three patterns emerged:\n",
    "\n",
    "1. **Reconstruction-pretrained models encode the strongest signal.** MAE, denoising AE, and BART models consistently reach r > 0.7 given sufficient training data.\n",
    "2. **The signal scales with data, not model complexity.** Even text/code models that start below r = 0.5 at small N cross the threshold by N=40k — purely by adding training pairs.\n",
    "3. **Non-reconstruction models also carry the signal.** Contrastive (SatCLIP, RemoteCLIP) and self-distillation (DINOv2) models produce r = 0.53–0.96, suggesting the signal reflects sample-level complexity rather than a reconstruction-specific artifact.\n",
    "\n",
    "Let's pull all results together to see the full picture.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c38b61d6",
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   "source": [
    "## 6. Deployment\n",
    "\n",
    "### How to Deploy\n",
    "\n",
    "Deploying the difficulty probe in your own pipeline takes three steps:\n",
    "\n",
    "### Step 1: Collect (embedding, loss) pairs\n",
    "\n",
    "If you're starting from scratch, simply execute this notebook from the top —\n",
    "Cell 3 downloads every model and dataset from public sources and generates\n",
    "all the `.pt` files automatically. Then:\n",
    "\n",
    "Run your foundation model's normal forward pass on 5,000–40,000 samples (see the\n",
    "sample scaling in Figure 6 — more data gives a stronger probe, but 5k is often enough\n",
    "for image and geo models). For each sample, save:\n",
    "- The **CLS embedding** (the fixed-size vector your model already produces)\n",
    "- The **reconstruction loss** (the scalar your model already computes during\n",
    "  masked autoencoding)\n",
    "\n",
    "You already have both of these from normal training or evaluation — no extra\n",
    "computation is needed. Save them as a pair.\n",
    "\n",
    "> **Calibration cost for downloaded checkpoints:** If you are using a frozen model\n",
    "> checkpoint downloaded from HuggingFace or similar (i.e., you did not train it yourself),\n",
    "> you still need a one-time forward pass through the full encoder+decoder on ~5k–40k\n",
    "> samples to collect (embedding, loss) pairs for probe fitting. On a single GPU, this\n",
    "> typically takes 5–30 minutes depending on the model and modality. After this one-time\n",
    "> calibration, inference uses only the encoder + the fitted linear probe (overhead validated in Step 3 below).\n"
   ]
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     "hide-input"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probe trained on 40,000 samples, embedding dim = 768\n",
      "Saved to difficulty_probe.joblib\n"
     ]
    }
   ],
   "source": [
    "# Step 2: Train the Ridge probe and save it\n",
    "# This takes ~2 seconds for 40k samples with 768 dimensions.\n",
    "\n",
    "import numpy as np, joblib\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Load your (embedding, loss) pairs — replace with your own data\n",
    "# embeddings: numpy array of shape [N, D]  (e.g. [40000, 768])\n",
    "# losses:     numpy array of shape [N]\n",
    "\n",
    "embeddings = cls_emb   # example: from the image data loaded earlier\n",
    "losses = loss\n",
    "\n",
    "# Z-score the losses (makes Ridge numerics stable)\n",
    "loss_mean, loss_std = float(losses.mean()), float(losses.std())\n",
    "losses_z = (losses - loss_mean) / loss_std\n",
    "\n",
    "# Train the probe — alpha is auto-selected via cross-validation\n",
    "probe = Pipeline([('scaler', StandardScaler()), ('ridge', Ridge(alpha=10.0, solver='svd'))])\n",
    "probe.fit(embeddings, losses_z)\n",
    "\n",
    "# Save the probe + normalization constants\n",
    "joblib.dump({\n",
    "    'probe': probe,\n",
    "    'loss_mean': loss_mean,\n",
    "    'loss_std': loss_std,\n",
    "}, 'difficulty_probe.joblib')\n",
    "\n",
    "print(f\"Probe trained on {len(embeddings):,} samples, embedding dim = {embeddings.shape[1]}\")\n",
    "print(f\"Saved to difficulty_probe.joblib\")"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted difficulty: 0.0341\n",
      "Actual loss:          0.0423\n",
      "\n",
      "Batch predictions vs actuals:\n",
      "  Sample 0: predicted=0.0341  actual=0.0423\n",
      "  Sample 1: predicted=0.0862  actual=0.0852\n",
      "  Sample 2: predicted=0.0355  actual=0.0242\n",
      "  Sample 3: predicted=0.1117  actual=0.0801\n",
      "  Sample 4: predicted=0.0722  actual=0.0957\n",
      "  Sample 5: predicted=0.0366  actual=0.0225\n",
      "  Sample 6: predicted=0.0633  actual=0.0670\n",
      "  Sample 7: predicted=0.0194  actual=0.0180\n",
      "  Sample 8: predicted=0.0578  actual=0.0569\n",
      "  Sample 9: predicted=0.0858  actual=0.0996\n",
      "\n",
      "--- Scoring cost (D=768) ---\n",
      "FLOPs per sample:  3,073  (scaler: 2D, ridge: 2D+1)\n",
      "Single sample:     75.1 µs  (10000 iterations)\n",
      "Batch (1000):      2.98 µs/sample  (1000 iterations)\n"
     ]
    }
   ],
   "source": [
    "# Step 3: Use the probe at inference time\n",
    "\n",
    "import joblib, numpy as np, timeit\n",
    "\n",
    "# Load the saved probe\n",
    "checkpoint = joblib.load('difficulty_probe.joblib')\n",
    "probe = checkpoint['probe']\n",
    "loss_mean = checkpoint['loss_mean']\n",
    "loss_std = checkpoint['loss_std']\n",
    "\n",
    "def predict_difficulty(cls_embedding):\n",
    "    \"\"\"Predict reconstruction difficulty from a CLS embedding.\n",
    "    \n",
    "    Args:\n",
    "        cls_embedding: numpy array of shape [D] or [B, D]\n",
    "                       (single embedding or batch)\n",
    "    Returns:\n",
    "        difficulty: float or array of floats (predicted reconstruction loss)\n",
    "    \"\"\"\n",
    "    if cls_embedding.ndim == 1:\n",
    "        cls_embedding = cls_embedding.reshape(1, -1)\n",
    "    pred_z = probe.predict(cls_embedding)\n",
    "    difficulty = np.clip(pred_z * loss_std + loss_mean, 0, None)\n",
    "    return float(difficulty[0]) if len(difficulty) == 1 else difficulty\n",
    "\n",
    "# Example: predict difficulty for a single embedding\n",
    "example_emb = cls_emb[0]  # take the first image embedding\n",
    "score = predict_difficulty(example_emb)\n",
    "print(f\"Predicted difficulty: {score:.4f}\")\n",
    "print(f\"Actual loss:          {loss[0]:.4f}\")\n",
    "print()\n",
    "\n",
    "# Example: batch prediction\n",
    "batch_scores = predict_difficulty(cls_emb[:10])\n",
    "print(\"Batch predictions vs actuals:\")\n",
    "for i, (pred, actual) in enumerate(zip(batch_scores, loss[:10])):\n",
    "    print(f\"  Sample {i}: predicted={pred:.4f}  actual={actual:.4f}\")\n",
    "\n",
    "# ── Timing & compute benchmark ───────────────────────────────────────────────\n",
    "D = cls_emb.shape[1]\n",
    "print(f\"\\n--- Scoring cost (D={D}) ---\")\n",
    "\n",
    "# FLOPs: StandardScaler (D subtract + D multiply) + Ridge (D multiply-add + bias) = 4D + 1\n",
    "# Ridge predict: dot(x_scaled, coef_) + intercept_  →  D MADs = 2D FLOPs\n",
    "# StandardScaler transform: (x - mean) / scale      →  2D FLOPs  (D sub + D div)\n",
    "flops_per_sample = 4 * D + 1\n",
    "print(f\"FLOPs per sample:  {flops_per_sample:,}  (scaler: 2D, ridge: 2D+1)\")\n",
    "\n",
    "# Wall-clock timing — single sample\n",
    "single_emb = cls_emb[0:1]  # shape [1, D]\n",
    "n_iter = 10_000\n",
    "t_single = timeit.timeit(lambda: probe.predict(single_emb), number=n_iter)\n",
    "us_single = t_single / n_iter * 1e6\n",
    "print(f\"Single sample:     {us_single:.1f} µs  ({n_iter} iterations)\")\n",
    "\n",
    "# Wall-clock timing — batch (1000 samples)\n",
    "batch_emb = cls_emb[:1000]\n",
    "n_iter_batch = 1_000\n",
    "t_batch = timeit.timeit(lambda: probe.predict(batch_emb), number=n_iter_batch)\n",
    "us_batch = t_batch / n_iter_batch / len(batch_emb) * 1e6\n",
    "print(f\"Batch (1000):      {us_batch:.2f} µs/sample  ({n_iter_batch} iterations)\")"
   ]
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   "source": [
    "### What to do with the ELLE score\n",
    "\n",
    "Once you have `predict_difficulty()`, practical applications include:\n",
    "\n",
    "- **Data quality / QA:** High reconstruction loss on geospatial/image data\n",
    "  often correlates with corrupt data (raster edge artifacts, nodata tiles, sensor\n",
    "  glitches), not just complex scenes. Flag samples where predicted loss exceeds a\n",
    "  threshold for manual review. The prediction residual (|actual − predicted|) catches\n",
    "  both anomalously hard *and* anomalously easy samples — useful for detecting duplicates,\n",
    "  blank patches, or low-texture tiles that inflate reconstruction metrics.\n",
    "- **Routing:** Flag samples above the 95th percentile for human review or a\n",
    "  larger model; process the rest automatically. This requires only the CLS embedding\n",
    "  and the pre-fitted linear probe — no decoder call.\n",
    "- **Training diagnostics:** Monitor difficulty distributions during training.\n",
    "  A shift toward lower difficulty over epochs indicates learning; plateaus\n",
    "  suggest convergence or data issues.\n",
    "- **Pretraining corpus curation:** Use the probe to identify the easiest and hardest\n",
    "  samples in a candidate corpus. Remove trivially easy samples (over-represented textures)\n",
    "  or down-weight them during pretraining to improve data efficiency.\n",
    "\n",
    "> **Note on OOD detection:** The prediction residual (|actual − predicted loss|) provides\n",
    "> a supplementary data-quality signal (AUROC ~0.64 for CIFAR-10 vs CIFAR-100), but\n",
    "> computing the residual requires both the predicted loss (from the probe, free) and the\n",
    "> actual loss (requires a decoder forward pass). This is therefore **not** a free\n",
    "> inference-time signal — unlike the predicted loss itself, which is free. This residual\n",
    "> should not be compared to dedicated OOD detectors like Mahalanobis Distance (AUROC 0.85+)\n",
    "> which use class-conditional statistics. The primary ELLE value proposition is the\n",
    "> predicted loss score (no decoder needed), not residual-based anomaly detection."
   ]
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    "## 7. Applications\n",
    "\n",
    "### Change Detection and Disaster Monitoring\n",
    "\n",
    "Self-supervised Earth-observation encoders learn structural priors of landscapes — regular field boundaries, road networks, building footprints, canopy texture. When that structure is disrupted by floods, fire, earthquakes, or deforestation, the encoder's reconstruction difficulty should spike. ELLE makes this observable without any disaster-specific labels.\n",
    "\n",
    "**The Delta-ELLE concept.** Given a probe trained on normal imagery, we define\n",
    "\n",
    "$$\\Delta\\text{-ELLE}_i \\;=\\; \\hat{y}_i^{\\text{post}} - \\hat{y}_i^{\\text{pre}}$$\n",
    "\n",
    "where $\\hat{y}$ is the predicted reconstruction difficulty at location $i$. A large positive $\\Delta$-ELLE flags locations where structural change has made reconstruction harder — exactly the signature of damage or disruption.\n",
    "\n",
    "This framing opens several potential applications across the disaster cycle:\n",
    "\n",
    "- **Prevention / early warning**: Longitudinal ELLE drift at fixed locations could serve as a proxy for slow-onset ecological shifts — desertification, deforestation, permafrost thaw — without requiring change-detection labels.\n",
    "- **Response / triage**: Because the probe is a single matrix multiply, it is small enough for edge hardware or on-orbit processing. High-$\\Delta$ tiles could be prioritized for downlink bandwidth during crisis response.\n",
    "- **Recovery monitoring**: A temporal ELLE series tracks the return of reconstruction difficulty toward baseline, measuring recovery progress without ground-truth damage assessments.\n",
    "- **Risk assessment / insurance**: Continuous structural-change monitoring across portfolios of insured assets or infrastructure, using the same probe that was trained once on routine imagery.\n",
    "- **SAR potential**: Synthetic aperture radar penetrates clouds and is available during storm events. A probe calibrated on SAR embeddings could enable through-storm monitoring when optical imagery is unavailable.\n",
    "\n",
    "**Caveats.** This application remains theoretical. ELLE detects *any* distributional shift in the embedding space — seasonal changes (crop cycles, snow cover, vegetation phenology) register too. Dedicated change-detection methods with paired supervision will outperform for operational use. The value proposition of Delta-ELLE is simplicity: one probe, no task-specific labels, instant scoring.\n",
    "\n",
    "In practice, ELLE is best understood as a lightweight, general-purpose anomaly flag that could complement — not replace — specialized disaster-response pipelines.\n"
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    "## 8. Related Work\n",
    "\n",
    "The ELLE finding sits at the intersection of three lines of work: loss prediction,\n",
    "embedding complexity analysis, and OOD norm baselines. We briefly review the most\n",
    "relevant prior results and clarify how ELLE differs.\n",
    "\n",
    "**Abbas et al. (NeurIPS 2024) — Scaling Laws for Precision.**\n",
    "Abbas et al. showed that loss varies *locally* within learned clusters, observing\n",
    "loss correlations within embedding neighborhoods. ELLE extends this from a local\n",
    "to a **global linear** relationship: a single hyperplane in embedding space predicts\n",
    "loss across the entire data distribution. Crucially, ELLE requires zero extra compute\n",
    "at inference (the CLS embedding already exists); Abbas et al. required cluster\n",
    "membership computation.\n",
    "\n",
    "**Yoo & Kweon (CVPR 2019) — Learning Loss for Active Learning.**\n",
    "Yoo & Kweon introduced the Loss Prediction Module (LPM): a small network co-trained\n",
    "with the main backbone to predict per-sample loss for active learning. ELLE shows\n",
    "that **no co-training is required** — the frozen CLS embedding from an already-trained\n",
    "model already encodes sufficient information for linear loss prediction. This makes\n",
    "ELLE applicable post-hoc to any existing checkpoint without retraining.\n",
    "\n",
    "**Azaria & Mitchell (2023) — The Internal State of an LLM Knows When It's Lying.**\n",
    "Azaria & Mitchell demonstrated that a classifier trained on internal LLM activations can\n",
    "predict whether the model's output is truthful. This is the discrete classification\n",
    "analogue of ELLE: both show that internal representations encode self-knowledge about\n",
    "output quality. ELLE extends this insight to continuous loss prediction and to\n",
    "non-language modalities (image, audio, geospatial), using the simpler linear probe\n",
    "rather than a trained classifier.\n",
    "\n",
    "**Sun et al. (2022) — Out-of-Distribution Detection via Embedding Norm.**\n",
    "Sun et al. demonstrated that the L2 norm of penultimate-layer embeddings correlates\n",
    "with OOD confidence, achieving useful AUROC on benchmark datasets. Our Trivial Baselines\n",
    "analysis (Section 5) shows that L2 norm correlation with loss varies widely across models\n",
    "(from r ≈ 0.01 to r ≈ 0.82), but the **directional linear probe consistently outperforms\n",
    "L2 norm** — e.g., SatCLIP probe r = 0.96 vs L2 r = 0.03, indicating that the\n",
    "direction, not just the magnitude, of the embedding carries difficulty information.\n",
    "\n",
    "**Alain & Bengio (2017) — Understanding Intermediate Representations with Linear Probes.**\n",
    "Alain & Bengio popularized linear probing as a tool to measure what information is\n",
    "encoded in intermediate representations of neural networks. ELLE applies this framework\n",
    "with a new target: **the model's own pretraining loss**, rather than downstream class labels.\n",
    "This shift reveals that self-supervised pretraining implicitly calibrates the embedding\n",
    "space to encode reconstruction difficulty — a property not studied by prior linear probing work.\n",
    "\n",
    "**Positioning ELLE.** Together, these works suggest that loss-predictive information\n",
    "is present in embeddings (Abbas), can be extracted with a lightweight module (Yoo & Kweon),\n",
    "is reflected in internal states for truthfulness (Azaria & Mitchell),\n",
    "exceeds norm-based signals in richness (Sun et al.), and can be studied via linear probes\n",
    "(Alain & Bengio). ELLE unifies and simplifies these findings: the signal is globally linear,\n",
    "post-hoc, modality-agnostic, and free at inference time.\n"
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    "# Note on AI use\n",
    "\n",
    "This exploration was done heavily using AI. As described, we were investigating losses with Clay and Sam from our team wondered if we could retrieve the loss from the embedding itself. We asked Claude Code to test the idea with a random forest and then with linear tools the same day. We then expanded using Claude Code and Google Antigravity to test with other geo models, then other domains, testing ablations, and slowly built up all sections of the paper with relatively little manual coding.\n",
    "\n",
    "This was based on several agentic instructions and tasks in batches, then left to download, implement, test, and create the figures and narrate findings. As results came back positive, they informed our initial Clay investigation. We then stopped and systematically reviewed the code, suggesting micro-changes and checks to ensure results were correct. We discovered several instances where the AI had produced placeholder outputs or run models incorrectly — for example, passing wrong input dimensions or silently substituting proxy metrics. It took substantial time to verify and fix these issues, but the human review process caught all of them before publication. \n",
    "\n",
    "Overall, this paper from idea to this notebook took one calendar week and roughly 1 day of focused developer thinking time. Every figure and number in this notebook has been verified against ground-truth model outputs by a human reviewer."
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    "\n",
    "<a href=\"https://raw.githubusercontent.com/EarthLegend/devlogs/main/posts/2026-03-01-self-aware-embeddings/index.ipynb\" download=\"ELLE-notebook.ipynb\" class=\"btn btn-primary\"><i class=\"bi bi-download\"></i> Download this notebook</a>\n"
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