{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "50483cd6",
   "metadata": {},
   "source": [
    "# Optimal Transport in linear ICA\n",
    "\n",
    "#### In this notebook we test the performance of a OT ICA on Stiefel Manifold (Orthogonal mtx space) versus Fast ICA over dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fac3658e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import FastICA\n",
    "from tqdm.notebook import tqdm\n",
    "import time\n",
    "from joblib import Parallel, delayed\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from wasserstein_ica import WassersteinICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41dc1406",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# Thesis Theme Configuration\n",
    "# ==========================================\n",
    "def set_thesis_theme():\n",
    "    thesis_colors = ['#0173B2', '#DE8F05', '#029E73', '#D55E00', '#CC78BC', '#CA9161']\n",
    "    plt.rcParams.update({\n",
    "        'figure.figsize': (10, 6),\n",
    "        'figure.dpi': 300,\n",
    "        'axes.prop_cycle': plt.cycler(color=thesis_colors),\n",
    "        'axes.grid': True,\n",
    "        'grid.alpha': 0.3,\n",
    "        'grid.linestyle': '--',\n",
    "        'axes.axisbelow': True,\n",
    "        'axes.spines.top': False,\n",
    "        'axes.spines.right': False,\n",
    "        'font.size': 12,\n",
    "        'axes.titlesize': 14,\n",
    "        'axes.labelsize': 12,\n",
    "        'legend.frameon': True,\n",
    "        'legend.fontsize': 11,\n",
    "        'lines.linewidth': 2.5\n",
    "    })\n",
    "set_thesis_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18192e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# 1. Helpers & Data Generation\n",
    "# ==========================================\n",
    "def amari_error(W_est, A_true):\n",
    "    if W_est is None or np.any(np.isnan(W_est)): return np.nan\n",
    "    P = np.abs(W_est @ A_true)\n",
    "    n = P.shape[0]\n",
    "    term1 = np.sum((np.sum(P, axis=1) / np.max(P, axis=1)) - 1)\n",
    "    term2 = np.sum((np.sum(P, axis=0) / np.max(P, axis=0)) - 1)\n",
    "    return (term1 + term2) / (2 * n)\n",
    "\n",
    "def generate_dataset(n_dim, n_samples, seed=None):\n",
    "    if seed is not None:\n",
    "        np.random.seed(seed)\n",
    "        torch.manual_seed(seed)\n",
    "    # Standard Laplace (Super-Gaussian)\n",
    "    sources = [np.random.laplace(0, 1, n_samples) for _ in range(n_dim)]\n",
    "    S = np.stack(sources)\n",
    "    \n",
    "    cond_num = 1000\n",
    "    while cond_num > 100:\n",
    "        A = np.random.randn(n_dim, n_dim)\n",
    "        cond_num = np.linalg.cond(A)\n",
    "    return torch.tensor(A @ S, dtype=torch.float32), A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd16b2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# 2. Parallel Worker Function\n",
    "# ==========================================\n",
    "def run_single_trial(dim, trial, n_samples, compute_level):\n",
    "    torch.set_num_threads(1)\n",
    "    trial_results = []\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    \n",
    "    X_torch, A_true = generate_dataset(n_dim=dim, n_samples=n_samples, seed=trial)\n",
    "    X_torch = X_torch.to(device)\n",
    "    X_np = X_torch.cpu().numpy()\n",
    "    \n",
    "    # Compute Parameters\n",
    "    if compute_level == 'Low Compute':\n",
    "        fast_max_iter = 1000\n",
    "        w2_restarts = min(dim * 4, 150)\n",
    "        w2_def_iter = 150\n",
    "        w2_sym_iter = 200\n",
    "    else: # High Compute\n",
    "        fast_max_iter = 5000\n",
    "        w2_restarts = min(dim * 15, 600)\n",
    "        w2_def_iter = 300\n",
    "        w2_sym_iter = 500\n",
    "\n",
    "    # --- FastICA ---\n",
    "    t0_fast = time.perf_counter()\n",
    "    try:\n",
    "        import warnings\n",
    "        from sklearn.exceptions import ConvergenceWarning\n",
    "        with warnings.catch_warnings():\n",
    "            warnings.simplefilter(\"ignore\", category=ConvergenceWarning)\n",
    "            fast_ica = FastICA(n_components=dim, max_iter=fast_max_iter, tol=1e-4, random_state=trial)\n",
    "            fast_ica.fit(X_np.T)\n",
    "            W_fast_total = fast_ica.components_\n",
    "            score_fast = amari_error(W_fast_total, A_true)\n",
    "    except Exception:\n",
    "        score_fast = np.nan\n",
    "    t_fast_total = time.perf_counter() - t0_fast\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, 'Method': 'FastICA', 'Compute Level': compute_level,\n",
    "        'Amari Error': score_fast, 'Total Time (s)': t_fast_total\n",
    "    })\n",
    "    \n",
    "    # --- OT-ICA ---\n",
    "    ica = WassersteinICA(X_torch)\n",
    "    ica.whiten()\n",
    "    W_white_np = ica.W_white.cpu().numpy()\n",
    "    \n",
    "    try:\n",
    "        t0_p1 = time.perf_counter()\n",
    "        extracted_ws = []\n",
    "        for _ in range(dim):\n",
    "            prev = torch.stack(extracted_ws) if extracted_ws else None\n",
    "            w, _ = ica.optimize_wasserstein2(\n",
    "                prev_components=prev, max_iter=w2_def_iter, \n",
    "                n_restarts=w2_restarts, dither_sigma=0.0\n",
    "            )\n",
    "            extracted_ws.append(w)\n",
    "        W_deflation_init = torch.stack(extracted_ws)\n",
    "        t_phase1 = time.perf_counter() - t0_p1\n",
    "        \n",
    "        t0_p2 = time.perf_counter()\n",
    "        W_stiefel_unmixed = ica.optimize_symmetric(\n",
    "            n_components=dim, max_iter=w2_sym_iter, lr=1.0, \n",
    "            init_w=W_deflation_init, optimizer='stiefel',\n",
    "            dither_sigma=0.0, batch_size=None\n",
    "        )\n",
    "        t_phase2 = time.perf_counter() - t0_p2\n",
    "        \n",
    "        W_wass_total = W_stiefel_unmixed.cpu().numpy() @ W_white_np\n",
    "        score_wass = amari_error(W_wass_total, A_true)\n",
    "    except Exception:\n",
    "        score_wass, t_phase1, t_phase2 = np.nan, np.nan, np.nan\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, 'Method': 'OT-ICA', 'Compute Level': compute_level,\n",
    "        'Amari Error': score_wass, \n",
    "        'Total Time (s)': t_phase1 + t_phase2 if not np.isnan(t_phase1) else np.nan\n",
    "    })\n",
    "    \n",
    "    return trial_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fed850bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- FastICA vs. OT-ICA: Laplace Scaling ---\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed077252baad4ef5abe89b1e46efe014",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running Trials:   0%|          | 0/78 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 3. Main Execution\n",
    "# ==========================================\n",
    "DIMENSIONS = [n for n in range(10, 71, 5)] # Scaled up to 40 for good visualization\n",
    "N_SAMPLES = 10000\n",
    "COMPUTE_LEVELS = ['Low Compute', 'High Compute']\n",
    "N_TRIALS = 3\n",
    "\n",
    "print(f\"--- FastICA vs. OT-ICA: Laplace Scaling ---\")\n",
    "tasks = [(dim, trial, N_SAMPLES, comp) for dim in DIMENSIONS for trial in range(N_TRIALS) for comp in COMPUTE_LEVELS]\n",
    "\n",
    "results_nested = Parallel(n_jobs=12)(\n",
    "    delayed(run_single_trial)(dim, trial, n_samples, comp) \n",
    "    for dim, trial, n_samples, comp in tqdm(tasks, desc=\"Running Trials\")\n",
    ")\n",
    "df = pd.DataFrame([item for sublist in results_nested for item in sublist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dea393aa",
   "metadata": {},
   "outputs": [
    {
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vnpo4cWKhn2Pjxo3Tcj7xxBO/bH/66adTtWrVKtBr36BBg9TLL79c6JqScg4ZMqTQ+VKpVOraa69Ny9m1a9c893/iiScK9Jw39VXQ995Vq1al7rnnnlSTJk0K9Dj16tVL3XLLLalVq1Zl9HplS9Lx1bhx44xynnPOOYnP/corr8wo79ixY1PHHntsqnz58gV6zf/whz8U+HfLxowePTq12267FaiGcuXKpa688spUTk5OKpVKpaZMmZK435QpU/JVQ3Gsnzt3bqpPnz75fr2POuqo1IwZMzb6uMOGDUt17NgxX/mqV6+euueee1Lr1q3L13PKr9JyHAEAAABkQ3H2mpLOxxfknFReOX57Tr8gnnvuuVSdOnUKdJ6nTp06qeeff/6XHEnnmgtyjjQ/z2nEiBH5Pi8WEalu3bqlRo8eXejXJVv0rQpO30rfKltf+laFe09Oom+V/qVv9RN9KwCA/+MWHAAAkE/jx4+Ptm3bxvnnnx8//PBDgdbOnDkzLrjggthrr73iyy+/zPe6GjVqRLt27dLiH3zwQYEe/9NPP40VK1YkbitorpycnMSrEfbo0aNAeQrr8ccfT4yfddZZUb169WKpoaD69+8fu+66a9x1112xfPnyAq394osv4ogjjoijjjoqFi5cmPXacnJy4rTTTotjjz02vv/++3ytmT9/fpx66qlx3nnnRSqVStv+9ddfR7t27eLWW2/N87j7rcGDB0e7du3ik08+KVD9+XHNNdfEKaecEosXLy7Qurlz50bv3r3juuuuy3pNvwfDhg2LVq1axQUXXLDRK7cmmT9/fvzjH/+IVq1axZgxY4qmwBJWp06dxPiCBQsKlW/VqlVx3nnnRZs2baJfv36xbt26Aq0fNGhQ7L333nHZZZfF+vXrC1XDz15++eXYZ5994uuvvy7QuvXr18ett94aRx55ZKxevTqjGorDiBEjomXLlvHUU0/l+/V+9dVXo0OHDjFu3Li0balUKvr27Rv7779/fPrpp/nKt2zZsrjgggvixBNPzPj7FlG6jiMAAAAAMnfppZfGiSeeWOD+wsKFC+OEE06I66+/vogq+z+pVCquu+666Ny5c77Pi0VEDBkyJDp27Bj9+/cvwuo2Td8qu/St8qZvlRl9q43Tt0qnb6VvBQDwWwbwAAAgH959993Yd999Y/z48RnlmTBhQnTq1CneeeedfK9JGmwr6NDcxvb/+OOPC3TC+LPPPktrxlWsWDH222+/AtVUGCtWrMjzuZx00klF/viFcfPNN8exxx5b6ObEzwYOHBgdOnQocENoY9avXx/HHntsPPnkk4Vaf//998cNN9ywQWzcuHHRtWvX+Oabbwqcb9myZXHQQQfF//73v0LVk+T++++PG2+8MaMc119/fdx0001Zquj34b777ovu3bvHpEmTMsozadKk6Ny5cwwaNChLlZUeK1euTIxXqFChwLlmzpwZnTp1ivvvvz9yc3MLXVNubm7cfvvtccQRR8SaNWsKlePNN9+M4447rtDrIyLeeuutOPXUUwu9vjgMHz48evToEfPmzSvw2lmzZsXBBx8cs2bN+iWWSqXinHPOiZtuuqlQ38MXX3wxzj333AKv+7XSdBwBAAAAkLkrr7wy7rjjjoxyXHfddfH//t//y1JFyf785z/H9ddfX6hzUjk5OXH88cfHwIEDi6CyTdO30rcqKH2rkqFvtWn6VnnTt9K3AgD4WfmSLgAAAEq7kSNHxtFHHx2rVq1K3F6+fPnYf//9Y6eddor69evHggULYvLkyTF06NBYu3Zt2v4rVqyIo48+Ot5///3o1KnTJh+/R48eccstt2wQ+/jjj2PNmjVRqVKlfD2HjQ3grV69OkaMGBHdu3cvdK699947qlSpkq/1mRg8eHDia9q4ceNo1apVkT9+Qd1zzz1x9dVX57m9Zs2a0b1792jUqFFUr149Zs+eHePHj49Ro0Yl7j958uTo3r17fPbZZ1GvXr2M67v88svTmtK1atWKbt26RaNGjaJmzZoxf/78GDVqVHzxxReJVw294YYb4tBDD4127drF3Llz49BDD4358+dvsM8uu+wS++yzTzRo0CDKli0bM2fOjPfffz9mz56dlm/JkiVxzjnnxIcffpjx8/vqq6/ivvvu2yC2ww47RJ8+feLwww+Pxo0bR82aNWPWrFkxYcKEeOGFF6Jfv36Jx9g111wT7du3j549e2Zc15bu9ttvj8suuyzP7TVq1IhOnTpFkyZNom7durFmzZqYPXt2fPTRRzFt2rS0/VeuXBlHHnlkjBw5Mlq3bl2ElRevH3/8MTGe1xVG8zJ79uzYf//947vvvstzn1133TXat28f9erVi6pVq8b8+fPj22+/jY8++ijxeP+5kfjf//63QLXMnDkzTjjhhMQrSJYpUyYOO+yw+NOf/hRt27aN7bbbLtatWxezZs2Kjz76KJ577rkN7q764osvRrNmzQr0+MVl+vTpcdRRR6UNr7dp0yZat24d22yzTeTk5MS0adPivffei0WLFqXlmDVrVpxzzjnx2muvRUTErbfeGg8//PAG+1SpUiX233//2HHHHaNOnTqxePHiGDduXAwfPjzxNX744YfjuOOOy/fv818rTccRAAAAAJl7/fXX45///GfitqpVq8af/vSnOPbYY2OXXXaJbbbZJpYsWRIzZsyIt956K5555pn49ttvf9n/wgsvjAsuuKBI6rz++uvjkUce2SBWsWLF6NSpUzRr1iwaNGgQq1atiunTp8cHH3yQeMe19evXx5///Ofo0qVL1K5du0jqzIu+lb5VQehblQx9q/zRt9K30rcCAMiHFAAAlGJDhgxJRUTa17XXXlssj79s2bJU06ZNE2uoUKFC6pprrknNmzcvce2CBQtSN954Y6pixYqJ65s0aZJaunTpJmtYtWpVqnLlymnrBw8enK/nsHz58lSFChUSa/j56x//+Ee+X5MuXbqkrb/++uvzvT4TV111VWL9ffr0KZbHL4ixY8fm+b2vX79+6umnn06tXr06ce13332XOuGEE/L8fvXu3btAtfTp0yctR/PmzVNlypT55d+NGzdOvfjii6mcnJzEHOPGjUu1b98+sZ79998/lUqlUgcddNAG8SOOOCI1YcKExHy5ubmpRx55JFWtWrXEnC+99FKBnmPjxo3TcpQvX36Df5933nmplStXbjTPuHHjUq1atcrzZ3bFihX5rikpx5AhQwr0vH7r2muvTcvZtWvXYlu/KQMGDNjguPr117777pt64403UuvWrctz/SeffJLq2rVr4vrddtsttWrVqqzVWhBJx1fjxo0zyrnDDjskPs8XXngh3zlWr16d6tChQ2Ke6tWrpy6//PLUzJkz81y/bNmy1M0335yqXr16Yo6nnnqqQM/pqKOOyvNnJz/H/osvvpiqU6fOBr9nk/JNmTIlX/VMmTKlSNbvsssuv/x3mTJlUqeeempq2rRpiTnWrl2b+uc//5nncxk6dGjq448/TpUtW3aD3xEPPvhgnsf71KlTU3/4wx8S87Vs2TJfz+3XSttxBAAAAJBtxdlrSjofX5BzUnnlKEgfZNmyZalGjRol1tGtW7dN1rJ27drUtddeu8E5q6TzWwU5R5r0nFq0aLHBY9StWzf173//O7Vs2bLEHOvWrUvdf//9eZ6HOv/88/NdT7boW/30pW+VTt+qeNZvir5V/ulbpdO3+r/fEfpWAAA/MYAHAECpVtIDeGeffXbi49etWzf1xRdf5CvH2LFjU/Xr10/Mc/rpp+crR/fu3dPWXnXVVfla+/bbb6etLVeu3Ab/7tixY75yrVixIrE5N3z48Hytz9QhhxyS+DrefffdxfL4+bVmzZrUHnvskVhrhw4dUnPnzs1XnmeeeWaDk9u//nr66afzXU9eDf+fv3r06JFnQ/vXli1blmrbtm1ijl83mcuWLZv6f//v/+WrtiFDhqQ1HCMi9Yc//CHfzy+VSm40/fqrIEOiixcvTu2+++6JeS677LJ850lavyU3MmfNmpWqVatWWv7y5cun7rjjjnznyc3NTV1xxRWJr99FF12UlVoLKtuNzOHDhyc+vzJlyqSmT5+e7zyXX355Yp62bdumvv/++3zn+d///pdq0qRJWp4aNWqkZsyYka8cb7zxRmItjRo1KtBz+vzzz/NsiP38VdKNzJ+/KlWqlHrllVfylev5559PzHHEEUds8PuidevWqR9//HGT+dauXZv24ZGfv0aMGJGvmn5Wmo4jAAAAgKLwexvAu+SSSxJrOPDAA1Nr1qzJd56HHnpoo+fHMh3A+/VXmzZtUnPmzMlXrhEjRqSqVKmSlqN27drFPgyjb/V/X/pWG9K3Kp71G6NvlX/6VnnTt9K3AgD4tbIBAAAkmjhxYjzyyCNp8SpVqsSbb74Zbdq0yVeePffcM956662oWrVq2rbHH388JkyYsMkcPXr0SIt98MEH+Xr83+5XpkyZOPnkkzeIff7557FkyZJN5vroo49i7dq1G8SqVasWHTp0yFctmfruu+8S47vuumuxPH5+PfnkkzF+/Pi0ePPmzeOtt96K+vXr5yvPSSedFP/5z38St11xxRWRk5OTUZ0REe3atYs333wzqlWrtsl9q1WrFg8//HCUKVMmbdvNN9/8y3/fddddcc455+Tr8ffff//EfT/44IOYPXt2vnJsyjHHHBPXXHNNvvevWbNmvP3221G9evW0bf/v//2/WL58eVbq2tJcdtllsXjx4rT4U089FRdffHG+85QpUyZuvfXWuOCCC9K2Pfjgg7Fw4cJMyixx69atiyuuuCJxW+fOnaNRo0b5yjNx4sS444470uJt2rSJDz/8MHbcccd819SiRYsYMmRI1KxZc4P40qVL47777stXjnvuuSctVrZs2Xj11Vfz/ZwiItq2bRuPP/54vvcvSc8991wcddRR+dr3T3/6Uxx55JFp8ddee+2X3xdNmjSJIUOGRIMGDTaZr0KFCvHII4/EVlttlbbtmWeeyVdNEaXvOAIAAAAgM6tWrUrsbW2//fYxYMCAqFixYr5znX322fHXv/41m+Ul2mWXXWLo0KGx9dZb52v/ffbZJ6699tq0+KJFi+K1117LdnkbpW/1f/St8k/fqnjoW+WPvtXG6VvpWwEA/JoBPAAAyMMDDzwQqVQqLX711VdHx44dC5SrXbt2ic3AiIj7779/k+uTBvBGjRoVS5cu3eTa3w7gtW7dOo477rgNYuvXr48PP/ywwLkiIvbbb7+oUKHCJtdmw8yZMxPj22+/fbE8fn4lNR/LlCkTTzzxRNStW7dAuc4555w45JBD0uKzZs2KAQMGFLrGiIhKlSrFM888E5UqVcr3mjZt2kSXLl3y3N69e/c4//zzC1THhRdemBZbv359fPzxxwXKk2SrrbaKO++8s8Drtt9++7jyyivT4suWLYsXXngh47q2NN988008//zzafGLLrooTjjhhELlvO2222K33XbbILZy5crED49sLnJycuLss8+O4cOHJ25POubycsMNN8T69es3iFWvXj1ee+21xIHvTWnSpEni76NHHnkkVq1atdG106ZNS/z9cOaZZ+Z7WP3XjjnmmOjevXuB1xWnU045JXr37l2gNX//+983uv2pp56KWrVq5Tvf9ttvH8cee2xafNiwYfnOUZqOIwAAAIDidv3110eZMmWy+vXUU0+V6HPq169f4gUXb7vttnwNNP3WjTfeWOC+RkGUK1cu/vvf/yYOFm3MBRdckPaB+oifLiRZnPSt/o++Vf7oWxUPfav80bfKH32r/NG3AgB+DwzgAQBAghUrViQ2SXfccce46KKLCpXzggsuiJ122ikt/uyzz25ykK5du3ZRo0aNDWLr16+PoUOHbnTdggULYuzYsRvEevTokTg09/777280V0TyAF7ScGBRWLFiRaxYsSJxW36vilochg8fHuPGjUuL/+lPf4p99923UDn//e9/R7ly5dLieV1lNL+OO+64aNGiRYHXJV0N72d9+/ZNvNLoxjRt2jT22GOPtPhvj93C6NOnT+ywww6FWvv3v/897ecuIjabqxwWp4ceeihyc3M3iNWrV69AV3D9rYoVK8bll1+eFt8cX/9UKhVvvvlm7LPPPvHEE08k7nPcccfFwQcfnK988+bNi379+qXFr7jiithuu+0KXecJJ5wQO++88waxhQsXxquvvrrRdc8991za9z8i4tJLLy10LZdddlmh1xa1smXLFurY3m+//aJ27dqJ27p27brRD4nkJen9eOLEibF69epNri1txxEAAAAAmXv22WfTYo0aNYrjjz++UPlq164dZ511VqZl5alXr16x1157FXhdpUqVEu/yM2bMmCxUlT/6VvpWhaFvVTz0rTZO36rg9K3yR98KANjSGcADAIAEw4YNS7xC6Omnn16gqy7+WsWKFeOMM85Iiy9fvjyGDBmy0bXlypWLrl27psWTBuJ+bciQIWl38evRo0dUrVo19t577wLlWrhwYWJjqbgG8FauXJnntsJc7ayovP7664nxv/zlL4XO2bx588TXefjw4bF48eJC5y1s03zPPfdMjDdr1iz233//QuVs3bp1WmzChAmFyvVrhb2KZcRPVyE9+uij0+KjRo1yRbxfSaVSiVdXPe200xKvQFwQxx13XNp77rfffhtz587NKG82LFmyJG666aY8v6655po4//zz4/DDD48GDRrEYYcdFl988UVirk6dOuXZ4EzSv3//yMnJ2SBWoUKFOPfcczN6TmXLlo2TTjopLb6pq0aPGDEiLda+ffu0ZlZBHHDAAdGgQYNCry9K3bp1Sxyo35Ry5crF7rvvnritsO/HSe+d69ati4kTJ25ybWk7jgAAAADITG5ubnzyySdp8eOPP77AA0i/lsl59k3JpHfSoUOHtFg2+gr5pW+lb1UY+lZFT99K30rfKn/0rfStAICCK1/SBQAAQGmU1KCMyLzJeOKJJ8ZVV12VNhT3ySefbPTqjBE/Dbr9tkm2qaG5326vWLHiL1cq69GjxwYnFb/++uuYPXt2bLvttom5hgwZknilwLyaWtm2sauiVaxYsVhqyI+kY6dx48bRqVOnjPKedNJJMWjQoA1iqVQqPv300+jZs2eB81WuXDk6duxYqFqaN2+eGC/MVfB+1qxZs7TYggULCp0vIqJOnTrRuXPnjHIcddRRaXfDXLduXYwZM6bQV4bd0owdOzbmzJmTFj/mmGMyzl2pUqXo2LFjDBs2bIP4iBEjEq9uXJwWL14cffv2zShHmTJl4qyzzop77723QMPd77zzTlrsgAMOiFq1amVUT0Tyz3FSo/LXRo0alRY76KCDMqqjXLlyceCBB8Zzzz2XUZ6iUNgPbET89P6Z1NBLGrLPjyZNmkSFChXSGpL5ef8sbccRAAAAAJmZOHFiLFu2LC2e6bm6PfbYI7bbbruYOXNmRnl+q1KlShmdZ991113TYkuWLInc3NwoW7bor0mvb6VvVVD6VsVD36rw9K3ypm+VP/pWAMCWzgAeAAAkSGpGbb/99tG0adOM8u6www7RpEmTmDJlyiYf77eSriQ5YcKEmDNnTmyzzTaJa347gLf33ntHlSpVfsl33XXXbbB98ODBceKJJ+YrV8RPV1PL5KqpBVGuXLk8t61evfqX51WS1q9fH59//nlafL/99sv4dcrrJPcnn3xSqEZmq1atokKFCoWqpUaNGonxdu3aFSpfRCRecXLp0qWFzhfx03PM9HXPa8B01KhRGpn/v6TmRMWKFaNt27ZZyd+sWbO0Rubo0aNLvJGZia222iqOPPLIuPTSS6NNmzYFXj9y5Mi0WLaOx6QPFXz11VeRk5OT+J4xY8aMxCu7tmrVKuNaWrVqVSobmZkc20nvn1tvvXVsv/32hcpXtmzZqFatWixatGiDeH7eP0vTcQQAAABQErp16xbdu3fPas4BAwbEmDFjspozv/K6k1G2ztVlewCvVatWBRrw+K3atWunxVKpVCxdujQrH9bfFH0rfauC0rcqHvpWBadvlT/6VpumbwUAbOkM4AEAQIKvvvoqLda6deus5G7dunXaAF7S4/3W7rvvHg0aNEg7YTx48ODEO/P98MMPMWnSpA1iBxxwwC//3bFjx6hatWqsWLHil9gHH3xQoAG8pKHAorLVVlvluW3JkiWlopH5/fffx8qVK9Pi2Th2dthhh6hTp04sXLhwg3h+jp0kDRo0KHQt1apVS4zXr18/qzmTrtRbEHvssUdG6yN+ukpf9erV02qZNm1axrm3FOPGjUuLNWvWbKMfPiiIunXrpsXmzZuXldwlZfvtt48jjzyyUE3M2bNnJz7/Fi1aZKO0xNd7/fr1sXDhwth6660T60nSsmXLjGvJRo6ikO33z0zeO3/O+dtG5qbeP0vbcQQAAABQErp06RJXX311VnN+9913JTaAl3SurkGDBlGvXr2Mc7ds2TLefvvtjPP8Wibn2SLyHrpatmxZsQzg6VvpWxWUvlXx0LcqOH2r/NG3yn9OfSsAYEtVtqQLAACA0ui3zaKIiF122SUruZNOEi5atChSqdQm1yZdiTVpMC6v+K8H5ipUqBBdunTJV64ZM2bEt99+u9F8Ra1atWp5XhVyyZIlxVbHxiQdNxHZO3aS8uT1mJuSSfO5bNnk/5XMds7169cXOl9EZOVkeZkyZRKbDL9tGvyeJTV1J0yYEGXKlMnK12233ZaWf3N//SdNmhR/+tOf4pRTTom1a9cWaG1eTfRjjz02K693Xh8Kyes1z+v9t06dOgV6XkWVoyhk+70u0w8DFeb9s7QdRwAAAABkLulcXbbOsRXFubqiOC8WkXlvIb/0rfStCkrfqnjoWxWcvlX+6FsVPqe+FQCwpTCABwAAv7FmzZpYvXp1WrxmzZpZyZ90wnL9+vX5umpi0sBbfgfwqlevHh06dNhovunTp8d3332Xr8fYYYcdYuedd95kzdlSoUKFPK+2tnjx4mKrY2PyqqMoj53CnhTO1lUeizpnJvK6+m028pSWY640mDNnTrE/ZmlohjRu3DhSqVTi19q1a2P+/PkxYsSIuP322/O8qu0zzzwTJ554YuTm5ub7cUvi9Y7I+zXP62ehevXqGT9mtn6Gsy3b73Ul8d5Z2o4jAAAAADKXdK4uG+fpIormXF358uWznrM46VvpWxWUvlXx0LfSt0qib1Vy+fKjtB1HAAB5MYAHAAC/kddJtqJsimzscX8taQBv2rRpMXny5LT44MGDN/h3165d05qZ+R3oS4ol3Y2vqO2www6J8W+++aaYK0lWEseOk8J5y+tKdgVVtWrVtNjy5cuzkntLsHLlymJ/zDVr1hT7YxZEhQoVom7durHPPvvEJZdcEl9++WU89NBDUbFixbR9+/fvH9ddd12+c5fE6x2R92ueV7xSpUoZP2Y2cpCstB1HAAAAAGQu6dxLts6xOVeXTN9K36og9K2Kh75VOn0rfavSrrQdRwAAeTGABwAA+VSmTJkSz7PjjjtGkyZN0uLvv//+Bv/+3//+F7NmzdogljRst+eee0bdunU3misieQAvKV9R23333RPjn3/+eTFXUjCl4dj5PcrWifoVK1akxapVq5aV3FuC9evXl3QJpV6ZMmXi7LPPjldffTXxqpE333xzfPzxx/nKVdpe77w+qJGfu7puSjZykKy0HUcAAAAAZC7pXF22zrE5V5dM30rfqiD0rYqH89+bpm9VOH4XFp3SdhwBAOSl/KZ3AQCA35datWolxpcuXZqV/EuWLEmM165dO1/re/ToEY899tgGsQ8++CD+/Oc/b/Dv3zrggAPSYmXKlIlu3bpF//79f4kNGTIkUqnUL02zb775Jm2YL6Jk7oDXtm3bePLJJ9Pio0aNKvZakpTEsZPf4+b3KFuve1KevL7Xv0eVK1dOi+2www5x1llnFdljNm7cuMhyF6WDDz44br311rjssss2iOfm5sYZZ5wR48ePjwoVKmw0R9LrHRFx6qmnxk477ZS1Wn9rxx13TIzn9bOwbNmyjH9OsvUzTLrSdhwBAAAAkLmk83HZGhZwri6ZvpW+VUHoWxUPfav807cqGL8Li05pO44AAPJiAA8AAH6jcuXKUbly5Vi9evUG8cWLF2clf1Izqly5clG9evV8rU8awPvt0NxvB/C23nrrPK/C2aNHjw0G8BYsWBBjx46NvfbaKzFXRMSuu+4aDRs2zFe92dS1a9fE+JdffhlLly7N84p2xSWvpmJRHjsamXmbO3duxjlSqVTMmzcvLV5Sjcx169aVyONuTNIxWLNmzbj66qtLoJrS7+KLL44BAwbEJ598skF84sSJce+998bFF1+80fV5/cwffvjh0atXr6zVmV951TN9+vRo1KhRRrl/+OGHjNaTt9J2HAEAAACQuaRzPrNmzYr169cn3uGoIJyrS6ZvpW9VEPpWxUPfqmD0rfLP78KiU9qOIwCAvJQt6QIAAKA0qlOnTlps0qRJWck9ceLEtFitWrV+GZ7blKQ7z82fPz++/PLLiPjpinRDhw7d5Jqf9ejRIy3266G7pAG8pDXFYY899kg8Mb5mzZp47rnnSqCiDSUdNxHZO3a+/fbbtJhGZt7GjRuXcY6pU6cmXiF4hx122OTapJ/p3NzcjOpZuHBhRuuLQtJrsWDBghKoZPNQtmzZ+M9//pN4fNxyyy2bvHpmXsdeSb3mTZs2TfzwTjZ+/n7+vUb2lbbjCAAAAIDMNW/ePC22evXqxN5CQTlXl0zfSt+qIPStioe+VcHoW+Wf34VFp7QdRwAAeTGABwAACVq2bJkWGzt2bFZyJ+XJ6+50SbbeeuvE+n4elPviiy/Srlx5wAEH5JmvWbNmac3Bn3MlDfNFlNwAXkTE8ccfnxh/5JFHirmSdDvuuGNUqVIlLZ6NY2fGjBkxf/78tHhBjp3fm3HjxkUqlcooR16NlPbt229ybdJdLZcvX55RPUlXNS1pu+22W1pszpw5iVe+5Sdt2rSJ4447Li2+cOHCuPPOOze6tmnTplG5cuW0+DfffJO1+gqiSpUqib+TPvvss4xzZyMHyUrbcQQAAABA5vI6b53pebZVq1bFV199lVGOLZm+1Yb0rfKmb1U89K0KTt8qf/Stik5pO44AAPJiAA8AABLss88+abHp06fHtGnTMso7c+bM+P777/P1eBuzsbvWFeaOdb/d/tFHH0VOTk6MHj06Fi1atMG2smXLxv7771+gerPpzDPPTIyPGTMmPvzww2KuZkPly5ePdu3apcU/+uijjHMPGzYsMV7QY+f3ZOHChTF8+PCMcrz66qtpsfLly0ebNm02ubZGjRqJNWUi08ZO2bLppwEybfbuvffeabHc3Nw8j1l+csMNNyRegfOee+5Je9/9tfLly0fbtm3T4knD0sWlQ4cOabGBAwfG2rVrC51z1qxZGf/8krfSeBwBAAAAkJnatWvHzjvvnBbv169fRnkHDhwYq1evzijHlkzfakP6VnnTt0qnb1V66FttnL5V0SqNxxEAQBIDeAAAkCDpxHxExPPPP59R3ueeey6xaZDX4+UlaaBu2LBhkZOTkzaAt/POO0fjxo0LlG/FihXxySefJA7ztW3bNmrVqlWgerOpefPmcdRRRyVuO/vss2PVqlXFW9BvJH0vp0yZEiNHjswo77PPPpsWK1OmTHTs2DGjvFu6TH5mV69eHa+88kpavF27dolXjP2tOnXqpMX+97//FbqeKVOmxPTp0wu9PiKiWrVqabFMf2b222+/xKumDhgwIKO8W7pmzZrFn/70p7T4kiVL4q677tro2kMOOSQtNnbs2JgyZUrW6iuIgw8+OC22aNGieO211wqd8+mnn47c3NxMymITSttxBAAAAEDmks7VDRo0KGbNmlXonE8++WQGFW359K3+j77VpulbbUjfqvTQt9o4fauiV9qOIwCAJAbwAAAgQZcuXaJmzZpp8ccffzxycnIKlTMnJycee+yxtHjVqlWjW7duBcrVtWvXtCvQrVixIj788MP4+OOPN4hv6u53ERHdu3dPi33wwQeJA3hJ+xa3m266KfEKfN9++21cddVVRfrYjz76aEyYMCHP7Ycffnhi/MEHHyz0Y06ePDnee++9tHjnzp1LdBhyc/DUU0/FDz/8UKi1d999dyxdujQtftppp+VrfcuWLdNin376aaFqiYh44IEHCr32Z0kNx0yvblqpUqXo1atXWvy5557L+K6hW7q+ffsmvpfde++9G/2+HH/88WlXhc3NzY1bb7016zXmx+GHHx4NGjRIi//jH/8o1NVE582bF//617+yURobUdqOIwAAAAAyd8YZZ6TFcnJy4sorryxUvvfeey/efffdTMva4ulb/UTfatP0rTakb1W66Fsl07cqHqXtOAIASGIADwAAElStWjX69OmTFv/uu+/i3nvvLVTO++67L7799tu0+Mknnxw1atQoUK6aNWtG27Zt0+I33nhj2lUB8zOA17Bhw9h11103iL311lsxfPjwtH3zk6+otWzZMi666KLEbXfffXc88cQTWX/MnJycuPTSS+Oss87a6BBm586do1WrVmnxZ555Jj7//PNCPfaFF14Y69atS4v/9a9/LVS+35NVq1bFxRdfXOB1M2bMiFtuuSUtXq1atcSrPyZJ+hn96KOPYvbs2QWu54cffoiHH364wOt+a5tttkmLTZs2LVavXp1R3vPOOy8tlpOTE3/9619dDXIjmjdvnng8LV26NO6888481zVt2jQOPfTQtPiTTz6Z+L5d1CpUqJD4O3PSpEkF/nBJbm5unHPOObF48eIsVUdeSttxBAAAAEDm9txzz2jfvn1a/Jlnnok33nijQLnmz58f5557brZK26LpW/1E32rT9K02pG9VuuhbpdO3Kj6l7TgCAEhiAA8AAPJw7rnnRpkyZdLi1113XYwZM6ZAucaMGRPXXntt4ra//e1vhaovaRBu2LBhG/y7TJky+b5j3W/zjRo1Km2Yr1KlStG5c+cCVlo0brjhhthjjz3S4qlUKs4888ysXoVuwoQJsd9++8Udd9yRr/2TGoypVCpOPfXUAp+cf/TRR+P1119Pi2+77baJV28kXb9+/eLGG2/M9/5LliyJQw89NJYtW5a27c9//nPi1TiTdOnSJS2Wm5sbN910U75rifipIXjSSSclXtW0oPbcc8+02Lp162LQoEEZ5W3btm0cc8wxafE333wzLr300oxy/9rKlSs3eiXfzVFeVxO97777Nno10ZtuuintKpA5OTnRq1ev+O6777JW3+jRo2P9+vWb3O+SSy6JOnXqpMXvuOOO+Oc//5mvx1q3bl385S9/iQEDBhS4TgqntB1HAAAAAGQuaUgnlUrFH//4xxg8eHC+cixcuDAOOeSQrJ4j2tLpW+lb5Ze+1f/Rtyp99K3+j75V8SttxxEAwG8ZwAMAgDzssssucdZZZ6XFly9fHgcffHCMHz8+X3nGjx8fBx98cCxfvjxt2+mnnx4tW7YsVH35uRNd69ato27dulnLt88++8RWW22Vr3xFrXLlyvH6669H/fr107bl5ubGFVdcEQceeGB88803hX6MmTNnxgUXXBCtW7eOTz/9NN/r+vTpk9hknTBhQhx++OEbbU782n//+98455xzErf961//igoVKuS7pt+j8uXL//Lf11xzTVxwwQVpQ6W/9dVXX0XXrl1j3LhxadsaN24c119/fb4fv3379ml3loyIePDBB2PgwIH5yrFs2bI47LDD0oZrC2uHHXZIvJroxRdfnHiHzoL497//HbVr106L33XXXXH00UdndGXI2bNnR9++faNRo0bxzDPPZFBl6ZPX1USXLVu20Q9PtGrVKvEqufPmzYv27dvHK6+8UuiacnNz4+23344ePXpE27ZtN3r15J81aNAgbr/99sRtV155ZfTs2XOjvzeHDx8eHTt23OCKubvvvnvBi6dASttxBAAAAEDmDjjggDj55JPT4qtWrYoDDzwwLrzwwpgzZ07i2jVr1sRzzz0Xu+22W4waNeqXuHN1m6ZvpW+1KfpW6fStSh99q5/oW5WM0nYcAQD8VvlN7wIAAKXPsGHDCnw1vk05+eSTo3HjxhvE7rzzznj//ffj+++/3yD+448/Rrt27aJv375x7rnnJl49bdGiRfHAAw/EjTfeGGvWrEnb3qRJk7j77rsLXW+nTp2icuXKsXr16jz3OeCAA/Kdb//994+yZctGbm5unvvkZ0ivODVu3DgGDRoUBx54YMyfPz9t+/vvvx8tW7aMo446Kvr06RMHHnjgJgcIly5dGkOGDInnnnsuBg4cGGvXri1wXZUqVYqnn346OnTokHbidvjw4dGyZcu46667onfv3lGxYsW09d9//31ce+218eyzzybm79WrV2LznA1dcMEFce+99/7yPbj33ntj4MCB0adPnzj88MOjcePGUaNGjZgzZ0589dVX8cILL8RLL72U5/f8gQceiKpVqxaohrPPPjsuvPDCDWK5ubnxxz/+Mfr27RuXXHJJVK5cOW3dmjVr4r///W9cc8018cMPP/wS32233eLrr78uUA2/ddJJJ6U1yL777rvYddddY999941WrVpF3bp1E4/NLl26JF4hNSJi++23j6eeeiqOOuqotPeRV199NYYOHRp/+ctf4uyzz44mTZpstMbc3NyYNGlSvPvuu9G/f//4+OOPN/retLm7+uqr47///W/alRbvu+++uPjii/McpL7ppptixIgR8fHHH28QX7x4cfTq1Su6du0aF198cfzhD3+ISpUqbbSGJUuWxKeffhoDBgyIV155JebOnVvg53H66afHgAED4s0330zbNmjQoGjVqlW0bNky2rVrFw0bNox169bFrFmzYvjw4TFt2rQN9q9Zs2bcd9990a1bt7RcSXenpfBK23EEAAAAQOb+/e9/x4cffhjTp0/fIJ6bmxt333133HvvvdG5c+do0aJFNGjQIJYtWxY//PBDfPDBB7FkyZIN1rRt2zbOOeectItWOk+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",
      "text/plain": [
       "<Figure size 3600x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 4. Plot 1: Amari Error (1x2 Side-by-Side)\n",
    "# ==========================================\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "palette = {'FastICA': '#0173B2', 'OT-ICA': '#D55E00'}\n",
    "\n",
    "for idx, comp in enumerate(COMPUTE_LEVELS):\n",
    "    sns.lineplot(\n",
    "        data=df[df['Compute Level'] == comp], x='Dimension', y='Amari Error', \n",
    "        hue='Method', palette=palette, marker='o', linewidth=2.5, markersize=8, ax=axes[idx]\n",
    "    )\n",
    "    axes[idx].set_title(f\"{comp} Regime\", fontsize=14)\n",
    "    axes[idx].set_xlabel(\"Number of Dimensions\", fontsize=13)\n",
    "    axes[idx].set_xticks(DIMENSIONS)\n",
    "    \n",
    "    # --- NEW: Lock the x-axis maximum to 65 ---\n",
    "    axes[idx].set_xlim(min(DIMENSIONS), 65)\n",
    "    \n",
    "    axes[idx].axhline(0.3, color='gray', linestyle=':', alpha=0.8, linewidth=2)\n",
    "    if idx == 0:\n",
    "        axes[idx].set_ylabel(\"Amari Error (Lower is Better)\", fontsize=13)\n",
    "        axes[idx].text(min(DIMENSIONS)+1, 0.35, 'Good Separation', color='gray', fontsize=11)\n",
    "        handles, labels = axes[idx].get_legend_handles_labels()\n",
    "        axes[idx].legend(handles=handles, labels=labels, title='Method', loc='upper left')\n",
    "    else:\n",
    "        axes[idx].set_ylabel(\"\")\n",
    "        if axes[idx].get_legend(): axes[idx].get_legend().remove()\n",
    "\n",
    "# --- NEW: Lock the y-axis strictly from 0 to 6 ---\n",
    "plt.ylim(0, 3)\n",
    "\n",
    "fig.suptitle(\"Amari Scores: FastICA vs OT-ICA\\nLaplacian Independent Sources\", fontsize=15, y=1.05)\n",
    "plt.tight_layout()\n",
    "plt.savefig('laplace_amari_ot_ica_scaling.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "92bbb0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s4/CZNGmSx2NdUFAQtrnZy+ERyYzZy+GzZs0aKyMjw+PxTkxMtF5//fWgxqutrfXrfs8884zXXmzZsqW1adOmJvvefPPNtvzMiBeRynjkyJFkFOVKS0utrKwsr/20YMECv/qzj2Eq6kHmox5ktmioFbCXQysaMmYvRz/qQeajHmQG6kHxgXpQ7KMeZD7qQfGBepD5qAehIdSDzMABPAAxZceOHVZZWVmj9wlFwbW4uNjrh57T6bTmz5/vV/+9e/da+fn5Hv1dLpe1YcOGZq/NNJEsqvM/kqFXVFTk95vtDXnzzTe98ne5XNaePXua7MteDr1IZ2xZ7OVYcdttt3nl/Jvf/KbJfuzj2BFsxpbFPo4GbrfbGjFihEcO//jHP+p+saY5b8iwj6NHKIvq7OPwiVQxjr0cPhRc48Po0aO9nn/ffPPNkM5ZU1Njde7c2WveV155xa/+FRUVVt++fb36B/PLt/EgEhlbFgXXWPDiiy96/dvo3bu3X33ZxzAZ9SDzUQ8yW6RrBezl0It0xpbFXo4V1IPMRz0otlEPig/Ug8xAPch81IPiA/Ug81EPQkOoB5khuGtSAkCEtGnTRqmpqWGf95VXXlFJSYlH25lnnqkTTjjBr/65ubn6wx/+4NHmdrv17LPP2rZGU9x2220qKSnR8uXL9fzzz+v666/Xscceq6SkpEgvDTbIycnRyJEjmzXG+eef73XZY7fbrQ8++KDJvuzl0It0xogdl1xyiVfbqlWrmuzHPo4dwWaM6PDII49o0aJFdbfPPPNMn5kGg30cHWpra3X55ZervLy8ru3xxx9X69atI7gqxBL2MmCfmTNnas6cOR5tl112mc4///yQzvvf//5XmzZt8mgbMGCAfvnLX/rVPzk5WX/+85+92p9++mlb1meSSGWM2PD88897tV111VV+9WUfw2TUg8xHPchska4VsJdDL9IZI3ZQDzIf9aDYRj3IfNSD0FzsZcA+1IPMRz0IjaEeZAYO4AGAH1577TWvtuuuuy6gMSZNmqTk5GSPtldffbVZ6zJRpIrqiC2nn366V9vGjRub7Mdejh3BZozY0bVrV6+2H374ocl+7OPYEWzGiLzvv/9ed911V93trKws/e1vf7NtfPZxdAhlUR3xgb0M2OfBBx/0uJ2cnKxHHnkk5PP62sfXXnttQGOcffbZOuaYYzza3n33XZWVlTVrbaaJVMaIfmvXrtX8+fM92hITE3XZZZf51Z99DNiP17nhQz0I/qAeZD7qQeajHmQ+6kGxi3pQfKAehOZiLwP2oR5kPupBaAj1IHNwAA8AmnDgwAGPNyIkKTMzU6eeempA47Rq1Uonn3yyR9vmzZv1/fffN3eJQNzp2LGjV9vOnTsb7cNeji3BZIzYUlFR4dXW1F+3Zh/HlmAyRuS53W5NmjRJlZWVdW2PPPKI2rdvb8v47OPoEOqiOszHXgbss2bNGi1YsMCjbfz48crLywvpvJZl6aOPPvJqD/QvcCYmJmrChAkebeXl5Zo3b16z1meSSGWM2ODrr52effbZfv37YB8D9uN1LhB9qAeZj3qQ+agHmY96UGyiHhQfqAehudjLgH2oB5mPehAaQz3IHBzAA4AmLFq0SG6326Nt+PDhcrlcAY914oknerXVP9EOoGm+/uJCU38pl70cW4LJGLHl66+/9mrz9Rcyj8Y+ji3BZIzIe+ihhzyyGzNmjH7961/bNj77OPJCXVRHfGAvA/Z54403vNouvvjikM+7Zs0a7d6926Ote/fuys/PD3gs9nHjIpUxol9NTY3+/ve/e7VfffXVfvVnHwP243UuEH2oB5mPepD5qAeZj3pQbKIeZD7qQbADexmwD/Ug81EPQkOoB5mFA3gA0IQlS5Z4tY0YMSKosY4//ni/xgfQuPXr13u1tWnTptE+7OXYEkzGiC0PP/ywV9u4ceMa7cM+ji3BZIzIWrlypf70pz/V3U5PT9ezzz5r6xzs48gLdVEd8YG9DNhn9uzZXm3Dhw8P+bzs4/CJVMaIfu+//77X1V06dOjg9/83sY8B+7GvgOhDPch81IPMRz3IfNSDYg/1oPhAPQh2YC8D9qEeZD7qQWgI9SCzJER6AQAQ7VavXu3V1q1bt6DG8vVXvtasWRPUWEC8qqmp0bvvvuvVfuyxxzbaj70cO4LNuCEbNmzQ//zP/2jhwoUqLCzUnj175HQ6lZubq9zcXA0cOFAnnXSSxo4dqw4dOjR3+WhCdXW1brrpJn388cce7Xl5ebr00ksb7cs+jg3Nybgh7OPQq6mp0aRJk1RVVVXXdv/996tz5862zsM+jqxwFNUReWVlZfrLX/6iBQsWaPXq1dqzZ48qKyvVsmVLtWzZUj169NBJJ52k0aNHa+DAgUHNwV4G7OF2u/Xll196tLVq1Urt2rWru33gwAG9/PLLmjVrlr799lvt3r1biYmJatWqldq0aaOTTjpJ48aN06hRo+R0+v/39uzcxx07dlRCQoJqamrq2tjHh0Uy44bs3r1bf/rTn7RgwQKtX79ee/fuldvtVsuWLZWbm6u+ffvqxBNP1CmnnKIePXo0ez407LnnnvNqu+KKK/zOmX0M2I/XuUB0oR5kPupBZqMeZD7qQbGJelB8oB4UH6gHAbGDepD5qAehMdSDDGMBgGEKCgosSR4fzXHCCSd4jTdv3rygxqqpqbFcLpfHWB06dGjW+uKR3Rk3NO4JJ5xg3XbbbdaJJ55odejQwUpJSbHS0tKsDh06WIMGDbImTZpkPf/889aWLVtsmR/+eeutt7zyb9mypVVdXd1oP/Zy7Ag24yN8PUf48+FyuayLLrrIWrp0aWi/wTi1adMm66mnnrJ69Ojh9dg7HA7rnXfeaXIM9nF0syPjI9jH4XfPPfd4PJbHH3+85Xa7ve63adMmr8d95MiRfs/DPo6c6upqa+jQoR6P19SpU73u19yMj+C1dfhMmjQpqOfMI3v93XffDXhO9nJ41c84Ly/Puv/++60zzjjD6tKli5WZmWklJSVZxxxzjNWnTx/rnHPOsR577DFr2bJlzZ6bvRxaq1at8tpLI0aMqPv69OnTrczMTL/2c9++fa233nrL77kvueQSrzFeeumloL+X+v9WXC6XVVVVFfR4pohkxkeMHDky6J8Tp59+etDP72jc9u3bvX7+ORwOa9OmTX6PwT4GqAfFA+pB8Y16kPmoB5mJepD5qAfFNupB5qMeZC7qQeajHmQu6kHmox6EhlAPMg8H8AAYx+5iXLdu3bzG27hxY9DjtW/f3mOspKSkZq0vHoWr4OrvB2/uhk95ebnPN/JvueWWJvuyl2NDczI+Iti9fPSevvfee63a2toQfqdmGjFihDVw4MC6jwEDBlhdu3Zt9A2EpKQk64UXXvBrfPZx5IU64yPYx+G1fPlyKzExse7xS0lJsVavXu3zvs0txrGPIydcRfUjeG0dPs0puB75mDhxonXgwAG/52Qvh1ckiupHsJdDa+bMmV6P3WmnnWZVVFRY48ePD+qx//Wvf+1XgeSUU07x6jtnzpygvxdfv4ixY8eOoMczRSQzPqI5BdcjH7/97W+tysrKED5S8ef+++/3epzHjh0b0BjsY4B6UDygHhS/qAeZj3pQbKMeZD7qQWaiHhQfqAeZi3qQ+agHmYt6kPmoB6Eh1IPM0/zrUwKA4YqKirzaMjIygh6vft+qqiodOnQo6PEQeW63W6+99pqGDRum++67T5ZlRXpJxpoyZYrWrl3r0ZaTk6Pbbrutyb7s5djQnIwb43Q6dcwxx6hHjx7q1auX8vLyGryEt9vt1p133qnzzjtPbre7WfPGm2+//VbLly+v+/j222+1YcMGHTx40Of9R48erS+//FJXXHGFX+OzjyMv1Bk3hn0cGtXV1Zo0aZKqq6vr2u6++2717NkzJPOxjyPj22+/1f/+7//W3U5JSdELL7zQ4B6KJF5b26dly5bq0qWL+vTpozZt2igxMbHB+77xxhsaOnSodu7c6dfY7OXYsXDhQk2YMEEXXnihSkpKwjYve9k/P/zwg1dbRkaGLr30Us2cOdOj3eFwKD8/X71791abNm0afA5/9tlnde6556q2trbRuUO9jyVp3759QY9nikhm3JS8vDx169ZNvXv3Vn5+vlwuV4P3feqppzRy5EiVlpY2a04cZlmWXnjhBa/2q6++OqBx2MeA/Xidi6bwOjd8qAeZj3pQbKMeZD7qQeahHhQfqAfFJ+pBkKgHRTvqQeajHgRfqAeZKfpeWQNAlPH1QiI1NTXo8Xz1LSsrC3o8hBZv7kaPt956S48//rhX+yOPPKK8vLwm+7OXo19zMz6ay+XS6NGj9fDDD+vrr7/WwYMH9cMPP2jNmjX6/vvvtXv3bu3bt08zZ87UBRdc4HNfv/POO7rxxhuD/XbQiAEDBuizzz7TJ598okGDBvndj30cO4LN+Gjs4/C49957tWzZsrrbQ4cO1R/+8IeQzcc+Dr9wF9Ubw2vr0Orfv7+mTJmiOXPmaO/evdq3b582bNiglStXaseOHTp48KDmz5+vm2++2ecbquvXr9dZZ53l1x5iL0eHUBbVG8Netk9xcbFX2/vvv6833nij7nZubq4ee+wxbdu2TTt37tSqVau0Y8cO7dq1S88884zatWvnc4w//elPjc7NPg6PSGZc3/Dhw3XPPffo888/14EDB7R7926tW7dOq1at0s6dO3XgwAF9/PHHuvLKK5WUlOTV/4svvtBFF13EXrbBvHnztH79eo+23NxcnXPOOQGNwz4G7Me+im+8zo0e1IPMRz0oflAPMh/1oNhBPch81IPiB/Wg+EM9KPZRDzIf9SD4Qj3IUJG7+B4AhIavy2E3h9Pp9Bqvuro66PGOP/54r/G2bt3arDXGG7szPnpcl8tljR492nr44Yetr7/+2iotLfW6X3FxsTVz5kzrggsu8PnvQ5J1/fXX27ImHLZ48WIrLS3N63E+//zz/R6DvRzd7Mj4iKeeesrasmVLQH2WL19u9erVy+d+fvPNNwNeQ7xKT0/3+5L1LpfLmjhxorVs2TK/x2cfR16oMz6CfRwe33zzjZWQkFD3OCUmJlrLly9vtM+mTZu8Ht+RI0f6PSf7OPzuuusuj8dn6NChVk1NTYP3b27GR/DaOnxef/116+uvvw6oz969e62zzjrL5+P929/+tsn+7OXwmjRpkiXJ6t+/vzVlyhRrzpw51t69e73uV1FRYc2fP9+6+eabrYyMDJ/5Dhs2zOdebAh7ObT+93//t9HXU4MGDbJ27tzZ6BglJSXWqFGjvPo6HA5r5cqVDfbr0qWLV59169YF/b388pe/9Bpv/vz5QY9nikhmfMQLL7xgrV69OqB1FxYWWiNGjPC55kceeSSgseDtkksu8Xpcb7rppoDHYR8D1IPiAfWg+EM9yHzUg8xAPch81IPMQj0oPlAPMh/1IPNRDzIX9SDzUQ+CL9SDzMQBPADGCUfBtbE3KJry85//3Gu8QN9QjHehKrjy5m502rBhg3XMMcd4Pb69evWySkpK/B6HvRy97Mq4uUpKSqyBAwd6raNnz57N+rcSz6qrq609e/ZYy5Yts5599llr3LhxlsPh8Hh8ExISrHvvvdev8djH0cfujJuLfey/yspKq3///h6P01133dVkv1AUXNnHoROJovoRvLaODVdeeaXXY52YmGht2LCh0X7s5fCKRFH9CPZyaN1zzz0+HydJVuvWra3du3f7NU5paanVo0cPrzEuvfTSBvv4KtSsX78+6O/lV7/6ldd4n332WdDjmSKSGTdXVVWVNW7cOK85W7Zsae3fvz9k85quuLjYSk1N9XpcV6xYEfBY7GOAelA8oB4UX6gHmY96kLmoB5mPelDsoh4UH6gHoSnUg2ID9SBzUQ8yH/Ug1Ec9yFy+rwULAKjj65Ld5eXlQY/nq6+vy/gi/H7zm9+oQ4cOAfUZMGCAvvrqKw0cONDra3fccQeXYW6m7du365RTTtHOnTs92jt06KD//ve/yszM9Hss9nJ0sjPj5srMzNR7772n9PR0j/Y1a9bovffeC9s6TJKQkKBWrVpp4MCBuvrqq/Xhhx/qm2++8XjOrKmp0Z133qnJkyc3OR77OPrYnXFzsY/99+c//1nfffdd3e1+/frpjjvuCPm87OPwqaqq0qRJk1RTU1PXNmXKFA0YMCAs8/PaOjZMnz5dQ4cO9Wirrq7WE0880Wg/9nJ4TZw4UcOGDQuoT25urv7zn//oyiuv9Pra9OnTtXHjRr/GYS+Hlq+9dMRDDz2kvLw8v8ZJS0vTtGnTvNpfffVVHTx40O+52cf2i2TGzZWYmKg333xT7du392gvKirSjBkzQjJnPPjnP//ptV+OO+449e3bN+Cx2MeA/dhX8YPXudGHepD5qAeZjXqQ+agHxS7qQeajHgR/UA+KDdSDzEU9yHzUg1Af9SBzcQAPAJqQlpbm1Wb3D676bwoitvDmbmjs3r1bp5xyijZt2uTRfswxx+jjjz9WQUFBQOOxl6OP3RnboUOHDrrpppu82j/44IOwr8VUgwYN0oIFC3Tcccd5tP/1r3/VG2+80Whf9nFsaE7GdmAfN23x4sV66KGH6m67XC698MILYXkzhX0cPpEqqjcXr63DKyEhQQ888IBXe1PPmezl2BFsUb252Mv+aejfeatWrXTxxRcHNNaYMWPUu3dvj7aamhp9/vnnPu/PPg6PSGZsh8zMTN19991e7by2Dt7zzz/v1Xb11VcHNRb7GLAf+wpN4XVuaFAPMh/1oPhEPch81IOiH/Wg+EA9CP6gHmQ+6kHRjXqQ+agHoT7qQebiAB4ANCE3N9er7dChQ0GPV79vUlKSMjIygh4P0YE3d+21b98+jRkzRqtXr/Zob9WqlT7++GP16NEj4DHZy9ElFBnb5Ze//KVX2yeffBKBlZgrIyND//rXv5SamurR/oc//MHjL/PVxz6OHcFmbBf2ccMqKyt1+eWXe+QwefJkHXvssWGZn30cHpEsqtuB19bhNWbMGOXn53u0rV27Vtu2bWuwD3s5dgRbVLcDe7lpvvaSJI0cOTKo5+yxY8d6tS1YsMDvue3cxw3NEW8imbFdLrzwQiUkJHi0ffbZZ6qurg7pvCZatmyZvvnmG4+2jIwMXXTRRUGNxz4G7MfrXPiD17n2oh5kPupB8Y16kPmoB0Uv6kHxgXoQAkE9yGzUg6Ib9SDzUQ/C0agHmY0DeADQhPr/4ymp0f/xbIzb7dYPP/zQ5PiITby5a4/i4mKNHTtWK1as8Ghv2bKlPv7446AuwSyxl6NJqDK2S58+fbwu+75169YIrcZcBQUFuvTSSz3aNm/erA8//LDBPuzj2BJMxnZhHzds6tSpWrlyZd3t7t27689//nPY5mcfh16ki+p24bV1+DgcDp100kle7Vu2bGmwD3s5tgRTVLcLe7lxbdq08dk+ePDgoMbz1W/Hjh0+72vnPpa8X2s5nU61atUq6PFMEcmM7ZKVleU1b0VFhfbs2RPSeU303HPPebVdeOGFQf+SEfsYsB+vc+EvXufag3qQ+agHQaIeFA+oB0Un6kHmox6EQFEPMh/1oOhFPch81INwNOpBZuMAHgA0oXPnzl5tmzdvDmqsHTt2eP2VL1/jIzbx5m7zHThwQGPHjtXSpUs92rOzs/XRRx9p4MCBQY/NXo4OoczYTvX/p7iqqkoHDhyI0GrMdfrpp3u1zZ07t8H7s49jT6AZ24l97Fv9N+QOHjyoESNGaNCgQX59nHHGGV5jLl682Od9fWEfh16ki+p24bV1ePkqCDT2Rjp7ObYEU1S3C3u5cV26dPHZHuxfGPTVb9++fT7va+c+rq2t1fbt2z3a2rdvr8TExKDGM0kkM7ZToD8n4K2iokKvvPKKV/vVV18d9JjsY8B+vM6Fv3id23zUg8xHPQhHox5kPupB0Yd6kPmoByEY1IPMRj0oelEPMh/1IBxBPch8CU3fBQDiW8+ePb3a1q9fH9RYGzZs8Grr1atXUGMhOrVp08bjBeeRN3dbtGgRwVXFhpKSEo0bN05LlizxaM/KytKHH36ooUOHNmt89nLkhTpjO6Wnp3u1lZeXs5dt1qlTJ6+2jRs3Nnh/9nHsCTRjO7GP/bNz507t3LmzWWOUlpZq+fLlft2XfRx6DRXV/VVVVeXVdqSoXt+yZcsCXV5AeG0dPg09ZzaEvRx7IlksYS83rEOHDsrIyNChQ4c82pOTk4MaLyUlxautoqLC533t3MdbtmxRdXW1Rxv7+LBIZmynQH9OwNvbb7+t/fv3e7T16dMnoNdp9bGPAfvxOheB4HVu8KgHmY96EOqjHmQ+6kHRj3qQeagHIRjUg8xHPSg6UQ8yH/UgHEE9yHwcwAOAJvgqACxatCiosRYuXOjVNmTIkKDGQnTizd3gHDx4UKeeeqq++uorj/bMzEx9+OGH+tnPftbsOdjLkRWOjO3k682nYP8iDRrm682CgwcPNnh/9nHsCTRjO7GPoxP7OPzCXVS3E6+tw8fXc2arVq0avD97OfZEsljCXm6Y0+nUkCFD9Nlnn3m0B/tX2usXc6SGX/+wj8MjkhnbKdCfE/D23HPPebVdddVVzRqTfQzYj32FQPA6NzjUg8xHPQi+UA8yH/Ug1Mc+Dj/qQfAH9SDzUQ+KTtSDzEc9CEdQDzKfM9ILAIBoN2LECLlcLo+2RYsWye12BzzWggULvNp8XfYbsYs3dwN36NAhnXbaafriiy882jMyMvTBBx9o+PDhtszDXo6ccGVsl9LSUm3ZssWjLTs7m8tsh8Du3bu92hr7n3b2cewJNGO7sI+jF/sYgeC1dfh8//33Xm15eXkN3p+9HHsiWSxhLzfu5JNP9mrbtGlTUGMVFhZ6tTW0l3v16uX1tbVr1/p8/dYU9nHjIpWxnQL9OQFPGzdu1Ny5cz3akpKSdNlllzVrXPYxYD9e5yIQvM4NHPUg81EPQkOoB5mPehDqYx8jELy2Dh/qQeajHhS9qAeZj3oQqAfFBw7gAUATsrOzvYoBBw8e1EcffRTQOEVFRfr000892jp27KjevXs3e42IDry5G7jS0lKdccYZXn9VIT09XbNmzdLPf/5z2+ZiL0dGODO2yyeffKKqqiqPtoEDB0ZoNWarX4SXpGOOOabB+7OPY0+gGduFfdywxx9/XJZlBf3h683BkSNH+ryvL+xj+IvX1uFTVFTk9XydkpKiHj16NNiHvRx7IlUsYS837cwzz/Rq8/WXB/3hq9/gwYN93tfhcGjcuHFe7W+99VZAc9bU1Oidd97xaEtJSdHIkSMDGsdkkcrYLt9++622b9/u0dapUyf+anEAXnjhBa/XxxMmTGj2L76wjwH78ToX/uJ1buCoB5mPehAaQz3IfNSDog/1IMQKXluHD/Wg+EA9KHpRDzIf9SBQD4oPHMADAD9cdNFFXm3Tp08PaIyXXnpJFRUVHm0XX3xxs9aF6MKbu4EpKyvT+PHjNX/+fI/2tLQ0vf/++zrxxBNtn5O9HF6RyNgODz74oFfb6aefHoGVmM2yLL322mte7SNGjGi0H/s4dgSbsR3Yx9GNfRxakS6q24XX1uHzyCOPeP2l0pNPPlmpqamN9mMvx45giup2YS837Wc/+5m6d+/u0bZ48WKfRfLG7N27Vx988IFX+5gxYxrsY8c+fu+99/TDDz94tE2YMEFpaWkBjWOySGZshwceeMCr7bTTTgvpnCZxu9168cUXvdqvuuoqW8ZnHwP243Uu/MHr3MBQDzIf9SA0hnqQ+agHoSHs49CiHoRAUQ8yH/Wg6EY9yHzUg+Ib9aA4YgGAYQoKCixJHh/NVVxcbGVmZnqM6XQ6rc8//9yv/vv27bPatGnj0d/lclnr169v9triUSgytsPxxx/vta4HHngg0suKSuXl5dYpp5zi9XilpqZac+bMCdm87OXwiVTGzfXUU095rdnpdFpr1qyJ9NKM88wzz3g91snJydbBgwcb7cc+jh3BZtxc7OPQ2rRpk9fjO3L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",
      "text/plain": [
       "<Figure size 3600x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- OT-ICA Time Breakdown ---\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Time (s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>73.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>125.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>186.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>262.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>380.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>486.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>546.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>607.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>665.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>677.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>632.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>510.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total Time (s)\n",
       "Dimension                \n",
       "10                  42.12\n",
       "15                  73.93\n",
       "20                 125.31\n",
       "25                 186.64\n",
       "30                 262.46\n",
       "35                 380.34\n",
       "40                 486.10\n",
       "45                 546.46\n",
       "50                 607.89\n",
       "55                 665.95\n",
       "60                 677.28\n",
       "65                 632.20\n",
       "70                 510.05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 5. Plot 2: Total Execution Time (1x2 Side-by-Side)\n",
    "# ==========================================\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "\n",
    "palette = {'FastICA': '#0173B2', 'OT-ICA': '#D55E00'}\n",
    "\n",
    "# --- Subplot 1: Low Compute ---\n",
    "df_low = df[df['Compute Level'] == 'Low Compute']\n",
    "sns.lineplot(\n",
    "    data=df_low, \n",
    "    x='Dimension', \n",
    "    y='Total Time (s)', \n",
    "    hue='Method', \n",
    "    palette=palette,\n",
    "    marker='o', \n",
    "    linewidth=2.5,\n",
    "    markersize=8,\n",
    "    ax=axes[0]\n",
    ")\n",
    "axes[0].set_title(\"Low Compute Regime\", fontsize=14)\n",
    "axes[0].set_ylabel(\"Total Time (Seconds)\", fontsize=13)\n",
    "axes[0].set_xlabel(\"Number of Dimensions\", fontsize=13)\n",
    "axes[0].set_xticks(DIMENSIONS)\n",
    "\n",
    "handles, labels = axes[0].get_legend_handles_labels()\n",
    "axes[0].legend(handles=handles, labels=labels, title='Method', loc='upper left', fontsize=11)\n",
    "\n",
    "# --- Subplot 2: High Compute ---\n",
    "df_high = df[df['Compute Level'] == 'High Compute']\n",
    "sns.lineplot(\n",
    "    data=df_high, \n",
    "    x='Dimension', \n",
    "    y='Total Time (s)', \n",
    "    hue='Method', \n",
    "    palette=palette,\n",
    "    marker='o', \n",
    "    linewidth=2.5,\n",
    "    markersize=8,\n",
    "    ax=axes[1]\n",
    ")\n",
    "axes[1].set_title(\"High Compute Regime\", fontsize=14)\n",
    "axes[1].set_ylabel(\"\") # Shared y-axis\n",
    "axes[1].set_xlabel(\"Number of Dimensions\", fontsize=13)\n",
    "axes[1].set_xticks(DIMENSIONS)\n",
    "\n",
    "if axes[1].get_legend() is not None:\n",
    "    axes[1].get_legend().remove()\n",
    "\n",
    "fig.suptitle(\"Time Complexity: FastICA vs OT-ICA\\nLaplacian Independent Sources\", fontsize=15, y=1.05)\n",
    "plt.tight_layout()\n",
    "plt.savefig('laplace_time_ot_ica_scaling.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# Display time breakdown table for OT-ICA\n",
    "otica_times = df[df['Method'] == 'OT-ICA'].groupby('Dimension')[['Total Time (s)']].mean()\n",
    "print(\"\\n--- OT-ICA Time Breakdown ---\")\n",
    "display(otica_times.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbb23f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# ==========================================\n",
    "# 3. Main Loop\n",
    "# ==========================================\n",
    "\n",
    "for dim in tqdm(DIMENSIONS, desc=\"Dimensions\"):\n",
    "    for trial in range(N_TRIALS):\n",
    "        # 1. Generate Data\n",
    "        X_torch, A_true = generate_dataset(n_dim=dim, n_samples=N_SAMPLES, seed=trial)\n",
    "        X_np = X_torch.numpy()\n",
    "        \n",
    "        # 2. FastICA (Baseline)\n",
    "        try:\n",
    "            fast_ica = FastICA(n_components=dim, max_iter=2000, tol=1e-4, random_state=trial)\n",
    "            fast_ica.fit(X_np.T)\n",
    "            W_fast_total = fast_ica.components_\n",
    "            score_fast = amari_error(W_fast_total, A_true)\n",
    "        except Exception as e:\n",
    "            score_fast = np.nan\n",
    "            W_fast_total = None\n",
    "            \n",
    "        results.append({'Dimension': dim, 'Method': 'FastICA', 'Amari Error': score_fast})\n",
    "        \n",
    "        # 3. Initialize WassersteinICA\n",
    "        ica = WassersteinICA(X_torch)\n",
    "        ica.whiten()\n",
    "        W_white_np = ica.W_white.cpu().numpy()\n",
    "        \n",
    "        # 4. Standalone WassersteinICA (Phase 1 + Stiefel Manifold Polish)\n",
    "        try:\n",
    "            # PHASE 1: Robust Deflation (Find the mountains)\n",
    "            extracted_ws = []\n",
    "            n_restarts = dim * 4 if dim * 4 < 150 else 150\n",
    "            for _ in range(dim):\n",
    "                prev = torch.stack(extracted_ws) if extracted_ws else None\n",
    "                # Using dynamic restarts to guarantee we avoid local minima in high dims\n",
    "                w, _ = ica.optimize_wasserstein2(prev_components=prev, max_iter=200, n_restarts=n_restarts)\n",
    "                extracted_ws.append(w)\n",
    "            W_deflation_init = torch.stack(extracted_ws)\n",
    "            \n",
    "            # PHASE 2: Stiefel Manifold (Climb the peaks while staying strictly orthogonal)\n",
    "            W_stiefel_unmixed = ica.optimize_symmetric(\n",
    "                n_components=dim, \n",
    "                max_iter=200, \n",
    "                lr=1.0, \n",
    "                init_w=W_deflation_init, \n",
    "                optimizer='stiefel',\n",
    "            )\n",
    "            \n",
    "            # No final orthogonalization needed; Stiefel guarantees W @ W.T = I\n",
    "            W_wass_total = W_stiefel_unmixed.cpu().numpy() @ W_white_np\n",
    "            score_wass = amari_error(W_wass_total, A_true)\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"Failed at dim {dim}: {e}\")\n",
    "            score_wass = np.nan\n",
    "            \n",
    "        results.append({'Dimension': dim, 'Method': 'W-ICA (Stiefel)', 'Amari Error': score_wass})\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d975c1ff",
   "metadata": {},
   "source": [
    "## What happens if we only do Stiefel step i.e. Phase 2, without Phase 1 with SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03630b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# ==========================================\n",
    "# 1. Experiment Setup\n",
    "# ==========================================\n",
    "DIMENSIONS = [n for n in range(10, 21)]  # 10 to 20 dimensions\n",
    "N_SAMPLES = 5000\n",
    "N_TRIALS = 5  \n",
    "\n",
    "print(f\"--- FastICA vs. W-ICA (Stiefel Cold Start Only) ---\")\n",
    "print(f\"Dimensions: {DIMENSIONS[0]} to {DIMENSIONS[-1]}\")\n",
    "print(f\"Learning Rate: 0.1\")\n",
    "\n",
    "# ==========================================\n",
    "# 2. Parallel Worker Function\n",
    "# ==========================================\n",
    "def run_cold_start_trial(dim, trial, n_samples):\n",
    "    torch.set_num_threads(1) \n",
    "    trial_results = []\n",
    "    \n",
    "    # Generate standard Laplace data for a clear baseline\n",
    "    X_torch, A_true = generate_dataset(n_dim=dim, n_samples=n_samples, seed=trial)\n",
    "    X_np = X_torch.numpy()\n",
    "    \n",
    "    # --- FastICA (Baseline) ---\n",
    "    t0_fast = time.perf_counter()\n",
    "    try:\n",
    "        fast_ica = FastICA(n_components=dim, max_iter=2000, tol=1e-4, random_state=trial)\n",
    "        fast_ica.fit(X_np.T)\n",
    "        W_fast_total = fast_ica.components_\n",
    "        score_fast = amari_error(W_fast_total, A_true)\n",
    "    except Exception:\n",
    "        score_fast = np.nan\n",
    "    t_fast_total = time.perf_counter() - t0_fast\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, \n",
    "        'Method': 'FastICA', \n",
    "        'Amari Error': score_fast,\n",
    "        'Time (s)': t_fast_total\n",
    "    })\n",
    "    \n",
    "    # --- W-ICA (Phase 2 Stiefel Cold Start ONLY) ---\n",
    "    t0_wass = time.perf_counter()\n",
    "    try:\n",
    "        ica = WassersteinICA(X_torch)\n",
    "        ica.whiten()\n",
    "        W_white_np = ica.W_white.cpu().numpy()\n",
    "        \n",
    "        # We completely skip optimize_wasserstein2 (Phase 1)\n",
    "        # Passing init_w=None forces optimize_symmetric to generate a random orthogonal matrix\n",
    "        W_stiefel_cold = ica.optimize_symmetric(\n",
    "            n_components=dim, \n",
    "            max_iter=300,       # Slightly higher max_iter to account for the slower 0.1 LR\n",
    "            lr=0.1,             # Your requested safe learning rate\n",
    "            init_w=None,        # Cold Start trigger\n",
    "            optimizer='stiefel',\n",
    "        )\n",
    "        \n",
    "        W_wass_total = W_stiefel_cold.cpu().numpy() @ W_white_np\n",
    "        score_wass = amari_error(W_wass_total, A_true)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"W-ICA Error (dim {dim}, trial {trial}): {e}\")\n",
    "        score_wass = np.nan\n",
    "        \n",
    "    t_wass_total = time.perf_counter() - t0_wass\n",
    "        \n",
    "    trial_results.append({\n",
    "        'Dimension': dim, \n",
    "        'Method': 'W-ICA (Cold Start)', \n",
    "        'Amari Error': score_wass,\n",
    "        'Time (s)': t_wass_total\n",
    "    })\n",
    "    \n",
    "    return trial_results\n",
    "\n",
    "# ==========================================\n",
    "# 3. Execute Main Loop\n",
    "# ==========================================\n",
    "tasks = [(dim, trial, N_SAMPLES) for dim in DIMENSIONS for trial in range(N_TRIALS)]\n",
    "\n",
    "results_nested = Parallel(n_jobs=12)(\n",
    "    delayed(run_cold_start_trial)(dim, trial, n_samples) \n",
    "    for dim, trial, n_samples in tqdm(tasks, desc=\"Running Cold Start Trials\")\n",
    ")\n",
    "\n",
    "results = [item for sublist in results_nested for item in sublist]\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "display(df.groupby(['Dimension', 'Method'])['Amari Error'].mean().unstack().round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11e3e82e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================\n",
    "# 4. Plotting the Cold Start Divergence\n",
    "# ==========================================\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot the Amari Error curves\n",
    "sns.lineplot(\n",
    "    data=df, \n",
    "    x='Dimension', \n",
    "    y='Amari Error', \n",
    "    hue='Method', \n",
    "    marker='o', \n",
    "    linewidth=2.5,\n",
    "    palette=['#1f77b4', '#ff7f0e'] # Force consistent colors (FastICA Blue, W-ICA Orange)\n",
    ")\n",
    "\n",
    "plt.title(\"Amari Error vs. Dimension\\n(The Local Minimum Trap: Phase 2 Cold Start)\", fontsize=14)\n",
    "plt.ylabel(\"Amari Error (Lower is Better)\", fontsize=12)\n",
    "plt.xlabel(\"Number of Dimensions\", fontsize=12)\n",
    "plt.xticks(DIMENSIONS) # Ensure every dimension is ticked from 10 to 20\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00598b64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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