{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "45f68dee",
   "metadata": {},
   "source": [
    "# Optimal Transport in linear Independent Component Analysis (ICA)\n",
    "#### Here, We validate OT-ICA Methodology in a simple 4 dimensional case of ICA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f30c3a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import FastICA\n",
    "import warnings\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "\n",
    "from wasserstein_ica import WassersteinICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57ceba4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a consistent Thesis Theme\n",
    "def set_thesis_theme():\n",
    "    # Academic, colorblind-friendly palette\n",
    "    # Blue, Orange, Green, Red, Purple, Brown\n",
    "    thesis_colors = ['#0173B2', '#DE8F05', '#029E73', '#D55E00', '#CC78BC', '#CA9161']\n",
    "    \n",
    "    mpl.rcParams.update({\n",
    "        # Figure and Layout\n",
    "        'figure.figsize': (8, 5),\n",
    "        'figure.dpi': 300,            # High resolution for print\n",
    "        'axes.prop_cycle': mpl.cycler(color=thesis_colors),\n",
    "        \n",
    "        # Grid lines (light and unobtrusive)\n",
    "        'axes.grid': True,\n",
    "        'grid.alpha': 0.3,\n",
    "        'grid.linestyle': '--',\n",
    "        'axes.axisbelow': True,       # Grid goes behind data\n",
    "        \n",
    "        # Spines (remove top and right borders for a cleaner look)\n",
    "        'axes.spines.top': False,\n",
    "        'axes.spines.right': False,\n",
    "        \n",
    "        # Fonts and Text\n",
    "        'font.size': 11,\n",
    "        'axes.titlesize': 13,\n",
    "        'axes.labelsize': 12,\n",
    "        'xtick.labelsize': 10,\n",
    "        'ytick.labelsize': 10,\n",
    "        \n",
    "        # Legends\n",
    "        'legend.frameon': False,      # No box around the legend\n",
    "        'legend.fontsize': 10,\n",
    "        \n",
    "        # Lines\n",
    "        'lines.linewidth': 2.0\n",
    "    })\n",
    "\n",
    "# Run this before plotting\n",
    "set_thesis_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "894060da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def amari_error(W_est, A_true):\n",
    "    if W_est is None or np.any(np.isnan(W_est)):\n",
    "        return np.nan\n",
    "    P = np.abs(W_est @ A_true)\n",
    "    n = P.shape[0]\n",
    "    row_sum = np.sum(P, axis=1)\n",
    "    row_max = np.max(P, axis=1)\n",
    "    term1 = np.sum((row_sum / row_max) - 1)\n",
    "    col_sum = np.sum(P, axis=0)\n",
    "    col_max = np.max(P, axis=0)\n",
    "    term2 = np.sum((col_sum / col_max) - 1)\n",
    "    return (term1 + term2) / (2 * n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93e45271",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Ground Truth Mixing Matrix (A) ---\n",
      "[[ 1.058  1.52  -0.249  1.012]\n",
      " [ 0.042 -0.486  0.388 -0.523]\n",
      " [-0.154 -1.572 -0.304 -1.234]\n",
      " [-0.006  0.503  0.053  0.928]]\n"
     ]
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 1. Simulation Setup (4 Dimensions)\n",
    "# ==========================================\n",
    "n_samples = 10000\n",
    "n_sources = 4\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Source 1: Laplace (Super-Gaussian, sharp peak)\n",
    "s1 = np.random.laplace(0, 1, n_samples)\n",
    "# Source 2: Uniform (Sub-Gaussian, flat)\n",
    "s2 = np.random.uniform(-np.sqrt(3), np.sqrt(3), n_samples)\n",
    "# Source 3: Student-t (Heavy tails, df=3)\n",
    "s3 = np.random.standard_t(df=3, size=n_samples)\n",
    "# Source 4: Beta(0.5, 0.5) (U-shaped, Bimodal)\n",
    "s4 = np.random.beta(0.5, 0.5, size=n_samples)\n",
    "\n",
    "# Stack, Center, and Normalize\n",
    "S_true = np.stack([s1, s2, s3, s4])\n",
    "S_true = (S_true - np.mean(S_true, axis=1, keepdims=True)) / np.std(S_true, axis=1, keepdims=True)\n",
    "source_names = ['Laplace', 'Uniform', 'Student-t', 'Beta']\n",
    "\n",
    "# Random well-conditioned Mixing Matrix\n",
    "A_true = np.random.randn(n_sources, n_sources)\n",
    "while np.linalg.cond(A_true) > 20:\n",
    "    A_true = np.random.randn(n_sources, n_sources)\n",
    "\n",
    "X_mixed = A_true @ S_true\n",
    "X_torch = torch.tensor(X_mixed, dtype=torch.float32)\n",
    "\n",
    "print(\"--- Ground Truth Mixing Matrix (A) ---\")\n",
    "print(np.round(A_true, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5345693c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Running W2-ICA ---\n",
      "\n",
      "--- Estimated Unmixing Matrix (W_est) ---\n",
      "[[ 0.154 -1.943  0.608 -0.456]\n",
      " [-0.092 -0.284 -0.632 -1.977]\n",
      " [ 0.146  0.719  1.097  1.691]\n",
      " [ 1.021  1.204  0.828  0.677]]\n",
      "\n",
      "W2-ICA Amari Error: 0.01552\n"
     ]
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 2. Wasserstein ICA Extraction (Stiefel)\n",
    "# ==========================================\n",
    "print(\"\\n--- Running W2-ICA ---\")\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "ica = WassersteinICA(X_torch.to(device))\n",
    "ica.whiten()\n",
    "W_white_np = ica.W_white.cpu().numpy()\n",
    "\n",
    "# 2.1 Deflation Initialization\n",
    "extracted_ws = []\n",
    "for i in range(n_sources):\n",
    "    prev = torch.stack(extracted_ws) if extracted_ws else None\n",
    "    w, _ = ica.optimize_wasserstein2(\n",
    "        prev_components=prev, \n",
    "        max_iter=200, \n",
    "        n_restarts=50,\n",
    "        dither_sigma=0.01 # Crucial for Bernoulli\n",
    "    )\n",
    "    extracted_ws.append(w)\n",
    "\n",
    "W_deflation_init = torch.stack(extracted_ws)\n",
    "\n",
    "# 2.2 Symmetric Stiefel Polish\n",
    "W_stiefel_unmixed = ica.optimize_symmetric(\n",
    "    n_components=n_sources, \n",
    "    max_iter=400,        \n",
    "    lr=0.25,             \n",
    "    init_w=W_deflation_init, \n",
    "    optimizer='stiefel',\n",
    "    dither_sigma=0.01,   \n",
    "    batch_size=1024      \n",
    ")\n",
    "\n",
    "W_wass_total = W_stiefel_unmixed.cpu().numpy() @ W_white_np\n",
    "score_wass = amari_error(W_wass_total, A_true)\n",
    "\n",
    "print(\"\\n--- Estimated Unmixing Matrix (W_est) ---\")\n",
    "print(np.round(W_wass_total, 3))\n",
    "print(f\"\\nW2-ICA Amari Error: {score_wass:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35e9e684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Global Transfer Matrix (P) ---\n",
      "[[-0.01  -0.007 -1.    -0.002]\n",
      " [ 0.    -0.003  0.    -1.   ]\n",
      " [ 0.006 -1.    -0.002 -0.012]\n",
      " [ 1.     0.006 -0.003  0.01 ]]\n"
     ]
    }
   ],
   "source": [
    "# ==========================================\n",
    "# 3. Source Matching\n",
    "# ==========================================\n",
    "# Global Transfer Matrix P = W_est @ A_true\n",
    "P = W_wass_total @ A_true\n",
    "print(\"\\n--- Global Transfer Matrix (P) ---\")\n",
    "print(np.round(P, 3))\n",
    "\n",
    "matches = []\n",
    "for i in range(n_sources):\n",
    "    row_abs = np.abs(P[i])\n",
    "    best_match_idx = np.argmax(row_abs)\n",
    "    sign = np.sign(P[i, best_match_idx])\n",
    "    matches.append({\n",
    "        \"extracted_idx\": i,\n",
    "        \"original_idx\": best_match_idx,\n",
    "        \"name\": source_names[best_match_idx],\n",
    "        \"sign\": sign\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9bf330e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3600x2400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract Signals\n",
    "Y_est = W_wass_total @ X_mixed\n",
    "\n",
    "# ==========================================\n",
    "# 4. Visualization\n",
    "# ==========================================\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for m in matches:\n",
    "    i = m['extracted_idx']\n",
    "    j = m['original_idx']\n",
    "    sign = m['sign']\n",
    "    \n",
    "    orig = S_true[j]\n",
    "    est = Y_est[i] * sign # Fix sign flip\n",
    "    \n",
    "    ax = axes[i]\n",
    "    \n",
    "    # Gray Histogram Bars for Original Data (No shading)\n",
    "    ax.hist(orig, bins=60, density=True, alpha=0.3, color='#555555', edgecolor='none', label='Original Data')\n",
    "    # Sharp Orange Line for Extracted Data\n",
    "    sns.kdeplot(est, ax=ax, color='#D55E00', linewidth=2.5, label='OT-ICA Extracted')\n",
    "        \n",
    "    ax.set_title(f\"Component {i+1}: Recovered {m['name']}\", fontsize=13)\n",
    "    ax.legend(loc='upper right', fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('otica_baseline_recovery.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22f1bcc",
   "metadata": {},
   "source": [
    "## Comparison: Wasserstein versus FAST ICA using Amari Similarity measure\n",
    "\n",
    "- P = U.M, where U is unmixing and M is mixing matrix and P should ideally have only one non zero entry per row\n",
    "\n",
    "**The Formula**\n",
    "$$\n",
    "E_{Amari} = \\frac{1}{2n(n-1)} \\left[ \\sum_{i=1}^{n} \\left( \\frac{\\sum_{j=1}^{n} |g_{ij}|}{\\max_j |g_{ij}|} - 1 \\right) + \\sum_{j=1}^{n} \\left( \\frac{\\sum_{i=1}^{n} |g_{ij}|}{\\max_i |g_{ij}|} - 1 \\right) \\right]\n",
    "$$\n",
    "\n",
    "**Simon's Correction**\n",
    "$$\n",
    "E_{Amari} = \\frac{1}{2n} \\left[ \\sum_{i=1}^{n} \\left( \\frac{\\sum_{j=1}^{n} |g_{ij}|}{\\max_j |g_{ij}|} - 1 \\right) + \\sum_{j=1}^{n} \\left( \\frac{\\sum_{i=1}^{n} |g_{ij}|}{\\max_i |g_{ij}|} - 1 \\right) \\right]\n",
    "$$\n",
    "\n",
    "**Interpretation**\n",
    "* **0.0**: Perfect Separation (The matrices match exactly up to permutation/scale).\n",
    "* **< 0.4**: Good/Acceptable Separation.\n",
    "* **> 0.5**: Poor Separation (Failed to unmix)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fff48052",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Running FastICA (sklearn) ---\n",
      "FastICA (logcosh) Amari Error: 0.01879\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import FastICA\n",
    "\n",
    "\n",
    "# ==========================================\n",
    "# 4. Method 2: FastICA (sklearn)\n",
    "# ==========================================\n",
    "print(\"\\n--- Running FastICA (sklearn) ---\")\n",
    "# FastICA expects shape (n_samples, n_features)\n",
    "X_sklearn = X_mixed.T \n",
    "\n",
    "# Algorithm 1: Parallel FastICA with logcosh (approx Negentropy)\n",
    "fastica = FastICA(n_components=n_sources, algorithm='parallel', fun='logcosh', random_state=42)\n",
    "S_est_fast = fastica.fit_transform(X_sklearn)\n",
    "W_total_fastica = fastica.components_ #@ fastica.whitening_ # Total unmixing matrix\n",
    "\n",
    "score_fastica = amari_error(W_total_fastica, A_true)\n",
    "print(f\"FastICA (logcosh) Amari Error: {score_fastica:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba8ce948",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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