{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fef9d297",
   "metadata": {},
   "source": [
    "# Optimal Transport in Linear ICA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2214e53e",
   "metadata": {},
   "source": [
    "# Experiment 1: Robust LinGAM in a Location-Scale Causal Environment\n",
    "\n",
    "**Objective:** Demonstrate that the OT-ICA framework can successfully recover causal ordering (the LinGAM problem) in environments where standard proxy-based solvers (like FastICA) suffer from mathematical collapse.\n",
    "\n",
    "**Theoretical Background:**\n",
    "Standard ICA-LiNGAM assumes linear mechanisms and strictly independent, non-Gaussian exogenous noise. However, in real-world Causal Component Analysis (CauCA), systems often exhibit **Location-Scale** dependencies. This means the variance (scale) of a child node dynamically changes based on the value (location) of its parent. \n",
    "\n",
    "As proven in Chapter 5 of the thesis, these dynamics create highly complex, heavy-tailed marginal distributions that trigger the **Vanishing Curvature Pitfall** in standard solvers relying on local density approximations (e.g., the `logcosh` contrast function). \n",
    "\n",
    "**The Experiment:**\n",
    "1. **DGP (Data Generating Process):** We simulate a 3-node Structural Causal Model (SCM): $Z_1 \\rightarrow Z_2 \\rightarrow Z_3$. We intentionally inject heteroskedasticity (location-scale noise) into the child nodes.\n",
    "2. **Mixing:** We apply an orthogonal mixing matrix to simulate the observational data $X = AZ$.\n",
    "3. **Recovery:** We deploy the `WassersteinICA` to evaluate the global geometry of the empirical CDF via exact sorting ($W_2^2$ metric), immune to local density blind spots.\n",
    "4. **Causal Discovery (LinGAM):** We permute the recovered unmixing matrix to reveal the strictly lower-triangular causal structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32f1a288",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import linear_sum_assignment\n",
    "from wasserstein_ica import WassersteinICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c48e494",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 1. SETUP GLOBAL PARAMETERS ---\n",
    "np.random.seed(42)\n",
    "n_samples = 10000\n",
    "dim = 3\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13ab731a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 2. GENERATE LOCATION-SCALE CAUSAL DATA ---\n",
    "# Exogenous noise (Laplace - Super-Gaussian, mimicking structural shocks)\n",
    "eps = np.random.laplace(0, 1, (dim, n_samples))\n",
    "\n",
    "# Structural Causal Model (Z_1 -> Z_2 -> Z_3) with Location-Scale dynamics\n",
    "z1 = eps[0, :]\n",
    "# The variance of z2 depends heavily on the absolute magnitude of z1\n",
    "z2 = 0.8 * z1 + (1.0 + 1.2 * np.abs(z1)) * eps[1, :] \n",
    "# The variance of z3 depends heavily on the absolute magnitude of z2\n",
    "z3 = 0.6 * z2 + (1.0 + 1.2 * np.abs(z2)) * eps[2, :] \n",
    "\n",
    "Z = np.stack([z1, z2, z3])\n",
    "\n",
    "# Mix the components observationally\n",
    "A_true = np.random.randn(dim, dim)\n",
    "Q, _ = np.linalg.qr(A_true) # Use orthogonal mixing for clean Stiefel manifold projection\n",
    "X_raw = Q @ Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f6b5862",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 3. PREPROCESSING (CENTERING & WHITENING) ---\n",
    "X_mean = X_raw.mean(axis=1, keepdims=True)\n",
    "X_centered = X_raw - X_mean\n",
    "cov = np.cov(X_centered)\n",
    "d, v = np.linalg.eigh(cov)\n",
    "W_white = v @ np.diag(1.0/np.sqrt(d)) @ v.T\n",
    "X_white = W_white @ X_centered\n",
    "\n",
    "X_torch = torch.from_numpy(X_white).float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d452fc8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 4. OPTIMAL TRANSPORT ICA (OT-ICA) ---\n",
    "# Initialize and whiten the data\n",
    "model = WassersteinICA(X_torch)\n",
    "model.whiten()\n",
    "\n",
    "# PHASE 1: Deflation Phase (Deploy n_restarts here to avoid local discrete traps)\n",
    "extracted_ws = []\n",
    "for _ in range(dim):\n",
    "    prev = torch.stack(extracted_ws) if extracted_ws else None\n",
    "    # Use 100 restarts for 1D parallel exploration\n",
    "    w, _ = model.optimize_wasserstein2(\n",
    "        prev_components=prev, \n",
    "        max_iter=200, \n",
    "        n_restarts=100, \n",
    "        dither_sigma=0.01\n",
    "    )\n",
    "    extracted_ws.append(w)\n",
    "\n",
    "# Stack the extracted 1D vectors into a 2D initialization matrix\n",
    "W_init = torch.stack(extracted_ws)\n",
    "\n",
    "# PHASE 2: Symmetric Stiefel Optimization (Stochastic Mini-batching)\n",
    "W_est_torch = model.optimize_symmetric(\n",
    "    n_components=dim,\n",
    "    max_iter=400,\n",
    "    lr=0.01,\n",
    "    init_w=W_init,       # Pass the 2D matrix here!\n",
    "    optimizer='stiefel', \n",
    "    batch_size=512,\n",
    "    dither_sigma=0.01\n",
    ")\n",
    "\n",
    "W_est = W_est_torch.cpu().numpy()\n",
    "\n",
    "# Recover the latent components (using X_white because W_est assumes pre-whitened mapping)\n",
    "Z_hat = W_est @ model.X_white.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0202294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 5. THE LINGAM STEP: CAUSAL ORDERING ---\n",
    "# In LinGAM, the global transfer matrix P = W_est @ Q should be a permuted diagonal matrix\n",
    "# By finding the optimal assignment, we can re-order the components to their causal roots\n",
    "P_matrix = np.abs(W_est @ np.linalg.inv(W_white) @ Q)\n",
    "\n",
    "# Hungarian algorithm to solve permutation ambiguity\n",
    "row_ind, col_ind = linear_sum_assignment(-P_matrix)\n",
    "Z_ordered = Z_hat[row_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82cd0a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 1 Complete: Note the tight linear correlation despite the heteroskedastic \"fan\" shapes in the data.\n"
     ]
    }
   ],
   "source": [
    "# Visualize the recovery of the Location-Scale distributions\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "for i in range(dim):\n",
    "    axes[i].scatter(Z[i], Z_ordered[i], alpha=0.3, s=2, color='#0173B2')\n",
    "    axes[i].set_title(f\"Causal Node $Z_{i+1}$: True vs Recovered\")\n",
    "    axes[i].set_xlabel(\"True SCM Generation\")\n",
    "    axes[i].set_ylabel(\"OT-ICA Recovery\")\n",
    "    axes[i].grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Experiment 1 Complete: Note the tight linear correlation despite the heteroskedastic \\\"fan\\\" shapes in the data.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b581d012",
   "metadata": {},
   "source": [
    "# Experiment 2: Causal Discovery on the Real-World Sachs Dataset\n",
    "\n",
    "**Objective:** Apply the OT-ICA causal discovery framework to a real-world biological dataset to recover a known structural directed acyclic graph (DAG).\n",
    "\n",
    "**The Dataset:**\n",
    "We will use the widely benchmarked **Sachs (2005) Protein Signaling Dataset**. It consists of flow cytometry measurements of 11 phosphorylated proteins and phospholipids across thousands of individual human immune system cells. \n",
    "* *Why this dataset?* Biological data naturally exhibits extreme non-Gaussianity, skewness, and location-scale dynamics (e.g., protein saturation limits). It is the gold standard in Causal Representation Learning because the true biological causal network is known and verified by literature.\n",
    "\n",
    "**Citation and data terms:** Cite Sachs et al. (2005) when using `Sachs2005.txt`. Bibliographic details, BibTeX, and a note on how this third-party data relates to the repository MIT License are in [`Sachs2005_CITATION.md`](Sachs2005_CITATION.md).\n",
    "\n",
    "**The Application:**\n",
    "1. **Data Loading:** We ingest the continuous cellular observation data.\n",
    "2. **Global Geometry Unmixing:** Biological noise causes standard proxy metrics to infinitely oscillate due to contradictory statistical structures. We use OT-ICA to enforce the $W_2^2$ cost directly on the residuals, guaranteeing that structural shocks are maximized away from Gaussianity without falling into local density traps.\n",
    "3. **Adjacency Matrix Extraction:** We estimate the causal mechanism matrix $\\mathbf{B}$ from the unmixing matrix. In a true causal system, thresholding this matrix reveals the biological pathways (edges) between the proteins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de26e9bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba94f93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded Sachs dataset: 7466 samples, 11 nodes.\n"
     ]
    }
   ],
   "source": [
    "# --- 1. LOAD THE LOCAL SACHS DATASET ---\n",
    "# Citation and terms: Sachs2005_CITATION.md (alongside Sachs2005.txt)\n",
    "df_sachs = pd.read_csv('Sachs2005.txt', sep='\\t')\n",
    "\n",
    "print(f\"Successfully loaded Sachs dataset: {df_sachs.shape[0]} samples, {df_sachs.shape[1]} nodes.\")\n",
    "\n",
    "protein_names = df_sachs.columns.tolist()\n",
    "X_real = df_sachs.values.T  # Shape: (dim, n_samples)\n",
    "dim_real = X_real.shape[0]\n",
    "n_samples_real = X_real.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69716545",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 2. PREPROCESSING (CENTERING & WHITENING) ---\n",
    "X_mean_r = X_real.mean(axis=1, keepdims=True)\n",
    "X_centered_r = X_real - X_mean_r\n",
    "cov_r = np.cov(X_centered_r)\n",
    "d_r, v_r = np.linalg.eigh(cov_r)\n",
    "W_white_r = v_r @ np.diag(1.0/np.sqrt(d_r)) @ v_r.T\n",
    "X_white_r = W_white_r @ X_centered_r\n",
    "\n",
    "X_torch_r = torch.from_numpy(X_white_r).float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a5d9b27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running OT-ICA on 11 biological nodes...\n"
     ]
    }
   ],
   "source": [
    "# --- 3. OPTIMAL TRANSPORT ICA ---\n",
    "print(f\"Running OT-ICA on {dim_real} biological nodes...\")\n",
    "model_real = WassersteinICA(X_torch_r)\n",
    "model_real.whiten()\n",
    "\n",
    "# Phase 1: Deflation (Higher compute regime for real biological data)\n",
    "extracted_ws_r = []\n",
    "for _ in range(dim_real):\n",
    "    prev = torch.stack(extracted_ws_r) if extracted_ws_r else None\n",
    "    w, _ = model_real.optimize_wasserstein2(\n",
    "        prev_components=prev, \n",
    "        max_iter=200, \n",
    "        n_restarts=150,   # 150 parallel restarts\n",
    "        dither_sigma=0.01\n",
    "    )\n",
    "    extracted_ws_r.append(w)\n",
    "\n",
    "W_init_r = torch.stack(extracted_ws_r)\n",
    "\n",
    "# Phase 2: Symmetric Stiefel Optimization\n",
    "W_est_torch_r = model_real.optimize_symmetric(\n",
    "    n_components=dim_real,\n",
    "    max_iter=600,\n",
    "    lr=0.01,\n",
    "    init_w=W_init_r,\n",
    "    optimizer='stiefel', \n",
    "    batch_size=1024,\n",
    "    dither_sigma=0.01\n",
    ")\n",
    "\n",
    "W_est_r = W_est_torch_r.cpu().numpy()\n",
    "\n",
    "# The estimated unmixing matrix mapping observations to independent biological shocks\n",
    "# Note: Ensure you multiply by the actual whitening matrix, not the internal PyTorch one\n",
    "W_full = W_est_r @ W_white_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a1ae000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- 4. EXTRACTING CAUSAL ADJACENCY (LiNGAM) ---\n",
    "# In LiNGAM, X = B*X + e. We can rewrite this as e = (I - B)X.\n",
    "# Therefore, W_full is a permuted and scaled version of (I - B).\n",
    "# Let's visualize the raw connection strength matrix |W_full|\n",
    "W_strength = np.abs(W_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8144eaf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment 2 Complete: In a full LiNGAM pipeline, this matrix would now be row-permuted to form a strictly lower-triangular DAG, revealing the protein signaling pathway.\n"
     ]
    }
   ],
   "source": [
    "# Plot the heatmap of connection strengths\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(W_strength, xticklabels=protein_names, yticklabels=protein_names, cmap=\"YlOrRd\")\n",
    "plt.title(\"OT-ICA Raw Unmixing Matrix (Absolute Connection Strengths)\")\n",
    "plt.xlabel(\"Observed Protein Nodes\")\n",
    "plt.ylabel(\"Estimated Independent Components (Shocks)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Experiment 2 Complete: In a full LiNGAM pipeline, this matrix would now be row-permuted to form a strictly lower-triangular DAG, revealing the protein signaling pathway.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4004e733",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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