{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Digits Conditional Modeling Study\n", "\n", "This notebook studies how different conditional regression models learn to generate digit images conditioned on their labels.\n", "\n", "**Problem Setup:**\n", "- **Targets**: 8 PCA components of 64-pixel digit images\n", "- **Conditioning**: 10-dimensional one-hot encoded digit labels (0-9)\n", "- **Goal**: Learn p(pixels | digit_label) - generate realistic digit images given the label\n", "\n", "**Models Compared:**\n", "1. **ConditionalGMMRegressor**: Joint GMM over [X, y] with analytical conditioning\n", "2. **MixtureOfExpertsRegressor**: Linear-softmax gating with Gaussian experts\n", "3. **DiscriminativeConditionalGMMRegressor**: Discriminative EM for conditional likelihood\n", "\n", "**Visualization**: For each model, we'll show a 9×5 grid where:\n", "- Rows represent digits 0-9\n", "- Columns show 5 random samples generated for each digit\n", "- This allows us to assess the quality and diversity of generated digits\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "ab720d0e", "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "import matplotlib\n", "matplotlib.use('module://matplotlib_inline.backend_inline') # must be before pyplot import\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.datasets import load_digits\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", "from sklearn.decomposition import PCA\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "from cgmm import ConditionalGMMRegressor, MixtureOfExpertsRegressor, DiscriminativeConditionalGMMRegressor\n", "\n", "# Set random seed for reproducibility\n", "np.random.seed(42)\n" ] }, { "cell_type": "markdown", "id": "fa24c4a3", "metadata": {}, "source": [ "## Data Preparation\n", "\n", "Load the digits dataset and prepare it for conditional modeling:\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "c1157248", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset shape: (1797, 64)\n", "Labels: [0 1 2 3 4 5 6 7 8 9]\n", "Label distribution: [178 182 177 183 181 182 181 179 174 180]\n", "\n", "PCA Results:\n", " Explained variance ratio: 0.793\n", " PCA components shape: (1797, 20)\n" ] } ], "source": [ "n_pca = 20\n", "\n", "# Load digits dataset\n", "digits = load_digits()\n", "X_original = digits.data # (1797, 64) - original pixel values\n", "y_labels = digits.target # (1797,) - digit labels 0-9\n", "\n", "print(f\"Dataset shape: {X_original.shape}\")\n", "print(f\"Labels: {np.unique(y_labels)}\")\n", "print(f\"Label distribution: {np.bincount(y_labels)}\")\n", "\n", "# Standardize features\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(X_original)\n", "\n", "# Apply PCA reduction to make the problem more manageable\n", "pca = PCA(n_components=n_pca)\n", "X_pca = pca.fit_transform(X_scaled)\n", "\n", "print(f\"\\nPCA Results:\")\n", "print(f\" Explained variance ratio: {pca.explained_variance_ratio_.sum():.3f}\")\n", "print(f\" PCA components shape: {X_pca.shape}\")\n", "\n", "# Prepare conditioning variables (one-hot encoded digit labels)\n", "encoder = OneHotEncoder(sparse_output=False)\n", "X_conditioning = encoder.fit_transform(y_labels.reshape(-1, 1)) # (1797, 10)\n", "\n", "# Set up the conditional modeling problem\n", "y_target = X_pca # (1797, 8) - PCA-reduced pixel values as targets\n", "X_conditioning = X_conditioning # (1797, 10) - one-hot digit labels as conditioning\n" ] }, { "cell_type": "markdown", "id": "0773521d", "metadata": {}, "source": [ "## Hyperparameter Optimization\n", "\n", "Perform out-of-sample hyperparameter search by sweeping over n_components = 2..20 for all three models:\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "0ce0d352", "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# Split data for hyperparameter optimization\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X_conditioning, y_target, test_size=0.3, random_state=42\n", ")\n", "\n", "# Define hyperparameter search range (reduced for faster doc builds)\n", "n_components_range = [2,3,4,5,7,10,15,20]\n", "\n", "# Initialize results storage\n", "hyperparameter_results = {\n", " 'ConditionalGMMRegressor': {'n_components': [], 'log_likelihood': [], 'mse': [], 'r2': []},\n", " 'MixtureOfExpertsRegressor': {'n_components': [], 'log_likelihood': [], 'mse': [], 'r2': []},\n", " 'DiscriminativeConditionalGMMRegressor': {'n_components': [], 'log_likelihood': [], 'mse': [], 'r2': []}\n", "}\n", "\n", "# Cache best models during search to avoid retraining\n", "best_models = {\n", " 'ConditionalGMMRegressor': {'model': None, 'score': -np.inf},\n", " 'MixtureOfExpertsRegressor': {'model': None, 'score': -np.inf},\n", " 'DiscriminativeConditionalGMMRegressor': {'model': None, 'score': -np.inf}\n", "}\n", "\n", "for n_comp in n_components_range:\n", " \n", "\n", " model = ConditionalGMMRegressor(n_components=n_comp, random_state=42)\n", " model.fit(X_train, y_train)\n", " y_pred = model.predict(X_test)\n", " mse = mean_squared_error(y_test, y_pred)\n", " r2 = r2_score(y_test, y_pred)\n", " log_likelihood = model.score(X_test, y_test)\n", " \n", " # Cache best model if this is the best score so far\n", " if log_likelihood > best_models['ConditionalGMMRegressor']['score']:\n", " best_models['ConditionalGMMRegressor']['model'] = model\n", " best_models['ConditionalGMMRegressor']['score'] = log_likelihood\n", " \n", " hyperparameter_results['ConditionalGMMRegressor']['n_components'].append(n_comp)\n", " hyperparameter_results['ConditionalGMMRegressor']['log_likelihood'].append(log_likelihood)\n", " hyperparameter_results['ConditionalGMMRegressor']['mse'].append(mse)\n", " hyperparameter_results['ConditionalGMMRegressor']['r2'].append(r2)\n", " \n", " \n", "\n", " model = MixtureOfExpertsRegressor(n_components=n_comp, random_state=42)\n", " model.fit(X_train, y_train)\n", " y_pred = model.predict(X_test)\n", " mse = mean_squared_error(y_test, y_pred)\n", " r2 = r2_score(y_test, y_pred)\n", " log_likelihood = model.score(X_test, y_test)\n", " \n", " # Cache best model if this is the best score so far\n", " if log_likelihood > best_models['MixtureOfExpertsRegressor']['score']:\n", " best_models['MixtureOfExpertsRegressor']['model'] = model\n", " best_models['MixtureOfExpertsRegressor']['score'] = log_likelihood\n", " \n", " hyperparameter_results['MixtureOfExpertsRegressor']['n_components'].append(n_comp)\n", " hyperparameter_results['MixtureOfExpertsRegressor']['log_likelihood'].append(log_likelihood)\n", " hyperparameter_results['MixtureOfExpertsRegressor']['mse'].append(mse)\n", " hyperparameter_results['MixtureOfExpertsRegressor']['r2'].append(r2)\n", " \n", "\n", " model = DiscriminativeConditionalGMMRegressor(\n", " n_components=n_comp, \n", " covariance_type='full',\n", " reg_covar=1e-2,\n", " random_state=42\n", " )\n", " model.fit(X_train, y_train)\n", " y_pred = model.predict(X_test)\n", " mse = mean_squared_error(y_test, y_pred)\n", " r2 = r2_score(y_test, y_pred)\n", " log_likelihood = model.score(X_test, y_test)\n", " \n", " # Cache best model if this is the best score so far\n", " if log_likelihood > best_models['DiscriminativeConditionalGMMRegressor']['score']:\n", " best_models['DiscriminativeConditionalGMMRegressor']['model'] = model\n", " best_models['DiscriminativeConditionalGMMRegressor']['score'] = log_likelihood\n", " \n", " hyperparameter_results['DiscriminativeConditionalGMMRegressor']['n_components'].append(n_comp)\n", " hyperparameter_results['DiscriminativeConditionalGMMRegressor']['log_likelihood'].append(log_likelihood)\n", " hyperparameter_results['DiscriminativeConditionalGMMRegressor']['mse'].append(mse)\n", " hyperparameter_results['DiscriminativeConditionalGMMRegressor']['r2'].append(r2)\n", " \n" ] }, { "cell_type": "code", "execution_count": 4, "id": "77eca66f", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal Hyperparameters (Out-of-Sample Performance):\n", "======================================================================\n", "ConditionalGMMRegressor:\n", " Optimal n_components: 7\n", " Best log-likelihood: -21.097\n", " Best MSE: 1.302\n", " Best R²: 0.247\n", "\n", "MixtureOfExpertsRegressor:\n", " Optimal n_components: 7\n", " Best log-likelihood: -18.816\n", " Best MSE: 1.833\n", " Best R²: -0.053\n", "\n", "DiscriminativeConditionalGMMRegressor:\n", " Optimal n_components: 15\n", " Best log-likelihood: -18.123\n", " Best MSE: 1.316\n", " Best R²: 0.227\n", "\n", "======================================================================\n" ] } ], "source": [ "# Find optimal hyperparameters for each model\n", "print(\"Optimal Hyperparameters (Out-of-Sample Performance):\")\n", "print(\"=\" * 70)\n", "\n", "optimal_params = {}\n", "\n", "for model_name, results in hyperparameter_results.items():\n", " # Find the best log-likelihood (excluding NaN values)\n", " valid_mask = ~np.isnan(results['log_likelihood'])\n", " if valid_mask.any():\n", " valid_ll = np.array(results['log_likelihood'])[valid_mask]\n", " valid_components = np.array(results['n_components'])[valid_mask]\n", " valid_mse = np.array(results['mse'])[valid_mask]\n", " valid_r2 = np.array(results['r2'])[valid_mask]\n", " \n", " best_idx = np.argmax(valid_ll)\n", " best_components = valid_components[best_idx]\n", " best_ll = valid_ll[best_idx]\n", " best_mse = valid_mse[best_idx]\n", " best_r2 = valid_r2[best_idx]\n", " \n", " optimal_params[model_name] = {\n", " 'n_components': best_components,\n", " 'log_likelihood': best_ll,\n", " 'mse': best_mse,\n", " 'r2': best_r2\n", " }\n", " \n", " print(f\"{model_name}:\")\n", " print(f\" Optimal n_components: {best_components}\")\n", " print(f\" Best log-likelihood: {best_ll:.3f}\")\n", " print(f\" Best MSE: {best_mse:.3f}\")\n", " print(f\" Best R²: {best_r2:.3f}\")\n", " print()\n", " else:\n", " print(f\"{model_name}: No valid results\")\n", " print()\n", "\n", "print(\"=\" * 70)\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "2e8f6f34", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create visualization plots for hyperparameter sweep results\n", "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", "\n", "# Plot 1: Log-likelihood vs n_components\n", "ax1 = ax\n", "for model_name, results in hyperparameter_results.items():\n", " valid_mask = ~np.isnan(results['log_likelihood'])\n", " if valid_mask.any():\n", " ax1.plot(results['n_components'], results['log_likelihood'], \n", " 'o-', label=model_name, linewidth=2, markersize=4)\n", "ax1.set_xlabel('Number of Components')\n", "ax1.set_ylabel('Out-of-Sample Log-Likelihood')\n", "ax1.set_title('Log-Likelihood vs Number of Components')\n", "ax1.legend()\n", "ax1.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Save the plot\n", "plt.savefig('hyperparameter_sweep_results.png', dpi=150, bbox_inches='tight')\n" ] }, { "cell_type": "markdown", "id": "89c8c582", "metadata": {}, "source": [ "## Digit Generation and Visualization\n", "\n", "Generate random samples for each digit (0-9) using each model and visualize them in a 10×5 grid:\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "434fd3aa", "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "def generate_digit_samples(model, digit_label, n_samples=5):\n", " \"\"\"Generate n_samples for a specific digit using the trained model.\"\"\"\n", " # Create one-hot encoding for the digit\n", " one_hot = np.zeros(10)\n", " one_hot[digit_label] = 1.0\n", " \n", " # Generate samples using the model's built-in sample() method\n", " X_conditioning = one_hot.reshape(1, -1) # (1, 10)\n", " \n", " # Use the model's sample() method to generate samples\n", " samples = model.sample(X_conditioning, n_samples=n_samples)\n", " \n", " return samples # (n_samples, 8)\n", "\n", "def reconstruct_image(pca_components, pca, scaler):\n", " \"\"\"Reconstruct image from PCA components back to original pixel space.\"\"\"\n", " # Inverse PCA transform\n", " X_reconstructed = pca.inverse_transform(pca_components) # (n_samples, 64)\n", " \n", " # Inverse scaling\n", " X_original_scale = scaler.inverse_transform(X_reconstructed) # (n_samples, 64)\n", " \n", " # Clip to valid pixel range [0, 16]\n", " X_original_scale = np.clip(X_original_scale, 0, 16)\n", " \n", " return X_original_scale\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "fdca1915", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating 8 samples for each digit (0-9) using best models...\n", " ConditionalGMMRegressor: n_components = 7\n", " MixtureOfExpertsRegressor: n_components = 7\n", " DiscriminativeConditionalGMMRegressor: n_components = 15\n", "\n" ] } ], "source": [ "# Generate samples using the best models from hyperparameter search\n", "n_samples_per_digit = 8\n", "digits_to_show = list(range(10)) # 0-9\n", "\n", "print(f\"Generating {n_samples_per_digit} samples for each digit (0-9) using best models...\")\n", "\n", "# Store generated samples for each model\n", "generated_samples = {}\n", "model_colors = {\n", " 'ConditionalGMMRegressor': 'blue',\n", " 'MixtureOfExpertsRegressor': 'green', \n", " 'DiscriminativeConditionalGMMRegressor': 'red'\n", "}\n", "\n", "for model_name in best_models.keys():\n", " if best_models[model_name]['model'] is not None:\n", " model = best_models[model_name]['model']\n", " print(f\" {model_name}: n_components = {model.n_components}\")\n", " \n", " generated_samples[model_name] = {}\n", " \n", " for digit in digits_to_show:\n", " # Generate samples for this digit\n", " pca_samples = generate_digit_samples(model, digit, n_samples_per_digit)\n", " \n", " # Reconstruct to original image space\n", " image_samples = reconstruct_image(pca_samples, pca, scaler)\n", " \n", " generated_samples[model_name][digit] = image_samples\n", " else:\n", " print(f\" {model_name}: No valid model found\")\n", " generated_samples[model_name] = None\n", "\n", "print()\n", " \n" ] }, { "cell_type": "code", "execution_count": 24, "id": "ea607bbb", "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create visualization grids for each model\n", "fig, axes = plt.subplots(3, 1, figsize=(12, 12))\n", "\n", "model_names = list(best_models.keys())\n", "for model_idx, model_name in enumerate(model_names):\n", " if generated_samples[model_name] is None:\n", " continue\n", " \n", " ax = axes[model_idx]\n", " \n", " # Create a 10x8 grid with 1 pixel spacing between 8x8 images\n", " # Grid size: 10 rows * (8 pixels + 1 spacing) - 1, 8 cols * (8 pixels + 1 spacing) - 1\n", " grid = np.zeros((10 * 9 - 1, n_samples_per_digit * 9 - 1)) # 89x71 pixels\n", " \n", " for digit in range(10):\n", " if digit in generated_samples[model_name]:\n", " samples = generated_samples[model_name][digit]\n", " for sample_idx in range(min(n_samples_per_digit, len(samples))):\n", " # Reshape to 8x8 image\n", " image = samples[sample_idx].reshape(8, 8)\n", " \n", " # Place in grid with 1 pixel spacing\n", " row_start = digit * 9 # 8 pixels + 1 spacing\n", " col_start = sample_idx * 9 # 8 pixels + 1 spacing\n", " grid[row_start:row_start+8, col_start:col_start+8] = image\n", " \n", " # Display the grid\n", " im = ax.imshow(grid, cmap='gray', vmin=0, vmax=16)\n", " ax.set_title(f'{model_name}')\n", " ax.set_xlabel('Sample Index')\n", " ax.set_ylabel('Digit')\n", " \n", " # Set ticks (adjusted for 1 pixel spacing)\n", " ax.set_xticks(range(4, n_samples_per_digit*9, 9)) # Center of each 8x8 image with spacing\n", " ax.set_xticklabels(range(1, n_samples_per_digit + 1))\n", " ax.set_yticks(range(4, 10*9, 9)) # Center of each 8x8 image with spacing\n", " ax.set_yticklabels(range(10))\n", " \n", " # Add grid lines\n", " ax.set_xticks(range(0, n_samples_per_digit*9, 9), minor=True)\n", " ax.set_yticks(range(0, 10*9, 9), minor=True)\n", " ax.grid(True, which='minor', color='red', linewidth=0.5, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "427afded", "metadata": {}, "source": [ "## Gallery Image" ] }, { "cell_type": "code", "execution_count": 30, "id": "5f4def92", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n", "\n", "model_name = 'DiscriminativeConditionalGMMRegressor'\n", "\n", "# Create a 8x10 grid with 1 pixel spacing between 8x8 images (transposed)\n", "# Grid size: 8 rows * (8 pixels + 1 spacing) - 1, 10 cols * (8 pixels + 1 spacing) - 1\n", "grid = np.zeros((n_samples_per_digit * 9 - 1, 10 * 9 - 1)) # 71x89 pixels\n", "\n", "# Fill grid with white background (16 is max pixel value, so 16 = white)\n", "grid.fill(16)\n", "\n", "for digit in range(10):\n", " if digit in generated_samples[model_name]:\n", " samples = generated_samples[model_name][digit]\n", " for sample_idx in range(min(n_samples_per_digit, len(samples))):\n", " # Reshape to 8x8 image\n", " image = samples[sample_idx].reshape(8, 8)\n", " \n", " # Place in grid with 1 pixel spacing (transposed)\n", " row_start = sample_idx * 9 # 8 pixels + 1 spacing\n", " col_start = digit * 9 # 8 pixels + 1 spacing\n", " grid[row_start:row_start+8, col_start:col_start+8] = image\n", "\n", "# Display the grid\n", "im = ax.imshow(grid, cmap='gray', vmin=0, vmax=16)\n", "ax.set_title('Random Handwritten Digits', fontsize=14)\n", "ax.set_xlabel('Digit')\n", "ax.set_ylabel('Sample Index')\n", "\n", "# Set ticks (adjusted for 1 pixel spacing)\n", "ax.set_xticks(range(4, 10*9, 9)) # Center of each 8x8 image with spacing\n", "ax.set_xticklabels(range(10))\n", "ax.set_yticks(range(4, n_samples_per_digit*9, 9)) # Center of each 8x8 image with spacing\n", "ax.set_yticklabels(range(1, n_samples_per_digit + 1))\n", "\n", "# No grid lines needed - white padding already provides separation\n", "\n", "plt.tight_layout()\n", "plt.savefig('gallery_images/digits.png', dpi=150, bbox_inches='tight')\n", "\n", "plt.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "cgmm-3qL4zxqN-py3.11", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 5 }