{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "colab-badge"
},
"source": [
"[](https://colab.research.google.com/github/seap-udea/multimin/blob/master/examples/multimin_mog_gmm.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"https://raw.githubusercontent.com/seap-udea/multimin/master/docs/multimin-logo-white.webp\")
\n",
"\n",
"# Comparison with scikit-learn's GaussianMixture\n",
"\n",
"In this notebook, we compare **MultiMin's MoG** with **scikit-learn's GaussianMixture** (GMM) to highlight similarities, differences, and the unique capabilities of each approach.\n",
"\n",
"Both methods fit Gaussian mixture models, but with different philosophies:\n",
"\n",
"- **scikit-learn GMM**: Focused on machine learning tasks, uses Expectation-Maximization (EM) algorithm\n",
"- **MultiMin MoG**: Focused on distribution modeling and physics applications, uses direct likelihood optimization\n",
"\n",
"Let's compare them across several dimensions: **syntax**, **performance**, **capabilities**, and **use cases**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation and importing\n",
"\n",
"If you're running this in Google Colab or need to install the package, uncomment and run the following cell:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:16.683165Z",
"iopub.status.busy": "2026-02-15T18:17:16.682839Z",
"iopub.status.idle": "2026-02-15T18:17:17.161101Z",
"shell.execute_reply": "2026-02-15T18:17:17.160019Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not running in Colab, skipping installation\n"
]
}
],
"source": [
"try:\n",
" from google.colab import drive\n",
" %pip install -Uq multimin\n",
"except ImportError:\n",
" print(\"Not running in Colab, skipping installation\")\n",
" %load_ext autoreload\n",
" %autoreload 2\n",
"!mkdir -p gallery/\n",
"# Uncomment to install from GitHub (development version)\n",
"# !pip install git+https://github.com/seap-udea/MultiMin.git"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:17.167611Z",
"iopub.status.busy": "2026-02-15T18:17:17.166961Z",
"iopub.status.idle": "2026-02-15T18:17:18.841253Z",
"shell.execute_reply": "2026-02-15T18:17:18.840514Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Welcome to MultiMin v0.11.2. \u00a1Al infinito y m\u00e1s all\u00e1!\n"
]
}
],
"source": [
"import multimin as mn\n",
"mn.show_watermark = True\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objects as go\n",
"from IPython.display import Markdown, display\n",
"\n",
"import numpy as np\n",
"np.random.seed(1)\n",
"deg = np.pi/180\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"figprefix = \"gmmcompare\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup: Import and prepare data\n",
"\n",
"First, let's import scikit-learn's GaussianMixture and generate test data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:18.850393Z",
"iopub.status.busy": "2026-02-15T18:17:18.850148Z",
"iopub.status.idle": "2026-02-15T18:17:20.980225Z",
"shell.execute_reply": "2026-02-15T18:17:20.978233Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated 1000 samples from 3 Gaussian components\n",
"Data shape: (1000, 2)\n"
]
}
],
"source": [
"# Import scikit-learn's GaussianMixture\n",
"from sklearn.mixture import GaussianMixture\n",
"import time\n",
"\n",
"# Generate test data: mixture of 3 Gaussians in 2D\n",
"np.random.seed(42)\n",
"n_samples = 1000\n",
"\n",
"# Component 1: centered at (0, 0)\n",
"samples1 = np.random.multivariate_normal([0, 0], [[1, 0.3], [0.3, 1]], size=300)\n",
"# Component 2: centered at (5, 5)\n",
"samples2 = np.random.multivariate_normal([5, 5], [[0.8, -0.2], [-0.2, 0.8]], size=400)\n",
"# Component 3: centered at (2, -3)\n",
"samples3 = np.random.multivariate_normal([2, -3], [[1.2, 0.5], [0.5, 1.2]], size=300)\n",
"\n",
"# Combine all samples\n",
"data_comparison = np.vstack([samples1, samples2, samples3])\n",
"\n",
"print(f\"Generated {len(data_comparison)} samples from 3 Gaussian components\")\n",
"print(f\"Data shape: {data_comparison.shape}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison 1: Syntax and Fitting\n",
"\n",
"Let's fit the same data with both methods and compare the syntax:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:20.987365Z",
"iopub.status.busy": "2026-02-15T18:17:20.986950Z",
"iopub.status.idle": "2026-02-15T18:17:21.749743Z",
"shell.execute_reply": "2026-02-15T18:17:21.748914Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"SCIKIT-LEARN GAUSSIANMIXTURE\n",
"============================================================\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting time: 0.2300 seconds\n",
"Converged: True\n",
"N iterations: 2\n",
"Log-likelihood: -3778.74\n",
"-LogL/N: 3.7787\n",
"\n",
"============================================================\n",
"MULTIMIN MoG\n",
"============================================================\n",
"Loading a FitMoG object.\n",
"Number of gaussians: 3\n",
"Number of variables: 2\n",
"Number of dimensions: 6\n",
"Number of samples: 1000\n",
"Log-likelihood per point (-log L/N): 12.117658034849903\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"FitMoG.fit_data executed in 0.48163723945617676 seconds\n",
"Fitting time: 0.4922 seconds\n",
"\n",
"============================================================\n",
"COMPARISON\n",
"============================================================\n",
"Speed ratio (sklearn/multimin): 0.47x\n",
"sklearn is faster\n"
]
}
],
"source": [
"# ==========================================\n",
"# FITTING WITH SCIKIT-LEARN GMM\n",
"# ==========================================\n",
"print(\"=\" * 60)\n",
"print(\"SCIKIT-LEARN GAUSSIANMIXTURE\")\n",
"print(\"=\" * 60)\n",
"\n",
"# Fit with scikit-learn\n",
"t0 = time.time()\n",
"gmm_sklearn = GaussianMixture(n_components=3, covariance_type='full', random_state=42)\n",
"gmm_sklearn.fit(data_comparison)\n",
"time_sklearn = time.time() - t0\n",
"\n",
"print(f\"Fitting time: {time_sklearn:.4f} seconds\")\n",
"print(f\"Converged: {gmm_sklearn.converged_}\")\n",
"print(f\"N iterations: {gmm_sklearn.n_iter_}\")\n",
"print(f\"Log-likelihood: {gmm_sklearn.score(data_comparison) * len(data_comparison):.2f}\")\n",
"print(f\"-LogL/N: {-gmm_sklearn.score(data_comparison):.4f}\")\n",
"print()\n",
"\n",
"# ==========================================\n",
"# FITTING WITH MULTIMIN MoG\n",
"# ==========================================\n",
"print(\"=\" * 60)\n",
"print(\"MULTIMIN MoG\")\n",
"print(\"=\" * 60)\n",
"\n",
"# Fit with MultiMin\n",
"t0 = time.time()\n",
"F_comparison = mn.FitMoG(data_comparison, ngauss=3)\n",
"F_comparison.fit_data(data_comparison, verbose=0, options={'maxiter': 500})\n",
"time_multimin = time.time() - t0\n",
"\n",
"print(f\"Fitting time: {time_multimin:.4f} seconds\")\n",
"print()\n",
"\n",
"# ==========================================\n",
"# COMPARISON SUMMARY\n",
"# ==========================================\n",
"print(\"=\" * 60)\n",
"print(\"COMPARISON\")\n",
"print(\"=\" * 60)\n",
"print(f\"Speed ratio (sklearn/multimin): {time_sklearn/time_multimin:.2f}x\")\n",
"print(f\"sklearn is {'faster' if time_sklearn < time_multimin else 'slower'}\")\n",
"#print(f\"-LogL/N difference: {abs(F_comparison.minres.fun - (-gmm_sklearn.score(data_comparison))):.6f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison 2: Parameter Access and Inspection\n",
"\n",
"Both methods give access to the fitted parameters, but with different interfaces:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:21.754892Z",
"iopub.status.busy": "2026-02-15T18:17:21.754760Z",
"iopub.status.idle": "2026-02-15T18:17:21.778127Z",
"shell.execute_reply": "2026-02-15T18:17:21.777291Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"SCIKIT-LEARN PARAMETERS\n",
"============================================================\n",
"Weights:\n",
"[0.40003196 0.29985149 0.30011655]\n",
"\n",
"Means:\n",
"[[ 4.98308814 5.08712197]\n",
" [ 1.89387311 -2.96269368]\n",
" [ 0.03248888 0.00985545]]\n",
"\n",
"Covariances shape: (3, 2, 2)\n",
"\n",
"============================================================\n",
"MULTIMIN PARAMETERS\n",
"============================================================\n",
"Weights:\n",
"[0.39955057 0.29919141 0.30125802]\n",
"\n",
"Means:\n",
"[[ 4.98308932e+00 5.08712011e+00]\n",
" [ 1.89529243e+00 -2.96671658e+00]\n",
" [ 3.04743275e-02 4.37246329e-03]]\n",
"\n",
"Covariances shape: (3, 2, 2)\n",
"\n",
"Note: Parameter values may differ between methods due to:\n",
" \u2022 Different initialization strategies\n",
" \u2022 Different optimization algorithms (EM vs. direct optimization)\n",
" \u2022 Label switching (components can be in different orders)\n"
]
}
],
"source": [
"# ==========================================\n",
"# SCIKIT-LEARN PARAMETERS\n",
"# ==========================================\n",
"print(\"=\" * 60)\n",
"print(\"SCIKIT-LEARN PARAMETERS\")\n",
"print(\"=\" * 60)\n",
"print(f\"Weights:\\n{gmm_sklearn.weights_}\")\n",
"print(f\"\\nMeans:\\n{gmm_sklearn.means_}\")\n",
"print(f\"\\nCovariances shape: {gmm_sklearn.covariances_.shape}\")\n",
"print()\n",
"\n",
"# ==========================================\n",
"# MULTIMIN PARAMETERS\n",
"# ==========================================\n",
"print(\"=\" * 60)\n",
"print(\"MULTIMIN PARAMETERS\")\n",
"print(\"=\" * 60)\n",
"print(f\"Weights:\\n{F_comparison.mog.weights}\")\n",
"print(f\"\\nMeans:\\n{F_comparison.mog.mus}\")\n",
"print(f\"\\nCovariances shape: {F_comparison.mog.Sigmas.shape}\")\n",
"print()\n",
"\n",
"# Note: Parameters may differ due to different initialization and optimization approaches\n",
"print(\"Note: Parameter values may differ between methods due to:\")\n",
"print(\" \u2022 Different initialization strategies\")\n",
"print(\" \u2022 Different optimization algorithms (EM vs. direct optimization)\")\n",
"print(\" \u2022 Label switching (components can be in different orders)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison 3: Visualization\n",
"\n",
"Let's visualize the fits from both methods:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:21.785056Z",
"iopub.status.busy": "2026-02-15T18:17:21.784872Z",
"iopub.status.idle": "2026-02-15T18:17:22.582127Z",
"shell.execute_reply": "2026-02-15T18:17:22.578569Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u2713 Both methods successfully fit the 3-component mixture\n"
]
}
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(8, 5))\n",
"\n",
"# Create grid for contour plots\n",
"x = np.linspace(data_comparison[:, 0].min() - 1, data_comparison[:, 0].max() + 1, 100)\n",
"y = np.linspace(data_comparison[:, 1].min() - 1, data_comparison[:, 1].max() + 1, 100)\n",
"X_grid, Y_grid = np.meshgrid(x, y)\n",
"grid_points = np.c_[X_grid.ravel(), Y_grid.ravel()]\n",
"\n",
"# ==========================================\n",
"# PLOT 1: Scikit-learn GMM\n",
"# ==========================================\n",
"ax1 = axes[0]\n",
"ax1.scatter(data_comparison[:, 0], data_comparison[:, 1], alpha=0.3, s=10, c='gray', label='Data')\n",
"\n",
"# Compute log-likelihood on grid\n",
"Z_sklearn = gmm_sklearn.score_samples(grid_points).reshape(X_grid.shape)\n",
"\n",
"# Plot contours\n",
"contours1 = ax1.contour(X_grid, Y_grid, np.exp(Z_sklearn), levels=10, cmap='viridis', alpha=0.7)\n",
"ax1.clabel(contours1, inline=True, fontsize=8)\n",
"\n",
"# Plot component means\n",
"ax1.scatter(gmm_sklearn.means_[:, 0], gmm_sklearn.means_[:, 1], \n",
" c='red', s=200, marker='x', linewidths=3, label='Component means')\n",
"\n",
"ax1.set_xlabel('Variable 1')\n",
"ax1.set_ylabel('Variable 2')\n",
"ax1.set_title('scikit-learn GaussianMixture\\n(EM algorithm)', fontsize=12, fontweight='bold')\n",
"ax1.legend()\n",
"ax1.grid(alpha=0.3)\n",
"\n",
"# ==========================================\n",
"# PLOT 2: MultiMin MoG\n",
"# ==========================================\n",
"ax2 = axes[1]\n",
"ax2.scatter(data_comparison[:, 0], data_comparison[:, 1], alpha=0.3, s=10, c='gray', label='Data')\n",
"\n",
"# Compute PDF on grid using MultiMin\n",
"Z_multimin = F_comparison.mog.pdf(grid_points).reshape(X_grid.shape)\n",
"\n",
"# Plot contours\n",
"contours2 = ax2.contour(X_grid, Y_grid, Z_multimin, levels=10, cmap='viridis', alpha=0.7)\n",
"ax2.clabel(contours2, inline=True, fontsize=8)\n",
"\n",
"# Plot component means\n",
"ax2.scatter(F_comparison.mog.mus[:, 0], F_comparison.mog.mus[:, 1], \n",
" c='red', s=200, marker='x', linewidths=3, label='Component means')\n",
"\n",
"ax2.set_xlabel('Variable 1')\n",
"ax2.set_ylabel('Variable 2')\n",
"ax2.set_title('MultiMin MoG\\n(Direct optimization)', fontsize=12, fontweight='bold')\n",
"ax2.legend()\n",
"ax2.grid(alpha=0.3)\n",
"mn.multimin_watermark(ax2)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(f'gallery/{figprefix}_comparison_sklearn_multimin.png', dpi=150, bbox_inches='tight')\n",
"plt.show()\n",
"\n",
"print(\"\u2713 Both methods successfully fit the 3-component mixture\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison 4: Unique Capabilities\n",
"\n",
"Each method has unique features that make it suitable for different use cases:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:22.599480Z",
"iopub.status.busy": "2026-02-15T18:17:22.599343Z",
"iopub.status.idle": "2026-02-15T18:17:22.794621Z",
"shell.execute_reply": "2026-02-15T18:17:22.793681Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"MULTIMIN UNIQUE FEATURES\n",
"============================================================\n",
"\n",
"1. Built-in visualization tools:\n",
" \u2713 plot_sample() - visualize data and fitted PDF\n",
" \u2713 plot_fit() - comprehensive fit visualization\n",
" \u2713 MultiPlot - advanced multi-dimensional plotting\n",
"\n",
"2. Direct sampling from fitted distribution:\n",
"MixtureOfGaussians.rvs executed in 0.0915231704711914 seconds\n",
" \u2713 Generated 1000 samples from MoG\n",
" \u2713 Sample shape: (1000, 2)\n",
"\n",
"3. Direct PDF evaluation:\n",
" \u2713 PDF at [2. 1.]: 0.006557\n",
"\n",
"4. Truncated domain support:\n",
" \u2713 Can fit distributions with bounded domains\n",
" \u2713 Useful for physical constraints (e.g., positive-only variables)\n",
"\n",
"5. Mathematical representation:\n",
" \u2713 get_function(type='python') - Python code\n",
" \u2713 get_function(type='latex') - LaTeX equations\n",
"\n",
"============================================================\n",
"SCIKIT-LEARN UNIQUE FEATURES\n",
"============================================================\n",
"\n",
"1. Built-in model selection:\n",
" \u2713 BIC/AIC for model selection\n",
" \u2713 Integration with scikit-learn pipeline\n",
"\n",
"2. Multiple covariance types:\n",
" \u2713 'full': BIC = 7674.91\n",
" \u2713 'tied': BIC = 7706.42\n",
" \u2713 'diag': BIC = 7721.07\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \u2713 'spherical': BIC = 7706.33\n",
"\n",
"3. Prediction and classification:\n",
" \u2713 Component assignment: [2 2 2 2 2 2 2 2 2 2]\n",
" \u2713 Probability matrix shape: (10, 3)\n",
"\n",
"4. Performance:\n",
" \u2713 EM algorithm is generally faster\n",
" \u2713 Better suited for very large datasets\n",
" \u2713 Mature, optimized implementation\n"
]
}
],
"source": [
"# ==========================================\n",
"# MultiMin UNIQUE FEATURES\n",
"# ==========================================\n",
"print(\"=\" * 60)\n",
"print(\"MULTIMIN UNIQUE FEATURES\")\n",
"print(\"=\" * 60)\n",
"print()\n",
"\n",
"# Feature 1: Integrated visualization with MultiPlot\n",
"print(\"1. Built-in visualization tools:\")\n",
"print(\" \u2713 plot_sample() - visualize data and fitted PDF\")\n",
"print(\" \u2713 plot_fit() - comprehensive fit visualization\")\n",
"print(\" \u2713 MultiPlot - advanced multi-dimensional plotting\")\n",
"print()\n",
"\n",
"# Feature 2: Sampling from fitted distribution\n",
"print(\"2. Direct sampling from fitted distribution:\")\n",
"samples_multimin = F_comparison.mog.rvs(1000)\n",
"print(f\" \u2713 Generated {len(samples_multimin)} samples from MoG\")\n",
"print(f\" \u2713 Sample shape: {samples_multimin.shape}\")\n",
"print()\n",
"\n",
"# Feature 3: PDF evaluation\n",
"print(\"3. Direct PDF evaluation:\")\n",
"test_point = np.array([[2.0, 1.0]])\n",
"pdf_val = F_comparison.mog.pdf(test_point)\n",
"print(f\" \u2713 PDF at {test_point[0]}: {pdf_val:.6f}\")\n",
"print()\n",
"\n",
"# Feature 4: Truncated domains\n",
"print(\"4. Truncated domain support:\")\n",
"print(\" \u2713 Can fit distributions with bounded domains\")\n",
"print(\" \u2713 Useful for physical constraints (e.g., positive-only variables)\")\n",
"print()\n",
"\n",
"# Feature 5: LaTeX output\n",
"print(\"5. Mathematical representation:\")\n",
"print(\" \u2713 get_function(type='python') - Python code\")\n",
"print(\" \u2713 get_function(type='latex') - LaTeX equations\")\n",
"print()\n",
"\n",
"# ==========================================\n",
"# SCIKIT-LEARN UNIQUE FEATURES\n",
"# ==========================================\n",
"print(\"=\" * 60)\n",
"print(\"SCIKIT-LEARN UNIQUE FEATURES\")\n",
"print(\"=\" * 60)\n",
"print()\n",
"\n",
"# Feature 1: Model selection tools\n",
"print(\"1. Built-in model selection:\")\n",
"from sklearn.model_selection import GridSearchCV\n",
"print(\" \u2713 BIC/AIC for model selection\")\n",
"print(\" \u2713 Integration with scikit-learn pipeline\")\n",
"print()\n",
"\n",
"# Feature 2: Different covariance types\n",
"print(\"2. Multiple covariance types:\")\n",
"for cov_type in ['full', 'tied', 'diag', 'spherical']:\n",
" gmm_test = GaussianMixture(n_components=3, covariance_type=cov_type, random_state=42)\n",
" gmm_test.fit(data_comparison)\n",
" print(f\" \u2713 '{cov_type}': BIC = {gmm_test.bic(data_comparison):.2f}\")\n",
"print()\n",
"\n",
"# Feature 3: Prediction and classification\n",
"print(\"3. Prediction and classification:\")\n",
"predictions = gmm_sklearn.predict(data_comparison[:10])\n",
"print(f\" \u2713 Component assignment: {predictions}\")\n",
"probs = gmm_sklearn.predict_proba(data_comparison[:10])\n",
"print(f\" \u2713 Probability matrix shape: {probs.shape}\")\n",
"print()\n",
"\n",
"# Feature 4: Speed and scalability\n",
"print(\"4. Performance:\")\n",
"print(\" \u2713 EM algorithm is generally faster\")\n",
"print(\" \u2713 Better suited for very large datasets\")\n",
"print(\" \u2713 Mature, optimized implementation\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Summary and Recommendations\n",
"\n",
"Both **MultiMin MoG** and **scikit-learn GaussianMixture** are excellent tools for Gaussian mixture modeling, but they excel in different contexts:\n",
"\n",
"#### When to use **MultiMin MoG**:\n",
"\n",
"\u2705 **Physics and science applications** where you need:\n",
"- Direct PDF evaluation and sampling\n",
"- Visualization of multivariate distributions\n",
"- Mathematical representation (LaTeX, Python code generation)\n",
"- Truncated/bounded domains for physical constraints\n",
"- Integration with physics simulations\n",
"\n",
"\u2705 **Educational purposes**:\n",
"- Understanding distribution components\n",
"- Visual exploration of mixtures\n",
"- Teaching mixture models\n",
"\n",
"\u2705 **Small to medium datasets** where:\n",
"- Interpretability is crucial\n",
"- You need flexible access to fitted distributions\n",
"- Visualization is a priority\n",
"\n",
"#### When to use **scikit-learn GaussianMixture**:\n",
"\n",
"\u2705 **Machine learning pipelines** where you need:\n",
"- Classification and prediction\n",
"- Integration with other sklearn tools\n",
"- Model selection (BIC/AIC)\n",
"- Cross-validation\n",
"\n",
"\u2705 **Large-scale applications**:\n",
"- Very large datasets (EM is faster)\n",
"- Production environments\n",
"- When speed is critical\n",
"\n",
"\u2705 **Different covariance structures**:\n",
"- When you need to test tied, diagonal, or spherical covariances\n",
"- When full covariance is too expensive\n",
"\n",
"#### Complementary Use:\n",
"\n",
"Both tools can be used together in a workflow:\n",
"1. Use **sklearn** for fast initial model selection (number of components, covariance type)\n",
"2. Use **MultiMin** for detailed analysis, visualization, and interpretation\n",
"3. Use **sklearn** for final deployment in production systems\n",
"\n",
"---\n",
"\n",
"**Key Takeaway**: Choose **MultiMin** for scientific analysis and visualization; choose **sklearn** for ML pipelines and large-scale applications. Both produce equivalent mixture models when using the same number of components and full covariance matrices."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example: Advanced MultiMin Visualization\n",
"\n",
"Let's demonstrate MultiMin's powerful visualization capabilities that are not available in scikit-learn:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:22.796739Z",
"iopub.status.busy": "2026-02-15T18:17:22.796400Z",
"iopub.status.idle": "2026-02-15T18:17:23.116901Z",
"shell.execute_reply": "2026-02-15T18:17:23.114966Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MultiMin Advantages:\n",
" \u2713 Automatic density plot creation\n",
" \u2713 Simultaneous histogram + scatter + PDF contours\n",
" \u2713 Publication-ready figures with minimal code\n",
" \u2713 Customizable properties and labels\n",
"\n",
"To achieve similar visualization with sklearn would require:\n",
" \u2022 Manual creation of MultiPlot grid\n",
" \u2022 Manual computation of PDF on grid\n",
" \u2022 Manual histogram and contour plotting\n",
" \u2022 ~50-100 lines of matplotlib code\n"
]
}
],
"source": [
"# Use MultiMin's built-in plot_fit method with histogram and scatter\n",
"fig = F_comparison.plot_fit(\n",
" properties=[\"X\u2081\", \"X\u2082\"],\n",
" pargs=dict(cmap='Blues'),\n",
" sargs=dict(s=0.5, edgecolor='None', color='red', alpha=0.5),\n",
" figsize=4\n",
")\n",
"plt.savefig(f'gallery/{figprefix}_multimin_advanced_viz.png', dpi=150, bbox_inches='tight')\n",
"plt.show()\n",
"\n",
"print(\"MultiMin Advantages:\")\n",
"print(\" \u2713 Automatic density plot creation\")\n",
"print(\" \u2713 Simultaneous histogram + scatter + PDF contours\")\n",
"print(\" \u2713 Publication-ready figures with minimal code\")\n",
"print(\" \u2713 Customizable properties and labels\")\n",
"print()\n",
"print(\"To achieve similar visualization with sklearn would require:\")\n",
"print(\" \u2022 Manual creation of MultiPlot grid\")\n",
"print(\" \u2022 Manual computation of PDF on grid\")\n",
"print(\" \u2022 Manual histogram and contour plotting\")\n",
"print(\" \u2022 ~50-100 lines of matplotlib code\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performance Benchmark\n",
"\n",
"Let's compare performance across different dataset sizes:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-15T18:17:23.124740Z",
"iopub.status.busy": "2026-02-15T18:17:23.124551Z",
"iopub.status.idle": "2026-02-15T18:17:24.727321Z",
"shell.execute_reply": "2026-02-15T18:17:24.724009Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running performance benchmark...\n",
"============================================================\n",
"Loading a FitMoG object.\n",
"Number of gaussians: 2\n",
"Number of variables: 2\n",
"Number of dimensions: 4\n",
"Number of samples: 100\n",
"Log-likelihood per point (-log L/N): 3.019933357776013\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"FitMoG.fit_data executed in 0.332993745803833 seconds\n",
"n= 100 | sklearn: 0.0060s | multimin: 0.3370s | ratio: 0.02x\n",
"Loading a FitMoG object.\n",
"Number of gaussians: 2\n",
"Number of variables: 2\n",
"Number of dimensions: 4\n",
"Number of samples: 500\n",
"Log-likelihood per point (-log L/N): 3.0092817332793156\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"FitMoG.fit_data executed in 0.23143672943115234 seconds\n",
"n= 500 | sklearn: 0.0054s | multimin: 0.2321s | ratio: 0.02x\n",
"Loading a FitMoG object.\n",
"Number of gaussians: 2\n",
"Number of variables: 2\n",
"Number of dimensions: 4\n",
"Number of samples: 1000\n",
"Log-likelihood per point (-log L/N): 3.065313790507079\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"FitMoG.fit_data executed in 0.2907719612121582 seconds\n",
"n=1000 | sklearn: 0.0079s | multimin: 0.2935s | ratio: 0.03x\n",
"Loading a FitMoG object.\n",
"Number of gaussians: 2\n",
"Number of variables: 2\n",
"Number of dimensions: 4\n",
"Number of samples: 2000\n",
"Log-likelihood per point (-log L/N): 2.959171520333672\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"FitMoG.fit_data executed in 0.3756368160247803 seconds\n",
"n=2000 | sklearn: 0.0193s | multimin: 0.3771s | ratio: 0.05x\n",
"============================================================\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u2713 Benchmark complete\n",
"Average speed ratio (sklearn/multimin): 0.03x\n"
]
}
],
"source": [
"# Benchmark with different sample sizes\n",
"sample_sizes = [100, 500, 1000, 2000]\n",
"results = {'n_samples': [], 'sklearn_time': [], 'multimin_time': []}\n",
"\n",
"print(\"Running performance benchmark...\")\n",
"print(\"=\" * 60)\n",
"\n",
"for n in sample_sizes:\n",
" # Generate data\n",
" data_bench = np.random.multivariate_normal([0, 0], [[1, 0.3], [0.3, 1]], size=n)\n",
" \n",
" # Benchmark sklearn\n",
" t0 = time.time()\n",
" gmm_bench = GaussianMixture(n_components=2, random_state=42)\n",
" gmm_bench.fit(data_bench)\n",
" t_sklearn = time.time() - t0\n",
" \n",
" # Benchmark multimin\n",
" t0 = time.time()\n",
" F_bench = mn.FitMoG(data_bench, ngauss=2)\n",
" F_bench.fit_data(data_bench, verbose=0, options={'maxiter': 200})\n",
" t_multimin = time.time() - t0\n",
" \n",
" results['n_samples'].append(n)\n",
" results['sklearn_time'].append(t_sklearn)\n",
" results['multimin_time'].append(t_multimin)\n",
" \n",
" print(f\"n={n:4d} | sklearn: {t_sklearn:6.4f}s | multimin: {t_multimin:6.4f}s | ratio: {t_sklearn/t_multimin:5.2f}x\")\n",
"\n",
"print(\"=\" * 60)\n",
"\n",
"# Plot results\n",
"fig, ax = plt.subplots(figsize=(10, 6))\n",
"ax.plot(results['n_samples'], results['sklearn_time'], 'o-', label='scikit-learn GMM', linewidth=2, markersize=8)\n",
"ax.plot(results['n_samples'], results['multimin_time'], 's-', label='MultiMin MoG', linewidth=2, markersize=8)\n",
"ax.set_xlabel('Number of samples', fontsize=12)\n",
"ax.set_ylabel('Fitting time (seconds)', fontsize=12)\n",
"ax.set_title('Performance Comparison: sklearn vs MultiMin', fontsize=14, fontweight='bold')\n",
"ax.legend(fontsize=11)\n",
"ax.grid(alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.savefig(f'gallery/{figprefix}_performance_comparison.png', dpi=150, bbox_inches='tight')\n",
"plt.show()\n",
"\n",
"print(f\"\\n\u2713 Benchmark complete\")\n",
"print(f\"Average speed ratio (sklearn/multimin): {np.mean([s/m for s, m in zip(results['sklearn_time'], results['multimin_time'])]):.2f}x\")"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"### Conclusion\n",
"\n",
"This comparison demonstrates that:\n",
"\n",
"1. **Both methods are equivalent** for fitting Gaussian mixture models with full covariance matrices\n",
"2. **scikit-learn is typically faster**, especially for large datasets (EM algorithm advantage)\n",
"3. **MultiMin provides superior visualization** and scientific analysis tools\n",
"4. **The choice depends on your use case**:\n",
" - **Scientific research, physics applications**: MultiMin\n",
" - **Machine learning pipelines, production**: scikit-learn\n",
" - **Teaching and exploration**: MultiMin (better visualization)\n",
" - **Large-scale data processing**: scikit-learn (better performance)\n",
"\n",
"#### Further Reading:\n",
"\n",
"- **MultiMin documentation**: https://github.com/seap-udea/multimin\n",
"- **scikit-learn GMM**: https://scikit-learn.org/stable/modules/mixture.html\n",
"- **EM algorithm**: Dempster, Laird, Rubin (1977) \"Maximum Likelihood from Incomplete Data via the EM Algorithm\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**MultiMin** - Multivariate Gaussian fitting\n",
"\n",
"\u00a9 2026 Jorge I. Zuluaga"
]
}
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