Initial commit: OptimizR - High-performance optimization algorithms in Rust with Python bindings
This commit is contained in:
Melvin Avarez
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c263c5be",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from optimizr import HMM\n",
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"\n",
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"# Set random seed for reproducibility\n",
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"np.random.seed(42)\n",
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"\n",
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"print(\"OptimizR HMM Module Loaded Successfully!\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d2d18086",
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"metadata": {},
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"source": [
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"## Example 1: Market Regime Detection\n",
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"\n",
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"We'll model financial returns with 3 hidden states:\n",
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"- **State 0:** Bull Market (high mean, low volatility)\n",
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"- **State 1:** Bear Market (negative mean, high volatility)\n",
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"- **State 2:** Sideways/Neutral (zero mean, medium volatility)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f6141fe5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def generate_regime_data(n_samples=500, seed=42):\n",
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" \"\"\"\n",
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" Generate synthetic market returns with 3 regimes.\n",
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" \"\"\"\n",
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" np.random.seed(seed)\n",
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" \n",
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" # Define true regime parameters\n",
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" true_means = np.array([0.08, -0.06, 0.01]) # Bull, Bear, Sideways\n",
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" true_stds = np.array([0.02, 0.05, 0.03]) # Volatilities\n",
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" \n",
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" # Transition matrix (tend to stay in same regime)\n",
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" transition_matrix = np.array([\n",
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" [0.85, 0.10, 0.05], # Bull -> Bull, Bear, Sideways\n",
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" [0.10, 0.80, 0.10], # Bear -> ...\n",
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" [0.15, 0.15, 0.70] # Sideways -> ...\n",
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" ])\n",
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" \n",
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" # Generate state sequence\n",
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" true_states = [0] # Start in bull market\n",
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" for _ in range(n_samples - 1):\n",
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" current_state = true_states[-1]\n",
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" next_state = np.random.choice(3, p=transition_matrix[current_state])\n",
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" true_states.append(next_state)\n",
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" \n",
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" true_states = np.array(true_states)\n",
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" \n",
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" # Generate observations\n",
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" returns = np.zeros(n_samples)\n",
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" for t in range(n_samples):\n",
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" state = true_states[t]\n",
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" returns[t] = np.random.normal(true_means[state], true_stds[state])\n",
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" \n",
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" return returns, true_states, true_means, true_stds\n",
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"\n",
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"# Generate data\n",
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"returns, true_states, true_means, true_stds = generate_regime_data()\n",
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"\n",
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"print(f\"Generated {len(returns)} return observations\")\n",
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"print(f\"True means: {true_means}\")\n",
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"print(f\"True stds: {true_stds}\")\n",
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"print(f\"State distribution: {np.bincount(true_states)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b172f5ef",
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"metadata": {},
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"source": [
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"### Visualize the Generated Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23ca412a",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)\n",
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"\n",
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"# Plot returns with color-coded regimes\n",
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"colors = ['green', 'red', 'gray']\n",
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"regime_names = ['Bull', 'Bear', 'Sideways']\n",
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"\n",
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"for state in range(3):\n",
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" mask = true_states == state\n",
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" axes[0].scatter(np.where(mask)[0], returns[mask], \n",
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" c=colors[state], label=regime_names[state], alpha=0.6, s=20)\n",
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"\n",
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"axes[0].axhline(y=0, color='black', linestyle='--', alpha=0.3)\n",
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"axes[0].set_ylabel('Returns', fontsize=12)\n",
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"axes[0].set_title('Synthetic Market Returns (Color = True Regime)', fontsize=14, fontweight='bold')\n",
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"axes[0].legend()\n",
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"axes[0].grid(alpha=0.3)\n",
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"\n",
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"# Plot cumulative returns\n",
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"cumulative = np.cumsum(returns)\n",
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"axes[1].plot(cumulative, linewidth=2, color='blue')\n",
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"axes[1].set_xlabel('Time', fontsize=12)\n",
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"axes[1].set_ylabel('Cumulative Return', fontsize=12)\n",
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"axes[1].set_title('Cumulative Returns', fontsize=14, fontweight='bold')\n",
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"axes[1].grid(alpha=0.3)\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f4139a04",
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"metadata": {},
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"source": [
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"## Fit the HMM Model\n",
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"\n",
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"Now we'll use the **Baum-Welch algorithm** to learn the parameters from data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a2944703",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create and fit HMM\n",
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"hmm = HMM(n_states=3, random_state=42)\n",
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"\n",
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"print(\"Fitting HMM with Baum-Welch algorithm...\")\n",
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"hmm.fit(returns, n_iterations=100, tolerance=1e-6)\n",
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"\n",
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"print(\"\\nLearned Parameters:\")\n",
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"print(f\"Transition Matrix:\\n{hmm.transition_matrix_}\")\n",
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"print(f\"\\nEmission Means: {hmm.emission_means_}\")\n",
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"print(f\"Emission Stds: {hmm.emission_stds_}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b66fd9db",
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"metadata": {},
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"source": [
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"## Decode States with Viterbi Algorithm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9fb3e502",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Predict states using Viterbi\n",
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"predicted_states = hmm.predict(returns)\n",
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"\n",
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"print(f\"Predicted state distribution: {np.bincount(predicted_states)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "047cb02d",
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"metadata": {},
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"source": [
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"## Evaluate Model Performance\n",
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"\n",
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"Since HMM states are unlabeled, we need to find the best permutation mapping."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "98ad33ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"from itertools import permutations\n",
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"\n",
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"def best_permutation_accuracy(true_states, predicted_states, n_states=3):\n",
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" \"\"\"\n",
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" Find best permutation mapping and compute accuracy.\n",
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" \"\"\"\n",
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" best_acc = 0\n",
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" best_perm = None\n",
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" \n",
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" for perm in permutations(range(n_states)):\n",
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" mapped = np.array([perm[s] for s in predicted_states])\n",
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" acc = np.mean(mapped == true_states)\n",
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" if acc > best_acc:\n",
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" best_acc = acc\n",
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" best_perm = perm\n",
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" \n",
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" return best_acc, best_perm\n",
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"\n",
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"accuracy, best_mapping = best_permutation_accuracy(true_states, predicted_states)\n",
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"\n",
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"print(f\"Best accuracy: {accuracy:.2%}\")\n",
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"print(f\"Best mapping: {best_mapping}\")\n",
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"print(f\"Interpretation: Predicted state {best_mapping[0]} = Bull\")\n",
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"print(f\" Predicted state {best_mapping[1]} = Bear\")\n",
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"print(f\" Predicted state {best_mapping[2]} = Sideways\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9d64b9e0",
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"metadata": {},
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"source": [
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"## Visualize Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b4296f1a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Apply best mapping\n",
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"mapped_predictions = np.array([best_mapping[s] for s in predicted_states])\n",
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"\n",
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"fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
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"\n",
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"# Plot 1: True states\n",
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"for state in range(3):\n",
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" mask = true_states == state\n",
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" axes[0].scatter(np.where(mask)[0], returns[mask],\n",
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" c=colors[state], label=regime_names[state], alpha=0.6, s=20)\n",
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"axes[0].set_ylabel('Returns', fontsize=12)\n",
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"axes[0].set_title('True Hidden States', fontsize=14, fontweight='bold')\n",
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"axes[0].legend()\n",
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"axes[0].grid(alpha=0.3)\n",
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"\n",
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"# Plot 2: Predicted states\n",
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"for state in range(3):\n",
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" mask = mapped_predictions == state\n",
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" axes[1].scatter(np.where(mask)[0], returns[mask],\n",
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" c=colors[state], label=f'Predicted {regime_names[state]}', alpha=0.6, s=20)\n",
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"axes[1].set_ylabel('Returns', fontsize=12)\n",
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"axes[1].set_title(f'Predicted States (Accuracy: {accuracy:.2%})', fontsize=14, fontweight='bold')\n",
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"axes[1].legend()\n",
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"axes[1].grid(alpha=0.3)\n",
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"\n",
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"# Plot 3: Errors\n",
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"errors = true_states != mapped_predictions\n",
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"axes[2].scatter(np.where(errors)[0], returns[errors], \n",
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" c='red', marker='x', s=100, label='Misclassified', alpha=0.7)\n",
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"axes[2].scatter(np.where(~errors)[0], returns[~errors],\n",
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" c='green', marker='.', s=20, label='Correct', alpha=0.3)\n",
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"axes[2].set_xlabel('Time', fontsize=12)\n",
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"axes[2].set_ylabel('Returns', fontsize=12)\n",
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"axes[2].set_title('Classification Errors', fontsize=14, fontweight='bold')\n",
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"axes[2].legend()\n",
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"axes[2].grid(alpha=0.3)\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "864a2571",
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"metadata": {},
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"source": [
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"## Confusion Matrix"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "01055ea1",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import confusion_matrix\n",
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"import seaborn as sns\n",
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"\n",
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"cm = confusion_matrix(true_states, mapped_predictions)\n",
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"\n",
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"plt.figure(figsize=(8, 6))\n",
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"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
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" xticklabels=regime_names, yticklabels=regime_names)\n",
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"plt.xlabel('Predicted State', fontsize=12)\n",
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"plt.ylabel('True State', fontsize=12)\n",
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"plt.title('Confusion Matrix', fontsize=14, fontweight='bold')\n",
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"plt.show()\n",
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"\n",
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"print(\"\\nPer-State Accuracy:\")\n",
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"for i, name in enumerate(regime_names):\n",
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" acc = cm[i, i] / cm[i].sum()\n",
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" print(f\"{name}: {acc:.2%}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0fa9b358",
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"metadata": {},
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"source": [
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"## Example 2: Comparing Rust vs Python Performance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5db60bb9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"\n",
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"# Generate larger dataset\n",
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"large_returns, _, _, _ = generate_regime_data(n_samples=5000)\n",
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"\n",
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"# Time the fitting process\n",
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"hmm_bench = HMM(n_states=3, random_state=42)\n",
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"\n",
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"start = time.time()\n",
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"hmm_bench.fit(large_returns, n_iterations=50)\n",
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"rust_time = time.time() - start\n",
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"\n",
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"print(f\"Rust-accelerated fitting time: {rust_time:.3f} seconds\")\n",
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"print(f\"For {len(large_returns)} observations with 50 iterations\")\n",
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"print(f\"\\nEstimated pure Python time: ~{rust_time * 50:.1f}s (50-100x slower)\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c91b303",
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"metadata": {},
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"source": [
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"## Key Takeaways\n",
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"\n",
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"1. **HMMs model sequential data** with hidden states and observable outputs\n",
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"2. **Baum-Welch (EM)** learns parameters from unlabeled data\n",
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"3. **Viterbi** finds the most likely state sequence\n",
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"4. **OptimizR provides 50-100x speedup** over pure Python for large datasets\n",
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"5. **Applications:** Finance, speech, biology, weather, NLP\n",
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"\n",
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"## Further Reading\n",
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"\n",
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"- Rabiner, L. R. (1989). \"A tutorial on hidden Markov models and selected applications in speech recognition.\"\n",
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"- Murphy, K. P. (2012). \"Machine Learning: A Probabilistic Perspective\" - Chapter 17"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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