From 6da870752ffb76888634e366a72ab6081742185a Mon Sep 17 00:00:00 2001 From: El Mekkaoui Date: Mon, 1 Dec 2025 14:19:01 +0100 Subject: [PATCH 1/4] Add a simple getting started notebook --- tutorials/getting_started.ipynb | 2314 +++++++++++++++++++++++++++++++ 1 file changed, 2314 insertions(+) create mode 100644 tutorials/getting_started.ipynb diff --git a/tutorials/getting_started.ipynb b/tutorials/getting_started.ipynb new file mode 100644 index 00000000..c7da0232 --- /dev/null +++ b/tutorials/getting_started.ipynb @@ -0,0 +1,2314 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4e242b4e", + "metadata": {}, + "source": [ + "## Getting started with Axtreme\n", + "\n", + "In this notebook we walk through the core ideas behind **Axtreme** on a simple toy problem:\n", + "\n", + "- Step 1: Define the problem in terms of:\n", + " - a **simulator** (the physics / system model), and \n", + " - an **environment distribution** (how the inputs vary in the real world).\n", + "- Step:2 Use **brute force** to compute a reference answer for our **Quantity of Interest** (QoI).\n", + "- Step 3: Build a **surrogate model** of the simulator using **Ax + BoTorch**.\n", + "- Step 4: Use that surrogate to **estimate the QoI much more cheaply**.\n", + "- Step 5: Use **Design of Experiments (DoE)** to choose simulator points intelligently and reduce QoI uncertainty faster.\n", + "\n", + "The imports below set up:\n", + "\n", + "- **Numerics & plotting**: `numpy`, `pandas`, `torch`, `matplotlib`\n", + "- **Ax / BoTorch**: experiment definition and Gaussian-process modelling\n", + "- **Axtreme**: helper functions for QoI estimation and DoE tailored to extreme responses\n", + "- **Toy example code**: a small Gumbel-based simulator and environment data used only for this tutorial\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f411681", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Setup complete\n" + ] + } + ], + "source": [ + "import sys\n", + "from collections.abc import Callable\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy\n", + "import torch\n", + "from ax import Experiment, SearchSpace\n", + "from ax.core import GeneratorRun, ObservationFeatures, ParameterType, RangeParameter\n", + "from ax.modelbridge.registry import Models\n", + "from scipy.stats import gumbel_r\n", + "from torch.distributions import Normal\n", + "from torch.utils.data import DataLoader\n", + "\n", + "from axtreme import sampling\n", + "from axtreme.acquisition import QoILookAhead\n", + "from axtreme.data import FixedRandomSampler, MinimalDataset\n", + "from axtreme.experiment import add_sobol_points_to_experiment, make_experiment\n", + "from axtreme.metrics import QoIMetric\n", + "from axtreme.plotting.doe import plot_qoi_estimates_from_experiment\n", + "from axtreme.plotting.histogram3d import histogram_surface3d\n", + "from axtreme.qoi import MarginalCDFExtrapolation\n", + "from axtreme.sampling.ut_sampler import UTSampler\n", + "from axtreme.utils import population_estimators, transforms\n", + "\n", + "# Configure torch\n", + "torch.set_default_dtype(torch.float64)\n", + "\n", + "# Load the toy problem\n", + "root_dir = Path(\"../\")\n", + "sys.path.append(str(root_dir))\n", + "from examples.basic_example_usecase.problem.brute_force import collect_or_calculate_results\n", + "from examples.basic_example_usecase.problem.env_data import collect_data\n", + "from examples.basic_example_usecase.problem.simulator import DummySimulatorSeeded\n", + "\n", + "print(\"✓ Setup complete\")" + ] + }, + { + "cell_type": "markdown", + "id": "24e96906", + "metadata": {}, + "source": [ + "### Axtreme Workflow Overview\n", + "\n", + "The following diagram illustrates the general Axtreme process for extreme response estimation:\n", + "\n", + "**Key Steps:**\n", + "\n", + "1. **Problem Definition**: Specify the simulator and environment distribution\n", + "2. **Surrogate Modeling**: Build a GP-based approximation using Ax/BoTorch\n", + "3. **QoI Estimation**: Efficiently estimate the Quantity of Interest using the surrogate\n", + "4. **Uncertainty Check**: Assess if the QoI uncertainty is acceptable\n", + "5. **Design of Experiments**: Iteratively refine the surrogate by intelligently selecting new evaluation points\n", + "\n", + "```mermaid\n", + "flowchart TD\n", + " A[1. Define Problem] --> A1[Simulator Function]\n", + " A[1. Define Problem] --> A2[Environment Distribution]\n", + " \n", + " A1 --> B[2. Build Surrogate]\n", + " A2 --> B\n", + " B --> B1[Define Search Space]\n", + " B1 --> B2[Generate Training Data]\n", + " B2 --> B3[Evaluate Simulator]\n", + " B3 --> B4[Fit GP Model]\n", + " \n", + " B4 --> C[3. Estimate QoI]\n", + " C --> C1[Setup Env Samples]\n", + " C1 --> C2[Choose QoI Estimator]\n", + " C2 --> C3[Compute QoI]\n", + " \n", + " C3 --> D{4. Acceptable?}\n", + " D -->|No| E[5. Design of Experiments]\n", + " D -->|Yes| F[Final QoI Estimate]\n", + " \n", + " E --> E1[Select Acquisition]\n", + " E1 -->|Space-filling| E2[Sobol/Random]\n", + " E1 -->|QoI-aware| E3[QoILookAhead]\n", + " \n", + " E2 --> E4[Next Point]\n", + " E3 --> E4\n", + " E4 --> E5[Evaluate]\n", + " E5 --> E6[Update Surrogate]\n", + " E6 --> C3\n", + " \n", + " style A fill:#e1f5ff\n", + " style B fill:#e8f5e9\n", + " style C fill:#f3e5f5\n", + " style D fill:#fff4e1\n", + " style E fill:#ffe0e0\n", + " style F fill:#c8e6c9\n", + "```\n", + "\n", + "### Tutorial Note:\n", + "This tutorial also computes a **brute-force reference** QoI using extensive simulation runs. This is done purely for validation purposes to demonstrate that Axtreme converges to the correct answer. In real applications, such brute-force computation is typically too expensive or infeasible.\n" + ] + }, + { + "cell_type": "markdown", + "id": "808c20b4", + "metadata": {}, + "source": [ + "## Step 1: Problem Inputs\n", + "\n", + "The `axtreme` package needs two core ingredients:\n", + "\n", + "1. A **simulator** \n", + "2. A set of **environment samples**\n", + "\n", + "These two objects completely define the *probabilistic problem* we want to solve.\n", + "\n", + "### 1.1 Simulator\n", + "\n", + "The simulator is a function\n", + "\n", + "$$\n", + "x \\mapsto y\n", + "$$\n", + "\n", + "where:\n", + "\n", + "- $x$ is a vector of input / environment variables (here a 2D point: `x = [x1, x2]` in $[0, 1]^2$).\n", + "- $y$ is a **random** output because the simulator includes noise.\n", + "\n", + "In this toy problem the noise model is a **Gumbel distribution**:\n", + "\n", + "- The simulator is parameterised by:\n", + " - a **location** function `loc(x)` and\n", + " - a **scale** function `scale(x)`\n", + "- For each input $x$ the output is:\n", + "\n", + " $$\n", + " y \\sim \\text{Gumbel}(\\text{loc}(x), \\text{scale}(x))\n", + " $$\n", + "\n", + "In a real application, we **don't know** the true `loc(x)` and `scale(x)`; we only see noisy simulator outputs. \n", + "Here we cheat a little: we have access to the \"true\" functions so we can visualise what is going on and check that our methods behave sensibly.\n", + "\n", + "**Visualizing the stochastic nature of the simulator:**\n", + "\n", + "Below we run the simulator 500 times at the same input point $x = [0.5, 0.5]$. The histogram shows the characteristic right-skewed shape of a Gumbel distribution — this is the noise model built into our toy simulator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "392d8b95", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The histogram shows the Gumbel-shaped distribution of simulator responses at a fixed input point.\n" + ] + } + ], + "source": [ + "# Use the seeded simulator for reproducible results\n", + "sim = DummySimulatorSeeded()\n", + "\n", + "# Run the simulator multiple times at the same point to visualize the response distribution\n", + "test_point = np.array([[0.5, 0.5]])\n", + "n_samples = 500\n", + "response = sim(test_point, n_simulations_per_point=n_samples)\n", + "\n", + "# Visualize the distribution of simulator responses\n", + "plt.figure(figsize=(10, 4))\n", + "plt.hist(response.flatten(), bins=40, density=True, alpha=0.7, edgecolor=\"black\")\n", + "plt.xlabel(\"Response value\")\n", + "plt.ylabel(\"Density\")\n", + "plt.title(\"Simulator Response Distribution at x = [0.5, 0.5]\")\n", + "plt.grid(alpha=0.3)\n", + "plt.show()\n", + "\n", + "print(\"The histogram shows the Gumbel-shaped distribution of simulator responses at a fixed input point.\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac0cfaf6", + "metadata": {}, + "source": [ + "### 1.2 Environment Data\n", + "\n", + "The second ingredient is a set of **environment samples**:\n", + "\n", + "- Each sample is a 2D point $x = [x1, x2]$\n", + "- Together they represent the **conditions your system actually experiences** in practice:\n", + " - These might come from historical data\n", + " - Or from a probabilistic model of the environment\n", + "\n", + "In this toy example we load a pre-generated dataset:\n", + "\n", + "- We treat it purely as **input samples** — no simulator has been run on them yet.\n", + "- We'll later use them to:\n", + " - Estimate our **Extreme Response Distribution (ERD)** by brute force, and\n", + " - Feed them into Axtreme's QoI estimators so we can reuse them efficiently with the surrogate." + ] + }, + { + "cell_type": "code", + "execution_count": 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"gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Environment Distribution" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load environment data\n", + "env_data = collect_data().to_numpy()\n", + "print(f\"Environment data shape: {env_data.shape}\")\n", + "print(f\"First 5 samples:\\n{env_data[:5]}\")\n", + "\n", + "# Visualize the environment distribution\n", + "fig = histogram_surface3d(env_data)\n", + "fig.update_layout(title_text=\"Environment Distribution\", scene_aspectmode=\"cube\", height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "62fbe922", + "metadata": {}, + "source": [ + "## Step 2: Brute-force Extreme Response and QoI\n", + "\n", + "Before we introduce any surrogate modelling, we first compute a **reference answer** using raw simulation.\n", + "\n", + "### 2.1 Extreme Response Distribution (ERD)\n", + "\n", + "We imagine observing the environment over **time periods** of length `N_ENV_SAMPLES_PER_PERIOD`. \n", + "For each period we:\n", + "\n", + "1. Draw `N_ENV_SAMPLES_PER_PERIOD` inputs from the environment data\n", + "2. Run the simulator on each input\n", + "3. Record the **maximum** response over that period\n", + "\n", + "Each period gives one sample from the **Extreme Response Distribution (ERD)**:\n", + "\n", + "$$\n", + "\\text{ERD sample} = \\max_{t \\in \\text{period}} y(t)\n", + "$$\n", + "\n", + "Repeating this many times gives us an empirical ERD, which we visualise as a histogram.\n", + "\n", + "### 2.2 Quantity of Interest (QoI)\n", + "\n", + "In this tutorial our **Quantity of Interest (QoI)** is:\n", + "\n", + "> The **median** of the Extreme Response Distribution.\n", + "\n", + "Intuitively:\n", + "\n", + "- We're asking: \"What is a *typical* extreme response over a period?\"\n", + "- This is a robust measure of tail behaviour that is easier to estimate than, say, a 1-in-1000-year extreme.\n", + "\n", + "We compute:\n", + "\n", + "- A **brute-force estimate** of the ERD median using many simulator calls\n", + "- An associated **uncertainty** for this estimate using Axtreme's `population_estimators` helpers\n", + "\n", + "This brute-force QoI estimate will serve as:\n", + "\n", + "- A **ground-truth** comparison for the surrogate-based estimates we build later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24645f41", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sarelm\\AppData\\Local\\Temp\\ipykernel_21132\\1551274040.py:9: UserWarning:\n", + "\n", + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brute force QoI (median ERD): 2.0480\n", + "This required 300,000 × 1000 = 300M simulator calls!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the period length (number of environment samples per period)\n", + "N_ENV_SAMPLES_PER_PERIOD = 1000\n", + "\n", + "# Load pre-calculated brute force results (300,000 ERD samples)\n", + "precalced_erd_samples, precalced_erd_x = collect_or_calculate_results(N_ENV_SAMPLES_PER_PERIOD, 300_000)\n", + "brute_force_qoi_estimate = np.median(precalced_erd_samples)\n", + "\n", + "# Calculate QoI uncertainty using population estimators\n", + "population_median_est_dist = population_estimators.sample_median_se(torch.tensor(precalced_erd_samples))\n", + "\n", + "print(f\"Brute force QoI (median ERD): {brute_force_qoi_estimate:.4f}\")\n", + "print(f\"This required 300,000 × {N_ENV_SAMPLES_PER_PERIOD} = 300M simulator calls!\")\n", + "\n", + "# Visualize the ERD with QoI uncertainty\n", + "fig, axes = plt.subplots(ncols=2, figsize=(14, 4))\n", + "\n", + "# Left plot: ERD histogram with QoI uncertainty overlay\n", + "axes[0].hist(precalced_erd_samples, bins=100, density=True, alpha=0.7, edgecolor=\"black\", label=\"ERD\")\n", + "axes[0].set_xlabel(\"Extreme Response\")\n", + "axes[0].set_ylabel(\"ERD Density\")\n", + "axes[0].set_title(\"Extreme Response Distribution (Brute Force)\")\n", + "axes[0].grid(alpha=0.3)\n", + "\n", + "# Add QoI uncertainty on secondary y-axis\n", + "ax_twin = axes[0].twinx()\n", + "population_estimators.plot_dist(population_median_est_dist, ax=ax_twin, c=\"red\", label=\"QoI estimate\")\n", + "ax_twin.set_ylabel(\"QoI Estimate PDF\")\n", + "ax_twin.legend(loc=\"upper right\")\n", + "\n", + "# Add median line\n", + "axes[0].axvline(brute_force_qoi_estimate, color=\"red\", linestyle=\"--\", linewidth=2, alpha=0.5)\n", + "axes[0].legend(loc=\"upper left\")\n", + "\n", + "# Right plot: Zoomed-in view of QoI uncertainty only\n", + "population_estimators.plot_dist(population_median_est_dist, ax=axes[1], c=\"red\")\n", + "axes[1].axvline(\n", + " brute_force_qoi_estimate,\n", + " color=\"red\",\n", + " linestyle=\"--\",\n", + " linewidth=2,\n", + " label=f\"Median ERD = {brute_force_qoi_estimate:.4f}\",\n", + ")\n", + "axes[1].set_xlabel(\"QoI Value (Median ERD)\")\n", + "axes[1].set_ylabel(\"Density\")\n", + "axes[1].set_title(\"QoI Estimate Distribution (Zoomed)\")\n", + "axes[1].legend()\n", + "axes[1].grid(alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bb0853fd", + "metadata": {}, + "source": [ + "## Step 3: Build a Surrogate Model with Ax + BoTorch\n", + "\n", + "Running the simulator many times is expensive. \n", + "Instead, we build a **statistical surrogate** (a Gaussian Process) that learns:\n", + "\n", + "- The mapping from inputs $(x1, x2)$ to the **location** parameter of the Gumbel noise\n", + "- The mapping from inputs $(x1, x2)$ to the **scale** parameter of the Gumbel noise\n", + "\n", + "### 3.1 Configuration\n", + "\n", + "Before creating the Ax experiment, we set up the core components:\n", + "\n", + "1. **Search space**: Define the valid input domain — here, `x1` and `x2` both in $[0, 1]$\n", + "2. **Noise distribution**: Specify the distribution family for the simulator noise (Gumbel)\n", + "3. **Simulations per point**: How many simulator runs to perform at each training point to estimate the distribution parameters (`loc` and `scale`)\n", + "\n", + "These configuration choices affect:\n", + "\n", + "- How much information each training point provides (more simulations → better estimates)\n", + "- The computational cost per training point\n", + "- The smoothness of the resulting surrogate model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3abb7b25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Search space: 2D unit square\n", + "✓ Distribution: Gumbel\n", + "✓ Simulations per point: 200\n" + ] + } + ], + "source": [ + "# 1. Define the search space (2D: x1 and x2 both in [0, 1])\n", + "search_space = SearchSpace(\n", + " parameters=[\n", + " RangeParameter(name=\"x1\", parameter_type=ParameterType.FLOAT, lower=0, upper=1),\n", + " RangeParameter(name=\"x2\", parameter_type=ParameterType.FLOAT, lower=0, upper=1),\n", + " ]\n", + ")\n", + "\n", + "# 2. Choose distribution for noise model\n", + "dist = gumbel_r\n", + "\n", + "# 3. Number of simulations per point\n", + "N_SIMULATIONS_PER_POINT = 200\n", + "\n", + "print(\"✓ Search space: 2D unit square\")\n", + "print(\"✓ Distribution: Gumbel\")\n", + "print(f\"✓ Simulations per point: {N_SIMULATIONS_PER_POINT}\")" + ] + }, + { + "cell_type": "markdown", + "id": "35793562", + "metadata": {}, + "source": [ + "### 3.2 Initial training data (Sobol design)\n", + "\n", + "We start by generating an initial, **space-filling** design using a Sobol sequence:\n", + "\n", + "- Sobol points cover the space well without any modelling assumptions.\n", + "- At each Sobol point `x`, we run the simulator multiple times to estimate:\n", + " - the mean (location) and\n", + " - variability (scale) of the response distribution.\n", + "\n", + "These initial points are used to fit the first GP surrogate using `Models.BOTORCH_MODULAR`.\n", + "\n", + "**What the GP actually learns:**\n", + "\n", + "The `make_experiment()` function wraps the simulator with logic that:\n", + "\n", + "1. Runs the simulator `N_SIMULATIONS_PER_POINT` times at each training point `x`\n", + "2. Fits the Gumbel distribution to those samples to estimate `loc(x)` and `scale(x)`\n", + "3. Provides these fitted parameters to the GP\n", + "\n", + "So the GP learns **two separate mappings**:\n", + "- `x → loc`: How the location parameter varies across the input space\n", + "- `x → scale`: How the scale parameter varies across the input space\n", + "\n", + "The GP is **not** trained on raw `(x, y)` pairs. Instead, it learns the underlying distribution parameters from multiple noisy observations at each `x`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "38f7b24d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Trained GP with 30 training points\n", + "✓ Total simulator calls: 6000\n" + ] + } + ], + "source": [ + "# Helper function to create experiments with consistent settings\n", + "def make_exp():\n", + " return make_experiment(sim, search_space, dist, n_simulations_per_point=N_SIMULATIONS_PER_POINT)\n", + "\n", + "\n", + "# Create experiment and add 30 initial training points\n", + "exp = make_exp()\n", + "add_sobol_points_to_experiment(exp, n_iter=30, seed=8)\n", + "\n", + "# Train the surrogate model\n", + "botorch_model_bridge = Models.BOTORCH_MODULAR(\n", + " experiment=exp,\n", + " data=exp.fetch_data(),\n", + ")\n", + "\n", + "print(f\"✓ Trained GP with {len(exp.trials)} training points\")\n", + "print(f\"✓ Total simulator calls: {len(exp.trials) * N_SIMULATIONS_PER_POINT}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b51a5336", + "metadata": {}, + "source": [ + "### 3.3 Comparing surrogate vs simulator\n", + "\n", + "To build intuition, we pick a single test point `x = [0.5, 0.5]` and:\n", + "\n", + "- Sample many responses from the **true simulator**\n", + "- Predict the mean Gumbel distribution parameters from the **surrogate** \n", + "- Sample from the GP posterior to visualize uncertainty\n", + "\n", + "The plot shows:\n", + "\n", + "- **Histogram**: 200 simulator runs at the test point, showing the true response distribution\n", + "- **Red line**: The surrogate's mean predicted Gumbel distribution \n", + "- **Grey lines**: 10 sampled Gumbel distributions from the GP posterior\n", + "\n", + "The GP posterior captures uncertainty about the true `loc` and `scale` parameters at this location. The grey lines show the range of plausible response distributions consistent with the training data. When the surrogate has high confidence, these lines cluster tightly around the mean; when uncertain, they spread out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35295f03", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Surrogate prediction: loc=0.598, scale=0.100\n" + ] + } + ], + "source": [ + "# Predict at x = [0.5, 0.5]\n", + "test_x = {\"x1\": 0.5, \"x2\": 0.5}\n", + "pred_mean, pred_covariance = botorch_model_bridge.predict([ObservationFeatures(parameters=test_x)])\n", + "\n", + "# Get simulator samples at the same point\n", + "simulator_samples = sim(np.array([[0.5, 0.5]]), n_simulations_per_point=200).flatten()\n", + "\n", + "# Mean prediction\n", + "pred_dist_mean = dist(loc=pred_mean[\"loc\"], scale=pred_mean[\"scale\"])\n", + "x_points = np.linspace(simulator_samples.min(), simulator_samples.max(), 100)\n", + "\n", + "# Sample from GP posterior to show uncertainty\n", + "mean = np.array([pred_mean[\"loc\"], pred_mean[\"scale\"]]).flatten()\n", + "covariance = np.array(\n", + " [\n", + " [pred_covariance[\"loc\"][\"loc\"], pred_covariance[\"loc\"][\"scale\"]],\n", + " [pred_covariance[\"scale\"][\"loc\"], pred_covariance[\"scale\"][\"scale\"]],\n", + " ]\n", + ").reshape(2, 2)\n", + "\n", + "surrogate_distribution = scipy.stats.multivariate_normal(mean, covariance)\n", + "posterior_samples = surrogate_distribution.rvs(size=10, random_state=42)\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(10, 4))\n", + "plt.hist(simulator_samples, bins=20, density=True, alpha=0.6, label=\"Simulator\", edgecolor=\"black\")\n", + "\n", + "# Plot posterior samples with transparency\n", + "for i, sample in enumerate(posterior_samples):\n", + " sample_dist = dist(loc=sample[0], scale=sample[1])\n", + " if i == 0:\n", + " plt.plot(\n", + " x_points, sample_dist.pdf(x_points), \"grey\", linewidth=1, alpha=0.5, label=\"GP Surrogate posterior samples\"\n", + " )\n", + " else:\n", + " plt.plot(x_points, sample_dist.pdf(x_points), \"grey\", linewidth=1, alpha=0.5)\n", + "\n", + "# Plot mean prediction on top\n", + "plt.plot(x_points, pred_dist_mean.pdf(x_points), \"r-\", linewidth=2, label=\"GP Surrogate (mean)\")\n", + "\n", + "plt.xlabel(\"Response value\")\n", + "plt.ylabel(\"Density\")\n", + "plt.title(\"Surrogate vs Simulator at x = [0.5, 0.5]\")\n", + "plt.legend()\n", + "plt.grid(alpha=0.3)\n", + "plt.show()\n", + "\n", + "print(f\"Surrogate prediction: loc={pred_mean['loc'][0]:.3f}, scale={pred_mean['scale'][0]:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "92d81f95", + "metadata": {}, + "source": [ + "## Step 4: Estimate the QoI from the Surrogate\n", + "\n", + "Now that we have a surrogate model, we can use it to estimate our **Quantity of Interest** (the median of the ERD) **without repeatedly calling the expensive simulator**.\n", + "\n", + "The key idea:\n", + "\n", + "- The surrogate is fast to evaluate.\n", + "- We use it to *emulate* what the simulator would do over many periods of environment samples.\n", + "- We propagate both:\n", + " - **environment randomness**, and\n", + " - **surrogate uncertainty**\n", + " into our QoI estimate.\n", + "\n", + "### 4.1 Environment sampling via DataLoader\n", + "\n", + "We wrap our environment samples in a small `Dataset` and `DataLoader`:\n", + "\n", + "- This lets the QoI estimator iterate over batches of environment points.\n", + "- We can easily control how many total environment samples we use for the QoI calculation.\n", + "- Different runs (with different seeds) can use different resampled subsets if desired.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "61afb67d", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up environment data loader\n", + "n_env_samples = 4_000\n", + "dataset = MinimalDataset(env_data)\n", + "sampler = FixedRandomSampler(dataset, num_samples=n_env_samples, seed=10, replacement=True)\n", + "dataloader = DataLoader(dataset, sampler=sampler, batch_size=256)" + ] + }, + { + "cell_type": "markdown", + "id": "01c7b44c", + "metadata": {}, + "source": [ + "### 4.2 QoI estimator: MarginalCDFExtrapolation\n", + "\n", + "We use Axtreme’s `MarginalCDFExtrapolation` as a ready-made QoI estimator that:\n", + "\n", + "1. Draws batches of environment inputs from the loader\n", + "2. Uses the surrogate model to obtain a distribution of responses at each input\n", + "3. Forms many **periods** of length `N_ENV_SAMPLES_PER_PERIOD`\n", + "4. Extracts the **maximum** response in each period\n", + "5. Estimates the **median** of the resulting extreme response distribution\n", + "6. Repeats under different draws from the GP posterior to capture model uncertainty\n", + "\n", + "The result is a **distribution over the QoI**, not just a single number.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "98377e34", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ QoI estimator configured\n" + ] + } + ], + "source": [ + "# Create QoI estimator\n", + "qoi_estimator = MarginalCDFExtrapolation(\n", + " env_iterable=dataloader,\n", + " period_len=N_ENV_SAMPLES_PER_PERIOD,\n", + " quantile=torch.tensor(0.5), # Median\n", + " quantile_accuracy=torch.tensor(0.01),\n", + " posterior_sampler=UTSampler(),\n", + ")\n", + "\n", + "print(\"✓ QoI estimator configured\")" + ] + }, + { + "cell_type": "markdown", + "id": "462ee0b3", + "metadata": {}, + "source": [ + "### 4.3 Effect of training data size\n", + "\n", + "To see the impact of more simulator data, we repeat:\n", + "\n", + "- Use `N` training points to fit a surrogate\n", + "- Use that surrogate to estimate the QoI distribution\n", + "\n", + "for several values of `N` (e.g. 30, 50, 128).\n", + "\n", + "For each `N` we:\n", + "\n", + "- Approximate the QoI distribution with a Normal using the sampler’s `mean` and `var` methods\n", + "- Plot the corresponding PDF\n", + "- Overlay the **brute-force QoI** as a vertical line\n", + "\n", + "Results:\n", + "\n", + "- With **few** training points, the QoI distribution is **wide** (high uncertainty).\n", + "- As we add more training points, the distribution becomes **sharper** (narrower), and its mean stays close to the brute-force value.\n", + "\n", + "This confirms that:\n", + "\n", + "> The surrogate-based QoI estimator converges towards the brute-force answer as more simulator data is added.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "596ec23c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Computed QoI with 30 training points\n", + "✓ Computed QoI with 64 training points\n", + "✓ Computed QoI with 128 training points\n" + ] + } + ], + "source": [ + "# Train surrogates with different numbers of training points\n", + "n_training_points = [30, 64, 128]\n", + "results = []\n", + "\n", + "for n_points in n_training_points:\n", + " # Create and train experiment\n", + " exp_temp = make_exp()\n", + " add_sobol_points_to_experiment(exp_temp, n_iter=n_points, seed=8)\n", + "\n", + " model_bridge = Models.BOTORCH_MODULAR(experiment=exp_temp, data=exp_temp.fetch_data())\n", + "\n", + " # Set up transforms\n", + " input_transform, outcome_transform = transforms.ax_to_botorch_transform_input_output(\n", + " transforms=list(model_bridge.transforms.values()), outcome_names=model_bridge.outcomes\n", + " )\n", + " qoi_estimator.input_transform = input_transform\n", + " qoi_estimator.outcome_transform = outcome_transform\n", + "\n", + " # Estimate QoI\n", + " model = model_bridge.model.surrogate.model\n", + " result = qoi_estimator(model)\n", + " results.append(result)\n", + "\n", + " print(f\"✓ Computed QoI with {n_points} training points\")" + ] + }, + { + "cell_type": "markdown", + "id": "7216da03", + "metadata": {}, + "source": [ + "Let's see how the QoI estimate improves with more training data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60f65f53", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sarelm\\AppData\\Local\\Temp\\ipykernel_21132\\844206477.py:6: UserWarning:\n", + "\n", + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + "\n", + "C:\\Users\\sarelm\\AppData\\Local\\Temp\\ipykernel_21132\\844206477.py:7: UserWarning:\n", + "\n", + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize QoI estimates\n", + "fig, axes = plt.subplots(nrows=len(n_training_points), figsize=(10, 3 * len(n_training_points)), sharex=True)\n", + "\n", + "for ax, estimate, n_points in zip(axes, results, n_training_points, strict=True):\n", + " # Extract mean and variance from the estimate\n", + " mean = qoi_estimator.posterior_sampler.mean(torch.tensor(estimate), -1)\n", + " var = qoi_estimator.posterior_sampler.var(torch.tensor(estimate), -1)\n", + " qoi_dist = Normal(mean, var**0.5)\n", + "\n", + " # Plot QoI distribution\n", + " x_range = torch.linspace(float(mean - 3 * var**0.5), float(mean + 3 * var**0.5), 200)\n", + " ax.plot(x_range.numpy(), torch.exp(qoi_dist.log_prob(x_range)).numpy(), \"b-\", linewidth=2, label=\"QoI estimate\")\n", + " ax.fill_between(x_range.numpy(), 0, torch.exp(qoi_dist.log_prob(x_range)).numpy(), alpha=0.3)\n", + "\n", + " # Add brute force reference\n", + " ax.axvline(brute_force_qoi_estimate, color=\"red\", linestyle=\"--\", linewidth=2, label=\"Brute force\")\n", + "\n", + " ax.set_title(f\"QoI Estimate with {n_points} Training Points\")\n", + " ax.set_ylabel(\"Density\")\n", + " ax.legend()\n", + " ax.grid(alpha=0.3)\n", + "\n", + "axes[-1].set_xlabel(\"Response\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7ae8adc8", + "metadata": {}, + "source": [ + "## 5. Design of Experiments (DoE)\n", + "\n", + "In the earlier sections, we saw that adding more simulator training data improves our surrogate model and reduces the uncertainty of our **Quantity of Interest (QoI)** estimate. However, **where** we choose to evaluate the simulator matters just as much as **how many** points we evaluate.\n", + "\n", + "Design of Experiments (DoE) helps us choose simulator inputs intelligently, so that each new simulation gives us the **maximum possible reduction in QoI uncertainty**.\n", + "\n", + "In this section we compare:\n", + "\n", + "- a baseline approach (random Sobol sampling), and\n", + "- a QoI-aware approach that uses Axtreme’s `QoILookAhead` acquisition function to pick the most informative points.\n", + "\n", + "We will track the QoI after each iteration to see which strategy converges faster.\n", + "\n", + "### 5.1 QoI tracking metric and stopping criteria\n", + "\n", + "To monitor the progress of the DoE, we attach a special metric — `QoIMetric` — to the Ax experiment. After each iteration:\n", + "\n", + "- The surrogate model is updated.\n", + "- The QoI is re-estimated using the updated surrogate.\n", + "- The QoI estimate (mean and standard error) is stored inside the experiment's data.\n", + "\n", + "This allows us to plot the QoI over time and optionally stop early when the QoI uncertainty becomes sufficiently small.\n", + "\n", + "We implement:\n", + "\n", + "1. **QoI tracking metric**: Automatically computes and logs the QoI after each surrogate update\n", + "2. **Stopping criterion** (`sem_stopping_criteria`): Checks if the standard error of the QoI drops below a threshold (e.g., 0.02)\n", + "3. **Trial runner** (`run_trials`): A helper function that:\n", + " - Runs warm-up trials using a space-filling design (e.g., Sobol)\n", + " - Then runs DoE trials using a specified acquisition strategy\n", + " - Checks the stopping criterion after each DoE iteration\n", + " - Returns early if the QoI uncertainty is sufficiently small\n", + "\n", + "This approach avoids unnecessary simulator calls once the QoI estimate is sufficiently accurate." + ] + }, + { + "cell_type": "markdown", + "id": "a2a66e5c", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01e67e82", + "metadata": {}, + "outputs": [], + "source": [ + "# Define QoI tracking metric\n", + "QOI_METRIC = QoIMetric(\n", + " name=\"QoIMetric\",\n", + " qoi_estimator=qoi_estimator,\n", + " minimum_data_points=3, # don't compute QoI until some data exists\n", + " attach_transforms=True,\n", + ")\n", + "\n", + "\n", + "# Define stopping criteria based on standard error of the mean (SEM)\n", + "def sem_stopping_criteria(experiment: Experiment, sem_threshold: float = 0.02, metric_name: str = \"QoIMetric\") -> bool:\n", + " \"\"\"Stop when the standard error of the QoI estimate drops below a threshold.\"\"\"\n", + " metrics = experiment.fetch_data()\n", + " df = metrics.df\n", + " qoi_rows = df[df[\"metric_name\"] == metric_name]\n", + "\n", + " if len(qoi_rows) == 0:\n", + " # No QoI yet\n", + " return False\n", + "\n", + " latest = qoi_rows.iloc[-1]\n", + " sem = latest[\"sem\"]\n", + "\n", + " # Stop if SEM is finite and sufficiently small\n", + " return (sem is not None) and (not np.isnan(sem)) and (sem <= sem_threshold)\n", + "\n", + "\n", + "# Define function to run trials with progress output\n", + "def run_trials(\n", + " experiment: Experiment,\n", + " warm_up_generator: Callable[[Experiment], GeneratorRun],\n", + " doe_generator: Callable[[Experiment], GeneratorRun],\n", + " warm_up_runs: int = 3,\n", + " doe_runs: int = 15,\n", + " stopping_criteria: Callable[[Experiment], bool] | None = None,\n", + ") -> int:\n", + " \"\"\"Run warm-up + DoE trials with simple progress output.\"\"\"\n", + "\n", + " total_iters = warm_up_runs + doe_runs\n", + " print(f\"Starting DoE run: {warm_up_runs} warm-up + {doe_runs} DoE iterations\")\n", + " print(\"-\" * 60)\n", + "\n", + " for i in range(total_iters):\n", + " phase = \"Warm-up\" if i < warm_up_runs else \"DoE\"\n", + "\n", + " # Select generator\n", + " gen_fn = warm_up_generator if i < warm_up_runs else doe_generator\n", + " gen = gen_fn(experiment)\n", + "\n", + " # Run trial\n", + " trial = experiment.new_trial(gen)\n", + " trial.run()\n", + " trial.mark_completed()\n", + "\n", + " # Print progress\n", + " print(f\"[{i + 1}/{total_iters}] {phase} iteration {i + 1} completed\")\n", + "\n", + " # Stopping criteria only valid during DoE\n", + " if (i >= warm_up_runs) and (stopping_criteria is not None) and stopping_criteria(experiment):\n", + " print(f\"✓ Stopping criterion met after {i - warm_up_runs + 1} DoE iterations.\")\n", + " print(\"-\" * 60)\n", + " return i + 1\n", + "\n", + " print(\"✓ DoE run completed.\")\n", + " print(\"-\" * 60)\n", + " return total_iters" + ] + }, + { + "cell_type": "markdown", + "id": "97c44be5", + "metadata": {}, + "source": [ + "### 5.2 Baseline: Sobol-only DoE\n", + "\n", + "As a baseline, we perform DoE using only Sobol points:\n", + "\n", + "- Sobol sequences are space-filling and require no modelling assumptions.\n", + "- They cover the full input domain evenly.\n", + "- They do not focus sampling effort on regions that matter most for the QoI.\n", + "\n", + "The Sobol-only experiment therefore provides a fair reference point to compare against more advanced acquisition strategies.\n", + "\n", + "We:\n", + "\n", + "1. Create a new Ax experiment with the same simulator, search space, and noise model as before.\n", + "2. Attach the QoI tracking metric.\n", + "3. Run:\n", + " - a few warm-up Sobol points, followed by\n", + " - many DoE Sobol points.\n", + "4. Record the number of iterations performed (possibly fewer if the stopping criterion triggers early).\n", + "\n", + "This gives us a curve describing how the QoI estimate improves when sampling is uniform and naïve." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a64d69bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Sobol-only experiment...\n", + "Starting DoE run: 3 warm-up + 100 DoE iterations\n", + "------------------------------------------------------------\n", + "[1/103] Warm-up iteration 1 completed\n", + "[2/103] Warm-up iteration 2 completed\n", + "[3/103] Warm-up iteration 3 completed\n", + "[4/103] DoE iteration 4 completed\n", + "[5/103] DoE iteration 5 completed\n", + "[6/103] DoE iteration 6 completed\n", + "[7/103] DoE iteration 7 completed\n", + "[8/103] DoE iteration 8 completed\n", + "[9/103] DoE iteration 9 completed\n", + "[10/103] DoE iteration 10 completed\n", + "[11/103] DoE iteration 11 completed\n", + "[12/103] DoE iteration 12 completed\n", + "[13/103] DoE iteration 13 completed\n", + "[14/103] DoE iteration 14 completed\n", + "[15/103] DoE iteration 15 completed\n", + "[16/103] DoE iteration 16 completed\n", + "[17/103] DoE iteration 17 completed\n", + "[18/103] DoE iteration 18 completed\n", + "[19/103] DoE iteration 19 completed\n", + "[20/103] DoE iteration 20 completed\n", + "[21/103] DoE iteration 21 completed\n", + "[22/103] DoE iteration 22 completed\n", + "[23/103] DoE iteration 23 completed\n", + "[24/103] DoE iteration 24 completed\n", + "[25/103] DoE iteration 25 completed\n", + "[26/103] DoE iteration 26 completed\n", + "[27/103] DoE iteration 27 completed\n", + "[28/103] DoE iteration 28 completed\n", + "[29/103] DoE iteration 29 completed\n", + "[30/103] DoE iteration 30 completed\n", + "[31/103] DoE iteration 31 completed\n", + "[32/103] DoE iteration 32 completed\n", + "[33/103] DoE iteration 33 completed\n", + "[34/103] DoE iteration 34 completed\n", + "[35/103] DoE iteration 35 completed\n", + "[36/103] DoE iteration 36 completed\n", + "[37/103] DoE iteration 37 completed\n", + "[38/103] DoE iteration 38 completed\n", + "[39/103] DoE iteration 39 completed\n", + "[40/103] DoE iteration 40 completed\n", + "[41/103] DoE iteration 41 completed\n", + "[42/103] DoE iteration 42 completed\n", + "[43/103] DoE iteration 43 completed\n", + "[44/103] DoE iteration 44 completed\n", + "[45/103] DoE iteration 45 completed\n", + "[46/103] DoE iteration 46 completed\n", + "[47/103] DoE iteration 47 completed\n", + "[48/103] DoE iteration 48 completed\n", + "[49/103] DoE iteration 49 completed\n", + "[50/103] DoE iteration 50 completed\n", + "[51/103] DoE iteration 51 completed\n", + "[52/103] DoE iteration 52 completed\n", + "[53/103] DoE iteration 53 completed\n", + "[54/103] DoE iteration 54 completed\n", + "[55/103] DoE iteration 55 completed\n", + "[56/103] DoE iteration 56 completed\n", + "[57/103] DoE iteration 57 completed\n", + "[58/103] DoE iteration 58 completed\n", + "✓ Stopping criterion met after 55 DoE iterations.\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# Baseline: Sobol-only DoE\n", + "exp_sobol = make_exp()\n", + "exp_sobol.add_tracking_metric(QOI_METRIC)\n", + "\n", + "# Sobol generator (kept outside the loop so its internal state persists)\n", + "sobol = Models.SOBOL(search_space=exp_sobol.search_space, seed=5)\n", + "\n", + "\n", + "# Sobol generator function\n", + "def sobol_generator_run(_: Experiment) -> GeneratorRun:\n", + " return sobol.gen(1)\n", + "\n", + "\n", + "print(\"Running Sobol-only experiment...\")\n", + "n_sobol_iters = run_trials(\n", + " experiment=exp_sobol,\n", + " warm_up_generator=sobol_generator_run,\n", + " doe_generator=sobol_generator_run,\n", + " warm_up_runs=3,\n", + " doe_runs=100,\n", + " stopping_criteria=sem_stopping_criteria,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "076a5f9a", + "metadata": {}, + "source": [ + "### 5.3 QoI-aware DoE with `QoILookAhead`\n", + "\n", + "Randomly exploring the search space is wasteful when our goal is to reduce QoI uncertainty, not to approximate the simulator everywhere equally.\n", + "\n", + "To address this, Axtreme provides a custom acquisition function, `QoILookAhead`.\n", + "\n", + "This acquisition function:\n", + "\n", + "- Uses the current GP surrogate.\n", + "- Anticipates how much one more simulator evaluation will reduce the QoI uncertainty.\n", + "- Selects the input location where this reduction is expected to be largest.\n", + "\n", + "Conceptually, it prioritizes simulator points that are most influential in shaping the extreme response distribution.\n", + "\n", + "This is significantly more efficient than Sobol sampling, especially when the QoI depends heavily on only a subset of the input space.\n", + "\n", + "To run QoI-aware DoE:\n", + "\n", + "1. For each DoE iteration, we build a BoTorch model bridge equipped with the `QoILookAhead` acquisition.\n", + "2. We use Ax’s built-in acquisition optimizer (for example, Nelder–Mead for robustness in non-smooth cases).\n", + "3. The best acquisition point becomes the next simulator evaluation.\n", + "4. After each point, the QoI is re-estimated and logged by the tracking metric.\n", + "\n", + "This creates a feedback loop where the model learns faster in QoI-critical regions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7033917d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running QoI-aware look-ahead experiment...\n", + "Starting DoE run: 3 warm-up + 40 DoE iterations\n", + "------------------------------------------------------------\n", + "[1/43] Warm-up iteration 1 completed\n", + "[2/43] Warm-up iteration 2 completed\n", + "[3/43] Warm-up iteration 3 completed\n", + "[4/43] DoE iteration 4 completed\n", + "[5/43] DoE iteration 5 completed\n", + "[6/43] DoE iteration 6 completed\n", + "[7/43] DoE iteration 7 completed\n", + "[8/43] DoE iteration 8 completed\n", + "[9/43] DoE iteration 9 completed\n", + "[10/43] DoE iteration 10 completed\n", + "[11/43] DoE iteration 11 completed\n", + "[12/43] DoE iteration 12 completed\n", + "[13/43] DoE iteration 13 completed\n", + "[14/43] DoE iteration 14 completed\n", + "[15/43] DoE iteration 15 completed\n", + "[16/43] DoE iteration 16 completed\n", + "[17/43] DoE iteration 17 completed\n", + "[18/43] DoE iteration 18 completed\n", + "[19/43] DoE iteration 19 completed\n", + "[20/43] DoE iteration 20 completed\n", + "[21/43] DoE iteration 21 completed\n", + "[22/43] DoE iteration 22 completed\n", + "✓ Stopping criterion met after 19 DoE iterations.\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "# QoI-aware DoE with QoILookAhead acquisition\n", + "\n", + "# Choose the acquisition function\n", + "acquisition_function_class = QoILookAhead\n", + "\n", + "\n", + "# Define QoILookAhead DoE generator function\n", + "def look_ahead_generator_run(experiment: Experiment) -> GeneratorRun:\n", + " \"\"\"Generate a new point by optimising the QoILookAhead acquisition.\"\"\"\n", + "\n", + " # First: build a model bridge to recover the Ax→BoTorch transforms\n", + " model_bridge_for_transforms = Models.BOTORCH_MODULAR(\n", + " experiment=experiment,\n", + " data=experiment.fetch_data(metrics=list(experiment.optimization_config.metrics.values())),\n", + " fit_tracking_metrics=False,\n", + " )\n", + "\n", + " input_transform, outcome_transform = transforms.ax_to_botorch_transform_input_output(\n", + " transforms=list(model_bridge_for_transforms.transforms.values()),\n", + " outcome_names=model_bridge_for_transforms.outcomes,\n", + " )\n", + "\n", + " # Feed the transforms into the QoI estimator so it interprets inputs/outputs correctly\n", + " qoi_estimator.input_transform = input_transform\n", + " qoi_estimator.outcome_transform = outcome_transform\n", + "\n", + " # Build a model bridge with a custom acquisition function\n", + " model_bridge_with_acq = Models.BOTORCH_MODULAR(\n", + " experiment=experiment,\n", + " data=experiment.fetch_data(),\n", + " botorch_acqf_class=acquisition_function_class,\n", + " fit_tracking_metrics=False,\n", + " acquisition_options={\n", + " \"qoi_estimator\": qoi_estimator,\n", + " \"sampler\": sampling.MeanSampler(),\n", + " },\n", + " )\n", + "\n", + " # Optimise the acquisition to find the next candidate\n", + " return model_bridge_with_acq.gen(\n", + " 1,\n", + " model_gen_options={\n", + " \"optimizer_kwargs\": {\n", + " \"num_restarts\": 20,\n", + " \"raw_samples\": 50,\n", + " \"options\": {\n", + " \"with_grad\": False, # QoILookAhead may not be smooth\n", + " \"method\": \"Nelder-Mead\",\n", + " \"maxfev\": 5,\n", + " },\n", + " \"retry_on_optimization_warning\": False,\n", + " }\n", + " },\n", + " )\n", + "\n", + "\n", + "# Run the QoI-aware DoE\n", + "exp_look_ahead = make_exp()\n", + "exp_look_ahead.add_tracking_metric(QOI_METRIC)\n", + "\n", + "# Sobol generator for warm-up (kept outside the loop so its internal state persists)\n", + "sobol_for_warmup = Models.SOBOL(search_space=exp_look_ahead.search_space, seed=5)\n", + "\n", + "\n", + "# Sobol generator function for warm-up\n", + "def sobol_warmup_run(_: Experiment) -> GeneratorRun:\n", + " return sobol_for_warmup.gen(1)\n", + "\n", + "\n", + "print(\"Running QoI-aware look-ahead experiment...\")\n", + "n_lookahead_iters = run_trials(\n", + " experiment=exp_look_ahead,\n", + " warm_up_generator=sobol_warmup_run,\n", + " doe_generator=look_ahead_generator_run,\n", + " warm_up_runs=3,\n", + " doe_runs=40,\n", + " stopping_criteria=sem_stopping_criteria,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6e533168", + "metadata": {}, + "source": [ + "### 5.4 Comparing Sobol and QoI-aware DoE\n", + "\n", + "Finally, we compare results from both strategies.\n", + "\n", + "Using Axtreme’s `plot_qoi_estimates_from_experiment`, we visualize:\n", + "\n", + "- The QoI estimate at each iteration.\n", + "- The uncertainty bands (SEM).\n", + "- The brute-force QoI as a reference line.\n", + "- Curves for both:\n", + " - Sobol-only DoE, and\n", + " - QoILookAhead-based DoE.\n", + "\n", + "This makes it easy to see:\n", + "\n", + "- How quickly each method reduces QoI uncertainty.\n", + "- Whether either method shows bias.\n", + "- How many simulator evaluations are required to reach a target confidence level.\n", + "\n", + "In typical problems, we expect:\n", + "\n", + "- Sobol: uncertainty decreases steadily but relatively slowly.\n", + "- QoILookAhead: uncertainty drops much faster because it samples only where the QoI is sensitive.\n", + "\n", + "In real applications, this difference can translate to far fewer simulator evaluations for similar or better QoI accuracy.\n", + "\n", + "This completes the end-to-end Axtreme workflow: from defining a simulation problem, through surrogate modeling and QoI estimation, to intelligent experiment design." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09c6755e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 5.4 Compare QoI evolution\n", + "\n", + "ax = plot_qoi_estimates_from_experiment(exp_sobol, name=\"Sobol\")\n", + "ax = plot_qoi_estimates_from_experiment(\n", + " exp_look_ahead,\n", + " ax=ax,\n", + " color=\"green\",\n", + " name=\"QoI look-ahead\",\n", + ")\n", + "\n", + "ax.axhline(float(brute_force_qoi_estimate), color=\"black\", linestyle=\"--\", label=\"Brute-force QoI\")\n", + "\n", + "ax.set_xlabel(\"Number of DoE iterations\")\n", + "ax.set_ylabel(\"QoI (median extreme response)\")\n", + "ax.legend()\n", + "ax.grid(alpha=0.3)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d4021aeb", + "metadata": {}, + "source": [ + "### 5.5 Spatial Distribution of Selected Points\n", + "\n", + "To understand **where** each DoE strategy chooses to evaluate the simulator, we visualize the selected points in the 2D input space overlaid on:\n", + "\n", + "1. **Environment density**: Shows the distribution of environment samples (grey histogram)\n", + "2. **Extreme response density**: Shows where extreme responses originated during brute-force simulation (red histogram)\n", + "3. **Combined view**: Both densities overlaid to reveal the relationship\n", + "\n", + "Key insights:\n", + "\n", + "- **Sobol points (blue circles)**: Distributed uniformly across the entire input space, regardless of where extremes occur\n", + "- **QoI-aware points (red triangles)**: Concentrated in regions that contribute most to extreme responses\n", + "\n", + "The point labels indicate the order in which they were selected during DoE.\n", + "\n", + "This visualization reveals why QoI-aware DoE is more efficient: it focuses simulator evaluations on the critical regions of the input space where the QoI is most sensitive." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "889096a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_2dtrials(\n", + " exp: Experiment, ax: plt.Axes | None = None, colour: str = \"blue\", marker: str = \"o\", label: str | None = None\n", + ") -> plt.Axes:\n", + " \"\"\"Plot the points and number the datapoints added over DoE.\"\"\"\n", + " if ax is None:\n", + " _, ax = plt.subplots(figsize=(8, 6))\n", + "\n", + " trials = []\n", + " trial_indices = []\n", + "\n", + " for trial_idx, trial in exp.trials.items():\n", + " if trial.arm:\n", + " params = trial.arm.parameters.values()\n", + " trials.append(list(params))\n", + " trial_indices.append(trial_idx)\n", + "\n", + " points = np.array(trials)\n", + " ax.scatter(points[:, 0], points[:, 1], alpha=0.8, s=50, label=label, c=colour, marker=marker)\n", + "\n", + " for i, (x, y) in enumerate(points):\n", + " ax.annotate(str(trial_indices[i]), (x, y), xytext=(2, 2), textcoords=\"offset points\", fontsize=8, color=colour)\n", + "\n", + " return ax\n", + "\n", + "\n", + "# Create three-panel visualization\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", + "\n", + "# Panel 1: Points vs Environment Density\n", + "axes[0].set_title(\"Points vs Environment Density\")\n", + "axes[0].hist2d(env_data[:, 0], env_data[:, 1], bins=20, alpha=0.6, cmap=\"Greys\", zorder=-1)\n", + "\n", + "# Panel 2: Points vs Extreme Response Density\n", + "axes[1].set_title(\"Points vs Extreme Response Density\")\n", + "axes[1].hist2d(precalced_erd_x[:, 0], precalced_erd_x[:, 1], bins=40, alpha=0.6, cmap=\"Purples\", zorder=-1)\n", + "\n", + "# Panel 3: Points vs Combined Density\n", + "axes[2].set_title(\"Points vs Env and Extreme Response Density\")\n", + "axes[2].hist2d(env_data[:, 0], env_data[:, 1], bins=20, alpha=0.6, cmap=\"Greys\", zorder=-1)\n", + "axes[2].hist2d(precalced_erd_x[:, 0], precalced_erd_x[:, 1], bins=40, alpha=0.5, cmap=\"Reds\", zorder=-1)\n", + "\n", + "# Add DoE points to all panels\n", + "for ax in axes:\n", + " ax.set_xlabel(\"x1\")\n", + " ax.set_ylabel(\"x2\")\n", + " plot_2dtrials(exp_sobol, colour=\"blue\", ax=ax, label=\"Sobol\")\n", + " plot_2dtrials(exp_look_ahead, colour=\"red\", ax=ax, label=\"QoI look-ahead\", marker=\"^\")\n", + " ax.legend()\n", + " ax.grid(visible=True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a05208a5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "axtreme", + "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": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e2b0a0c24dcff00274537c82fe96809a1c234b81 Mon Sep 17 00:00:00 2001 From: El Mekkaoui Date: Tue, 2 Dec 2025 10:44:24 +0100 Subject: [PATCH 2/4] Convert getting_started notebook to interactive Python file --- tutorials/getting_started.ipynb | 2314 ------------------------------- tutorials/getting_started.py | 1014 ++++++++++++++ 2 files changed, 1014 insertions(+), 2314 deletions(-) delete mode 100644 tutorials/getting_started.ipynb create mode 100644 tutorials/getting_started.py diff --git a/tutorials/getting_started.ipynb b/tutorials/getting_started.ipynb deleted file mode 100644 index c7da0232..00000000 --- a/tutorials/getting_started.ipynb +++ /dev/null @@ -1,2314 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4e242b4e", - "metadata": {}, - "source": [ - "## Getting started with Axtreme\n", - "\n", - "In this notebook we walk through the core ideas behind **Axtreme** on a simple toy problem:\n", - "\n", - "- Step 1: Define the problem in terms of:\n", - " - a **simulator** (the physics / system model), and \n", - " - an **environment distribution** (how the inputs vary in the real world).\n", - "- Step:2 Use **brute force** to compute a reference answer for our **Quantity of Interest** (QoI).\n", - "- Step 3: Build a **surrogate model** of the simulator using **Ax + BoTorch**.\n", - "- Step 4: Use that surrogate to **estimate the QoI much more cheaply**.\n", - "- Step 5: Use **Design of Experiments (DoE)** to choose simulator points intelligently and reduce QoI uncertainty faster.\n", - "\n", - "The imports below set up:\n", - "\n", - "- **Numerics & plotting**: `numpy`, `pandas`, `torch`, `matplotlib`\n", - "- **Ax / BoTorch**: experiment definition and Gaussian-process modelling\n", - "- **Axtreme**: helper functions for QoI estimation and DoE tailored to extreme responses\n", - "- **Toy example code**: a small Gumbel-based simulator and environment data used only for this tutorial\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f411681", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Setup complete\n" - ] - } - ], - "source": [ - "import sys\n", - "from collections.abc import Callable\n", - "from pathlib import Path\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import scipy\n", - "import torch\n", - "from ax import Experiment, SearchSpace\n", - "from ax.core import GeneratorRun, ObservationFeatures, ParameterType, RangeParameter\n", - "from ax.modelbridge.registry import Models\n", - "from scipy.stats import gumbel_r\n", - "from torch.distributions import Normal\n", - "from torch.utils.data import DataLoader\n", - "\n", - "from axtreme import sampling\n", - "from axtreme.acquisition import QoILookAhead\n", - "from axtreme.data import FixedRandomSampler, MinimalDataset\n", - "from axtreme.experiment import add_sobol_points_to_experiment, make_experiment\n", - "from axtreme.metrics import QoIMetric\n", - "from axtreme.plotting.doe import plot_qoi_estimates_from_experiment\n", - "from axtreme.plotting.histogram3d import histogram_surface3d\n", - "from axtreme.qoi import MarginalCDFExtrapolation\n", - "from axtreme.sampling.ut_sampler import UTSampler\n", - "from axtreme.utils import population_estimators, transforms\n", - "\n", - "# Configure torch\n", - "torch.set_default_dtype(torch.float64)\n", - "\n", - "# Load the toy problem\n", - "root_dir = Path(\"../\")\n", - "sys.path.append(str(root_dir))\n", - "from examples.basic_example_usecase.problem.brute_force import collect_or_calculate_results\n", - "from examples.basic_example_usecase.problem.env_data import collect_data\n", - "from examples.basic_example_usecase.problem.simulator import DummySimulatorSeeded\n", - "\n", - "print(\"✓ Setup complete\")" - ] - }, - { - "cell_type": "markdown", - "id": "24e96906", - "metadata": {}, - "source": [ - "### Axtreme Workflow Overview\n", - "\n", - "The following diagram illustrates the general Axtreme process for extreme response estimation:\n", - "\n", - "**Key Steps:**\n", - "\n", - "1. **Problem Definition**: Specify the simulator and environment distribution\n", - "2. **Surrogate Modeling**: Build a GP-based approximation using Ax/BoTorch\n", - "3. **QoI Estimation**: Efficiently estimate the Quantity of Interest using the surrogate\n", - "4. **Uncertainty Check**: Assess if the QoI uncertainty is acceptable\n", - "5. **Design of Experiments**: Iteratively refine the surrogate by intelligently selecting new evaluation points\n", - "\n", - "```mermaid\n", - "flowchart TD\n", - " A[1. Define Problem] --> A1[Simulator Function]\n", - " A[1. Define Problem] --> A2[Environment Distribution]\n", - " \n", - " A1 --> B[2. Build Surrogate]\n", - " A2 --> B\n", - " B --> B1[Define Search Space]\n", - " B1 --> B2[Generate Training Data]\n", - " B2 --> B3[Evaluate Simulator]\n", - " B3 --> B4[Fit GP Model]\n", - " \n", - " B4 --> C[3. Estimate QoI]\n", - " C --> C1[Setup Env Samples]\n", - " C1 --> C2[Choose QoI Estimator]\n", - " C2 --> C3[Compute QoI]\n", - " \n", - " C3 --> D{4. Acceptable?}\n", - " D -->|No| E[5. Design of Experiments]\n", - " D -->|Yes| F[Final QoI Estimate]\n", - " \n", - " E --> E1[Select Acquisition]\n", - " E1 -->|Space-filling| E2[Sobol/Random]\n", - " E1 -->|QoI-aware| E3[QoILookAhead]\n", - " \n", - " E2 --> E4[Next Point]\n", - " E3 --> E4\n", - " E4 --> E5[Evaluate]\n", - " E5 --> E6[Update Surrogate]\n", - " E6 --> C3\n", - " \n", - " style A fill:#e1f5ff\n", - " style B fill:#e8f5e9\n", - " style C fill:#f3e5f5\n", - " style D fill:#fff4e1\n", - " style E fill:#ffe0e0\n", - " style F fill:#c8e6c9\n", - "```\n", - "\n", - "### Tutorial Note:\n", - "This tutorial also computes a **brute-force reference** QoI using extensive simulation runs. This is done purely for validation purposes to demonstrate that Axtreme converges to the correct answer. In real applications, such brute-force computation is typically too expensive or infeasible.\n" - ] - }, - { - "cell_type": "markdown", - "id": "808c20b4", - "metadata": {}, - "source": [ - "## Step 1: Problem Inputs\n", - "\n", - "The `axtreme` package needs two core ingredients:\n", - "\n", - "1. A **simulator** \n", - "2. A set of **environment samples**\n", - "\n", - "These two objects completely define the *probabilistic problem* we want to solve.\n", - "\n", - "### 1.1 Simulator\n", - "\n", - "The simulator is a function\n", - "\n", - "$$\n", - "x \\mapsto y\n", - "$$\n", - "\n", - "where:\n", - "\n", - "- $x$ is a vector of input / environment variables (here a 2D point: `x = [x1, x2]` in $[0, 1]^2$).\n", - "- $y$ is a **random** output because the simulator includes noise.\n", - "\n", - "In this toy problem the noise model is a **Gumbel distribution**:\n", - "\n", - "- The simulator is parameterised by:\n", - " - a **location** function `loc(x)` and\n", - " - a **scale** function `scale(x)`\n", - "- For each input $x$ the output is:\n", - "\n", - " $$\n", - " y \\sim \\text{Gumbel}(\\text{loc}(x), \\text{scale}(x))\n", - " $$\n", - "\n", - "In a real application, we **don't know** the true `loc(x)` and `scale(x)`; we only see noisy simulator outputs. \n", - "Here we cheat a little: we have access to the \"true\" functions so we can visualise what is going on and check that our methods behave sensibly.\n", - "\n", - "**Visualizing the stochastic nature of the simulator:**\n", - "\n", - "Below we run the simulator 500 times at the same input point $x = [0.5, 0.5]$. The histogram shows the characteristic right-skewed shape of a Gumbel distribution — this is the noise model built into our toy simulator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "392d8b95", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The histogram shows the Gumbel-shaped distribution of simulator responses at a fixed input point.\n" - ] - } - ], - "source": [ - "# Use the seeded simulator for reproducible results\n", - "sim = DummySimulatorSeeded()\n", - "\n", - "# Run the simulator multiple times at the same point to visualize the response distribution\n", - "test_point = np.array([[0.5, 0.5]])\n", - "n_samples = 500\n", - "response = sim(test_point, n_simulations_per_point=n_samples)\n", - "\n", - "# Visualize the distribution of simulator responses\n", - "plt.figure(figsize=(10, 4))\n", - "plt.hist(response.flatten(), bins=40, density=True, alpha=0.7, edgecolor=\"black\")\n", - "plt.xlabel(\"Response value\")\n", - "plt.ylabel(\"Density\")\n", - "plt.title(\"Simulator Response Distribution at x = [0.5, 0.5]\")\n", - "plt.grid(alpha=0.3)\n", - "plt.show()\n", - "\n", - "print(\"The histogram shows the Gumbel-shaped distribution of simulator responses at a fixed input point.\")" - ] - }, - { - "cell_type": "markdown", - "id": "ac0cfaf6", - "metadata": {}, - "source": [ - "### 1.2 Environment Data\n", - "\n", - "The second ingredient is a set of **environment samples**:\n", - "\n", - "- Each sample is a 2D point $x = [x1, x2]$\n", - "- Together they represent the **conditions your system actually experiences** in practice:\n", - " - These might come from historical data\n", - " - Or from a probabilistic model of the environment\n", - "\n", - "In this toy example we load a pre-generated dataset:\n", - "\n", - "- We treat it purely as **input samples** — no simulator has been run on them yet.\n", - "- We'll later use them to:\n", - " - Estimate our **Extreme Response Distribution (ERD)** by brute force, and\n", - " - Feed them into Axtreme's QoI estimators so we can reuse them efficiently with the surrogate." - ] - }, - { - "cell_type": "code", - "execution_count": 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"gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Environment Distribution" - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Load environment data\n", - "env_data = collect_data().to_numpy()\n", - "print(f\"Environment data shape: {env_data.shape}\")\n", - "print(f\"First 5 samples:\\n{env_data[:5]}\")\n", - "\n", - "# Visualize the environment distribution\n", - "fig = histogram_surface3d(env_data)\n", - "fig.update_layout(title_text=\"Environment Distribution\", scene_aspectmode=\"cube\", height=500)\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "62fbe922", - "metadata": {}, - "source": [ - "## Step 2: Brute-force Extreme Response and QoI\n", - "\n", - "Before we introduce any surrogate modelling, we first compute a **reference answer** using raw simulation.\n", - "\n", - "### 2.1 Extreme Response Distribution (ERD)\n", - "\n", - "We imagine observing the environment over **time periods** of length `N_ENV_SAMPLES_PER_PERIOD`. \n", - "For each period we:\n", - "\n", - "1. Draw `N_ENV_SAMPLES_PER_PERIOD` inputs from the environment data\n", - "2. Run the simulator on each input\n", - "3. Record the **maximum** response over that period\n", - "\n", - "Each period gives one sample from the **Extreme Response Distribution (ERD)**:\n", - "\n", - "$$\n", - "\\text{ERD sample} = \\max_{t \\in \\text{period}} y(t)\n", - "$$\n", - "\n", - "Repeating this many times gives us an empirical ERD, which we visualise as a histogram.\n", - "\n", - "### 2.2 Quantity of Interest (QoI)\n", - "\n", - "In this tutorial our **Quantity of Interest (QoI)** is:\n", - "\n", - "> The **median** of the Extreme Response Distribution.\n", - "\n", - "Intuitively:\n", - "\n", - "- We're asking: \"What is a *typical* extreme response over a period?\"\n", - "- This is a robust measure of tail behaviour that is easier to estimate than, say, a 1-in-1000-year extreme.\n", - "\n", - "We compute:\n", - "\n", - "- A **brute-force estimate** of the ERD median using many simulator calls\n", - "- An associated **uncertainty** for this estimate using Axtreme's `population_estimators` helpers\n", - "\n", - "This brute-force QoI estimate will serve as:\n", - "\n", - "- A **ground-truth** comparison for the surrogate-based estimates we build later." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24645f41", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sarelm\\AppData\\Local\\Temp\\ipykernel_21132\\1551274040.py:9: UserWarning:\n", - "\n", - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brute force QoI (median ERD): 2.0480\n", - "This required 300,000 × 1000 = 300M simulator calls!\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define the period length (number of environment samples per period)\n", - "N_ENV_SAMPLES_PER_PERIOD = 1000\n", - "\n", - "# Load pre-calculated brute force results (300,000 ERD samples)\n", - "precalced_erd_samples, precalced_erd_x = collect_or_calculate_results(N_ENV_SAMPLES_PER_PERIOD, 300_000)\n", - "brute_force_qoi_estimate = np.median(precalced_erd_samples)\n", - "\n", - "# Calculate QoI uncertainty using population estimators\n", - "population_median_est_dist = population_estimators.sample_median_se(torch.tensor(precalced_erd_samples))\n", - "\n", - "print(f\"Brute force QoI (median ERD): {brute_force_qoi_estimate:.4f}\")\n", - "print(f\"This required 300,000 × {N_ENV_SAMPLES_PER_PERIOD} = 300M simulator calls!\")\n", - "\n", - "# Visualize the ERD with QoI uncertainty\n", - "fig, axes = plt.subplots(ncols=2, figsize=(14, 4))\n", - "\n", - "# Left plot: ERD histogram with QoI uncertainty overlay\n", - "axes[0].hist(precalced_erd_samples, bins=100, density=True, alpha=0.7, edgecolor=\"black\", label=\"ERD\")\n", - "axes[0].set_xlabel(\"Extreme Response\")\n", - "axes[0].set_ylabel(\"ERD Density\")\n", - "axes[0].set_title(\"Extreme Response Distribution (Brute Force)\")\n", - "axes[0].grid(alpha=0.3)\n", - "\n", - "# Add QoI uncertainty on secondary y-axis\n", - "ax_twin = axes[0].twinx()\n", - "population_estimators.plot_dist(population_median_est_dist, ax=ax_twin, c=\"red\", label=\"QoI estimate\")\n", - "ax_twin.set_ylabel(\"QoI Estimate PDF\")\n", - "ax_twin.legend(loc=\"upper right\")\n", - "\n", - "# Add median line\n", - "axes[0].axvline(brute_force_qoi_estimate, color=\"red\", linestyle=\"--\", linewidth=2, alpha=0.5)\n", - "axes[0].legend(loc=\"upper left\")\n", - "\n", - "# Right plot: Zoomed-in view of QoI uncertainty only\n", - "population_estimators.plot_dist(population_median_est_dist, ax=axes[1], c=\"red\")\n", - "axes[1].axvline(\n", - " brute_force_qoi_estimate,\n", - " color=\"red\",\n", - " linestyle=\"--\",\n", - " linewidth=2,\n", - " label=f\"Median ERD = {brute_force_qoi_estimate:.4f}\",\n", - ")\n", - "axes[1].set_xlabel(\"QoI Value (Median ERD)\")\n", - "axes[1].set_ylabel(\"Density\")\n", - "axes[1].set_title(\"QoI Estimate Distribution (Zoomed)\")\n", - "axes[1].legend()\n", - "axes[1].grid(alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "bb0853fd", - "metadata": {}, - "source": [ - "## Step 3: Build a Surrogate Model with Ax + BoTorch\n", - "\n", - "Running the simulator many times is expensive. \n", - "Instead, we build a **statistical surrogate** (a Gaussian Process) that learns:\n", - "\n", - "- The mapping from inputs $(x1, x2)$ to the **location** parameter of the Gumbel noise\n", - "- The mapping from inputs $(x1, x2)$ to the **scale** parameter of the Gumbel noise\n", - "\n", - "### 3.1 Configuration\n", - "\n", - "Before creating the Ax experiment, we set up the core components:\n", - "\n", - "1. **Search space**: Define the valid input domain — here, `x1` and `x2` both in $[0, 1]$\n", - "2. **Noise distribution**: Specify the distribution family for the simulator noise (Gumbel)\n", - "3. **Simulations per point**: How many simulator runs to perform at each training point to estimate the distribution parameters (`loc` and `scale`)\n", - "\n", - "These configuration choices affect:\n", - "\n", - "- How much information each training point provides (more simulations → better estimates)\n", - "- The computational cost per training point\n", - "- The smoothness of the resulting surrogate model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3abb7b25", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Search space: 2D unit square\n", - "✓ Distribution: Gumbel\n", - "✓ Simulations per point: 200\n" - ] - } - ], - "source": [ - "# 1. Define the search space (2D: x1 and x2 both in [0, 1])\n", - "search_space = SearchSpace(\n", - " parameters=[\n", - " RangeParameter(name=\"x1\", parameter_type=ParameterType.FLOAT, lower=0, upper=1),\n", - " RangeParameter(name=\"x2\", parameter_type=ParameterType.FLOAT, lower=0, upper=1),\n", - " ]\n", - ")\n", - "\n", - "# 2. Choose distribution for noise model\n", - "dist = gumbel_r\n", - "\n", - "# 3. Number of simulations per point\n", - "N_SIMULATIONS_PER_POINT = 200\n", - "\n", - "print(\"✓ Search space: 2D unit square\")\n", - "print(\"✓ Distribution: Gumbel\")\n", - "print(f\"✓ Simulations per point: {N_SIMULATIONS_PER_POINT}\")" - ] - }, - { - "cell_type": "markdown", - "id": "35793562", - "metadata": {}, - "source": [ - "### 3.2 Initial training data (Sobol design)\n", - "\n", - "We start by generating an initial, **space-filling** design using a Sobol sequence:\n", - "\n", - "- Sobol points cover the space well without any modelling assumptions.\n", - "- At each Sobol point `x`, we run the simulator multiple times to estimate:\n", - " - the mean (location) and\n", - " - variability (scale) of the response distribution.\n", - "\n", - "These initial points are used to fit the first GP surrogate using `Models.BOTORCH_MODULAR`.\n", - "\n", - "**What the GP actually learns:**\n", - "\n", - "The `make_experiment()` function wraps the simulator with logic that:\n", - "\n", - "1. Runs the simulator `N_SIMULATIONS_PER_POINT` times at each training point `x`\n", - "2. Fits the Gumbel distribution to those samples to estimate `loc(x)` and `scale(x)`\n", - "3. Provides these fitted parameters to the GP\n", - "\n", - "So the GP learns **two separate mappings**:\n", - "- `x → loc`: How the location parameter varies across the input space\n", - "- `x → scale`: How the scale parameter varies across the input space\n", - "\n", - "The GP is **not** trained on raw `(x, y)` pairs. Instead, it learns the underlying distribution parameters from multiple noisy observations at each `x`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "38f7b24d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Trained GP with 30 training points\n", - "✓ Total simulator calls: 6000\n" - ] - } - ], - "source": [ - "# Helper function to create experiments with consistent settings\n", - "def make_exp():\n", - " return make_experiment(sim, search_space, dist, n_simulations_per_point=N_SIMULATIONS_PER_POINT)\n", - "\n", - "\n", - "# Create experiment and add 30 initial training points\n", - "exp = make_exp()\n", - "add_sobol_points_to_experiment(exp, n_iter=30, seed=8)\n", - "\n", - "# Train the surrogate model\n", - "botorch_model_bridge = Models.BOTORCH_MODULAR(\n", - " experiment=exp,\n", - " data=exp.fetch_data(),\n", - ")\n", - "\n", - "print(f\"✓ Trained GP with {len(exp.trials)} training points\")\n", - "print(f\"✓ Total simulator calls: {len(exp.trials) * N_SIMULATIONS_PER_POINT}\")" - ] - }, - { - "cell_type": "markdown", - "id": "b51a5336", - "metadata": {}, - "source": [ - "### 3.3 Comparing surrogate vs simulator\n", - "\n", - "To build intuition, we pick a single test point `x = [0.5, 0.5]` and:\n", - "\n", - "- Sample many responses from the **true simulator**\n", - "- Predict the mean Gumbel distribution parameters from the **surrogate** \n", - "- Sample from the GP posterior to visualize uncertainty\n", - "\n", - "The plot shows:\n", - "\n", - "- **Histogram**: 200 simulator runs at the test point, showing the true response distribution\n", - "- **Red line**: The surrogate's mean predicted Gumbel distribution \n", - "- **Grey lines**: 10 sampled Gumbel distributions from the GP posterior\n", - "\n", - "The GP posterior captures uncertainty about the true `loc` and `scale` parameters at this location. The grey lines show the range of plausible response distributions consistent with the training data. When the surrogate has high confidence, these lines cluster tightly around the mean; when uncertain, they spread out." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35295f03", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Surrogate prediction: loc=0.598, scale=0.100\n" - ] - } - ], - "source": [ - "# Predict at x = [0.5, 0.5]\n", - "test_x = {\"x1\": 0.5, \"x2\": 0.5}\n", - "pred_mean, pred_covariance = botorch_model_bridge.predict([ObservationFeatures(parameters=test_x)])\n", - "\n", - "# Get simulator samples at the same point\n", - "simulator_samples = sim(np.array([[0.5, 0.5]]), n_simulations_per_point=200).flatten()\n", - "\n", - "# Mean prediction\n", - "pred_dist_mean = dist(loc=pred_mean[\"loc\"], scale=pred_mean[\"scale\"])\n", - "x_points = np.linspace(simulator_samples.min(), simulator_samples.max(), 100)\n", - "\n", - "# Sample from GP posterior to show uncertainty\n", - "mean = np.array([pred_mean[\"loc\"], pred_mean[\"scale\"]]).flatten()\n", - "covariance = np.array(\n", - " [\n", - " [pred_covariance[\"loc\"][\"loc\"], pred_covariance[\"loc\"][\"scale\"]],\n", - " [pred_covariance[\"scale\"][\"loc\"], pred_covariance[\"scale\"][\"scale\"]],\n", - " ]\n", - ").reshape(2, 2)\n", - "\n", - "surrogate_distribution = scipy.stats.multivariate_normal(mean, covariance)\n", - "posterior_samples = surrogate_distribution.rvs(size=10, random_state=42)\n", - "\n", - "# Plot\n", - "plt.figure(figsize=(10, 4))\n", - "plt.hist(simulator_samples, bins=20, density=True, alpha=0.6, label=\"Simulator\", edgecolor=\"black\")\n", - "\n", - "# Plot posterior samples with transparency\n", - "for i, sample in enumerate(posterior_samples):\n", - " sample_dist = dist(loc=sample[0], scale=sample[1])\n", - " if i == 0:\n", - " plt.plot(\n", - " x_points, sample_dist.pdf(x_points), \"grey\", linewidth=1, alpha=0.5, label=\"GP Surrogate posterior samples\"\n", - " )\n", - " else:\n", - " plt.plot(x_points, sample_dist.pdf(x_points), \"grey\", linewidth=1, alpha=0.5)\n", - "\n", - "# Plot mean prediction on top\n", - "plt.plot(x_points, pred_dist_mean.pdf(x_points), \"r-\", linewidth=2, label=\"GP Surrogate (mean)\")\n", - "\n", - "plt.xlabel(\"Response value\")\n", - "plt.ylabel(\"Density\")\n", - "plt.title(\"Surrogate vs Simulator at x = [0.5, 0.5]\")\n", - "plt.legend()\n", - "plt.grid(alpha=0.3)\n", - "plt.show()\n", - "\n", - "print(f\"Surrogate prediction: loc={pred_mean['loc'][0]:.3f}, scale={pred_mean['scale'][0]:.3f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "92d81f95", - "metadata": {}, - "source": [ - "## Step 4: Estimate the QoI from the Surrogate\n", - "\n", - "Now that we have a surrogate model, we can use it to estimate our **Quantity of Interest** (the median of the ERD) **without repeatedly calling the expensive simulator**.\n", - "\n", - "The key idea:\n", - "\n", - "- The surrogate is fast to evaluate.\n", - "- We use it to *emulate* what the simulator would do over many periods of environment samples.\n", - "- We propagate both:\n", - " - **environment randomness**, and\n", - " - **surrogate uncertainty**\n", - " into our QoI estimate.\n", - "\n", - "### 4.1 Environment sampling via DataLoader\n", - "\n", - "We wrap our environment samples in a small `Dataset` and `DataLoader`:\n", - "\n", - "- This lets the QoI estimator iterate over batches of environment points.\n", - "- We can easily control how many total environment samples we use for the QoI calculation.\n", - "- Different runs (with different seeds) can use different resampled subsets if desired.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "61afb67d", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up environment data loader\n", - "n_env_samples = 4_000\n", - "dataset = MinimalDataset(env_data)\n", - "sampler = FixedRandomSampler(dataset, num_samples=n_env_samples, seed=10, replacement=True)\n", - "dataloader = DataLoader(dataset, sampler=sampler, batch_size=256)" - ] - }, - { - "cell_type": "markdown", - "id": "01c7b44c", - "metadata": {}, - "source": [ - "### 4.2 QoI estimator: MarginalCDFExtrapolation\n", - "\n", - "We use Axtreme’s `MarginalCDFExtrapolation` as a ready-made QoI estimator that:\n", - "\n", - "1. Draws batches of environment inputs from the loader\n", - "2. Uses the surrogate model to obtain a distribution of responses at each input\n", - "3. Forms many **periods** of length `N_ENV_SAMPLES_PER_PERIOD`\n", - "4. Extracts the **maximum** response in each period\n", - "5. Estimates the **median** of the resulting extreme response distribution\n", - "6. Repeats under different draws from the GP posterior to capture model uncertainty\n", - "\n", - "The result is a **distribution over the QoI**, not just a single number.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "98377e34", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ QoI estimator configured\n" - ] - } - ], - "source": [ - "# Create QoI estimator\n", - "qoi_estimator = MarginalCDFExtrapolation(\n", - " env_iterable=dataloader,\n", - " period_len=N_ENV_SAMPLES_PER_PERIOD,\n", - " quantile=torch.tensor(0.5), # Median\n", - " quantile_accuracy=torch.tensor(0.01),\n", - " posterior_sampler=UTSampler(),\n", - ")\n", - "\n", - "print(\"✓ QoI estimator configured\")" - ] - }, - { - "cell_type": "markdown", - "id": "462ee0b3", - "metadata": {}, - "source": [ - "### 4.3 Effect of training data size\n", - "\n", - "To see the impact of more simulator data, we repeat:\n", - "\n", - "- Use `N` training points to fit a surrogate\n", - "- Use that surrogate to estimate the QoI distribution\n", - "\n", - "for several values of `N` (e.g. 30, 50, 128).\n", - "\n", - "For each `N` we:\n", - "\n", - "- Approximate the QoI distribution with a Normal using the sampler’s `mean` and `var` methods\n", - "- Plot the corresponding PDF\n", - "- Overlay the **brute-force QoI** as a vertical line\n", - "\n", - "Results:\n", - "\n", - "- With **few** training points, the QoI distribution is **wide** (high uncertainty).\n", - "- As we add more training points, the distribution becomes **sharper** (narrower), and its mean stays close to the brute-force value.\n", - "\n", - "This confirms that:\n", - "\n", - "> The surrogate-based QoI estimator converges towards the brute-force answer as more simulator data is added.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "596ec23c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Computed QoI with 30 training points\n", - "✓ Computed QoI with 64 training points\n", - "✓ Computed QoI with 128 training points\n" - ] - } - ], - "source": [ - "# Train surrogates with different numbers of training points\n", - "n_training_points = [30, 64, 128]\n", - "results = []\n", - "\n", - "for n_points in n_training_points:\n", - " # Create and train experiment\n", - " exp_temp = make_exp()\n", - " add_sobol_points_to_experiment(exp_temp, n_iter=n_points, seed=8)\n", - "\n", - " model_bridge = Models.BOTORCH_MODULAR(experiment=exp_temp, data=exp_temp.fetch_data())\n", - "\n", - " # Set up transforms\n", - " input_transform, outcome_transform = transforms.ax_to_botorch_transform_input_output(\n", - " transforms=list(model_bridge.transforms.values()), outcome_names=model_bridge.outcomes\n", - " )\n", - " qoi_estimator.input_transform = input_transform\n", - " qoi_estimator.outcome_transform = outcome_transform\n", - "\n", - " # Estimate QoI\n", - " model = model_bridge.model.surrogate.model\n", - " result = qoi_estimator(model)\n", - " results.append(result)\n", - "\n", - " print(f\"✓ Computed QoI with {n_points} training points\")" - ] - }, - { - "cell_type": "markdown", - "id": "7216da03", - "metadata": {}, - "source": [ - "Let's see how the QoI estimate improves with more training data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60f65f53", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sarelm\\AppData\\Local\\Temp\\ipykernel_21132\\844206477.py:6: UserWarning:\n", - "\n", - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - "\n", - "C:\\Users\\sarelm\\AppData\\Local\\Temp\\ipykernel_21132\\844206477.py:7: UserWarning:\n", - "\n", - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualize QoI estimates\n", - "fig, axes = plt.subplots(nrows=len(n_training_points), figsize=(10, 3 * len(n_training_points)), sharex=True)\n", - "\n", - "for ax, estimate, n_points in zip(axes, results, n_training_points, strict=True):\n", - " # Extract mean and variance from the estimate\n", - " mean = qoi_estimator.posterior_sampler.mean(torch.tensor(estimate), -1)\n", - " var = qoi_estimator.posterior_sampler.var(torch.tensor(estimate), -1)\n", - " qoi_dist = Normal(mean, var**0.5)\n", - "\n", - " # Plot QoI distribution\n", - " x_range = torch.linspace(float(mean - 3 * var**0.5), float(mean + 3 * var**0.5), 200)\n", - " ax.plot(x_range.numpy(), torch.exp(qoi_dist.log_prob(x_range)).numpy(), \"b-\", linewidth=2, label=\"QoI estimate\")\n", - " ax.fill_between(x_range.numpy(), 0, torch.exp(qoi_dist.log_prob(x_range)).numpy(), alpha=0.3)\n", - "\n", - " # Add brute force reference\n", - " ax.axvline(brute_force_qoi_estimate, color=\"red\", linestyle=\"--\", linewidth=2, label=\"Brute force\")\n", - "\n", - " ax.set_title(f\"QoI Estimate with {n_points} Training Points\")\n", - " ax.set_ylabel(\"Density\")\n", - " ax.legend()\n", - " ax.grid(alpha=0.3)\n", - "\n", - "axes[-1].set_xlabel(\"Response\")\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7ae8adc8", - "metadata": {}, - "source": [ - "## 5. Design of Experiments (DoE)\n", - "\n", - "In the earlier sections, we saw that adding more simulator training data improves our surrogate model and reduces the uncertainty of our **Quantity of Interest (QoI)** estimate. However, **where** we choose to evaluate the simulator matters just as much as **how many** points we evaluate.\n", - "\n", - "Design of Experiments (DoE) helps us choose simulator inputs intelligently, so that each new simulation gives us the **maximum possible reduction in QoI uncertainty**.\n", - "\n", - "In this section we compare:\n", - "\n", - "- a baseline approach (random Sobol sampling), and\n", - "- a QoI-aware approach that uses Axtreme’s `QoILookAhead` acquisition function to pick the most informative points.\n", - "\n", - "We will track the QoI after each iteration to see which strategy converges faster.\n", - "\n", - "### 5.1 QoI tracking metric and stopping criteria\n", - "\n", - "To monitor the progress of the DoE, we attach a special metric — `QoIMetric` — to the Ax experiment. After each iteration:\n", - "\n", - "- The surrogate model is updated.\n", - "- The QoI is re-estimated using the updated surrogate.\n", - "- The QoI estimate (mean and standard error) is stored inside the experiment's data.\n", - "\n", - "This allows us to plot the QoI over time and optionally stop early when the QoI uncertainty becomes sufficiently small.\n", - "\n", - "We implement:\n", - "\n", - "1. **QoI tracking metric**: Automatically computes and logs the QoI after each surrogate update\n", - "2. **Stopping criterion** (`sem_stopping_criteria`): Checks if the standard error of the QoI drops below a threshold (e.g., 0.02)\n", - "3. **Trial runner** (`run_trials`): A helper function that:\n", - " - Runs warm-up trials using a space-filling design (e.g., Sobol)\n", - " - Then runs DoE trials using a specified acquisition strategy\n", - " - Checks the stopping criterion after each DoE iteration\n", - " - Returns early if the QoI uncertainty is sufficiently small\n", - "\n", - "This approach avoids unnecessary simulator calls once the QoI estimate is sufficiently accurate." - ] - }, - { - "cell_type": "markdown", - "id": "a2a66e5c", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01e67e82", - "metadata": {}, - "outputs": [], - "source": [ - "# Define QoI tracking metric\n", - "QOI_METRIC = QoIMetric(\n", - " name=\"QoIMetric\",\n", - " qoi_estimator=qoi_estimator,\n", - " minimum_data_points=3, # don't compute QoI until some data exists\n", - " attach_transforms=True,\n", - ")\n", - "\n", - "\n", - "# Define stopping criteria based on standard error of the mean (SEM)\n", - "def sem_stopping_criteria(experiment: Experiment, sem_threshold: float = 0.02, metric_name: str = \"QoIMetric\") -> bool:\n", - " \"\"\"Stop when the standard error of the QoI estimate drops below a threshold.\"\"\"\n", - " metrics = experiment.fetch_data()\n", - " df = metrics.df\n", - " qoi_rows = df[df[\"metric_name\"] == metric_name]\n", - "\n", - " if len(qoi_rows) == 0:\n", - " # No QoI yet\n", - " return False\n", - "\n", - " latest = qoi_rows.iloc[-1]\n", - " sem = latest[\"sem\"]\n", - "\n", - " # Stop if SEM is finite and sufficiently small\n", - " return (sem is not None) and (not np.isnan(sem)) and (sem <= sem_threshold)\n", - "\n", - "\n", - "# Define function to run trials with progress output\n", - "def run_trials(\n", - " experiment: Experiment,\n", - " warm_up_generator: Callable[[Experiment], GeneratorRun],\n", - " doe_generator: Callable[[Experiment], GeneratorRun],\n", - " warm_up_runs: int = 3,\n", - " doe_runs: int = 15,\n", - " stopping_criteria: Callable[[Experiment], bool] | None = None,\n", - ") -> int:\n", - " \"\"\"Run warm-up + DoE trials with simple progress output.\"\"\"\n", - "\n", - " total_iters = warm_up_runs + doe_runs\n", - " print(f\"Starting DoE run: {warm_up_runs} warm-up + {doe_runs} DoE iterations\")\n", - " print(\"-\" * 60)\n", - "\n", - " for i in range(total_iters):\n", - " phase = \"Warm-up\" if i < warm_up_runs else \"DoE\"\n", - "\n", - " # Select generator\n", - " gen_fn = warm_up_generator if i < warm_up_runs else doe_generator\n", - " gen = gen_fn(experiment)\n", - "\n", - " # Run trial\n", - " trial = experiment.new_trial(gen)\n", - " trial.run()\n", - " trial.mark_completed()\n", - "\n", - " # Print progress\n", - " print(f\"[{i + 1}/{total_iters}] {phase} iteration {i + 1} completed\")\n", - "\n", - " # Stopping criteria only valid during DoE\n", - " if (i >= warm_up_runs) and (stopping_criteria is not None) and stopping_criteria(experiment):\n", - " print(f\"✓ Stopping criterion met after {i - warm_up_runs + 1} DoE iterations.\")\n", - " print(\"-\" * 60)\n", - " return i + 1\n", - "\n", - " print(\"✓ DoE run completed.\")\n", - " print(\"-\" * 60)\n", - " return total_iters" - ] - }, - { - "cell_type": "markdown", - "id": "97c44be5", - "metadata": {}, - "source": [ - "### 5.2 Baseline: Sobol-only DoE\n", - "\n", - "As a baseline, we perform DoE using only Sobol points:\n", - "\n", - "- Sobol sequences are space-filling and require no modelling assumptions.\n", - "- They cover the full input domain evenly.\n", - "- They do not focus sampling effort on regions that matter most for the QoI.\n", - "\n", - "The Sobol-only experiment therefore provides a fair reference point to compare against more advanced acquisition strategies.\n", - "\n", - "We:\n", - "\n", - "1. Create a new Ax experiment with the same simulator, search space, and noise model as before.\n", - "2. Attach the QoI tracking metric.\n", - "3. Run:\n", - " - a few warm-up Sobol points, followed by\n", - " - many DoE Sobol points.\n", - "4. Record the number of iterations performed (possibly fewer if the stopping criterion triggers early).\n", - "\n", - "This gives us a curve describing how the QoI estimate improves when sampling is uniform and naïve." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a64d69bb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running Sobol-only experiment...\n", - "Starting DoE run: 3 warm-up + 100 DoE iterations\n", - "------------------------------------------------------------\n", - "[1/103] Warm-up iteration 1 completed\n", - "[2/103] Warm-up iteration 2 completed\n", - "[3/103] Warm-up iteration 3 completed\n", - "[4/103] DoE iteration 4 completed\n", - "[5/103] DoE iteration 5 completed\n", - "[6/103] DoE iteration 6 completed\n", - "[7/103] DoE iteration 7 completed\n", - "[8/103] DoE iteration 8 completed\n", - "[9/103] DoE iteration 9 completed\n", - "[10/103] DoE iteration 10 completed\n", - "[11/103] DoE iteration 11 completed\n", - "[12/103] DoE iteration 12 completed\n", - "[13/103] DoE iteration 13 completed\n", - "[14/103] DoE iteration 14 completed\n", - "[15/103] DoE iteration 15 completed\n", - "[16/103] DoE iteration 16 completed\n", - "[17/103] DoE iteration 17 completed\n", - "[18/103] DoE iteration 18 completed\n", - "[19/103] DoE iteration 19 completed\n", - "[20/103] DoE iteration 20 completed\n", - "[21/103] DoE iteration 21 completed\n", - "[22/103] DoE iteration 22 completed\n", - "[23/103] DoE iteration 23 completed\n", - "[24/103] DoE iteration 24 completed\n", - "[25/103] DoE iteration 25 completed\n", - "[26/103] DoE iteration 26 completed\n", - "[27/103] DoE iteration 27 completed\n", - "[28/103] DoE iteration 28 completed\n", - "[29/103] DoE iteration 29 completed\n", - "[30/103] DoE iteration 30 completed\n", - "[31/103] DoE iteration 31 completed\n", - "[32/103] DoE iteration 32 completed\n", - "[33/103] DoE iteration 33 completed\n", - "[34/103] DoE iteration 34 completed\n", - "[35/103] DoE iteration 35 completed\n", - "[36/103] DoE iteration 36 completed\n", - "[37/103] DoE iteration 37 completed\n", - "[38/103] DoE iteration 38 completed\n", - "[39/103] DoE iteration 39 completed\n", - "[40/103] DoE iteration 40 completed\n", - "[41/103] DoE iteration 41 completed\n", - "[42/103] DoE iteration 42 completed\n", - "[43/103] DoE iteration 43 completed\n", - "[44/103] DoE iteration 44 completed\n", - "[45/103] DoE iteration 45 completed\n", - "[46/103] DoE iteration 46 completed\n", - "[47/103] DoE iteration 47 completed\n", - "[48/103] DoE iteration 48 completed\n", - "[49/103] DoE iteration 49 completed\n", - "[50/103] DoE iteration 50 completed\n", - "[51/103] DoE iteration 51 completed\n", - "[52/103] DoE iteration 52 completed\n", - "[53/103] DoE iteration 53 completed\n", - "[54/103] DoE iteration 54 completed\n", - "[55/103] DoE iteration 55 completed\n", - "[56/103] DoE iteration 56 completed\n", - "[57/103] DoE iteration 57 completed\n", - "[58/103] DoE iteration 58 completed\n", - "✓ Stopping criterion met after 55 DoE iterations.\n", - "------------------------------------------------------------\n" - ] - } - ], - "source": [ - "# Baseline: Sobol-only DoE\n", - "exp_sobol = make_exp()\n", - "exp_sobol.add_tracking_metric(QOI_METRIC)\n", - "\n", - "# Sobol generator (kept outside the loop so its internal state persists)\n", - "sobol = Models.SOBOL(search_space=exp_sobol.search_space, seed=5)\n", - "\n", - "\n", - "# Sobol generator function\n", - "def sobol_generator_run(_: Experiment) -> GeneratorRun:\n", - " return sobol.gen(1)\n", - "\n", - "\n", - "print(\"Running Sobol-only experiment...\")\n", - "n_sobol_iters = run_trials(\n", - " experiment=exp_sobol,\n", - " warm_up_generator=sobol_generator_run,\n", - " doe_generator=sobol_generator_run,\n", - " warm_up_runs=3,\n", - " doe_runs=100,\n", - " stopping_criteria=sem_stopping_criteria,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "076a5f9a", - "metadata": {}, - "source": [ - "### 5.3 QoI-aware DoE with `QoILookAhead`\n", - "\n", - "Randomly exploring the search space is wasteful when our goal is to reduce QoI uncertainty, not to approximate the simulator everywhere equally.\n", - "\n", - "To address this, Axtreme provides a custom acquisition function, `QoILookAhead`.\n", - "\n", - "This acquisition function:\n", - "\n", - "- Uses the current GP surrogate.\n", - "- Anticipates how much one more simulator evaluation will reduce the QoI uncertainty.\n", - "- Selects the input location where this reduction is expected to be largest.\n", - "\n", - "Conceptually, it prioritizes simulator points that are most influential in shaping the extreme response distribution.\n", - "\n", - "This is significantly more efficient than Sobol sampling, especially when the QoI depends heavily on only a subset of the input space.\n", - "\n", - "To run QoI-aware DoE:\n", - "\n", - "1. For each DoE iteration, we build a BoTorch model bridge equipped with the `QoILookAhead` acquisition.\n", - "2. We use Ax’s built-in acquisition optimizer (for example, Nelder–Mead for robustness in non-smooth cases).\n", - "3. The best acquisition point becomes the next simulator evaluation.\n", - "4. After each point, the QoI is re-estimated and logged by the tracking metric.\n", - "\n", - "This creates a feedback loop where the model learns faster in QoI-critical regions." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7033917d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running QoI-aware look-ahead experiment...\n", - "Starting DoE run: 3 warm-up + 40 DoE iterations\n", - "------------------------------------------------------------\n", - "[1/43] Warm-up iteration 1 completed\n", - "[2/43] Warm-up iteration 2 completed\n", - "[3/43] Warm-up iteration 3 completed\n", - "[4/43] DoE iteration 4 completed\n", - "[5/43] DoE iteration 5 completed\n", - "[6/43] DoE iteration 6 completed\n", - "[7/43] DoE iteration 7 completed\n", - "[8/43] DoE iteration 8 completed\n", - "[9/43] DoE iteration 9 completed\n", - "[10/43] DoE iteration 10 completed\n", - "[11/43] DoE iteration 11 completed\n", - "[12/43] DoE iteration 12 completed\n", - "[13/43] DoE iteration 13 completed\n", - "[14/43] DoE iteration 14 completed\n", - "[15/43] DoE iteration 15 completed\n", - "[16/43] DoE iteration 16 completed\n", - "[17/43] DoE iteration 17 completed\n", - "[18/43] DoE iteration 18 completed\n", - "[19/43] DoE iteration 19 completed\n", - "[20/43] DoE iteration 20 completed\n", - "[21/43] DoE iteration 21 completed\n", - "[22/43] DoE iteration 22 completed\n", - "✓ Stopping criterion met after 19 DoE iterations.\n", - "------------------------------------------------------------\n" - ] - } - ], - "source": [ - "# QoI-aware DoE with QoILookAhead acquisition\n", - "\n", - "# Choose the acquisition function\n", - "acquisition_function_class = QoILookAhead\n", - "\n", - "\n", - "# Define QoILookAhead DoE generator function\n", - "def look_ahead_generator_run(experiment: Experiment) -> GeneratorRun:\n", - " \"\"\"Generate a new point by optimising the QoILookAhead acquisition.\"\"\"\n", - "\n", - " # First: build a model bridge to recover the Ax→BoTorch transforms\n", - " model_bridge_for_transforms = Models.BOTORCH_MODULAR(\n", - " experiment=experiment,\n", - " data=experiment.fetch_data(metrics=list(experiment.optimization_config.metrics.values())),\n", - " fit_tracking_metrics=False,\n", - " )\n", - "\n", - " input_transform, outcome_transform = transforms.ax_to_botorch_transform_input_output(\n", - " transforms=list(model_bridge_for_transforms.transforms.values()),\n", - " outcome_names=model_bridge_for_transforms.outcomes,\n", - " )\n", - "\n", - " # Feed the transforms into the QoI estimator so it interprets inputs/outputs correctly\n", - " qoi_estimator.input_transform = input_transform\n", - " qoi_estimator.outcome_transform = outcome_transform\n", - "\n", - " # Build a model bridge with a custom acquisition function\n", - " model_bridge_with_acq = Models.BOTORCH_MODULAR(\n", - " experiment=experiment,\n", - " data=experiment.fetch_data(),\n", - " botorch_acqf_class=acquisition_function_class,\n", - " fit_tracking_metrics=False,\n", - " acquisition_options={\n", - " \"qoi_estimator\": qoi_estimator,\n", - " \"sampler\": sampling.MeanSampler(),\n", - " },\n", - " )\n", - "\n", - " # Optimise the acquisition to find the next candidate\n", - " return model_bridge_with_acq.gen(\n", - " 1,\n", - " model_gen_options={\n", - " \"optimizer_kwargs\": {\n", - " \"num_restarts\": 20,\n", - " \"raw_samples\": 50,\n", - " \"options\": {\n", - " \"with_grad\": False, # QoILookAhead may not be smooth\n", - " \"method\": \"Nelder-Mead\",\n", - " \"maxfev\": 5,\n", - " },\n", - " \"retry_on_optimization_warning\": False,\n", - " }\n", - " },\n", - " )\n", - "\n", - "\n", - "# Run the QoI-aware DoE\n", - "exp_look_ahead = make_exp()\n", - "exp_look_ahead.add_tracking_metric(QOI_METRIC)\n", - "\n", - "# Sobol generator for warm-up (kept outside the loop so its internal state persists)\n", - "sobol_for_warmup = Models.SOBOL(search_space=exp_look_ahead.search_space, seed=5)\n", - "\n", - "\n", - "# Sobol generator function for warm-up\n", - "def sobol_warmup_run(_: Experiment) -> GeneratorRun:\n", - " return sobol_for_warmup.gen(1)\n", - "\n", - "\n", - "print(\"Running QoI-aware look-ahead experiment...\")\n", - "n_lookahead_iters = run_trials(\n", - " experiment=exp_look_ahead,\n", - " warm_up_generator=sobol_warmup_run,\n", - " doe_generator=look_ahead_generator_run,\n", - " warm_up_runs=3,\n", - " doe_runs=40,\n", - " stopping_criteria=sem_stopping_criteria,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6e533168", - "metadata": {}, - "source": [ - "### 5.4 Comparing Sobol and QoI-aware DoE\n", - "\n", - "Finally, we compare results from both strategies.\n", - "\n", - "Using Axtreme’s `plot_qoi_estimates_from_experiment`, we visualize:\n", - "\n", - "- The QoI estimate at each iteration.\n", - "- The uncertainty bands (SEM).\n", - "- The brute-force QoI as a reference line.\n", - "- Curves for both:\n", - " - Sobol-only DoE, and\n", - " - QoILookAhead-based DoE.\n", - "\n", - "This makes it easy to see:\n", - "\n", - "- How quickly each method reduces QoI uncertainty.\n", - "- Whether either method shows bias.\n", - "- How many simulator evaluations are required to reach a target confidence level.\n", - "\n", - "In typical problems, we expect:\n", - "\n", - "- Sobol: uncertainty decreases steadily but relatively slowly.\n", - "- QoILookAhead: uncertainty drops much faster because it samples only where the QoI is sensitive.\n", - "\n", - "In real applications, this difference can translate to far fewer simulator evaluations for similar or better QoI accuracy.\n", - "\n", - "This completes the end-to-end Axtreme workflow: from defining a simulation problem, through surrogate modeling and QoI estimation, to intelligent experiment design." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09c6755e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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4eDsrK4sDnPHZq0gwcu8FI6fgH0oiYC5HWiuKiYVSkyOZ+Ln0Z/p82+c0PHu4NyjZ1Ui/Pei3ZDMciBPAVR2uyHDVGE6gqdCaYPMIlw0CRHHl2zZdW2gPfr7ggoJLRjq+dx2Zx8gg89h98Bu4bds2trLCwur/+9jV87ecqQQf5fXlpNd4gwEhbhCQLHE6giAIQjwjQkdgPIqHM67Q+gFoNVq+OqlqrJIZEgRBEOIWEToCU+eo40BkpJar6LV6Fj+CIAiCEK+I0BGYGkcNN/M06w7U47DpbVw40OkOXPNCEARBEGIdEToCg+BjhRR2WamkGlNZ/CDVXBAEQRDiERE6AoOgY21K66qsJp2JmlxSOFAQBEGIX0ToCBx0vNe+1xeI7A9Sdvc37JdZEgRBEOISEToC1Tvrye6wtwpEVrHqrbSnbk/E+/sIgiAIQk8gLSAEjs+B2AnUwBP1dKqaqrimTppRCiz2Jugq3+xu7pF9GbQGMus7bhQpCIIQD4jQETjjyu1xk06jC2jR2Ve/j+vpiNDpXZHz7sZ3e6yuUaY5k84YcYaInRBAP6Wnn36aK10vWLCA3nnnHe6phNtgHHvssdy1/fHHH4/kx5aUc4957kqT0HgZD/pndXY8dYWUlBQ+XgP1mosmO3bs4MrvK1eu5O9ATyCuK4FPnsHK5/sKBzZJ4cDeBJYcfE5I/880ZUZ1wT6wr3CsR2ggiaadaJyIJo5HHHEEN8/0B8fRnXfeyd3NsQ4aKKJJpIrD4eAmiCjtjqaRbZsqPvzww9woMRRQKh7NFdFYFK000AUc+0Ozxki6YdevX0933303NwZFd24003ziiSfo+eefp0QAvwvqgjYb/fv3pxtvvJE/q3jiv//9L02ePJm7lqO1wDHHHEMffPBB2NuBQMVxLsQXYtERvBWRde0Dkf3dGHvr9tLBBT2jvoXgIBMuUCxVpEGfs3D4/e9/T2vWrKEXX3yRO5K/9NJLLCzWrVvH/WoAumajczROOmr38pNOOonXgRiZN28eLV++nL7//nv6+OOP6fzzz6d9+/bxSRb9v5599llatmxZp2PB1S86TKMfDrql4wSHk/RXX31Ff/rTn+j444+njIwMigRbt27lW3Q8Vy8WjEYjJRLz58+nGTNmcMfvX375hTvIW61W7jAfD/zxj3/kjvc4FmC9wPvA8YnPDKL0mmuu6e0hClFGLDpxxtbKrbR63+qIukRQJydQxpVKqiGVyuxlPRYfIsQXjY2N9NZbb7GQOfroo2no0KFswsctXDoAVhS4aW6//XY+wYwbN45eeOEF2rt3r88kD+vI6aefTmPGjKGrr76aysvLaf9+b8bflVdeSQ899FBIjfz+8pe/sHn8xx9/pFmzZtHo0aPZQnT55ZezS8Fm8zapraqqoosvvpgyMzP5Sh/WGH8LE6wyEESffvopjRo1il+HEz4sNwDv8bTTTuP7aDyoCh24GvzdAfX19bwfvB7WrEcffbTdmGEhwQkZohAi4rDDDqMvv/wy5LGo/Oc//+H5g9jCvvxP4hCAEKS5ubk8jxB8EC6dgf3CItavXz869dRT+fNbsWJFq3XwOQ8ZMoQMBgONGDGCBa8KPgvMjb87B2PBc+p7xC0ef/755zRp0iT+PGAV3LhxY6v9PPjgg5Sfn89Wmcsuu4wbQHbEDz/8wPMNayDmF8ck5u++++5jywysU7t27fKtj+NYnb+BAwcG/KzCbd57zz33UN++fXmbcNV88sknrdZZvXo1fxawcmZnZ9P//d//kd1uD7pNWErxGeL70NE606dPp5ycHG6CCQtW288M4Pt11lln8XwPGzaM3nvvvVZ/x8ULvhc43jDvsLiq30mA94KLChwjGDuOD1X8qyxdupQmTJjAFzP4bOGy6mlE6MQZO2t20k97f+KWDRGriOys71DoqA0+pe+VEAh0D3e73e26reOH+9tvv+X7sMiUlpaylUcFP8A4ocOCA8aPH8/rQzjhhI4TNX6oX375Zd42fpBDObG8+uqrdMEFF7Blqd2xbLOxdUcVJLAQ4ccdY4AYmzlzJl/xqzQ0NNAjjzzCJ+6vv/6aiouL+YQJcAtrB4DgaCs6VG6++Wa2Jr377ru0cOFCPqm3PelAkGAMGPuqVavonHPOYSHjL7w6GosqNiAQcaLEyRPvCyd2FWwTcUSwlsFyNnHiRJo2bRpVVlZSqGzatIm++OIL/txUEOdx/fXX00033cQnxiuuuIKtPosXL6ZwgbsR4gKfCz6nSy+91Pe3119/ncXl/fffz3/H8fHUU091uL1XXnmFP3OMqS0YLz5riBuAOTn33HPpN7/5Dc8f9gWrY3fckLAY4f3gc8PnCgsmxLz6uUIE4zmIbYiTN954g122waxMmHsIGAi1P//5zx26kiHy8X364YcfWMTg2Mbz/sDtiveMseHv+N6oxwPEKAQYRArmG6IGFlasr4LxQyzi7xCpEPz4nuJ7CCDYIH5wsYH5xZz6H7M9hpJk1NTUwEHPt/HIq6tfVe796l5l2Z5lEdnexv0blUeWPKK8t+G9Dhess758veJ2u5WSkhK+FbpOsHlsbGxU1q1bx7f+VDdWK08tfUp5ZdUrnX5W3V2wD+wL+wyVKVOmKMccc4yyZ88exeVyKS+++KKi0WiU4cOH89+XLFnC37u9e/e2et0555yjnHvuuXy/ublZueqqq5SBAwcqkyZNUr755huloqJCGTx4sFJcXKzcdtttypAhQ5QTTzxR2b17N7/G4/Hw63AL9u3bx/t57LHHOhzvpk2beD2MS2X//v2K2WxWXn/9dX48f/58XmfLli2+dZ588kklPz/f93jBggW8jj+zZs1SzjjjDL5fV1enGAwG3zYB3hP2c/311/PjnTt3KlqtlufOn2nTpim33npryGMpKiriOQoE5jItLU1pampq9Tzm85lnnmk3jyrYp8lkUqxWq2I0GvnxqaeeyuuqHHHEEcrll1/e7nOdOXMm39++fTu/buXKlb6/V1VV8XOLFy/mx7jF488++8y3zocffsjPqd8FHGM4Pvw57LDDlPHjxyvBmDFjRod/x5xceeWVfP/8889Xpk+f3urvN998szJ69GjfYxzj6ucWiDvvvFMZN26cbx7xmdx3332t1pk8ebLvfcybN0/JzMxU7HZ7q/eN705paWmr4+ntt99WbDab8uqrryrh4na7ldTUVOX999/3PYe5vf32232PMQY89/HHH/Pje++9l79r/uzatYvX2bhxY8D9lJeX899Xr17Nj3FsZWdnt/o9e/rpp9sdD/5g3bVr1/J3vu3vY1fP32LRiSPgZoJlBcGia8rWcHuG7lLd6DUhd4YUDhQ6AlYG/HbC9QITPWJxfvvb3/IVXqjo9Xp68skn2fqDq1uYxHHVfd1117G5Gy4uuFoOP/xwfi4QoQYaw00Gi4G/ZQKmd7hd8DcVmPThklGBFQFWkVCBGb+5ubnVfrKysng/KrAewCIG9xqsD+oCK5C/G6CjseAWbkBYaAKBecPVNd6j/z4w121dDW3529/+xm4nbAMBvLDqwIWhgvmaOnVqq9fgsf88hgpcmv7vT31v6n785xFMmTKl0212dkzA3dbR+4D1BZ9PuCAgHp9JR3ODW1gy4a70/zssIv5uO7hhYZHD9+y8887zPQ+rnv/nCWsXgOUFrlpYctLT09lVic8f6webb4wB66nzjc8bVjn/7SO4H6jHDOYG3/PBgwfza+HuU8elvj/sw9/aG8pnFmkkGDmOqHXUstjpn96fdlTvoC2VW2h8wfhubbO0vpSM2s6DJ9Hgc3ftbikcKAQEJ2CcmGHKxg88TlL4QcYPIECMh/oDrJ7A1MfBUkzxI7t27Vr697//ze4fmNbxYwzTOYJLA4HYBcQLbNiwISKfFMRXW8Ef6eKZOAFptVo27ePWHzWeqLOxwE3Y2T4w7/5xPyqdBWbjs1NdYBBocH/g5IbgXn/XWDBUses/b/7uQX/836N6Aaa6QboCTvRw30BsqoJGBSIExyoEZjx8vyBSEYN1yimn+OYJ7ln/2CeIaAC3VUVFBbvOBgwYwBcfEBiYB38CHVP+bifEoAWKBVK/w/g7to9EAYwFrx07dmy7/fQ2YtGJI2DNcbgdnHmTbkynX/b9wsKnqyC4uKKhgmvldAbidFBYEGJLEIIBIYIfQQT6Is4GgasAWVY4YcKPr4KTDK5UA13hIcgU8SZI28bJH1fU6skRt8GusHFSRYwF4npwImsLfrwRU4SAVNxi/yo4MeAqGvEEkTxB4WTivx/MDawiKoiBwPvBlTSEg/+iCsTOQHAurqb959cfxOMgRgpWrLb7QBxUOKhiDLFUAHO5ZMmSVuvgsTqPEJ/AP4apK3VmsB//eQSIP+kICDJ85jiO2oK4GVgaVAtJsPcBIdRWgIYCLBw4+Xc0N9gnLCe4QPD/O45jf6sfPiPE52zZsoWFvvpdaPt5qkIH24DVExcHY1qCq/2DiEMBxwwuNHBctT1m8D1Xvy9IMIAlEe8Fx7Y/eA7xP/5B4519ZtFAhE4c4R+AnGfNo7L6MrbqdBWIlgZnQ4eByCpYB0HL1Q7pZN6boMkqXJbRXLCPcIGoQbAiXCGLFi2i4447js3cCEpVrxSR5QIrAIJk4a5BJhJOBIEKliF1GT/SEAGqOR81cPCjCWtOW3eAPwjURIYQ3BzI7EL6OkzsuBrG9nDiw5U+RBjM+7jix8nmwgsvZNebKs4iASwyyA6CRQonKgTrIgja36WHEymCQDEfeI+YQ2SqPPDAA/Thhx+GvC8EeiLwFW5DvF8EPP/jH//gvyEIHIISc42AaGRCfffddxz821nKPoJSIZIgHGG1QxYRxoyTGMB7Q8AugqGx38cee4zfhxp0CmsT3I3ImIIrA9vAyTFcEPCMzxAB4BCKc+bM4RNxR+A943UYI+YGLhdY+7B/zBMsEbCUALhJIRRx7GH7KIOAY607wbPYLywir732GouCW265hUUexgTwuUNswQKDYwNWTNSKgmsQWU7+5OXl8TGE8UPAQagHA8c33FyY7x9//JH305nVry240EBgMvYFVzLmDt9zfKchzBFAjblDWQgIMIwNgcn+oEQEvvv4nuF7+NFHH7HA7GnEdRVHVDRWkEFj8BXyQ9o3rDrDsoexlSdcYKHBSS2U16qFA9HlvI/GWxdF6DlQywjVipH5Fm6Nm66AfWGfoYKaNbfeeivt3r2bryrPPvtsFhz+pnHUsMGVK7KC1Fo3EEdts7Xwg48MG/+r/l//+tfsdjnqqKP4Svd///tf0LFg/7hqxIkVwmrnzp38o3zQQQdxmjFiFgBOmDjhICsEpnakxuOHuK05v7tgn6obAJYXnFAxX/5gLBgr/rZnzx6+goc4wNhCBSdLXDkjpgYnZ2wD8wZwssF7g7DBiQqp+7AW4T23PaG2xV+sqq9BLIiavQbxBBcJTmCYT1jv8H5QXE8FAgWC75BDDuHPD6UITjzxxLDmEZYXnGxxHOF94hhD2QGcfDsCZQ0QJ4IMLQgcvBZuLJyY8V78LRg47lDUEmIHlkmIOgjTrgKrCj5rfK6w2MGSA6EPIaLGXWH8mDe1oCHeF8RiIDD/GDfmFuIF34NA1qbnnnuOv2d4T/369ePPK1zBplqjkN2FzwolEOCmQjagWk4BWYJ4j3BX4XOFePT/3CH033//fZo9ezZfZOD9Q/jhPfYkKS3R10kDzOX4ocPBF0pNjlgBH9Mrq19hq0qBzWvOdnlctK1qG80YOoPG5I0Je5srSlbQ4u2LaXh2aD7q4ppijg+anD6Zry7CCTQVWgNfNn742s4jfoRxRY+TRVsBIL2uAn8vcGWLk24oQfVCYJJpHmHNQl0ZWHvg4uyKWyoYyTSP0QK/gdu2beOLAlhY/X8fu3r+FotOnAAXE0QOMq5U0JsKLqVV+1axVSecK3Cwz74vrNegIm95fTk5bYEDCYXogiab0mhTELoHYk5gHYRrClZDWJmExEYuyeMoEBlX9G1PdPnWfNpTt4ctO+GAJp6I8QklENk/88rebOdFEAQhXoHFFDFNInKSAxE6cRSI7PQ421lg9Fo9W3lg1XG6nVEJRFYx6oy8j54QOhibIAiCIHQXETpxZNFRKHA4VaGtkHbV7KLt1dvDav2AoNZwXSGaFA2/Nprsqd1DH2z8gC1OgiAIgtAdROjECfvr9weNp4FVB0X/YNVBgHKoGVcInINwCQfU00HtHY/S9SJenbF+/3pelu9dHtX9CIIgCImPCJ04ACf78obyVoHIbSlM9Vp1UDE5FLA9BDOHC4QO3EqRairalv0N+7k2UFFqEW3Yv4G7tQuCIAhCVxGhE0cZVx3F08Dag1o3sOog0Lgz4VRmLwsrPkcFr3G4HNw6IhpA5CAGCCn0eE/L9i7rVvVnQRAEIbkRoRMHwHrCGVcdWHRAka2IiquLaWfNzg7Xg5Coc9aFlXGlgtoQep2evtrxFa3YuyJkV1kooCrvuvJ1lGXyljHvk9qH+2tBvAmCIAhCVxChEwcgQwqCArE4nWVFobgSYlvgwgoW34L4nIbm8DKu/Mk153JM0Bc7vqBFWxdRdVNk2kIgRb6yoZKyLd6S7LBQ5Vpy6ed9P3P9HkEQBEGIK6GDXi4oe40KiKgQi1Li/q3pA4HeJCgDj5LuWNDDBX1hEj3jKlT6pvalEnsJvbvhXVq4dSGV2ksDCieIIAiJrgIxMiB9AK0tX0vvbXyPXU7dKbKNBqNrytZwDJB/gDT2Y3fYaXmJBCYLkQHHKcrjo1UELJRdaTApCEL80KtCB83d0DgMfWnQCBAdWdFTw7+Ta1tQ0RJNxtD87Pvvv+c+HngN+sMkKrBmwIISCrD6DMoYxDEucAMtWL+Avtr5FfdI8u+ZFW62VSDQI2tY1jAWIh9t/oi+2/VdlxpCgp3VO6mkroSblbalb1pf2lAugcmxDPoBQTSoC5r9oScOmnBGavuBmn92BfTXQhPKDz74gDtqo09PrIGqvWrvI1wIomUBxhsu6DuEZqqCkMz0qtDBDw5+wNBGfvz48fzjU1xcTMuXLw/6GvQmueqqq+jggw/m7sj//ve/uW8Qus4mIrC8IBMp3Ho3cEtBhKDx59LdS+mtdW/R0j1LOQ4GVp6uuq3aAsHUL70fx9Us2bWEBQ9aS4T7HpFOjiywQO45jBXPLyuRwORYBsIGwgELvo/o99NZU0pc3PQ0aAyJho1HHHEEN0lUm1N2padRNEDzxSuuuIKbWEIowmKNBqjoqo5u2oIgxHGMjtrRFyblUGloaOAfy2CvQcdVNALzXwDEUTwstU21HE9j1pr5xzXcJc2YRsOzhpOGNPTl9i9Z8NQ21pJVZ+3S9jraz9CMobSjage9v+F9Wl++PuT3uKdmD+2o3MHtLIJtv4+tD+2q3sWByb39mURqwfsK9nygBR2wgy2NjY0hr4vvTGfrhvv5A6PRyJ2wseDCBV2Pd+3axc1LsQ6alaodj2GhQNPSl156iebMmcMXLv7bQwdu9CTCffwdFo53333XZzGCRRd/w/Zh4YUbG78BEAPYT7Bx4sLq2muv5QsqbEfdBxoJ4nm40DEuCAsIDPV12J/aARxtA/Bev/nmG3K73dyNeejQofxc//79uQu5+jrs59xzz6WMjIyQxgcr9aOPPsrdvdHxesiQIXxBh22iw/WNN97I21TXf/PNN/lCEfvGe0EH8bafS6ifXzjryyLzqETxOFCPw0C/j10hZpp64g3AxDp16tSwTMn4MUU7ecTqBIsDuvvuu9s9X15ezj9usQ6K81EDkZnMRF28+E2hFMqhHMoyZlF1XTU5XU7KtmUTdfWCFNMWoDGvjnQ01DCUKhsr6fsN31NjfiMVpRV1urm1pWvJ0GTwWq0cgdeBUOuT0ofW71hPGa4MSjelUzyD4x3CHl9m/+68EO34G6wFbS0GcGEE4+STT2YhoAKxAUETiKOPPpo+++yzVn1/9u/f32qd5ubmsN+POm4AsfTiiy+yAEC3Yf/3c+utt7I4QLwdRAVuMQ/+71f9QcNz+F1Yt24d1dXV8boAogHi7qSTTqJDDz2UvvjiC7bM4PsOy9KKFSvIYGhfYBNCAILgueeeo++++447V2MfN998M7399tv8PMQKxAa2s379et4XBA245ZZbeOyYM4gr/P785z//oYcffph/u0pLSznOENvEZ4nxHX744SGP73//+x/ZbDa67LLL2n3+EDoQgG+88QZdd911vA1Yfe644w4655xzOAQAYg3juvjii/k16omjM+sT1lHfo3Td7joyj90Hxyq+//i+4yLJ//cRz8W10EGszpo1a+jbb78N+TUPPvggXx0ibgc/mIHAjyquglRg0UFcT25ublht3nuLqooqsuvsXBCwu0AsZKWGbi0LiNKyWAOLHZBly6K9tXtpWe0yOjH3ROqf3r9DIbfDuYNsmTaiwB+hj1RbKm2u2EzbndvphH4nRCTOqLfAFxknFByH/l9kiG98mXFSDMelgm2Fun4o64brzsF7gLUDJ1mAODu4h95//33fCV3dJk7YODH7v7btmNQ5wXOwhlitVhYOffv29a0DaxBOLPPmzfPtA+5vjAG/I4jdawtihyC8IHDUbWGszzzzDM2fP9/naoNLHGIGliSIIKwP7rnnHhYqAJ8TXEn/+Mc/6NJLL+XnRowYwdYqgN8mjA/iSRUPnY1vy5YtbMVBbE5bIMDwm4V1MC9///vfadq0aWzxAqNHj6YNGzbQY4895huPagEL9fPU6zvO7BRCQ+ax6+BYxfcfgh8WVv/fx2Dn+U63STHANddcw4F2X3/9dasfso7AlRmEDq5Mx40bF3Q9mHSxtAWT5z+BsQpXIE6JsassDKWTMfVJ78MBxot3LKYZQ2cEFWpbq7dyVlmftD4h7bpvel/aULGBhmYPpaFZQymewfy1PQ7Vk766+AMrSTBwIvZfH1dCwVD3obJjR/tq2l053o477jh6+umn+X5VVRU99dRTNHPmTHYBDRgwwLdNBNn6b1+939lzbR8jfgUnfYgXfyAWt23bxmICli4ViJkLLrig3baxLkQU3FXqcxBOsBRBOPh/Fv5jx9/gGoc1OdB8qeNre0Glji/YHEMcdTT/+D3D32FtgivMf128hyeeeIKFtCrOAh1LHe0zpn5r4gyZx+7jfxwG+n2MO6GDgwKm1gULFrBVBldQoQD/9X333UeffvopTZo0iRIZtGpAdlM8AkvOtupt9Pn2z1ns5FhyAhYIzDa3PlF1BAcma/RcMRkFBcMN0o5nYNXo7XU72w5cVSqwisB6AncTYkyC7Q8/XqpfPpwgZQg/xMvASoKrQP8TNCxlECv+qeNw53UX/7GbzeaQxocEirZgfIEYNmwYCzS4Dtu6tvbu3csW6eHDh3d5/IKQjGh6210F8zP80og/gH8bC3zvKvA1w/2kAv84fNLwi8PXrr6mo6vdeAWtHDjjqpOKyLEKTjxIdUe7ic+3fd6usGDbAoGhgj5YxTXFtHSvN1hUiE3UKzL/73MgcNLHd9j/s2xb2wYnfTWGRGXixIm0efNmNm9DYPkvEFgQIv7PBYtxgqsI21+yZEkrofXTTz+xOygYECXYR7CMz87GFwgEVuO3DNanQFZsmO4RlwNGjRrVaswAjyGEVGuOIAi9LHRg5kZAJmo9wJ+vLq+99ppvHWQYIF3V/zW42vn1r3/d6jX4EUg00Kqh0dUY11YLxNEMzhxMu2p30Rfbv+D31FGBwFBAGjoKI64sWUmbKjZFaeRCuMCNo154wK0Cay1O2qeddlqHr8P3H8kBsNQi9fvJJ5+kjz/+uNU6uKiBKwiBvgichhCBGyonJ4fOPvtszoBCNhMswwjU3b17d1hWmiuvvJJjcVDyAoHPl19+OQdzIyg4GBAdCEb+05/+RC+88AKPHQHBiMkB6vjgXgp1fFOmTOEYJowFAdHYJlxkt99+O8fkwDqmuuqQlQWRde+999KmTZs4nggxQ0hPFwQhhlxXnYEfBn8CxRMkKohdQY+rAmsBxTOowDwkcwhXT/5S8yVNGzyNe1ihQODAjIFd2maqMZXnB7V7YBFq6xYTeh6IBFx08OeTmspp0cgQgpDpCFgmEM9z//3380kbwgUnawQZq0B44LcArmqIJ6R7Y7soOgqhgdcgOLhPnz4coBtuogHi/RDXctFFF/F2sB+4xtXg6mDAugy32Z133smuJbz/2bNn898QUIy4Q4ihX/3qVyGP7/HHH+e4Q8wJBA5iemBxQuYWMub8LUavv/467xvzhn0jWBop9IIgHCBFSTLbP3zcMBvDkhTrWVeoRYMCfMOzY8cnz4cLjDK28IMWYcXZWrWVxuePJ4fbwTV3BmUO6tZYNldupiFZQ+jkoSdzr694ASdVBAy3zSrASQ1X/ohX62qGQTKhpk63jdFJJHBxh0wuWHsQ7xMNt1QyzGNPIPPYfdRgfVws4cLA//exq+fv2E87SmJqHDVcAydRMGgNHLODon+Iz8m35UckBggp5whOTjLNLiQJcNvBmgULmfTlEoQ4TS8XAlNWXxa3GVfBwPtBNhYyriLRhgKtIQpthdyxHX2yhmUPi8g4BSGWgIXvrrvu6u1hCEJcIhadGMXlcXGF4XgORA4GBE6uNXB6bVdAlWQIHjQVxZwJgiAIgooInRguFIhA5HhNLe9pUFMHNYeWFC/hWKB4R9xwgiAkI0oUQhBE6MR4xlWiua6iBTdoTB9IG/ZvoBUlKyjeS8cH61MlCIKQyDS39NmLZOcCidGJIE63k7OARuaM7HYfJlh0FFI4NVsIDWRdIcAZgcmI10H9nngDGTXo7aS2cECKsmTBBEeyXCKDzKPMY6xko6KmFn73ROjEKJUN1fRzyRrKteR2OwaFM64kzTNsMk2ZLBIRr4PCggW2As72iicKCgo67VclHDhB48exbf8uITxkHiODzGP3wXcZPS+rq1tX0u8OYtGJMOX11VTRWNFtoYOMK4nP6Rp90/py+vqC9Qso3ZjOjUARw4PPJMucFfNdz3HCRvE31NgJpedTMgORU1FRwdWC46FJb6wi8yjzGCu07fEWCUToRJiqRjuLFLivuuMCq26sFqHTRSBk0NkcQcm1jlpuNfFzyc9kMVgo05zJsTxwcSEtPZaz2uDGkp5FnZ+gEdeE4ooidLqOzGNkkHmM3Dz2utDBVSb62SBgEg35srKyIjqoeKax2UW7a3aTR/F02XKAQOQGV0NYXb2F9sBlhdYQWGBSRt+wmqYa+r7ueyKFKM+WR5OKJtGwrGESCyUIgpCghHwmRp8WNNREKXKUXka1TvSogdAZMGAA96JBt99kp8np5vganFC7CmJMmlxNknEVYXcQ6vcUphaysEGgMub5o00fcZuNPbV7Irk7QRAEIZ6EzmOPPcbCZv78+XTCCSfQO++8w6XI0TH3+++/pzlz5nCflBNPPJFmzJhBmzdvpmQFFreqhtpuFa6DRQcWiFiPJYlnkM2GWB5UaUaz0Xc2vkNf7fyKXV2CIAhC4hCS6wqWGnThHTNmTMC/H3rooXTppZfSv/71LxZD33zzDQ0blryl+O3NTbS/YT83m+wK1U3VkkHSgynpiOeBBW7p7qW0vWo7HVJ4CI3IGRF32VqCIAhCF4XOK6+8EspqZDQaafbs2ZTspJCRimuK6dA+h3ZJsEjGVc+DNhKpxlSe+4VbF9LGio00sXAiDUgfIPE7giAIcUyXs662bNlCW7dupaOPPprMZjO7WqSOhRctWaiqqYrszXY+eYYDMoVg0YnlbKBEBa5C1N1BEPju2t28wL01Nm8sDcwYKDFTgiAIySB0ULPivPPOoy+++IKFDeJxBg8eTJdddhllZmbSo48+SslOCplY5CBOJ1yhwz2uXI2UY86J2viEjkGD0EGZg8jhctA++z7aUb2DBdDonNE0NHsopRnTZAoFQRDihLCjXW+44QbS6XRUXFzMZZpVIH4++eSTSI8vLvF4NNTs8nYf70ogssPpEOtBjMTvIFh5aOZQ7jv2+fbP6fU1r9O3xd+yAJLGm4IgCAlo0Vm4cCF9+umnXKLZHwQf79y5M5Jji2sUxcCujwmFE8J6HbJ+0ONKMq5iK0MLFp18az5VNlXSD7t/oFX7VtGQzCE0Jm8MV10Wt60gCEKCCJ36+vpWlhyVyspKDkYWWiZWY6HyhnK2BIQTb4PYHm2KNPKMRSBmEL+DBa7JdeXrOGgZdXkOyj9IBI8gCEIiuK6OOuooeuGFF1r9+KNc89y5c+m4446L9PjiFo1iYutMOO4rBCLvqtlFVoM1qmMTuo/NYOO09DxLHm3Yv4HeXv82fbLlE/78xKUlCIIQxxYdCJpp06bRsmXLqLm5mf70pz/R2rVr2aKzZMmS6IwyDnG6NERaNwudPml9QnoNqvMivRm9mIT4AKIUgqe+uZ4Fz+bKzWzhQaYWMrbEpSUIghBnQmfs2LFcEfmf//wnpaamkt1up1/96ld09dVXc8dlwYvTrZCeNFRqL2W3RihsrdpKKZTCWT9CfAqeBmeDT/AgiBnZW9mWbO6artNID11BEISepku/vOnp6XTbbbdFfjQJhi7FSnvq9nA38s7ECyrzoiovTopC/IJ+Wqrg2VS5idbvX8/ZW0hJh4UHHdPxGWcYM3p7qIIgCElB2EIHKeQ2m42OPPJIfvzkk0/Ss88+S6NHj+b7qKUjeNGSieocNey+yrfldzgtu2p3caHAEdkjZPoSRPAgKwugQSvqI60qXUUrlZVe4WNIo4KUAspVcnldBKybdCYy67y3YtUTBEHoJaFz880300MPPcT3V69eTTfeeCPddNNNtHjxYr6PXleCF49HT06luVOh41E87O7ACU9iOhIPCBcsudbcA8KnqY62122n1fbVnBKQonhdlgadgQwaAx8LKDaZacrkW6veygHQcJFBDMlxIgiCECWhs337drbegLfeeotOO+00uv/++2nFihU0c+bMcDeX0DQ0u8lk1nKA8ajcUUHXQ/G5kroSyrPm9ej4hN4BosdoNRIpSN9qyVxUPOziROZds6eZXZloDLvBvcHbXoVSWASZtCYWQZnmTE5zh4AekDFAPkpBEIRICR2DwUANDQ18/7PPPqOLL76Y72dlZVFtbW24m0v4gOR0rYULB+JEFqwIIFoMONwOPoFJz7DkBMcGXFpYAoHjAiII1qB6Zz1bCX9u/plG5owUoSMIghBJoYPYHLiopk6dSkuXLqXXXnuNn0cmVttqyQICks1cTwfxN8i8aQtOXAhahYvCoyhU3dhMWRYpvCi0BlYfVQilUzo/t7duLwtoQRAEIYJCB2nlV111Fb355pv09NNPU58+3hoxH3/8Mc2YMSPczSU+HiPVu/ZRRUNFQKEDa8/++v00OHMw1TY6qa7RJUJHEAShC1TYHeRW0EInhbQpKXyr0cBieuA+ntdqUhIyzs3l9lCD001Ol8f3nlN87x8Np/G+0dYm5cBz3ZgHj0fh/dU7XC2Lm+wOF/9typDs+BU6/fv3pw8++KDd83/7298iNaaEotHlJg1pON5iWPawdn/fUrmFeymhxsr+ejsfqIIgCELolNc5aGu5nexN3pNsKOBk77/ocPLH/RYhpPHdku95Fg4sDg5sp5VQUDxUY3eQ04DwDg1b6T0KEk4UUhSvCxqheQDbhfRQxQYeqtv37c9PmGnUMaakkBOCptndsrioseV+syv880cK9sf7anmv/u+z5f15x+gdr/p8o9PN+8X7aovFoI3/Ojpo+bBlyxYqKyvj+/4cffTRkRpbQtDY7KEMm5UtN23jbxBnsbN6J+WYc8jhcvOXFH/GlwIHmiAIghCcqvpmFjjVDc6wp8ntUXiJJPiNd9c3ktZhjxuLkaIQW8EiPRdxLXR++OEHOv/887lTeduePvhg3W53JMcX90Bhm3UW7npd11zHheNU0BcJ8TsoIldS08TPYUohesx6qaIrCEL8UFLTSBX2ZjLoNKTXYkHJhPb3YZ3oLrVNTtpaZuf9CUJnhH02nT17Nk2aNIk+/PBDbvkQL6q1N0khE9U7ytmCowodt8fNna9RGwVysbLhwBcWJkEROoIgxAtNTjdtKK0jt7tzq4DqmoEbRnXL6LS41fBzqlDy3qaQwSeWNOyy2VZeT/tqvReGghAVobN582YORB46dGi4L01aHE6UTFE46HhghrdhZ4m9hGvn9EntQzWNTnL5/UA0NXuILL04YEEQhDDYGKLI8blK3ErI6wtCdwlc2KUDDjvsMI7PEUKHLTQ6M+2u2+17Dn2tXB4XpwvvtzvarS8IghAPlNU1cTCwICSMRefaa6/llg+lpaV00EEHkV7fulnluHHjIjm+hACR6QVWG2deodkjQHdrpJtD1CAlr9X6InQEQYgDkCW6qdTe28MQYvC4aHZ52P0Yl0Ln7LPP5ttLL73U9xzidNSMongJRq6vryettn0KHJ4zmUyt1guGRqMhs9ncal1nUzM1k6OVrQzRN31SLWR3VnKcTn1zPZVWldLg9MG0t7yWmhtbB9Q1p6Cqso30LeNzNDraBX63KiRnPlBg0NHkIKWD6HmTxdThutgPLE3ou+S/brOjmTxuj3ccjV5boH98VqB1g4Hxqq91NjvJ7XJHZF2DycCfSaTX1Rv1vmOlo3UxN7XuWtpas5XLBlTXV5NNa6M0Uxo38UTPKsRopRvTyaa3kdllppr6GipvKKfS2lLaV7OP72NB3aWqpireLqeeIoZBp/VW1/YQadwa8pCHhmUNoxkDZrSrXq5egLhcLnI4gl9t+6+L725TU/DYB6yH9cNdF5mZjY2NEVlXp9OR0Wj0zTe+c6jUjlv18wy2rlrRPRDhfO+7+xsR6roYb0ffe4vF0qV1Mb9ts2XxWJ3H1NTUDtf1Z4/dw/E5oNnR1OHvv9li9d3vbF2T+UDfv+ZmB7ldroisazSZ/b73zeRyOSOyrsFoOvAb4Wym5oaGVinowdZ1OZ28ftDtGoyk1enCXhdzgLkIhl5vIJ36vQ9nXbebP7tg6HR6qncRzft6G00ekEF/mTG0S997/+MRn4H/ul1CCZMdO3Z0uMQ6NTU1+DUIusycObPV+haLJei6xxxzTKt1s7Kzg647eMwQ5eFvH1Z+LvlZeX/D+0pWQVbQdXMH9FFe+vkt5b0N7/HSb2i/oOvmFeX51sMydOzQoOumZaa1Wnfs5LFB1zWYDK3WnXTMpA7nzX/dI046osN1X1/xum/d4888vsN1X/zuRd+6M8+f2eG6z372rG/dsy49q8N1//HeP3zr/ubq33S47qNvPOpb93c3/67DdWkWKXRXyzKzg/WwnO+37hmdrHuO37rndLzu/PnzfcfkBx980OG6//znP33rLl68uMN1586d61t36dKlHa47Z84c37pr1qzpcN0//vGPvnW3b9/e4bpXXXWVb92ysrIO1501a5ZvXbvd3uG6v/71r1t9l6P1G5GTkxN03UmTJrVad8CAAUHXHT16dKt18TjYutiOP9hPsHUxPn8w/mDr4n1/tq5UWbTWuxx69LQO501dD8tRJ57a4brv/bTVt+70M87tcN03vlnjW/e033T8/Xxx4VLfuudccmWH6z777pe+dS+66qaOv0evfszrLVxTolx2zR87XPeR+W/5tnvNbfd3uO5fn3rRt+4f//p4h+ve/tg837q439G6f/zr4751sY+O1sUY1XUx9o7WPeuKPynj7vpUGfDnD5TBv38i4r8R6vkbt+EQtkVnwABpINgVcFGEpoxbq7ZyE8+2V59twVVSmqm1W7AngVUnFvpu3f3V3TRxyEQ6KO8gzlSLFDcsvIEMqw1sYWne1nGK6jsb3iGNXcPlAYo3FXe4LiwuAzMH0tDMoVSxq4KW0/IO19emaCnHkkP6ND3tpgMxXG25bMJldPDxB7MVZ6VuJT3/xvOdvENBiC4wBgcxIglJyleb9lNqhpP6ZZrpj9MOpjP/TTFBSsvVS1hs3bqVHn/8cVq/fj0/Rjfz66+/noYMGUKxDhqPpqen0969eykt7UBNm0iYpbeV7qC5X/2X0k05ZNC07leVaTNQVgZOUx52SwyyDqLNZbXU4AhgFk4hys+y0YBsa4+6rvbV76NrP77W9/jmY2+mo/of1d51hSmxRs51ta1iG72y5hVaUbKCn4PbLMOYwS4cgtZr2Y3Wo6XhGcNpTO4YGpc/jt02qCodijvK3myndze+Sx9t+Yi7hLPTVtWasHR3VFA0yLp4D31T+9KQrCE0JHMIt/EYmjeUzAbvMeFyunhpC7tcmutJaVIoMy+TdFpd0HVV9AY9u64A3hfeHzL30MH8zFFnJq3rym63U3l5OeXm5vaa6wpj2LG/nkx6LZkNWr7FEm+uK3UeQ3Fd7a5soE376sJyRyWD6wqfQVN1GSkGW9K4rpwuhV5dVkw/bKukFK2WJg/Oo6uOHULHDM/p8m+E//Ho77pSz981NTUBz9/BCNui8+mnn9Lpp59OBx98MDf2BEuWLKExY8bQ+++/T9OnT6d4wGq18hLKeuFsU28ykMFkJIO2tdBxaTRkM1i4dg6u4D06Lbk0emo5J7bDPyDZX8h0htHU9XUrayqJ/Nygz654liYUTOBaPwaj9w/8Y4rfPUvwHinqup1Rai9lgfPlji85/V5j1NCJg0+k88aexydw/H112WpavW8131Y0VtD62vW8vLn1TW6EOqXfFJradyqNzh3d6kQHYYAFHb8/3PwhvbHuDRY7pCUalT+KLjzoQso0Z3LBRnWpcdT47tc56sigNXBcTaohtd0tLEH4HE26Aye8tuj0Ol4CwcIQMZyaztdtCwQPFqPbyNvp6BjFyR5LSNvVakM+3sNZF59LNNbF8Yd1IR5w25GVVF03VMJZ15lioNIGCBh8Z73fW9SGgdhBKXyzwUUmfGaIs0rR+toNeB8faD+g07Yev7846Yxw1vUXU/4nFnUeO1sX1ua9DfWthIt6Ag+VsNY1GPGCiK+rh8gPMe4jrHX1BtJaDoivjoCAUEVEJNeF4DGH+r3XhbGuVtvqc0dvsSe/3krFlQ2kNZro1xP70omj8/nY785vhP/x2Jn3IypC55ZbbqEbbriBHnzwwXbP//nPf44bodPTOJwe0mn0NCB9AJ8s91R3XPDK4fL0eCsIWAgAxE1ZfRntqdtDL6x6ga6adFVE94NO7hAeH2/5mF1k4Mh+R9IFB11AfdK8TWJBga2Al+mDp7PAwvhW7VvFogfWHwTrfrT5I14Q4Dul7xSa2m8qjc0by69fvGMx/W/N/zjbDfRP708Xj7uYJhdN7nWXnJA4lAVIrUY5fbXRYahA8CBLhZeWgnnqfaNOQ0ZYjPTaXs9kgSVHauAI6/bW0rxvtnETT5tRR1ccPZhGFYZuZelJwhY6cFe9/vrr7Z5HFhbcWUJwYKVJN6WT2+OhKr9KyIHojVYQqtCBIPj1qF/TbYtvo0+3fErHDzyeRuaM7Na24a77ae9PtGzvMvp5389saQEH5x9MF42/iN1QHQFhUpRaxMuMoTPY/QTRs2TXEvpxz49sjflk6ye8qJaXvXV7+bWwvFww9gI6duCxrVxdghCpOjKRAOIIpSiwdAQsQbAQwU0G4YPFZND47re1DEUS1Mspq5WaObEILgarGpy0t7qR6tDc1NeUk7hrOeDuG36PgXrN1/bSr6OLQVhw3l+1l89TA7MtdOUxQyjbFro3oacJ+ywKn9nPP/9Mw4a1PjHhuby8vEiOLSEbfOJYwMHYQcZmr7WCUIUOem8dlH8QTRs0jT7f/jk9+dOT9LeT/sYd1kPFo3hoa+VWFjdYEITtz9CsoTRr3CwaXzC+S2NFHM8hRYfwAqsQ3FsQPT/s+cHremquY5fbOaPPoVOGncJuKEGINDUNTrbW9iSwptS7g1uL0E6BRU+LEFJjh9BCIdiJDSdJh9PNLRaMQVwFEGKogCxEBv9O5rjjTTHyPpPi19U8ELCiQNDsqWqk3dWN3vvVjdzBvCeZOiSbLjx8gO/YilXCPotefvnl9H//93+0bds2OuKII3wxOg899BDdeOON0RhjwtDQjB8mI/s1Q6GnW0GgJYUqdMAlB19CS/cspZ01OzmQ9+xRZ4e0jTfXv8niBi4qFXxxh2cPZ7cRFrTCiJT7CAJsQuEEXq70XElrytdQeX05Hd73cBY7ghDr1pxIgnYydW6X96o+rK7bdbSpVuvtQ6Vt32cKFma1Zk4sAkvYTzsrW7XTCQR+dtTYKLxX3308z7eBrB0pvvsIKUAxPCyOlsJ4vLTcR1CtQ6lqmS8PNbXMG993unmdcDKAVMGj3qa0hDYEWzc/zUSZFoNXNLUIKADrC55TY9Z9t36jCTU1CXM0dWg2HTk0Jy7CAMIWOnfccQdH5T/66KN066238nNFRUV011130XXXXReNMSYMXAW52UWNIV4B9mSFZFhgEPwLClO9QgcBt5cefCk9sfQJDhpG/Eu+NT/oDyVibub/PJ8cbq+QQ9sLiA8Im0MKD6EMU0bU3wdcU+Pzu2YlEoRwScTWBzjZOV0eQhJgQ0twdTzw/Hc7aHmxt8hmIsGJsRCi/OiAEsm2GqhPhpn6ZJp9twVpppi3rsSF0IF6QzAylro6rxnTPx1RCA5U+P4wfhh7UuigYrPT4+SskFxLru/54wcdz+4rWEnmLZ9Htx91eyv/LkCw7z+W/oNWlq7kx6h5A5cR0sDhYhKERKSuydnjrgIhMGv21LDIgUVjQv/MdvEm/sAig9ABl+LhW7jk8BxuXS3lNtRU/RaN0XJf8VkzjAECxtVbnaeZLFarz21o0mu8t8iU1HsDy7ENf2tRyx1fTI23RpES8BZjRY01uCNjFV2Mia0uB4CUlZXRxo0b+f7IkSM5dkfoGHxhEJ8TKjDBwmfeEwpddVvlWfNaBexC2F41+Sq67pPraFnJMvpu93c0NdNbVgBfvK92fkXPLH+G6p31HAeDrKZTh5/qbVUgCEmWbSX0PPiNfHmpt5DntFH5dN6kfr32MXhdgNWktWbEhUsnGuBtjyiILeNH2GcjWHEuuugidlcdc8wxvOD+hRdeyEV8hMjSU1YdXyByi9vKn75pfTkLS62tA1FT01RDDy15iB774TF+jKypx096nE4fcbqInCi7GJtcTTz/mHeh95Dso9jg4zWl7ELMMOvpjPFFvT2cpGdQjpXSzfr4Fjq///3v6ccff6QPP/yQqqurefnggw9o2bJldMUVV4S1rQceeIAmT57Mri9kbJ155pk+K1FHvPHGG2xFQnVSdFD/6KOPKFHpqYwOVeigbk0gfj3619QntQ/Xrnl45cN07SfXsnUHri7Uv5l7wlwWRELkBA2CuXfX7uaMNRSa3FSxiTPZUOOo0dXILsZAwlSIPuHWyBGiw77aJvpotfe36zeT+/kqUgu9Q4ZFz0In1gjbdQVRg+rIRx55pO+5k046iZ599lmaMaN1F+XO+Oqrr+jqq69msYNS9X/5y1/oxBNPpHXr1gWtqPjdd9/Rb3/7WxZJp556Kv3vf/9jgbRixQoaO9ZbKC6RaOS2AMYeTS0PBNxSV066km5ffDutLF/pq7dzw2E3cAsEoftwS4NmO1eARkA3gsHzbflcARr1lyx6S6sFVZnFRdg7iNsqNr4v/1tazHE1YwrT6JABmb09pKRGq02hMUXpMemyC1voZGdnc6+JtuC5zMzwDrRPPvmk1ePnn3+eLTvLly+no48+OuBrnnjiCRZUN998Mz++9957adGiRfTPf/6T/vWvf7VbH31+/Hv9oFeGWmI6UA+X7oC+UfwRqz1XI0CDwx20h020UsuD7Q9BxqcMPYU+3voxnT78dLbkQAD1xPgSDZ6zluOk0dnIBRXhikI6/MD0gSweURwRYif4RryWn2QG32EO0ozwd7kz9tU0JtRxzzVdWpZ4YfnOKlq7t5bbZ/z2UG9cTm+PPx7nMVIMz7ORUZcSke9isO91V7cdttC5/fbbuV7Oiy++SAUFXjdHaWkpCw+knncHNcYnKysr6Drff/99u3o9sCi98847AdeH5efuu+9u9zwahnXUcKwrVDfWUDbZyOoxkK7rcd6tSPEQKXYIqOipZG6voAodTaG3B1MQfj/89zSrzywy2AyU0hh7yj0WQbYGOq+7lZbF7eYihxqHhux1du5qX2AqoKKcIu7xBbGT4kmhppomwn9CcPDDh98NHMOR6IkTCqiDUl3hvWBKpGPU46hvVzU3VkFNmldbApBPHJ5BOdomctf3/ncl3uYxUmRYdKR1IEmpLqrfazXTO1zCPhs//fTTtGXLFurfvz8voLi4mLsEQzw888wzvnXhTgrnjf3hD3/gRqEduaAgqvLzW9dywWM8HwjU+vEXRrDo9OvXj7PEwul+GhJ1GqogO7k0ZjJoI3eQO4wKmfTR+9IgsLXJ3cRfzPzcfG58GYwUJYUM6PwJz2LyfI/DxuFyUHGN94cYplxdio5T7dGpHM1GjTojWRwWGlk4kl2A2ZZscUN1AfxuYH4DdS+PFrvQwNCqSUgLo9YSH9lCHy3fTdVNbsqxGeiUCQNJ28v9v+J1HiMBUuYnDMqKaHZwsO814nJ7ROggHiYaIFZnzZo19O2330Z0uxBgWNqCyYv0D2OKJsXrsWrpJxIpUFkzmjUTSutLfT2hYF3oFLVfSpJ8kcMFcTYIIh6XP44GZQ4io9bIwka9hbtPQxou0QBXbU+doBMVrhobhe9zMMrtzQl57HPV3ZYllkHbg8/W7+P75x/an5udxhLxMo+RAnE5xii0Kgr0ve7qdzzs0c2ZM4cizTXXXMNBzl9//TX17dtx5g7cZfv2eQ9yFTxW3WiJmmKe2YOtH4TuWccgHFEN+oj+RwTtsdXTMSVCZEBZ/5rG0GthCZG3mLz0404unDehfwaN6xv9autCcPpnW2K6madK2PJo165dtHv3bt/jpUuXsstp3rx5XTpoIXIWLFhAX3zxBQ0aNKjT10yZMoU+//zzVs8hGBnPJyqddTPuLmrrh2Cp5UJoIKC4vKGcjux/JB014ChpJJqgtXOSMM40Zvh+WwVtLkNcm4Z+04uFAQUim0lHQ3Pjo5dg2Bad888/n5t6omgg4mJOOOEEjql5+eWX+fGdd94ZlrsK6eHvvvsu19JR42yQwWU2m/n+xRdfTH369OGgYnD99ddzkUL02jrllFPo1Vdf5Ro+XRFa8UK0G+l1VCxQCF0sorbNsQOP5Y7skvadmJSH2JA3GUHRPlif0YYBLQ7UhplqM0o0zFRbHAB+5NdJHc+rXbvx2hR1Oy0vQN2iN5Z7L7JPG1cYF5aEREWjIRrbJ50/p4QUOoijOfTQQ/n+66+/zgX70L184cKFNHv27LCEDgKbwbHHHtvq+fnz59Pvfvc7X6Czv18OHdMhjpD9hbo7w4YN44yrRKyho+KMcisIcV11j101u/gYnT54Oo3KHRWhT0WINfAdrKpv7u1hxGyvqcc/3xy17bNYohRyKwoVpZto+qjAzYWTHdSySTPpyKjT+vpppbTMnfdWFZoHOrazuNS0iNE2r/HvmK4+h//R2d5iiHxcTrQIe6ROp9MX3PvZZ5/R6aefzvdRqbikxGsZCJVQag18+eWX7Z4755xzeEkmEJAcNaEjFp0ugRo2O2t2klVvpeMGHUeDMwdH+qMRYsxiIW6rwL/j7/6yl+9bDFo+gWKe1GaZahNNiJSu4u21qfC2Lzx8QMw1jewNMBepJh2lmfXeW5OerMb4ER89SdizMmbMGC7MB7cRYmNQsA/s3buXiwkK0aGp2UOpxuhkCNU1e2sTFFh7JkanuqGZmt0eyrEZfWbpeAN1cLZXbac8Wx4dN/A46pPWp7eHJEQZqYYcmA2ldbR9fz1f5d97xtgO+xxxMT2+o9bL9LUG93UK94mjNh27PR6U2dDG3ckc1hJcpB5YvO441eJywKXX4rbz+5t38bO8tGS8wppiNWiTJrOru4R9xDz00EN01lln0cMPP0yzZs2i8ePH8/Pvvfeez6UlxE8rCNWagzYDZr03LiraVDU2U02Di/bbm6kw3USZlhBS2mME/PDub9jPbRoGZAxgkZNrze3tYQlRxuX2UGW9xOcE4qM13t+QI4fmdNrMUXWDHCi/EX8naggTBENDtHhvU8jQch/lfOzVTsrLyySTHrWzUsT6FI9CB/E0+/fv58J7/i0fEKBssVgiPT4hyplXPR2fgyuzuiaXr8LszooG2m93sOCxGWOr420g69eeuj2UYcygaYOm0ei80dxvKp5P3iU1TaTTev31eo2Gffwoqa/T4EfbG1AqEItyqQjQnh3762l9SR1bGk4a0/tZm5lWA8eoqMHP/oHR6v22MStAtZr43/e+vnVQNJ7rCJSNKHPoWfBJfazYQdfVq1r0o9q6dStnYSFjymAwiNCJIk0ub++PSJsqezq1vL7Z1e6EUe9w05ayekq36Kgo3cyBdN2lzuGk1AgJJ6fbyQIH839wwcE0sXAiZZmDtymJl8Dan3dVU02DM6SrV15aTO+4b9QduEUxy0h8Zl2h0u7gk+1+dw0ZdLiC9go01UWAWA6M26TXdOu7U1bX++0FYtmac9igbHZF9zajClPjKkhW6BnCPiJ27tzJTTWRDYVmmdOnT2ehA5cWHgdqrCl0H/iuHS4P+6jjORC5ttFrzQkE3Fm1jXX8gwkLT1fjd/bVNrGlAgF6fTO7LpwgbPbV7+MigAMzB9Kkokk0IH1A3PvFIXJWFldTbQiF7xBQCmtiZxZFWIUQO2FuiaGwGrV8wrHotVGxCiHOa2u5nSrtzeRucFJdioNSUoJnROEjw9ggynhcBvW+lp9XP1PEgajxIbhV56DCLtlWbdlb3cjHEZgxtvetOekWvYgcITJCB3VsJk2aRL/88kur4GPE7Vx++eXhbk4Is55OxIVOD7uuOju54tyC7JZmt5sGZlnDFhWoWlta6736hotsY2kd5aeZKC/VGPK2IHCqm6pZ5KAH1fQh3rTxYFWO403krNhZ5XMfRgqXW2HrUA21/nwx5TDjZ1gMlG01tJj0uy586pqctLW8nvbXhRcvg+OqodnNSwW1Fi3qYSEZVeHxydpSDiA+uF8G9cnomfi+jihIi183shBjQuebb76h7777jl1V/gwcOJD27NkTybEJbWhwuinSBc9V11VPCB2Uz4dVKhRg3dmV0kj9syxhCcGdlfWtTli4Mod1p6qhmfplWjrM2EAmVXl9OdU4aijdlE6H9jmUi/9lmFrPOgqXbdxXx2IB1gC4Rby33gWWAjzuzJ/f0yAmakVxFdkjLHI6Ap9FdYOTF7iYMCe48kYAepbFQGlmbzxFZzQ0u2hbeT2V1kTehSQCJ3wq7A76cVsl358ZA9YcZDbhgkYQIiJ0EGzldrc3Y6MtBFxYQvxUSG5yNVFlU2WPua46clsForK+mV0iiNvpDJfHwymuwQJGm5we2lJuZ6sC3GJavyKUDc4G2mffRy7FRXnWPHZRDc4a3E7gwIWBfRRXHtiP0+Wh2sbA+4Tg4RO61UAZFn3ErXGxLnICgTmEuwnL1haXF+aFA6Bb4msghvwfQ1CW1DSKIIkhPl23j+vijCxIpcEx0AYg22rkmDFBiIjQOfHEE+nxxx/3tVzA1ZjdbudmnzNnzgx3c0KYtXQ6Az8+qG4ZTnxOqiGVbIbo/1jVNDm71FsI76ejqzW4mpC91Zm1CFfuyJ6Be6sg3UgeslNVUyW7pBCDMypnFPVP788dxtuNo66JNpXawxKb3tiWRo5lABajlkUPWzJMPRcwCUvaip3VbImKNeDysrtjb1xCx+7Dbzfv5/szx8ZG2xhcvAhCMML+tX3kkUc4GHn06NHU1NTEWVebN2+mnJwceuWVV8LdnBAGKLIHywVSf9tS2+SkinoH1TW6aHh+akjWg550W7k9ni6faNUUaFy1BWJvTVNYMSewbvywcx1ZDDYamTOCxhWMpJF5/SjDbAwoVuCmCjcmJBANDjc1OBppdyWsEwppm2tpm13HlgutXwl2LiDWkt7K2UOalhodWu9jb/ZTaPU5IHKW76zifQtCJPhsfRn/Fg3MtnCWU2+D70UsZHwJCSR0+vXrx4HIr732Gt/CmnPZZZfRBRdc4GvEKUQPWBRsRu8Jzul2U2W9k108/taMPdWNNCQEc7IaiNwTqeUQIt2Jhdhd1cgiAEGt/kDcIXg5HCqb9pHNkEaHFU6nTFMu2euJlm2vZjEBi0u2zcAuJ1hidlQEd4d1l2anh9wOV5ezuKB3IXrVVGq18qpB562Do9dpaGdFvYgcIWJA+H+xoYzvnzy2MCYyEGHtlXpPQsSEDvpcoafVBx98wMIGi9CzNDo9pJCT013hggkkHiAqqhubKcNsiJnUclicugPe587KBi5op9bHgYUIAigcGpx2crodND7vCBY5bS09CHaNRsBrNIAAa/Z4eNyC0BN8uamMO5QXpJtoQv9Ip0Z0DXFbCREVOnq9nt1VQu8BK0MolpGS6iZu8tZRLZqeTC2vjUAQLN43goFhrYIFY3tF6wyrzkBW1f7GEhqdfQj1tQ3p9ngEIZmAoF60bh/fP3lsQUz0qUPAf1srryC0Jeww9auvvpqLA7pcEkDYG4R6YocrqzOXTk9VRUY1ZASdRsqKAbGDVONwtomYmLKGYupjG0Qjsg6OCZO7IMQTS7bu5wsWuHcPGxQblcElpVyISozOTz/9RJ9//jktXLiQDjroILJara3+/vbbb4e7SSFKoEJwplVPBq02YFuD8obyHnFddddt1RYInHCFU1VTGZl1qTQmZzIZtJKhIQjhlgX4dK33wuik0fkBEyJ6g6IM+S4LURA6GRkZdPbZZ4f7MqEX4GJ51U00ILu1GAWo+quQQmadmZtURpPaTvopRZtGVz01uhtocsGx7eJyBCHegbUSdaKQkQmLr9LynPf2wH2H08MXHYjt8902ou2Kk0s/NOCCRLMrqNDB+mircuSwHIoF0szS8kGIktCZP39+uC8RepGqBidl25ztOoOrgchwW0XTjYPMMARQ9xZuVDtu2Mvuqn6pw3ptHIIQKINyb00j7a1qojJ7U7vsPv+vpbfXnZsb4MIVzO0sHC6q57YWLr6o6QlmjCnotQaubZEgZCFUpM1rEoDMpBH5rUvt91QgciSCkLtDWcMuKrT2p5FZE0iTEhvmdiGxQPNPWEVUCwq1sagAZCohkWBPy4L7KF4ZDfAtx1cd33ffffJ2oUehSlhCkKiAvmNoweF9rCOTu5F0llReNxAoV1AUI4X5pOWDEA4idJIAmLX31zdTrl9RrZ5KLQ+lQ3a0qGoqJ5PWSmNzDyOTLvSeWYIQKhA0D3+6kTaX2bs0aRAYaIiJdG1fAUg/64xXLnlBEVB0W7e2dF/nLvG4z53itVxIsqvWWbwPd71CWmv4jXR7gyxp+SCEgQidJKG0ppEyzHouKNdTVZFxpVvXS20HmlyN1OCqo0Pyj6EsU16vjEFIDmupKnLQwxXWEFUn+FtV8L2DqwWiBksRLyZKNbV2KQuhIW4rIRxE6CQJbg9RaW0Td/BuG6MTLewOV9SqCndEvbOOKhpLaVjmOBqQNqLnByAkDcuLq/j24H4ZdM1xQ3t7OEkBWj74W6cFodNjpjtThOKBJlNs+GyFzkGriBybgQzaFO7WHW2LTjj9pyKBR/HQ/sa9fIuigAhAlrgcIZqgjxg4pH+mTHQPkZcqLR+E8Ag7OtPj8dC9995Lffr0IZvNRtu2bePn77jjDnruuefC3ZzQgyAwEqZ21M9xK27Sa/SUbclOiPicJlcD7anbSjZ9Oh1eOJ3G5hwm9XKEqIKAYjScRfPV8f3SZbZ7CHFbCVEXOn/961/p+eefp7lz55LBcKD09tixY+nf//532AMQehakp67Zt9PntoqWxQOps/6NRqMFgijhpkKjzqGZ42hK0UlUaBsQFwGVQnyzosVthQ7eFoNEAfQECMjOtErLByE8wj7LvfDCCzRv3jxu6Kn1q7g7fvx42rBhQ7ibE3qBbRW7E8Jt1ex20G77VjJojTS54Hg6OG8qWfSdd20XhEggbqueB9lpghAuYV+G7Nmzh4YOHRrQpYXu5kLsA+sHyDZHLxsJlVaD4XQ3U6PLTmnGrG6ljiPoeFDaSBqZPZFSDbHRSVlIDsrqmmhXVSNnWiEQWQgNuPk0mhTSpqTwfe+CjLUUbiuB+jjIUMPzeo2GtFo8ry4ashpjo1ihkOBCZ/To0fTNN9/QgAEDWj3/5ptv0oQJEyI5NiFKVDaV8a2OsrjaaqQrnbo9HqrvIK18X0MxGTQmjhPqSksGiByXx0mH5B9NA9JHkDZFfvyE3rHmjMhPlRTxFrJsBipKN7NIgTBRRYpXxHhFjbiUhbgQOnfeeSfNmjWLLTuw4qCJ58aNG9ml9cEHH0RnlEJULDoZxjzuAj4s3xZykz5kbtVXuajB7iaDXsNXXyhUhpRPVF7FVViD0xW0y3qNo5KsujQamDGKNlQsp3qnkaz6tJDHbm+uIYe7kSbmHyWp40Lvu60GSLaVysBsK3c2F4S4FzpnnHEGvf/++3TPPfdw53IIn4kTJ/Jz06dPj84ohYiB1GvVopNtyueA4R0VDTQ4x8pXXsFodrtpV2Uj1TW6KNWtoybyUFOYwcaw4NQ6Kmh83lQanjmen1u7fynpUvRk1JlDyqyqduznjKr+qcPD2rcgRIoKu4O/M/i2TJC0csZs0IrIEWKWLqUKHHXUUbRo0aLIj0aIOvbmanb7aFK0lG7ydiG2N7k47bx/VuA2CfvtDm4+2N3ifxWNJZRrKaKB6SPYhD0iczw1Ou20tXotFdoGkk4T/HB0epqpvGEPDc8az4uYwIXeLhI4NM/G/aIESfkWYptu5UTa7XZ2X/mTlha6G0LoeSparDkZxuxWsS1wSZn0Gi7GpYL4HQRcQgh1F4erkQXWsMzxZNR6rTdajY7G5h5KTe4G2mvfTkW2wQHT3WEJKrHvpAFpw2h09mSJyRF6lRU7q/lW3FZeYAhGSwtBSJj08u3bt9Mpp5zCbqv09HTKzMzkJSMjg2+F+IjPyTLlt/sbip9VN3o7KpfbHbSxtC4iIoe317iX+qUOpSLbwFbPQ/SMy53C49lXXxywTk5p/Q7Kt/alcblHcCq5IPQW1Q3NtLXc29tqoritGNS1QX0bQUgYi86FF17IJ5///Oc/lJ+fLy6ECIJ5bfY4yKiNXq2IysbgQgcBxMUVDVRucHBhwUhR66gis87KvacCWWyQGo64naUln3Hxv2zzgf5bZQ27Kc2QLTVyhJhgRXE19xNHTJsE3npBppUgJJTQ+eWXX2j58uU0YoQ0S4w0nxe/Sd/u/pCO6XcGL9GoWqwGIgfr6O1RvNWTIxn8jABiWG06SiXPMRfwOstKv2RhlGbM5LHqNHo6OO8IyjB644kEIRaqIYvbyguyLfNSxcoqxDZhn0knT55Mu3btis5okpyNlT+Thzy0eNcCemX9E5xlFK6o2F6znoN7YblxeVzBXVfm9hadaAALDUTMoPSRna7bN3UIjc6eRLXNlbS/sYTTyA/KPZzyLH17ZKyC0BF1TU7auK+O74vb6kClYhQAFISEsuign9Xs2bO5jg76W+n1rbMOxo0bF8nxJQ0QJYhjAQgS3li1kp75ZQ79dtT1IZ3od9VtoY+2vUR77N4mqyCFUijVkMmBxxmmXLaKQHiA7CAWnUi3aHB6HDQ860gy6QJndLVlaOZB1OiqZ8E2OmsS9U8dFvVxCkIorITbSiHOTswVKwZTKG4rIRGFTnl5OW3dupUuueQS33NI9UV8CW7d7si5PZKJ/Y17yaO4yaS10Kwxf6JXN/6DKpr20bxf7qazhl1OY3IODfi6WkclLdr5Ov1S/h0/RsVhuH3gLkKWE6wjWIrrNrcSQBA+0aa8cTf1syEAeVDIr4G7bnTOJMqxFFKBtb/EgAkxl1YubisvNpNO0uuFxBQ6l156Kbd6eOWVVyQYOYLsq/e6A/Ot/ahP6mCaPf5uen3jk2zZeG3jP+lI+yl0woBzfHE76Be1ZO9H9M3uD7jGDMTLhLyjaNqAX3NwL4QnekFVO8pZ9FQ17W+5X0GD00eTXhPdCqZ1zdWcUTUsa3zY6eAYW58wxJEgRBu0NNlQ4nVbidDxIkHIQsIKnZ07d9J7770XsLGn0HVKG1qEjqUf36ItwsVj/kSLdrxO3+39mL7d8yGV1O+kc4ZfSdtq1tPCHa+ygAFw78wcfGErywmsazZDGi+IfelJOAC5aT9bZoIFPQtCPPHz7mpyKwr1yTBTQZp00EbHGOkkLiSs0Dn++OM580qETnQsOgVWr9ABsITMGPRbtm68s+XftLV6DT267Aa24IA0QxadNPA33BIhlioFVzWVUZY5j4ZkjOntoQhCRFjR0ttqYn/pVA5ybEbubScICSl0TjvtNLrhhhto9erVdNBBB7ULRj799NMjOb6kdF21BZlHaJ3wyoa/s4iAa+fIPqfQ1D4zY66AHqoYI5h4bM6hXDtHEOKdxmY3rd1by/cnDcjq7eHEBBKELCS00EHGFUBTz7ZIMHLXqHfWUp3TW1Y+WIYVAnMRt7Nu/080JHNszNaVgcsK9XLaVkAWhHhl1R70h1MoP81IRRnitjLqNZRjky7lQgILnba9rYTus69+N98inqWjqsiwkBxScGzMTjlicxpcdTQ6+xAyRLG6syD0Vm+rWHIR9xaF6SaZByF5mno2NTWRySQntO5S2lDcKhA5XqlxVHDNnkLbgN4eipBkeBSFPB6F3B6F7+PWt7R93OZ5WGsamt3c183uaFn87pfUNPI+DpHeVow08BQSXuigTs79999P//rXv2jfvn20adMmGjx4MN1xxx00cOBAuuyyy6Iz0gSmrMWiEyg+J16ANcfurKGDc6dKbI7QI1TYHfT6sl20clc1ty6JJigSiCXZybDoyWLo1vWxIPQ4YR+x9913H/33v/+luXPn0uWXX+57HlWSH3/8cRE63Ugt98+4ijfqmqsozZDR46nsQvLhdHvo07Wl9NHqUmp2B3elozOBJiWFtJoDi0693+Z5s0FLqUY92Yw6LoTHt35LYYa4a0BhhjTwFJJA6Lzwwgs0b948mjZtmi8wGYwfP542bNgQ6fElPLCEoEM3yLf0p3gExQlrHJXclNOit/X2cIQEZtXuanrlp11UXufgx8PzbXT26AzKzcnmBpOqiEH/JYgcIXJgbvOl9YWQDEIHPa4C1dBBkLLT6YzUuJIGb/NNJ6eMd9TdO5ZBFeRUfTr1E2uOECUgbF77aRcX7gPpZj2dO6kvTR6QSZ6GGtKadBIgG0UgIpFSrtNK7RwhCYTO6NGj6ZtvvqEBA1oHnL755pvcGkLomtsKaeVqe4d4AxWax+RMJpshvbeHIoQJgnEdLjc5XB5yOD2++01ONzW7POwaQqYRjky+TfHewlgCe4nGzx2k12q8txoNabW4TeETI/6OEyWeD7XTNQcJu737X7yxnD5eU0JOt8LWmhNG5dFp44vIpNeyNVHoOvgcEXNjM2rIozdRdm4qGXRaMuBz02pIr03h+5JtJiSV0Lnzzjtp1qxZbNmBFeftt9+mjRs3skvrgw8+CGtbX3/9NT388MO0fPlyKikpoQULFtCZZ57Z4Wtefvlljg/avHkzpaen08knn8zbyM7OpnhkX31xXMfn2JtryKpPpf6p0hIknmJcfthWQZ9vKKPdVd6Mop4COkfvEz/eEym0CjKfMC6XWyGnx8PPtWVkQSqdf2h/yfrpIhCfqS0xSKkmbzwSHkN84re8rMxBeelm0qC/gyAks9A544wz6P333+eCgVarlYXPxIkT+bnp06eHta36+nqO7UGj0F/96ledrr9kyRK6+OKL6W9/+xtXaIbYQpwQgqIhuOKRfW16XMUbaBQ6LHM8pRmlYmysU9PopC83ltGXm8qprsnV6m+wlKAQnFGHRcvl/U16DVthoDlgOUFmk4L/FG86t3oL64sT6dpupGp7WLS41PtuvOIA2AZbj/iRO6Rxozjd2RP70iSpY9MpsKjB0mUxeBcEWcNig/v4bMUyIyQjYQkdl8vFqeUQJosWLer2zmGNwRIq33//PaewX3fddfx40KBBdMUVV9BDDz1E8Uqpr/VD/AUiNzjryKi10IC04b09FKEDdlU10KJ1+2jp9koWISDLYqDjR+bR4YOz+Mo+WrEXEEioV8PCp8Vaw7d+1hs4s9jKo4UbzGvlYWtPi9UHJ28hMLDOZFkNlGkxUKpJxyJHEIRuCB2dTsduI1hVeoMpU6bQX/7yF/roo49YIJWVlXFs0MyZM4O+xuFw8KJSW+vtWQNTbaSrPCsehX+0vZfAna/f5GrwdSDPN/cN6TW9jvreFLR7KKdB6aO5HYXESoQ5jWwR8S6Ri6/xxtggtgb3UQRv6Y5K2lBa53vNoBwrTR+VRxP7Z7YSENH8/DiVW5dCRv61Cf9E3NHYIjGP8QQsNJkWPQvVDKuerW/+dPU3Da9jq51Uvu8WMo/RnceuHp9hu66QVv7VV1+xZaWnmTp1KsfonHfeeVyVGRYmuLCefPLJoK954IEH6O677273fHl5OW8jklQ31lA22cjqMZAuhKktt5fwbYYhi/I1GaFa8nsXhcjs0VKzp4nyNZk00NCX3PXeTJhkBV9IBMo2OD3UiMXlvcXzVoOWbEYt2QwaMvm5DuDQ8TjqOaI3xSuPWZzUOdy82HGLar243+zx3vJ9/N1D9c1u3l8oQM9MKLLStGEZNDirpZJ5Y01cHG6dEWge4xWExqi1ftQgbr3fLVxQRu6h7CZyNFHNgeu3boMTSE1NDR+zEqMj89jbBDse6+oOXLRFVejAknLLLbdw9/JDDjmE43R6qnv5unXr6Prrr+e4oJNOOokDmG+++WaO03nuuecCvubWW2+lG2+8sZVFp1+/fpSbm0tpaWmRHWCdhirITi6NmQzazn90tzft4NtcSz+q07aOmYhZWi6cdzp2UV/bMMrIGJh0fn+4XeYv2UE7Kuqp0enm7tbuEAwKOCSsRh0vcBfpyU31rjpuM1DX5KTmUDYSZLvIlEEMBmJrvHE2uK+l/llmOnZ4LmXbYqvLfaRgS45CpLVkxOVxaDFqqU+GifJSTb3qdsKJBfOH30UROjKPvU2w47GrLafCFjpXXXUV3z722GM93r0c1hlYdSBuwLhx41hoHXXUUfTXv/6VCgsL273GaDTy0hZMXqS/0CmaFK8OwO9tSuiByJxxFUe/0U6Pg1JSNDQgY3hS/iguXLePftpZ1e55nGfNLYGguAX1bIVxsTsJOqa2ycVLMBCfggq9qWZvRoyaHcOZMm1u8Tz2k+y1Tbzp7t4lHoDVJi/NSH0yzJRhiZ0u4Fw+IAq/i8mGzGP05rGrx2ZcdS9vaGjgOCF/tFrvCSUeffQHApHjK+PK7qymfOsAyjEXUbJRWttEH6zyuhzPm9SPRhemtWS2dJzVAqGjNomsb7HgNDXY2aqYZtKzoEEwqWTGJC5pZj0VZZioIM2U9OJUEGK+BQRiZNpaSZqbm+nVV18NK1DZbrfTli1bfI+3b99OP//8M2VlZVH//v3Z7YQUcuwTIB4HqeRPP/20z3X1hz/8gQ499FAqKiqKw9YP8dfjqtZRSbYUHQ3JGBW3BQ67CsT0Sz/s5MylMYVpXLguVCsCXEpZOgNnyKjbctdrSGuNT5eLEBy1d5bFL7UbIgcWOEEQep6wv3mXXHIJzZgxg/Ly8lo9jyAh/C0cobNs2TI67rjjfI/VWBoUJHz++edZyBQXewvqgd/97ne8n3/+85900003UUZGBh1//PFxmV5e46ggh7uJtCk6yjYVUDzQ5GqkOmc1jUo/lHItfSnZ+H5bBWcwwb104eEDRKAIDHTq8PxUjr2CqJEUb0GIc6GDK9FAV6C7d+/mSsXhcOyxx3bocoLYacu1117LS7xT2lIROddSRFpN7F/puTwuKm/YQ0MzDqICW+v2H8kAXE2vL/M2Xz1tXBHlSnNDoYUMi576ZVlkPgQhRgn5DIs+VmrAH1LM/WNlEIAMtxMsPUJo+AKR46AiMsQoWlUU2QbS6JxDSNMUwbzWOOGN5bs5vgYBpCeOye/t4QgxBDKmBEFIAKGj9qBCDA3iY2w2m+9vBoOB6+qcffbZ0RllArIvjioi72/cS6mGDBqbeygZtWZytxTwTxY2lNbSd1sr+P7FUwZw9V5BUBHrniAkiNCZM2cO30LQIBi5q/nsQnxlXNU1V3Pg9EG5hydlBWTUzHnxh518/5jhuTQk94DAFwS4rSQmRxBim7AvTZENFUzkPPPMM5EYU8LT7HZQZdO+mHddOVyNHDQ9KvsQdlslIx+tLqF9tQ5KN+vp7Il9ens4QowhbitBSEChgzgcFOxzOp2+5/bv38+p36iYLHQOgnpRut6qTyObIbwA7p7C7XHRvobdNCh9FA3NGEvJSElNI328ppTv/2ZyP04VFgR/UPhPEIQEEzqLFy+mBQsW0OTJk7klw4cffkhjx47l1gqI3xE6p7QlEDk/Rq05HHzcUEwF1v40JmdyXGSFRWMOXmypmXNQn3SaNCCzt4ckxBjp4rYShMQUOkcccQQLGoibiRMn0llnnUU33HADffnllzRgQPKlHXcnEDlWCwVWNJWytWlc7uFk1rXuZZYsLNlSQZv22bnQ3wWH9ZeaOUI78qTEgCDEBV26VN+0aRMX++vbty/t3buXNm7cyO0Z2jb4FOIvEBkuK4e7gcbnnkCZplyKR7aW2+mXXdVk1KNzuI47iKuNNH0NNbUp1OT0UE2jk6obm6m20cW3eIzll101vK0zxhdRToI2xBS6R36aJGQIQkIKnQcffJAzsP7v//6PHn74YW7hcNFFF3GDzZdeeommTJkSnZEmkEvE1/ohBl1XTo+TDBpzXIoch9NNb6/cQ19sKFObrAdFiwa0nWSQ9c+y0LRRrSuACwJASwfJthKEBBU6TzzxBL3zzjt08skn82O4sJYuXUp/+ctfuNKxw5FcNVa6kq7d4LKThjSUY4m9/lwuj5N0Gj0ZtPFlxVhfUkv//X4H7bc38+OJ/TO4s3d9s5ubaKoNNRscbhY4qsjBOmlmHWdVZZgNfIvHmRYDTeiXITVzhICI20oQEljorF69mnJyclo9p9fr2bpz6qmnRnJsCV0ROdtcSHqNt8FjLOFSmlnk6DXxIXQaml305vLd9PXm/fwYTTMvPnwAje2THtSiBpdVo9PNLi24twQhXMRtJQgJLHQgcqqrq+nNN9+krVu3cqo5uo2vWLGChg4dGp1RJmB8TqwGIrvcTkozZMZFZ/JVu6s5M6qqwVvq4LgRuXT2xL4duhTQwgSdpbEIQldINenk+BGERBY6q1atohNOOIEbeO7YsYMuv/xyFjpvv/02dxp/4YUXojPSBLPoxGIgMnApTrLoo1f9d19tE/2wrYJdSkBtD9u2TyzaLBj1GjLqsGjJpNNwBhREjF6roS83ldEP2yp9boRZUwbSiILUqI1bEFTyJAhZEBJb6CCV/He/+x3NnTuXUlMPnFhmzpxJ559/fqTHl7g9rix9KRZxe5xk0UVWMMBdtL6kjj7bsI9W767pNFA4VCCOpo/KpzMOLmIxJAg9Qb4UCRSExBY6SCufN29eu+f79OlDpaXeKrJCYFweF5U37o1pi45CKRELRHa43Gx1+XzDPtpb3eR7HgX4BmRZfIIHVaLVB7hBnLDL46Fml4ccWJy4dXvv8+LmwOFzJ/WlwdJ7SuhBbCadVMgWhEQXOkajkasgB6qtk5sbfynJPd0F3KO4yaS1ULohm2IThTuUd5TdtGhNCZlNlZxim2ZqWcy6lls9uT0Kfb25nL7eVO5zUcEFNXVIDh0/Ko8KxPQvxCmSbSUISSB0Tj/9dLrnnnvo9ddf9wV3Ijbnz3/+M5199tnRGGPiua2s/WKy0q5bcXMQckcWnQUr99D2igbkO4W0zRybgY4fmUdHDs2RK2Eh7pFsK0FIAqHz6KOP0q9//WvKy8ujxsZGOuaYY9hlhUKB9913X3RGmSDEeo8rbw0dAxm0gSu+wmVUXOkVOKePL6Rml0K1TU6qbXRSbZOLb+uaXFyjZni+jeNnxvfNII0m9kSdIIQLqmpjEQQhvgj7W4tsq0WLFtGSJUvol19+Ibvdzj2vkIkldExpfXFsp5Z7mkmPYoFBauhs319PboUow6SlUw8qJI2mfQq6R1HI6fZIcLCQcEinckGIT7p8eTJ16lRehM5pcNbRpztepa3Va/hxoTU2m586WegEt+hsKbPz7ZBsU1DXmyYlRUSOkJCI20oQ4hOxw0YRpFX/Ur6EPtn+CjW46iiFUmhK0UlUZBtEseq6SjWmBy0WqAqdoTnBg5UFIRGxGL0NYgVBiD/kmxsl9jeW0Ptb/0vba9bx4zxLXzp9yCXUP20YxSoQOhZ94Bo6Ho9CW8vrfRYdQUgm8lLlmBeEeEWEToRxe1z0ze4PaMmej7jKMFxBx/Y7k44omkFaTWxPt1txkjWI0NlT08j9oZAm3ict9np0CUI0kSKBghC/xPaZN86Y+/nH9L8NT1JN8z5+PDTjIDp1yCzKMuVRXKCkBI/P2dcSn5NrJa1kUQkJDI5vtBqxtPREQ4f7VJO+t4clCEJPCh2Px0NbtmyhsrIyvu/P0UcfTcnIW2s+ocd+mcX3dZROk7Nn0XGDJ5EpjowfqFAcVOiUq0Inen2wBKEngZBBpWPE3lgMOhY0JoO3t5ogCEksdH744QfuabVz504OtvUHmThut7cSbrJxcN4RlGsYQ00NfSndeQnt3W2jV/cqNCC3mYYXOagoy9WucWUs4VE83mKBQVLLN7cEIg/Lg9BpLW4FIZbRalMotaUGDjqPpxr1ZDVqSacNHHQvCEKSC53Zs2fTpEmT6MMPP6TCwsKYrPDbGwzJS6Nlsz+h+z7/H1XUuGlbiYsq7Trats/Ii83kpmGFzTS8j4NsJk+MFgvUkzGARaeyvpkXeKwG5ViJmut6ZYyCEAizQUPpqUYyGbRsjfF2vNeQUe+9j273giAkL2ELnc2bN9Obb75JQ4cOjc6I4hijzkgGvZtG9W2gcf3dVFGnpU17jbS11ED2Ji2t3G6mVTtNdPxBduqf66SYq6GTYiB9gPYPalp5vywLxy64m3thgILQBp02hUbkpVKKgygvLz1gAUtBEISwfxkOO+wwjs8ROgaGrpw0Nx0xsoF+e1Q1HTvWTnnpTnJ7UujzVTbaUaaPPYuOVh8wRsdXP0fic4QYITfVSFOGZFN+uqR9C4IQYYvOtddeSzfddBP3tzrooINIr299wh43bly4m0x4ENs4pKCZBuU105drrbR9n5G+WG2j48baaVC+M2baP6QZM0mb0j4Qc3NZnV98jiD0shWnIJUK071FK9smQwiCIHRb6Kgdyi+99FLfc4jTQWByMgcjhwIs68eOqedYl62lRlq82kYepZ5FUCxYdKy69jV0GpvdtLu6ke8PFaEj9CI5qUYaWZDK7lNBEISoCZ3t27eH+xKhjdg5ukXsbC4x0ldrrISL0mFFzb1u0bHo09o9v22/nZBcl2MzUIbF0C7TThB62oojCIIQVaEzYEBsNqSMJyByjhpdTxqNQhv3mOjrdVbyKCk0oo+j18YE+WLsKD5HrDlCDwgaU0umFG7V+1lWg1hxBEHo+crI69ato+LiYmpubm2JOP3007s+miQLVp46soG0KUTrdpvo2/VWcnuIRvfrHbGDIgGBApEP1M8J3BpCEMLFoNNwPZs0s55vrQYdCxmpuC0IQkwInW3bttFZZ51Fq1ev9sXmALWejsTohA6m7PARDYRm4WuLTfT9Rlh2iMb2d/R4sUBIHUOb1HKXx0Pb93sbeYpFR+iqqEH14TSTntJaxI3E2AiCENNC5/rrr6dBgwbR559/zrdLly6liooKzsR65JFHojPKBBc7hw2DZUehVTvN9OMmK5n0Cg0tbO7RQGQ0H23rutpd2UgOl4dL5RdKGq/gV2lYr0EhvhTS6zRk0MLVpPEr1qclo977vEb6ogmCEG9C5/vvv6cvvviCcnJyuEAXliOPPJIeeOABuu6662jlypXRGWmCi51JQxs5Tmb1TjN9t8FKeekuSrN4eiwQGVWR27qu1P5WqJ+jkQrYcWdJQZsDWOocLjcLVre780ByWFvQyBLi1gqXksErWNAugYUNf+elGrogCAksdOCaSk31xmtA7Ozdu5dGjBjBQcobN26MxhiTSuyU1+iotFpPi9fY6NRJtdQT1et9xQLb9LlS43PEbRW7xwyECXo4WSFOWm7xOFDbA5fbw4LHu7jJ4fQKabVLNxpbSpyMIAiU7EJn7Nix9Msvv7DbClWS586dSwaDgebNm0eDBw+OziiTBFwoHzO2nhb8kEb7a3W0fKuZDh3mrWETTZweJ6Ua0kmrOXA4IPZKMq5iC4iQNLOO0s0GyrToKd2sD6sxJdbFYg3ct1UQBCEhCVvo3H777VRf7w1Qveeee+jUU0+lo446irKzs+m1116LxhiTCjT8ROr556tS2Y1VlOWkvtmuqFt0LLrWVY/325upptHJJ9eB2dao7l8IDOJfMsx6yrDoKcNs4AwlcRsJgiBEWeicdNJJvvto7LlhwwaqrKykzMxM6WQeIQbmOWlk3ybasNtEX6+10VmH15DZoEQ1Rsfapligas0ZmG3heA8h+jVkkJHEmUkmpF3r2Z0kCIIg9FIdHX+ysrIisRnBD2RilVbpqLpeR1+vtdKJB9s5JiMaKKSQUWcO2N9KGnlGtiq2Sadl4WjUpZBLa6TCgjRKtxhF1AiCIPSm0PnVr35Fzz//PKWlpfH9jnj77bcjNTZK9kagxx1UT+8tTaPdFQZaW2yksQOiU18H+skYLONKKiKHDerGZFoMfGtC+nVLpV//AGE0oywrc1BemokzFwVBEIReFDrp6ek+txTuCz1Dls1Nhw1v4HTzn7ZYqCDTRTlp7igUC0RV5AMRqnaHi/ZWN/F9ETodg6+FzaijTCt6gelZ4ATKeBIEQRBiWOjMnz8/4H0h+ozs46A9FXraWW7glPMzD60hfUQcjl7cHhfpNIZWNXS2tVhzCtJMHCsitAcF8UYVpnGwcDiZT4IgCELPIr/QcWAxQBaW1eim2gYtt4mIJE61WKDmgNCR+jmdU5RhphybUUSOIAhCjBOSbWDChAkhZ1StWLGiu2MS2mDUK1xf5+PlqbS5xEjpFjeNG9gUkeBkbv+gbd3+QerndE5ReuvgbUEQBCGOhc6ZZ57pu9/U1ERPPfUUjR49mqZMmcLP/fDDD7R27Vq66qqrojfSJKcw00UHD26kldsstGyrhfZU6tnSk2r2RCC1PNVXLNDpPtDIc5gEIgcky2aQLClBEIREEjpz5szx3f/973/PPa3uvffeduvs2rUr8iMUfEwY1EQWg0I/brZQSZWeKyhPGdHADUC7at1xKU4y6w8UC9xZ0UAuj8LF6fJSpYRuIMSaIwiCkMAxOm+88QZdfPHF7Z6/8MIL6a233orUuJKWvfbtVO3YH/BvEDMj+zrorMNqKC/dSU63hr5eZ6PPV9mosblrSsfpbiabPj2g2ypUd2WyFfYTASgIgpDAQsdsNtOSJUvaPY/nTKbWtVg64+uvv6bTTjuNioqK+KT6zjvvdPoah8NBt912GzcRNRqNNHDgQPrPf/5DiQJiZhqcXrERDHQ1P2VSHU0a0kCaFIUzst7+IZ2Ky/VdLhbY2OzmIoErd1Xx81IoMDCF6WZpwyAIghBHhJ2o/Ic//IGuvPJKDjo+9NBD+bkff/yRxcYdd9wR1rbQM2v8+PF06aWXdlqIUOXcc8+lffv20XPPPcctKEpKSrj4WiKAVG+9xkAuxR1SA9Dxg5qob46Tvlxj5QrKi35JpeFFTZySjhp0EEHeW1TlVby3KUQNDg1V2rVUZdfS3uqh9MNqB9U0rGy1/WH5rXtfCV6KMsIT84IgCEKcCZ1bbrmFu5Q/8cQT9NJLL/Fzo0aN4vo6ECHhcPLJJ/MSKp988gl99dVXtG3bNl/bCVh0OrMAYVGpra3lW4ijSAskxaNwlWFCW6outKZyup2kTzGQR2kM+fXZNjedMbmWlm8z05piE23a611CB9lD3p2hI3afDDPXhxmYZeEO5m3Bc+qSbCBuyWrQRuS4wTYwh4ki0nsLmUeZx1hCjsfozmNXfy+7VHoOgiZcURMJ3nvvPZo0aRLNnTuXXnzxRbJarXT66adzYDRcaoF44IEH6O677273fHl5OWeQRZLqxhrKJhtZPQbSdWFq9W4NWTTp5FCMZHVpSJMSumfxhMFOGpnloW+3Gqm+WUNuBQcFKh+n+O63yDDSaxXKsbopy+oim6mOJhYOpaFZmXwSV/E01AR1dXkc9dw3IqVle8mC1WCmsrLIdJLHF7ampoa/zNICQuaxt5HjUeYxHo7HujpvD8YeETrV1dX05ptvsmXlj3/8I1tX4MrKz8+nPn36ULTA/r799luOBVqwYAHt37+fU9orKiqCVmy+9dZb6cYbb2xl0enXrx/l5uZy766IUqehCrKTS2MmgzZ8EVDlrGXXVZPHQW6qJ5M2vFot6dkuOiU7eD8sGGE8itd9hTjjRlc9NTrraGT/qWTRp4a0D7bkKERaS0ZSBStrNSk0fGB2xNo74IuM+cNxKEJH5rG3keNR5jEejsdw44C7LHRWrVpFJ5xwAve82rFjB6ebQ+igmWdxcTG98MILFO03//LLL/t6bj322GP061//mmv7BLLqIGAZS1sweZE+waRoUrxOIJz/u6ABXEozpekzyE1OcioOMqVEtigddIm2TWq5VqvnYORwRAvWVZdkAc03jZHsvdEyj9E4DpMNmUeZx1hCjsfozWNXfyvDfhWsI7/73e9o8+bNrdTVzJkzOYsqmhQWFrLFyL+xKOKDYGXYvXs3xTtOj5PSjdlk0lrI6Xb0wP6auSIyWkAIHYPYJUEQBCH+CFvo/PTTT3TFFVe0ex4CpLS0lKLJ1KlTae/evWS3H0i/3rRpE6u8vn37UryjKG4y66xkM6SzCOmJVHb/YoFCYCwGLXcnFwRBEJJA6MANpGYu+QPBAX9aOECw/Pzzz7yA7du38324wNT4Gv/ihOeffz5lZ2fTJZdcQuvWrWML0s0338zp6cGCkeMJBPqii7hNn8GtGaKNt/1DhOOUEpBCseYIgiAkj9BBltM999xDTqfT50eDMPnzn/9MZ599dljbWrZsGTcMxaK6xXD/zjvv5MeokaOKHmCz2WjRokUcDI3sqwsuuIALDv7973+neMejIP5IQyadmZeeSN5WFA+ZtZYe2FP8gjCkwnSpnSMIghCvhB1d+eijj3Lwb15eHjU2NtIxxxzDLis0+LzvvvvC2taxxx7bYT2W559/vt1zI0eOZLGTaKBYoC5FzxYdiB6AuYlmwC9mHvsTgpNtM5JJ7x/CLQiCICS00EEgMIQG0ryRgQX308SJEzkTS+g6iMlBarlRa2aBwxWSPU7Sa6MTG6IKTINWGnd2hFRCFgRBiG+6nC975JFH8iJETujotAYyaIz8qUDoNHscURM6bsVFOo2OhZUQGINOQ7k2EYKCIAhJJ3SQebV48WIqKytrV5IZdW2ErgUGpxoySQvxkaJhS4vTgxTz0Ar5ddWCJBad4CA2J5lqBQmCICQiYQud+++/n26//XYaMWIEV0L2PxHISaF7wiPV4M2AQusHZEPtb4xeuj7cYqifIzE6wSmSbCtBEITkEzpo5olO5SgaKEQOt+Iki+5AqneqIYNK6ndGVegYdSa26oRLullHaRkm7qHldCvkdHvI1XLrRo+JBKAg3URWY2QrIQuCIAg9T9i/5CjOh8J9QmRRlBQWHiroPeVuyb6KlqssUxde3SP/dgiDCtMCluP2eBQqq3PQmj2Bm4LGMjBO5qWaaGCOhVJNUi1aEAQhKevo3HDDDfTkk09GZzRJjdIqMBj3NVFuN2HrYrFABOkGQ6NJofw0Ixn18dO/CXqtT6aZpgzJpoP6povIEQRBSGaLDrqVn3LKKTRkyBAaPXo06fWtr3zR3FMIv4aONkXbKl4GRQNRQJD/pom8C8WjuMmks3Spi3dnHbwRq1WYbqYd++spltFqU6hvhpn6ZVmkVo4gCEKCEvYZ9LrrruOMq+OOO47bMUgAcmSsKzquoWNqZdHRa5B55YyK0IEFqSuByN7iea6QmmDGqtDRaVOof5aFBU5nok0QBEGIb8I+g/73v/+lt956i606QuTiZfQafSuhA2uLmmJuomjUuknpUmq5KUSXlNmgpSybgSrt0e/ZFY7LbUC2hUWYTgSOIAhCUhC20MnKymK3lRDZ1HKIHFhwVJANZdKaqd5ZF/GpdrW4w7pSLNBsCN0CArdQLAgdWKFUgYMYIkEQBCF5CNtuf9ddd9GcOXOooaEhOiNKUosOsqzaugFthnSujhwtCxJXYQ4Tcxh9n3Jsxg4Dl6ONxaClUUVpdMSQbHZTicgRBEFIPsK26KBT+NatW7lY4MCBA9sFI69YsSKS40sKgmVA2fQZ5PZ4u8RHvFhgSwPRcDHrtKR0HqLDQFigV9SO/Q1RDSg2aDUsqHy3Og2lGnWUm2qUGDJBEIQkJ2yhc+aZZ0ZnJEmMorjJrLO2e96sDz8rKlRXGeJzulIs0KDXkqMpvOrCkRY6mVYDjSpMJaNOy1lggiAIghAxoQO3lRBZlCAZUNzJvKXTeCSz22DRyTB2LWMOrqtwnGkWg44yrXqqqndGTOQc3C9DBI4gCIIQEiEFUOBEK0QHj+JhwYG6OW1B5hWsLrDARDpGB720uuQm6kLMTZ+MyFimROQIgiAI4RLSWWvMmDH06quvUnNzxyfczZs305VXXkkPPvhg2ANJVlAQUJdiCGrRQcBwpIWOG64yfXtXWWeYdKEHIvuTl2okfTeDkpGqLpYcQRAEISquq3/84x/05z//ma666iqaPn06TZo0iYqKishkMlFVVRWtW7eOvv32W1q7di1dc801LHaE0ICIQRfxQKneXDRQ562lQ5Qa0Sn1r9kTKqiN0xUQlFyYbqLiioaui5y+GZI1JQiCIERH6EybNo2WLVvGYua1116jl19+mXbu3EmNjY2Uk5NDEyZMoIsvvpguuOACyszMDH8USQynemsNAVO9NSkasupSaX9jScT3a9CYoppaHigouStCJ9tmoPEicgRBEISeCEY+8sgjeREia9FJNSC4NvBHkWrMpJL6nVHtq9UTQsdm1FGGRU/VDaEHJYvIEQRBELqLNPqJAaHTURdxi87GAcuRAEHlDS4799WKZvuHjqw6oZKTahRLjiAIgtBtotEtUggDt+Ikqz496N+70qbBH4ikemctLxBVJq2FcsyFAev2dIapizE6KvlpJtq0r45c7uBZfBoNccPNwTk2ickRBEEQuo0InV5GUVLIqAvuRkLaOWJ12OUUYhdzCJr65lpqcNWSR1E4lbyPbRDlW/tSpjGXbIYM3mZPuq4AivsVpJtod2VjwL/jb0PzbC0d0gVBEASh+4jQ6XWUDq02sMB4u5g3hyR0ah2VVNdcTWnGLBqeeTBlmwso05TbJQtO2xo6eq2GPJ7uudHQWLOt0EF9nGH5NkoztW4nIgiCIAjdRYROLxJKYLBRZ+au5ux2os4L79U2V9HIrAk0KvsQTluPFN215qikmvSUZtZTbaOTrEYdCxw0/xQEQRCEXhU6tbW1Ia2XlhZ+xd1kbuaJwOCOatqgMrJJaya7sy4k4YQqy7mWooiKnEgKHTAg20Iuj0JF6SZpuikIgiDEhtDJyMjo8KSk9mNyu92RGlty1NDhYoEdp3oj/bzKsb/T7dmdtWTTp1OWKY8iTSTjZhCULAiCIAgxJXQWL14c3ZEkIXBHQeTANdURCB52ezqvP1PfXE1DMsd2qUZOT1p0BEEQBCHmhM4xxxwT3ZEkqUUHgcKddRFH5hU6nHeWRu4hhXLMRREeZcsYDFJySRAEQUiSYGS4p9555x1av369r+nn6aefTlqtXPWHG6PTUbFA/8wrohSfezAQDc46bhcRDbcVEIuOIAiCkBRCZ8uWLTRz5kzas2cPjRgxgp974IEHqF+/fvThhx/SkCFDojHOhERBF/EQ0r69mVcGdnUFq2hsd9ZQkW0gWfS2KIw0sjE6giAIgtBThO2PuO6661jM7Nq1i1asWMFLcXExDRo0iP8mhA6cUaHE06DODpp+QugE3I6i8N8KrP2iMv26lho6giAIgpDwFp2vvvqKfvjhB8rKyvI9l52dTQ8++CBNnTo10uNLWLxuKG/8TShCR68zktPtINKntvt7k7uBzDoLZUbJbSXWHEEQBCFeCfsy3Wg0Ul1d+5oudrudDAZDpMaV8LhQQyfFEJJFB+0akDbu9DgC/t3eXM2tHVL1GVEYqcTnCIIgCEkkdE499VT6v//7P/rxxx/ZKoEFFp7Zs2dzQLIQGnA1oahfqE07bYb0oK6rJlcjFdoGRq34nrmbzTwFQRAEIW6Ezt///neO0ZkyZQqZTCZe4LIaOnQoPfHEE9EZZaIWC9QaOPYmFCw6G6eQt8XhbuIA5WhlWwHJuBIEQRCSJkYHFZLfffdd2rx5M23YsIGfGzVqFAsdIXRgnUHF41A7kgez/NibayjdmMVNPKOFUS+ByIIgCEKSNfXMzMzkIoLS26rrQieUGjoqJp2FG4ByI1A/cdToqqNhGWP5b9FCLDqCIAhCvBLWpXp1dTVdffXVlJOTQ/n5+Sx2CgoK6NZbb6WGhobojTJBg5Gt+vSQ10djT7i6/ON0cF+boqMscz5FExE6giAIQsJbdCorKzkuB4UCL7jgAnZXgXXr1tE//vEPWrRoEX377be0atUqDk6WmjqdY9SZwrLooCdWs9vB90F9cy33wcow5VC0QA0dndTQEQRBEBJd6Nxzzz2cPr5161a25rT924knnkgXXXQRLVy4kAOWhc4JNeOKPyiNnqso1zXX+J6rd9XQiMyJXDU5Wog1RxAEQUgK1xV6Wz3yyCPtRA6A+2ru3Ln01ltv0Y033kizZs2K9DgTCsTZoDZOuF3GUw0Haum4FTf3v8oxF1A0kdRyQRAEISmETklJCTfvDMbYsWNJo9HQnDlzIjW2hG7mCTeUMUyhg5gexPb43Fb6NO5+Hk3EoiMIgiAkhdBBAPKOHTuC/n379u2Ulxe9Wi4JV0OHiwWGJ3S8sTnokEVU76yhXHMfX7xOtJD2D4IgCEJSCJ2TTjqJbrvtNmpubl+d1+Fw0B133EEzZsyI9PgSEm8XchNbdcIBmVeofozCgXBd5Vn7RG2Mvn1K13JBEAQhWYKRJ02aRMOGDeMU85EjR3L7h/Xr19NTTz3FYueFF16I7mgTyKIDl1O4LRuMOjP3x6p1VHKl5CxTdNPKgcToCIIgCEkhdPr27Uvff/89XXXVVVw3ByIH4GQ9ffp0+uc//0n9+/eP5lgTKkYnnGKBKiathVtGVDaV0eCM0WQN0Mk80kiMjiAIgpA0lZEHDRpEH3/8MVVVVXELCIDWD1lZ0Ws/kIgoiptTxcMF7i6DzkQpDqICaz+KNnqdhrSa6DQKFQRBEISYbQGBisiHHnpo5EeTJMAWFm5qOUBKulWfxkUCo9nEU0WsOYIgCEK806vdGr/++ms67bTTqKioiF1gqNUTKkuWLCGdTkcHH3wwxRNw+SE0J9yMKxWbIZ0yjNmUZoi+Fc0kzTwFQRCEOKdXhU59fT2NHz+ennzySQq359bFF19M06ZNo3gDdXAQUIzA4q5QaO1Pw7PGhx3I3BXEoiMIgiAkbffySHDyySfzEi6zZ8+m888/n7RabVhWoFhJLUc7h3DaP/iTYy6knkJSywVBEIR4p1eFTleYP38+bdu2jV566SX661//2un6SHvHolJbW8u3Ho+Hl0iieBRKUYNwvElp7XC5USzQQPoUgy9zLVYx6VLazREeY9yRnrtkQ+ZR5jGWkONR5jEejseunnfiSugg0+uWW26hb775huNzQuGBBx6gu+++u93z5eXl1NTUFNHxVTfWUDbZyOoxkC7Y1HoMZEY140Y7oVtVLGOvcZO7QdvuQKupqeGDEC0/hK4h8xgZZB5lHmMJOR6jO491dXWJLXTcbje7qyBahg8fHvLrUPMHjUb9LTr9+vWj3NxcSksLv5ZNh9RpqILs5NKYyaANHENTplRRH2Mmaa0ZFOv0Lcxtl16OAxDxQZg/ETpdR+YxMsg8yjzGEnI8RnceTSZTYgsdKLlly5bRypUr6Zprrmll3oJ1Z+HChXT88ce3e53RaOSlLZi8SJ+oUzQpXo8VtEGQWOFmTzOnh/dEMHF3MOg0pNe1tuaoYOzRmL9kQ+ZR5jGWkONR5jHWj8eunnPiRujA+rJ69epWz6H1xBdffEFvvvkmFzOMF4y6rqnSnkQCkQVBEIREoFeFjt1upy1btrTqgP7zzz9zpWW0k4Dbac+ePdxDC0pu7NixrV6PbukwZbV9PtbpasZVTyKp5YIgCEIi0KtCB66o4447zvdYjaWZNWsWPf/881RSUkLFxcWUKLg9LtKmaLtUFbmnMRvELSUIgiDEP70qdI499tgOU6whdjrirrvu4iVecLgb2ZrTE804u4u4rgRBEIREQC7be5AmdyOZ9BbuQh7riNARBEEQEgEROj1Is7uJMo25MZ9xBSRGRxAEQUgEROj0cJ+rdGM2xQMidARBEIREQIROD6HGIll0Nop1UENH06ZQoCAIgiDEIyJ0etBtZdAa4yIQ2WwIXChQEARBEOINETo9nHFliQehoxehIwiCICQGInR6UOikGTJJp9FTrCMZV4IgCEKiIEKnh2j2NFGGCQ3KKOYx6eNgkIIgCIIQAnJG68Fg5AxTOuXa4qAqsriuBEEQhARBhE4P4OLWDzoqSM2kdHNsu65Q4sdqjJter4IgCILQISJ0ejAQuSgtm9ItsS10+mdZJEZHEARBSBhE6PSQ0DHrrVSUlkFpJh1pY7RGjcWgpcG5sV/nRxAEQRBCRYROD+BwNVK2KZfSLQZu/5Bmjk3X0KjCtJgVYYIgCILQFUTo9ABuxUUFaXk+ERGLcTp9Ms2UaTX09jAEQRAEIaKI0IkyHsXDtwhEVkmLMaFj1GtoWJ64rARBEITEQ4ROlHG6Hdz6oU96lu+5DHNsWU5GFqSRTiuHgiAIgpB4yNmtBwKRTToL9U3PbtU0E4G/sUBBuolyU429PQxBEARBiAoidHpA6ORYsslmbF0oMBbSzPU6DQ3Pj/3eW4IgCILQVUTo9EDrh/7phe2ej4WA5JEFqWxdEgRBEIRERc5yPdD6ocjPbaWSYendOB24q/LTYr8dhSAIgiB0BxE60W79oNFTn7QDgcgqVoOWdNreqVmD/Y4oEJeVIAiCkPiI0IlyfI5Fb6HCtPYWHW/hwPDdVzkRCBwelp8qbR4EQRCEpECETtQDkdNZ7AQiI0yhg4abowpTKcvWdbdXplVPfTLMXX69IAiCIMQTInSiiMPVQP0zCtl6E4hwA5KzrAYy6rRc3C/IJjsErxlRkBb+CwVBEAQhThGhE0Xcipv6ZeQF/TuETjiCpTDda4lJNel998PtTG4zxmafLUEQBEGIBiJ0otj6QaNJoaLU9oHIKqhGbA1ReGi1Ka0K+w3Js/Jz4bR5GJRjDXl9QRAEQUgEROhEiWa3g9KMFsq0ZHS4Xqjuq1ybsVVncbiwBmaHLlyG5aVKmwdBEAQh6RChE8VA5CyzjVINHadxZ4RYIbkw3RTQFQVLTWegKzlaPQiCIAhCsiFCJ4pCpyg9j/RafbctOhAzCERuCyw8QzvpOq7ReCsgC4IgCEIyIkInSjjdTTQkq0+n61kMuk7bMBSkmYJmbiEouaN6PP0yLSHHAQmCIAhCoiFCJ4oNM7MtmSGt25lVpzO3E9LNAyEByIIgCEKyI0InCrg8TrIZjZRmTOu20LGZdJxO3lkMjn9Glgo6kyOzSxAEQRCSFTkLRisQ2WILWeh0FJAMt1UoDMu3cTyOCqonS9NOQRAEIdkRoRMloZNnzQja+qEtaSZ9K5GigrCcULOlEOvTN9O7P2xrRL4EIAuCIAiCCJ0o4FaaaEBGn6ABxG1BYUGbsb1VJ8NiCKv5JgoCojM50s4lAFkQBEEQxKITFXQ6hbIswSsih+q+ClQ7pyP0Wg2NKkyjQTkdp5wLgiAIQrIgecdRIN1oDTk+J1hAMmrk5AUIMO4MicsRBEEQhAOI6yoKZFtDD0QOJnRybEbJmBIEQRCEbiJCJ8KkaIiyzKlhCx3E4vjH40jLBkEQBEHoPiJ0IoxZr6Ucaw7pNOF7BVWrDooN5tjat3wQBEEQBCE8ROhEGKtRT3mWvC69Vg1I7qjlgyAIgiAIoSNCJ8KgY3m6Kb1Lr1V7VoVaJFAQBEEQhI4RoRNBYIXJtoQfn6OSZtJxy4f0DiolC4IgCIIQOiJ0IojFYKI0UyplmkNr5hlIKElFY0EQBEGIHFJHJ4LYDDaaOWxmt+Jr0KBTEARBEITIIBadCCNBxIIgCIIQO4jQEQRBEAQhYRGhIwiCIAhCwiJCRxAEQRCEhEWEjiAIgiAICUuvCp2vv/6aTjvtNCoqKuIg3nfeeafD9d9++22aPn065ebmUlpaGk2ZMoU+/fTTHhuvIAiCIAjxRa8Knfr6eho/fjw9+eSTIQsjCJ2PPvqIli9fTscddxwLpZUrV0Z9rIIgCIIgxB+9Wkfn5JNP5iVUHn/88VaP77//fnr33Xfp/fffpwkTJgR8jcPh4EWltraWbz0eDy9CeGDOFEWRuesmMo+RQeZR5jGWkOMxuvPY1XN2XBcMxJuuq6ujrKysoOs88MADdPfdd7d7vry8nJqamqI8wsQDc15TU8MHoUYjIV4yj3I8JgLyvZZ5jIfjEef7pBM6jzzyCNntdjr33HODrnPrrbfSjTfe2Mqi069fP1+cjxD+AYh4KsyfCJ2uI/MYGWQeZR5jCTkeozuPJpMpuYTO//73P7bUwHWVl5cXdD2j0chLWzB5cqLuGjgAZf66j8xjZJB5lHmMJeR4jN48dvWcHZdC59VXX6Xf//739MYbb9AJJ5zQ28MRBEEQBCFGibsgi1deeYUuueQSvj3llFN6eziCIAiCIMQwvWrRQXzNli1bfI+3b99OP//8MwcX9+/fn+Nr9uzZQy+88ILPXTVr1ix64okn6LDDDqPS0lJ+3mw2U3p6eq+9D0EQBEEQYpNetegsW7aM08LV1HAEDeP+nXfeyY9LSkqouLjYt/68efPI5XLR1VdfTYWFhb7l+uuv77X3IAiCIAhC7NKrFp1jjz2W08eC8fzzz7d6/OWXX3Z7n+r+1Ho6QtdS+hH9LsHcXUfmMTLIPMo8xhJyPEZ3HtXzdke6IWGCkbuDmoePFHNBEARBEOLvPB5OuEqKEq40SgCluHfvXkpNTeX0NSE81DpEu3btkjpE3UDmMTLIPMo8xhJyPEZ3HiFXIHLQHzMcj0LSWXQwOX379u3tYcQ9OPik4KLMY6wgx6PMYywhx2P05rEriUdxl14uCIIgCIIQKiJ0BEEQBEFIWEToCGGBdhpz5swJ2FZDkHnsaeR4lHmMJeR4jM15TLpgZEEQBEEQkgex6AiCIAiCkLCI0BEEQRAEIWERoSMIgiAIQsIiQkcQBEEQhIRFhI4QkK+//ppOO+00rkCJCtLvvPNOq78jhh3NV9FUFd3jTzjhBNq8ebPMZhseeOABmjx5MlfizsvLozPPPJM2btzYap2mpiZuVJudnU02m43OPvts2rdvn8ylH08//TSNGzfOV0BsypQp9PHHH8scdoMHH3yQv9t/+MMfZB7D5K677uK5819Gjhwp8xgme/bsoQsvvJB/+3AeOeigg7jZd6TPMyJ0hIDU19fT+PHj6cknnwz497lz59Lf//53+te//kU//vgjWa1WOumkk/ikLRzgq6++YhHzww8/0KJFi8jpdNKJJ57I86tyww030Pvvv09vvPEGr48WJb/61a9kGv1ANXOcmJcvX84/hMcffzydccYZtHbtWpnDLvDTTz/RM888w+LRHzkWQ2fMmDFUUlLiW7799luZxzCoqqqiqVOnkl6v54uWdevW0aOPPkqZmZmRP88gvVwQOgKHyYIFC3yPPR6PUlBQoDz88MO+56qrqxWj0ai88sorMpkdUFZWxvP51Vdf+eZNr9crb7zxhm+d9evX8zrff/+9zGUHZGZmKv/+979lDsOkrq5OGTZsmLJo0SLlmGOOUa6//no5FsNkzpw5yvjx4wP+Tb7TofHnP/9ZOfLII4P+PZLnGbHoCGGzfft2Ki0tZTOif/+Rww47jL7//nuZ0Q6oqanh26ysLL6FhQJWHv+5hAm8f//+MpdBcLvd9Oqrr7JVDC4smcPwgIXxlFNOaXXMybEYPnChwLU/ePBguuCCC6i4uFjmMQzee+89mjRpEp1zzjns1p8wYQI9++yzUTnPiNARwgYHH8jPz2/1PB6rfxPa4/F4OB4C5tqxY8f65tJgMFBGRobMZSesXr2aY5hQLXX27Nm0YMECGj16tMxhGEAgrlixgmPHAn2v5VgMDZxsn3/+efrkk084fgwn5aOOOoo7a8s8hsa2bdt47oYNG0affvopXXnllXTdddfRf//734ifZ5Kue7kg9OaV9Jo1a1r58oXQGTFiBP38889sFXvzzTdp1qxZHNMkhMauXbvo+uuv51gxk8kk09YNTj75ZN99xDlB+AwYMIBef/11DpoVQrvwg0Xn/vvv58ew6OD3EfE4+G5HErHoCGFTUFDAt20zg/BY/ZvQmmuuuYY++OADWrx4MQfW+s9lc3MzVVdXy1x2AqwNQ4cOpUMOOYQtEgiWf+KJJ2QOQwQuvrKyMpo4cSLpdDpeIBQR7In7uFKWY7FrwCI7fPhw2rJlixyPIYJMKlhk/Rk1apTPBRjJ84wIHSFsBg0axAfa559/7nuutraWo+IRMyEcALHcEDlws3zxxRc8d/7gpI2sA/+5RPo5vuwyl51fETocDpnDEJk2bRq7/2AVUxdcUSO+RL0vx2LXsNvttHXrVj55y3c6NODCb1tqY9OmTWwZi/h5JqzQZSGpMjNWrlzJCw6Txx57jO/v3LmT//7ggw8qGRkZyrvvvqusWrVKOeOMM5RBgwYpjY2NvT30mOLKK69U0tPTlS+//FIpKSnxLQ0NDb51Zs+erfTv31/54osvlGXLlilTpkzhRTjALbfcwplq27dv5+MNj1NSUpSFCxfKHHYD/6wrORZD56abbuLvNI7HJUuWKCeccIKSk5PDWZUyj6GxdOlSRafTKffdd5+yefNm5eWXX1YsFovy0ksv+daJ1HlGhI4QkMWLF7PAabvMmjXLl/p3xx13KPn5+ZzuN23aNGXjxo0ym22/YAHmEMv8+fN96+BLe9VVV3G6NL7oZ511Fosh4QCXXnqpMmDAAMVgMCi5ubl8vKkiR+YwckJHjsXQOO+885TCwkI+Hvv06cOPt2zZIvMYJu+//74yduxYPoeMHDlSmTdvXqu/R+o8k4J/wrMBCYIgCIIgxAcSoyMIgiAIQsIiQkcQBEEQhIRFhI4gCIIgCAmLCB1BEARBEBIWETqCIAiCICQsInQEQRAEQUhYROgIgiAIgpCwiNARBEEQBCFhEaEjCEnIjh07KCUlhXscxQobNmygww8/nDtrH3zwwRSLPP/889zAMdb43e9+R2eeeWZvD0MQYhIROoLQSycmCI0HH3yw1fPvvPMOP5+MzJkzh6xWKzf682/kF2jesKABJTpuT58+nf7zn/9wk89wRYu6Lf8FQisY5513HjceVLnrrrt6VJQFE6jo4o73IwhCe0ToCEIvgRPqQw89RFVVVQnzGTQ3N3f5tej+fOSRR3L34uzs7KDrzZgxg0pKSvik//HHH9Nxxx1H119/PZ166qnkcrnC2mdaWhpvy3/ZuXNn0PXNZjPl5eVRLM0bSE9Pj0lLkyDEAiJ0BKGXOOGEE6igoIAeeOCBoOsEshg8/vjjNHDgwHZui/vvv58tHDjh3XPPPXzSv/nmmykrK4v69u1L8+fPD+guOuKII1h0jR07lr766qtWf1+zZg2dfPLJZLPZeNsXXXQR7d+/3/f3Y489lq655hr6wx/+QDk5OXTSSScFfB+wtmBMGIfRaOT39Mknn/j+DivF8uXLeR3cx/sOBl6PeevTpw9NnDiR/vKXv9C7777LosffqlFcXExnnHEGjx2C5txzz6V9+/a12hb2hW35L3ifobiucP/uu++mX375xWcNUvdfXV1Nv//97yk3N5f3ffzxx/N6bT/Xf//73zRo0CCfFQlzArGHfUDsQbxBAKpgXTBhwgTeH+Y/kOvK4XDQddddx6IM28Y2f/rpJ9/fv/zyS349LGeTJk0ii8XCxwGsaSoYL0Rkamoqv4dDDjmEli1bFnRuBCFWEaEjCL2EVqtlcfKPf/yDdu/e3a1tffHFF7R37176+uuv6bHHHmM3EE6SmZmZ9OOPP9Ls2bPpiiuuaLcfCKGbbrqJVq5cSVOmTKHTTjuNKioqfCdrnKBxUsUJDidhCAUIBn/++9//ksFgoCVLltC//vWvgOODa+XRRx+lRx55hFatWsWC6PTTT6fNmzfz32FJGTNmDI8F9//4xz+G9f4xzvHjx9Pbb7/tE1YQOZWVlSzeFi1aRNu2bWPXU6TAtjBejFu1BqnbP+ecc6isrIzFFwQcBNm0adN4PCpbtmyht956i8esuqLq6+vpxhtv5PmGCNFoNHTWWWf53HJLly7l288++4z3p77ftvzpT3/ibeOzWbFiBQ0dOpTn3H//4LbbbuPPBfvT6XR06aWX+v52wQUXsDCFQMJ7uOWWW9hdKAhxR9j9zgVB6DazZs1SzjjjDL5/+OGHK5deeinfX7BggeL/tZwzZ44yfvz4Vq/929/+pgwYMKDVtvDY7Xb7nhsxYoRy1FFH+R67XC7FarUqr7zyCj/evn077+fBBx/0reN0OpW+ffsqDz30ED++9957lRNPPLHVvnft2sWv27hxIz8+5phjlAkTJnT6fouKipT77ruv1XOTJ09WrrrqKt9jvE+831DnrS3nnXeeMmrUKL6/cOFCRavVKsXFxb6/r127lse+dOlSfjx//nx+jHnxX2bMmBF0/3hNenp6h5/PN998o6SlpSlNTU2tnh8yZIjyzDPP+F6n1+uVsrKyDt9veXk5j3H16tWtPreVK1cGnRe73c7bfvnll31/b25u5s9g7ty5/Hjx4sW8nc8++8y3zocffsjPNTY28uPU1FTl+eef73B8ghAP6HpbaAlCsoM4HVgkwrVi+AOrAq7+VeB+gSvK33oEVwisDP7AiqOCK3q4MdavX+9zXSxevJhdP22BO2X48OF8Hy6NjqitrWVr09SpU1s9j8f+7pzuoiiKL5Ab76Ffv368qIwePZpdQvjb5MmT+Tm4ZWDxaBuH0x3wnux2e7s4o8bGxlZuKMQiwbXlDyxcd955J1vh4CJULTlww/l/nh2BfTidzlbzDUvMoYce6vtsVcaNG+e7X1hYyLc4Rvr378+WJbjfXnzxRXazwko1ZMiQsOZCEGIBETqC0MscffTR7Fa49dZbOdbCH4gXnMD9wUmsLW1dCmpWUtvnwslMwskariwIsbaoJ0WATKlYACdxNYYlVDC/cOtEEswb5gdxMG3xDxgONG+YbwigZ599loqKivjzgsDpbrByMPyPEVUkqscI4ojOP/98+vDDD9kFB3foq6++yq40QYgnJEZHEGIApJm///779P3337d6Hlf8paWlrcROJGvf/PDDD777CF5GLMaoUaP4MeJK1q5dy4HPEAP+SzjiBoGsOGkjhscfPIaVJRIgRmn16tV09tln82O8h127dvGism7dOo47itQ+AWKT3G53q+cwb/jMYCFrO28I2A4GYqMQDHz77bdzPA/eQ9uMPOwPtN2nP7C6qDFT/uIYsTbhvndY7W644QZauHAh/epXvwoY0C4IsY4IHUGIAQ466CAO/vz73//e6nlk1ZSXl9PcuXPZJfHkk0/y1XWkwPYWLFjA2VdXX301n1jVgFQ8RvDqb3/7Wz5JYv+ffvopXXLJJR2eaAOBoGdYhl577TU+mSOwFYINaeHhgowiCIk9e/aw2wkB3Qg8RvD1xRdfzOvA1aLOKdZBEC/+dswxx7B7TgUCEttqu4Rq+YII3L59O78XuJowNuwbLkFkQUEgIA3+u+++48DfjrKWEDgOd9e8efM4UBniDe4jf5BFBdeaGhheU1PTbjsQoVdeeSXPOdaDwLv88supoaGBLrvsspDeF9xsyKaDVQrp9hBNOAZUESwI8YQIHUGIEZBa3fYEixPLU089xYIEWUU4YXcnlieQJQkLtv3tt9/Se++957M6qFYYiJoTTzyRhQPSyOF+8Y8HCgWkOuOkjSwlbAcnYOxr2LBhYY8Zr4VrCCIDNXUQRwSBiBRzxCKpbhg8hniAaxDiY/DgwSy02sYPYVttl7axTMGABQljQBo2rG+vvPIK7/ujjz7i/UIUwirym9/8hgVDR6nrmFO4hmBVg7sKlpSHH3641TqwEuG9PvPMM/z5QOAFAp8pxoZyALAwQThBpGI+QgHzCAsTxCHGj0w7lBlAOr0gxBspiEju7UEIgiAIgiBEA7HoCIIgCIKQsIjQEQRBEAQhYRGhIwiCIAhCwiJCRxAEQRCEhEWEjiAIgiAICYsIHUEQBEEQEhYROoIgCIIgJCwidARBEARBSFhE6AiCIAiCkLCI0BEEQRAEIWERoSMIgiAIAiUq/w9AMjMUFfosIQAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 5.4 Compare QoI evolution\n", - "\n", - "ax = plot_qoi_estimates_from_experiment(exp_sobol, name=\"Sobol\")\n", - "ax = plot_qoi_estimates_from_experiment(\n", - " exp_look_ahead,\n", - " ax=ax,\n", - " color=\"green\",\n", - " name=\"QoI look-ahead\",\n", - ")\n", - "\n", - "ax.axhline(float(brute_force_qoi_estimate), color=\"black\", linestyle=\"--\", label=\"Brute-force QoI\")\n", - "\n", - "ax.set_xlabel(\"Number of DoE iterations\")\n", - "ax.set_ylabel(\"QoI (median extreme response)\")\n", - "ax.legend()\n", - "ax.grid(alpha=0.3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d4021aeb", - "metadata": {}, - "source": [ - "### 5.5 Spatial Distribution of Selected Points\n", - "\n", - "To understand **where** each DoE strategy chooses to evaluate the simulator, we visualize the selected points in the 2D input space overlaid on:\n", - "\n", - "1. **Environment density**: Shows the distribution of environment samples (grey histogram)\n", - "2. **Extreme response density**: Shows where extreme responses originated during brute-force simulation (red histogram)\n", - "3. **Combined view**: Both densities overlaid to reveal the relationship\n", - "\n", - "Key insights:\n", - "\n", - "- **Sobol points (blue circles)**: Distributed uniformly across the entire input space, regardless of where extremes occur\n", - "- **QoI-aware points (red triangles)**: Concentrated in regions that contribute most to extreme responses\n", - "\n", - "The point labels indicate the order in which they were selected during DoE.\n", - "\n", - "This visualization reveals why QoI-aware DoE is more efficient: it focuses simulator evaluations on the critical regions of the input space where the QoI is most sensitive." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "889096a2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot_2dtrials(\n", - " exp: Experiment, ax: plt.Axes | None = None, colour: str = \"blue\", marker: str = \"o\", label: str | None = None\n", - ") -> plt.Axes:\n", - " \"\"\"Plot the points and number the datapoints added over DoE.\"\"\"\n", - " if ax is None:\n", - " _, ax = plt.subplots(figsize=(8, 6))\n", - "\n", - " trials = []\n", - " trial_indices = []\n", - "\n", - " for trial_idx, trial in exp.trials.items():\n", - " if trial.arm:\n", - " params = trial.arm.parameters.values()\n", - " trials.append(list(params))\n", - " trial_indices.append(trial_idx)\n", - "\n", - " points = np.array(trials)\n", - " ax.scatter(points[:, 0], points[:, 1], alpha=0.8, s=50, label=label, c=colour, marker=marker)\n", - "\n", - " for i, (x, y) in enumerate(points):\n", - " ax.annotate(str(trial_indices[i]), (x, y), xytext=(2, 2), textcoords=\"offset points\", fontsize=8, color=colour)\n", - "\n", - " return ax\n", - "\n", - "\n", - "# Create three-panel visualization\n", - "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", - "\n", - "# Panel 1: Points vs Environment Density\n", - "axes[0].set_title(\"Points vs Environment Density\")\n", - "axes[0].hist2d(env_data[:, 0], env_data[:, 1], bins=20, alpha=0.6, cmap=\"Greys\", zorder=-1)\n", - "\n", - "# Panel 2: Points vs Extreme Response Density\n", - "axes[1].set_title(\"Points vs Extreme Response Density\")\n", - "axes[1].hist2d(precalced_erd_x[:, 0], precalced_erd_x[:, 1], bins=40, alpha=0.6, cmap=\"Purples\", zorder=-1)\n", - "\n", - "# Panel 3: Points vs Combined Density\n", - "axes[2].set_title(\"Points vs Env and Extreme Response Density\")\n", - "axes[2].hist2d(env_data[:, 0], env_data[:, 1], bins=20, alpha=0.6, cmap=\"Greys\", zorder=-1)\n", - "axes[2].hist2d(precalced_erd_x[:, 0], precalced_erd_x[:, 1], bins=40, alpha=0.5, cmap=\"Reds\", zorder=-1)\n", - "\n", - "# Add DoE points to all panels\n", - "for ax in axes:\n", - " ax.set_xlabel(\"x1\")\n", - " ax.set_ylabel(\"x2\")\n", - " plot_2dtrials(exp_sobol, colour=\"blue\", ax=ax, label=\"Sobol\")\n", - " plot_2dtrials(exp_look_ahead, colour=\"red\", ax=ax, label=\"QoI look-ahead\", marker=\"^\")\n", - " ax.legend()\n", - " ax.grid(visible=True, alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a05208a5", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "axtreme", - "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": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/getting_started.py b/tutorials/getting_started.py new file mode 100644 index 00000000..56f62f1d --- /dev/null +++ b/tutorials/getting_started.py @@ -0,0 +1,1014 @@ +# %% [markdown] +# ## Getting started with Axtreme +# +# In this notebook we walk through the core ideas behind **Axtreme** on a simple toy problem: +# +# - Step 1: Define the problem in terms of: +# - a **simulator** (the physics / system model), and +# - an **environment distribution** (how the inputs vary in the real world). +# - Step:2 Use **brute force** to compute a reference answer for our **Quantity of Interest** (QoI). +# - Step 3: Build a **surrogate model** of the simulator using **Ax + BoTorch**. +# - Step 4: Use that surrogate to **estimate the QoI much more cheaply**. +# - Step 5: Use **Design of Experiments (DoE)** to choose simulator points intelligently +# and reduce QoI uncertainty faster. +# +# The imports below set up: +# +# - **Numerics & plotting**: `numpy`, `torch`, `matplotlib` +# - **Ax / BoTorch**: experiment definition and Gaussian-process modelling +# - **Axtreme**: helper functions for QoI estimation and DoE tailored to extreme responses +# - **Toy example code**: a small Gumbel-based simulator and environment data used only for this tutorial + +# %% +import sys +from collections.abc import Callable +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import scipy +import torch +from ax import Experiment, SearchSpace +from ax.core import GeneratorRun, ObservationFeatures, ParameterType, RangeParameter +from ax.modelbridge.registry import Models +from scipy.stats import gumbel_r +from torch.distributions import Normal +from torch.utils.data import DataLoader + +from axtreme import sampling +from axtreme.acquisition import QoILookAhead +from axtreme.data import FixedRandomSampler, MinimalDataset +from axtreme.experiment import add_sobol_points_to_experiment, make_experiment +from axtreme.metrics import QoIMetric +from axtreme.plotting.doe import plot_qoi_estimates_from_experiment +from axtreme.plotting.histogram3d import histogram_surface3d +from axtreme.qoi import MarginalCDFExtrapolation +from axtreme.sampling.ut_sampler import UTSampler +from axtreme.utils import population_estimators, transforms + +# Configure torch +torch.set_default_dtype(torch.float64) + +# Load the toy problem +root_dir = Path("../") +sys.path.append(str(root_dir)) +from examples.basic_example_usecase.problem.brute_force import collect_or_calculate_results +from examples.basic_example_usecase.problem.env_data import collect_data +from examples.basic_example_usecase.problem.simulator import DummySimulatorSeeded + +print("✓ Setup complete") + +# %% [markdown] +# ### Axtreme Workflow Overview +# +# The following diagram illustrates the general Axtreme process for extreme response estimation: +# +# **Key Steps:** +# +# 1. **Problem Definition**: Specify the simulator and environment distribution +# 2. **Surrogate Modeling**: Build a GP-based approximation using Ax/BoTorch +# 3. **QoI Estimation**: Efficiently estimate the Quantity of Interest using the surrogate +# 4. **Uncertainty Check**: Assess if the QoI uncertainty is acceptable +# 5. **Design of Experiments**: Iteratively refine the surrogate by intelligently selecting new evaluation points +# +# ```mermaid +# flowchart TD +# A[1. Define Problem] --> A1[Simulator Function] +# A[1. Define Problem] --> A2[Environment Distribution] +# +# A1 --> B[2. Build Surrogate] +# A2 --> B +# B --> B1[Define Search Space] +# B1 --> B2[Generate Training Data] +# B2 --> B3[Evaluate Simulator] +# B3 --> B4[Fit GP Model] +# +# B4 --> C[3. Estimate QoI] +# C --> C1[Setup Env Samples] +# C1 --> C2[Choose QoI Estimator] +# C2 --> C3[Compute QoI] +# +# C3 --> D{4. Acceptable?} +# D -->|No| E[5. Design of Experiments] +# D -->|Yes| F[Final QoI Estimate] +# +# E --> E1[Select Acquisition] +# E1 -->|Space-filling| E2[Sobol/Random] +# E1 -->|QoI-aware| E3[QoILookAhead] +# +# E2 --> E4[Next Point] +# E3 --> E4 +# E4 --> E5[Evaluate] +# E5 --> E6[Update Surrogate] +# E6 --> C3 +# +# style A fill:#e1f5ff +# style B fill:#e8f5e9 +# style C fill:#f3e5f5 +# style D fill:#fff4e1 +# style E fill:#ffe0e0 +# style F fill:#c8e6c9 +# ``` +# +# ### Tutorial Note: +# This tutorial also computes a **brute-force reference** QoI using extensive simulation +# runs. This is done purely for validation purposes to demonstrate that Axtreme converges +# to the correct answer. In real applications, such brute-force computation is typically +# too expensive or infeasible. + +# %% [markdown] +# ## Step 1: Problem Inputs +# +# The `axtreme` package needs two core ingredients: +# +# 1. A **simulator** +# 2. A set of **environment samples** +# +# These two objects completely define the *probabilistic problem* we want to solve. +# +# ### 1.1 Simulator +# +# The simulator is a function +# +# $$ +# x \mapsto y +# $$ +# +# where: +# +# - $x$ is a vector of input / environment variables (here a 2D point: `x = [x1, x2]` in $[0, 1]^2$). +# - $y$ is a **random** output because the simulator includes noise. +# +# In this toy problem the noise model is a **Gumbel distribution**: +# +# - The simulator is parameterised by: +# - a **location** function `loc(x)` and +# - a **scale** function `scale(x)` +# - For each input $x$ the output is: +# +# $$ +# y \sim \text{Gumbel}(\text{loc}(x), \text{scale}(x)) +# $$ +# +# In a real application, we **don't know** the true `loc(x)` and `scale(x)`; we only see +# noisy simulator outputs. Here we cheat a little: we have access to the "true" functions +# so we can visualise what is going on and check that our methods behave sensibly. +# +# **Visualizing the stochastic nature of the simulator:** +# +# Below we run the simulator 500 times at the same input point $x = [0.5, 0.5]$. The +# histogram shows the characteristic right-skewed shape of a Gumbel distribution — this +# is the noise model built into our toy simulator. + +# %% +# Use the seeded simulator for reproducible results +sim = DummySimulatorSeeded() + +# Run the simulator multiple times at the same point to visualize the response distribution +test_point = np.array([[0.5, 0.5]]) +n_samples = 500 +response = sim(test_point, n_simulations_per_point=n_samples) + +# Visualize the distribution of simulator responses +plt.figure(figsize=(10, 4)) +plt.hist(response.flatten(), bins=40, density=True, alpha=0.7, edgecolor="black") +plt.xlabel("Response value") +plt.ylabel("Density") +plt.title("Simulator Response Distribution at x = [0.5, 0.5]") +plt.grid(alpha=0.3) +plt.show() + +print("The histogram shows the Gumbel-shaped distribution of simulator responses at a fixed input point.") + +# %% [markdown] +# ### 1.2 Environment Data +# +# The second ingredient is a set of **environment samples**: +# +# - Each sample is a 2D point $x = [x1, x2]$ +# - Together they represent the **conditions your system actually experiences** in practice: +# - These might come from historical data +# - Or from a probabilistic model of the environment +# +# In this toy example we load a pre-generated dataset: +# +# - We treat it purely as **input samples** — no simulator has been run on them yet. +# - We'll later use them to: +# - Estimate our **Extreme Response Distribution (ERD)** by brute force, and +# - Feed them into Axtreme's QoI estimators so we can reuse them efficiently with the surrogate. + +# %% +# Load environment data +env_data = collect_data().to_numpy() +print(f"Environment data shape: {env_data.shape}") +print(f"First 5 samples:\n{env_data[:5]}") + +# Visualize the environment distribution +fig = histogram_surface3d(env_data) +fig.update_layout(title_text="Environment Distribution", scene_aspectmode="cube", height=500) +fig.show() + +# %% [markdown] +# ## Step 2: Brute-force Extreme Response and QoI +# +# Before we introduce any surrogate modelling, we first compute a **reference answer** using raw simulation. +# +# ### 2.1 Extreme Response Distribution (ERD) +# +# We imagine observing the environment over **time periods** of length `N_ENV_SAMPLES_PER_PERIOD`. +# For each period we: +# +# 1. Draw `N_ENV_SAMPLES_PER_PERIOD` inputs from the environment data +# 2. Run the simulator on each input +# 3. Record the **maximum** response over that period +# +# Each period gives one sample from the **Extreme Response Distribution (ERD)**: +# +# $$ +# \text{ERD sample} = \max_{t \in \text{period}} y(t) +# $$ +# +# Repeating this many times gives us an empirical ERD, which we visualise as a histogram. +# +# ### 2.2 Quantity of Interest (QoI) +# +# In this tutorial our **Quantity of Interest (QoI)** is: +# +# > The **median** of the Extreme Response Distribution. +# +# Intuitively: +# +# - We're asking: "What is a *typical* extreme response over a period?" +# - This is a robust measure of tail behaviour that is easier to estimate than, say, a 1-in-1000-year extreme. +# +# We compute: +# +# - A **brute-force estimate** of the ERD median using many simulator calls +# - An associated **uncertainty** for this estimate using Axtreme's `population_estimators` helpers +# +# This brute-force QoI estimate will serve as: +# +# - A **ground-truth** comparison for the surrogate-based estimates we build later. + +# %% +# Define the period length (number of environment samples per period) +N_ENV_SAMPLES_PER_PERIOD = 1000 + +# Load pre-calculated brute force results (300,000 ERD samples) +precalced_erd_samples, precalced_erd_x = collect_or_calculate_results(N_ENV_SAMPLES_PER_PERIOD, 300_000) +brute_force_qoi_estimate = np.median(precalced_erd_samples) + +# Calculate QoI uncertainty using population estimators +population_median_est_dist = population_estimators.sample_median_se(torch.tensor(precalced_erd_samples)) + +print(f"Brute force QoI (median ERD): {brute_force_qoi_estimate:.4f}") +print(f"This required 300,000 × {N_ENV_SAMPLES_PER_PERIOD} = 300M simulator calls!") + +# Visualize the ERD with QoI uncertainty +fig, axes = plt.subplots(ncols=2, figsize=(14, 4)) + +# Left plot: ERD histogram with QoI uncertainty overlay +axes[0].hist(precalced_erd_samples, bins=100, density=True, alpha=0.7, edgecolor="black", label="ERD") +axes[0].set_xlabel("Extreme Response") +axes[0].set_ylabel("ERD Density") +axes[0].set_title("Extreme Response Distribution (Brute Force)") +axes[0].grid(alpha=0.3) + +# Add QoI uncertainty on secondary y-axis +ax_twin = axes[0].twinx() +population_estimators.plot_dist(population_median_est_dist, ax=ax_twin, c="red", label="QoI estimate") +ax_twin.set_ylabel("QoI Estimate PDF") +ax_twin.legend(loc="upper right") + +# Add median line +axes[0].axvline(brute_force_qoi_estimate, color="red", linestyle="--", linewidth=2, alpha=0.5) +axes[0].legend(loc="upper left") + +# Right plot: Zoomed-in view of QoI uncertainty only +population_estimators.plot_dist(population_median_est_dist, ax=axes[1], c="red") +axes[1].axvline( + brute_force_qoi_estimate, + color="red", + linestyle="--", + linewidth=2, + label=f"Median ERD = {brute_force_qoi_estimate:.4f}", +) +axes[1].set_xlabel("QoI Value (Median ERD)") +axes[1].set_ylabel("Density") +axes[1].set_title("QoI Estimate Distribution (Zoomed)") +axes[1].legend() +axes[1].grid(alpha=0.3) + +plt.tight_layout() +plt.show() + +# %% [markdown] +# ## Step 3: Build a Surrogate Model with Ax + BoTorch +# +# Running the simulator many times is expensive. +# Instead, we build a **statistical surrogate** (a Gaussian Process) that learns: +# +# - The mapping from inputs $(x1, x2)$ to the **location** parameter of the Gumbel noise +# - The mapping from inputs $(x1, x2)$ to the **scale** parameter of the Gumbel noise +# +# ### 3.1 Configuration +# +# Before creating the Ax experiment, we set up the core components: +# +# 1. **Search space**: Define the valid input domain — here, `x1` and `x2` both in +# $[0, 1]$ +# 2. **Noise distribution**: Specify the distribution family for the simulator noise +# (Gumbel) # noqa: ERA001 +# 3. **Simulations per point**: How many simulator runs to perform at each training point +# to estimate the distribution parameters (`loc` and `scale`) +# +# These configuration choices affect: +# +# - How much information each training point provides (more simulations → better estimates) +# - The computational cost per training point +# - The smoothness of the resulting surrogate model + +# %% +# 1. Define the search space (2D: x1 and x2 both in [0, 1]) +search_space = SearchSpace( + parameters=[ + RangeParameter(name="x1", parameter_type=ParameterType.FLOAT, lower=0, upper=1), + RangeParameter(name="x2", parameter_type=ParameterType.FLOAT, lower=0, upper=1), + ] +) + +# 2. Choose distribution for noise model +dist = gumbel_r + +# 3. Number of simulations per point +N_SIMULATIONS_PER_POINT = 200 + +print("✓ Search space: 2D unit square") +print("✓ Distribution: Gumbel") +print(f"✓ Simulations per point: {N_SIMULATIONS_PER_POINT}") + +# %% [markdown] +# ### 3.2 Initial training data (Sobol design) +# +# We start by generating an initial, **space-filling** design using a Sobol sequence: +# +# - Sobol points cover the space well without any modelling assumptions. +# - At each Sobol point `x`, we run the simulator multiple times to estimate: +# - the mean (location) and +# - variability (scale) of the response distribution. +# +# These initial points are used to fit the first GP surrogate using `Models.BOTORCH_MODULAR`. +# +# **What the GP actually learns:** +# +# The `make_experiment()` function wraps the simulator with logic that: +# +# 1. Runs the simulator `N_SIMULATIONS_PER_POINT` times at each training point `x` +# 2. Fits the Gumbel distribution to those samples to estimate `loc(x)` and `scale(x)` +# 3. Provides these fitted parameters to the GP +# +# So the GP learns **two separate mappings**: +# - `x → loc`: How the location parameter varies across the input space +# - `x → scale`: How the scale parameter varies across the input space +# +# The GP is **not** trained on raw `(x, y)` pairs. Instead, it learns the underlying +# distribution parameters from multiple noisy observations at each `x`. + + +# %% +# Helper function to create experiments with consistent settings +def make_exp() -> Experiment: + return make_experiment(sim, search_space, dist, n_simulations_per_point=N_SIMULATIONS_PER_POINT) + + +# Create experiment and add 30 initial training points +exp = make_exp() +add_sobol_points_to_experiment(exp, n_iter=30, seed=8) + +# Train the surrogate model +botorch_model_bridge = Models.BOTORCH_MODULAR( + experiment=exp, + data=exp.fetch_data(), +) + +print(f"✓ Trained GP with {len(exp.trials)} training points") +print(f"✓ Total simulator calls: {len(exp.trials) * N_SIMULATIONS_PER_POINT}") + +# %% [markdown] +# ### 3.3 Comparing surrogate vs simulator +# +# To build intuition, we pick a single test point `x = [0.5, 0.5]` and: +# +# - Sample many responses from the **true simulator** +# - Predict the mean Gumbel distribution parameters from the **surrogate** +# - Sample from the GP posterior to visualize uncertainty +# +# The plot shows: +# +# - **Histogram**: 200 simulator runs at the test point, showing the true response distribution +# - **Red line**: The surrogate's mean predicted Gumbel distribution +# - **Grey lines**: 10 sampled Gumbel distributions from the GP posterior +# +# The GP posterior captures uncertainty about the true `loc` and `scale` parameters at +# this location. The grey lines show the range of plausible response distributions +# consistent with the training data. When the surrogate has high confidence, these lines +# cluster tightly around the mean; when uncertain, they spread out.# %% +# Predict at x = [0.5, 0.5] +test_x = {"x1": 0.5, "x2": 0.5} +pred_mean, pred_covariance = botorch_model_bridge.predict([ObservationFeatures(parameters=test_x)]) + +# Get simulator samples at the same point +simulator_samples = sim(np.array([[0.5, 0.5]]), n_simulations_per_point=200).flatten() + +# Mean prediction +pred_dist_mean = dist(loc=pred_mean["loc"], scale=pred_mean["scale"]) +x_points = np.linspace(simulator_samples.min(), simulator_samples.max(), 100) + +# Sample from GP posterior to show uncertainty +mean = np.array([pred_mean["loc"], pred_mean["scale"]]).flatten() +covariance = np.array( + [ + [pred_covariance["loc"]["loc"], pred_covariance["loc"]["scale"]], + [pred_covariance["scale"]["loc"], pred_covariance["scale"]["scale"]], + ] +).reshape(2, 2) + +surrogate_distribution = scipy.stats.multivariate_normal(mean, covariance) +posterior_samples = surrogate_distribution.rvs(size=10, random_state=42) + +# Plot +plt.figure(figsize=(10, 4)) +plt.hist(simulator_samples, bins=20, density=True, alpha=0.6, label="Simulator", edgecolor="black") + +# Plot posterior samples with transparency +for i, sample in enumerate(posterior_samples): + sample_dist = dist(loc=sample[0], scale=sample[1]) + if i == 0: + plt.plot( + x_points, + sample_dist.pdf(x_points), + "grey", + linewidth=1, + alpha=0.5, + label="GP Surrogate posterior samples", + ) + else: + plt.plot(x_points, sample_dist.pdf(x_points), "grey", linewidth=1, alpha=0.5) + +# Plot mean prediction on top +plt.plot(x_points, pred_dist_mean.pdf(x_points), "r-", linewidth=2, label="GP Surrogate (mean)") + +plt.xlabel("Response value") +plt.ylabel("Density") +plt.title("Surrogate vs Simulator at x = [0.5, 0.5]") +plt.legend() +plt.grid(alpha=0.3) +plt.show() + +print(f"Surrogate prediction: loc={pred_mean['loc'][0]:.3f}, scale={pred_mean['scale'][0]:.3f}") + +# %% [markdown] +# ## Step 4: Estimate the QoI from the Surrogate +# +# Now that we have a surrogate model, we can use it to estimate our **Quantity of +# Interest** (the median of the ERD) **without repeatedly calling the expensive +# simulator**. +# +# The key idea: +# +# - The surrogate is fast to evaluate. +# - We use it to *emulate* what the simulator would do over many periods of environment samples. +# - We propagate both: +# - **environment randomness**, and +# - **surrogate uncertainty** +# into our QoI estimate. +# +# ### 4.1 Environment sampling via DataLoader +# +# We wrap our environment samples in a small `Dataset` and `DataLoader`: +# +# - This lets the QoI estimator iterate over batches of environment points. +# - We can easily control how many total environment samples we use for the QoI calculation. +# - Different runs (with different seeds) can use different resampled subsets if desired. + +# %% +# Set up environment data loader +n_env_samples = 4_000 +dataset = MinimalDataset(env_data) +sampler = FixedRandomSampler(dataset, num_samples=n_env_samples, seed=10, replacement=True) +dataloader = DataLoader(dataset, sampler=sampler, batch_size=256) + +# %% [markdown] +# ### 4.2 QoI estimator: MarginalCDFExtrapolation +# +# We use Axtreme's `MarginalCDFExtrapolation` as a ready-made QoI estimator that: +# +# 1. Draws batches of environment inputs from the loader +# 2. Uses the surrogate model to obtain a distribution of responses at each input +# 3. Forms many **periods** of length `N_ENV_SAMPLES_PER_PERIOD` +# 4. Extracts the **maximum** response in each period +# 5. Estimates the **median** of the resulting extreme response distribution +# 6. Repeats under different draws from the GP posterior to capture model uncertainty +# +# The result is a **distribution over the QoI**, not just a single number. + +# %% +# Create QoI estimator +qoi_estimator = MarginalCDFExtrapolation( + env_iterable=dataloader, + period_len=N_ENV_SAMPLES_PER_PERIOD, + quantile=torch.tensor(0.5), # Median + quantile_accuracy=torch.tensor(0.01), + posterior_sampler=UTSampler(), +) + +print("✓ QoI estimator configured") + +# %% [markdown] +# ### 4.3 Effect of training data size +# +# To see the impact of more simulator data, we repeat: +# +# - Use `N` training points to fit a surrogate +# - Use that surrogate to estimate the QoI distribution +# +# for several values of `N` (e.g. 30, 50, 128). +# +# For each `N` we: +# +# - Approximate the QoI distribution with a Normal using the sampler's `mean` and `var` methods +# - Plot the corresponding PDF +# - Overlay the **brute-force QoI** as a vertical line +# +# Results: +# +# - With **few** training points, the QoI distribution is **wide** (high uncertainty). +# - As we add more training points, the distribution becomes **sharper** (narrower), +# and its mean stays close to the brute-force value. +# +# This confirms that: +# +# > The surrogate-based QoI estimator converges towards the brute-force answer as more simulator data is added. + +# %% +# Train surrogates with different numbers of training points +n_training_points = [30, 64, 128] +results = [] + +for n_points in n_training_points: + # Create and train experiment + exp_temp = make_exp() + add_sobol_points_to_experiment(exp_temp, n_iter=n_points, seed=8) + + model_bridge = Models.BOTORCH_MODULAR(experiment=exp_temp, data=exp_temp.fetch_data()) + + # Set up transforms + input_transform, outcome_transform = transforms.ax_to_botorch_transform_input_output( + transforms=list(model_bridge.transforms.values()), outcome_names=model_bridge.outcomes + ) + qoi_estimator.input_transform = input_transform + qoi_estimator.outcome_transform = outcome_transform + + # Estimate QoI + model = model_bridge.model.surrogate.model + result = qoi_estimator(model) + results.append(result) + + print(f"✓ Computed QoI with {n_points} training points") + +# %% [markdown] +# Let's see how the QoI estimate improves with more training data: + +# %% +# Visualize QoI estimates +fig, axes = plt.subplots(nrows=len(n_training_points), figsize=(10, 3 * len(n_training_points)), sharex=True) + +for ax, estimate, n_points in zip(axes, results, n_training_points, strict=True): + # Extract mean and variance from the estimate + mean = qoi_estimator.posterior_sampler.mean(torch.tensor(estimate), -1) + var = qoi_estimator.posterior_sampler.var(torch.tensor(estimate), -1) + qoi_dist = Normal(mean, var**0.5) + + # Plot QoI distribution + x_range = torch.linspace(float(mean - 3 * var**0.5), float(mean + 3 * var**0.5), 200) + ax.plot(x_range.numpy(), torch.exp(qoi_dist.log_prob(x_range)).numpy(), "b-", linewidth=2, label="QoI estimate") + ax.fill_between(x_range.numpy(), 0, torch.exp(qoi_dist.log_prob(x_range)).numpy(), alpha=0.3) + + # Add brute force reference + ax.axvline(brute_force_qoi_estimate, color="red", linestyle="--", linewidth=2, label="Brute force") + + ax.set_title(f"QoI Estimate with {n_points} Training Points") + ax.set_ylabel("Density") + ax.legend() + ax.grid(alpha=0.3) + +axes[-1].set_xlabel("Response") +plt.tight_layout() +plt.show() + +# %% [markdown] +# ## 5. Design of Experiments (DoE) +# +# In the earlier sections, we saw that adding more simulator training data improves our +# surrogate model and reduces the uncertainty of our **Quantity of Interest (QoI)** +# estimate. However, **where** we choose to evaluate the simulator matters just as much +# as **how many** points we evaluate. +# +# Design of Experiments (DoE) helps us choose simulator inputs intelligently, so that +# each new simulation gives us the **maximum possible reduction in QoI uncertainty**. +# +# In this section we compare: +# +# - a baseline approach (random Sobol sampling), and +# - a QoI-aware approach that uses Axtreme's `QoILookAhead` acquisition function to pick the most informative points. +# +# We will track the QoI after each iteration to see which strategy converges faster. +# +# ### 5.1 QoI tracking metric and stopping criteria +# +# To monitor the progress of the DoE, we attach a special metric — `QoIMetric` — to +# the Ax experiment. After each iteration: +# +# - The surrogate model is updated. +# - The QoI is re-estimated using the updated surrogate. +# - The QoI estimate (mean and standard error) is stored inside the experiment's data. +# +# This allows us to plot the QoI over time and optionally stop early when the QoI +# uncertainty becomes sufficiently small. +# +# We implement: +# +# 1. **QoI tracking metric**: Automatically computes and logs the QoI after each +# surrogate update +# 2. **Stopping criterion** (`sem_stopping_criteria`): Checks if the standard error of +# the QoI drops below a threshold (e.g., 0.02) +# 3. **Trial runner** (`run_trials`): A helper function that: +# - Runs warm-up trials using a space-filling design (e.g., Sobol) +# - Then runs DoE trials using a specified acquisition strategy +# - Checks the stopping criterion after each DoE iteration +# - Returns early if the QoI uncertainty is sufficiently small +# +# This approach avoids unnecessary simulator calls once the QoI estimate is sufficiently accurate. + +# %% [markdown] + +# %% +# Define QoI tracking metric +QOI_METRIC = QoIMetric( + name="QoIMetric", + qoi_estimator=qoi_estimator, + minimum_data_points=3, # don't compute QoI until some data exists + attach_transforms=True, +) + + +# Define stopping criteria based on standard error of the mean (SEM) +def sem_stopping_criteria(experiment: Experiment, sem_threshold: float = 0.02, metric_name: str = "QoIMetric") -> bool: + """Stop when the standard error of the QoI estimate drops below a threshold.""" + metrics = experiment.fetch_data() + df = metrics.df + qoi_rows = df[df["metric_name"] == metric_name] + + if len(qoi_rows) == 0: + # No QoI yet + return False + + latest = qoi_rows.iloc[-1] + sem = latest["sem"] + + # Stop if SEM is finite and sufficiently small + return (sem is not None) and (not np.isnan(sem)) and (sem <= sem_threshold) + + +# Define function to run trials with progress output +def run_trials( + experiment: Experiment, + warm_up_generator: Callable[[Experiment], GeneratorRun], + doe_generator: Callable[[Experiment], GeneratorRun], + warm_up_runs: int = 3, + doe_runs: int = 15, + stopping_criteria: Callable[[Experiment], bool] | None = None, +) -> int: + """Run warm-up + DoE trials with simple progress output.""" + + total_iters = warm_up_runs + doe_runs + print(f"Starting DoE run: {warm_up_runs} warm-up + {doe_runs} DoE iterations") + print("-" * 60) + + for i in range(total_iters): + phase = "Warm-up" if i < warm_up_runs else "DoE" + + # Select generator + gen_fn = warm_up_generator if i < warm_up_runs else doe_generator + gen = gen_fn(experiment) + + # Run trial + trial = experiment.new_trial(gen) + trial.run() + trial.mark_completed() + + # Print progress + print(f"[{i + 1}/{total_iters}] {phase} iteration {i + 1} completed") + + # Stopping criteria only valid during DoE + if (i >= warm_up_runs) and (stopping_criteria is not None) and stopping_criteria(experiment): + print(f"✓ Stopping criterion met after {i - warm_up_runs + 1} DoE iterations.") + print("-" * 60) + return i + 1 + + print("✓ DoE run completed.") + print("-" * 60) + return total_iters + + +# %% [markdown] +# ### 5.2 Baseline: Sobol-only DoE +# +# As a baseline, we perform DoE using only Sobol points: +# +# - Sobol sequences are space-filling and require no modelling assumptions. +# - They cover the full input domain evenly. +# - They do not focus sampling effort on regions that matter most for the QoI. +# +# The Sobol-only experiment therefore provides a fair reference point to compare +# against more advanced acquisition strategies. +# +# We: +# +# 1. Create a new Ax experiment with the same simulator, search space, and noise model as before. +# 2. Attach the QoI tracking metric. +# 3. Run: +# - a few warm-up Sobol points, followed by +# - many DoE Sobol points. +# 4. Record the number of iterations performed (possibly fewer if the stopping criterion triggers early). +# +# This gives us a curve describing how the QoI estimate improves when sampling is uniform and naïve. + +# %% +# Baseline: Sobol-only DoE +exp_sobol = make_exp() +exp_sobol.add_tracking_metric(QOI_METRIC) + +# Sobol generator (kept outside the loop so its internal state persists) +sobol = Models.SOBOL(search_space=exp_sobol.search_space, seed=5) + + +# Sobol generator function +def sobol_generator_run(_: Experiment) -> GeneratorRun: + return sobol.gen(1) + + +print("Running Sobol-only experiment...") +n_sobol_iters = run_trials( + experiment=exp_sobol, + warm_up_generator=sobol_generator_run, + doe_generator=sobol_generator_run, + warm_up_runs=3, + doe_runs=100, + stopping_criteria=sem_stopping_criteria, +) + +# %% [markdown] +# ### 5.3 QoI-aware DoE with `QoILookAhead` +# +# Randomly exploring the search space is wasteful when our goal is to reduce QoI +# uncertainty, not to approximate the simulator everywhere equally. +# +# To address this, Axtreme provides a custom acquisition function, `QoILookAhead`. +# +# This acquisition function: +# +# - Uses the current GP surrogate. +# - Anticipates how much one more simulator evaluation will reduce the QoI uncertainty. +# - Selects the input location where this reduction is expected to be largest. +# +# Conceptually, it prioritizes simulator points that are most influential in shaping +# the extreme response distribution. +# +# This is significantly more efficient than Sobol sampling, especially when the QoI +# depends heavily on only a subset of the input space. +# +# To run QoI-aware DoE: +# +# 1. For each DoE iteration, we build a BoTorch model bridge equipped with the +# `QoILookAhead` acquisition. +# 2. We use Ax's built-in acquisition optimizer (for example, Nelder-Mead for +# robustness in non-smooth cases). +# 3. The best acquisition point becomes the next simulator evaluation. +# 4. After each point, the QoI is re-estimated and logged by the tracking metric. +# +# This creates a feedback loop where the model learns faster in QoI-critical regions. + +# %% +# QoI-aware DoE with QoILookAhead acquisition + +# Choose the acquisition function +acquisition_function_class = QoILookAhead + + +# Define QoILookAhead DoE generator function +def look_ahead_generator_run(experiment: Experiment) -> GeneratorRun: + """Generate a new point by optimising the QoILookAhead acquisition.""" + + # First: build a model bridge to recover the Ax→BoTorch transforms + model_bridge_for_transforms = Models.BOTORCH_MODULAR( + experiment=experiment, + data=experiment.fetch_data(metrics=list(experiment.optimization_config.metrics.values())), + fit_tracking_metrics=False, + ) + + input_transform, outcome_transform = transforms.ax_to_botorch_transform_input_output( + transforms=list(model_bridge_for_transforms.transforms.values()), + outcome_names=model_bridge_for_transforms.outcomes, + ) + + # Feed the transforms into the QoI estimator so it interprets inputs/outputs correctly + qoi_estimator.input_transform = input_transform + qoi_estimator.outcome_transform = outcome_transform + + # Build a model bridge with a custom acquisition function + model_bridge_with_acq = Models.BOTORCH_MODULAR( + experiment=experiment, + data=experiment.fetch_data(), + botorch_acqf_class=acquisition_function_class, + fit_tracking_metrics=False, + acquisition_options={ + "qoi_estimator": qoi_estimator, + "sampler": sampling.MeanSampler(), + }, + ) + + # Optimise the acquisition to find the next candidate + return model_bridge_with_acq.gen( + 1, + model_gen_options={ + "optimizer_kwargs": { + "num_restarts": 20, + "raw_samples": 50, + "options": { + "with_grad": False, # QoILookAhead may not be smooth + "method": "Nelder-Mead", + "maxfev": 5, + }, + "retry_on_optimization_warning": False, + } + }, + ) + + +# Run the QoI-aware DoE +exp_look_ahead = make_exp() +exp_look_ahead.add_tracking_metric(QOI_METRIC) + +# Sobol generator for warm-up (kept outside the loop so its internal state persists) +sobol_for_warmup = Models.SOBOL(search_space=exp_look_ahead.search_space, seed=5) + + +# Sobol generator function for warm-up +def sobol_warmup_run(_: Experiment) -> GeneratorRun: + return sobol_for_warmup.gen(1) + + +print("Running QoI-aware look-ahead experiment...") +n_lookahead_iters = run_trials( + experiment=exp_look_ahead, + warm_up_generator=sobol_warmup_run, + doe_generator=look_ahead_generator_run, + warm_up_runs=3, + doe_runs=40, + stopping_criteria=sem_stopping_criteria, +) + +# %% [markdown] +# ### 5.4 Comparing Sobol and QoI-aware DoE +# +# Finally, we compare results from both strategies. +# +# Using Axtreme's `plot_qoi_estimates_from_experiment`, we visualize: +# +# - The QoI estimate at each iteration. +# - The uncertainty bands (SEM). +# - The brute-force QoI as a reference line. +# - Curves for both: +# - Sobol-only DoE, and +# - QoILookAhead-based DoE. +# +# This makes it easy to see: +# +# - How quickly each method reduces QoI uncertainty. +# - Whether either method shows bias. +# - How many simulator evaluations are required to reach a target confidence level. +# +# In typical problems, we expect: +# +# - Sobol: uncertainty decreases steadily but relatively slowly. +# - QoILookAhead: uncertainty drops much faster because it samples only where the QoI +# is sensitive. +# +# In real applications, this difference can translate to far fewer simulator evaluations +# for similar or better QoI accuracy. +# +# This completes the end-to-end Axtreme workflow: from defining a simulation problem, +# through surrogate modeling and QoI estimation, to intelligent experiment design. + +# %% +# 5.4 Compare QoI evolution + +ax = plot_qoi_estimates_from_experiment(exp_sobol, name="Sobol") +ax = plot_qoi_estimates_from_experiment( + exp_look_ahead, + ax=ax, + color="green", + name="QoI look-ahead", +) + +ax.axhline(float(brute_force_qoi_estimate), color="black", linestyle="--", label="Brute-force QoI") + +ax.set_xlabel("Number of DoE iterations") +ax.set_ylabel("QoI (median extreme response)") +ax.legend() +ax.grid(alpha=0.3) + +plt.show() + +# %% [markdown] +# ### 5.5 Spatial Distribution of Selected Points +# +# To understand **where** each DoE strategy chooses to evaluate the simulator, we +# visualize the selected points in the 2D input space overlaid on: +# +# 1. **Environment density**: Shows the distribution of environment samples +# (grey histogram) +# 2. **Extreme response density**: Shows where extreme responses originated during +# brute-force simulation (red histogram) +# 3. **Combined view**: Both densities overlaid to reveal the relationship +# +# Key insights: +# +# - **Sobol points (blue circles)**: Distributed uniformly across the entire input +# space, regardless of where extremes occur +# - **QoI-aware points (red triangles)**: Concentrated in regions that contribute most +# to extreme responses +# +# The point labels indicate the order in which they were selected during DoE. +# +# This visualization reveals why QoI-aware DoE is more efficient: it focuses simulator +# evaluations on the critical regions of the input space where the QoI is most +# sensitive. + + +# %% +def plot_2dtrials( + exp: Experiment, ax: plt.Axes | None = None, colour: str = "blue", marker: str = "o", label: str | None = None +) -> plt.Axes: + """Plot the points and number the datapoints added over DoE.""" + if ax is None: + _, ax = plt.subplots(figsize=(8, 6)) + + trials = [] + trial_indices = [] + + for trial_idx, trial in exp.trials.items(): + if trial.arm: + params = trial.arm.parameters.values() + trials.append(list(params)) + trial_indices.append(trial_idx) + + points = np.array(trials) + ax.scatter(points[:, 0], points[:, 1], alpha=0.8, s=50, label=label, c=colour, marker=marker) + + for i, (x, y) in enumerate(points): + ax.annotate(str(trial_indices[i]), (x, y), xytext=(2, 2), textcoords="offset points", fontsize=8, color=colour) + + return ax + + +# Create three-panel visualization +fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + +# Panel 1: Points vs Environment Density +axes[0].set_title("Points vs Environment Density") +axes[0].hist2d(env_data[:, 0], env_data[:, 1], bins=20, alpha=0.6, cmap="Greys", zorder=-1) + +# Panel 2: Points vs Extreme Response Density +axes[1].set_title("Points vs Extreme Response Density") +axes[1].hist2d(precalced_erd_x[:, 0], precalced_erd_x[:, 1], bins=40, alpha=0.6, cmap="Purples", zorder=-1) + +# Panel 3: Points vs Combined Density +axes[2].set_title("Points vs Env and Extreme Response Density") +axes[2].hist2d(env_data[:, 0], env_data[:, 1], bins=20, alpha=0.6, cmap="Greys", zorder=-1) +axes[2].hist2d(precalced_erd_x[:, 0], precalced_erd_x[:, 1], bins=40, alpha=0.5, cmap="Reds", zorder=-1) + +# Add DoE points to all panels +for ax in axes: + ax.set_xlabel("x1") + ax.set_ylabel("x2") + plot_2dtrials(exp_sobol, colour="blue", ax=ax, label="Sobol") + plot_2dtrials(exp_look_ahead, colour="red", ax=ax, label="QoI look-ahead", marker="^") + ax.legend() + ax.grid(visible=True, alpha=0.3) + +plt.tight_layout() +plt.show() + +# %% From 86b8f128d8a05d4f1839af355f60a32e3866e04b Mon Sep 17 00:00:00 2001 From: saraelme Date: Fri, 26 Dec 2025 10:06:02 +0100 Subject: [PATCH 3/4] Address PR review comments on getting_started tutorial --- tutorials/getting_started.py | 101 ++++++++++------------------- tutorials/img/axtreme_workflow.png | Bin 0 -> 1009770 bytes 2 files changed, 33 insertions(+), 68 deletions(-) create mode 100644 tutorials/img/axtreme_workflow.png diff --git a/tutorials/getting_started.py b/tutorials/getting_started.py index 56f62f1d..6d6f8bfb 100644 --- a/tutorials/getting_started.py +++ b/tutorials/getting_started.py @@ -1,16 +1,10 @@ # %% [markdown] # ## Getting started with Axtreme # -# In this notebook we walk through the core ideas behind **Axtreme** on a simple toy problem: -# -# - Step 1: Define the problem in terms of: -# - a **simulator** (the physics / system model), and -# - an **environment distribution** (how the inputs vary in the real world). -# - Step:2 Use **brute force** to compute a reference answer for our **Quantity of Interest** (QoI). -# - Step 3: Build a **surrogate model** of the simulator using **Ax + BoTorch**. -# - Step 4: Use that surrogate to **estimate the QoI much more cheaply**. -# - Step 5: Use **Design of Experiments (DoE)** to choose simulator points intelligently -# and reduce QoI uncertainty faster. +# This notebook introduces the key concepts of **Axtreme** using a simple toy problem. +# We demonstrate how to define a simulation problem, build a surrogate model, estimate +# a Quantity of Interest (QoI), and use Design of Experiments (DoE) to efficiently +# reduce uncertainty. # # The imports below set up: # @@ -70,46 +64,13 @@ # 3. **QoI Estimation**: Efficiently estimate the Quantity of Interest using the surrogate # 4. **Uncertainty Check**: Assess if the QoI uncertainty is acceptable # 5. **Design of Experiments**: Iteratively refine the surrogate by intelligently selecting new evaluation points -# -# ```mermaid -# flowchart TD -# A[1. Define Problem] --> A1[Simulator Function] -# A[1. Define Problem] --> A2[Environment Distribution] -# -# A1 --> B[2. Build Surrogate] -# A2 --> B -# B --> B1[Define Search Space] -# B1 --> B2[Generate Training Data] -# B2 --> B3[Evaluate Simulator] -# B3 --> B4[Fit GP Model] -# -# B4 --> C[3. Estimate QoI] -# C --> C1[Setup Env Samples] -# C1 --> C2[Choose QoI Estimator] -# C2 --> C3[Compute QoI] -# -# C3 --> D{4. Acceptable?} -# D -->|No| E[5. Design of Experiments] -# D -->|Yes| F[Final QoI Estimate] -# -# E --> E1[Select Acquisition] -# E1 -->|Space-filling| E2[Sobol/Random] -# E1 -->|QoI-aware| E3[QoILookAhead] -# -# E2 --> E4[Next Point] -# E3 --> E4 -# E4 --> E5[Evaluate] -# E5 --> E6[Update Surrogate] -# E6 --> C3 -# -# style A fill:#e1f5ff -# style B fill:#e8f5e9 -# style C fill:#f3e5f5 -# style D fill:#fff4e1 -# style E fill:#ffe0e0 -# style F fill:#c8e6c9 -# ``` -# + +# %% +from IPython.display import Image + +Image(filename="img/axtreme_workflow.png", width=800) + +# %% [markdown] # ### Tutorial Note: # This tutorial also computes a **brute-force reference** QoI using extensive simulation # runs. This is done purely for validation purposes to demonstrate that Axtreme converges @@ -215,7 +176,11 @@ # # ### 2.1 Extreme Response Distribution (ERD) # -# We imagine observing the environment over **time periods** of length `N_ENV_SAMPLES_PER_PERIOD`. +# A **period** represents a time window over which we want to find the extreme (maximum) response. +# For example, in offshore engineering this might represent a return period of 25 years, containing +# many individual sea states. The number of environment samples per period (`N_ENV_SAMPLES_PER_PERIOD`) +# determines how many conditions we observe within that time window. +# # For each period we: # # 1. Draw `N_ENV_SAMPLES_PER_PERIOD` inputs from the environment data @@ -244,7 +209,7 @@ # We compute: # # - A **brute-force estimate** of the ERD median using many simulator calls -# - An associated **uncertainty** for this estimate using Axtreme's `population_estimators` helpers +# - An associated **estimated uncertainty** using Axtreme's `population_estimators` helpers # # This brute-force QoI estimate will serve as: # @@ -320,7 +285,10 @@ # 2. **Noise distribution**: Specify the distribution family for the simulator noise # (Gumbel) # noqa: ERA001 # 3. **Simulations per point**: How many simulator runs to perform at each training point -# to estimate the distribution parameters (`loc` and `scale`) +# to estimate the distribution parameters (`loc` and `scale`). Higher values lead to less +# uncertainty in the GP fit, but increase computation time. Axtreme is designed to work well +# with few simulations per point, but higher values can be useful for debugging and testing +# purposes. # # These configuration choices affect: # @@ -361,11 +329,12 @@ # # **What the GP actually learns:** # -# The `make_experiment()` function wraps the simulator with logic that: +# The `make_experiment()` function is a helper that configures an Ax experiment with +# the appropriate settings. When trials are later added and run, the experiment will: # -# 1. Runs the simulator `N_SIMULATIONS_PER_POINT` times at each training point `x` -# 2. Fits the Gumbel distribution to those samples to estimate `loc(x)` and `scale(x)` -# 3. Provides these fitted parameters to the GP +# 1. Run the simulator `N_SIMULATIONS_PER_POINT` times at each training point `x` +# 2. Fit the Gumbel distribution to those samples to estimate `loc(x)` and `scale(x)` +# 3. Provide these fitted parameters to the GP # # So the GP learns **two separate mappings**: # - `x → loc`: How the location parameter varies across the input space @@ -412,7 +381,9 @@ def make_exp() -> Experiment: # The GP posterior captures uncertainty about the true `loc` and `scale` parameters at # this location. The grey lines show the range of plausible response distributions # consistent with the training data. When the surrogate has high confidence, these lines -# cluster tightly around the mean; when uncertain, they spread out.# %% +# cluster tightly around the mean; when uncertain, they spread out. + +# %% # Predict at x = [0.5, 0.5] test_x = {"x1": 0.5, "x2": 0.5} pred_mean, pred_covariance = botorch_model_bridge.predict([ObservationFeatures(parameters=test_x)]) @@ -501,14 +472,8 @@ def make_exp() -> Experiment: # %% [markdown] # ### 4.2 QoI estimator: MarginalCDFExtrapolation # -# We use Axtreme's `MarginalCDFExtrapolation` as a ready-made QoI estimator that: -# -# 1. Draws batches of environment inputs from the loader -# 2. Uses the surrogate model to obtain a distribution of responses at each input -# 3. Forms many **periods** of length `N_ENV_SAMPLES_PER_PERIOD` -# 4. Extracts the **maximum** response in each period -# 5. Estimates the **median** of the resulting extreme response distribution -# 6. Repeats under different draws from the GP posterior to capture model uncertainty +# We use Axtreme's `MarginalCDFExtrapolation` as a ready-made QoI estimator that efficiently +# computes the QoI from the surrogate model while propagating uncertainty from the GP posterior. # # The result is a **distribution over the QoI**, not just a single number. @@ -742,7 +707,7 @@ def run_trials( # - many DoE Sobol points. # 4. Record the number of iterations performed (possibly fewer if the stopping criterion triggers early). # -# This gives us a curve describing how the QoI estimate improves when sampling is uniform and naïve. +# This gives us a curve describing how the QoI estimate improves when sampling is uniform and naive. # %% # Baseline: Sobol-only DoE @@ -779,7 +744,7 @@ def sobol_generator_run(_: Experiment) -> GeneratorRun: # This acquisition function: # # - Uses the current GP surrogate. -# - Anticipates how much one more simulator evaluation will reduce the QoI uncertainty. +# - Anticipates how much adding a new training point to the surrogate will reduce the QoI uncertainty. # - Selects the input location where this reduction is expected to be largest. # # Conceptually, it prioritizes simulator points that are most influential in shaping diff --git a/tutorials/img/axtreme_workflow.png b/tutorials/img/axtreme_workflow.png new file mode 100644 index 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zh6L~bn?dSUz`o|k9lOoOCqgLy|JVn>fMTFEDO^LYn-;Lqn}BUwwYMIAJ^52WYf>WQ zk8e{~%#mRBCzh08U&%=joLtH2*j0r0{kC6ZCq%-E=fCXBv%??^vlJ7jl~?)^s2>tm zjAHN7`H=!3Nw)i~FaoQ&Uj$p8!0{N*YVIJgt%!VK0Hy%P4nJ&psqze~__hvUAGUwG z<8`oMR*M{PRtDy8MYdrgyGH#0XOVHg{?|M)@uV>^P@1egNbHj6w(J`}APT~z!@p`j z$_@awJ$qGxp6J@ne6YX%HAgoQO&|v1#LX(YS??S@V{HBBI3p5(_#bPDJZrlp1`uhY zxj!gWVP@}p5VW0SO-|my2`H^1fz9t7)@>e;3Xjq||KXA^-h7y`63|}m%S$dy*})9 z>R2=XM~j5G45eNfxVieZ7`(6=Vn;NGS9{qs5GyXFdV3!#Jz9D|XvW!0StR zZ!~T4?5Ab|Y~qvS&ymBK0*t!*&JhygMFYki>!M4VC zHA<%>jzd_ZeI&LC#y Date: Tue, 30 Dec 2025 17:01:14 +0100 Subject: [PATCH 4/4] Fix mypy and pyright type errors in getting_started.py --- tutorials/getting_started.py | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/tutorials/getting_started.py b/tutorials/getting_started.py index 6d6f8bfb..8faefa6d 100644 --- a/tutorials/getting_started.py +++ b/tutorials/getting_started.py @@ -24,7 +24,9 @@ import torch from ax import Experiment, SearchSpace from ax.core import GeneratorRun, ObservationFeatures, ParameterType, RangeParameter +from ax.core.trial import Trial from ax.modelbridge.registry import Models +from matplotlib.axes import Axes from scipy.stats import gumbel_r from torch.distributions import Normal from torch.utils.data import DataLoader @@ -417,17 +419,17 @@ def make_exp() -> Experiment: if i == 0: plt.plot( x_points, - sample_dist.pdf(x_points), + sample_dist.pdf(x_points), # type: ignore[union-attr] "grey", linewidth=1, alpha=0.5, label="GP Surrogate posterior samples", ) else: - plt.plot(x_points, sample_dist.pdf(x_points), "grey", linewidth=1, alpha=0.5) + plt.plot(x_points, sample_dist.pdf(x_points), "grey", linewidth=1, alpha=0.5) # type: ignore[union-attr] # Plot mean prediction on top -plt.plot(x_points, pred_dist_mean.pdf(x_points), "r-", linewidth=2, label="GP Surrogate (mean)") +plt.plot(x_points, pred_dist_mean.pdf(x_points), "r-", linewidth=2, label="GP Surrogate (mean)") # type: ignore[union-attr] plt.xlabel("Response value") plt.ylabel("Density") @@ -550,12 +552,15 @@ def make_exp() -> Experiment: for ax, estimate, n_points in zip(axes, results, n_training_points, strict=True): # Extract mean and variance from the estimate - mean = qoi_estimator.posterior_sampler.mean(torch.tensor(estimate), -1) - var = qoi_estimator.posterior_sampler.var(torch.tensor(estimate), -1) - qoi_dist = Normal(mean, var**0.5) + # Cast to UTSampler to access mean/var methods (not part of PosteriorSampler protocol) + ut_sampler = qoi_estimator.posterior_sampler + assert isinstance(ut_sampler, UTSampler) + qoi_mean = ut_sampler.mean(torch.tensor(estimate), -1) + qoi_var = ut_sampler.var(torch.tensor(estimate), -1) + qoi_dist = Normal(qoi_mean, qoi_var**0.5) # Plot QoI distribution - x_range = torch.linspace(float(mean - 3 * var**0.5), float(mean + 3 * var**0.5), 200) + x_range = torch.linspace(float(qoi_mean - 3 * qoi_var**0.5), float(qoi_mean + 3 * qoi_var**0.5), 200) ax.plot(x_range.numpy(), torch.exp(qoi_dist.log_prob(x_range)).numpy(), "b-", linewidth=2, label="QoI estimate") ax.fill_between(x_range.numpy(), 0, torch.exp(qoi_dist.log_prob(x_range)).numpy(), alpha=0.3) @@ -776,6 +781,7 @@ def look_ahead_generator_run(experiment: Experiment) -> GeneratorRun: """Generate a new point by optimising the QoILookAhead acquisition.""" # First: build a model bridge to recover the Ax→BoTorch transforms + assert experiment.optimization_config is not None model_bridge_for_transforms = Models.BOTORCH_MODULAR( experiment=experiment, data=experiment.fetch_data(metrics=list(experiment.optimization_config.metrics.values())), @@ -924,8 +930,8 @@ def sobol_warmup_run(_: Experiment) -> GeneratorRun: # %% def plot_2dtrials( - exp: Experiment, ax: plt.Axes | None = None, colour: str = "blue", marker: str = "o", label: str | None = None -) -> plt.Axes: + exp: Experiment, ax: Axes | None = None, colour: str = "blue", marker: str = "o", label: str | None = None +) -> Axes: """Plot the points and number the datapoints added over DoE.""" if ax is None: _, ax = plt.subplots(figsize=(8, 6)) @@ -934,7 +940,7 @@ def plot_2dtrials( trial_indices = [] for trial_idx, trial in exp.trials.items(): - if trial.arm: + if isinstance(trial, Trial) and trial.arm: params = trial.arm.parameters.values() trials.append(list(params)) trial_indices.append(trial_idx)