From 954fca33e99362057512af81311824a2f0b1d23d Mon Sep 17 00:00:00 2001 From: Eric Hans Lee Date: Fri, 7 Jul 2023 11:00:18 -0700 Subject: [PATCH] tutorial --- .../cost_aware_bayesian_optimization.ipynb | 651 ++++++++++++++++++ website/tutorials.json | 4 + 2 files changed, 655 insertions(+) create mode 100644 tutorials/cost_aware_bayesian_optimization.ipynb diff --git a/tutorials/cost_aware_bayesian_optimization.ipynb b/tutorials/cost_aware_bayesian_optimization.ipynb new file mode 100644 index 0000000000..5c4b6469b0 --- /dev/null +++ b/tutorials/cost_aware_bayesian_optimization.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "26281547", + "metadata": {}, + "source": [ + "# Cost-aware Bayesian Optimization\n", + "\n", + "This tutorial covers cost-aware Bayesian optimization, a situation in which the cost of evaluation is unknown but assumed to depend on the set or a subset of the optimization parameters. \n", + "\n", + "Note that cost-aware Bayesian optimization is a more general form of multifidelity Bayesian optimization: \n", + "* In multi-fidelity Bayesian optimization, the fidelity parameters are typically known ahead of time, an the relationship between cost and performance is typically known e.g., the highest fidelity parameters are the least noisy and the most costly. \n", + "* In cost-aware Bayesian optimization, we do not know a-priori which parameters dictate cost, nor do we make any assumptions about the relationship between cost and performance. \n", + "\n", + "Cost-aware Bayesian optimization is well suited to any problem for which the user suspects there to be a heterogenous cost of evaluation. It can also be used as a simpler alternative to multifidelity optimization, although we recommend a dedicated multifidelity algorithm for more experienced users. In this tutorial, the acquisition function we use for cost-aware Bayesian optimization is expected improvement per unit (EIpu), which has the following formula:\n", + "\n", + "$$\n", + "EIpu(x) = \\frac{EI(x)}{c(x)^\\alpha}\n", + "$$\n", + "\n", + "$c(x)$ is a cost model that predicts the evaluation cost and $\\alpha \\in [0, 1]$ is a decay factor that reduces or increases the cost model's effect to prevent cheap points from dominating the optimization routine. We recommend starting $\\alpha$ at 1 and decreasing it to 0 as the optimization budget is exhausted. \n", + "\n", + "[1]: [Lee, Eric Hans, et al. Cost-aware Bayesian Optimization. International Conference on Machine Learning, AutoML Workshop. 2020. ](https://arxiv.org/pdf/2003.10870.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "05735702", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import torch\n", + "import warnings\n", + "\n", + "from botorch.acquisition import AnalyticAcquisitionFunction, ExpectedImprovement\n", + "from botorch.acquisition.cost_aware import CostAwareUtility\n", + "from botorch.fit import fit_gpytorch_mll\n", + "from botorch.models import SingleTaskGP\n", + "from botorch.optim import optimize_acqf\n", + "from botorch.test_functions import Ackley\n", + "from botorch.utils import standardize\n", + "from botorch.utils.sampling import draw_sobol_samples\n", + "\n", + "from gpytorch.mlls import ExactMarginalLogLikelihood\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n", + "tkwargs = {\n", + " \"device\": device,\n", + " \"dtype\": torch.double,\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "id": "a6f3faf8", + "metadata": {}, + "source": [ + "# Cost Modeling\n", + "\n", + "The first thing we do in this tutorial is define a simple cost model, in which we make no assumptions other than a positive cost. We will use the mean of a GP for the cost model. To enforce positivity, we will model the log cost and then exponentiate when we perform predictions. Users can use more bespoke cost models should they have a better understanding of their problem. \n", + "\n", + "Having defined the cost model, we'll also generate some simple plots of a 1D synthetic problem for illustrative purposes, where the objective is the Ackley function and the cost is quadratic." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e4aca71c", + "metadata": {}, + "outputs": [], + "source": [ + "class CostModelGP(CostAwareUtility):\n", + " \"\"\"\n", + " A basic cost model that assumes the cost is positive.\n", + " It models the log cost to guarantee positive cost predictions.\n", + " \"\"\"\n", + "\n", + " def __init__(self, X, Y_cost):\n", + " super().__init__()\n", + " assert torch.all(Y_cost > 0)\n", + " self.X = X\n", + " self.Y_cost = Y_cost\n", + " log_costs = torch.log(self.Y_cost)\n", + " gp = SingleTaskGP(X, log_costs)\n", + " mll = ExactMarginalLogLikelihood(gp.likelihood, gp)\n", + " fit_gpytorch_mll(mll)\n", + " self.gp = gp\n", + "\n", + " def forward(self, X):\n", + " return torch.exp(self.gp(X).mean)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "12bcb738", + "metadata": {}, + "outputs": [], + "source": [ + "def synthetic_objective_with_cost(x):\n", + " dim = 1\n", + " f = Ackley(dim) # synthetic objective is the Ackley\n", + " fx = f(x).unsqueeze(1)\n", + " cx = 200 * (1.1 - x) ** 2 # synthetic cost is quadratric\n", + " return fx, cx\n", + "\n", + "\n", + "# Generate training data\n", + "dim = 1\n", + "num = 4\n", + "bounds = torch.tensor([[0] * dim, [1] * dim], **tkwargs)\n", + "train_X = draw_sobol_samples(bounds=bounds, n=num, q=1, seed=111).squeeze(1)\n", + "train_Y, cost_Y = synthetic_objective_with_cost(train_X)\n", + "\n", + "# Fit GP to data\n", + "train_Y = standardize(train_Y)\n", + "gp = SingleTaskGP(train_X, train_Y)\n", + "mll = ExactMarginalLogLikelihood(gp.likelihood, gp)\n", + "fit_gpytorch_mll(mll)\n", + "\n", + "# Fit cost model to data\n", + "cost_model_gp = CostModelGP(train_X, cost_Y)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a500755e", + "metadata": {}, + "source": [ + "# Plot the GP and the Cost Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "686cd4d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Cost of Evaluation')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "\n", + "# Plot GP\n", + "X_preds = torch.linspace(0, 1, 100, **tkwargs).unsqueeze(1)\n", + "Y_preds = gp.posterior(X_preds)\n", + "Y_mean = Y_preds.mean.squeeze().detach().numpy()\n", + "Y_var = Y_preds.variance.squeeze().detach().numpy()\n", + "axes[0].plot(X_preds, Y_preds.mean.detach().numpy(), \"r\")\n", + "axes[0].plot(train_X, train_Y, \"k^\")\n", + "axes[0].fill_between(\n", + " X_preds.numpy()[:, 0], Y_mean - Y_var, Y_mean + Y_var, color=\"m\", alpha=0.5\n", + ")\n", + "axes[0].set_title(\"Gaussian Process Model\")\n", + "axes[0].set_ylabel(\"Objective Value\")\n", + "\n", + "# Plot Cost Model\n", + "cost_preds = cost_model_gp.forward(X_preds)\n", + "axes[1].plot(X_preds, cost_preds.detach().numpy())\n", + "axes[1].plot(train_X, cost_Y, \"kv\")\n", + "axes[1].set_title(\"Cost Model\")\n", + "axes[1].set_ylabel(\"Cost of Evaluation\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ca3d020", + "metadata": {}, + "source": [ + "# Expected Improvement Per Unit\n", + "\n", + "Having defined the cost model, we can now define our EIpu acquisition function and plot it for different values of $\\alpha$. Note that when $\\alpha=0$, EIpu simply reduces to EI. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "73667f9d", + "metadata": {}, + "outputs": [], + "source": [ + "class ExpectedImprovementWithCost(AnalyticAcquisitionFunction):\n", + " \"\"\"\n", + " This is the acquisition function EI(x) / c(x) ^ alpha, where alpha is a decay\n", + " factor that reduces or increases the emphasis of the cost model c(x).\n", + " \"\"\"\n", + "\n", + " def __init__(self, model, fmax, cost_model, alpha=1):\n", + " super().__init__(model=model)\n", + " self.model = model\n", + " self.cost_model = cost_model\n", + " self.ei = ExpectedImprovement(model, fmax)\n", + " self.alpha = alpha\n", + "\n", + " def forward(self, X):\n", + " return self.ei(X) / torch.pow(self.cost_model(X)[:, 0], self.alpha)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4510d1e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", + "X_preds = torch.linspace(0, 1, 100, **tkwargs).unsqueeze(1)\n", + "X_batch = X_preds.unsqueeze(1)\n", + "\n", + "\n", + "def normalize_acquisition_values(values):\n", + " max_value = values.max().item()\n", + " min_value = values.min().item()\n", + " return (values - min_value) / (max_value - min_value)\n", + "\n", + "\n", + "# Compute EI\n", + "fmax = torch.max(train_Y)\n", + "ei = ExpectedImprovement(gp, fmax)\n", + "ei_values = normalize_acquisition_values(ei(X_batch))\n", + "\n", + "# Compute and plot EIpu vs EI\n", + "fig.suptitle(\"EIpu (green) vs EI (blue)\")\n", + "for i in range(3):\n", + " alpha = 1 - i / 2\n", + " eipu = ExpectedImprovementWithCost(\n", + " gp,\n", + " fmax,\n", + " cost_model_gp,\n", + " alpha=alpha,\n", + " )\n", + " eipu_values = normalize_acquisition_values(eipu(X_batch).squeeze())\n", + " axes[i].plot(X_preds, eipu_values.detach().numpy(), \"-g\", linewidth=3)\n", + " axes[i].plot(X_preds, ei_values.detach().numpy(), \"--b\", alpha=1, linewidth=3)\n", + " axes[i].set_title(f\"alpha={alpha}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b55bc51c", + "metadata": {}, + "source": [ + "# A Practial Problem\n", + "\n", + "To make things more interesting, let's look at the classic problem of least squares estimation:\n", + "\n", + "$$\n", + "\\text{arg} \\min_{x \\in \\mathbb{R}^d} \\| Ax - b \\|_2\n", + "$$\n", + "\n", + "$A$ is a matrix of size $n \\times d$ and $b$ is a vector of length $n$. Assuming that $n \\geq d$, the solution to this problem is unique and has the following closed form: $(A^T A) ^{-1} (A^T b)$. The problem with explicitly computing this solution is that it will have an $\\mathcal{O}(n^3)$ complexity due to the need to compute a Cholesky factorization of the matrix $A^T A$. \n", + "\n", + "\n", + "These difficulties in computing an explicit solution when $n$ is large lead us to a cost-aware twist on the least squares estimation. An alternative solution is to perform batched gradient descent by sampling rows of $A$. Because the batching introduces noise, we'll use Adam to perform the optimization. This introduces hyperparameters such as the learning rate, batch size, and the number of optimization iterations. These hyperparameters influence the cost immensely, as we'll see in a bit. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "875dd79a", + "metadata": {}, + "outputs": [], + "source": [ + "class NoisyLinearLeastSquares:\n", + " \"\"\"\n", + " The standard linear least squares problem min_x ||Ax - b||_2.\n", + " We compute the loss via batching that introduces noise.\n", + " \"\"\"\n", + "\n", + " def __init__(self, A, b, batch_size=50):\n", + " self.A = A\n", + " self.b = b\n", + " self.batch_size = min(batch_size, self.A.shape[0])\n", + "\n", + " def fit(self, lr=1, niters=100):\n", + " x = torch.zeros(A.shape[1], 1, requires_grad=True, **tkwargs)\n", + " optimizer = torch.optim.Adam([x], lr=lr)\n", + " batch_indices = torch.randperm(A.shape[1])[: self.batch_size]\n", + " for i in range(niters):\n", + " res = torch.matmul(self.A[batch_indices, :], x) - self.b[batch_indices]\n", + " loss = torch.norm(res)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " return x, loss\n" + ] + }, + { + "cell_type": "markdown", + "id": "baf37696", + "metadata": {}, + "source": [ + "# Cost Analysis\n", + "\n", + "Here, we examine the variation in runtime as we vary both the batch size and the number of Adam iterations. Perhaps unsurpsingly, the runtime varies significantly with these parameters. Though we expect the runtime to be stricly linear in both the batch size and the number of Adam iterations, we can see that in practice the graph is a little variance due to the nuances in which the computer executes the matrix operations." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "07ac8ad5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "n = 30000\n", + "d = 3000\n", + "A = torch.rand(n, d, **tkwargs)\n", + "b = torch.rand(n, 1, **tkwargs)\n", + "\n", + "# Timings varying batch size\n", + "batch_sizes = 100 * torch.arange(4, 10, device=device)\n", + "times_batch = []\n", + "for batch_size in batch_sizes:\n", + " model = NoisyLinearLeastSquares(A, b, batch_size=batch_size)\n", + " t_start = time.time()\n", + " model.fit(lr=0.1, niters=200)\n", + " times_batch.append(time.time() - t_start)\n", + "\n", + "axes[0].set_title(\"Time vs Batch Size\")\n", + "axes[0].set_xlabel(\"Batch Size\")\n", + "axes[0].set_ylabel(\"Runtime (s)\")\n", + "axes[0].plot(batch_sizes, times_batch, \"b\")\n", + "\n", + "# Timings varying number of Adam iterations\n", + "iter_count = 10 * torch.arange(1, 20, device=device)\n", + "times_iters = []\n", + "for niters in iter_count:\n", + " model = NoisyLinearLeastSquares(A, b)\n", + " t_start = time.time()\n", + " model.fit(lr=0.1, niters=niters)\n", + " times_iters.append(time.time() - t_start)\n", + "\n", + "axes[1].set_title(\"Time vs Iterations\")\n", + "axes[1].set_xlabel(\"Iteration Count\")\n", + "axes[1].set_ylabel(\"Runtime (s)\")\n", + "axes[1].plot(iter_count, times_iters, \"g\")\n", + "\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "419ea7eb", + "metadata": {}, + "source": [ + "# Full Optimization Loop\n", + "\n", + "Having defined our problem, let's now run a full optimization loop and see how EIpu does compared to EI. Let's tune three hyperparameters in our least squares estimator: the learning rate, the batch size, and the number of adam iterations. \n", + "\n", + "* $ \\textit{learning_rate} \\in [0.05, 1.0]$\n", + "* $ \\textit{batch_size} \\in [40, 1000] $ \n", + "* $\\textit{num_iters} \\in [10, 400]$. \n", + "\n", + "Previously, we mentioned that we can use bespoke cost models tailored to the specific problem to increase performance. Let's do this by replacing the generic GP cost model with a custom linear one. Note that we can only do this because we performed some cost analysis above and understand well the relationship between hyperparameters and cost. Our cost model will simply scale linearly with both the batch size and the number of iterations: \n", + "\n", + "$$Cost\\big(\\textit{learning_rate}, \\textit{batch_size}, \\textit{num_iters}\\big) \\propto \\textit{batch_size} \\times \\textit{num_iters} $$ " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ae38594f", + "metadata": {}, + "outputs": [], + "source": [ + "# Assume x0 is learning rate, x1 is batch_size, x2 is iterations\n", + "bounds = torch.tensor([[0.05, 40, 10], [1, 1000, 400]], **tkwargs)\n", + "\n", + "\n", + "def objective(x):\n", + " learning_rate = x[0]\n", + " batch_size = int(x[1])\n", + " num_iters = int(x[2])\n", + " model = NoisyLinearLeastSquares(A, b, batch_size=batch_size)\n", + " t_start = time.time()\n", + " x, loss = model.fit(lr=learning_rate, niters=num_iters)\n", + " cost = time.time() - t_start\n", + " return loss.item(), cost\n", + "\n", + "\n", + "# Simplified cost model based on analysis above\n", + "class LinearCostModel(CostAwareUtility):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " # Assume x1 is batch_size, x2 is iterations\n", + " def forward(self, X):\n", + " return X[:, :, 1] * X[:, :, 2]\n", + "\n", + "\n", + "def generate_initial_data(obj, bounds, num):\n", + " dim = bounds.shape[1]\n", + " train_x = draw_sobol_samples(bounds=bounds, n=num, q=1, seed=111).squeeze(1)\n", + " train_y = []\n", + " cost_y = []\n", + " for x in train_x:\n", + " y, c = obj(x)\n", + " train_y.append(y)\n", + " cost_y.append(c)\n", + " return (\n", + " train_x,\n", + " torch.tensor(train_y, **tkwargs).unsqueeze(-1),\n", + " torch.tensor(cost_y, **tkwargs).unsqueeze(-1),\n", + " )\n", + "\n", + "\n", + "# Generate initial data\n", + "budget = 25\n", + "num_initial = 5\n", + "init_X, init_Y, init_C = generate_initial_data(objective, bounds, num_initial)\n" + ] + }, + { + "cell_type": "markdown", + "id": "45e64dbd", + "metadata": {}, + "source": [ + "# Run Bayesian optimization with EIpu" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "488fdef7", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "train_X = init_X\n", + "train_Y = init_Y\n", + "cost_Y = init_C\n", + "\n", + "for i in range(budget):\n", + " alpha = (budget - i - 1) / (budget - 1)\n", + "\n", + " # Train GP\n", + " train_Y_flip = -1 * standardize(train_Y) # we want to minimize so we negate\n", + " gp = SingleTaskGP(train_X, train_Y_flip)\n", + " mll = ExactMarginalLogLikelihood(gp.likelihood, gp)\n", + " fit_gpytorch_mll(mll)\n", + "\n", + " # Train Cost Model\n", + " cost_model = LinearCostModel()\n", + " fmax = torch.max(train_Y_flip)\n", + " eipu = ExpectedImprovementWithCost(\n", + " gp,\n", + " fmax,\n", + " cost_model,\n", + " alpha=alpha,\n", + " )\n", + " new_x, acq_value = optimize_acqf(\n", + " eipu,\n", + " bounds=bounds,\n", + " q=1,\n", + " num_restarts=5,\n", + " raw_samples=20,\n", + " )\n", + "\n", + " # Get objective value and cost\n", + " new_y, cost_y = objective(new_x.squeeze())\n", + "\n", + " # update training points\n", + " train_X = torch.cat([train_X, new_x])\n", + " train_Y = torch.cat([train_Y, torch.tensor([new_y], **tkwargs).unsqueeze(1)])\n", + " cost_Y = torch.cat([cost_Y, torch.tensor([cost_y], **tkwargs).unsqueeze(1)])\n", + "\n", + "costs_eipu = cost_Y[:, 0]\n", + "results_ei_cost, _ = torch.cummin(train_Y, dim=0)\n", + "times_ei_cost = torch.cumsum(costs_eipu, dim=0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1a06bd79", + "metadata": {}, + "source": [ + "# Run Bayesian optimization with EI" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e3e6d5cd", + "metadata": {}, + "outputs": [], + "source": [ + "train_X = init_X\n", + "train_Y = init_Y\n", + "cost_Y = init_C\n", + "\n", + "for i in range(budget):\n", + " # Train GP\n", + " train_Y_flip = -1 * standardize(train_Y) # we want to minimize so we negate\n", + " gp = SingleTaskGP(train_X, train_Y_flip)\n", + " mll = ExactMarginalLogLikelihood(gp.likelihood, gp)\n", + " fit_gpytorch_mll(mll)\n", + "\n", + " # Train Cost Model\n", + " fmax = torch.max(train_Y_flip)\n", + " ei = ExpectedImprovement(gp, fmax)\n", + " new_x, acq_value = optimize_acqf(\n", + " ei,\n", + " bounds=bounds,\n", + " q=1,\n", + " num_restarts=5,\n", + " raw_samples=20,\n", + " )\n", + "\n", + " # Get objective value and cost\n", + " new_y, cost_y = objective(new_x.squeeze())\n", + "\n", + " # update training points\n", + " train_X = torch.cat([train_X, new_x])\n", + " train_Y = torch.cat([train_Y, torch.tensor([new_y], **tkwargs).unsqueeze(1)])\n", + " cost_Y = torch.cat([cost_Y, torch.tensor([cost_y], **tkwargs).unsqueeze(1)])\n", + "\n", + "costs_ei = cost_Y[:, 0]\n", + "results_ei, _ = torch.cummin(train_Y, dim=0)\n", + "times_ei = torch.cumsum(costs_ei, dim=0)\n" + ] + }, + { + "cell_type": "markdown", + "id": "6db897f5", + "metadata": {}, + "source": [ + "# Plotting Results\n", + "\n", + "Unlike the usual optimization progress plots, which measure performance by comparing loss to iterations, in the cost aware setting, we measure performance by comparing loss to cumulative training time. \n", + "\n", + "EIpu and EI take the same number of iterations, but we can see that EIpu takes less time to execute those iterations (and finds a better result). We've also plotted a histogram of the evaluation times on the right. We can see that because EI is not cost aware, it has a pretty even spread of evaluation costs, whereas EIpu evaluates many more cheap points. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a9f10a98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "axes[0].plot(times_ei_cost, results_ei_cost, \"--b\", marker=\"^\")\n", + "axes[0].plot(times_ei, results_ei, \"--r\", marker=\"v\", alpha=0.5)\n", + "axes[0].set_xlabel(\"Cumulative Training Time (s)\")\n", + "axes[0].set_ylabel(\"Loss\")\n", + "axes[0].set_title(\"Loss over time\")\n", + "axes[0].legend([\"EIpu\", \"EI\"])\n", + "\n", + "axes[1].hist(costs_eipu, bins=30, color=\"b\")\n", + "axes[1].hist(costs_ei, bins=30, color=\"r\", alpha=0.5)\n", + "axes[1].set_xlabel(\"Evaluation Time\")\n", + "axes[1].set_ylabel(\"Number of Evaluations\")\n", + "axes[1].set_title(\"Histogram of Evaluation Times\")\n", + "axes[1].legend([\"EIpu\", \"EI\"])\n", + "\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a0cef29", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/website/tutorials.json b/website/tutorials.json index 7f61e0b3a1..2ffa1f93a3 100644 --- a/website/tutorials.json +++ b/website/tutorials.json @@ -38,6 +38,10 @@ "id": "saasbo", "title": "High-dimensional Bayesian optimization with SAASBO" }, + { + "id": "cost_aware_bayesian_optimization", + "title": "Cost-aware Bayesian optimization" + }, { "id": "Multi_objective_multi_fidelity_BO", "title": "Multi-Objective-Multi-Fidelity optimization with MOMF"