{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "db85c74b",
   "metadata": {},
   "source": [
    "## Test computational scaling \n",
    "\n",
    "Here we give an example of how to measure the training computation time as a function of training iteration and number of dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c28f189",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-21T08:21:38.133751Z",
     "iopub.status.busy": "2026-02-21T08:21:38.133557Z",
     "iopub.status.idle": "2026-02-21T08:21:39.173499Z",
     "shell.execute_reply": "2026-02-21T08:21:39.169219Z"
    }
   },
   "outputs": [],
   "source": [
    "import alabi\n",
    "from alabi.core import SurrogateModel\n",
    "from alabi import benchmarks as bm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import partial\n",
    "from scipy.stats import multivariate_normal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46523e6c",
   "metadata": {},
   "source": [
    "Define common settings for all tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7bdb28fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-21T08:21:39.177192Z",
     "iopub.status.busy": "2026-02-21T08:21:39.176940Z",
     "iopub.status.idle": "2026-02-21T08:21:39.183870Z",
     "shell.execute_reply": "2026-02-21T08:21:39.181221Z"
    }
   },
   "outputs": [],
   "source": [
    "ncore = 1                       # we'll run one active learning chain on one core\n",
    "ntrain = 100                    # number of initial training samples\n",
    "savedir = \"results/scaling\"     # directory to save results\n",
    "\n",
    "gp_kwargs = {\"kernel\": \"ExpSquaredKernel\", \n",
    "             \"fit_amp\": True, \n",
    "             \"fit_mean\": True, \n",
    "             \"fit_white_noise\": False, \n",
    "             \"white_noise\": -12,\n",
    "             \"gp_opt_method\": \"l-bfgs-b\",\n",
    "             \"gp_scale_rng\": [-2,2],\n",
    "             \"optimizer_kwargs\": {\"max_iter\": 50, \"xatol\": 1e-3, \"fatol\": 1e-2, \"adaptive\": True}}\n",
    "\n",
    "al_kwargs = {\"algorithm\": \"bape\",\n",
    "             \"gp_opt_freq\": 20,\n",
    "             \"obj_opt_method\": \"nelder-mead\",\n",
    "             \"nopt\": 1,\n",
    "             \"optimizer_kwargs\": {\"max_iter\": 50, \"xatol\": 1e-3, \"fatol\": 1e-2, \"adaptive\": True}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07801a5",
   "metadata": {},
   "source": [
    "Define a general function that trains `alabi` on an N-dimensional Gaussian for `max_iter` iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3c90bc1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-21T08:21:39.187717Z",
     "iopub.status.busy": "2026-02-21T08:21:39.187585Z",
     "iopub.status.idle": "2026-02-21T08:21:39.215724Z",
     "shell.execute_reply": "2026-02-21T08:21:39.215227Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_alabi(ndim, max_iter=100, savedir=\"results/scaling\"):\n",
    "\n",
    "    path = f\"{savedir}/multivariate_normal_{ndim}d/\"\n",
    "\n",
    "    # Define the multivariate normal likelihood function\n",
    "    mean = np.zeros(ndim)\n",
    "    cov = bm.random_gaussian_covariance(ndim)\n",
    "    lnlike = partial(multivariate_normal.logpdf, mean=mean, cov=cov)\n",
    "\n",
    "    # Train surrogate model\n",
    "    sm = SurrogateModel(lnlike_fn=lnlike,\n",
    "                        bounds=[(-3., 3.) for _ in range(ndim)],\n",
    "                        savedir=path,\n",
    "                        cache=True,\n",
    "                        verbose=True,\n",
    "                        ncore=ncore)\n",
    "    \n",
    "    # save the true mean and covariance to a file\n",
    "    np.savez(f\"{path}gaussian_mean_cov.npz\", mean=mean, cov=cov)\n",
    "\n",
    "    # Initialize surrogate model \n",
    "    if ndim >= 40:\n",
    "        sm.init_samples(ntrain=ntrain, sampler=\"uniform\")\n",
    "    else:\n",
    "        sm.init_samples(ntrain=ntrain, sampler=\"sobol\")\n",
    "\n",
    "    sm.init_gp(**gp_kwargs)\n",
    "    sm.active_train(niter=max_iter, **al_kwargs)\n",
    "    sm.plot(plots=[\"gp_all\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d24630",
   "metadata": {},
   "source": [
    "For a quick test, we'll just run `alabi` for a 5-dimensional gaussian and measure the amount of computational overhead `alabi` takes to train the surrogate model. To test more dimensions add them to the `dimensions` array, or to test for more iterations increase `max_iter`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21367840",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-21T08:21:39.216949Z",
     "iopub.status.busy": "2026-02-21T08:21:39.216814Z",
     "iopub.status.idle": "2026-02-21T08:21:50.664918Z",
     "shell.execute_reply": "2026-02-21T08:21:50.664322Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized GP with squared exponential kernel.\n",
      "Successfully initialized GP on attempt 1\n",
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running 200 active learning iterations using bape...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/200 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  4%|▍         | 8/200 [00:00<00:02, 76.26it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 12%|█▎        | 25/200 [00:00<00:06, 26.48it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 16%|█▋        | 33/200 [00:00<00:04, 35.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 5.290769548379788e-16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 20%|██        | 40/200 [00:01<00:07, 21.80it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 24%|██▍       | 49/200 [00:01<00:05, 29.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 2.8140615805435077e-16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 29%|██▉       | 58/200 [00:01<00:03, 38.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 32%|███▎      | 65/200 [00:02<00:06, 22.41it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 36%|███▋      | 73/200 [00:02<00:04, 28.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 4.740593184211731e-16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|████      | 80/200 [00:03<00:06, 19.32it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 44%|████▎     | 87/200 [00:03<00:04, 24.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 1.5158058487886292e-15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 47%|████▋     | 94/200 [00:03<00:03, 29.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 100/200 [00:04<00:05, 18.06it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 54%|█████▍    | 108/200 [00:04<00:03, 24.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 5.561937160794517e-15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 57%|█████▊    | 115/200 [00:04<00:02, 29.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|██████    | 121/200 [00:04<00:04, 17.00it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 64%|██████▍   | 128/200 [00:05<00:03, 21.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 1.3563998408462552e-14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 68%|██████▊   | 135/200 [00:05<00:02, 27.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 70%|███████   | 141/200 [00:06<00:03, 15.12it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 74%|███████▎  | 147/200 [00:06<00:02, 18.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 3.814731814602212e-12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 76%|███████▋  | 153/200 [00:06<00:02, 23.37it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 80%|███████▉  | 159/200 [00:06<00:01, 27.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 82%|████████▏ | 164/200 [00:07<00:03, 11.96it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 84%|████████▍ | 168/200 [00:07<00:02, 14.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 2.661067977783123e-10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 86%|████████▋ | 173/200 [00:07<00:01, 17.36it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|████████▉ | 179/200 [00:07<00:00, 22.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 92%|█████████▏| 184/200 [00:09<00:01,  9.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 2.6148026975461192e-09\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 94%|█████████▍| 189/200 [00:09<00:00, 12.68it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 98%|█████████▊| 195/200 [00:09<00:00, 16.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimizing GP hyperparameters using 5-fold cross-validation...\n",
      "Evaluating 100 hyperparameter candidates using 5-fold CV...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|██████████| 200/200 [00:10<00:00,  9.13it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|██████████| 200/200 [00:10<00:00, 18.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train MSE: 2.0152473853508697e-07\n",
      "Caching model to results/scaling/multivariate_normal_5d/surrogate_model...\n",
      "Plotting the gp mean squared error with 0 test samples...\n",
      "Saving to results/scaling/multivariate_normal_5d//gp_mse_vs_iteration.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dimensions = [5]\n",
    "# dimensions = [10, 20, 30]\n",
    "\n",
    "# Test the scaling of ALABI with dimensionality \n",
    "for ndim in dimensions:\n",
    "    run_alabi(ndim, max_iter=200, savedir=savedir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42483c9",
   "metadata": {},
   "source": [
    "Get the recorded train time for each step of `alabi`'s training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1fb2c1f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-21T08:21:50.666700Z",
     "iopub.status.busy": "2026-02-21T08:21:50.666564Z",
     "iopub.status.idle": "2026-02-21T08:21:50.671547Z",
     "shell.execute_reply": "2026-02-21T08:21:50.670975Z"
    }
   },
   "outputs": [],
   "source": [
    "train_time = []\n",
    "opt_time = []\n",
    "test_mse = []\n",
    "hp_opt_time = []\n",
    "hp_opt_iter = []\n",
    "\n",
    "for ndim in dimensions[::-1]: # Reverse order for plotting\n",
    "    path = f\"{savedir}/multivariate_normal_{ndim}d/\"\n",
    "    sm = alabi.load_model_cache(path)\n",
    "    train_time.append(sm.training_results['gp_train_time'])\n",
    "    opt_time.append(sm.training_results['obj_fn_opt_time'])\n",
    "    test_mse.append(sm.training_results['test_mse'])\n",
    "    hp_opt_time.append(sm.training_results['gp_hyperparam_opt_time'])\n",
    "    hp_opt_iter.append(sm.training_results['gp_hyperparameter_opt_iteration'])\n",
    "iterations = sm.training_results['iteration']\n",
    "\n",
    "train_time = np.array(train_time)\n",
    "opt_time = np.array(opt_time)\n",
    "test_mse = np.array(test_mse)\n",
    "hp_opt_time = np.array(hp_opt_time)\n",
    "hp_opt_iter = np.array(hp_opt_iter)\n",
    "\n",
    "cum_time = np.sum(train_time, axis=1) + np.sum(opt_time, axis=1) + np.sum(hp_opt_time, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3bb58035",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-21T08:21:50.672737Z",
     "iopub.status.busy": "2026-02-21T08:21:50.672629Z",
     "iopub.status.idle": "2026-02-21T08:21:54.511957Z",
     "shell.execute_reply": "2026-02-21T08:21:54.511335Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logy = False\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 18), sharex=True)\n",
    "plt.subplots_adjust(hspace=0.05)\n",
    "\n",
    "# Top panel - Training time\n",
    "for ii, ndim in enumerate(dimensions):\n",
    "    ax1.plot(iterations, train_time[ii], label=f\"{dimensions[ii]} dimensions\", linewidth=1)\n",
    "ax1.set_ylabel(\"GP training time (s)\", fontsize=22)\n",
    "ax1.legend(loc=\"upper left\", fontsize=18)\n",
    "ax1.minorticks_on()\n",
    "\n",
    "# Middle panel - Optimization time\n",
    "for ii, ndim in enumerate(dimensions):\n",
    "    ax2.plot(iterations, opt_time[ii], linewidth=1)\n",
    "ax2.set_ylabel(\"BAPE optimization time (s)\", fontsize=22)\n",
    "ax2.minorticks_on()\n",
    "\n",
    "# # Bottom panel - Total time\n",
    "for ii, ndim in enumerate(dimensions):\n",
    "    ax3.plot(hp_opt_iter[ii], hp_opt_time[ii][1:], linewidth=1.5)\n",
    "ax3.set_xlabel(\"Number of active learning iterations\", fontsize=22)\n",
    "ax3.set_ylabel(\"Hyperparameter optimization time (s)\", fontsize=22)\n",
    "ax3.minorticks_on()\n",
    "\n",
    "if logy:\n",
    "    ax1.set_yscale(\"log\")\n",
    "    ax2.set_yscale(\"log\")\n",
    "    # ax3.set_yscale(\"log\")\n",
    "ax1.set_xlim(0, iterations[-1])\n",
    "\n",
    "plt.savefig(f\"{savedir}/multivariate_normal_training_time.png\", bbox_inches='tight', dpi=800)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25f0132e",
   "metadata": {},
   "source": [
    "Plot the cummulative training time as a bar chart for each dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "975eb5fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-21T08:21:54.513439Z",
     "iopub.status.busy": "2026-02-21T08:21:54.513301Z",
     "iopub.status.idle": "2026-02-21T08:21:54.952728Z",
     "shell.execute_reply": "2026-02-21T08:21:54.952010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(dimensions, cum_time, width=1, color='blue', alpha=0.7)\n",
    "plt.xlabel(\"Number of dimensions\", fontsize=22)\n",
    "plt.ylabel(\"Total training time (s)\", fontsize=22)\n",
    "plt.xticks(dimensions, fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.savefig(f\"{savedir}/multivariate_normal_total_training_time.png\", bbox_inches='tight', dpi=600)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py310",
   "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": "3.14.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}