noisy_optimization_BayBE_extendedBounds.ipynb

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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97c15027-9275-43e5-9613-ecbfe31d4914",
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    {
     "name": "stderr",
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     "text": [
      "/home/brendenpelkie/miniconda3/envs/noisybo/lib/python3.12/site-packages/baybe/telemetry.py:222: UserWarning: WARNING: BayBE Telemetry endpoint https://public.telemetry.baybe.p.uptimize.merckgroup.com:4317 cannot be reached. Disabling telemetry. The exception encountered was: ConnectionError, HTTPConnectionPool(host='verkehrsnachrichten.merck.de', port=80): Max retries exceeded with url: / (Caused by NameResolutionError(\"<urllib3.connection.HTTPConnection object at 0x7f6d19881010>: Failed to resolve 'verkehrsnachrichten.merck.de' ([Errno -2] Name or service not known)\"))\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMOKE_TEST None\n",
      "SMOKE_TEST None\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('./src/baybe_utils')\n",
    "from run_grid_experiments_baybe import run_grid_experiments\n",
    "from run_grid_experiments_baybe_random import run_grid_experiments_random\n",
    "from src import visualization\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from src import schwefel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcc2d3b-c62b-422c-bdee-15eee6f1452c",
   "metadata": {},
   "source": [
    "## Intro\n",
    "\n",
    "This work lightly explores the impact of a noisy oracle on Bayesian optimization performance. Here, the 2-dimensional Schwefel function with Gaussian noise is used as an optimization objective. However, this work is motivated by the need to deal with noisy data when applying BO to experimental optimization. \n",
    "\n",
    "## Implementation Notes\n",
    "\n",
    "- This work was done as an entry in the 2024 Bayesian optimization for materials hackathon\n",
    "- Our team pursued multiple implementations in parallel\n",
    "- This notebook serves as an entry point to the BayBE implementation of the project\n",
    "- Individual optimization campaigns are run from the run_experiments() function in run_experiment_babye.py\n",
    "- Grid screening of parameters builds on this with funcitonality in the run_grid_experiments_babye.py\n",
    "- As this was a hackathon project, there are some hacks. Watch out for hard-coded gotchas throughout. Would not recommend directly re-using code. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f05875-7a5c-4f50-8b55-fc0e30340897",
   "metadata": {},
   "source": [
    "## 1. Define grid search parameters \n",
    "\n",
    "This is to run a grid search over experiment parameters like number of BO trials to run, noise level, etc. These values were chosen by the team as 'reasonable' sounding values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5319bdb1-b6a0-4dfa-b05c-bd854456278d",
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   "source": [
    "seeds = list(range(5)) # run 5 replicates of each parameter set\n",
    "n_inits = [2, 4, 8, 10] # Number of initial randomly collected data points\n",
    "noise_levels = [1, 5, 10, 20] # variance (?) of gaussian noise. Bigger number -> more noise\n",
    "noise_bools = [True] # carryover from BoTorch side of project, ignore\n",
    "budget = 30 # Run 30 iterations of BO\n",
    "bounds = (0, 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdf100a-84fe-466f-9b60-67d698e7578e",
   "metadata": {},
   "source": [
    "## 2. Run grid search\n",
    "\n",
    "Run the grid search over parameters. This will take a minute or 60. Results are written to disk so if you are just following along, skip this step and load below "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8ffedfe-8639-479f-9cee-63f4a0f32e54",
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     "text": [
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 1 budget 30 seed 0, time: 9.77s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 5 budget 30 seed 0, time: 20.17s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 10 budget 30 seed 0, time: 29.14s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 20 budget 30 seed 0, time: 37.97s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 1 budget 30 seed 0, time: 46.36s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 5 budget 30 seed 0, time: 53.80s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 10 budget 30 seed 0, time: 63.23s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 20 budget 30 seed 0, time: 73.00s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 1 budget 30 seed 0, time: 82.27s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 5 budget 30 seed 0, time: 92.22s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 10 budget 30 seed 0, time: 101.83s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 20 budget 30 seed 0, time: 111.08s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 1 budget 30 seed 0, time: 122.14s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 5 budget 30 seed 0, time: 133.72s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 10 budget 30 seed 0, time: 143.48s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 20 budget 30 seed 0, time: 154.43s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 1 budget 30 seed 1, time: 164.97s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 5 budget 30 seed 1, time: 173.89s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 10 budget 30 seed 1, time: 182.85s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 20 budget 30 seed 1, time: 192.51s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 1 budget 30 seed 1, time: 201.54s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 5 budget 30 seed 1, time: 212.40s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 10 budget 30 seed 1, time: 221.70s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 20 budget 30 seed 1, time: 230.91s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 1 budget 30 seed 1, time: 243.10s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 5 budget 30 seed 1, time: 255.56s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 10 budget 30 seed 1, time: 270.03s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 20 budget 30 seed 1, time: 280.28s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 1 budget 30 seed 1, time: 289.97s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 5 budget 30 seed 1, time: 299.25s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 10 budget 30 seed 1, time: 308.92s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 20 budget 30 seed 1, time: 319.49s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 1 budget 30 seed 2, time: 329.27s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 5 budget 30 seed 2, time: 338.76s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 10 budget 30 seed 2, time: 347.93s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 20 budget 30 seed 2, time: 357.74s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 1 budget 30 seed 2, time: 367.02s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 5 budget 30 seed 2, time: 376.51s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 10 budget 30 seed 2, time: 385.96s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 20 budget 30 seed 2, time: 397.90s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 1 budget 30 seed 2, time: 408.44s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 5 budget 30 seed 2, time: 419.22s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 10 budget 30 seed 2, time: 428.92s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 20 budget 30 seed 2, time: 439.04s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 1 budget 30 seed 2, time: 449.04s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 5 budget 30 seed 2, time: 460.35s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 10 budget 30 seed 2, time: 471.40s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 20 budget 30 seed 2, time: 482.81s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 1 budget 30 seed 3, time: 491.03s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 5 budget 30 seed 3, time: 504.92s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 10 budget 30 seed 3, time: 519.03s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 20 budget 30 seed 3, time: 533.70s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 1 budget 30 seed 3, time: 551.65s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 5 budget 30 seed 3, time: 566.76s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 10 budget 30 seed 3, time: 581.07s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 20 budget 30 seed 3, time: 595.80s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 1 budget 30 seed 3, time: 609.09s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 5 budget 30 seed 3, time: 624.71s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 10 budget 30 seed 3, time: 638.26s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 20 budget 30 seed 3, time: 656.84s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 1 budget 30 seed 3, time: 674.62s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 5 budget 30 seed 3, time: 691.10s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 10 budget 30 seed 3, time: 705.66s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 20 budget 30 seed 3, time: 717.10s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 1 budget 30 seed 4, time: 728.05s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 5 budget 30 seed 4, time: 739.18s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 10 budget 30 seed 4, time: 752.93s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 2 noise 20 budget 30 seed 4, time: 763.88s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 1 budget 30 seed 4, time: 777.25s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 5 budget 30 seed 4, time: 791.57s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 10 budget 30 seed 4, time: 804.90s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 4 noise 20 budget 30 seed 4, time: 816.67s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 1 budget 30 seed 4, time: 828.14s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 5 budget 30 seed 4, time: 841.69s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 10 budget 30 seed 4, time: 858.32s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 8 noise 20 budget 30 seed 4, time: 872.05s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 1 budget 30 seed 4, time: 887.12s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 5 budget 30 seed 4, time: 898.55s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 10 budget 30 seed 4, time: 908.63s\n",
      "Collecting initial observations\n",
      "Beginning optimization campaign\n",
      "Started problem 10 noise 20 budget 30 seed 4, time: 918.71s\n",
      "all experiments done, time: 918.71s\n"
     ]
    }
   ],
   "source": [
    "run_grid_experiments(seeds, n_inits, noise_levels, noise_bools, budget, bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16aeaf0f-14b6-4827-b94a-cb87cd2a4d7c",
   "metadata": {},
   "source": [
    "### Run random search as well\n",
    "\n",
    "Generate some random baseline data to compare against\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5aac4ea7-cb9f-48a4-a874-e4396d8b9e22",
   "metadata": {
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     "text": [
      "Collecting random observations observations\n",
      "Started problem 2 noise 1 budget 30 seed 0, time: 0.97s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 5 budget 30 seed 0, time: 1.90s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 10 budget 30 seed 0, time: 2.83s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 20 budget 30 seed 0, time: 3.75s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 1 budget 30 seed 0, time: 4.67s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 5 budget 30 seed 0, time: 5.60s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 10 budget 30 seed 0, time: 6.52s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 20 budget 30 seed 0, time: 7.44s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 1 budget 30 seed 0, time: 8.38s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 5 budget 30 seed 0, time: 9.32s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 10 budget 30 seed 0, time: 10.27s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 20 budget 30 seed 0, time: 11.24s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 1 budget 30 seed 0, time: 12.23s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 5 budget 30 seed 0, time: 13.18s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 10 budget 30 seed 0, time: 14.14s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 20 budget 30 seed 0, time: 15.09s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 1 budget 30 seed 1, time: 16.02s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 5 budget 30 seed 1, time: 16.94s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 10 budget 30 seed 1, time: 17.85s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 20 budget 30 seed 1, time: 18.77s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 1 budget 30 seed 1, time: 19.70s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 5 budget 30 seed 1, time: 20.62s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 10 budget 30 seed 1, time: 21.55s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 20 budget 30 seed 1, time: 22.48s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 1 budget 30 seed 1, time: 23.41s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 5 budget 30 seed 1, time: 24.34s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 10 budget 30 seed 1, time: 25.28s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 20 budget 30 seed 1, time: 26.21s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 1 budget 30 seed 1, time: 27.15s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 5 budget 30 seed 1, time: 28.10s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 10 budget 30 seed 1, time: 29.05s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 20 budget 30 seed 1, time: 29.99s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 1 budget 30 seed 2, time: 30.90s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 5 budget 30 seed 2, time: 31.82s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 10 budget 30 seed 2, time: 32.85s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 20 budget 30 seed 2, time: 33.78s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 1 budget 30 seed 2, time: 34.70s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 5 budget 30 seed 2, time: 35.63s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 10 budget 30 seed 2, time: 36.56s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 20 budget 30 seed 2, time: 37.48s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 1 budget 30 seed 2, time: 38.43s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 5 budget 30 seed 2, time: 39.37s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 10 budget 30 seed 2, time: 40.31s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 20 budget 30 seed 2, time: 41.25s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 1 budget 30 seed 2, time: 42.20s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 5 budget 30 seed 2, time: 43.15s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 10 budget 30 seed 2, time: 44.10s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 20 budget 30 seed 2, time: 45.05s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 1 budget 30 seed 3, time: 45.96s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 5 budget 30 seed 3, time: 46.89s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 10 budget 30 seed 3, time: 47.80s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 20 budget 30 seed 3, time: 48.73s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 1 budget 30 seed 3, time: 49.66s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 5 budget 30 seed 3, time: 50.58s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 10 budget 30 seed 3, time: 51.53s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 20 budget 30 seed 3, time: 52.47s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 1 budget 30 seed 3, time: 53.41s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 5 budget 30 seed 3, time: 54.36s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 10 budget 30 seed 3, time: 55.30s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 20 budget 30 seed 3, time: 56.25s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 1 budget 30 seed 3, time: 57.21s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 5 budget 30 seed 3, time: 58.15s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 10 budget 30 seed 3, time: 59.11s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 20 budget 30 seed 3, time: 60.05s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 1 budget 30 seed 4, time: 61.04s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 5 budget 30 seed 4, time: 61.97s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 10 budget 30 seed 4, time: 62.90s\n",
      "Collecting random observations observations\n",
      "Started problem 2 noise 20 budget 30 seed 4, time: 63.83s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 1 budget 30 seed 4, time: 64.75s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 5 budget 30 seed 4, time: 65.68s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 10 budget 30 seed 4, time: 66.60s\n",
      "Collecting random observations observations\n",
      "Started problem 4 noise 20 budget 30 seed 4, time: 67.53s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 1 budget 30 seed 4, time: 68.47s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 5 budget 30 seed 4, time: 69.41s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 10 budget 30 seed 4, time: 70.35s\n",
      "Collecting random observations observations\n",
      "Started problem 8 noise 20 budget 30 seed 4, time: 71.29s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 1 budget 30 seed 4, time: 72.23s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 5 budget 30 seed 4, time: 73.17s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 10 budget 30 seed 4, time: 74.25s\n",
      "Collecting random observations observations\n",
      "Started problem 10 noise 20 budget 30 seed 4, time: 75.20s\n",
      "all experiments done, time: 75.20s\n"
     ]
    }
   ],
   "source": [
    "run_grid_experiments_random(seeds, n_inits, noise_levels, noise_bools, budget, bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ac9617-f371-40b1-b1a1-5203d84b1a65",
   "metadata": {},
   "source": [
    "## 3. Process Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7509bc0b-63de-4329-a75f-a8acdf522165",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_min = torch.tensor([420,420])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ce441669-a569-468f-a482-fec91efef95f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22503/1164103272.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_init</th>\n",
       "      <th>noise_level</th>\n",
       "      <th>seed</th>\n",
       "      <th>noise_bool</th>\n",
       "      <th>best</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <td>219.849106</td>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>23.180445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.325259</td>\n",
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       "      <td>True</td>\n",
       "      <td>234.490707</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>0.764173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.684985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1.410233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>0.926888</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>2.214593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>16.486259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_init noise_level seed noise_bool        best\n",
       "0       2           1    0       True  219.849106\n",
       "0       2           1    1       True   23.180445\n",
       "0       2           1    2       True    0.325259\n",
       "0       2           1    3       True  234.490707\n",
       "0       2           1    4       True    0.764173\n",
       "..    ...         ...  ...        ...         ...\n",
       "0      10          20    0       True    0.684985\n",
       "0      10          20    1       True    1.410233\n",
       "0      10          20    2       True    0.926888\n",
       "0      10          20    3       True    2.214593\n",
       "0      10          20    4       True   16.486259\n",
       "\n",
       "[80 rows x 5 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load BO results \n",
    "sm_list_bo = {}\n",
    "sm_x_list_bo = {}\n",
    "bo_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n",
    "for noise_bool in noise_bools:\n",
    "    for n_init in n_inits:\n",
    "        for noise_level in noise_levels:\n",
    "            sm_agg = torch.zeros((len(seeds), n_init+budget))\n",
    "            for idx, seed in enumerate(seeds):\n",
    "                X, Y, Y_real, model = torch.load(f\"results_baybe_extendedBounds/results_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n",
    "                sliding_min = torch.zeros(Y.shape[0])\n",
    "                sliding_x_min = torch.zeros(Y.shape[0])\n",
    "                for i in range(Y_real.shape[0]):\n",
    "                    sliding_min[i] = Y_real[:i+1].min().item()\n",
    "                    min_x = X[Y_real[:i+1].argmin().item()]\n",
    "                    x_rmsd = np.sqrt((min_x[0].item() - global_min[0].item())** 2 + (min_x[1].item() -global_min[1].item())**2)\n",
    "                    sliding_x_min[i] = x_rmsd\n",
    "                \n",
    "                sm_agg[idx] = sliding_min\n",
    "                sm = pd.Series(sliding_min.numpy())\n",
    "                \n",
    "                bo_results = pd.concat([bo_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n",
    "                                                  \"best\": [sliding_min[-1].item()]})])\n",
    "                \n",
    "            sm_mean = sm_agg.mean(0)\n",
    "            sm_std = sm_agg.std(0)\n",
    "            sm_list_bo[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n",
    "            sm_x_list_bo[(n_init, noise_level, noise_bool)] = sliding_x_min\n",
    "bo_results  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "af01900d-a2bf-4c49-90a0-b3614a59c2fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22503/129970788.py:22: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_init</th>\n",
       "      <th>noise_level</th>\n",
       "      <th>seed</th>\n",
       "      <th>noise_bool</th>\n",
       "      <th>best</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>382.194916</td>\n",
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       "      <td>12.435197</td>\n",
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       "      <td>True</td>\n",
       "      <td>176.200272</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>382.194916</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
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       "      <td>12.435197</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>295.999420</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>2.214593</td>\n",
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       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>176.200272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_init noise_level seed noise_bool        best\n",
       "0       2           1    0       True  382.194916\n",
       "0       2           1    1       True   12.435197\n",
       "0       2           1    2       True  295.999420\n",
       "0       2           1    3       True    2.214593\n",
       "0       2           1    4       True  176.200272\n",
       "..    ...         ...  ...        ...         ...\n",
       "0      10          20    0       True  382.194916\n",
       "0      10          20    1       True   12.435197\n",
       "0      10          20    2       True  295.999420\n",
       "0      10          20    3       True    2.214593\n",
       "0      10          20    4       True  176.200272\n",
       "\n",
       "[80 rows x 5 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load random results \n",
    "sm_list_random = {}\n",
    "sm_x_list_random = {}\n",
    "random_results = pd.DataFrame(columns=[\"n_init\", \"noise_level\", \"seed\", \"noise_bool\", \"best\"])\n",
    "for noise_bool in noise_bools:\n",
    "    for n_init in n_inits:\n",
    "        for noise_level in noise_levels:\n",
    "            sm_agg = torch.zeros((len(seeds), n_init+budget))\n",
    "            for idx, seed in enumerate(seeds):\n",
    "                X, Y, Y_real, model = torch.load(f\"results_baybe_extendedBounds/results_random_baybe/Schwe_n_init_{n_init}_noiselvl_{noise_level}_budget_{budget}_seed_{seed}_noise_{noise_bool}.pt\")\n",
    "                #print(torch.min(Y_real))\n",
    "                sliding_min = torch.zeros(Y.shape[0])\n",
    "                for i in range(Y_real.shape[0]):\n",
    "                    sliding_min[i] = Y_real[:i+1].min().item()\n",
    "                    min_x = X[Y_real[:i+1].argmin().item()]\n",
    "                    x_rmsd = np.sqrt((min_x[0].item() - global_min[0].item())** 2 + (min_x[1].item() -global_min[1].item())**2)\n",
    "                    sliding_x_min[i] = x_rmsd\n",
    "                \n",
    "                sm_agg[idx] = sliding_min\n",
    "                sm = pd.Series(sliding_min.numpy())\n",
    "                \n",
    "                random_results = pd.concat([random_results, pd.DataFrame({\"n_init\": [n_init], \"noise_level\": [noise_level], \"seed\": [seed], \"noise_bool\": [noise_bool],\n",
    "                                                  \"best\": [sliding_min[-1].item()]})])\n",
    "\n",
    "                #print(sliding_min)\n",
    "\n",
    "            \n",
    "            sm_mean = sm_agg.mean(0)\n",
    "            sm_std = sm_agg.std(0)\n",
    "            sm_list_random[(n_init, noise_level, noise_bool)] = (sm_mean, sm_std)\n",
    "            sm_x_list_random[(n_init, noise_level, noise_bool)] = sliding_x_min\n",
    "random_results  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62c64e2-b44c-4cb2-8e3c-4955abb334de",
   "metadata": {},
   "source": [
    "Calculate 'performance matrix' to generate iterations vs noise heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f9d3ca6c-22f6-459e-9107-cfa6cc663673",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_matrix_bo = np.zeros((len(n_inits), len(noise_levels)))\n",
    "\n",
    "for i, init in enumerate(n_inits):\n",
    "    for j, noise in enumerate(noise_levels):\n",
    "        y_vals = torch.load(f'results_baybe_extendedBounds/results_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[2]\n",
    "        best_y = torch.min(y_vals)\n",
    "        performance_matrix_bo[i,j] = best_y\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cd2d4601-b8ee-4d96-8c54-9dad0285151c",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_matrix_random = np.zeros((len(n_inits), len(noise_levels)))\n",
    "\n",
    "for i, init in enumerate(n_inits):\n",
    "    for j, noise in enumerate(noise_levels):\n",
    "        y_vals = torch.load(f'results_baybe_extendedBounds/results_random_baybe/Schwe_n_init_{init}_noiselvl_{noise}_budget_30_seed_0_noise_True.pt')[2]\n",
    "        best_y = torch.min(y_vals)\n",
    "        performance_matrix_random[i,j] = best_y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba43cdf-7017-4853-a200-945af8e20e61",
   "metadata": {},
   "source": [
    "## 4. Plot\n",
    "\n",
    "### backtesting plots\n",
    "\n",
    "1. Fix n_init, compare noise level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8f2b0de9-e2b8-40a6-a910-a4945f06aaba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_init_val = 10\n",
    "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n",
    "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n",
    "\n",
    "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n",
    "plot_bo = bo_results[bo_results['seed'] == 0]\n",
    "plot_rand = random_results[random_results['seed'] == 0]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for idx, row in plot_bo.iterrows():\n",
    "    if row['n_init'] == n_init_val:\n",
    "        mean = sm_list_bo[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n",
    "        std = sm_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n",
    "        plt.plot(mean, label=f\"BO, noise_level={row['noise_level']}\")\n",
    "        #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n",
    "    \n",
    "for idx, row in plot_rand.iterrows():\n",
    "    if row['n_init'] == n_init_val:\n",
    "        mean = sm_list_random[(n_init_val, row['noise_level'], True)][0][n_init_val:]\n",
    "        std = sm_list_random[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n",
    "        plt.plot(mean, label=f\"Random Baseline\", linestyle=\"--\")\n",
    "        break\n",
    "        #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n",
    "\n",
    "# aaawaaay\n",
    "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n",
    "plt.title(f\"BayBE Optimization, {n_init_val} initial observations\")\n",
    "\n",
    "ax.set_xlabel('Campaign iteration')\n",
    "ax.set_ylabel('Schwefel function value')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e856df-7791-447b-8f6e-fc78cf1cd769",
   "metadata": {},
   "source": [
    "Note: The random baseline is a flatline because the random search finds the best point at the 10th iteration with our seeds, which is not shown here because it falls within the initial random data collection phase. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8989570b-6b02-4e6a-99c7-f7ccafb0bb7f",
   "metadata": {},
   "source": [
    "2. Fix noise value, compare initial data number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "409f03ea-c50e-461d-850d-f7723b031f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_level_val = 10\n",
    "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n",
    "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n",
    "\n",
    "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n",
    "plot_bo = bo_results[bo_results['seed'] == 0]\n",
    "plot_rand = random_results[random_results['seed'] == 0]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for idx, row in plot_bo.iterrows():\n",
    "    if row['noise_level'] == noise_level_val:\n",
    "        n_init_val = row['n_init']\n",
    "        mean = sm_list_bo[(row['n_init'], noise_level_val, True)][0][n_init_val:]\n",
    "        std = sm_list_bo[(row['n_init'], noise_level_val, True)][1][n_init_val:]\n",
    "        plt.plot(mean, label=f\"BO, n_init={row['n_init']}\")\n",
    "        #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n",
    "    \n",
    "for idx, row in plot_rand.iterrows():\n",
    "    if row['noise_level'] == noise_level_val:\n",
    "        n_init_val = row['n_init']\n",
    "        mean = sm_list_random[(row['n_init'], noise_level_val, True)][0][n_init_val:]\n",
    "        std = sm_list_random[(row['n_init'], noise_level_val, True)][1][n_init_val:]\n",
    "        plt.plot(mean, label=f\"Random Baseline, n_init={row['n_init']}\", linestyle=\"--\")\n",
    "        #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n",
    "        #break\n",
    "\n",
    "# aaawaaay\n",
    "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n",
    "plt.title(f\"BayBE Optimization, noise level {noise_level_val}\")\n",
    "\n",
    "ax.set_xlabel('Campaign iteration')\n",
    "ax.set_ylabel('Schwefel function value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3f1ea5-856d-47a3-9010-7a4d4ceb8b1f",
   "metadata": {},
   "source": [
    "### Heat map plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e90c4e71-e671-415d-950b-377a463730b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Bayesian Optimization')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_bo)\n",
    "\n",
    "ax.set_title('Bayesian Optimization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5d63c7cd-755c-4fd3-8506-a317bf5bdd43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "visualization.grid_search_heatmap(n_inits, noise_levels, performance_matrix_random)\n",
    "ax.set_title('Random')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ab425a-61a7-464d-812c-502b9ecd39d7",
   "metadata": {},
   "source": [
    "## 'X-distance' plot\n",
    "This plot looks at the distance from the true global minimum of the best point found so far in the optization. Unlike the 'best point found' backtesting plots above, the values do fluctuate up and down as a new 'best' local minimum that is found could be further from the global minimum. It looks like most BO schemes are settling into local minima that are not close to the global minimum, while high-noise BO and random baseline seem to have gotten lucky and found the global minima early. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "caba841d-9015-4097-a8f6-f046370d33d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distance of best point from global min\n",
    "\n",
    "n_init_val = 10\n",
    "#df_bo = bo_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n",
    "#df_rand = random_results.groupby([\"n_init\", \"noise_level\"]).agg({\"best\": [\"mean\", \"std\"]})\n",
    "\n",
    "# we already got the statistics from all seeds above, but only want to plot one example for each so just pick first seed \n",
    "plot_bo = bo_results[bo_results['seed'] == 0]\n",
    "plot_rand = random_results[random_results['seed'] == 0]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for idx, row in plot_bo.iterrows():\n",
    "    if row['n_init'] == n_init_val:\n",
    "        mean = sm_x_list_bo[(n_init_val, row['noise_level'], True)][n_init_val:]\n",
    "        #std = sm_x_list_bo[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n",
    "        plt.plot(mean, label=f\"BO, noise_level={row['noise_level']}\")\n",
    "        #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n",
    "    \n",
    "for idx, row in plot_rand.iterrows():\n",
    "    if row['n_init'] == n_init_val:\n",
    "        mean = sm_x_list_random[(n_init_val, row['noise_level'], True)][n_init_val:]\n",
    "        #std = sm_x_list_random[(n_init_val, row['noise_level'], True)][1][n_init_val:]\n",
    "        plt.plot(mean, label=f\"Random Baseline\", linestyle=\"--\")\n",
    "        break\n",
    "        #plt.fill_between(range(len(mean)), mean-std, mean+std, alpha=0.1)\n",
    "\n",
    "# aaawaaay\n",
    "plt.legend(loc=\"upper right\", bbox_to_anchor=(1.3, 1))\n",
    "plt.title(f\"BayBE Optimization, {n_init_val} initial observations\")\n",
    "\n",
    "ax.set_xlabel('Campaign iteration')\n",
    "ax.set_ylabel('RMSD of best found point from true min')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "873019f4-a6f1-4674-b2eb-714e6d5f172d",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}