examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb8c4686",
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom imports\n",
    "import utils\n",
    "from zombihop import *\n",
    "\n",
    "# standard imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "import random\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8835354",
   "metadata": {},
   "source": [
    "# ZoMBI-Hop: \n",
    "## Working Example for Running Virtual Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9599225",
   "metadata": {},
   "source": [
    "### Load Example Dataset\n",
    "\n",
    "<div>\n",
    "<img src=\"./data/needle_manifold_R1.png\" align = \"center\" width=\"500\" />\n",
    "</div>\n",
    "\n",
    "#### Dataset: Negative Poisson's Ratio Materials Discovery (see [https://www.nature.com/articles/s41524-023-01048-x](https://www.nature.com/articles/s41524-023-01048-x))\n",
    "\n",
    "The virtual optimization mode uses a Random Forest Regression over a dataset of 146,000 materials from the Materials Project in place of real experimental measurements. This RF is withheld from the optimizer and is only used to draw experiments.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f052644a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Negative Poisson's Ratio data set for virtual optimization\n",
    "\n",
    "# === USER PARAMETERS ===#\n",
    "n_init = 20 # number of initial data points sampled to construct initialization set\n",
    "seed = 1 # random seed for model reproducibility, IMPORTANT: *change every time a new independent trial is run*\n",
    "# =======================#\n",
    "\n",
    "# Load full dataset to draw random initialization set\n",
    "chem_props = pd.read_pickle(os.getcwd()+'\\\\data\\\\Material_Properties_Negative_Poisson').reset_index(drop=True)\n",
    "X = chem_props.loc[:,['density','energy_per_atom','efermi','energy_above_hull','band_gap']]\n",
    "Y = chem_props[['homogeneous_poisson']]\n",
    "X_norm = utils.m_norm(X) # normalize X-coordinates\n",
    "\n",
    "# Load pseudo-experimental data: random forest regression of dataset\n",
    "poisson_model = joblib.load(os.getcwd()+'\\\\data\\\\poisson_RF_trained.pkl') # load the trained model\n",
    "poisson_model = poisson_model.predict # call the prediction function\n",
    "\n",
    "# Draw random initialization set of n_init points\n",
    "random.seed(seed) # initialize a random seed\n",
    "rand_init = random.sample(range(X_norm.index.values.min(), X_norm.index.values.max()), n_init)\n",
    "X_init = X_norm.iloc[rand_init,:]\n",
    "Y_init = pd.DataFrame(poisson_model(np.array(X_init)), columns = Y.columns.values) # grab init data from \"experimental\" RF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a2b534",
   "metadata": {},
   "source": [
    "### Run the ZoMBI-Hop Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07ef3bad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Initiating hop . . . (1/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n",
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚        ] 10 / 10 experiments completed . . .                                    \n",
      "    Activate ZoMBI . . . (2/4)\n",
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                        ] 8 / 10 experiments completed . . .                            \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (2/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚        ] 10 / 10 experiments completed . . .                                    \n",
      "    Activate ZoMBI . . . (2/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚        ] 10 / 10 experiments completed . . .                                    \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (3/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚        ] 10 / 10 experiments completed . . .                                    \n",
      "    Activate ZoMBI . . . (2/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                                                ] 5 / 10 experiments completed . . .                \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (4/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                                ] 7 / 10 experiments completed . . .                        \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (5/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                        ] 8 / 10 experiments completed . . .                            \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (6/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                ] 9 / 10 experiments completed . . .                                \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (7/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                                        ] 6 / 10 experiments completed . . .                    \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (8/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                                        ] 6 / 10 experiments completed . . .                    \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (9/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                                        ] 6 / 10 experiments completed . . .                    \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n",
      "\n",
      "Initiating hop . . . (10/10)\n",
      "\n",
      "    Activate ZoMBI . . . (1/4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚❚                                                ] 5 / 10 experiments completed . . .                \n",
      "Found a needle!\n",
      "\n",
      "Pruning memory and zooming out. Keep top k=5 points\n",
      "\n"
     ]
    }
   ],
   "source": [
    "optimizer = ZombiHop(\n",
    "                    seed = seed,                         # A random seed for model reproducibility, IMPORTANT: *change every time a new independent trial is run*\n",
    "                    X_init = X_init,                     # An (n, d) array of normalized [0,1] initialization coordinates, where n is the number of initial points and d is the number of dimensions\n",
    "                    Y_init = Y_init,                     # An (n, 1) array of values, corresponding to the X_init coordinates, where n is the number of initial points\n",
    "                    Y_experimental = poisson_model,      # A model of the form Y_experimental() that can take an (n, d) dimensional array of coordinates (X) to predict an output (Y) => supplements for experimental data\n",
    "                    Gammas = 10,                         # Number of hops to other needles\n",
    "                    alphas = 4,                          # Number of ZoMBI zoom-ins for each hop\n",
    "                    n_draws_per_activation = 10,         # Number of samples drawn for each zoom-in\n",
    "                    acquisition_type = LCB_ada,          # Acquisition function model to conduct sampling of new points\n",
    "                    tolerance = 0.03,                    # Error tolerance of GP prediction, used to end a ZoMBI zoom-in and move to the next needle\n",
    "                    penalty_width = 0.2,                 # Width of penalty region about needle => inhibits BO searching from areas surrounding previously found needles\n",
    "                    m = 5,                               # Top m-number of data points used to zoom in bounds\n",
    "                    k = 5,                               # Top k-number of data points to keep\n",
    "                    lower_bound = np.zeros(X.shape[1]),  # An (d,) array of lower bound coordinates for each dimension (d) to begin the search\n",
    "                    upper_bound = np.ones(X.shape[1]),   # An (d,) array of upper bound coordinates for each dimension (d) to begin the search\n",
    "                    resolution = 10,                     # Number for the resolution of the mesh search space, e.g., resolution=10\n",
    "                    sampler = None                       # Either None or a sampler that takes in X_ask (1,d), dimension_meshes (n,d), acquisitions (n,), and Y_experimental (model) \n",
    "                                                         # to generate X_tell (n,d) and Y_tell (n,)\n",
    "                    )\n",
    "\n",
    "X_all, Y_all, needle_locs, needles = optimizer.run_virtual(verbose = False, plot = True) # Run virtual optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c07ea42",
   "metadata": {},
   "source": [
    "### Plot Optimizer Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "169fbb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,4))\n",
    "for n in range(needles.shape[0]):\n",
    "    plt.axhline(needles.iloc[n,0], c='r', ls='-', lw=0.5, alpha=1, label='Discovered Needles')\n",
    "plt.scatter(Y_all.index, Y_all, c='k')\n",
    "plt.ylabel('Poission\\'s Ratio')\n",
    "plt.xlabel('Number of Experiments')\n",
    "plt.grid()\n",
    "plt.legend(['Discovered Needles'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7703d407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,4))\n",
    "for n in range(needles.shape[0]):\n",
    "    plt.axhline(needles.iloc[n,0], c='r', ls='-', lw=0.5, alpha=1, label='Discovered Needles')\n",
    "plt.plot(Y_all, c='k')\n",
    "plt.ylabel('Poission\\'s Ratio')\n",
    "plt.xlabel('Number of Experiments')\n",
    "plt.grid()\n",
    "plt.legend(['Discovered Needles'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4d18160",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}