Merge pull request #8 from gkwt/main

Example scripts added
aspuru-guzik-group · Nov 29, 2024 · 7121e61 · 7121e61
2 parents cecf394 + 07c6a3b
commit 7121e61
Show file tree

Hide file tree

Showing 9 changed files with 1,069 additions and 540 deletions.
diff --git a/docs/source/examples/notebooks/general.ipynb → docs/.todo/general.ipynb b/docs/source/examples/notebooks/general.ipynb → docs/.todo/general.ipynb
diff --git a/...xamples/notebooks/known_constraints.ipynb → docs/.todo/known_constraints.ipynb b/...xamples/notebooks/known_constraints.ipynb → docs/.todo/known_constraints.ipynb
diff --git a/...ce/examples/notebooks/meta_learning.ipynb → docs/.todo/meta_learning.ipynb b/...ce/examples/notebooks/meta_learning.ipynb → docs/.todo/meta_learning.ipynb
diff --git a/...source/examples/notebooks/molecular.ipynb → docs/.todo/molecular.ipynb b/...source/examples/notebooks/molecular.ipynb → docs/.todo/molecular.ipynb
diff --git a/docs/source/examples/notebooks/robust.ipynb → docs/.todo/robust.ipynb b/docs/source/examples/notebooks/robust.ipynb → docs/.todo/robust.ipynb
diff --git a/docs/source/examples/notebooks/multi_fidelity.ipynb b/docs/source/examples/notebooks/multi_fidelity.ipynb
@@ -10,12 +10,277 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Some experiments can be very expensive. These may be supplemented by simpler alternatives, or perhaps high-throughput calculations. This would give measurements of lower *fidelity*, and the planner can take advantage of these measurements to guide high fidelity optimization.\n",
+    "\n",
+    "This can also be used in a virtual screening setting. Expensive quantum chemistry calculations can be supplemented by faster semi-empirical methods. Another example could also be the virtual screening of compounds for drug activity, with high fidelity free-energy perturbation calcualtions being approximated by faster and lower fidelity docking calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/garyk/mambaforge/envs/atlas/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "from olympus.datasets import Dataset\n",
+    "from olympus.objects import (\n",
+    "\tParameterContinuous,\n",
+    "\tParameterDiscrete, \n",
+    "\tParameterCategorical, \n",
+    "\tParameterVector\n",
+    ")\n",
+    "from olympus.campaigns import ParameterSpace, Campaign\n",
+    "\n",
+    "from atlas.planners.multi_fidelity.planner import MultiFidelityPlanner      # specially designed planner for multi-fidelity optimization\n",
+    "\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this example, we will perform a screening of the bandgap of perovskites. There's two fidelities of measurements, one using GGA (*low*), and one use HSE06 (*high*). You can set the associated cost to each one, but we will consider queries to GGA calculations as 10 times cheaper than with HSE06."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COST_BUDGET = 50            # this time the budget is a cost\n",
+    "NUM_INIT_DESIGN = 10\n",
+    "NUM_CHEAP = 8               # this is the ratio of low:high measurements (ie. 8:1 low/high fidelity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we will create an additional fidelity parameter `s`, which can only be the permitted fidelities. The `MultiFidelityPlanner` will be allowed to vary this parameter, and perform optimization with an additional constrained *fidelity* parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = Dataset(kind='perovskites')\n",
+    "\n",
+    "# build parameter space\n",
+    "param_space = ParameterSpace()\n",
+    "\n",
+    "# fidelity param\n",
+    "param_space.add(ParameterDiscrete(name='s', options=[0.1, 1.0], low=0.1, high=1.0))\n",
+    "for param in dataset.param_space: # add perovskite component parameters ('organic', 'cation', and 'anion')\n",
+    "\tparam_space.add(param)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lower fidelity data calucated using GGA is available in the examples folder\n",
+    "# so we will load it here to create a new function for measurements\n",
+    "# fill in the ATLAS_PATH\n",
+    "ATLAS_PATH = '.'\n",
+    "LOOKUP = pickle.load(open(f'{ATLAS_PATH}/examples/multi_fidelity/perovskites/lookup/lookup_table.pkl', 'rb'))\n",
+    "\n",
+    "def measure(params, s):\n",
+    "\t# high-fidelity is hse06, low-fidelity is gga\n",
+    "\tif s == 1.0:\n",
+    "\t\tmeasurement = np.amin(\n",
+    "\t\t\tLOOKUP[params.organic.capitalize()][params.cation][params.anion]['bandgap_hse06']\n",
+    "\t\t)\n",
+    "\telif s == 0.1:\n",
+    "\t\tmeasurement = np.amin(\n",
+    "\t\t\tLOOKUP[params.organic.capitalize()][params.cation][params.anion]['bandgap_gga']\n",
+    "\t\t)\n",
+    "\treturn measurement\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #051923; text-decoration-color: #051923\">───────────────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;5;25;35m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #05a6fb; text-decoration-color: #05a6fb; font-weight: bold\">                                                                                                                   </span>\n",
+       "<span style=\"color: #05a6fb; text-decoration-color: #05a6fb; font-weight: bold\">                                                 Welcome to ATLAS!                                                 </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;38;2;5;166;251m                                                 \u001b[0m\u001b[1;38;2;5;166;251m                 \u001b[0m\u001b[1;38;2;5;166;251m                                                 \u001b[0m\n",
+       "\u001b[1;38;2;5;166;251m                                                 \u001b[0m\u001b[1;38;2;5;166;251mWelcome to ATLAS!\u001b[0m\u001b[1;38;2;5;166;251m                                                 \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #006494; text-decoration-color: #006494\">                                                Made with 💕 in 🇨🇦                                                 </span>\n",
+       "<span style=\"color: #006494; text-decoration-color: #006494\">                                                                                                                   </span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;0;100;148m                                                \u001b[0m\u001b[38;2;0;100;148mMade with 💕 in 🇨🇦\u001b[0m\u001b[38;2;0;100;148m                                                 \u001b[0m\n",
+       "\u001b[38;2;0;100;148m                                                \u001b[0m\u001b[38;2;0;100;148m                  \u001b[0m\u001b[38;2;0;100;148m                                                 \u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #051923; text-decoration-color: #051923\">───────────────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;5;25;35m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #05a6fb; text-decoration-color: #05a6fb\">───────────────────────────── Initial design phase ─────────────────────────────</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;5;166;251m───────────────────────────── Initial design phase ─────────────────────────────\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "campaign = Campaign()\n",
+    "campaign.set_param_space(param_space)\n",
+    "\n",
+    "planner = MultiFidelityPlanner(\n",
+    "    goal='minimize',\n",
+    "    init_design_strategy='random',\n",
+    "    num_init_design=NUM_INIT_DESIGN,\n",
+    "    use_descriptors=True,\n",
+    "    batch_size=1,\n",
+    "    acquisition_optimizer_kind='pymoo',     # this is required\n",
+    "    fidelity_params=0,                      # this dimension is the fidelity parameter (we use the first one)\n",
+    "    fidelities=[0.1, 1.],                   # these are the possible fidelities (GGA = 0.1, and HSE = 1.0)\n",
+    ")\n",
+    "\n",
+    "planner.set_param_space(param_space)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# accumulated cost, the budget is also cost\n",
+    "COST = 0.\n",
+    "\n",
+    "target_rec_measurements = []\n",
+    "iter_ = 0\n",
+    "while COST < COST_BUDGET:\n",
+    "    print(f'\\nITER : {iter_+1}\\tCOST : {COST}\\n')\n",
+    "\n",
+    "    # this is how much the corresponding measurement will cost\n",
+    "    if iter_ % NUM_CHEAP == 0:\n",
+    "        planner.set_ask_fidelity(1.0)\n",
+    "    else:\n",
+    "        planner.set_ask_fidelity(0.1)\n",
+    "\n",
+    "    samples = planner.recommend(campaign.observations)\n",
+    "    for sample in samples:\n",
+    "        measurement = measure(sample, sample.s)\n",
+    "        campaign.add_observation(sample, measurement)\n",
+    "\n",
+    "        print('SAMPLE : ', sample)\n",
+    "        print('MEASUREMENT : ', measurement)\n",
+    "\n",
+    "        iter_+=1\n",
+    "\n",
+    "    # do a check to see if model will find the optimal\n",
+    "    if campaign.num_obs > NUM_INIT_DESIGN:\n",
+    "        # make greedy recommendation on the target fidelity\n",
+    "        # use this to make a high-fidelity measurement\n",
+    "        rec_sample = planner.recommend_target_fidelity(batch_size=1)[0]\n",
+    "        rec_measurement = measure(rec_sample, rec_sample.s)\n",
+    "        print('REC SAMPLE : ', rec_sample)\n",
+    "        print('REC MEASUREMENT : ', rec_measurement)\n",
+    "\n",
+    "        target_rec_measurements.append(rec_measurement)\n",
+    "        # kill the run if we have found the lowest hse06 bandgap\n",
+    "        # on the most recent high-fidelity measurement\n",
+    "        if rec_measurement == min_hse06_bandgap:\n",
+    "            print('found the min hse06 bandgap!')\n",
+    "            break\n",
+    "    else:\n",
+    "        target_rec_measurements.append(measurement)\n",
+    "        if measurement == min_hse06_bandgap and samples[0].s == 1.:\n",
+    "            print('found the min hse06 bandgap!')\n",
+    "            break"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "atlas",
+   "language": "python",
+   "name": "atlas"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.20"
   },
   "orig_nbformat": 4
  },