Pareto_Bruteforce.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "747e58db",
   "metadata": {},
   "source": [
    "# Loads packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80d8907d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from scipy.spatial import ConvexHull\n",
    "from matplotlib.patches import Polygon\n",
    "import sys,os\n",
    "sys.path.append(os.path.realpath('./src/'))\n",
    "import pandas as pd\n",
    "from materialEncoder import MaterialEncoder\n",
    "import time\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb7b898",
   "metadata": {},
   "source": [
    "# Loads the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60593859",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessData():\n",
    "  df = pd.read_excel('./data/MaterialDatabase_FULL.xlsx')# # \n",
    "  dataIdentifier = {'name': df[df.columns[0]], 'className':df[df.columns[1]], 'classID':df[df.columns[2]]} # name of the material and type\n",
    "  trainInfo = torch.tensor((df[df.columns[3:]].to_numpy())).float()\n",
    "\n",
    "  return dataIdentifier, trainInfo, df\n",
    "dataIdentifier, trainInfo, df = preprocessData()\n",
    "\n",
    "props = df.columns[3:].tolist()\n",
    "nmat, nprop = trainInfo.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abacbe1",
   "metadata": {},
   "source": [
    "# Loads the truss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "152f0b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "from examples import getExample\n",
    "from trussFE import TrussFE\n",
    "\n",
    "#Loads the example truss\n",
    "exampleName, nodeXY, connectivity, bc = getExample(4)\n",
    "#Changes the force\n",
    "bc['forces'] = {'nodes':np.array([2]), 'fx':1.E3*torch.tensor([0.]), 'fy':1.E2*torch.tensor([125.])}\n",
    "\n",
    "\n",
    "#Loads the truss in truss\n",
    "truss = TrussFE(nodeXY, connectivity, bc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d76e8f",
   "metadata": {},
   "source": [
    "# Sets the constraints and objectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85991eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defines constraints and objectives\n",
    "constraints = {'massCons': {'isOn':False, 'maxMass':5}, \\\n",
    "               'costCons': {'isOn':False, 'maxCost':5},\\\n",
    "               'tensionCons': {'isOn':True, 'FOS':4.},\\\n",
    "               'complianceCons': {'isOn':False, 'maxJ':35},\\\n",
    "               'compressionCons': {'isOn':True, 'FOS':4.},\\\n",
    "               'co2Cons': {'isOn':False, 'maxco2':30},\\\n",
    "               'energyCons': {'isOn':False, 'maxenergy' : 1e10},\\\n",
    "               'waterCons': {'isOn':False, 'maxwater':4e3}}\n",
    "\n",
    "\n",
    "objectives = {'cost': {'isOn':True},\\\n",
    "              'mass': {'isOn':False}, \\\n",
    "              'co2': {'isOn':True},\\\n",
    "              'water': {'isOn':False},\\\n",
    "              'energy': {'isOn':False},\\\n",
    "              'compliance': {'isOn': False}}\n",
    "               \n",
    "# count the number of entries with 'isOn' set to True\n",
    "no = sum(1 for v in objectives.values() if v['isOn'])\n",
    "nc = sum(1 for v in constraints.values() if v['isOn'])\n",
    "\n",
    "# print an error message in red if there are 3 or more entries with 'isOn' set to True\n",
    "if no >= 3:\n",
    "    print('Error: Three or more entries have isOn=True')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbb78a4",
   "metadata": {},
   "source": [
    "# Defines the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82f9e266",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "from pymoo.operators.crossover.sbx import SBX\n",
    "from pymoo.operators.mutation.pm import PM\n",
    "from pymoo.operators.sampling.rnd import FloatRandomSampling\n",
    "from pymoo.termination import get_termination\n",
    "from pymoo.termination.default import DefaultMultiObjectiveTermination\n",
    "from pymoo.optimize import minimize\n",
    "from pymoo.core.problem import ElementwiseProblem\n",
    "\n",
    "\n",
    "\n",
    "#Problem of minimizing mass with the tensile and compressio constraints. We have 2 variables of the latent space constrained\n",
    "#to the limits in the map and 6 areas with limits.\n",
    "class MyProblem_fix(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self,truss, matprop, constraints, objectives, nc, no):\n",
    "        super().__init__(n_var=6,\n",
    "                         n_obj=no,\n",
    "                         n_ieq_constr=nc,\n",
    "                         xl=np.array([2e-6, 2e-6, 2e-6, 2e-6, 2e-6, 2e-6]),\n",
    "                         xu=np.array([2e-2, 2e-2, 2e-2, 2e-2, 2e-2, 2e-2]))\n",
    "\n",
    "        self.truss=truss\n",
    "        self.matprop=matprop\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives\n",
    "\n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "\n",
    "        x_tensor = torch.from_numpy(x)\n",
    "        area = x_tensor * torch.ones((connectivity.shape[0]))   \n",
    "        u, _, _, _, internalForce = truss.solveFE(E = torch.tensor([matprop[0]]).float(), A=area)\n",
    "        compliance = truss.computeCompliance(u)\n",
    "        volume = truss.getVolume(area)\n",
    "        mass = matprop[2]*volume\n",
    "        cost = mass*matprop[1]\n",
    "        co2 = mass*matprop[4]\n",
    "        energy = mass*matprop[5]        \n",
    "        water = mass*matprop[6] \n",
    "        \n",
    "        \n",
    "        con_values = []\n",
    "        \n",
    "        if(constraints['massCons']['isOn']):\n",
    "            constraints['massCons']['value'] = (mass/constraints['massCons']['maxMass']) - 1.\n",
    "            con_values.append(constraints['massCons']['value'])\n",
    "\n",
    "        if(constraints['costCons']['isOn']):\n",
    "            constraints['costCons']['value'] = (cost/constraints['costCons']['maxCost']) - 1.\n",
    "            con_values.append(constraints['costCons']['value'])\n",
    "\n",
    "        if(constraints['tensionCons']['isOn']):\n",
    "            tensileForce = 0.001+torch.relu(internalForce)\n",
    "            tensileStress = tensileForce/area\n",
    "            maxTensileStressExperienced = torch.pow(torch.sum(tensileStress**6), 1./6)*constraints['tensionCons']['FOS']\n",
    "            constraints['tensionCons']['value'] = (maxTensileStressExperienced/matprop[3]) - 1.\n",
    "            con_values.append(constraints['tensionCons']['value'])\n",
    "\n",
    "        if(constraints['complianceCons']['isOn']):\n",
    "            constraints['complianceCons']['value'] = (compliance/constraints['complianceCons']['maxJ']) - 1.\n",
    "            con_values.append(constraints['complianceCons']['value'])\n",
    "\n",
    "        if(constraints['compressionCons']['isOn']):\n",
    "            compressiveForce = 0.001+torch.relu(-internalForce)\n",
    "            k = 4. # used in geom factor... see buckling theory\n",
    "            geomFactor = k*(self.truss.barLength/(np.pi*area))**2\n",
    "            compressiveStress = compressiveForce*geomFactor\n",
    "            maxCompressiveStressExperienced = torch.pow(torch.sum(compressiveStress**6), 1./6)*constraints['compressionCons']['FOS']\n",
    "            constraints['compressionCons']['value'] = (maxCompressiveStressExperienced/matprop[0]) - 1.\n",
    "            con_values.append(constraints['compressionCons']['value'])\n",
    "\n",
    "        if(constraints['co2Cons']['isOn']):\n",
    "            constraints['co2Cons']['value'] = (co2/constraints['co2Cons']['maxco2']) - 1.\n",
    "            con_values.append(constraints['co2Cons']['value'])\n",
    "\n",
    "        if(constraints['energyCons']['isOn']):\n",
    "            constraints['energyCons']['value'] = (energy/constraints['energyCons']['maxenergy']) - 1.\n",
    "            con_values.append(constraints['energyCons']['value'])\n",
    "\n",
    "        if(constraints['waterCons']['isOn']):\n",
    "            constraints['waterCons']['value'] = (water/constraints['waterCons']['maxwater']) - 1.\n",
    "            con_values.append(constraints['waterCons']['value'])\n",
    "            \n",
    "        \n",
    "        \n",
    "        obj_values = []\n",
    "        \n",
    "        if(objectives['cost']['isOn']):\n",
    "            obj_values.append(cost)\n",
    "            \n",
    "        if(objectives['mass']['isOn']):\n",
    "            obj_values.append(mass)\n",
    "            \n",
    "        if(objectives['co2']['isOn']):\n",
    "            obj_values.append(co2)\n",
    "            \n",
    "        if(objectives['water']['isOn']):\n",
    "            obj_values.append(water)\n",
    "            \n",
    "        if(objectives['energy']['isOn']):\n",
    "            obj_values.append(energy)\n",
    "            \n",
    "        if(objectives['compliance']['isOn']):\n",
    "            obj_values.append(compliance)            \n",
    "  \n",
    "\n",
    "        out[\"F\"] = np.array(obj_values)\n",
    "        out[\"G\"] = np.array(con_values)\n",
    "\n",
    "\n",
    "\n",
    "algorithm_fix = NSGA2(\n",
    "    pop_size=50,\n",
    "    n_offsprings=20,\n",
    "    sampling=FloatRandomSampling(),\n",
    "    crossover=SBX(prob=0.9, eta=15),\n",
    "    mutation=PM(eta=20),\n",
    "    eliminate_duplicates=True\n",
    ")\n",
    "\n",
    "\n",
    "termination_fix = DefaultMultiObjectiveTermination(\n",
    "    xtol=1e-7,\n",
    "    cvtol=1e-6,\n",
    "    ftol=0.001,\n",
    "    period=10,\n",
    "    n_max_gen=1000,\n",
    "    n_max_evals=50000\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d5aa07",
   "metadata": {},
   "source": [
    "# Runs the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1533362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luisy\\Desktop\\CO2-Topology-Optimization-main\\Codes\\src\\trussFE.py:74: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\utils\\tensor_new.cpp:233.)\n",
      "  Kasm = torch.sparse_coo_tensor(self.nodeIdx, sK,\\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Solver time : 390.77\n"
     ]
    }
   ],
   "source": [
    "N=nmat\n",
    "results = np.empty((N, 2))\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "start = time.perf_counter()\n",
    "for i in range(N):\n",
    "\n",
    "    matlist=[i for i in range(N)]\n",
    "    matprop=trainInfo[i,:]\n",
    "    print(i)\n",
    "    res = minimize(MyProblem_fix(truss, matprop, constraints, objectives, nc, no),\n",
    "               algorithm_fix,\n",
    "               termination_fix,\n",
    "               seed=1,\n",
    "               save_history=False,\n",
    "               verbose=False)\n",
    "    results[i] = np.hstack(res.F)\n",
    "    matname = dataIdentifier['name'][i]\n",
    "    mattype = dataIdentifier['className'][i]\n",
    "    names.append(matname)\n",
    "    types.append(mattype)\n",
    "print('Solver time : {:.2F}'.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7420c5",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11e9bd2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat_colors = {}\n",
    "for i, type in enumerate(types):\n",
    "    mat_colors[type] = plt.cm.get_cmap('jet')(i / len(types))\n",
    "    \n",
    "#print(mat_colors)    \n",
    "# Create scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(len(results)):\n",
    "    x = results[i][0]\n",
    "    y = results[i][1]\n",
    "    label = names[i]\n",
    "    color = mat_colors.get(types[i], (0.8, 0.8, 0.8))\n",
    "    ax.scatter(x, y, color=color, label=types[i])\n",
    "    ax.text(x + 0.1, y + 0.1, label)\n",
    "    \n",
    "# Add legend and axis labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "unique_labels = list(set(labels))\n",
    "unique_handles = [handles[labels.index(label)] for label in unique_labels]\n",
    "ax.legend(unique_handles, unique_labels)\n",
    "\n",
    "\n",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy']\n",
    "enabled_entries = []\n",
    "\n",
    "for entry in entry_order:\n",
    "    if objectives[entry]['isOn']:\n",
    "        enabled_entries.append(entry)\n",
    "\n",
    "\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures/brute.png',bbox_inches='tight', dpi = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838d27b1",
   "metadata": {},
   "source": [
    "# Plot with zoom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b721734f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If you want to zoom a part of the plot\n",
    "xinf=0\n",
    "xsup=20\n",
    "yinf=0\n",
    "ysup=60\n",
    "\n",
    "mat_colors = {}\n",
    "for i, type in enumerate(types):\n",
    "    mat_colors[type] = plt.cm.get_cmap('jet')(i / len(types))\n",
    "    \n",
    "#print(mat_colors)    \n",
    "# Create scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "for i in range(len(results)):\n",
    "    x = results[i][0]\n",
    "    y = results[i][1]\n",
    "    label = names[i]\n",
    "    color = mat_colors.get(types[i], (0.8, 0.8, 0.8))\n",
    "    if (xinf<= x <= xsup) & (yinf <= y<= ysup):\n",
    "        ax.scatter(x, y, color=color, label=types[i])\n",
    "        ax.text(x + 0.1, y + 0.1, label)\n",
    "    \n",
    "# Add legend and axis labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "unique_labels = list(set(labels))\n",
    "unique_handles = [handles[labels.index(label)] for label in unique_labels]\n",
    "ax.legend(unique_handles, unique_labels)\n",
    "\n",
    "\n",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy']\n",
    "enabled_entries = []\n",
    "\n",
    "for entry in entry_order:\n",
    "    if objectives[entry]['isOn']:\n",
    "        enabled_entries.append(entry)\n",
    "\n",
    "\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures/brute_zoom.png',bbox_inches='tight', dpi = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ed23c1",
   "metadata": {},
   "source": [
    "# Plots Pareto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe5d742a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the Pareto front\n",
    "# We need to convert the cost and mass values to negative since we want to minimize both\n",
    "def is_pareto_efficient(costs):\n",
    "    is_efficient = np.ones(costs.shape[0], dtype=bool)\n",
    "    for i, c in enumerate(costs):\n",
    "        if is_efficient[i]:\n",
    "            is_efficient[is_efficient] = np.any(costs[is_efficient]>=c, axis=1) \n",
    "            is_efficient[i] = True  \n",
    "    return is_efficient\n",
    "\n",
    "pareto_front = results[is_pareto_efficient(-results)]\n",
    "\n",
    "\n",
    "\n",
    "# Create scatter plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot Pareto front\n",
    "pareto_front_results = [list(p) for p in pareto_front]\n",
    "pareto_front_names = [names[i] for i in np.where(is_pareto_efficient(-results))[0]]\n",
    "pareto_front_types = [types[i] for i in np.where(is_pareto_efficient(-results))[0]]\n",
    "\n",
    "# Create a dictionary to map types to colors\n",
    "mat_colors = {}\n",
    "for i, type in enumerate(set(pareto_front_types)):\n",
    "    mat_colors[type] = plt.cm.get_cmap('jet')(i / len(set(pareto_front_types)))\n",
    "    \n",
    "    \n",
    "for i in range(len(pareto_front_results)):\n",
    "    x = pareto_front_results[i][0]\n",
    "    y = pareto_front_results[i][1]\n",
    "    label = pareto_front_names[i]\n",
    "    color = mat_colors[pareto_front_types[i]]\n",
    "    ax.scatter(x, y, color=color, label=pareto_front_types[i])\n",
    "    ax.text(x + 0.1, y + 0.1, label)\n",
    "\n",
    "# Add legend and axis labels\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "unique_labels = list(set(labels))\n",
    "unique_handles = [handles[labels.index(label)] for label in unique_labels]\n",
    "ax.legend(unique_handles, unique_labels)\n",
    "\n",
    "# Show plot\n",
    "plt.title(\"Pareto Front\")\n",
    "ax.set_xlabel('Cost')\n",
    "ax.set_ylabel('Mass')\n",
    "plt.savefig('./figures/Pareto.png',bbox_inches='tight', dpi = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebb3d9b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}