EDL_12_5_Genetic_Plasticity.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDL_12_5_Genetic_Plasticity.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOhK7IoBXb+l1ZU92DW6aOz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/EvolutionaryDeepLearning/blob/main/EDL_12_5_Genetic_Plasticity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WvPKxQatmSn",
        "cellView": "form"
      },
      "source": [
        "#@title Scenario Imports\n",
        "import numpy as np\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import os\n",
        "import time\n",
        "import cv2\n",
        "import urllib.request\n",
        "from IPython.display import clear_output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "1ujS-2dLNZce"
      },
      "source": [
        "#@title Image Helpers\n",
        "def load_target_icon(icon_url, size):\n",
        "  icon_path = \"target_icon\"    \n",
        "  urllib.request.urlretrieve(icon_url,icon_path)\n",
        "  target = cv2.imread(icon_path, cv2.IMREAD_GRAYSCALE) \n",
        "  return cv2.resize(src=target, dsize=size, interpolation=cv2.INTER_AREA)\n",
        "\n",
        "def show_icon(icon_arr):    \n",
        "    plt.figure(figsize=(5,5))\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow(255-icon_arr, cmap='Greys')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "WdQqaeWJNtZD",
        "cellView": "form",
        "outputId": "c77b31d8-d520-4a33-bf0d-8954e4bb5a5f"
      },
      "source": [
        "#@title Select the Target Image/Icon { run: \"auto\" }\n",
        "target_icon = \"Mona Lisa\" #@param [\"Mona Lisa\", \"Wolf\", \"Trash\", \"Happy\"]\n",
        "size = 64 #@param {type:\"slider\", min:16, max:64, step:1}\n",
        "\n",
        "target_urls = { \n",
        "    \"Mona Lisa\" : \"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRZgVzl5ashVmS9cFLqcoAJT6SxCMxWlgbJOw&usqp=CAU\",\n",
        "    \"Wolf\" : 'https://www.clipartmax.com/png/middle/235-2350420_other-wolf-icon-16x16-images-loup-icon.png',\n",
        "    \"Trash\" : 'data:image/png;base64,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',\n",
        "    \"Happy\" : 'data:image/png;base64,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'  \n",
        "    }\n",
        "\n",
        "target_icon_url = target_urls[target_icon]\n",
        "target = load_target_icon(target_icon_url, (size, size))\n",
        "show_icon(target)\n",
        "print(target.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(64, 64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qADmbyzLtXTM",
        "outputId": "ff185faf-6028-4116-bce4-0a20722d05d4"
      },
      "source": [
        "#initial population\n",
        "population = 300\n",
        "genes = size*size\n",
        "generations = 10000\n",
        "\n",
        "pop = np.random.uniform(0,1, size=(population,genes))\n",
        "print(pop)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.54853855 0.36050232 0.91620458 ... 0.28044466 0.19880014 0.79889185]\n",
            " [0.72677849 0.23323529 0.0245627  ... 0.77331516 0.77028589 0.48616122]\n",
            " [0.18998844 0.59326996 0.65364363 ... 0.71253222 0.03061934 0.6689522 ]\n",
            " ...\n",
            " [0.46190685 0.51580344 0.55379753 ... 0.53566433 0.73421052 0.87314137]\n",
            " [0.45164026 0.62047723 0.70110461 ... 0.01015341 0.36619834 0.1834855 ]\n",
            " [0.54971437 0.33238306 0.72919939 ... 0.19878361 0.50419132 0.85250932]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gKEtXtDTmtK"
      },
      "source": [
        "def render(individual):\n",
        "  return np.reshape(individual, (-1, size))\n",
        "\n",
        "def fitness(individual):\n",
        "  \"\"\"Calculates Mean Square Error Fitness for a render\"\"\"   \n",
        "  fitness = (np.square(render(individual) - target)).mean(axis=None)\n",
        "  return 1/fitness"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "kMVtvpxaUbrI",
        "outputId": "3447a5c9-f769-4f3e-95bb-4fc65064770b"
      },
      "source": [
        "def evaluate(pop):\n",
        "  return np.array([fitness(ind) for ind in pop])\n",
        "\n",
        "plt.hist(evaluate(pop))\n",
        "show_icon(render(pop[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3tP3fEhtQ_Z",
        "outputId": "7f23d542-1097-494d-ff53-c199f85c0bde"
      },
      "source": [
        "def elite_selection(fitness):\n",
        "  return fitness.argsort()[:-2][::-1] \n",
        "\n",
        "parents = elite_selection(evaluate(pop))\n",
        "print(parents)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[136 227 184 126  15  89  83 235 246 123 202 101  33  39  49 216 100 163\n",
            " 177 241 128 111  64 263  58 230 280 143  84 289 195  32   3 223 288  71\n",
            " 293 265  77  59 204  60 115 218 182 298 122  56 264 124 272  14 181 192\n",
            "  74  48   1 119 256 125 274 170 129  36 113 214 152 211  18  55 131 257\n",
            "  35  40   5 282 213 197 151  95 279  21   9  76 240  96 180  19 196  24\n",
            " 108 249 273 166  37 106 297 248 237  41  65 171 148 225 221 168 291  66\n",
            " 146  26 243  91  61 169 259 189  57 141 160 285 103 296  68 135 209 277\n",
            "  53  90  16 200 299 114  73 239 242 215 271 164 137  38  78 286 144 105\n",
            " 287 236  46  99 175  54 198 226 173  23 205 174 121 276 107  80 231 116\n",
            " 210 109 199 251 281 153  63  69   6  22  50 186  86  44 252 140 208  30\n",
            " 222 104 228 120  85 234 260  88 295 283   4 255 220 172 139 250 207 134\n",
            " 132 110  42 138 284 158 292  62  79  98 133 193 162   8 203 102 149   7\n",
            " 278 261 176 194 217 290 190 147 244  75 269  70 154  87 267  11 254 270\n",
            "  27  82 247 275 238  51   0 262 294 229  17  72 127 212 142 117  43  94\n",
            " 268 159 233  34 112  25  52  28 165 118 150 191 245 183 187 253  12 155\n",
            " 224 266 206 201  81  10  97 188  31 219  47 157 161 232 258  67  92 178\n",
            " 167   2  45 156  29 179 145  13 130 185]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4QSmgaxu86U",
        "outputId": "b7d356f5-f9ed-4095-cda9-f55a588eb753"
      },
      "source": [
        "def crossover(parent1, parent2, crossover_rate):\n",
        "  # children are copies of parents by default\n",
        "  child1, child2 = parent1.copy(), parent2.copy()  \n",
        "  # check for recombination\n",
        "  if random.random() < crossover_rate:\n",
        "    # select crossover point that is not on the end of the string\n",
        "    pt = random.randint(1, len(parent1)-2)\n",
        "    # perform crossover    \n",
        "    child1 = np.concatenate((parent1[:pt], parent2[pt:]))\n",
        "    child2 = np.concatenate((parent2[:pt], parent1[pt:]))\n",
        "  return [child1, child2]\n",
        "\n",
        "crossover(parents[0],parents[1], .15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[136, 227]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmZi7iYJywBo",
        "outputId": "52c50135-18cf-48ab-f8da-cb1280d0f7db"
      },
      "source": [
        "def mutation(individual, mutation_rate):\n",
        "  for i in range(len(individual)):\n",
        "\t  # check for a mutation\n",
        "    if random.random() < mutation_rate:      \n",
        "      individual[i] = individual[i] + random.uniform(-.1,.1)\n",
        "  return individual\n",
        "\n",
        "mutation(pop[parents[0]], .5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.77836349, 0.45576452, 0.64377903, ..., 0.08807663, 0.57325956,\n",
              "       0.19620773])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plasticity(individual):\n",
        "  original = individual.copy()\n",
        "  f1 = fitness(individual)\n",
        "  indvidual = mutation(individual, 1.0)\n",
        "  f2 = fitness(individual)\n",
        "  if f1 < f2:\n",
        "    return individual\n",
        "  else:\n",
        "    return original\n",
        "\n",
        "plasticity(pop[parents[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nx-R6Ze1B_fg",
        "outputId": "d74852a9-e9a4-4c78-eb9e-1b2402bf51b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.78884338, 0.38132252, 0.65878433, ..., 0.07478428, 0.63420725,\n",
              "       0.19891541])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWdlxwFDz9H-"
      },
      "source": [
        "def simple_GA(pop, crossover_rate=.5, mutation_rate=.05):   \n",
        "  parents = elite_selection(evaluate(pop))\n",
        "  children = np.zeros((population,genes))  \n",
        "  for i in range(population):\n",
        "    offspring = crossover(pop[parents[0]],pop[parents[1]], crossover_rate)\n",
        "    children[i] = mutation(offspring[0],mutation_rate)  \n",
        "    children[i] = plasticity(children[i])\n",
        "  return children\n",
        "\n",
        "pop = simple_GA(pop)\n",
        "pop = simple_GA(pop)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "8palCtnsAhhH",
        "outputId": "9a0f5f38-d1a2-4d50-d166-e0705ba22b53"
      },
      "source": [
        "#initial population\n",
        "pop = np.random.uniform(0,1, size=(population,genes))\n",
        "render_gen = 100\n",
        "\n",
        "for i in range(generations):\n",
        "  pop = simple_GA(pop)\n",
        "  if i % render_gen == 0:  \n",
        "    clear_output()\n",
        "    fit = evaluate(pop) \n",
        "    parents = elite_selection(fit) \n",
        "    plt.hist(fit)\n",
        "    plt.show()\n",
        "    print(f\"Generation {i+1}\")\n",
        "    print(f\"    Max fitness {np.max(fit)}\")\n",
        "    print(f\"    Min fitness {np.min(fit)}\")\n",
        "    print(f\"    Mean fitness {np.mean(fit)}\")\n",
        "    print(f\"    Std fitness {np.std(fit)}\")\n",
        "    show_icon(render(pop[parents[0]]))\n",
        "    time.sleep(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generation 9901\n",
            "    Max fitness 7.331313160769754e-05\n",
            "    Min fitness 7.330826774340672e-05\n",
            "    Mean fitness 7.331030854373338e-05\n",
            "    Std fitness 9.706239340739723e-10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}