EDL_2_4_Simulating_Evolution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDL_2_4_Simulating_Evolution.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNU/nEm7LdhudcyfD2uR2Ku",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "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_2_4_Simulating_Evolution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6oZdYFXMBjK"
      },
      "source": [
        "#@title INSTALLS\n",
        "!pip install livelossplot --quiet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fhzb2-auwAzl"
      },
      "source": [
        "import random\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "from livelossplot import PlotLosses"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ViAjsf6PuxFk"
      },
      "source": [
        "MUTATE_RNG = 5\n",
        "MUTATE_RATE = 10\n",
        "\n",
        "def clamp(num, min_value, max_value):\n",
        "   return max(min(num, max_value), min_value)\n",
        "\n",
        "def create_cell(parent1, parent2):\n",
        "  if parent1 is None or parent2 is None:\n",
        "    return dict(\n",
        "        fitness = 0,\n",
        "        a = random.randint(1, 100),\n",
        "        b = random.randint(1, 100),\n",
        "        c = random.randint(1, 100)\n",
        "      ) \n",
        "  else:    \n",
        "    return dict(\n",
        "        fitness = 0,\n",
        "        a = (parent1[\"a\"] + parent2[\"a\"])/2,\n",
        "        b = (parent1[\"b\"] + parent2[\"b\"])/2,\n",
        "        c = (parent1[\"c\"] + parent2[\"c\"])/2,\n",
        "      )                 \n",
        "\n",
        "def birth(offspring):\n",
        "  return [create_cell(None, None) for i in range(offspring)]\n",
        "\n",
        "def evaluate(cells):\n",
        "  for cell in cells:\n",
        "    cell[\"fitness\"] = 2 * cell[\"a\"] - cell[\"b\"] + cell[\"c\"]\n",
        "  return cells\n",
        "\n",
        "def mutate(cells):\n",
        "  for cell in cells:\n",
        "    if random.randint(1,100) < MUTATE_RATE:\n",
        "      cell[\"a\"] = clamp(\n",
        "          cell[\"a\"] + random.randint(-MUTATE_RNG, MUTATE_RNG), 1, 100)\n",
        "      cell[\"b\"] = clamp(\n",
        "          cell[\"b\"] + random.randint(-MUTATE_RNG, MUTATE_RNG), 1, 100)\n",
        "      cell[\"b\"] = clamp(\n",
        "          cell[\"b\"] + random.randint(-MUTATE_RNG, MUTATE_RNG), 1, 100)      \n",
        "  return cells\n",
        "\n",
        "def reproduce(cells): \n",
        "  parents = sorted(cells, key=lambda d: d['fitness'])[int(len(cells)/2):]    \n",
        "  children = [] \n",
        "  for i in range(len(parents)*2):\n",
        "    mates = random.sample(parents, 2) \n",
        "    children.append(create_cell(mates[0], mates[1]))\n",
        "  return mutate(children)\n",
        "\n",
        "def run_generation(cells):  \n",
        "  cells = evaluate(cells)\n",
        "  cells = reproduce(cells) \n",
        "  cells = evaluate(cells)\n",
        "  return cells"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "IL8S79ewzSHT",
        "outputId": "a706ba6d-54b8-4455-f078-1a7292449c0b"
      },
      "source": [
        "#@title Default title text\n",
        "generations = 25 #@param {type:\"slider\", min:1, max:25, step:1}\n",
        "initial_offspring = 10 #@param {type:\"slider\", min:10, max:1000, step:1}\n",
        "mutation_rate = 10 #@param {type:\"slider\", min:1, max:100, step:1}\n",
        "mutation_range = 20 #@param {type:\"slider\", min:1, max:100, step:1}\n",
        "\n",
        "MUTATE_RNG = mutation_range\n",
        "MUTATE_RATE = mutation_rate\n",
        "cells = birth(initial_offspring)\n",
        "\n",
        "groups = {'Generation': ['population','avg_fitness'], 'Attrributes' : [\"avg_a\", \"avg_b\", \"avg_c\"]}\n",
        "liveloss = PlotLosses(groups=groups)\n",
        "\n",
        "history = {}\n",
        "for i in range(generations):  \n",
        "  cells = run_generation(cells)  \n",
        "  history[\"population\"] = len(cells)   \n",
        "  history[\"avg_fitness\"] = sum([cell[\"fitness\"] for cell in cells])/(len(cells)+1)\n",
        "  history[\"avg_a\"] = sum([cell[\"a\"] for cell in cells])/(len(cells)+1) \n",
        "  history[\"avg_b\"] = sum([cell[\"b\"] for cell in cells])/(len(cells)+1) \n",
        "  history[\"avg_c\"] = sum([cell[\"c\"] for cell in cells])/(len(cells)+1) \n",
        "  liveloss.update(history)\n",
        "  liveloss.send() \n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attrributes\n",
            "\tavg_a            \t (min:   50.636, max:   87.041, cur:   87.041)\n",
            "\tavg_b            \t (min:   16.447, max:   40.864, cur:   17.215)\n",
            "\tavg_c            \t (min:   49.369, max:   53.182, cur:   49.631)\n",
            "Generation\n",
            "\tpopulation       \t (min:   10.000, max:   10.000, cur:   10.000)\n",
            "\tavg_fitness      \t (min:  114.000, max:  206.497, cur:  206.497)\n"
          ]
        }
      ]
    }
  ]
}