EDL_10_5_NEAT_Speciation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDL_10_5_NEAT_Speciation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNRhuysrrLGaWDVIu+LnflU",
      "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_10_5_NEAT_Speciation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Install NEAT\n",
        "!pip install git+https://github.com/CodeReclaimers/neat-python.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Z3Ayt-wXGOD",
        "outputId": "eda730e7-c427-41c2-8fbb-54300707963a",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/CodeReclaimers/neat-python.git\n",
            "  Cloning https://github.com/CodeReclaimers/neat-python.git to /tmp/pip-req-build-aon7o_qd\n",
            "  Running command git clone -q https://github.com/CodeReclaimers/neat-python.git /tmp/pip-req-build-aon7o_qd\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gEpTXjwrHo-Q",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title Do the Imports\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import sklearn.datasets\n",
        "import sklearn.linear_model\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "import time\n",
        "\n",
        "import neat"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Dataset Parameters  { run: \"auto\" }\n",
        "number_samples = 15 #@param {type:\"slider\", min:10, max:1000, step:5}\n",
        "difficulty = 1 #@param {type:\"slider\", min:1, max:5, step:1}\n",
        "problem = \"circles\" #@param [\"classification\", \"blobs\", \"gaussian quantiles\", \"moons\", \"circles\"]\n",
        "number_features = 2\n",
        "number_classes = 2 \n",
        "\n",
        "def load_data(problem):  \n",
        "  if problem == \"classification\":\n",
        "    clusters = 1 if difficulty < 3 else 2\n",
        "    informs = 1 if difficulty < 4 else 2\n",
        "    data = sklearn.datasets.make_classification(\n",
        "        n_samples = number_samples,\n",
        "        n_features=number_features, \n",
        "        n_redundant=0, \n",
        "        class_sep=1/difficulty,\n",
        "        n_informative=informs, \n",
        "        n_clusters_per_class=clusters)\n",
        "    \n",
        "  if problem == \"blobs\":\n",
        "    data = sklearn.datasets.make_blobs(\n",
        "        n_samples = number_samples,\n",
        "        n_features=number_features, \n",
        "        centers=number_classes,\n",
        "        cluster_std = difficulty)\n",
        "  \n",
        "  if problem == \"gaussian quantiles\":\n",
        "    data = sklearn.datasets.make_gaussian_quantiles(mean=None, \n",
        "                                                  cov=difficulty,\n",
        "                                                  n_samples=number_samples,\n",
        "                                                  n_features=number_features,\n",
        "                                                  n_classes=number_classes,\n",
        "                                                  shuffle=True,\n",
        "                                                  random_state=None)\n",
        "    \n",
        "  if problem == \"moons\":\n",
        "    data = sklearn.datasets.make_moons(\n",
        "        n_samples = number_samples)\n",
        "    \n",
        "  if problem == \"circles\":\n",
        "    data = sklearn.datasets.make_circles(\n",
        "        n_samples = number_samples)\n",
        "    \n",
        "  return data\n",
        "\n",
        "data = load_data(problem)\n",
        "X, Y = data\n",
        "\n",
        "\n",
        "# Input Data\n",
        "plt.figure(\"Input Data\")\n",
        "plt.scatter(X[:, 0], X[:, 1], c=Y, s=40, cmap=plt.cm.Spectral)"
      ],
      "metadata": {
        "id": "6S1csdi_Xdhb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "b1aad074-5643-40a5-fe2c-16e8e913cbe7",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff81a4c8550>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile config\n",
        "[NEAT]\n",
        "fitness_criterion     = max\n",
        "fitness_threshold     = .9\n",
        "pop_size              = 100\n",
        "reset_on_extinction   = 1\n",
        "\n",
        "[DefaultGenome]\n",
        "num_inputs              = 2\n",
        "num_hidden              = 25\n",
        "num_outputs             = 1\n",
        "initial_connection      = partial_direct 0.5\n",
        "feed_forward            = True\n",
        "compatibility_disjoint_coefficient    = 1.0\n",
        "compatibility_weight_coefficient      = 0.6\n",
        "conn_add_prob           = 0.02\n",
        "conn_delete_prob        = 0.02\n",
        "node_add_prob           = 0.02\n",
        "node_delete_prob        = 0.02\n",
        "activation_default      = sigmoid\n",
        "activation_options      = sigmoid\n",
        "activation_mutate_rate  = 0.0\n",
        "aggregation_default     = sum\n",
        "aggregation_options     = sum\n",
        "aggregation_mutate_rate = 0.0\n",
        "bias_init_mean          = 0.0\n",
        "bias_init_stdev         = 1.0\n",
        "bias_replace_rate       = 0.1\n",
        "bias_mutate_rate        = 0.7\n",
        "bias_mutate_power       = 0.5\n",
        "bias_max_value          = 30.0\n",
        "bias_min_value          = -30.0\n",
        "response_init_mean      = 1.0\n",
        "response_init_stdev     = 0.0\n",
        "response_replace_rate   = 0.0\n",
        "response_mutate_rate    = 0.0\n",
        "response_mutate_power   = 0.0\n",
        "response_max_value      = 30.0\n",
        "response_min_value      = -30.0\n",
        "\n",
        "weight_max_value        = 30\n",
        "weight_min_value        = -30\n",
        "weight_init_mean        = 0.0\n",
        "weight_init_stdev       = 1.0\n",
        "weight_mutate_rate      = 0.08\n",
        "weight_replace_rate     = 0.01\n",
        "weight_mutate_power     = 0.1\n",
        "enabled_default         = True\n",
        "enabled_mutate_rate     = 0.01\n",
        "\n",
        "[DefaultSpeciesSet]\n",
        "compatibility_threshold = 1.0\n",
        "\n",
        "[DefaultStagnation]\n",
        "species_fitness_func = max\n",
        "max_stagnation  = 25\n",
        "\n",
        "[DefaultReproduction]\n",
        "elitism            = 2\n",
        "survival_threshold = 0.2"
      ],
      "metadata": {
        "id": "hd9WgGFyX5aZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89e033ff-9fbe-4ae1-b4d0-a941ebb3f2d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting config\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Configuration.\n",
        "config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n",
        "                     neat.DefaultSpeciesSet, neat.DefaultStagnation,\n",
        "                     'config')\n",
        "\n",
        "print(config.genome_type, config.genome_config,config.pop_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWw1ctXr5FPM",
        "outputId": "6172c147-6260-4c5c-88d6-c8aad3272e61",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'neat.genome.DefaultGenome'> <neat.genome.DefaultGenomeConfig object at 0x7ff81a4c80d0> 100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Create 'fred' the Genome\n",
        "key = \"fred\"\n",
        "genome = config.genome_type(key)\n",
        "genome.configure_new(config.genome_config)\n",
        "\n",
        "net = neat.nn.FeedForwardNetwork.create(genome, config)"
      ],
      "metadata": {
        "id": "Pr22TQRl7nTc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Update the Evaluation Fitness Function\n",
        "\n",
        "results = []\n",
        "for x, y in zip(X,Y):   \n",
        "  yi = net.activate(x)[0] \n",
        "  if y < .5:\n",
        "    error = yi - y\n",
        "  else:\n",
        "    error = y - yi  \n",
        "  print(yi, error)\n",
        "  \n",
        "  results.append(error*error)\n",
        "fitness = 1 - np.mean(results)\n",
        "\n",
        "print(fitness)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZshMKDF85fm",
        "outputId": "c05bc707-8ad9-46ea-ad29-621ebfb1e128"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0 0.0\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.0 0.0\n",
            "0.0 0.0\n",
            "0.0 1.0\n",
            "0.0 0.0\n",
            "0.0 0.0\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.0 1.0\n",
            "0.0 0.0\n",
            "0.0 1.0\n",
            "0.0 0.0\n",
            "0.4666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_genomes(genomes, config):\n",
        "  for genome_id, genome in genomes:\n",
        "    genome.fitness = 4.0    \n",
        "    net = neat.nn.FeedForwardNetwork.create(genome, config)    \n",
        "    for x, y in zip(X,Y):            \n",
        "      output = net.activate(x)[0] \n",
        "      genome.fitness -= (output-y)**2        "
      ],
      "metadata": {
        "id": "xP2r1cae_NrH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_genomes(genomes, config):\n",
        "  for genome_id, genome in genomes:     \n",
        "    net = neat.nn.FeedForwardNetwork.create(genome, config)  \n",
        "    results = []  \n",
        "    for x, y in zip(X,Y):\n",
        "      yi = net.activate(x)[0]  \n",
        "      if y < .5:\n",
        "        error = yi - y\n",
        "      else:\n",
        "        error = y - yi                    \n",
        "      results.append(error*error)\n",
        "    genome.fitness = 1 - np.mean(results)"
      ],
      "metadata": {
        "id": "9INUscXzTVcd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Helper function to show prediction results\n",
        "def show_predictions(net, X, Y, name=\"\"):\n",
        "    \"\"\" display the labeled data X and a surface of prediction of model \"\"\"\n",
        "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))\n",
        "    X_temp = np.c_[xx.flatten(), yy.flatten()]\n",
        "    Z = []    \n",
        "    for x in X_temp:\n",
        "      Z.append(net.activate(x))\n",
        "    Z = np.array(Z)\n",
        "    plt.figure(\"Predictions \" + name)\n",
        "    plt.contourf(xx, yy, Z.reshape(xx.shape), cmap=plt.cm.Spectral)\n",
        "    plt.ylabel('x2')\n",
        "    plt.xlabel('x1')\n",
        "    plt.scatter(X[:, 0], X[:, 1],c=Y, s=40, cmap=plt.cm.Spectral)\n",
        "    plt.show()\n",
        "\n",
        "#show_predictions(winner_net, X, Y)"
      ],
      "metadata": {
        "id": "EZdg6UAiDJxb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Draw Network from Genome\n",
        "import graphviz\n",
        "\n",
        "def draw_net(config, genome, view=False, filename=None, node_names=None, show_disabled=True, prune_unused=False,\n",
        "             node_colors=None, fmt='svg'):\n",
        "    \"\"\" Receives a genome and draws a neural network with arbitrary topology. \"\"\"\n",
        "    # Attributes for network nodes.\n",
        "    if graphviz is None:\n",
        "        print(\"This display is not available due to a missing optional dependency (graphviz)\")\n",
        "        return\n",
        "\n",
        "    # If requested, use a copy of the genome which omits all components that won't affect the output.\n",
        "    if prune_unused:\n",
        "        genome = genome.get_pruned_copy(config.genome_config)\n",
        "\n",
        "    if node_names is None:\n",
        "        node_names = {}\n",
        "\n",
        "    assert type(node_names) is dict\n",
        "\n",
        "    if node_colors is None:\n",
        "        node_colors = {}\n",
        "\n",
        "    assert type(node_colors) is dict\n",
        "\n",
        "    node_attrs = {\n",
        "        'shape': 'circle',\n",
        "        'fontsize': '9',\n",
        "        'height': '0.2',\n",
        "        'width': '0.2'}\n",
        "\n",
        "    dot = graphviz.Digraph(format=fmt, node_attr=node_attrs)\n",
        "\n",
        "    inputs = set()\n",
        "    for k in config.genome_config.input_keys:\n",
        "        inputs.add(k)\n",
        "        name = node_names.get(k, str(k))\n",
        "        input_attrs = {'style': 'filled', 'shape': 'box', 'fillcolor': node_colors.get(k, 'lightgray')}\n",
        "        dot.node(name, _attributes=input_attrs)\n",
        "\n",
        "    outputs = set()\n",
        "    for k in config.genome_config.output_keys:\n",
        "        outputs.add(k)\n",
        "        name = node_names.get(k, str(k))\n",
        "        node_attrs = {'style': 'filled', 'fillcolor': node_colors.get(k, 'lightblue')}\n",
        "\n",
        "        dot.node(name, _attributes=node_attrs)\n",
        "\n",
        "    used_nodes = set(genome.nodes.keys())\n",
        "    for n in used_nodes:\n",
        "        if n in inputs or n in outputs:\n",
        "            continue\n",
        "\n",
        "        attrs = {'style': 'filled',\n",
        "                 'fillcolor': node_colors.get(n, 'white')}\n",
        "        dot.node(str(n), _attributes=attrs)\n",
        "\n",
        "    for cg in genome.connections.values():\n",
        "        if cg.enabled or show_disabled:\n",
        "            # if cg.input not in used_nodes or cg.output not in used_nodes:\n",
        "            #    continue\n",
        "            input, output = cg.key\n",
        "            a = node_names.get(input, str(input))\n",
        "            b = node_names.get(output, str(output))\n",
        "            style = 'solid' if cg.enabled else 'dotted'\n",
        "            color = 'green' if cg.weight > 0 else 'red'\n",
        "            width = str(0.1 + abs(cg.weight / 5.0))\n",
        "            dot.edge(a, b, _attributes={'style': style, 'color': color, 'penwidth': width})\n",
        "\n",
        "    dot.render(filename, view=view)\n",
        "    dot.view()\n",
        "\n",
        "    return dot\n",
        "\n",
        "node_names = {-1: 'X1', -2: 'X2', 0: 'Classify'}\n",
        "draw_net(config, genome, True, node_names=node_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "R23BCj8ss_Cd",
        "outputId": "3e059eb5-9232-4037-80c6-ddd9620ffeb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7ff81aadbe50>"
            ],
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          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Reporter Class\n",
        "from neat.math_util import mean, stdev\n",
        "\n",
        "class CustomReporter(neat.reporting.BaseReporter):\n",
        "  \"\"\"Uses `print` to output information about the run; an example reporter class.\"\"\"\n",
        "\n",
        "  def __init__(self, show_species_detail, gen_display=100):\n",
        "      self.show_species_detail = show_species_detail\n",
        "      self.generation = None\n",
        "      self.generation_start_time = None\n",
        "      self.generation_times = []\n",
        "      self.num_extinctions = 0   \n",
        "      self.gen_display = gen_display        \n",
        "      \n",
        "  def start_generation(self, generation):\n",
        "      clear_output()\n",
        "      self.generation = generation\n",
        "      print('\\n ****** Running generation {0} ****** \\n'.format(generation))\n",
        "      self.generation_start_time = time.time()\n",
        "\n",
        "  def end_generation(self, config, population, species_set):\n",
        "      ng = len(population)\n",
        "      ns = len(species_set.species)      \n",
        "      if self.show_species_detail:\n",
        "          print('Population of {0:d} members in {1:d} species:'.format(ng, ns))\n",
        "          print(\"   ID   age  size   fitness   adj fit  stag\")\n",
        "          print(\"  ====  ===  ====  =========  =======  ====\")\n",
        "          for sid in sorted(species_set.species):\n",
        "              s = species_set.species[sid]\n",
        "              a = self.generation - s.created\n",
        "              n = len(s.members)\n",
        "              f = \"--\" if s.fitness is None else f\"{s.fitness:.3f}\"\n",
        "              af = \"--\" if s.adjusted_fitness is None else f\"{s.adjusted_fitness:.3f}\"\n",
        "              st = self.generation - s.last_improved\n",
        "              print(f\"  {sid:>4}  {a:>3}  {n:>4}  {f:>9}  {af:>7}  {st:>4}\")\n",
        "      else:\n",
        "          print('Population of {0:d} members in {1:d} species'.format(ng, ns))\n",
        "\n",
        "      elapsed = time.time() - self.generation_start_time\n",
        "      self.generation_times.append(elapsed)\n",
        "      self.generation_times = self.generation_times[-10:]\n",
        "      average = sum(self.generation_times) / len(self.generation_times)\n",
        "      print('Total extinctions: {0:d}'.format(self.num_extinctions))\n",
        "      if len(self.generation_times) > 1:\n",
        "          print(\"Generation time: {0:.3f} sec ({1:.3f} average)\".format(elapsed, average))\n",
        "      else:\n",
        "          print(\"Generation time: {0:.3f} sec\".format(elapsed))\n",
        "\n",
        "  def post_evaluate(self, config, population, species, best_genome):\n",
        "      # pylint: disable=no-self-use\n",
        "      fitnesses = [c.fitness for c in population.values()]\n",
        "      fit_mean = mean(fitnesses)\n",
        "      fit_std = stdev(fitnesses)\n",
        "      best_species_id = species.get_species_id(best_genome.key)\n",
        "      print('Population\\'s average fitness: {0:3.5f} stdev: {1:3.5f}'.format(fit_mean, fit_std))\n",
        "      print(\n",
        "          'Best fitness: {0:3.5f} - size: {1!r} - species {2} - id {3}'.format(best_genome.fitness,\n",
        "                                                                                best_genome.size(),\n",
        "                                                                                best_species_id,\n",
        "                                                                                best_genome.key))\n",
        "      \n",
        "      if (self.generation) % self.gen_display == 0 : \n",
        "        members = [len(s.members) for s in species.species.values()]      \n",
        "        num_generations = len(members)\n",
        "        curves = np.array(members).T\n",
        "\n",
        "        fig, ax = plt.subplots()\n",
        "        ax.stackplot(range(num_generations), *curves)\n",
        "\n",
        "        plt.title(\"Speciation\")\n",
        "        plt.ylabel(\"Size per Species\")\n",
        "        plt.xlabel(\"Generations\")\n",
        "        plt.show()\n",
        "              \n",
        "        self.best_fit = best_genome.fitness\n",
        "        net = neat.nn.FeedForwardNetwork.create(best_genome, config)      \n",
        "        show_predictions(net, X, Y)     \n",
        "        time.sleep(5) \n",
        "      \n",
        "  def complete_extinction(self):\n",
        "      self.num_extinctions += 1\n",
        "      print('All species extinct.')\n",
        "\n",
        "  def found_solution(self, config, generation, best):\n",
        "      print('\\nBest individual in generation {0} meets fitness threshold - complexity: {1!r}'.format(\n",
        "          self.generation, best.size()))\n",
        "\n",
        "  def species_stagnant(self, sid, species):\n",
        "      if self.show_species_detail:\n",
        "          print(\"\\nSpecies {0} with {1} members is stagnated: removing it\".format(sid, len(species.members)))\n",
        "\n",
        "  def info(self, msg):\n",
        "      print(msg)"
      ],
      "metadata": {
        "id": "03p8sG4bRYbJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the population, which is the top-level object for a NEAT run.\n",
        "p = neat.Population(config)\n",
        "\n",
        "# Add a stdout reporter to show progress in the terminal.\n",
        "p.add_reporter(CustomReporter(True, gen_display=10))\n",
        "\n",
        "# Run until a solution is found.\n",
        "winner = p.run(eval_genomes)\n",
        "\n",
        "# Display the winning genome.\n",
        "print('\\nBest genome:\\n{!s}'.format(winner))\n",
        "\n",
        "# Show output of the most fit genome against training data.\n",
        "print('\\nOutput:')\n",
        "winner_net = neat.nn.FeedForwardNetwork.create(winner, config)\n",
        "show_predictions(winner_net, X, Y)\n",
        "draw_net(config, winner, True, node_names=node_names)\n",
        "for x, y in zip(X, Y):\n",
        "    output = winner_net.activate(x)\n",
        "    print(\"  input {!r}, expected output {!r}, got {!r}\".format(x, y, output))"
      ],
      "metadata": {
        "id": "pYYZ4cXVX1Gu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "draw_net(config, winner, True)"
      ],
      "metadata": {
        "id": "j_xN08JIuisb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}