contents/notebooks/experiments_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9de8d0b1",
   "metadata": {},
   "source": [
    "# Study of experiment results\n",
    "\n",
    "After execution of each experiment the results of execution will be saved in Numpy NPZ format. The saved data can be used to analyse and visualize the evolutionary process.\n",
    "\n",
    "In this notebook we are going to examine results of experiments provided with goNEAT implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a670043c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00ff4cb",
   "metadata": {},
   "source": [
    "## The XOR experiment results\n",
    "\n",
    "We are going to start with the most basic experiment - XOR experiment. You can start it be executing the following command from the root directory of the repository:\n",
    "\n",
    "```bash\n",
    "$ run-xor\n",
    "```\n",
    "\n",
    "By default the results of the experiment will be saved to the [**out/xor**] folder and the collected experimental data will be saved to the [**out/xor/XOR.npz**] file.\n",
    "\n",
    "We are going to use that file to examine experimental results. Your results may vary from our results, however the general direction remain the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3044814",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trials_fitness\n",
      "trials_ages\n",
      "trials_complexity\n",
      "trial_0_epoch_mean_fitnesses\n",
      "trial_0_epoch_mean_ages\n",
      "trial_0_epoch_mean_complexities\n",
      "trial_0_epoch_best_fitnesses\n",
      "trial_0_epoch_best_ages\n",
      "trial_0_epoch_best_complexities\n",
      "trial_0_epoch_diversity\n",
      "trial_1_epoch_mean_fitnesses\n",
      "trial_1_epoch_mean_ages\n",
      "trial_1_epoch_mean_complexities\n",
      "trial_1_epoch_best_fitnesses\n",
      "trial_1_epoch_best_ages\n",
      "trial_1_epoch_best_complexities\n",
      "trial_1_epoch_diversity\n",
      "trial_2_epoch_mean_fitnesses\n",
      "trial_2_epoch_mean_ages\n",
      "trial_2_epoch_mean_complexities\n",
      "trial_2_epoch_best_fitnesses\n",
      "trial_2_epoch_best_ages\n",
      "trial_2_epoch_best_complexities\n",
      "trial_2_epoch_diversity\n",
      "trial_3_epoch_mean_fitnesses\n",
      "trial_3_epoch_mean_ages\n",
      "trial_3_epoch_mean_complexities\n",
      "trial_3_epoch_best_fitnesses\n",
      "trial_3_epoch_best_ages\n",
      "trial_3_epoch_best_complexities\n",
      "trial_3_epoch_diversity\n",
      "trial_4_epoch_mean_fitnesses\n",
      "trial_4_epoch_mean_ages\n",
      "trial_4_epoch_mean_complexities\n",
      "trial_4_epoch_best_fitnesses\n",
      "trial_4_epoch_best_ages\n",
      "trial_4_epoch_best_complexities\n",
      "trial_4_epoch_diversity\n",
      "trial_5_epoch_mean_fitnesses\n",
      "trial_5_epoch_mean_ages\n",
      "trial_5_epoch_mean_complexities\n",
      "trial_5_epoch_best_fitnesses\n",
      "trial_5_epoch_best_ages\n",
      "trial_5_epoch_best_complexities\n",
      "trial_5_epoch_diversity\n",
      "trial_6_epoch_mean_fitnesses\n",
      "trial_6_epoch_mean_ages\n",
      "trial_6_epoch_mean_complexities\n",
      "trial_6_epoch_best_fitnesses\n",
      "trial_6_epoch_best_ages\n",
      "trial_6_epoch_best_complexities\n",
      "trial_6_epoch_diversity\n",
      "trial_7_epoch_mean_fitnesses\n",
      "trial_7_epoch_mean_ages\n",
      "trial_7_epoch_mean_complexities\n",
      "trial_7_epoch_best_fitnesses\n",
      "trial_7_epoch_best_ages\n",
      "trial_7_epoch_best_complexities\n",
      "trial_7_epoch_diversity\n",
      "trial_8_epoch_mean_fitnesses\n",
      "trial_8_epoch_mean_ages\n",
      "trial_8_epoch_mean_complexities\n",
      "trial_8_epoch_best_fitnesses\n",
      "trial_8_epoch_best_ages\n",
      "trial_8_epoch_best_complexities\n",
      "trial_8_epoch_diversity\n",
      "trial_9_epoch_mean_fitnesses\n",
      "trial_9_epoch_mean_ages\n",
      "trial_9_epoch_mean_complexities\n",
      "trial_9_epoch_best_fitnesses\n",
      "trial_9_epoch_best_ages\n",
      "trial_9_epoch_best_complexities\n",
      "trial_9_epoch_diversity\n"
     ]
    }
   ],
   "source": [
    "# Loading the NPZ file\n",
    "xor_data = np.load('XOR.npz')\n",
    "\n",
    "# Printing the content of the file\n",
    "for f in xor_data:\n",
    "    print(f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c41c4de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fitness</th>\n",
       "      <th>complexity</th>\n",
       "      <th>diversity</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.627044</td>\n",
       "      <td>9.112676</td>\n",
       "      <td>29.676056</td>\n",
       "      <td>68.239437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.959789</td>\n",
       "      <td>4.540451</td>\n",
       "      <td>15.632178</td>\n",
       "      <td>4.207017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.032803</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.802319</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.734502</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fitness  complexity  diversity        age\n",
       "count  71.000000   71.000000  71.000000  71.000000\n",
       "mean    1.627044    9.112676  29.676056  68.239437\n",
       "std     1.959789    4.540451  15.632178   4.207017\n",
       "min     0.032803    7.000000   1.000000  41.000000\n",
       "25%     0.692308    7.000000  17.500000  69.000000\n",
       "50%     1.500000    7.000000  30.000000  70.000000\n",
       "75%     1.802319   11.000000  42.500000  70.000000\n",
       "max    15.734502   36.000000  52.000000  70.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are going to convert results of 8-th trial to the Pandas data frame for better maintainability\n",
    "# The data frame will be constructed from the results collected from the best organisms (champions) found in each\n",
    "# generation (epoch) of evolution.\n",
    "xor_best_org_df_8 = pd.DataFrame(\n",
    "    {\n",
    "        \"fitness\": xor_data['trial_8_epoch_best_fitnesses'],\n",
    "        \"complexity\": xor_data['trial_8_epoch_best_complexities'],\n",
    "        \"diversity\": xor_data['trial_8_epoch_diversity'],\n",
    "        \"age\": xor_data['trial_8_epoch_best_ages'],\n",
    "    }\n",
    ")\n",
    "\n",
    "# Now lets look into the statistics of the collected data\n",
    "xor_best_org_df_8.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b0743d",
   "metadata": {},
   "source": [
    "From the table we can see that trial took 71 generation to complete, i.e., to find the successful solver of the XOR experiment. The mean age of winner species is close to the number of generations executed (68.239437), which means that many of the winner species survived almost until successful solver was found by evolution (at least 50%).\n",
    "\n",
    "Another interesting point to notice is the maximal value of the diversity of the population. The diversity is the number of species in the population. For XOR experiment we used population of 200 organisms. If diversity has value 52 it means that most of the species include only two to three organisms. We got very diverse population with tiny species.\n",
    "\n",
    "The maximal fitness score is close to the maximal possible (16.0) and considerably exceeds the threshold value of 15.5, which was used to define the successful solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b677bc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we are going to plot all collected data in one plot as the collected values approximately in the same scale\n",
    "ax = sns.lineplot(data=xor_best_org_df_8)\n",
    "ax.set_xlabel('epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8704e4b8",
   "metadata": {},
   "source": [
    "The plot above confirms our previous observations and demonstrates that diversity of the population (# of species) increased during the evolution with old species still remaining in the genetic pool. It is interesting to note the massive extinction event occured at about generation #56 when about half of old species was rejected by evolution. Our assumption, that it was caused by sudden spike of genome complexity happened at the same epoch wich was making species too cumbersome to continue the evolution.\n",
    "\n",
    "The optimal genome configuration of the successful XOR solver is to have 5 nodes with 7 connection genes. \n",
    "The five nodes of the optimal genome as follows:\n",
    "* two input nodes\n",
    "* one bias node\n",
    "* one output node\n",
    "* one hidden node (found during the evolution)\n",
    "\n",
    "In the trial #8 the optimal genome was not found. However, in trial #7 it was found.\n",
    "\n",
    "It could be interesting to compare the results collected for these both trials to see the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "376c6607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fitness</th>\n",
       "      <th>complexity</th>\n",
       "      <th>diversity</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.274090</td>\n",
       "      <td>9.611111</td>\n",
       "      <td>11.944444</td>\n",
       "      <td>14.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.621386</td>\n",
       "      <td>2.725455</td>\n",
       "      <td>8.270943</td>\n",
       "      <td>3.038425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.036416</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.125074</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.450120</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.729051</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.722252</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fitness  complexity  diversity        age\n",
       "count  18.000000   18.000000  18.000000  18.000000\n",
       "mean    1.274090    9.611111  11.944444  14.055556\n",
       "std     3.621386    2.725455   8.270943   3.038425\n",
       "min     0.036416    7.000000   1.000000  11.000000\n",
       "25%     0.125074    7.000000   3.750000  11.000000\n",
       "50%     0.450120    9.500000  12.000000  14.500000\n",
       "75%     0.729051   12.000000  18.000000  17.000000\n",
       "max    15.722252   14.000000  26.000000  17.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xor_best_org_df_7 = pd.DataFrame(\n",
    "    {\n",
    "        \"fitness\": xor_data['trial_7_epoch_best_fitnesses'],\n",
    "        \"complexity\": xor_data['trial_7_epoch_best_complexities'],\n",
    "        \"diversity\": xor_data['trial_7_epoch_diversity'],\n",
    "        \"age\": xor_data['trial_7_epoch_best_ages'],\n",
    "    }\n",
    ")\n",
    "\n",
    "# Now lets look into the statistics of the collected data\n",
    "xor_best_org_df_7.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edcc4e43",
   "metadata": {},
   "source": [
    "We can see that trial #7 was able to find successful solver in only 18 generations.\n",
    "\n",
    "Now, lets see how best species performed during the evolution in this trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ab3ecca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we are going to plot all collected data in one plot as the collected values approximately in the same scale\n",
    "ticks = len(xor_best_org_df_7['fitness'])\n",
    "ax = sns.lineplot(data=xor_best_org_df_7)\n",
    "ax.set_xlabel('epochs')\n",
    "ax.set_xticks(range(0, ticks))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75743853",
   "metadata": {},
   "source": [
    "As in previous example, we can see that number of active species (diversity) increases with each epoch of the evolution and many species survive until the successfull solver was found. Also, at the middle of the evolutionary process we can see sudden jump in the complexity of the genomes, which caused mass extinction of old species (about third of original species) and creation of younger ones. After that, starting from epoch #10 the genome's compexity become optimal (12 = 5 + 7) and evolutionary process only worked to find the relevant weights of connections between nodes of the genome.\n",
    "\n",
    "The successfull solver was found in sudden evolutionary leap when fitness of the champion organism in the population increased almost eightfold. This is typical for evolutionary processes we observed before. The evolution tries variety of low performing combinations until finding one suitable in one quick move."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b2d038",
   "metadata": {},
   "source": [
    "## The cart-pole experiment results\n",
    "\n",
    "Now, let's consider results of more advanced experiment and see how it looks compared to the XOR experiment outlined above.\n",
    "\n",
    "You can run it by executing the following command in the root folder of this repository:\n",
    "```bash\n",
    "$ make run-cartpole\n",
    "```\n",
    "\n",
    "We start with loading experiment data and visualization, the same as previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30e0210a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the NPZ file\n",
    "cart_pole_data = np.load('cart_pole.npz')\n",
    "\n",
    "# Printing the content of the file\n",
    "# for f in cart_pole_data:\n",
    "#     print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76726dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fitness</th>\n",
       "      <th>complexity</th>\n",
       "      <th>diversity</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.00000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.182451</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>70.548387</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.196707</td>\n",
       "      <td>1.48324</td>\n",
       "      <td>35.088306</td>\n",
       "      <td>5.46504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.002151</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.063178</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.124977</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.212611</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>98.500000</td>\n",
       "      <td>15.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>21.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fitness  complexity   diversity       age\n",
       "count  31.000000    31.00000   31.000000  31.00000\n",
       "mean    0.182451    19.00000   70.548387  10.00000\n",
       "std     0.196707     1.48324   35.088306   5.46504\n",
       "min     0.002151    17.00000    1.000000   1.00000\n",
       "25%     0.063178    18.00000   42.500000   6.00000\n",
       "50%     0.124977    19.00000   82.000000   9.00000\n",
       "75%     0.212611    20.00000   98.500000  15.00000\n",
       "max     1.000000    24.00000  105.000000  21.00000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are going to convert results of 9-th trial to the Pandas data frame for better maintainability\n",
    "# The data frame will be constructed from the results collected from the best organisms (champions) found in each\n",
    "# generation (epoch) of evolution.\n",
    "cart_pole_best_org_df_9 = pd.DataFrame(\n",
    "    {\n",
    "        \"fitness\": cart_pole_data['trial_9_epoch_best_fitnesses'],\n",
    "        \"complexity\": cart_pole_data['trial_9_epoch_best_complexities'],\n",
    "        \"diversity\": cart_pole_data['trial_9_epoch_diversity'],\n",
    "        \"age\": cart_pole_data['trial_9_epoch_best_ages'],\n",
    "    }\n",
    ")\n",
    "\n",
    "# Now lets look into the statistics of the collected data\n",
    "cart_pole_best_org_df_9.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b71acc44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we are going to plot all collected data in one plot as the collected values approximately in the same scale\n",
    "ax = sns.lineplot(data=cart_pole_best_org_df_9)\n",
    "ax.set_xlabel('epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2939edf",
   "metadata": {},
   "source": [
    "In trial #9 of the Cart-Pole experiment we can see another interesting evolution path. The evolution started by accumulating the number of diverse species and quickly reached the theoretical maximum (we have population of 150 organims). After that at generation #23 the catastrophic drop of number of species happened (almost 10-fold) which continued by fast creation of the new species in the epochs that followed this event (almost 8-fold).\n",
    "\n",
    "Interesting to note, that complexity of the genomes remain almost constant through the evolutionary process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89c0cb2f",
   "metadata": {},
   "source": [
    "## The Double-Pole Cart experiment\n",
    "\n",
    "After looking at experiment intended to find successful balancer of cart with single-pole we are ready to take a look at even more advanced variant. In this experiment, we are trying to find balancer able maintain equlibrium of the cart with two poles attached. We consider the Markovian version of this experiment.\n",
    "\n",
    "You can run it by executing the following command in the root folder of this repository:\n",
    "```bash\n",
    "$ make run-cartpole-two-markov\n",
    "```\n",
    "\n",
    "As usually, we start with loading the experiment data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89f75035",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the NPZ file\n",
    "cart_2pole_data = np.load('cart_2pole_markov.npz')\n",
    "\n",
    "# Printing the content of the file\n",
    "# for f in cart_2pole_data:\n",
    "#     print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0f8133a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fitness</th>\n",
       "      <th>complexity</th>\n",
       "      <th>diversity</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.064654</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>620.750000</td>\n",
       "      <td>11.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.228458</td>\n",
       "      <td>8.294577</td>\n",
       "      <td>257.404837</td>\n",
       "      <td>5.206019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000280</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>565.750000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000777</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>761.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.001560</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>795.750000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>822.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fitness  complexity   diversity        age\n",
       "count  20.000000   20.000000   20.000000  20.000000\n",
       "mean    0.064654   21.800000  620.750000  11.550000\n",
       "std     0.228458    8.294577  257.404837   5.206019\n",
       "min     0.000002   15.000000    2.000000   3.000000\n",
       "25%     0.000280   15.000000  565.750000   5.500000\n",
       "50%     0.000777   16.000000  761.000000  15.000000\n",
       "75%     0.001560   28.000000  795.750000  15.000000\n",
       "max     1.000000   39.000000  822.000000  16.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are going to convert results of 9-th trial to the Pandas data frame for better maintainability\n",
    "# The data frame will be constructed from the results collected from the best organisms (champions) found in each\n",
    "# generation (epoch) of evolution.\n",
    "cart_2pole_best_org_df_9 = pd.DataFrame(\n",
    "    {\n",
    "        \"fitness\": cart_2pole_data['trial_9_epoch_best_fitnesses'],\n",
    "        \"complexity\": cart_2pole_data['trial_9_epoch_best_complexities'],\n",
    "        \"diversity\": cart_2pole_data['trial_9_epoch_diversity'],\n",
    "        \"age\": cart_2pole_data['trial_9_epoch_best_ages'],\n",
    "    }\n",
    ")\n",
    "\n",
    "# Now lets look into the statistics of the collected data\n",
    "cart_2pole_best_org_df_9.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca8c90c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we are going to plot all collected data in one plot as the collected values approximately in the same scale\n",
    "ticks = len(cart_2pole_best_org_df_9['fitness'])\n",
    "ax0 = plt.subplot(212)\n",
    "ax0.set_xlabel('epochs')\n",
    "ax0.set_xticks(range(0, ticks))\n",
    "sns.lineplot(data=cart_2pole_best_org_df_9[['complexity', 'age']], ax=ax0 )\n",
    "\n",
    "ax1 = plt.subplot(221)\n",
    "ax1.set_xlabel('epochs')\n",
    "sns.lineplot(data=cart_2pole_best_org_df_9[['diversity']], ax=ax1 )\n",
    "\n",
    "ax2 = plt.subplot(222)\n",
    "ax2.set_xlabel('epochs')\n",
    "sns.lineplot(data=cart_2pole_best_org_df_9[['fitness']], ax=ax2 )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c47ac6",
   "metadata": {},
   "source": [
    "In this experiment we used population of 1000 organisms. As in previous cases, you can see that diversity of the population almost reached theoretically possible (about 800). However, after genome complexity suddenly increased at generation #16, the diversity of the population began to decline rapidly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c24ac6",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "It is always a good idea to visualize the data collected during your experiments. We are visual beings, and good visualization always allows us to better understand the behavior of the process.\n",
    "\n",
    "The goNEAT library allows you to collect and save data into common formats (Numpy NPZ), which can later be analysed using plenty of available tools.\n",
    "\n",
    "With help of visualizations presented on this page we was able to make important conclusions about the evolutionary process. The one of the important conclusions is that increasing complexity of the genomes leads to extinction events. In such cases, the evolutionary process tries to rebalance existing species, either by replacing old with new ones, or by drastically reducing the size of the species.\n",
    "\n",
    "As a rule of a thumb, each extinction event, usually leads to discovery of the successfull solver over time. Thus, we can consider these events as benefitial."
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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