problem_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# mlrose_hiive Generator and Runner Usage Examples - Andrew Rollings"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Overview"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "These examples will not solve assignment 2 for you, but they will give you\n",
    "some idea on how to use the problem generator and runner classes.\n",
    "\n",
    "Hopefully this will result in slightly fewer\n",
    "\"How do I &lt;insert basic usage here&gt;?\" questions every semester...\n",
    "\n",
    "Also, and in case it hasn't been made clear enough by the TAs, using any of the visualization code in here for your\n",
    "assignment is a bad idea, for two reasons... (1) It provides nothing useful as far as the assignment goes,\n",
    "and (2) the TAs will undoubtedly frown upon it.\n",
    "\n",
    "Visualization is part of the analysis and, for the most part, you're supposed to do that by yourself. Just including\n",
    "images of the before/after state of a problem really isn't useful in terms of what you're supposed to be analyzing."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Import Libraries"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from IPython.core.display import display, HTML # for some notebook formatting.\n",
    "\n",
    "import mlrose_hiive\n",
    "import numpy as np\n",
    "import logging\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import string\n",
    "\n",
    "\n",
    "from ast import literal_eval\n",
    "import chess\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from mlrose_hiive import QueensGenerator, MaxKColorGenerator, TSPGenerator\n",
    "from mlrose_hiive import SARunner, GARunner, NNGSRunner\n",
    "\n",
    "# switch off the chatter\n",
    "logging.basicConfig(level=logging.WARNING)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Example 1: Generating and Running 8-Queens using the SA algorithm"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# Generate a new 8-Queen problem using a fixed seed.\n",
    "problem = QueensGenerator().generate(seed=123456, size=8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The initial state is as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 3 2 7 0 1 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": "Board('2Q5/3Q4/3Q4/2Q5/7Q/Q7/1Q6/1Q6 w - - 0 1')",
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0-1.805.86-.64.859-.64 2.421 0 1.555.64 2.415.648.859 1.805.859 1.164 0 1.804-.86.649-.859.649-2.414zm1.437 3.391q0 2.234-.992 3.32-.992 1.094-3.04 1.094-.757 0-1.429-.117-.672-.11-1.304-.344v-1.398q.632.344 1.25.508.617.164 1.257.164 1.414 0 2.118-.743.703-.734.703-2.226v-.711q-.446.773-1.141 1.156-.695.383-1.664.383-1.61 0-2.594-1.227-.984-1.226-.984-3.25 0-2.03.984-3.257.985-1.227 2.594-1.227.969 0 1.664.383t1.14 1.156V5.664h1.438z\" /></g><g fill=\"#e5e5e5\" stroke=\"#e5e5e5\" transform=\"translate(335, 0) scale(0.75, 0.75)\"><path d=\"M26.164 9.133v5.281h-1.437V9.18q0-1.243-.485-1.86-.484-.617-1.453-.617-1.164 0-1.836.742-.672.742-.672 2.024v4.945h-1.445V2.258h1.445v4.765q.516-.789 1.211-1.18.703-.39 1.617-.39 1.508 0 2.282.938.773.93.773 2.742z\" /></g><g fill=\"#e5e5e5\" stroke=\"#e5e5e5\" transform=\"translate(335, 375) scale(0.75, 0.75)\"><path d=\"M26.164 9.133v5.281h-1.437V9.18q0-1.243-.485-1.86-.484-.617-1.453-.617-1.164 0-1.836.742-.672.742-.672 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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = problem.get_state()\n",
    "print(state)\n",
    "board_layout = '/'.join([''.join(([str(s)] if s > 0 else []) + ['Q'] + ([str((7-s))] if s < 7 else [])) for s in state])\n",
    "board = chess.Board(board_layout)\n",
    "board"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# create a runner class and solve the problem\n",
    "sa = SARunner(problem=problem,\n",
    "              experiment_name='queens8_sa',\n",
    "              output_directory=None, # note: specify an output directory to have results saved to disk\n",
    "              seed=123456,\n",
    "              iteration_list=2 ** np.arange(11),\n",
    "              max_attempts=500,\n",
    "              temperature_list=[0.1, 0.5, 0.75, 1.0, 2.0, 5.0],\n",
    "              decay_list=[mlrose_hiive.GeomDecay])\n",
    "\n",
    "# the two data frames will contain the results\n",
    "df_run_stats, df_run_curves = sa.run()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The preceding code will run the `SA` algorithm six times for at most 1024 iterations.\n",
    "Each run is initialized with the temperature specified in the `temperature_list`\n",
    "using the temperature values specified.\n",
    "\n",
    "If the fitness remains static for `max_attempts` iterations, it will terminate that run.\n",
    "\n",
    "Note that the initial state parameters here are just toy values picked specifically\n",
    "for this example. You will have to choose your own range of values for your\n",
    "assignment. I strongly recommend you don't just copy these, or you will find\n",
    "that the grading is unlikely to go the way you would like.\n",
    "\n",
    "The output in the `df_run_stats` dataframe contains snapshots of the state of the algorithm at the iterations\n",
    "specified in the `iteration_list` passed into the runner class.\n",
    "\n",
    "The first 12 rows (corresponding to the first run of this algorithm) are as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n      <th>State</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>0.001407</td>\n      <td>[1, 2, 2, 1, 0, 3, 7, 3]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>9.0</td>\n      <td>2</td>\n      <td>0.004973</td>\n      <td>[1, 2, 2, 0, 0, 3, 7, 3]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>8.0</td>\n      <td>4</td>\n      <td>0.007838</td>\n      <td>[1, 2, 2, 0, 0, 3, 7, 5]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>8.0</td>\n      <td>7</td>\n      <td>0.010544</td>\n      <td>[1, 2, 2, 5, 0, 3, 7, 5]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>5.0</td>\n      <td>13</td>\n      <td>0.013995</td>\n      <td>[1, 2, 7, 5, 0, 3, 5, 5]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>16</td>\n      <td>4.0</td>\n      <td>24</td>\n      <td>0.018867</td>\n      <td>[1, 2, 7, 5, 3, 0, 5, 5]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>32</td>\n      <td>4.0</td>\n      <td>47</td>\n      <td>0.026599</td>\n      <td>[1, 5, 7, 5, 0, 0, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>64</td>\n      <td>1.0</td>\n      <td>86</td>\n      <td>0.039504</td>\n      <td>[1, 5, 2, 6, 3, 0, 7, 4]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>128</td>\n      <td>1.0</td>\n      <td>155</td>\n      <td>0.062218</td>\n      <td>[1, 5, 2, 6, 3, 0, 4, 7]</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>256</td>\n      <td>1.0</td>\n      <td>295</td>\n      <td>0.108992</td>\n      <td>[1, 7, 2, 6, 3, 5, 0, 4]</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>512</td>\n      <td>0.0</td>\n      <td>461</td>\n      <td>0.169440</td>\n      <td>[1, 5, 0, 6, 3, 7, 2, 4]</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1024</td>\n      <td>0.0</td>\n      <td>461</td>\n      <td>0.169440</td>\n      <td>[1, 5, 0, 6, 3, 7, 2, 4]</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(df_run_stats[['Iteration', 'Fitness', 'FEvals', 'Time', 'State']][0:12].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The state information is excluded from the previous output.\n",
    "\n",
    "A sample of this (based on the state of the `GeomDecay` object) is below:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>schedule_current_value</th>\n      <th>schedule_init_temp</th>\n      <th>schedule_min_temp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.099999</td>\n      <td>0.1</td>\n      <td>0.001</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_sample = df_run_stats[['schedule_current_value', 'schedule_init_temp', 'schedule_min_temp']][:1]\n",
    "HTML(state_sample.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "So, to pick out the most performant run from the dataframe, you need to find the row with the best fitness.\n",
    "As 8-Queens is a minimization problem, you'd pick the row with the minimum fitness.\n",
    "\n",
    "However, I'm going to look in the `run_curves` (which stores minimal basic information every iteration) to\n",
    "find out which input state achieved the best fitness in the fewest fitness evaluations."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Time</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Temperature</th>\n      <th>max_iters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>414</th>\n      <td>414</td>\n      <td>0.169440</td>\n      <td>0.0</td>\n      <td>461.0</td>\n      <td>0.1</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>829</th>\n      <td>414</td>\n      <td>0.169440</td>\n      <td>0.0</td>\n      <td>461.0</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2416</th>\n      <td>561</td>\n      <td>0.077231</td>\n      <td>0.0</td>\n      <td>639.0</td>\n      <td>1.0</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2739</th>\n      <td>322</td>\n      <td>0.134266</td>\n      <td>0.0</td>\n      <td>427.0</td>\n      <td>2.0</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>3164</th>\n      <td>424</td>\n      <td>0.015155</td>\n      <td>0.0</td>\n      <td>583.0</td>\n      <td>5.0</td>\n      <td>1024</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_fitness = df_run_curves['Fitness'].min()\n",
    "best_runs = df_run_curves[df_run_curves['Fitness'] == best_fitness]\n",
    "\n",
    "HTML(best_runs.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "This gives us five candidates for the best run. We are going to pick the one with\n",
    "that reached the best fitness value in the fewest number of evaluations.\n",
    "\n",
    "(We could also have chosen to use `Iterations` as our criteria.)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "minimum_evaluations = best_runs['FEvals'].min()\n",
    "\n",
    "best_curve_run = best_runs[best_runs['FEvals'] == minimum_evaluations]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The best run using these criteria is as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Time</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Temperature</th>\n      <th>max_iters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2739</th>\n      <td>322</td>\n      <td>0.134266</td>\n      <td>0.0</td>\n      <td>427.0</td>\n      <td>2.0</td>\n      <td>1024</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(best_curve_run.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Which has the following identifying state information:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best initial temperature: 2.0\n"
     ]
    }
   ],
   "source": [
    "best_init_temperature = best_curve_run['Temperature'].iloc()[0].init_temp\n",
    "\n",
    "print(f'Best initial temperature: {best_init_temperature}')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "To map this back to the `run_stats` we look at the configuration data included in\n",
    "the curve data. The curve data includes at least the minimum identifying information\n",
    "to determine which run each row came from.\n",
    "\n",
    "In this case, the value we are looking for is the `Temperature`, which is the initial temperature\n",
    "used to initialize the `GeomDecay` object.\n",
    "\n",
    "So, in this case, we are looking for all rows in `df_run_stats` where the temperature is equal to 1."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n      <th>State</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>48</th>\n      <td>0</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>0.000646</td>\n      <td>[1, 2, 2, 1, 0, 3, 7, 3]</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>1</td>\n      <td>9.0</td>\n      <td>2</td>\n      <td>0.006700</td>\n      <td>[1, 2, 2, 0, 0, 3, 7, 3]</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>2</td>\n      <td>8.0</td>\n      <td>4</td>\n      <td>0.013422</td>\n      <td>[1, 2, 2, 0, 0, 3, 7, 5]</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>4</td>\n      <td>8.0</td>\n      <td>7</td>\n      <td>0.020229</td>\n      <td>[1, 2, 2, 5, 0, 3, 7, 5]</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>8</td>\n      <td>7.0</td>\n      <td>14</td>\n      <td>0.027899</td>\n      <td>[1, 2, 2, 5, 0, 3, 5, 5]</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>16</td>\n      <td>6.0</td>\n      <td>27</td>\n      <td>0.037195</td>\n      <td>[3, 2, 3, 5, 0, 1, 5, 5]</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>32</td>\n      <td>4.0</td>\n      <td>57</td>\n      <td>0.052080</td>\n      <td>[3, 5, 6, 5, 5, 0, 4, 7]</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>64</td>\n      <td>5.0</td>\n      <td>114</td>\n      <td>0.074471</td>\n      <td>[2, 0, 3, 6, 1, 2, 1, 7]</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>128</td>\n      <td>3.0</td>\n      <td>205</td>\n      <td>0.112543</td>\n      <td>[2, 0, 6, 3, 5, 0, 4, 3]</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>256</td>\n      <td>2.0</td>\n      <td>358</td>\n      <td>0.182122</td>\n      <td>[7, 1, 3, 6, 6, 4, 0, 5]</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>512</td>\n      <td>0.0</td>\n      <td>427</td>\n      <td>0.221128</td>\n      <td>[7, 1, 3, 0, 6, 4, 2, 5]</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>1024</td>\n      <td>0.0</td>\n      <td>427</td>\n      <td>0.221128</td>\n      <td>[7, 1, 3, 0, 6, 4, 2, 5]</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_stats_best_run = df_run_stats[df_run_stats['schedule_init_temp'] == best_init_temperature]\n",
    "HTML(run_stats_best_run[['Iteration', 'Fitness', 'FEvals', 'Time', 'State']].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "And the best state associated with this is:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>schedule_current_value</th>\n      <th>schedule_init_temp</th>\n      <th>schedule_min_temp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>59</th>\n      <td>1.99556</td>\n      <td>2.0</td>\n      <td>0.001</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_state = run_stats_best_run[['schedule_current_value', 'schedule_init_temp', 'schedule_min_temp']].tail(1)\n",
    "HTML(best_state.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The final state is as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 1, 3, 0, 6, 4, 2, 5]\n"
     ]
    },
    {
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = literal_eval(run_stats_best_run['State'].tail(1).values[0])\n",
    "print(state)\n",
    "board_layout = '/'.join([''.join(([str(s)] if s > 0 else []) + ['Q'] + ([str((7-s))] if s < 7 else [])) for s in state])\n",
    "board = chess.Board(board_layout)\n",
    "board"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Example 2: Generating and Running Max K Color using the GA algorithm"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "# Generate a new Max K problem using a fixed seed.\n",
    "problem = MaxKColorGenerator().generate(seed=123456, number_of_nodes=10, max_connections_per_node=3, max_colors=3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The input graph generated for the problem looks like this:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(problem.source_graph,\n",
    "        pos=nx.spring_layout(problem.source_graph, seed = 3))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "# create a runner class and solve the problem\n",
    "ga = GARunner(problem=problem,\n",
    "              experiment_name='max_k_ga',\n",
    "              output_directory=None, # note: specify an output directory to have results saved to disk\n",
    "              seed=123456,\n",
    "              iteration_list=2 ** np.arange(11),\n",
    "              population_sizes=[10, 20, 50],\n",
    "              mutation_rates=[0.1, 0.2, 0.5])\n",
    "\n",
    "# the two data frames will contain the results\n",
    "df_run_stats, df_run_curves = ga.run()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The preceding code will run the `GA` algorithm nine times for at most 1024 iterations per run.\n",
    "Each run is a permutation from the list of `population_sizes` and `mutation_rates`.\n",
    "\n",
    "Note that the initial state parameters here are just toy values picked specifically\n",
    "for this example. You will have to choose your own range of values for your\n",
    "assignment. I strongly recommend you don't just copy these, or you will find\n",
    "that the grading is unlikely to go the way you would like.\n",
    "\n",
    "Really. I mean it... A mutation rate of 0.5 is little better than a pure random search.\n",
    "\n",
    "The output in the `df_run_stats` dataframe contains snapshots of the state of the algorithm at the iterations\n",
    "specified in the `iteration_list` passed into the runner class.\n",
    "\n",
    "The first row (corresponding to the first run of this algorithm) are as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n      <th>State</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>10</td>\n      <td>0.00072</td>\n      <td>[1, 2, 2, 1, 0, 0, 0, 0, 2, 2]</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(df_run_stats[['Iteration', 'Fitness', 'FEvals', 'Time', 'State']][0:1].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The state information is excluded from the previous output.\n",
    "\n",
    "A sample of this is below:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Population Size</th>\n      <th>Mutation Rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>0.1</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_sample = df_run_stats[['Population Size', 'Mutation Rate']][:1]\n",
    "HTML(state_sample.to_html())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "So, to pick out the most performant run from the dataframe, you need to find the row with the best fitness.\n",
    "As Max-K-Color is a minimization problem, you'd pick the row with the minimum fitness.\n",
    "\n",
    "However, I'm going to look in the `run_curves` (which stores minimal basic information every iteration) to\n",
    "find out which input state achieved the best fitness in the fewest fitness evaluations."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Time</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Population Size</th>\n      <th>Mutation Rate</th>\n      <th>max_iters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.012456</td>\n      <td>0.0</td>\n      <td>57.0</td>\n      <td>10</td>\n      <td>0.1</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>4</td>\n      <td>0.012456</td>\n      <td>0.0</td>\n      <td>57.0</td>\n      <td>10</td>\n      <td>0.2</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>8</td>\n      <td>0.011575</td>\n      <td>0.0</td>\n      <td>101.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>5</td>\n      <td>0.000608</td>\n      <td>0.0</td>\n      <td>127.0</td>\n      <td>20</td>\n      <td>0.1</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2</td>\n      <td>0.007827</td>\n      <td>0.0</td>\n      <td>64.0</td>\n      <td>20</td>\n      <td>0.2</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>3</td>\n      <td>0.011192</td>\n      <td>0.0</td>\n      <td>85.0</td>\n      <td>20</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>9</td>\n      <td>0.012944</td>\n      <td>0.0</td>\n      <td>511.0</td>\n      <td>50</td>\n      <td>0.1</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>3</td>\n      <td>0.011192</td>\n      <td>0.0</td>\n      <td>205.0</td>\n      <td>50</td>\n      <td>0.2</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>4</td>\n      <td>0.012456</td>\n      <td>0.0</td>\n      <td>256.0</td>\n      <td>50</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_fitness = df_run_curves['Fitness'].min()\n",
    "best_runs = df_run_curves[df_run_curves['Fitness'] == best_fitness]\n",
    "\n",
    "HTML(best_runs.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "This gives us nine candidates for the best run. We are going to pick the one with\n",
    "that reached the best fitness value in the fewest number of evaluations.\n",
    "\n",
    "(We could also have chosen to use `Iterations` as our criteria.)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "minimum_evaluations = best_runs['FEvals'].min()\n",
    "\n",
    "best_curve_run = best_runs[best_runs['FEvals'] == minimum_evaluations]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The best runs using these criteria is as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Time</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Population Size</th>\n      <th>Mutation Rate</th>\n      <th>max_iters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.012456</td>\n      <td>0.0</td>\n      <td>57.0</td>\n      <td>10</td>\n      <td>0.1</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>4</td>\n      <td>0.012456</td>\n      <td>0.0</td>\n      <td>57.0</td>\n      <td>10</td>\n      <td>0.2</td>\n      <td>1024</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(best_curve_run.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We will arbitrarily pick the first row for this example,\n",
    "which has the following identifying state information:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Mutation Rate: 0.1, best Population Size: 10\n"
     ]
    }
   ],
   "source": [
    "best_mr = best_curve_run['Mutation Rate'].iloc()[0]\n",
    "best_pop_size = best_curve_run['Population Size'].iloc()[0]\n",
    "print(f'Best Mutation Rate: {best_mr}, best Population Size: {best_pop_size}')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "To map this back to the `run_stats` we look at the configuration data included in\n",
    "the curve data. The curve data includes at least the minimum identifying information\n",
    "to determine which run each row came from.\n",
    "\n",
    "In this case, the values we are looking for are the `Mutation Rate` and `Population Size`.\n",
    "\n",
    "So, we are looking for all rows in `df_run_stats` where the mutation rate and population size are equal to our best values."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>10</td>\n      <td>0.000720</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>3.0</td>\n      <td>21</td>\n      <td>0.004370</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2.0</td>\n      <td>33</td>\n      <td>0.007827</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>16</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>32</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>64</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>128</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>256</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>512</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1024</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>0.012456</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_stats_best_run = df_run_stats[(df_run_stats['Mutation Rate'] == best_mr) & (df_run_stats['Population Size'] == best_pop_size)]\n",
    "HTML(run_stats_best_run[['Iteration', 'Fitness', 'FEvals', 'Time']].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "And the best state associated with this is:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11</th>\n      <td>[0, 1, 2, 0, 0, 2, 0, 1, 2, 1]</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_state = run_stats_best_run[['State']].tail(1)\n",
    "HTML(best_state.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For the following node ordering:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 8, 1, 3, 4, 6, 7, 9, 5]\n"
     ]
    }
   ],
   "source": [
    "print([n for n in problem.source_graph.nodes])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Reordering the state by ascending node number gives the following:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 2, 1, 0, 0, 0, 1, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "color_indexes = literal_eval(run_stats_best_run['State'].tail(1).values[0])\n",
    "ordered_state = [color_indexes[n] for n in problem.source_graph.nodes]\n",
    "print(ordered_state)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Which results in a graph looking like this:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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EUPmrr6hRogSr3nwTJ72eN1etorGvLxNbtHji2DgHB840aICPjw+lS5fGxcXFVm/B7oWHh1OrVi3Onj1L0aJFn3n8xIkTCQsLY/369bmo12tbsmSZEM9w4sQJ3nnnHQIDA20eegA6nQ5fX19tLndm8MH6qFc3uF49iufLh5eHB8MbNGD7xYupjvXw9KRFixaUL19eQu8pU6ZMYdiwYZkKPUi+8nDu3DnWrl1r5cpyLwk+ITIQGRlJly5d+Pbbb7O9t152aDWyU+funjyDOg0F3dwolT//E0Ms0otJneYjKO3ThQsX2LhxI2PHjs30a1xcXFi2bBmjRo3i7t27Vqwu95LgEyIdBoOBbt260bNnT7p3765pLVoNcLnj4YExnRGdAG/WqMGiw4eJjI3lfnw88w8donXFik8e5OSEk2ykm6ZPP/2U0aNHU7BgwSy9rkGDBrzxxhu89957Vqosd5PgEyIdo0ePxtPTk6lTp2pdik17fElJSaxbt44OHTpQtU4dTsfFkd5AgA+aNaNWyZLUnjOHunPnUq14ccY2afLkQUrhomFv2V6dOnWK3bt3M+p/26pnybRp09i3bx+//vqrhSvL/WQ6gxBpWLx4MTt37uT333//Z8dsbfn6+nL06FGrnuPUqVMsW7aMlStX4ufnR//+/Vm9ejUuUVE8/OEHMBhSvcbJwYEvO3Tgyw4d0m5Up8OpcmV0rq5WrT0nmjhxIh9++CGemZzf+DRPT08WLVrE22+/TVhYmM0HPuVk0uMT4ikhISGMHz+eoKAgCjzaF0Zj1urxRUVFMW/ePP71r3/Rrl07PD09OXjwIMHBwfTt2xcPDw8cvb1x8vMjzT10MmBWiocmE85PjfAUcPjwYY4dO8awYcOy1c6rr75KixYtGDdunIUqyxtkOoMQj4mIiKBu3bosXbqUtm3bal1Oilu3blG1alUiIyOz3ZbZbGbXrl0sX76cLVu20KZNG/r370/Lli3T7d0qs5m41auJO38e58wModfpUM7ODAsORl+4MMuXL89gp4K8p1WrVrz22msMGTIk221FRUVRtWpVVq9ebbPVhHI66fEJ8Y/4+Hi6dOnCu+++a1ehB1C0aFFiY2N5+PDhc7dx5coVJk2axIsvvsiHH35Iw4YN+fPPP1m1ahWtW7fO8JKuTq8n2NOTH0NDUY6O6ff+dDpwdMShZEnyDxvGssBAYmNj6dSpE7FpLGeWF+3du5fLly8zYMAAi7T3wgsvMGfOHAYOHEhCQoJF2sztpMcnBMmTxPv27YvJZGLlypV2OTH4pZdeYv369VSpUiXTr4mLi2P9+vUsW7aMU6dO8eabb9K/f/8sT824desWNWrUYM2aNTSuW5ek0FASQ0JQMTHJ0x3MZtDrcfL3x6V+fRwem+9oNBoZMmQIp06dYvPmzZrMhbQXSimaNGnCkCFD6NOnj0Xbfv3116lYsSKfffaZRdvNlZQQQn3xxReqVq1a6uHDh1qXkq7WrVurzZs3P/M4s9msfv/9dzVkyBD1wgsvqLZt26r//ve/KiEh4bnOazabVdu2bdX48eNTP2c0KlNsrDInJiqz2ZxhGx9//LGqWLGi+uuvv56rjtxg69atqkqVKspoNFq87Rs3bqgiRYqoP/74w+Jt5zYyqlPkedu2bePLL7/k0KFDuLu7a11Oup41wOXWrVusWLGCZcuWkZiYyIABAwgNDcXb2ztb5/3222+5c+cOkyZNSvWczsEBnYfHM9vQ6XR89tlnFCtWjMaNG7N161aqVq2arbpyGqUUEyZMYMqUKVYZKVy8eHFmzpzJgAEDOHz4sE3Xk81p5B6fyNMuXrxI3759WbNmDWXscKfwx6UVfAaDgY0bN9KlSxdeeuklwsLCWLBgARcvXmTcuHHZDr3Tp08zefJkVq5caZEP0v/7v/9jxowZtGjRggMHDmS7vZxkw4YNmM1munTpYrVz9O3bl6JFi/Lll19a7Ry5gtZdTiG0Eh0drSpXrqwWLlyodSmZ8tNPP6lu3boppZQ6c+aMev/991Xx4sVVo0aN1JIlS1RMTIxFz5eQkKD8/f3VkiVLLNquUkpt27ZNFSlSRAUFBVm8bXtkNBpVlSpVMnWpOruuXLmivLy81Llz56x+rpxKLnWKPMlsNtO7d2+aNWvG4MGDtS4nU4oUKcKRI0do0KAB4eHh9O3blz179lCpUiWrnG/cuHGUL1/eYqMPH9eqVSs2b95Mp06diIyMZODAgRY/hz35+eefKViwoE1GC/v6+jJp0iTefvtt9u7di/5Zu9vmRVonrxBamDBhgmrSpIlKTEzUupQMmUwmFRwcrPr27avy58+vXFxc1KZNm5TBYLDqebdv3668vb3VnTt3rHqe8+fPK19fX/XZZ59lODgmJ0tKSlLlypVTwcHBNjun0WhUDRs2VHPnzrXZOXMSCT6R56xZs0aVKVNG3bp1S+tS0nX16lU1depUVbZsWVW1alX11VdfqZs3bypXV1erjzyNjIxUpUqVUjt37rTqeR75+++/VbVq1dSoUaOUyWSyyTltaeHChaply5Y2P++ZM2eUl5dXnh5Fmx6ZxyfylJMnT9KyZUu2bdtGrVq1tC7nCQkJCQQFBbFs2TKOHj3KG2+8wYABA6hdu3bKvMJKlSoRGBhI5cqVrVKDUoouXbpQoUIFZs6caZVzpOX+/ft06tSJUqVK8f333+eaVV4SEhKoUKEC69ato27dujY//7///W8OHDjAli1b7HJuqlbk4q/IM+7cuUPnzp2ZM2eOXYXe8ePHGTlyJN7e3ixZsoS33nqLiIgI5s2bR506dZ74wPLx8bHqLg1LliwhPDycadOmWe0caSlYsCDbtm0jISGB9u3b8+DBA5ue31oWLFhArVq1NAk9gA8++IAbN26wYsUKTc5vr6THJ/IEg8FAq1atqF+/PtOnT9e6HO7evcvKlStZtmwZ0dHRvPXWW/Tr1w9fX98MXzd48GBq1arF0KFDLV7T+fPnady4Mfv27bNaj/JZjEYjw4cP5/jx42zZsiVHr/ISGxtL+fLl2b59O/7+/prV8ccff9C2bVtCQ0MpVqyYZnXYE+nxiTzhvffew93d3eY9mceZTCa2bt1K9+7dKVeuHIcPH+arr77i8uXLTJo06ZmhB9bbpSEpKYlevXoxZcoUzUIPwNHRkYULF9K2bVsaNWqkya7zljJnzhxefvllTUMPoFatWvTv3/+59/3LlTS9wyiEDSxZskRVqlRJ3b9/X5PzX7x4UY0bN06VKlVK1a1bV82fP19FRUU9V1srV65Ub7zxhoUrVOqjjz5SHTp0sKuRlXPmzFGlSpVSJ0+e1LqULIuKilKFCxe2m7l0cXFxqkKFCmrDhg1al2IXJPhErhYSEqKKFCli8w+gBw8eqOXLl6smTZqookWLqvfee0+dOnUq2+2GhISoevXqWaDC/9mzZ48qWbKkXY5yXbVqlSpSpIjau3ev1qVkyYQJE1T//v21LuMJe/fuVaVKlXruH7pyEwk+kWtdu3ZNlSxZ0iarZSiVvBDzgQMH1MCBA1XBggVVx44d1YYNGyw6VzAiIkIVK1bMYu3du3dPlSlTRm3dutVibVrajh07VJEiRXJMb+X27duqUKFC6sqVK1qXksqwYcPUwIEDtS5DczK4ReRK8fHxNG3alNdff50PP/zQque6ceMGP/zwA8uWLUOv19O/f3/69OlDiRIlLH4us9mMu7s7UVFRuLm5ZastpRQ9evSgWLFifPPNNxaq0DqOHTtGx44dmTx5MoMGDdK6nAyNGTOGxMRE5s6dq3UpqcTExFC1alWWL19OixYttC5HOxoHrxAWZzabVe/evVWPHj2sds8qMTFRrVu3TrVv314VLFhQvf322+q3336zyT2y8uXLW+TS7ffff6/8/PxUXFycBaqyvgsXLqiyZcuqqVOn2tW9yMdFRESoQoUKqevXr2tdSro2bdqkXnzxRRUbG6t1KZqRUZ0i15k1axanT59m6dKlFp+0e+rUKd577z28vb355ptv6N69OxERESxevJgGDRrYZJKwJUZ2Xr58mTFjxvDTTz9lu+doKxUqVODAgQOsXbuWkSNHYjKZtC4plWnTpjFw4ECr9PYtpX379jRs2JCJEydqXYp2tE5eISxp27Ztqnjx4io8PNxibUZFRal58+apOnXqKG9vbzV+/Hh18eJFi7WfVW+//bZasGDBc7/eYDCo+vXrq1mzZlmwKtu5f/++atasmerevftzb65rDX/++acqVKiQ1dc3tYTIyEhVvHhxdfDgQa1L0YT0+ESucenSJfr06cPq1auzvbee2Wxm586dvPnmm/j6+rJnzx6mTZvGX3/9xbRp0yhfvryFqs667K7eMm3aNPLnz59j53UVKFCAX3/9FaPRaFervEyePJkRI0bg5eWldSnPVLhwYWbPns3AgQNJTEzUuhzb0zp5hbCER3vrzZ8/P1vtXLlyRU2aNEn5+PiomjVrqjlz5tjdT/A//vij6tmz53O9NiQkRBUrVsyu70FlltFoVEOGDFG1atXSfCrGmTNnVJEiRTSbK/o8zGaz6tSpk5o0aZLWpdicTUd1KrMZ46VLJB48iPnuXTAYwMkJvZcXLg0a4Fi+PDrZO0qkwaRMmDHjiGOq+2iPdrUuUaIECxYsyHLb8fHxrF+/nmXLlhEaGkrPnj3p378/NWvWtFT5FnXgwAE++OADfvvttyy9LiYmhho1ajBr1iwCAgKsVJ1tKaVSdojftm0bZcuW1aSO7t27U7t2bauPILa0v//+mxo1arB7926qVaumdTk2Y5PgU0qReOgQSQcOoIxGSEpKfZCzMzpHR5wbN8alfn1ZSVwQaYzkWMIxLhkuYcac8nhRh6LUca1DWaey6HV6PvnkE4KDg9m1a1emV/VXSnHkyBGWLVvGmjVrqFevHgMGDKBTp064uLhY6y1ZREREBHXr1uX69etZel2/fv1wc3N7rh8O7N38+fOZNm0amzdvpkaNGjY99/Hjx2nfvj0XL17Ew8PDpue2hMWLF7No0SIOHjyIo2Pe2Jvc6sGnTCbi1q3DeOlScg/vWZyccKxQAfeuXdE5OFizNGGn7prusjV2K9HmaEyYUKT+FnXCCQedA65hrnzS6xMOHz6cqQV4b9++zYoVK1i2bBkJCQn079+fvn37Urp0aWu8FaswmUy4u7sTHR2Nq6trpl6zatUqPv30U44dO5YjP5wzY+3atQwfPpw1a9bw8ssv2+y8HTp0oHXr1owcOdJm57QkpRQtWrSgffv2jBkzRutybMKqwaeUIn7DBgznzmUu9B5xdMSpShXcOneWnl8e87fxb4IeBGEgc98vSfFJlEsoR9eyXdM9xmg0snXrVpYtW0ZwcDCdO3dmwIABNGnSJMd+f5UrV46tW7dSsWLFZx579epV6tSpw6+//mpX2zFZw+7du+nRowfz58/ntddes/r5Dh48SI8ePbhw4YLdXynIyKVLl6hfvz6HDh3SdOCWrVj1hprx3LkMQ+/y3bsUmzqVwevWPfVCI4azZzGeO2fN8oSduWu6m6XQA3B2c+bGCzc4mXAy1XPnzp3jww8/pHTp0nz++ed06NCBa9eu8d1339G0adMcG3qQPJcvPDz8mceZTCb69OnD2LFjc33oAbzyyits27aNkSNH2uSS7vjx4/nkk09ydOgBlC9fno8//phBgwZhw2EfmrFq8CUeOJBhT2/s5s3UKlUq7ScNBhJDQqxUmbBHOx7uSDP09i/ez5evfMmY4mNY+c7KVM8bMbI/fj9x5jhiYmJYsmQJDRs2pHnz5gAEBwcTEhLCwIEDyZcvn9Xfhy1kdhL7jBkz0Ov1eeYSFkDNmjXZv38/X3zxBZMnT7baB/muXbu4du0a/fr1s0r7tvbuu+8SFxfHkiVLtC7F6qx2J9MUGYnp9u10n1936hQFXF2pW6QIV+7dS7uNW7cw3bmDQ+HC1ipT2Il7pnvcNd1N87n8xfPz6phXObf7HIaEtH+QMpvNfLriUxaNWsQrr7zCuHHjaNOmTa69WZ+Z4Dty5AizZs3i2LFjOOSx++XlypUjJCSEtm3bcuvWLebMmWPRvwOlFBMmTGDy5Mm55nvMwcGBpUuX0rx5c9q1a38eCHMAACAASURBVEep9DoluYDVenxJJ09COksKxSQk8FlwMP9u3TrjRsxmkk6csEJ1wt4cTziOibS/X6p3rI5/e388CqU/KEPpFSVbluTchXOsX7+eDh065JoPpLQ8K/hiY2Pp1asX3377bY4auGNJxYoVY8+ePZw/f54ePXpYdKL25s2biY2NpUePHhZr0x5UrVqVd955h2HDhuXqS55W+2RQ9+9DOn9x/w4Opk+tWpQqUCDjRsxm7v31F3dDQ9HpdOj1eov9ntljc/J9oJzkouFimqM3s8LZzRldvrzw/0vh55fAuXNHge+B/IAf8L+BLqNHj6ZRo0Z069ZNoxrtQ/78+dmyZQu9e/embdu2BAYGkj9//my1aTabmTBhAlOnTkWfC+cdf/zxx9SuXZvVq1fnumB/xHrBl869vdAbN9j755/sGzIkU+2cOHqUj7/5BrPZjFLKor8/65hHP/FYKkStEcw5ua3H23R52wWdPnuhpUNHgkrIVhv2LRZYCcygRo2blC8fD4wg+cKNgeTw+5DAQEVwcDDHjx/XsFb74eLiwqpVqxg1ahTNmjVj69atFC9e/LnbW7t2Lc7OzrlmEYCnubi4sHTpUgICAmjZsiWFc+GtJqtNZ4gLCsKQxmXKeQcPMm33bjz/mWj8MCkJk1JULFyYfUOHpjreqUYN3DX8BrNUiFoykLVu0xr1lfigxDODb/O/N3P/+n16fdsr7f9XiQqP4x6UdS6Lt7c33t7euWjOWgjQHjACD9M9ymz24MaNeG7fXk3Nmq/bqrgcQSnFtGnT+O6779i+fTvlypXLchtGo5GqVavyzTff0KpVKytUaT/ee++9lHmvuY3VenyOvr4YTp9ONarzrdq1ea1q1ZQ/z/ntN67ev89XHTqkbsTZGUdfX2uVmCk6nS7PDQzQwryoeVmaxpAWheLYwWP8tP8nIiIiiIiIwM3NDW9vb0qXLp0Sho++fvS7/YfjLqATEPfMI/X6h5QooaNUqUGAP49f/szrdDodEydOpFixYjRp0oTNmzenuSydSZn4y/AX0eZoDMqAs86ZQg6FKO1YmhUrVlCsWDFeffVVDd6BbU2dOpVq1aqxefNm2rdvr3U5FmW14HOqUoX4LVtSPe7u7Iz7Y8tKeTg74+roSOF0Pnyc/PysVaKwI6WdSvOn4c80nzMZTZiNZswmM8qkMCQY0DvqcXB88gcSFxcX5k6ei6Mu+dtaKcXdu3eJiIjg2rVrKb/v3r37icdcXV1TheHTAaldOF4AOpOZ0HtEr1dANNAMOAsUtEplOdXgwYMpXLgwrVu3ZtWqVbzyyisAPDA/IDQhlNDEUBQKE8nrwzrggB49jjiyI3wHkz+fnCfu/Xt4eLB48WL69+9PWFhYtu+N2hOrrtwSv3UrSUePgtn87IOfptfj/K9/4damjeULE3bnuvE6gQ8C0+z1bf18K9tmbHvisdYftKbtR21T/qxHj7+LP83cm2XpvI+H4+Nh+PTXLi4uafYWH//a09Pz+d58hnoBq+GxEa+JiTB8OOzcCffuQblyMH06tG379GvdgSlA3pnDlxV79+6lW7dufPvtt9QKqMWvD3/F/M9/6TElmnBzcSMgXwAlHUvasFrtDBo0CCcnJ+bNm6d1KRZj1eAzR0fzYP785H+pWeXiQr5hw9A/a+SnyBWUUnwX8x0x5pjner0x0UiNqzVoUbeFhStLru3evXupeo5PB6Szs3OqYHw6ILMWjlFASeDJATsPH8LMmfDWW1CmDGzZAj17wqlTkPrOQAkgAiuvVZFjnTx5kndnvUvnmZ2zdP3LEUc65+tMKcfcO9ftkfv371O1alV++uknmjZtqnU5FmH1RaqN167x8Mcfs7ZWp5MTHn364JhH5x/lVeGGcDbFbsKIMUuvc8QR5whnPm3zKR07duTzzz+nYEHbXt5TShEVFZVhMF67du2JcEwvIP+3usxXwEQyc5nT3x8mTYLUy1N6AuuB3H9P6nlEGiNZHbMaky7tOaQZccaZPgX64Km3Rk/fvgQFBfH+++9z8uRJ3NzctC4n22yyLZHx+vXk8DOZMg5AJydwcEgOvZJ54zKCeNLphNPsid+T6fBzxBFvJ286enQkJjqGjz76iF9++YWvv/6a1157za7uxTwKx4yCMSIiAicnJ7y9vfnuu1vUqXPnme3eugU+PnDiBLz00tPP6oBPgE8t/4ZygU2xm7hsuJzq8Zvnb7Lug3VcO3ENz8KedJrcCf8O/k8co0dPLZdaNHJvZKtyNfXGG2/g6+vLf/7zH61LyTabbUSrkpJICg0lMSQE9fAh6HTJ9/70elAKnYcHLo0a4ezvjy6Te6qJ3Oly0mW2PUy+p5feSE8Hkge2+Dn70cy9GXrd/y7lHThwgMGDB1O+fPkct3KJUor79+9z7do1ypTpRsGCFzI83mBIvrdXrhwsXJj2MaGhjfn99764urri6uqKm5tbqq+ffszFxSVXTs5+XJw5jmXRy1KtGGQymvi8wec0fKshzYY241LIJZa8uYSxe8ZStHzRJ451xpnBBQfjoMv9I79v3bqFv78/mzdvpk6dOlqXky023YEdkv9hm65fx3z3LioxEZ2LC3ovLxxKlrSrn86FtozKyIWkCxxNOMoD8wP0/9yjUih0Oh3+zv74u/qTT5/2otOJiYnMmDGDr7/+mokTJzJixIgcOC2lKbA/3WfNZnjzTYiJgaCg5Asmadm1qyarVtUmPj6ehIQEEhIS0vz68ccSEhJwdnZONxifFZzZed7JyckmnwVH4o9wOOFwqqsLN87cYFbrWfzn6n9S6pjfdT4+tX1oN77dE8c64URLj5ZUdM4b00Z+/PFHvvjiC44ePYpTet9wOYDNg0+IrIo2RROv4jFhwlXnSkF9wUz/hH3+/HkGDx5MfHw8ixYtsvnu3NnTG/gJ0ljKTSkYMAD++it5cEv6t11cgenAu1k6s9lsJikpKd1gfFZwZud5s9mc5eB8npA9VeQUt51TL6SfVvDN6zIPF08XBv44MNXxtV1q09i9cZb+fnMqpRTt2rWjcePGjB8/XutynlvuXcVX5BoFHApQgOcb3VupUiWCg4NZvnw5rVq1on///kyaNAl3d3cLV2kNA4Egkpcqe9KwYXD2bPKUhmePNcj6ep16vT4lMGw9UMhoND7R88xqcN67dy9Tx7af255SNVKPyixaoSj5Cudj95zdvDzsZS7uv8jl3y5TvnHaG7TGq3hr/5XYDZ1Ox8KFC6lduzZdu3alcuXKWpf0XKTHJ/KMW7duMXr0aH7//Xfmz5+fA5acUoAPcO2JR8PDk6ctuLjA4xtQLFwIvVKt5tYWSL2QhIANDzZw1Xg1zeeun77Oug/XcePsDUrXKI1nYU8cnR3pOadnqmNruNTI8vzRnG7evHmsWLGC/fv358BbCBJ8Ig/aunUrw4cPp1GjRnz11VcULVr02S/SzFzgQ7Kycsv/eAC/AM0tWlFuERwXzKnEU5naFWR269n8q+e/aPTWkyM4HXCgkVsjarqmXvosNzObzTRr1oxu3boxatQorcvJstw9bEuINLRt25awsDCKFy9OtWrV+O677+x477GhQF3AJYuvcwf6IaGXvqrOVVNGBz/t+unrGBIMJMUlsXvObmJuxVCvZ700j63kXMmaZdolvV7PkiVLmDJlyjM3RLZH0uMTedoff/zBoEGDKFCgAAsXLqRChQpal5SGWKA1cBx49v2kuDgdjo7dcXb+CfnZNmMrYlZw13Q31eNBnwRx6MdDmIwmytYvy2v/eY0iZYukOq6sU1k6ena0Ral26fPPP2f37t1s27YtR43Kl+ATeZ7RaGTOnDn8+9//ZvTo0bz//vs4291cUgMwHpj/z59TD3iBfIAL//1vZdauLc6qVatz1IeRFi4kXWDHwx1ZXi0IkhdP6JqvKyUcS1ihspzBYDBQr149Ro0axVtvvaV1OZkmPw6KPM/R0ZHRo0dz9OhRQkJCqFWrFgcPHtS6rKc4ATOASGAeUBPwIvk+XlGSd2L4GbhJx47bOXfuPMuWLdOq2ByjglMFKjhXwDGLA9wdcaSOa508HXoATk5OLF26lA8++ICbN29qXU6mSY9PiMcopVizZg2jR4+mS5cufPbZZxTIgQulnz17lqZNm7Jv374cO+TcVszKzPaH2zkffz5TE7wccaSGSw0aujWUHvU/xo0bx4ULF1i7dq3WpWSK9PiEeIxOp+ONN97g9OnTGAwG/Pz82LBhg9ZlZVnlypWZPn06PXr0ICEh4dkvyMP0Oj31DPXYMmkLzknOOJF6RRIdOhxxxEvvRRuPNjRybySh95hPPvmEsLAw1q1bp3UpmSI9PiEysG/fPgYPHsxLL73E3Llz8fb21rqkTFNK0aNHD4oWLcqcOXO0LseujRgxgqSkJBYuXMh103VOJpwkyhRFEkk440xRx6LUcKlBEcfUA1xEspCQELp160ZYWBiFChXSupwMSfAJ8QyJiYlMnz6duXPn8umnnzJs2LAcM2n3/v371KxZk6+//ppOnTppXY5dOnr0KB06dODMmTN2/4Ft70aOHElsbCzLly/XupQMSfAJkUlnz55l8ODBGAwGFi1ahL+//7NfZAcOHjxIly5dOHr0aI7qsdqCyWSiXr16jBgxIkeNSrRXDx48oFq1aixatMiuV0aSe3xCZFLlypXZu3cvAwcOpEWLFnz88cfEx9v/Oo0NGjRg1KhR9O7dG5Mp6xuu5mYLFizA3d2dfv36aV1KrpAvXz4WLFjA4MGDiY1Na8qNfZAenxDP4caNG7z77rscO3aMBQsW0LJlS61LypDJZKJVq1a8/PLLTJw4Uety7MLNmzepVq0ae/bswc/PT+tycpV+/fpRsGBBvv76a61LSZMEnxDZsGnTJt555x1efvllvvzySwoXLqx1Sem6fv06tWvXZu3atTRqlDd2Dc9Ir1698Pb2zhU7itubu3fvUrVqVdatW0fDhg21LicVudQpRDZ06NCB06dP4+XlRdWqVfnhhx/sdt3PkiVLsmTJEnr16kVUVJTW5Whq165dHDhwgE8++UTrUnIlLy8vvvnmGwYOHGiX02mkxyeEhRw9epRBgwbh5eXFggULKF8+7f3btPbuu+8SERHBf//73zw5Fy0xMRF/f39mzpwpI12tSCnFa6+9hp+fH1OnTgUg1hxLhDGCRHMiOp0ON50bZZzK4KLL6iLs2SPBJ4QFGY1GZs+ezeeff87YsWMZM2YMTk6pJ0RrKTExkfr16zN06FCGDBmidTk2N3XqVI4ePUpQUJDWpeR6N27coHr16qzZt4b7pe4TYYxAjx4zZnToUr6u6FyRmq41Kexgm1sFEnxCWMGVK1cYNmwY169fZ/HixdSrl/aWNlo5f/48jRs3znMDOy5fvky9evU4duwYPj4+WpeT6xmUgYUXFpLgmYCTe/o/AOrQ4YAD1Vyq0cStidWvRMg9PiGs4MUXX2Tr1q18/PHHdO7cmZEjRxITE6N1WSkqVarEzJkzeeONN3LElAxLUEoxYsQI3n//fQk9GzAqI/998F8oSoahB6BQGDFyKvEU2+O2W/0+uQSfEFai0+no2bMnp0+fJi4uDj8/P7u6vNavXz/8/f0ZM2aM1qXYxLp167h69SqjR4/WupQ8YevDrdwz3cNE5ueOGjFyKekSxxKOWbEyudQphM0EBwczZMgQqlWrxjfffEOpUqW0LomYmBhq1qzJzJkz6dq1q9blWM2DBw+oUqUKK1eupGnTplqXk+vdM93j55ifU+1z+EHpD574syHeQOOBjXntP6898bgTTgwuOBhHXda2i8os6fEJYSPNmzcnNDQUPz8/atSowbx58zCbzZrWlD9/fn7++WeGDRvG1atXNa3FmiZNmkSLFi0k9GzkeMLxNHt6M67NSPk19exUnNycqBFQI802LiVdslp9EnxC2JCrqytTpkxhz549rFy5ksaNGxMWFqZpTXXr1mXMmDH06tULozHrO5Hbu5MnT7JixQpmzpypdSl5gkEZOJd0DkXGFxNP/nKSfIXzUbZB2dRtYOBIwhFrlSjBJ4QW/Pz82L9/P3379qV58+ZMmDBB04m+Y8eOxdXVlWnTpmlWgzWYzWaGDRvGtGnTKFJEthSyhfum++h49qjMI6uOUOeNOumO4IwyR1ltkIsEnxAa0ev1DB06lJMnT3Lu3Dn8/f0JDg7WrJYffviBhQsXsnfvXk1qsIalS5eilOLtt9/WupQ8I0klPTP47l27x6WQS9TtWTfD456+R2gpEnxCaKxkyZKsXbuWL774gn79+tG/f3/u3r1r8zpKlCjBsmXL6NOnjybnt7TIyEjGjx/P/Pnz0evlo85WHHTP3qvy6OqjlK1fFi8fr3SPUSgcsM6+l/LdIISd6NSpE6dPnyZ//vz4+fmxcuVKm6/72bZtW7p168bAgQPtds3RzPrggw/o1asXNWqkPXhCWIe7zh2D2ZDhMUdWH+FfPf6V4THOOKPXWSeiJPiEsCP58uXj66+/ZuPGjcyYMYM2bdrw559/2rSG6dOnExERwfz58216Xkvav38/O3bsYMqUKVqXkickJSWxbds2hg8fThWfKtw6fyvdY6/8foXoG9HpjuYE0KOniksVa5T6T/tCCLtTt25djh49SosWLahbty4zZszAYMj4p2hLcXZ25ueff2bSpEmEhoba5JyWZDAYGDZsGLNnzyZfvnxal5NrRUdH8/PPP9OjRw+KFSvG5MmT8fX1ZdeuXbxV5y2cSHu1lsOrDuPfwR/XfK7ptq1DR3WX6tYqXSawC2HvLl++zLBhw7h9+zaLFy/mX//K+BKRpfz4449Mnz6do0eP4u7ubpNzWsKMGTMIDg5my5YteXL3CWuKiIggKCiIoKAgDh06RNOmTencuTMdO3akWLFiKceZlIkl0UtIUFkfqaxDR0nHkrye73VLlv7kOST4hLB/SilWrlzJ2LFj6dGjB1OnTrVJb6ZPnz64ubmxaNEiq5/LEsLDw6lduza///475cqV07qcHE8pRVhYGEFBQQQGBnLlyhXat29P586dadWqFZ6enum+9rrxOhsebMjyyExXnSu98vfCU59+29klwSdEDnLnzh3Gjh1LcHAwc+fOpWPHjlY934MHD6hVqxafffYZ3bp1s+q5LKFz587Url2biRMnal1KjmU0GgkJCUkJO6UUAQEBdO7cmcaNG+PomPllxP4y/MXm2M2ZCj8dOlx1rrye73UKORTKzlt49rkk+ITIeXbt2sXQoUOpUaMG33zzDSVKlLDauY4ePUq7du04fPgwvr6+VjtPdm3cuJH333+f0NBQXFxsu7FpTvfw4UO2b99OYGAgmzdvxsfHJyXsqlWrlq1LxpHGSPbF7+OG8QYKhZknl+lzxBGFopxTOZq6N8VD75Hdt/NMEnxC5FDx8fFMmzaNRYsWMW3aNAYNGmS1+Wpffvkl69atY9++fVn6id9WHj58iJ+fH0uXLqVFixZal5Mj3L59m19++YXAwED27t1LvXr1CAgIoFOnTpQpU8bi54sxxXAy8SR/Gv5MmeTuqnOliksVqjhXwVWf/mAXS5PgEyKHCwsLY9CgQTg4OLBo0SKqVLH8MHCz2Uy7du2oU6eOXS5r9tFHH3H16lV++uknrUuxaxcuXEi5hHn69Glat25NQEAA7dq1o2DBglqXZzMSfELkAmazmQULFjBp0iSGDRvGuHHjcHW17E/Qt27dombNmqxcuZLmzZtbtO3sOH36NC+//DKnTp2iePHiWpdjV8xmM4cPH04Ju5iYGDp16kRAQADNmzfPs5eEJfiEyEX+/vtvRo0aRVhYGIsWLaJZs2YWbX/79u0MHDiQ48ePU7hwYYu2/TyUUrz88st069aNESNGaF2OXUhISGD37t0EBQWxceNGvLy8Uu7X1a5dW5ZvQ4JPiFwpMDCQkSNH0rp1a2bMmEGhQpYbJffhhx9y5swZNm7cqPk8ue+//545c+bw+++/4+BgnXUdc4KoqCg2b95MUFAQO3bswN/fn4CAAAICAihfvrzW5dkdCT4hcqmYmBjGjx/P2rVrmTVrFm+88YZFgiopKYnGjRvTu3dvRo0aZYFKn8+9e/eoUqUKmzZtok6dOprVoZXw8PCUyeRHjhzhlVdeISAggA4dOsgWTM8gwSdELnfo0CEGDRqEt7c38+fPt8iUhMuXL1O/fn127Nih2SLQQ4YMwdHRkW+//VaT89uaUooTJ06khF1ERAQdO3YkICCAV199NUetrqM1CT4h8gCDwcCXX37JF198wccff8z//d//ZXtawk8//cSUKVM4duwYHh7Wn3v1uEOHDtG1a1fOnDmTq0cjGgwG9u3blxJ2Tk5OdO7cmYCAABo2bJinL+9mhwSfEHnIpUuXGDp0KPfu3WPx4sXUrl07W+31798fvV7P0qVLLVThsxmNRurUqcP7779Pr169bHZeW3nw4AG//vorQUFBbNmyhfLly6eEXZUqVTS/r5obSPAJkccopfjxxx9TgmPKlCkZrrmYkdjYWGrXrs3kyZPp0aOHhStN2+zZs/nll1/YuXNnrgmBGzdusHHjRoKCgjhw4AANGzZMWfy5VKlSWpeX60jwCZFH3blzhzFjxrB3717mzZtHu3btnqud48eP07p1aw4dOkTZsmUtXOWT/v77b6pXr05ISAiVKlWy6rmsSSnFuXPnCAwMJCgoiAsXLtC2bVsCAgJo06YN+fPn17rEXE2CT4g8bufOnQwdOpQ6deowe/bs55oEPnv2bFatWsX+/ftxckp7HzZL6N69O5UqVWLq1KlWO4e1mEwmDh06lBJ28fHxKZcwmzZtirOzs9Yl5hkSfEII4uLimDp1KkuXLuWzzz5jwIABWZrorJSiY8eOVKtWjenTp1ulxkc7fIeFheHm5maVc1hafHw8O3fuJDAwkE2bNlG8ePGUsKtZs2auuVSb00jwCSFShIaGMmjQIFxcXFi0aBEvvfRSpl8bGRlJzZo1+e6772jZsqVF64qPj6datWrMmTOHtm3bWrRtS7tz5w6bN28mMDCQ3bt3U6tWLTp37kynTp148cUXtS5PIMEnhHiKyWRi/vz5fPrpp4wcOZKPPvoo02s67tq1i759+3L8+HGKFi1qsZomTZrE6dOnWbt2rcXatKTLly+nTDk4ceIELVu2pHPnzrRr1w4vLy+tyxNPkeATQqTp2rVrjBgxggsXLrBo0SKaNGmSqdeNGzeOEydOsGnTJousC3nhwgUaNmzIiRMn8Pb2znZ7lqCU4tixYyn36yIjI+nYsSOdO3emRYsWFl8gXFiWBJ8QIl1KKTZs2MCoUaNo164d//nPf3jhhRcyfI3BYKBp06Z0796d0aNHZ/v8rVq1om3btrz33nvZaiu7kpKS2LNnD4GBgWzcuBFPT8+UxZ/r1asniz/nIBJ8Qohnio6OZty4cWzYsIHZs2fTrVu3DAdmXLlyhXr16rF169ZsTZJftWoV06dP59ixY5psgBsdHc3WrVsJDAxk27ZtVK5cOWXx56zc/xT2RYJPCJFpv/32G4MHD8bX15dvv/0WHx+fdI9dvXo1EydO5NixY+TLly/L54qOjqZy5cqsXbuWhg0bZqfsLImIiGDjxo0EBgZy6NAhmjZtSkBAAB07dpT9/nIJCT4hRJYkJSUxc+ZMZs2axYQJExg5cmS6a0a+/fbbGAwGvv/++yyfZ+TIkSQkJLB48eLslpwhpRRhYWEpm7VeuXKF9u3bExAQQOvWrZ97VRthvyT4hBDP5cKFCwwdOpSYmBgWL15MzZo1Ux3z8OFD6tSpw/jx4+ndu3em2z527Bjt27fn9OnTVhkVaTQaCQkJSRmJaTabUy5hNmnSRJPLqsJ2JPiEEM9NKcX333/Phx9+SN++ffn0009T7dRw8uRJWrZsycGDB1M2RVUJCRguXkQ9fAgmEzo3Nxx8fHDw8sJkMlG/fn2GDx9O//79LVbrw4cP2b59O0FBQWzevJkyZcqkhJ2/v79MJs9DJPiEENl2+/Zt3nvvPUJCQpg/fz5t2rR54vm5c+fy/fffs3/9esxHj2I4cwb0ejCZQClwcAClcChWjN0PHvBVYCC7g4OzPVLy9u3b/PLLLwQFBbFnzx7q1atHQEAAnTp1okyZMtlqW+RcEnxCCIvZtm0bw4YNo379+syaNYtixYoBYDab+fmdd2hVrBjOen1y2KXjYVISTkWL4jV4MPrnWJrswoULKZcww8LCaN26NQEBAbRr1y5X790nMk+CTwhhUXFxcUyePJnly5fz+eef89Zbb5GwaRNJp06hMxoz14iDA7p8+cg3ZAi6Z0wGN5vNHD58OCXs7t+/n3IJs3nz5pledUbkHRJ8QgirOHHiBIMGDaJHpUoMeOkl9CZT1hpwcMChRAk8BgxIdf8tISGB3bt3ExQUxMaNGylUqFDK4s916tSRyeQiQxJ8QgirMSYlce/zz3FJ42Nm8Lp17L1yhbikJIp6evJ/jRrR9+nJ7k5OePTti6O3N1FRUWzevJmgoCB27NiBv79/Ss/u0aAZITJDgk8IYTWGM2eICwqCpKRUz529fZuyhQrh4ujIhchIOnz3HWt69aJGyZIpxygg3MmJ93bt4siRI7zyyisEBATQoUMHihQpYsN3InITmawihLCaxN9+SzP0ACo/tnuDTqdDp9Nx5d69J4JPB5RKTOS94cNp3rYt7u7u1i5Z5AESfEIIqzHdvp3h82M2beKnEyeINxrxL16cVytUSHWMk5sbrevUwVFCT1iIBJ8QwiqUUmAwZHjMlx06MKNdOw5fu8aBv/7CJZ0VU1RCgjVKFHmUDH0SQliFTqdLnpj+DA56PQ18fLgeE8PSI0fSbsvJydLliTxMgk8IYTW6LFyeNJrNXImKSv2E2Ywuf34LViXyOgk+IYTVONeuDWlcvoyMjWXdqVPEJiZiMpvZdekS68LC5Lu/AwAAB25JREFUaPbii6mO1RcogEPhwrYoV+QRco9PCGE1zrVrk7h/f6rHdTodS48eZfSmTSilKF2wINPbtKHd05u7Ojvj0qiRjaoVeYXM4xNCWNXD1asxXryYvCB1Vrm4kH/MGLnHJyxKLnUKIazKvVMndJ6ekNVtfxwd8ejZU0JPWJwEnxDCqnRubngOGICuQIFMjfIEwMkJ927dcPTxsW5xIk+SS51CCJtQCQkk7N5N0okTyb2/p1d0+ScUHXx8cHv1VRyKF9egSpEXSPAJIWxKGQwYwsJIOn4cc2wsiXFx/H3nDpXatMGlbl30MnVBWJkEnxBCU+Hh4TRu3Jhr165pXYrIIyT4hBCaMhqNeHh48ODBA5ydnbUuR+QBMrhFCKEpR0dHSpYsKT0+YTMSfEIIzfn6+vLXX39pXYbIIyT4hBCak+ATtiTBJ4TQnASfsCUJPiGE5iT4hC1J8AkhNCfBJ2xJgk8IoTkJPmFLMo9PCKE5mcsnbEl6fEIIzT2ay3f16lWtSxF5gASfEMIuvPjii3K5U9iEBJ8Qwi7IfT5hKxJ8Qgi7IMEnbEWCTwhhFyT4hK1I8Akh7IIEn7AVCT4hhF2Q4BO2IvP4hBB24dFcvpiYGFxcXLQuR+Ri0uMTQtgFR0dHSpUqJfvyCauT4BNC2A253ClsQYJPCGE3JPiELUjwCSHshgSfsAUJPiGE3ZDgE7YgwSeEsBsSfMIWJPiEEHZDgk/YgszjE0LYDZnLJ2xBenxCCLshc/mELUjwCSHsilzuFNYmwSeEsCsSfMLaJPiEEHZFgk9YmwSfEMKuSPAJa5PgE0LYFQk+YW0SfEIIuyLBJ6xN5vEJIeyKyWTC3d1d5vIJq5EenxDCrjg4OODt7c3Vq1e1LkXkUhJ8Qgi7I5c7hTVJ8Akh7I4En7AmCT4hhN2R4BPWJMEnhLA7EnzCmiT4hBB2R4JPWJMEnxDC7kjwCWuSeXxCCLsjc/mENUmPTwhhd2Qun7AmCT4hhF2Sy53CWiT4hBB2SYJPWIsEnxDCLknwCWuR4BNC2CUJPmEtEnxCCLskwSesRYJPCGGXJPiEtcg8PiGEXZK5fMJapMcnhLBLMpdPWIsEnxDCbsnlTmENEnxCCLslwSesQYJPCGG3JPiENUjwCSHslgSfsAYJPiGE3ZLgE9YgwSeEsFu+vr5cuXJF6zJELiPz+IQQduvRXL7o6GhcXV21LkfkEtLjE0LYLQcHB0qXLi1z+YRFSfAJIeya3OcTlibBJ4SwaxJ8wtIk+IQQdk2CT1iaBJ8Qwq5J8AlLk+ATQtg1CT5haRJ8Qgi7JsEnLE3m8Qkh7JrM5ROWJj0+IYRdk7l8wtIk+IQQdk8udwpLkuATQtg9CT5hSRJ8Qgi7J8EnLEmCTwhh9yT4hCVJ8Akh7J4En7AkCT4hhN2T4BOWJPP4hBB2T+byCUuSHp8Qwu7JXD5hSRJ8QogcQS53CkuR4BNC5AgSfMJSJPiEEDmCBJ+wFAk+IUSOIMEnLEWCTwiRI/j6+nLlyhWtyxC5gASfECJHkB6fsBSZxyeEyBHMZjPu7u5ERUXh5uamdTkiB5MenxAiR9Dr9TKXT1iEBJ8QIseQy53CEiT4hBA5hgSfsAS5xyeEyAEUsIcTJ8bi4XGPChV8gSJAe6A7IPf8ROZJ8Akh7FgSsACYCdxHqVh0usef9/zn9/7AR0BJ25YnciQJPiGEnboPtAFOAXHPONaJ5BDcBdS0cl0ip5PgE0LYoXigAXAOSMzC6/IBh4GXrFGUyCVkcIsQwg6NAi7wdOi9/DK4uv5/e3evE0UUxmH8wSVZPxYTE+JHtLEQuAIpKEzEKA3XQEdhZ6xI6GykJOECjJHKxGwjCR2hsTAxFhZLAbEA6o2sCYY1WJxCXWZc1B09M+f5JdPsnNm81f7nnT3nDDQa4Rgf772uA9wDvv6LIlVSBp+kyLSBF4Su76SVFeh0wrG11Xv2GPgErBVaocrN4JMUmWf83U/TAfB0MKWokgw+SZFZ5leTWRYWYHQUpqZgYyNv1DvAHV6UzeCTFJn93DNLS7CzA3t7MD8Ps7OwvZ01sg58LKg+lZ3BJykix4S1e9kmJ2FkBOp1mJsLXd9a7t95B0UUqAow+CRFZIjQrZ1y9BDkL8i6OIiCVEEGn6TI3Mz8tN2G9XU4PIRuF1ZXYXMTZmayRn8BbhVZpEps+H8XIEk/eww8IqzJ++7oCBYXodWCWg0mJqDZhLGxrO+4A1wtvlSVkju3SIrMZ+Ay/bcpy9MAmsD0wCpStfioU1JkLgAPgfN/cG0NuAHcHWhFqhY7PkkR6gIPgDfk7eBy0hngEvCeEH5SNjs+SREaBl4D9wkdYD/ngOvAWww99WPwSYrUWeAV8By4TQi33vl4DeAa8AT4QN6MUOlHPuqUVBIt4CWwS1iucIXwJoZpvIfX7zD4JElJ8TZJkpQUg0+SlBSDT5KUFINPkpQUg0+SlBSDT5KUFINPkpQUg0+SlBSDT5KUFINPkpQUg0+SlBSDT5KUFINPkpQUg0+SlBSDT5KUlG/3W/g8mlavdQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['lightcoral', 'lightgreen', 'yellow']\n",
    "node_color_map = [colors[s] for s in ordered_state]\n",
    "\n",
    "nx.draw(problem.source_graph,\n",
    "        pos=nx.spring_layout(problem.source_graph, seed = 3),\n",
    "        with_labels=True,\n",
    "        node_color=node_color_map)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Example 3: Generating and Running TSP using the GA algorithm"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "# Generate a new TSP problem using a fixed seed.\n",
    "problem = TSPGenerator().generate(seed=123456, number_of_cities=20)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The input graph generated for the problem looks like this:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)         # Prepare 2 plots\n",
    "ax.set_yticklabels([])\n",
    "ax.set_xticklabels([])\n",
    "for i, (x,y) in enumerate(problem.coords):\n",
    "    ax.scatter(x,y, s=200, c='cornflowerblue')             # plot A\n",
    "node_labels = {k:str(v) for k, v in enumerate(string.ascii_uppercase) if k < len(problem.source_graph.nodes)}\n",
    "for i in node_labels.keys():\n",
    "    x,y = problem.coords[i]\n",
    "    plt.text(x, y, node_labels[i], ha=\"center\", va=\"center\", c='white', fontweight='bold',\n",
    "             bbox = dict(boxstyle=f\"circle,pad=0.15\", fc='cornflowerblue'))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "# create a runner class and solve the problem\n",
    "ga = GARunner(problem=problem,\n",
    "              experiment_name='tsp_ga',\n",
    "              output_directory=None, # note: specify an output directory to have results saved to disk\n",
    "              seed=123456,\n",
    "              iteration_list=2 ** np.arange(11),\n",
    "              population_sizes=[10, 20],\n",
    "              mutation_rates=[0.1, 0.25, 0.5])\n",
    "\n",
    "# the two data frames will contain the results\n",
    "df_run_stats, df_run_curves = ga.run()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The preceding code will run the `GA` algorithm nine times for at most 1024 iterations per run.\n",
    "Each run is a permutation from the list of `population_sizes` and `mutation_rates`.\n",
    "\n",
    "Note that the initial state parameters here are just toy values picked specifically\n",
    "for this example. You will have to choose your own range of values for your\n",
    "assignment. I strongly recommend you don't just copy these, or you will find\n",
    "that the grading is unlikely to go the way you would like.\n",
    "\n",
    "Really. I mean it... A mutation rate of 0.5 is little better than a pure random search.\n",
    "\n",
    "The output in the `df_run_stats` dataframe contains snapshots of the state of the algorithm at the iterations\n",
    "specified in the `iteration_list` passed into the runner class.\n",
    "\n",
    "The first row (corresponding to the first run of this algorithm) are as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n      <th>State</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2722.031402</td>\n      <td>10</td>\n      <td>0.001058</td>\n      <td>[19, 13, 12, 9, 5, 6, 2, 3, 18, 8, 4, 7, 0, 14, 16, 15, 11, 17, 10, 1]</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(df_run_stats[['Iteration', 'Fitness', 'FEvals', 'Time', 'State']][0:1].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The state information is excluded from the previous output.\n",
    "\n",
    "A sample of this is below:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Population Size</th>\n      <th>Mutation Rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10</td>\n      <td>0.1</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_sample = df_run_stats[['Population Size', 'Mutation Rate']][:1]\n",
    "HTML(state_sample.to_html())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "So, to pick out the most performant run from the dataframe, you need to find the row with the best fitness.\n",
    "As TSP is a minimization problem, you'd pick the row with the minimum fitness.\n",
    "\n",
    "However, I'm going to look in the `run_curves` (which stores minimal basic information every iteration) to\n",
    "find out which input state achieved the best fitness in the fewest fitness evaluations."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Time</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Population Size</th>\n      <th>Mutation Rate</th>\n      <th>max_iters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2724</th>\n      <td>792</td>\n      <td>1.615769</td>\n      <td>939.836374</td>\n      <td>8760.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2725</th>\n      <td>793</td>\n      <td>1.618006</td>\n      <td>939.836374</td>\n      <td>8771.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2726</th>\n      <td>794</td>\n      <td>1.620256</td>\n      <td>939.836374</td>\n      <td>8782.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2727</th>\n      <td>795</td>\n      <td>1.622528</td>\n      <td>939.836374</td>\n      <td>8793.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2728</th>\n      <td>796</td>\n      <td>1.624695</td>\n      <td>939.836374</td>\n      <td>8804.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2729</th>\n      <td>797</td>\n      <td>1.626893</td>\n      <td>939.836374</td>\n      <td>8815.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2730</th>\n      <td>798</td>\n      <td>1.629253</td>\n      <td>939.836374</td>\n      <td>8826.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2731</th>\n      <td>799</td>\n      <td>1.631371</td>\n      <td>939.836374</td>\n      <td>8837.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2732</th>\n      <td>800</td>\n      <td>1.633683</td>\n      <td>939.836374</td>\n      <td>8848.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n    <tr>\n      <th>2733</th>\n      <td>801</td>\n      <td>1.635748</td>\n      <td>939.836374</td>\n      <td>8859.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_fitness = df_run_curves['Fitness'].min()\n",
    "best_runs = df_run_curves[df_run_curves['Fitness'] == best_fitness]\n",
    "\n",
    "HTML(best_runs[0:10].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "This gives us nine candidates for the best run. We are going to pick the one with\n",
    "that reached the best fitness value in the fewest number of evaluations.\n",
    "\n",
    "(We could also have chosen to use `Iterations` as our criteria.)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "minimum_evaluations = best_runs['FEvals'].min()\n",
    "\n",
    "best_curve_run = best_runs[best_runs['FEvals'] == minimum_evaluations]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The best runs using these criteria is as follows:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Time</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Population Size</th>\n      <th>Mutation Rate</th>\n      <th>max_iters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2724</th>\n      <td>792</td>\n      <td>1.615769</td>\n      <td>939.836374</td>\n      <td>8760.0</td>\n      <td>10</td>\n      <td>0.5</td>\n      <td>1024</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(best_curve_run.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "This has the following identifying state information:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Mutation Rate: 0.5, best Population Size: 10\n"
     ]
    }
   ],
   "source": [
    "best_mr = best_curve_run['Mutation Rate'].iloc()[0]\n",
    "best_pop_size = best_curve_run['Population Size'].iloc()[0]\n",
    "print(f'Best Mutation Rate: {best_mr}, best Population Size: {best_pop_size}')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "To map this back to the `run_stats` we look at the configuration data included in\n",
    "the curve data. The curve data includes at least the minimum identifying information\n",
    "to determine which run each row came from.\n",
    "\n",
    "In this case, the values we are looking for are the `Mutation Rate` and `Population Size`.\n",
    "\n",
    "So, we are looking for all rows in `df_run_stats` where the mutation rate and population size are equal to our best values."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>24</th>\n      <td>0</td>\n      <td>2722.031402</td>\n      <td>10</td>\n      <td>0.000707</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>1</td>\n      <td>2257.114802</td>\n      <td>22</td>\n      <td>0.006444</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2</td>\n      <td>2257.114802</td>\n      <td>33</td>\n      <td>0.012500</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>4</td>\n      <td>2132.447496</td>\n      <td>56</td>\n      <td>0.020088</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>8</td>\n      <td>2042.852057</td>\n      <td>101</td>\n      <td>0.030391</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>16</td>\n      <td>1676.952309</td>\n      <td>192</td>\n      <td>0.051568</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>32</td>\n      <td>1620.511778</td>\n      <td>372</td>\n      <td>0.083747</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>64</td>\n      <td>1335.720410</td>\n      <td>730</td>\n      <td>0.148626</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>128</td>\n      <td>1221.503390</td>\n      <td>1442</td>\n      <td>0.261459</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>256</td>\n      <td>1074.768322</td>\n      <td>2857</td>\n      <td>0.516374</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>512</td>\n      <td>956.906962</td>\n      <td>5677</td>\n      <td>0.978203</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>1024</td>\n      <td>939.836374</td>\n      <td>11312</td>\n      <td>1.987658</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_stats_best_run = df_run_stats[(df_run_stats['Mutation Rate'] == best_mr) & (df_run_stats['Population Size'] == best_pop_size)]\n",
    "HTML(run_stats_best_run[['Iteration', 'Fitness', 'FEvals', 'Time']].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "And the best state associated with this is:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>State</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>35</th>\n      <td>[3, 7, 4, 11, 13, 10, 2, 18, 6, 1, 5, 12, 8, 16, 0, 19, 15, 14, 17, 9]</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_state = run_stats_best_run[['State']].tail(1)\n",
    "HTML(best_state.to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Which results in a graph looking like this:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "ordered_state = literal_eval(run_stats_best_run['State'].tail(1).values[0])\n",
    "edge_labels = {(ordered_state[i], ordered_state[(i+1) % len(ordered_state)]):f'{str(i+1)}➜' for i in range(len(ordered_state))}\n",
    "# print(ordered_state)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)         # Prepare 2 plots\n",
    "ax.set_yticklabels([])\n",
    "ax.set_xticklabels([])\n",
    "for i, (x,y) in enumerate(problem.coords):\n",
    "    ax.scatter(x,y, s=1,c='green' if i == 5 else 'cornflowerblue')             # plot A\n",
    "\n",
    "\n",
    "for i in range(len(ordered_state)):\n",
    "    start_node = ordered_state[i]\n",
    "    end_node = ordered_state[(i+1) % len(ordered_state)]\n",
    "    start_pos = problem.coords[start_node]\n",
    "    end_pos = problem.coords[end_node]\n",
    "    ax.annotate(\"\",\n",
    "            xy=start_pos, xycoords='data',\n",
    "            xytext=end_pos, textcoords='data',\n",
    "            c='red',\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            ec='red',\n",
    "                            connectionstyle=\"arc3\"))\n",
    "node_labels = {k:str(v) for k, v in enumerate(string.ascii_uppercase) if k < len(problem.source_graph.nodes)}\n",
    "\n",
    "for i in node_labels.keys():\n",
    "    x,y = problem.coords[i]\n",
    "    plt.text(x, y, node_labels[i], ha=\"center\", va=\"center\", c='white', fontweight='bold',\n",
    "             bbox = dict(boxstyle=f\"circle,pad=0.15\", fc='green' if i == ordered_state[0] else 'cornflowerblue'))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "And, to verify that the route is correct (or at least, the shortest one found):"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "route_length: (939.836374) equal to best_fitness: (939.836374)\n"
     ]
    }
   ],
   "source": [
    "all_edge_lengths = {(x, y):d for x, y, d in problem.distances}\n",
    "all_edge_lengths.update({(y, x):d for x, y, d in problem.distances})\n",
    "\n",
    "route_length = sum([all_edge_lengths[k] for k in edge_labels.keys()])\n",
    "print(f'route_length: ({round(route_length, 6)}) equal to best_fitness: ({round(best_fitness, 6)})')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Example 4: Using the NNGSRunner with the RHC algorithm"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:    5.7s remaining:    8.6s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:    5.8s finished\n"
     ]
    }
   ],
   "source": [
    "# Load and Split data into training and test sets\n",
    "data = load_iris()\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.data, data.target, test_size = 0.3, random_state = 123456\n",
    ")\n",
    "\n",
    "# Normalize feature data\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# One hot encode target values\n",
    "one_hot = OneHotEncoder()\n",
    "y_train_hot = one_hot.fit_transform(y_train.reshape(-1, 1)).todense()\n",
    "y_test_hot = one_hot.transform(y_test.reshape(-1, 1)).todense()\n",
    "\n",
    "grid_search_parameters = {\n",
    "    'max_iters': [1000],                          # nn params\n",
    "    'learning_rate': [1e-2],                       # nn params\n",
    "    'activation': [mlrose_hiive.relu],            # nn params\n",
    "    'restarts': [1],                             # rhc params\n",
    "}\n",
    "\n",
    "nnr = NNGSRunner(\n",
    "    x_train=X_train_scaled,\n",
    "    y_train=y_train_hot,\n",
    "    x_test=X_test_scaled,\n",
    "    y_test=y_test_hot,\n",
    "    experiment_name='nn_test_rhc',\n",
    "    algorithm=mlrose_hiive.algorithms.rhc.random_hill_climb,\n",
    "    grid_search_parameters=grid_search_parameters,\n",
    "    iteration_list=[1, 10, 50, 100, 250, 500, 1000],\n",
    "    hidden_layer_sizes=[[2]],\n",
    "    bias=True,\n",
    "    early_stopping=True,\n",
    "    clip_max=5,\n",
    "    max_attempts=500,\n",
    "    n_jobs=5,\n",
    "    seed=123456,\n",
    "    output_directory=None\n",
    ")\n",
    "\n",
    "\n",
    "run_stats_df, curves_df, cv_results_df, grid_search_cv = nnr.run()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The runner returns the `run_stats` and `curves` corresponding to *best* hyperparameter combination,\n",
    "as well as the cross validation results and the underlying `GridSearchCV` object used in the run."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = grid_search_cv.predict(X_test_scaled)\n",
    "y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)\n",
    "print(y_test_accuracy)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = grid_search_cv.predict(X_train_scaled)\n",
    "y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)\n",
    "print(y_train_accuracy)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>current_restart</th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n      <th>learning_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1.306448</td>\n      <td>1</td>\n      <td>0.003884</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1.290536</td>\n      <td>3</td>\n      <td>0.012704</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>10</td>\n      <td>1.246087</td>\n      <td>14</td>\n      <td>0.029880</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>50</td>\n      <td>1.115004</td>\n      <td>67</td>\n      <td>0.094185</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>100</td>\n      <td>1.044654</td>\n      <td>127</td>\n      <td>0.158927</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>250</td>\n      <td>0.877647</td>\n      <td>308</td>\n      <td>0.366079</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>500</td>\n      <td>0.739120</td>\n      <td>614</td>\n      <td>0.766410</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>1000</td>\n      <td>0.732424</td>\n      <td>1175</td>\n      <td>1.546452</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1.306448</td>\n      <td>1175</td>\n      <td>1.554133</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1.306448</td>\n      <td>1176</td>\n      <td>1.560976</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1</td>\n      <td>10</td>\n      <td>1.247648</td>\n      <td>1188</td>\n      <td>1.578570</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1</td>\n      <td>50</td>\n      <td>1.148455</td>\n      <td>1239</td>\n      <td>1.658799</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1</td>\n      <td>100</td>\n      <td>1.105441</td>\n      <td>1298</td>\n      <td>1.762235</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1</td>\n      <td>250</td>\n      <td>0.931072</td>\n      <td>1490</td>\n      <td>2.064673</td>\n      <td>0.01</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(run_stats_df[['current_restart', 'Iteration', 'Fitness', 'FEvals', 'Time', 'learning_rate']][0:14].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>current_restart</th>\n      <th>Iteration</th>\n      <th>Fitness</th>\n      <th>FEvals</th>\n      <th>Time</th>\n      <th>learning_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1.290536</td>\n      <td>1.0</td>\n      <td>0.003884</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1.290536</td>\n      <td>3.0</td>\n      <td>0.012704</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>2</td>\n      <td>1.290536</td>\n      <td>4.0</td>\n      <td>0.018902</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>3</td>\n      <td>1.290536</td>\n      <td>5.0</td>\n      <td>0.020048</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>4</td>\n      <td>1.263664</td>\n      <td>7.0</td>\n      <td>0.022160</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>5</td>\n      <td>1.263664</td>\n      <td>8.0</td>\n      <td>0.023259</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>6</td>\n      <td>1.246087</td>\n      <td>10.0</td>\n      <td>0.025376</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>7</td>\n      <td>1.246087</td>\n      <td>11.0</td>\n      <td>0.026472</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0</td>\n      <td>8</td>\n      <td>1.246087</td>\n      <td>12.0</td>\n      <td>0.027573</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0</td>\n      <td>9</td>\n      <td>1.246087</td>\n      <td>13.0</td>\n      <td>0.028750</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0</td>\n      <td>10</td>\n      <td>1.246087</td>\n      <td>14.0</td>\n      <td>0.029880</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0</td>\n      <td>11</td>\n      <td>1.246087</td>\n      <td>15.0</td>\n      <td>0.036280</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0</td>\n      <td>12</td>\n      <td>1.246087</td>\n      <td>16.0</td>\n      <td>0.037807</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>0</td>\n      <td>13</td>\n      <td>1.246087</td>\n      <td>17.0</td>\n      <td>0.039168</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0</td>\n      <td>14</td>\n      <td>1.246087</td>\n      <td>18.0</td>\n      <td>0.040967</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0</td>\n      <td>15</td>\n      <td>1.246087</td>\n      <td>19.0</td>\n      <td>0.042132</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>0</td>\n      <td>16</td>\n      <td>1.246087</td>\n      <td>20.0</td>\n      <td>0.043211</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>0</td>\n      <td>17</td>\n      <td>1.232142</td>\n      <td>22.0</td>\n      <td>0.045225</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>0</td>\n      <td>18</td>\n      <td>1.216927</td>\n      <td>24.0</td>\n      <td>0.047344</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0</td>\n      <td>19</td>\n      <td>1.216927</td>\n      <td>25.0</td>\n      <td>0.048530</td>\n      <td>0.01</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(curves_df[['current_restart', 'Iteration', 'Fitness', 'FEvals', 'Time', 'learning_rate']][0:20].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean_test_score</th>\n      <th>rank_test_score</th>\n      <th>mean_train_score</th>\n      <th>param_activation</th>\n      <th>param_hidden_layer_sizes</th>\n      <th>param_learning_rate</th>\n      <th>param_max_iters</th>\n      <th>param_restarts</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.333333</td>\n      <td>1</td>\n      <td>0.333333</td>\n      <td>relu</td>\n      <td>[2]</td>\n      <td>0.01</td>\n      <td>1000</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(cv_results_df[['mean_test_score', 'rank_test_score', 'mean_train_score', 'param_activation', 'param_hidden_layer_sizes', 'param_learning_rate',\n",
    "                    'param_max_iters', 'param_restarts']].to_html())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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