Knapsack Problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import mlrose_hiive as mlrh\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [7, 7]\n",
    "\n",
    "random_state = 2020\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fitness_iteration(curve, title, x=\"Iteration\", y=\"Fitness\"):\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(y)\n",
    "    plt.title(title)\n",
    "    length = len(curve)\n",
    "    plt.plot(range(length), curve, label=y, lw=2)\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Knapsack Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Problem definition for comparison\n",
    "number_of_items_types = 40\n",
    "max_weight_per_item = 25\n",
    "max_value_per_item = 15\n",
    "\n",
    "weights = 1 + np.random.randint(max_weight_per_item, size=number_of_items_types)\n",
    "values = 1 + np.random.randint(max_value_per_item, size=number_of_items_types)\n",
    "max_weight_pct = 0.35\n",
    "max_item_count = 2\n",
    "\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "\n",
    "base_fit_func = mlrh.Knapsack(weights, values, max_weight_pct=max_weight_pct).evaluate\n",
    "\n",
    "def knapsack_fit(state):\n",
    "    global eval_count\n",
    "    eval_count += 1\n",
    "\n",
    "    return base_fit_func(state)\n",
    "\n",
    "\n",
    "# Initialize custom fitness function object\n",
    "fitness = mlrh.CustomFitness(knapsack_fit)\n",
    "\n",
    "prob = mlrh.KnapsackOpt(length=number_of_items_types,\n",
    "                        max_val=max_item_count,\n",
    "                        max_weight_pct=max_weight_pct,\n",
    "                        fitness_fn=fitness,\n",
    "                        maximize=True)\n",
    "\n",
    "experiment_name = \"knapsack\"\n",
    "output_directory = \"knapsack\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MIMIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running mimic\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[25], iteration:[25], done:[True], time:[0.37], fitness:[107.0000]\n",
      "\t[0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0.// 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[0.38], fitness:[118.0000]\n",
      "\t[1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.// 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[0.41], fitness:[118.0000]\n",
      "\t[1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.// 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[0.41], fitness:[118.0000]\n",
      "\t[1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.// 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[10], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[0.42], fitness:[118.0000]\n",
      "\t[1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1.// 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[20], iteration:[25], done:[True], time:[0.38], fitness:[130.0000]\n",
      "\t[1. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0.// 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.25], max_iters:[25]\n",
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      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.25], max_iters:[25]\n",
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      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.// 1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[24], iteration:[25], done:[True], time:[0.47], fitness:[127.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.// 1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[24], iteration:[25], done:[True], time:[0.48], fitness:[127.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.// 1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[25], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[24], iteration:[25], done:[True], time:[0.48], fitness:[127.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.// 1. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[25], iteration:[25], done:[True], time:[0.43], fitness:[143.0000]\n",
      "\t[1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1.// 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[23], iteration:[25], done:[True], time:[0.51], fitness:[138.0000]\n",
      "\t[0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.// 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n"
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      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[23], iteration:[25], done:[True], time:[0.52], fitness:[138.0000]\n",
      "\t[0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.// 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[23], iteration:[25], done:[True], time:[0.52], fitness:[138.0000]\n",
      "\t[0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.// 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.8], max_iters:[25]\n",
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      "\t[0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.// 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.1], max_iters:[25]\n",
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      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[9], iteration:[25], done:[True], time:[0.60], fitness:[136.0000]\n",
      "\t[0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
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      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.5], max_iters:[25]\n",
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      "\n",
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      "\t[0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.7], max_iters:[25]\n",
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      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[9], iteration:[25], done:[True], time:[0.61], fitness:[136.0000]\n",
      "\t[0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.8], max_iters:[25]\n",
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      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[9], iteration:[25], done:[True], time:[0.61], fitness:[136.0000]\n",
      "\t[0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.1], max_iters:[25]\n",
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      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[0.79], fitness:[151.0000]\n",
      "\t[1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.// 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[0.79], fitness:[151.0000]\n",
      "\t[1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.// 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[0.78], fitness:[151.0000]\n",
      "\t[1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.// 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[0.78], fitness:[151.0000]\n",
      "\t[1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.// 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[0.78], fitness:[151.0000]\n",
      "\t[1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.// 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.03], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[1.46], fitness:[158.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.04], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[1.47], fitness:[158.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.04], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[1.47], fitness:[158.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.04], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[1.48], fitness:[158.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.03], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[22], iteration:[25], done:[True], time:[1.48], fitness:[158.0000]\n",
      "\t[1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 1.// 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.08], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.1], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[2.51], fitness:[153.0000]\n",
      "\t[1. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0.// 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.07], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.25], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[2.49], fitness:[153.0000]\n",
      "\t[1. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0.// 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.06], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.5], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[2.49], fitness:[153.0000]\n",
      "\t[1. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0.// 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.06], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.7], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[2.49], fitness:[153.0000]\n",
      "\t[1. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0.// 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.07], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.8], max_iters:[25]\n",
      "runner_name:[mimic], experiment_name:[knapsack], attempt:[19], iteration:[25], done:[True], time:[2.49], fitness:[153.0000]\n",
      "\t[1. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0.// 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 34.19944464601576\n",
      "Saving: [knapsack/knapsack/mimic__knapsack__run_stats_df.csv]\n",
      "Saving: [knapsack/knapsack/mimic__knapsack__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "mim = mlrh.MIMICRunner(problem=prob,\n",
    "                       experiment_name=experiment_name,\n",
    "                       output_directory=output_directory,\n",
    "                       seed=random_state,\n",
    "                       population_sizes=[50, 100, 200, 500, 1000, 2000],\n",
    "                       keep_percent_list=[0.1, 0.25, 0.5, 0.7, 0.8],\n",
    "                       iteration_list=[50],\n",
    "                       use_fast_mimic=True)\n",
    "mim_stats, mim_curve = mim.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Population Size</th>\n",
       "      <th>Keep Percent</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.475960</td>\n",
       "      <td>158.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.80</td>\n",
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       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1.477638</td>\n",
       "      <td>158.0</td>\n",
       "      <td>500</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.467474</td>\n",
       "      <td>158.0</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
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       "      <td>158.0</td>\n",
       "      <td>500</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
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       "      <td>153.0</td>\n",
       "      <td>1000</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2.494532</td>\n",
       "      <td>153.0</td>\n",
       "      <td>1000</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2.493451</td>\n",
       "      <td>153.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.50</td>\n",
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       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2.494513</td>\n",
       "      <td>153.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.25</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2.511004</td>\n",
       "      <td>153.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.10</td>\n",
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      "text/plain": [
       "        Time  Fitness  Population Size  Keep Percent\n",
       "59  1.475960    158.0              500          0.80\n",
       "57  1.477638    158.0              500          0.70\n",
       "55  1.467474    158.0              500          0.50\n",
       "53  1.473435    158.0              500          0.25\n",
       "51  1.461270    158.0              500          0.10\n",
       "69  2.489413    153.0             1000          0.80\n",
       "67  2.494532    153.0             1000          0.70\n",
       "65  2.493451    153.0             1000          0.50\n",
       "63  2.494513    153.0             1000          0.25\n",
       "61  2.511004    153.0             1000          0.10"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Population Size', 'Keep Percent']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./knapsack/knapsack/mimic__knapsack__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MIMIC - Total Function Evaluations: 18022\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 10\n",
    "max_iters = 25\n",
    "keep_pct=0.25\n",
    "pop_size = 1000\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, mimic_curve = mlrh.mimic(prob,\n",
    "                                             max_attempts=max_attempts,\n",
    "                                             max_iters=max_iters,\n",
    "                                             random_state=random_state,\n",
    "                                             pop_size=pop_size,\n",
    "                                             keep_pct=keep_pct,\n",
    "                                             curve=True)\n",
    "print(\"MIMIC - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(mimic_curve,\n",
    "                       \"Knapsack - Mimic: Population Size: {}, Keep Percent: {}\".format(pop_size, keep_pct))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running ga\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[67], done:[True], time:[1.08], fitness:[185.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[54], done:[True], time:[0.85], fitness:[185.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[55], done:[True], time:[0.84], fitness:[186.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[64], done:[True], time:[0.97], fitness:[185.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.01], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[63], done:[True], time:[0.91], fitness:[182.0000]\n",
      "\t[1 1 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.03], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[48], done:[True], time:[3.89], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.03], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[44], done:[True], time:[3.46], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.03], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[47], done:[True], time:[3.63], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.04], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[38], done:[True], time:[2.90], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.03], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[47], done:[True], time:[3.46], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.06], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[49], done:[True], time:[8.11], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.07], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[41], done:[True], time:[6.72], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.06], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[50], done:[True], time:[7.84], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.06], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pop_size:[1000], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[40], done:[True], time:[6.08], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.07], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[knapsack], attempt:[21], iteration:[44], done:[True], time:[6.58], fitness:[187.0000]\n",
      "\t[1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 57.55827337998198\n",
      "Saving: [knapsack/knapsack/ga__knapsack__run_stats_df.csv]\n",
      "Saving: [knapsack/knapsack/ga__knapsack__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "ga = mlrh.GARunner(problem=prob,\n",
    "                   experiment_name=experiment_name,\n",
    "                   output_directory=output_directory,\n",
    "                   seed=random_state,\n",
    "                   max_attempts=20,\n",
    "                   iteration_list=[100],\n",
    "                   population_sizes=[100, 500, 1000],\n",
    "                   mutation_rates=[0.1, 0.25, 0.5, 0.75, 0.9])\n",
    "ga_stats, ga_curve = ga.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Population Size</th>\n",
       "      <th>Mutation Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.625344</td>\n",
       "      <td>187.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.886663</td>\n",
       "      <td>187.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6.083674</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.838819</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.718493</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8.114269</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.461853</td>\n",
       "      <td>187.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.901424</td>\n",
       "      <td>187.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.455903</td>\n",
       "      <td>187.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6.579120</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time  Fitness  Population Size  Mutation Rate\n",
       "15  3.625344    187.0              500           0.50\n",
       "11  3.886663    187.0              500           0.10\n",
       "27  6.083674    187.0             1000           0.75\n",
       "25  7.838819    187.0             1000           0.50\n",
       "23  6.718493    187.0             1000           0.25\n",
       "21  8.114269    187.0             1000           0.10\n",
       "19  3.461853    187.0              500           0.90\n",
       "17  2.901424    187.0              500           0.75\n",
       "13  3.455903    187.0              500           0.25\n",
       "29  6.579120    187.0             1000           0.90"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Population Size', 'Mutation Rate']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./knapsack/knapsack/ga__knapsack__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genetic Alg - Total Function Evaluations: 10438\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 20\n",
    "max_iters = 25\n",
    "mutation_prob=0.6\n",
    "pop_size = 400\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, gen_curve = mlrh.genetic_alg(prob,\n",
    "                                                   max_attempts=max_attempts,\n",
    "                                                   max_iters=max_iters,\n",
    "                                                   random_state=random_state,\n",
    "                                                   pop_size=pop_size,\n",
    "                                                   mutation_prob=mutation_prob,\n",
    "                                                   curve=True)\n",
    "print(\"Genetic Alg - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(gen_curve,\n",
    "                       \"Knapsack - Genetic Alg: mutation_prob: {}, pop_size: {}\".format(mutation_prob, pop_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulated Annealing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sa\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.05], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[706], done:[True], time:[0.09], fitness:[148.0000]\n",
      "\t[1 1 0 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[341], done:[True], time:[0.04], fitness:[134.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[517], done:[True], time:[0.06], fitness:[143.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[402], done:[True], time:[0.04], fitness:[149.0000]\n",
      "\t[1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[689], done:[True], time:[0.09], fitness:[152.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[1], iteration:[2500], done:[True], time:[0.57], fitness:[133.0000]\n",
      "\t[1 1 0 0 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[475], done:[True], time:[0.06], fitness:[147.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[719], done:[True], time:[0.10], fitness:[151.0000]\n",
      "\t[1 0 0 0 1 1 1 0 1 0 1 1 0 1 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 1 0 0// 1 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[1], iteration:[2500], done:[True], time:[0.59], fitness:[0.0000]\n",
      "\t[0 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 0 0 0 0 0 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 1// 0 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[537], done:[True], time:[0.06], fitness:[156.0000]\n",
      "\t[1 1 0 0 1 0 0 1 1 0 1 1 0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[201], iteration:[865], done:[True], time:[0.13], fitness:[154.0000]\n",
      "\t[1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 1 1 1 0// 1 1 1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[knapsack], attempt:[2], iteration:[2500], done:[True], time:[0.58], fitness:[109.0000]\n",
      "\t[0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0// 0 1 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 4.5006845309981145\n",
      "Saving: [knapsack/knapsack/sa__knapsack__run_stats_df.csv]\n",
      "Saving: [knapsack/knapsack/sa__knapsack__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "sa = mlrh.SARunner(problem=prob,\n",
    "                   experiment_name=experiment_name,\n",
    "                   output_directory=output_directory,\n",
    "                   seed=random_state,\n",
    "                   max_attempts=200,\n",
    "                   iteration_list=[2500],\n",
    "                   temperature_list=[0.05, 0.1, 0.5, 1, 10, 20, 25],\n",
    "                   decay_list=[mlrh.GeomDecay, mlrh.ExpDecay, mlrh.ArithDecay])\n",
    "sa_stats, sa_curve = sa.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>schedule_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.064876</td>\n",
       "      <td>156.0</td>\n",
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       "      <th>39</th>\n",
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       "      <th>27</th>\n",
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       "      <td>149.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>geometric</td>\n",
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       "      <th>17</th>\n",
       "      <td>0.086248</td>\n",
       "      <td>148.0</td>\n",
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       "      <th>31</th>\n",
       "      <td>0.059440</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>geometric</td>\n",
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       "      <th>23</th>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.037956</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>exponential</td>\n",
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       "      <th>15</th>\n",
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       "      <td>exponential</td>\n",
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      "text/plain": [
       "        Time  Fitness  Temperature schedule_type\n",
       "37  0.064876    156.0         25.0     geometric\n",
       "39  0.125914    154.0         25.0   exponential\n",
       "27  0.090298    152.0         10.0   exponential\n",
       "33  0.098334    151.0         20.0   exponential\n",
       "25  0.043515    149.0         10.0     geometric\n",
       "17  0.086248    148.0          0.5    arithmetic\n",
       "31  0.059440    147.0         20.0     geometric\n",
       "23  0.058411    143.0          1.0    arithmetic\n",
       "21  0.037956    134.0          1.0   exponential\n",
       "15  0.039891    134.0          0.5   exponential"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Temperature', 'schedule_type']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./knapsack/knapsack/sa__knapsack__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Annealing - Total Function Evaluations: 472\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 200\n",
    "max_iters = 2500\n",
    "init_temp = 0.1\n",
    "schedule = mlrh.ExpDecay(init_temp)\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, sa_curve = mlrh.simulated_annealing(prob,\n",
    "                                                             max_attempts=max_attempts,\n",
    "                                                             max_iters=max_iters,\n",
    "                                                             random_state=random_state,\n",
    "                                                             schedule=schedule,\n",
    "                                                             curve=True)\n",
    "print(\"Simulated Annealing - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(sa_curve,\n",
    "                       \"Knapsack - Simulated Annealing: schedule: {}, init_temp: {}\".format(schedule.__class__.__name__, init_temp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Randomized Hill Climbing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running rhc\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "restarts:[10], max_iters:[2500], current_restart:[0]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[0]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.02], fitness:[0.0000]\n",
      "\t[0 0 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0// 1 1 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[1]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.03], fitness:[0.0000]\n",
      "\t[1 0 1 0 0 0 1 0 0 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1// 0 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[1]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.06], fitness:[0.0000]\n",
      "\t[1 0 1 0 0 0 1 0 0 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1// 0 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[2]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.07], fitness:[0.0000]\n",
      "\t[0 1 1 1 1 1 1 1 0 0 0 0 1 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1// 1 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[2]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.09], fitness:[0.0000]\n",
      "\t[0 1 1 1 1 1 1 1 0 0 0 0 1 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1// 1 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[3]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.11], fitness:[0.0000]\n",
      "\t[1 1 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 1 0 1 1 0 1 1 1 0 0 0// 1 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[3]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.14], fitness:[0.0000]\n",
      "\t[1 1 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 1 0 1 1 0 1 1 1 0 0 0// 1 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[4]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.16], fitness:[0.0000]\n",
      "\t[1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 1 0 1 0 1 1 1 1 0 1 1// 0 1 1]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[4]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.19], fitness:[0.0000]\n",
      "\t[1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 1 0 1 0 1 1 1 1 0 1 1// 0 1 1]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[5]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.21], fitness:[0.0000]\n",
      "\t[1 0 1 0 1 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1// 1 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[5]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.25], fitness:[0.0000]\n",
      "\t[1 0 1 0 1 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1// 1 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[6]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.29], fitness:[0.0000]\n",
      "\t[0 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 1 0 1 1 0// 0 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[6]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[249], done:[True], time:[0.35], fitness:[115.0000]\n",
      "\t[0 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0// 0 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[7]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.37], fitness:[0.0000]\n",
      "\t[1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 0 0 1 0 1 0 0 1 1 1 0 1 1 1 1// 1 1 1]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[7]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.42], fitness:[0.0000]\n",
      "\t[1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 0 0 1 0 1 0 0 1 1 1 0 1 1 1 1// 1 1 1]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[8]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.45], fitness:[0.0000]\n",
      "\t[0 0 1 1 1 1 0 0 1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0// 1 1 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[8]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[206], done:[True], time:[0.50], fitness:[128.0000]\n",
      "\t[0 0 1 1 1 1 0 0 1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 0 0 0 0 1 1 1 0 0 0 0// 1 1 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[9]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.53], fitness:[0.0000]\n",
      "\t[0 1 0 1 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 1 0// 1 1 1]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[9]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.59], fitness:[0.0000]\n",
      "\t[0 1 0 1 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 1 0// 1 1 1]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[10]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[0], iteration:[0], done:[False], time:[0.63], fitness:[0.0000]\n",
      "\t[1 0 0 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 1 0 1 1 1 1// 1 0 0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[10]\n",
      "runner_name:[rhc], experiment_name:[knapsack], attempt:[201], iteration:[200], done:[True], time:[0.69], fitness:[0.0000]\n",
      "\t[1 0 0 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 1 0 1 1 1 1// 1 0 0]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 0.7314738039858639\n",
      "Saving: [knapsack/knapsack/rhc__knapsack__run_stats_df.csv]\n",
      "Saving: [knapsack/knapsack/rhc__knapsack__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "rhc = mlrh.RHCRunner(problem=prob,\n",
    "                    experiment_name=experiment_name,\n",
    "                    output_directory=output_directory,\n",
    "                    seed=random_state,\n",
    "                    max_attempts=200,\n",
    "                    iteration_list=[2500],\n",
    "                    restart_list=[10])\n",
    "rhc_stats, rhc_curve = rhc.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Restarts</th>\n",
       "      <th>current_restart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.497094</td>\n",
       "      <td>128.0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.346358</td>\n",
       "      <td>115.0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.017088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.631292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.591161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.534745</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.446908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.418557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.373873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time  Fitness  Restarts  current_restart\n",
       "17  0.497094    128.0        10                8\n",
       "13  0.346358    115.0        10                6\n",
       "0   0.000193      0.0        10                0\n",
       "1   0.017088      0.0        10                0\n",
       "20  0.631292      0.0        10               10\n",
       "19  0.591161      0.0        10                9\n",
       "18  0.534745      0.0        10                9\n",
       "16  0.446908      0.0        10                8\n",
       "15  0.418557      0.0        10                7\n",
       "14  0.373873      0.0        10                7"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Restarts', 'current_restart']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./knapsack/knapsack/rhc__knapsack__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randomized Hill Climbing - Total Function Evaluations: 5589\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 500\n",
    "max_iters = 2500\n",
    "restarts = 10\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, rhc_curve = mlrh.random_hill_climb(prob,\n",
    "                                                         max_attempts=max_attempts,\n",
    "                                                         max_iters=max_iters,\n",
    "                                                         random_state=random_state,\n",
    "                                                         restarts=restarts,\n",
    "                                                         curve=True)\n",
    "print(\"Randomized Hill Climbing - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(rhc_curve,\n",
    "                       \"Queens - Randomized Hill Climbing: restarts: {}\".format(restarts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitness vs Iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_curves = {\"MIMIC\": mimic_curve, \"Genetic Alg\": gen_curve, \"Simulated Annealing\": sa_curve, \"Random Hill Climb\": rhc_curve}\n",
    "\n",
    "np.array([len(x) for x in all_curves]).max()\n",
    "\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Fitness\")\n",
    "plt.title(\"Knapsack Problem\")\n",
    "\n",
    "for name, curve in all_curves.items():\n",
    "    plt.plot(range(len(curve)), curve, label=name, lw=2)\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function Evaluations vs Input Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_gen(input=40):\n",
    "    number_of_items_types = input\n",
    "    max_weight_per_item = 25\n",
    "    max_value_per_item = 15\n",
    "\n",
    "    weights = 1 + np.random.randint(max_weight_per_item, size=number_of_items_types)\n",
    "    values = 1 + np.random.randint(max_value_per_item, size=number_of_items_types)\n",
    "    max_weight_pct = 0.35\n",
    "    max_item_count = 2\n",
    "\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "\n",
    "    base_fit_func = mlrh.Knapsack(weights, values, max_weight_pct=max_weight_pct).evaluate\n",
    "\n",
    "    def knapsack_fit(state):\n",
    "        global eval_count\n",
    "        eval_count += 1\n",
    "\n",
    "        return base_fit_func(state)\n",
    "\n",
    "\n",
    "    # Initialize custom fitness function object\n",
    "    fitness = mlrh.CustomFitness(knapsack_fit)\n",
    "\n",
    "    prob = mlrh.KnapsackOpt(length=number_of_items_types,\n",
    "                        max_val=max_item_count,\n",
    "                        max_weight_pct=max_weight_pct,\n",
    "                        fitness_fn=fitness,\n",
    "                        maximize=True)\n",
    "    \n",
    "    return prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_rhc(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 500\n",
    "    max_iters = 2500\n",
    "    restarts = 10\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, rhc_curve = mlrh.random_hill_climb(prob,\n",
    "                                                             max_attempts=max_attempts,\n",
    "                                                             max_iters=max_iters,\n",
    "                                                             random_state=random_state,\n",
    "                                                             restarts=restarts,\n",
    "                                                             curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(rhc_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_sa(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 200\n",
    "    max_iters = 2500\n",
    "    init_temp = 0.1\n",
    "    schedule = mlrh.ExpDecay(init_temp)\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, sa_curve = mlrh.simulated_annealing(prob,\n",
    "                                                             max_attempts=max_attempts,\n",
    "                                                             max_iters=max_iters,\n",
    "                                                             random_state=random_state,\n",
    "                                                             schedule=schedule,\n",
    "                                                             curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(sa_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_gen(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 20\n",
    "    max_iters = 25\n",
    "    mutation_prob=0.6\n",
    "    pop_size = 400\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, gen_curve = mlrh.genetic_alg(prob,\n",
    "                                                   max_attempts=max_attempts,\n",
    "                                                   max_iters=max_iters,\n",
    "                                                   random_state=random_state,\n",
    "                                                   pop_size=pop_size,\n",
    "                                                   mutation_prob=mutation_prob,\n",
    "                                                   curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(gen_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_mimic(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 10\n",
    "    max_iters = 25\n",
    "    keep_pct=0.25\n",
    "    pop_size = 1000\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, mimic_curve = mlrh.mimic(prob,\n",
    "                                             max_attempts=max_attempts,\n",
    "                                             max_iters=max_iters,\n",
    "                                             random_state=random_state,\n",
    "                                             pop_size=pop_size,\n",
    "                                             keep_pct=keep_pct,\n",
    "                                             curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(mimic_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "inputs = [10, 20, 30, 40, 50, 60, 70, 80]\n",
    "\n",
    "mimic_curve_eval = [eval_count_mimic(prob_gen(x)) for x in inputs]\n",
    "gen_curve_eval = [eval_count_gen(prob_gen(x)) for x in inputs]\n",
    "sa_curve_eval = [eval_count_sa(prob_gen(x)) for x in inputs]\n",
    "rhc_curve_eval = [eval_count_rhc(prob_gen(x)) for x in inputs]\n",
    "\n",
    "all_curves = {\"MIMIC\": mimic_curve_eval,\n",
    "              \"Genetic Alg\": gen_curve_eval,\n",
    "              \"Simulated Annealing\": sa_curve_eval,\n",
    "              \"Random Hill Climb\": rhc_curve_eval}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = [(\"Inputs\", \"Function Evaluations\"),\n",
    "     (\"Inputs\", \"Iterations\"),\n",
    "     (\"Inputs\", \"Wall Clock Time (sec)\")]\n",
    "\n",
    "for i in range(3):\n",
    "    plt.xlabel(l[i][0])\n",
    "    plt.ylabel(l[i][1])\n",
    "    plt.title(\"Knapsack Problem\")\n",
    "\n",
    "    for name, curve in all_curves.items():\n",
    "        plt.plot(inputs, [x[i] for x in curve], label=name, lw=2)\n",
    "\n",
    "        plt.legend(loc=\"best\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Inputs\")\n",
    "plt.ylabel(\"Avg. Time (sec) / Iteration\")\n",
    "plt.title(\"Knapsack Problem\")\n",
    "\n",
    "for name, curve in all_curves.items():\n",
    "    avg_time_div_iterations = np.array([x[2] for x in curve]) / np.array([x[1] for x in curve])\n",
    "    plt.plot(inputs, avg_time_div_iterations, label=name, lw=2)\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}