Visualizing_Samplesets-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with samplesets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need a model to submit to the CQM Hybrid Solver through Leap.<br>\n",
    "For this example, we will submit a CQM based on a Quadratic Assignment Problem.\n",
    "\n",
    "### Build a CQM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Variables: 4\n"
     ]
    }
   ],
   "source": [
    "from dimod import ConstrainedQuadraticModel\n",
    "import random\n",
    "\n",
    "# Set size parameter. Model will have n^2 variables.\n",
    "n=2\n",
    "\n",
    "# Instantiate CQM and add grid of variables\n",
    "cqm = ConstrainedQuadraticModel()\n",
    "cqm.add_variables('BINARY', [(a,b) for a in range(n) for b in range(n)])\n",
    "\n",
    "# Add psuedorandom dense objective.\n",
    "random.seed(1)\n",
    "cqm.set_objective([(v,w,random.randint(5,20)) for v in cqm.variables for w in cqm.variables])\n",
    "\n",
    "# Add assignment constraint.\n",
    "for i in range(n):\n",
    "    cqm.add_constraint_from_iterable([((i,j), 1) for j in range(n)], '==', rhs=1, label=f'({i},*)')\n",
    "    cqm.add_constraint_from_iterable([((j,i), 1) for j in range(n)], '==', rhs=1, label=f'(*, {i})')\n",
    "    \n",
    "print('Number of Variables:', len(cqm.variables))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a sampleset by submitting the CQM to the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dwave.system import LeapHybridCQMSampler\n",
    "\n",
    "cqm_sampler = LeapHybridCQMSampler()\n",
    "\n",
    "sampleset = cqm_sampler.sample_cqm(cqm, time_limit=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Raw samplesets are best viewed with a print call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    (0, 0) (0, 1) (1, 0) (1, 1) energy num_oc. is_sat. is_fea.\n",
      "99     1.0    0.0    0.0    0.0    9.0       1 arra...   False\n",
      "100    1.0    0.0    0.0    0.0    9.0       1 arra...   False\n",
      "101    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "102    0.0    0.0    0.0    1.0   19.0       1 arra...   False\n",
      "103    0.0    0.0    0.0    1.0   19.0       1 arra...   False\n",
      "104    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "105    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "106    0.0    0.0    0.0    1.0   19.0       1 arra...   False\n",
      "107    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "108    0.0    0.0    0.0    1.0   19.0       1 arra...   False\n",
      "109    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "110    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "111    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "112    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "113    0.0    0.0    0.0    1.0   19.0       1 arra...   False\n",
      "114    0.0    1.0    0.0    0.0   19.0       1 arra...   False\n",
      "115    0.0    0.0    0.0    1.0   19.0       1 arra...   False\n",
      "116    0.0    0.0    1.0    0.0   20.0       1 arra...   False\n",
      "117    0.0    0.0    1.0    0.0   20.0       1 arra...   False\n",
      "118    0.0    0.0    1.0    0.0   20.0       1 arra...   False\n",
      "119    0.0    0.0    1.0    0.0   20.0       1 arra...   False\n",
      "0      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "1      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "2      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "3      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "4      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "5      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "6      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "7      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "8      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "9      1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "10     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "11     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "12     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "13     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "14     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "15     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "16     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "17     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "18     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "19     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "20     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "21     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "22     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "23     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "24     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "25     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "26     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "27     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "28     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "29     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "30     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "31     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "32     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "33     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "34     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "35     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "36     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "37     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "38     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "39     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "40     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "41     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "42     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "43     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "44     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "45     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "46     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "47     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "48     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "49     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "50     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "51     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "52     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "53     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "54     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "55     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "56     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "57     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "58     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "59     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "60     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "61     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "62     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "63     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "64     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "65     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "66     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "67     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "68     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "69     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "70     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "71     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "72     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "73     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "74     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "75     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "76     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "77     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "78     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "79     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "80     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "81     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "82     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "83     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "84     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "85     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "86     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "87     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "88     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "89     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "90     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "91     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "92     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "93     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "94     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "95     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "96     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "97     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "98     0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "['INTEGER', 120 rows, 120 samples, 4 variables]\n"
     ]
    }
   ],
   "source": [
    "print(sampleset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We filter for the best feasible solution found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 0.0}, energy=9.0, num_occurrences=1, is_satisfied=array([ True, False, False,  True]), is_feasible=False) \n",
      "\n",
      "Sample energy:  9.0 \n",
      "\n",
      "Are all constraints satisfied?  False\n"
     ]
    }
   ],
   "source": [
    "# Best solution (objective minimized)\n",
    "print(sampleset.first, \"\\n\")\n",
    "\n",
    "print(\"Sample energy: \", sampleset.first.energy, \"\\n\")\n",
    "\n",
    "print(\"Are all constraints satisfied? \", sampleset.first.is_feasible)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filter for the best feasible sample - where all hard constraints are satisfied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Samples: 120\n",
      "# Feasible: 99 \n",
      "\n",
      "   (0, 0) (0, 1) (1, 0) (1, 1) energy num_oc. is_sat. is_fea.\n",
      "0     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "1     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "2     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "3     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "4     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "5     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "6     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "7     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "8     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "9     1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "10    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "11    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "12    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "13    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "14    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "15    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "16    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "17    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "18    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "19    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "20    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "21    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "22    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "23    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "24    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "25    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "26    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "27    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "28    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "29    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "30    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "31    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "32    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "33    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "34    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "35    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "36    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "37    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "38    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "39    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "40    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "41    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "42    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "43    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "44    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "45    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "46    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "47    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "48    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "49    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "50    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "51    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "52    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "53    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "54    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "55    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "56    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "57    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "58    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "59    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "60    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "61    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "62    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "63    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "64    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "65    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "66    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "67    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "68    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "69    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "70    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "71    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "72    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "73    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "74    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "75    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "76    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "77    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "78    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "79    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "80    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "81    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "82    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "83    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "84    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "85    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "86    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "87    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "88    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "89    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "90    1.0    0.0    0.0    1.0   53.0       1 arra...    True\n",
      "91    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "92    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "93    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "94    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "95    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "96    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "97    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "98    0.0    1.0    1.0    0.0   67.0       1 arra...    True\n",
      "['INTEGER', 99 rows, 99 samples, 4 variables]\n"
     ]
    }
   ],
   "source": [
    "feasible_sampleset = sampleset.filter(lambda d: d.is_feasible)\n",
    "\n",
    "print('# Samples:', len(sampleset))\n",
    "print('# Feasible:', len(feasible_sampleset), \"\\n\")\n",
    "\n",
    "print(feasible_sampleset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample energy:  53.0 \n",
      "\n",
      "Are all constraints satisfied?  True\n"
     ]
    }
   ],
   "source": [
    "# The best feasible solution found is\n",
    "best = feasible_sampleset.first\n",
    "\n",
    "print(\"Sample energy: \", best.energy, \"\\n\")\n",
    "\n",
    "print(\"Are all constraints satisfied? \", best.is_feasible)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are several ways to pass the information from a sampleset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1): 1.0}, energy=53.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n",
      "Sample(sample={(0, 0): 0.0, (0, 1): 1.0, (1, 0): 1.0, (1, 1): 0.0}, energy=67.0)\n"
     ]
    }
   ],
   "source": [
    "# Looping through the data.\n",
    "for datum in feasible_sampleset.data(fields=['sample', 'energy']):\n",
    "    print(datum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['energy', 'num_occurrences', 'is_satisfied', 'is_feasible'])\n"
     ]
    }
   ],
   "source": [
    "# As a Dictionary.\n",
    "dictionary = feasible_sampleset.data_vectors\n",
    "print(dictionary.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[([1., 0., 0., 1.], 53., 1, [ True,  True,  True,  True],  True)\n",
      " ([1., 0., 0., 1.], 53., 1, [ True,  True,  True,  True],  True)\n",
      " ([1., 0., 0., 1.], 53., 1, [ True,  True,  True,  True],  True)]\n"
     ]
    }
   ],
   "source": [
    "# As a NumPy recarray.\n",
    "array = sampleset.record\n",
    "print(array[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>energy</th>\n",
       "      <th>num_occurrences</th>\n",
       "      <th>is_satisfied</th>\n",
       "      <th>is_feasible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[True, True, True, True]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[True, True, True, True]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[True, True, True, True]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[True, True, True, True]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[True, True, True, True]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              sample  energy  num_occurrences  \\\n",
       "0  {(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...    53.0                1   \n",
       "1  {(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...    53.0                1   \n",
       "2  {(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...    53.0                1   \n",
       "3  {(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...    53.0                1   \n",
       "4  {(0, 0): 1.0, (0, 1): 0.0, (1, 0): 0.0, (1, 1)...    53.0                1   \n",
       "\n",
       "               is_satisfied  is_feasible  \n",
       "0  [True, True, True, True]         True  \n",
       "1  [True, True, True, True]         True  \n",
       "2  [True, True, True, True]         True  \n",
       "3  [True, True, True, True]         True  \n",
       "4  [True, True, True, True]         True  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As a Pandas dataframe.\n",
    "dataframe = sampleset.to_pandas_dataframe(True)\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas even has some of its own quick charting functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'energy'}>,\n",
       "        <AxesSubplot:title={'center':'num_occurrences'}>]], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kB8CHgEPAz7p9PgfsAe6rqhdm5zeRTo+30kvTSDICvFBVzyd5N/D33Yekxz+8/Dbwoap6eB7H1ALmOXBpem8A7uti/RzdVSxJVnLsw9HtxlvzySNwSWqU58AlqVEGXJIaZcAlqVEGXJIaZcAlqVH/D664u+nu2KyVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe.boxplot('energy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}