Partition-problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partition problem\n",
    "### Introduction\n",
    "The background to the partition problem is described in **MOD007357 Coursework Brief 2021-22 (Task C)**, including the description of six simple splitting approaches (labelled A-F).\n",
    "\n",
    "### Implementation summary\n",
    "The splitting algorithms have been implemented in the *python* programming language, and can be conveniently applied to an input array (*multiset*) by initialising an `ArraySplitter` class with the array, and calling the class' `split()` method - providing a string key for the required method.\n",
    "\n",
    "The described methods A-F have been extended with method 'G' - an implementation of the KK splitting algorithm<sup>1</sup>.\n",
    "\n",
    "### ZIP archive\n",
    "The code and output of running the `main()` method can be found in the associated ZIP archive (`partition-problem.zip`).  Contents:\n",
    "\n",
    "```\n",
    "partition-problem.zip\n",
    "  - Partition problem.html\n",
    "  - Partition problem.ipynb\n",
    "  - partition_problem.py\n",
    "  - README.md\n",
    "  - environment.yml\n",
    "  results/\n",
    "    - comparison_pandas.png\n",
    "    - comparison_seaborn.png\n",
    "    - testing_df.csv\n",
    "    - walkthrough_examples.txt\n",
    "```\n",
    "\n",
    "### Dependencies\n",
    "The `conda` environment required to run the `partition_problem.py` code, and this notebook, can be recreated from the *environment.yml* file in the ZIP archive, and activated as follows:\n",
    "\n",
    "```bash\n",
    "conda env create -f environment.yml\n",
    "conda activate partition-problem\n",
    "```\n",
    "\n",
    "We can use a jupyter 'magic' command to generate the *environment.yml* file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda env export --from-history > environment.yml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook\n",
    "This notebook was created within the same python `conda` environment used to run the `partition_problem.py`.\n",
    "\n",
    "To generate the PDF version, the notebook was exported to HTML format and then printed to PDF format from Google Chrome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from partition_problem import ArraySplitter\n",
    "from partition_problem import generate_random_array, run_tests, seaborn_plot, pandas_plot\n",
    "\n",
    "pd.set_option('display.max_rows', 10)  # Keep table displays short\n",
    "sns.set_context('talk')  # Make seaborn plots have good font sizes, line widths, etc "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split method G - the Karmarkar-Karp heuristic (KK)\n",
    "The KK method - described in section 2.3 of [1] - was implemented as an additional method of the `ArraySplitter` class, and is reproduced below for reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approach G - time complexity O(nlogn)\n",
    "def _split_kk(self, array):\n",
    "    \"\"\"Sorts high to low, then removes the first two members of the array.\n",
    "    The 2nd value is subtracted from the first value, and the difference is appended to the \n",
    "    end of the list. The list is then resorted, and the process is repeated until the list \n",
    "    length is 1.\n",
    "\n",
    "    Finally the list is passed back as s1_, and an empty list is passed back as s2_.\n",
    "    This is a different approach to the other algorithms, hence the different variable \n",
    "    naming for clarity.\n",
    "\n",
    "    Passing back the two lists allows downstream calculation of the absolute subset sum \n",
    "    difference (even though we already know the answer)\n",
    "    \"\"\"\n",
    "    s1_ = []\n",
    "    s2_ = []\n",
    "\n",
    "    reverse_sorted_array = sorted(array, reverse=True)  # O(nlogn)\n",
    "    while len(reverse_sorted_array) > 1:  # O(n)\n",
    "        elem_0 = reverse_sorted_array.pop(0)\n",
    "        elem_1 = reverse_sorted_array.pop(0)\n",
    "        reverse_sorted_array.append(elem_0 - elem_1)\n",
    "        reverse_sorted_array.sort(reverse=True)  # O(nlogn)\n",
    "    # We know the difference at this point, but we are not using or returning it\n",
    "    s1_ = reverse_sorted_array\n",
    "    return s1_, s2_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Walkthrough examples\n",
    "The splitting applied by each method can be seen below for a single input array of cardinality 32."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[147, 218, 164, 206, 7, 90, 169, 295, 290, 26, 194, 166, 106, 91, 77, 141, 310, 300, 147, 132, 281, 236, 103, 318, 74, 317, 78, 131, 115, 163, 320, 234]\n"
     ]
    }
   ],
   "source": [
    "array = generate_random_array(min_val=1, max_val=320, cardinality=32)\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply each splitting method in turn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method: A\n",
      "Time complexity: O(n)\n",
      "  Subset 1: [147, 218, 164, 206, 7, 90, 169, 295, 290, 26, 194, 166, 106, 91, 77, 141]\n",
      "  Subset 2: [310, 300, 147, 132, 281, 236, 103, 318, 74, 317, 78, 131, 115, 163, 320, 234]\n",
      "  Absolute partition difference: 872\n",
      "\n",
      "Method: B\n",
      "Time complexity: O(n)\n",
      "  Subset 1: [218, 164, 206, 90, 290, 26, 194, 166, 106, 310, 300, 132, 236, 318, 74, 78, 320, 234]\n",
      "  Subset 2: [147, 7, 169, 295, 91, 77, 141, 147, 281, 103, 317, 131, 115, 163]\n",
      "  Absolute partition difference: 1278\n",
      "\n",
      "Method: C\n",
      "Time complexity: O(n)\n",
      "  Subset 1: [147, 164, 7, 90, 169, 290, 166, 91, 310, 147, 281, 103, 74, 317, 115, 320]\n",
      "  Subset 2: [218, 206, 295, 26, 194, 106, 77, 141, 300, 132, 236, 318, 78, 131, 163, 234]\n",
      "  Absolute partition difference: 64\n",
      "\n",
      "Method: D\n",
      "Time complexity: O(n)\n",
      "  Subset 1: [218, 290, 310, 281, 318, 320, 147, 7, 90, 26, 166, 91, 141, 132, 74, 131]\n",
      "  Subset 2: [206, 295, 194, 300, 236, 317, 234, 164, 169, 106, 77, 147, 103, 78, 115, 163]\n",
      "  Absolute partition difference: 162\n",
      "\n",
      "Method: E\n",
      "Time complexity: O(nlogn)\n",
      "  Subset 1: [320, 317, 300, 290, 236, 218, 194, 166, 163, 147, 132, 115, 103, 90, 77, 26]\n",
      "  Subset 2: [318, 310, 295, 281, 234, 206, 169, 164, 147, 141, 131, 106, 91, 78, 74, 7]\n",
      "  Absolute partition difference: 142\n",
      "\n",
      "Method: F\n",
      "Time complexity: O(nlogn)\n",
      "  Subset 1: [320, 310, 300, 290, 234, 206, 194, 164, 147, 141, 132, 115, 91, 90, 74, 7]\n",
      "  Subset 2: [318, 317, 295, 281, 236, 218, 169, 166, 163, 147, 131, 106, 103, 78, 77, 26]\n",
      "  Absolute partition difference: 16\n",
      "\n",
      "Method: G\n",
      "Time complexity: O(nlogn)\n",
      "  Subset 1: [0]\n",
      "  Subset 2: []\n",
      "  Absolute partition difference: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "splitter = ArraySplitter(array)\n",
    "results = splitter.compare_methods(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input parameters\n",
    "The parameters below can be varied and will control the subsequent code blocks.  These are the same parameters accepted by the `main()` function of `partition_problem.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "CARDINALITIES = [32, 64, 128, 256, 512, 1024]\n",
    "REPEATS = 10000\n",
    "MIN_ARRAY_VAL = 1\n",
    "MAX_VAL_FACTOR = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running tests\n",
    "For each *cardinality* value we will run *number_of_tests* tests.\n",
    "\n",
    "Each test involves the following steps:\n",
    "1. Make a new random array\n",
    "2. Make an `ArraySplitter` instance for the random array\n",
    "3. Use the ArraySplitter's `compare_methods()` function to get the absolute difference of the subset sums for each different split method\n",
    "4. Add the results from the iteration to a list that keeps track of all of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_results = []  # a list to keep each iteration's results in\n",
    "for cardinality in CARDINALITIES:  # iterate over cardinalities\n",
    "    for test in range(REPEATS):  # then iterate over n tests (eg 100)\n",
    "        # 1. Make a new random array\n",
    "        array = generate_random_array(min_val=MIN_ARRAY_VAL,\n",
    "                                      max_val=MAX_VAL_FACTOR * cardinality,\n",
    "                                      cardinality=cardinality)\n",
    "        # 2. Use an ArraySplitter to get the results for each available split method\n",
    "        splitter = ArraySplitter(array)\n",
    "        results = splitter.compare_methods()\n",
    "        # 3. Add the cardinality info to the result\n",
    "        results['cardinality'] = cardinality\n",
    "        # 4. add the result to the testing_results list\n",
    "        testing_results.append(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `partition_problem.py` the above steps can be conveniently run by simply calling the `run_tests()` method.\n",
    "\n",
    "```python\n",
    "testing_results = run_tests(cardinalities=CARDINALITIES, repeats=REPEATS,\n",
    "                            min_array_val=MIN_ARRAY_VAL, max_val_factor=MAX_VAL_FACTOR)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example result in the list (showing the dictionary structure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 195,\n",
       " 'B': 249,\n",
       " 'C': 115,\n",
       " 'D': 139,\n",
       " 'E': 153,\n",
       " 'F': 21,\n",
       " 'G': 1,\n",
       " 'cardinality': 32}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_results[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we convert the whole `testing_results` list of dictionaries into a DataFrame, we get the following shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>195</td>\n",
       "      <td>249</td>\n",
       "      <td>115</td>\n",
       "      <td>139</td>\n",
       "      <td>153</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>1914</td>\n",
       "      <td>286</td>\n",
       "      <td>42</td>\n",
       "      <td>220</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>463</td>\n",
       "      <td>1405</td>\n",
       "      <td>207</td>\n",
       "      <td>7</td>\n",
       "      <td>201</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>1444</td>\n",
       "      <td>20</td>\n",
       "      <td>94</td>\n",
       "      <td>196</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227</td>\n",
       "      <td>277</td>\n",
       "      <td>107</td>\n",
       "      <td>69</td>\n",
       "      <td>133</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>202326</td>\n",
       "      <td>142858</td>\n",
       "      <td>1382</td>\n",
       "      <td>88</td>\n",
       "      <td>5142</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>16631</td>\n",
       "      <td>56079</td>\n",
       "      <td>5151</td>\n",
       "      <td>4215</td>\n",
       "      <td>5515</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>188464</td>\n",
       "      <td>243190</td>\n",
       "      <td>5762</td>\n",
       "      <td>30</td>\n",
       "      <td>5094</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>23092</td>\n",
       "      <td>12468</td>\n",
       "      <td>3900</td>\n",
       "      <td>2384</td>\n",
       "      <td>4818</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>181439</td>\n",
       "      <td>116555</td>\n",
       "      <td>3389</td>\n",
       "      <td>2041</td>\n",
       "      <td>5261</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B     C     D     E   F  G  cardinality\n",
       "0         195     249   115   139   153  21  1           32\n",
       "1          90    1914   286    42   220   4  0           32\n",
       "2         463    1405   207     7   201   3  1           32\n",
       "3         322    1444    20    94   196   2  0           32\n",
       "4         227     277   107    69   133   5  1           32\n",
       "...       ...     ...   ...   ...   ...  .. ..          ...\n",
       "59995  202326  142858  1382    88  5142  20  0         1024\n",
       "59996   16631   56079  5151  4215  5515  25  1         1024\n",
       "59997  188464  243190  5762    30  5094  12  0         1024\n",
       "59998   23092   12468  3900  2384  4818  10  0         1024\n",
       "59999  181439  116555  3389  2041  5261  11  1         1024\n",
       "\n",
       "[60000 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_df = pd.DataFrame(testing_results)\n",
    "testing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with Seaborn\n",
    "The above shape of the data is convenient for readability, but not so good for certain plotting approaches.  The `seaborn` package makes plotting data (including data from a `pandas.DataFrame`) easy, but it is best to provide the data in a \"tall, skinny\" format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert raw results into tall-skinny DataFrame\n",
    "The raw data can be unpivotted using `pandas.melt()`.  This gives us the tall, skinny version of the output data where each method appears not as a column, but as a value in a single 'method' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>cardinality</th>\n",
       "      <th>method</th>\n",
       "      <th>absolute_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>A</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>A</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>A</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>A</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>A</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419995</th>\n",
       "      <td>1024</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419996</th>\n",
       "      <td>1024</td>\n",
       "      <td>G</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419997</th>\n",
       "      <td>1024</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419998</th>\n",
       "      <td>1024</td>\n",
       "      <td>G</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419999</th>\n",
       "      <td>1024</td>\n",
       "      <td>G</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cardinality method  absolute_diff\n",
       "0                32      A            195\n",
       "1                32      A             90\n",
       "2                32      A            463\n",
       "3                32      A            322\n",
       "4                32      A            227\n",
       "...             ...    ...            ...\n",
       "419995         1024      G              0\n",
       "419996         1024      G              1\n",
       "419997         1024      G              0\n",
       "419998         1024      G              0\n",
       "419999         1024      G              1\n",
       "\n",
       "[420000 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tall_df = pd.melt(testing_df, id_vars=['cardinality'],\n",
    "                  value_vars=splitter.func_dict.keys(),\n",
    "                  var_name='method', value_name='absolute_diff')\n",
    "tall_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot\n",
    "Seaborn's `lineplot()` method is an appropriate choice.  This method will plot the mean of the data, and can automatically show variation from the mean in a number of ways.  As we are probably most interested in how confident we are in the predicted mean values, 95% confidence intervals have been chosen (rather than standard deviations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 9), facecolor='white')\n",
    "ax = sns.lineplot(data=tall_df, x='cardinality', y='absolute_diff', hue='method', ci=95)\n",
    "ax.set_xlabel('multiset cardinality')\n",
    "ax.set_xscale('log', base=2)\n",
    "ax.set_ylabel('mean absolute partition difference')\n",
    "ax.set_yscale('log')\n",
    "# Put legend outside plot\n",
    "ax.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "# Underscore assignment to supress Text object output\n",
    "_ = ax.set_title(f'Comparison of multiset splitting methods ({REPEATS} repeats)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of the above steps can be conveniently run by calling the `seaborn_plot()` method from `partition_problem.py`.  The inputs to this function are simply the raw result dictionary list, and the list of cardinality values used to generate the results:\n",
    "\n",
    "```python\n",
    "fig = seaborn_plot(results=testing_results, repeats=REPEATS)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with pandas\n",
    "This is a bit more 'DIY' than using Seaborn.\n",
    "\n",
    "We will use the `groupby()` method of the `pandas.DataFrame` to aggregate the results by cardinality, and generate corresponding statistical values (*mean*, *std*).\n",
    "\n",
    "This grouped form can then be easily used to plot with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardinality</th>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">32</th>\n",
       "      <th>A</th>\n",
       "      <td>417.1434</td>\n",
       "      <td>315.032901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>835.5898</td>\n",
       "      <td>632.621641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>107.0974</td>\n",
       "      <td>75.873346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>53.2330</td>\n",
       "      <td>38.639325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>154.6836</td>\n",
       "      <td>27.448644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1024</th>\n",
       "      <th>C</th>\n",
       "      <td>3442.7974</td>\n",
       "      <td>2437.622184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1725.2598</td>\n",
       "      <td>1211.461576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>5116.2232</td>\n",
       "      <td>162.388149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>7.7386</td>\n",
       "      <td>8.492083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.5042</td>\n",
       "      <td>0.500007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean          std\n",
       "cardinality method                        \n",
       "32          A        417.1434   315.032901\n",
       "            B        835.5898   632.621641\n",
       "            C        107.0974    75.873346\n",
       "            D         53.2330    38.639325\n",
       "            E        154.6836    27.448644\n",
       "...                       ...          ...\n",
       "1024        C       3442.7974  2437.622184\n",
       "            D       1725.2598  1211.461576\n",
       "            E       5116.2232   162.388149\n",
       "            F          7.7386     8.492083\n",
       "            G          0.5042     0.500007\n",
       "\n",
       "[42 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by cardinality and method\n",
    "group = tall_df.groupby(['cardinality', 'method'])\n",
    "# Dropping level 0 of axis 1 allows us to use 'mean' and 'std' as the column names\n",
    "# instead of ('absolute_diff', 'mean') and ('absolute_diff', 'std')\n",
    "stats_df = group.agg(['mean', 'std']).droplevel(axis=1, level=0)\n",
    "# Some of the seaborn code above\n",
    "stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot\n",
    "We will iterate over (using `group()` again) each method, and add a plot to the axis.  The `pandas` plot will show error bars (not area) by default and, as we have calculated standard deviation in the initial aggregation, we will plot that instead of the 95% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 9), facecolor='white')\n",
    "# Iterate over each method and add the data to the plot\n",
    "for key, group in stats_df.groupby('method'):\n",
    "    group.reset_index(inplace=True)\n",
    "    group.plot('cardinality', 'mean', yerr='std', label=key, ax=ax)\n",
    "ax.set_xlabel('multiset cardinality')\n",
    "ax.set_xscale('log', base=2)\n",
    "ax.set_ylabel('mean absolute partition difference')\n",
    "ax.set_yscale('log')\n",
    "# Put legend outside plot\n",
    "ax.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "# Underscore assignment to supress Text object output\n",
    "_ = ax.set_title(f'Comparison of multiset splitting methods ({REPEATS} repeats)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, this functionality can be easily accessed by using the `pandas_plot()` method from `partition_problem.py`:\n",
    "\n",
    "```python\n",
    "fig = pandas_plot(results=testing_results, repeats=REPEATS)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "[1] https://www.ijcai.org/Proceedings/09/Papers/096.pdf\n",
    "\n",
    "### Resources used but not directly referenced\n",
    "- https://en.wikipedia.org/wiki/Partition_problem\n",
    "- https://pandasguide.readthedocs.io/en/latest/\n",
    "- https://peps.python.org/pep-0008/\n",
    "- https://seaborn.pydata.org/api.html\n",
    "- https://en.wikipedia.org/wiki/Timsort"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "234372afb06e39c9937c8e8b8bcb51cd036d89122c0bbbb520553b9ebad79899"
  },
  "kernelspec": {
   "display_name": "Python 3.10.4 ('partition-problem')",
   "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": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}