2_results_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c00174320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using a color-blind friendly palette with 10 colors\n",
    "color_blind_palette_10 = ['#cfcfcf', '#ffbc79', '#a2c8ec', '#898989', '#c85200',\n",
    "                          '#5f9ed1', '#595959', '#ababab', '#ff800e', '#006ba4']\n",
    "\n",
    "sns.palplot(color_blind_palette_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load background data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movie info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_n_movie_ratings = pd.read_csv('data/top-n-movies_user-ratings.csv').drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>307</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Toy Story</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>250</td>\n",
       "      <td>99</td>\n",
       "      <td>4</td>\n",
       "      <td>Fargo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>180</td>\n",
       "      <td>5</td>\n",
       "      <td>Return of the Jedi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>287</td>\n",
       "      <td>1</td>\n",
       "      <td>Scream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>293</td>\n",
       "      <td>3</td>\n",
       "      <td>Liar Liar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating         movie_title\n",
       "0      307         0       4           Toy Story\n",
       "1      250        99       4               Fargo\n",
       "2       24       180       5  Return of the Jedi\n",
       "3       19       287       1              Scream\n",
       "4       31       293       3           Liar Liar"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_movie_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Toy Story',\n",
       " 49: 'Star Wars',\n",
       " 99: 'Fargo',\n",
       " 120: 'Independence Day',\n",
       " 180: 'Return of the Jedi',\n",
       " 257: 'Contact',\n",
       " 285: 'English Patient, The',\n",
       " 287: 'Scream',\n",
       " 293: 'Liar Liar',\n",
       " 299: 'Air Force One'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dictionary between movie_id and movie_title (since the results output doesn't have titles)\n",
    "movie_titles = top_n_movie_ratings.groupby(['movie_id','movie_title']).size().to_frame() \\\n",
    "                                    .reset_index('movie_title').movie_title \\\n",
    "                                    .to_dict()\n",
    "movie_titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dictionary to map these movies to colors for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Air Force One': '#cfcfcf',\n",
       " 'Contact': '#ffbc79',\n",
       " 'English Patient, The': '#a2c8ec',\n",
       " 'Fargo': '#898989',\n",
       " 'Independence Day': '#c85200',\n",
       " 'Liar Liar': '#5f9ed1',\n",
       " 'Return of the Jedi': '#595959',\n",
       " 'Scream': '#ababab',\n",
       " 'Star Wars': '#ff800e',\n",
       " 'Toy Story': '#006ba4'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_map = dict(zip(top_n_movie_ratings.movie_title.sort_values().unique().tolist(), color_blind_palette_10))\n",
    "color_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the results from the simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A/B Test Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ab_test_1k = pd.read_csv('output/ab_test_1k.csv').drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
       "      <th>visit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>767</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>276</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>907</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>580</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fraction_relevant  iteration  item_id  reward  total_reward  visitor_id  \\\n",
       "0           1.000000          0      299       1             1         767   \n",
       "1           1.000000          0       99       1             2         144   \n",
       "2           0.666667          0      257       0             2         276   \n",
       "3           0.500000          0      180       0             2         907   \n",
       "4           0.400000          0       49       0             2         580   \n",
       "\n",
       "   visit  \n",
       "0      0  \n",
       "1      1  \n",
       "2      2  \n",
       "3      3  \n",
       "4      4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_test_1k.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ab_test_5k = pd.read_csv('output/ab_test_5k.csv').drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
       "      <th>visit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>767</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>276</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>907</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>580</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fraction_relevant  iteration  item_id  reward  total_reward  visitor_id  \\\n",
       "0           1.000000          0      299       1             1         767   \n",
       "1           1.000000          0       99       1             2         144   \n",
       "2           0.666667          0      257       0             2         276   \n",
       "3           0.500000          0      180       0             2         907   \n",
       "4           0.400000          0       49       0             2         580   \n",
       "\n",
       "   visit  \n",
       "0      0  \n",
       "1      1  \n",
       "2      2  \n",
       "3      3  \n",
       "4      4  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_test_5k.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\epsilon$-Greedy Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_greedy_05 = pd.read_csv('output/epsilon_greedy_05.csv').drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
       "      <th>visit</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.500000</td>\n",
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       "      <td>458</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>921</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fraction_relevant  iteration  item_id  reward  total_reward  visitor_id  \\\n",
       "0           1.000000          0        0       1             1          20   \n",
       "1           0.500000          0        0       0             1         458   \n",
       "2           0.666667          0        0       1             2         921   \n",
       "3           0.500000          0        0       0             2         212   \n",
       "4           0.400000          0        0       0             2          72   \n",
       "\n",
       "   visit  \n",
       "0      0  \n",
       "1      1  \n",
       "2      2  \n",
       "3      3  \n",
       "4      4  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps_greedy_05.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_greedy_10 = pd.read_csv('output/epsilon_greedy_10.csv').drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
       "      <th>visit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>654</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>553</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>653</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>395</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fraction_relevant  iteration  item_id  reward  total_reward  visitor_id  \\\n",
       "0                0.0          0        0       0             0         654   \n",
       "1                0.0          0        0       0             0         553   \n",
       "2                0.0          0        0       0             0         653   \n",
       "3                0.0          0        0       0             0         203   \n",
       "4                0.0          0        0       0             0         395   \n",
       "\n",
       "   visit  \n",
       "0      0  \n",
       "1      1  \n",
       "2      2  \n",
       "3      3  \n",
       "4      4  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps_greedy_10.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thompson Sampling Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "thompson = pd.read_csv('output/thompson_sampling.csv').drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
       "      <th>visit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>885</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>868</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>593</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>285</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>586</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fraction_relevant  iteration  item_id  reward  total_reward  visitor_id  \\\n",
       "0                0.0          0      120       0             0         114   \n",
       "1                0.0          0        0       0             0         885   \n",
       "2                0.0          0       49       0             0         868   \n",
       "3                0.0          0       99       0             0         593   \n",
       "4                0.0          0      285       0             0         586   \n",
       "\n",
       "   visit  \n",
       "0      0  \n",
       "1      1  \n",
       "2      2  \n",
       "3      3  \n",
       "4      4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thompson.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's visualize the Thompson Sampling output for a single run. We'll look at each movie's percentage of the total recommendations as the simulation progressed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe with running totals of how many times each recommendation was taken\n",
    "thompson_running_ttl = thompson.query('iteration == 0') \\\n",
    "                                .eval('selected = 1') \\\n",
    "                                .pivot(index='visit', columns='item_id', values='selected') \\\n",
    "                                .fillna(0) \\\n",
    "                                .cumsum(axis=0) \\\n",
    "                                .reset_index() \\\n",
    "                                .rename(columns=movie_titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale the value by the visit number to get turn the running total into a percentage\n",
    "thompson_running_ttl.iloc[:,1:] = thompson_running_ttl.iloc[:,1:].div((thompson_running_ttl.visit + 1)/100, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>item_id</th>\n",
       "      <th>visit</th>\n",
       "      <th>Toy Story</th>\n",
       "      <th>Star Wars</th>\n",
       "      <th>Fargo</th>\n",
       "      <th>Independence Day</th>\n",
       "      <th>Return of the Jedi</th>\n",
       "      <th>Contact</th>\n",
       "      <th>English Patient, The</th>\n",
       "      <th>Scream</th>\n",
       "      <th>Liar Liar</th>\n",
       "      <th>Air Force One</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "item_id  visit  Toy Story  Star Wars  Fargo  Independence Day  \\\n",
       "0            0   0.000000   0.000000    0.0        100.000000   \n",
       "1            1  50.000000   0.000000    0.0         50.000000   \n",
       "2            2  33.333333  33.333333    0.0         33.333333   \n",
       "3            3  25.000000  25.000000   25.0         25.000000   \n",
       "4            4  20.000000  20.000000   20.0         20.000000   \n",
       "\n",
       "item_id  Return of the Jedi  Contact  English Patient, The  Scream  Liar Liar  \\\n",
       "0                       0.0      0.0                   0.0     0.0        0.0   \n",
       "1                       0.0      0.0                   0.0     0.0        0.0   \n",
       "2                       0.0      0.0                   0.0     0.0        0.0   \n",
       "3                       0.0      0.0                   0.0     0.0        0.0   \n",
       "4                       0.0      0.0                  20.0     0.0        0.0   \n",
       "\n",
       "item_id  Air Force One  \n",
       "0                  0.0  \n",
       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thompson_running_ttl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c005c75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(color_blind_palette_10)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "ax.stackplot(thompson_running_ttl.visit,\n",
    "             thompson_running_ttl.iloc[:,1:11].T,\n",
    "             labels=thompson_running_ttl.iloc[:,1:11].columns.values.tolist(),\n",
    "             colors=[color_map[x] for x in thompson_running_ttl.iloc[:,1:].columns.values]\n",
    "            )\n",
    "\n",
    "ax.set_xlim(0,1000)\n",
    "ax.set_xticks(range(0, 1100, 250))\n",
    "\n",
    "ax.set_title('Thompson Sampling Algorithm')\n",
    "ax.set_xlabel('Recommendation #')\n",
    "ax.set_ylabel('% of Recommendations')\n",
    "\n",
    "lgd = plt.legend(bbox_to_anchor=(1.02, 0.15), loc=2, borderaxespad=0., labelspacing=-2.3)\n",
    "\n",
    "ax.set_facecolor('w')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('output/images/bandit_results.png', transparent=False, bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group the data to average over the runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A/B Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ab_test_1k_avg = ab_test_1k.groupby('visit', as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit</th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3500</td>\n",
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       "      <td>0.70</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>9.5</td>\n",
       "      <td>168.70</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.05</td>\n",
       "      <td>472.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>9.5</td>\n",
       "      <td>242.25</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.35</td>\n",
       "      <td>542.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>9.5</td>\n",
       "      <td>188.80</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.65</td>\n",
       "      <td>553.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visit  fraction_relevant  iteration  item_id  reward  total_reward  \\\n",
       "0      0             0.3500        9.5   226.40    0.35          0.35   \n",
       "1      1             0.3500        9.5   160.35    0.35          0.70   \n",
       "2      2             0.3500        9.5   168.70    0.35          1.05   \n",
       "3      3             0.3375        9.5   242.25    0.30          1.35   \n",
       "4      4             0.3300        9.5   188.80    0.30          1.65   \n",
       "\n",
       "   visitor_id  \n",
       "0      498.80  \n",
       "1      559.10  \n",
       "2      472.75  \n",
       "3      542.90  \n",
       "4      553.35  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_test_1k_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ab_test_5k_avg = ab_test_5k.groupby(['visit'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit</th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>231.80</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>450.75</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>199.20</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.45</td>\n",
       "      <td>418.50</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>9.5</td>\n",
       "      <td>201.45</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.65</td>\n",
       "      <td>437.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>183.00</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.00</td>\n",
       "      <td>486.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>186.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.20</td>\n",
       "      <td>437.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visit  fraction_relevant  iteration  item_id  reward  total_reward  \\\n",
       "0      0           0.300000        9.5   231.80    0.30          0.30   \n",
       "1      1           0.225000        9.5   199.20    0.15          0.45   \n",
       "2      2           0.216667        9.5   201.45    0.20          0.65   \n",
       "3      3           0.250000        9.5   183.00    0.35          1.00   \n",
       "4      4           0.240000        9.5   186.00    0.20          1.20   \n",
       "\n",
       "   visitor_id  \n",
       "0      450.75  \n",
       "1      418.50  \n",
       "2      437.90  \n",
       "3      486.85  \n",
       "4      437.50  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_test_5k_avg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\epsilon$-Greedy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_greedy_05_avg = eps_greedy_05.groupby(['visit'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>fraction_relevant</th>\n",
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       "      <th>0</th>\n",
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       "      <td>560.60</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>12.85</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.70</td>\n",
       "      <td>423.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>9.5</td>\n",
       "      <td>19.90</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>568.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>14.95</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.20</td>\n",
       "      <td>442.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29.30</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.45</td>\n",
       "      <td>490.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visit  fraction_relevant  iteration  item_id  reward  total_reward  \\\n",
       "0      0           0.250000        9.5     0.00    0.25          0.25   \n",
       "1      1           0.350000        9.5    12.85    0.45          0.70   \n",
       "2      2           0.333333        9.5    19.90    0.30          1.00   \n",
       "3      3           0.300000        9.5    14.95    0.20          1.20   \n",
       "4      4           0.290000        9.5    29.30    0.25          1.45   \n",
       "\n",
       "   visitor_id  \n",
       "0      560.60  \n",
       "1      423.25  \n",
       "2      568.70  \n",
       "3      442.15  \n",
       "4      490.95  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps_greedy_05_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_greedy_10_avg = eps_greedy_10.groupby(['visit'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit</th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
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       "      <td>0.30</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>20.35</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>438.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.183333</td>\n",
       "      <td>9.5</td>\n",
       "      <td>14.95</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.55</td>\n",
       "      <td>507.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>26.65</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.70</td>\n",
       "      <td>383.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>14.35</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>594.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visit  fraction_relevant  iteration  item_id  reward  total_reward  \\\n",
       "0      0           0.300000        9.5    12.85    0.30          0.30   \n",
       "1      1           0.200000        9.5    20.35    0.10          0.40   \n",
       "2      2           0.183333        9.5    14.95    0.15          0.55   \n",
       "3      3           0.175000        9.5    26.65    0.15          0.70   \n",
       "4      4           0.200000        9.5    14.35    0.30          1.00   \n",
       "\n",
       "   visitor_id  \n",
       "0      462.55  \n",
       "1      438.35  \n",
       "2      507.60  \n",
       "3      383.10  \n",
       "4      594.30  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps_greedy_10_avg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thompson Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "thompson_avg = thompson.groupby(['visit'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\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>visit</th>\n",
       "      <th>fraction_relevant</th>\n",
       "      <th>iteration</th>\n",
       "      <th>item_id</th>\n",
       "      <th>reward</th>\n",
       "      <th>total_reward</th>\n",
       "      <th>visitor_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>9.5</td>\n",
       "      <td>187.60</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>280.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>9.5</td>\n",
       "      <td>176.40</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.55</td>\n",
       "      <td>445.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>9.5</td>\n",
       "      <td>183.80</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>473.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>9.5</td>\n",
       "      <td>184.60</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.15</td>\n",
       "      <td>450.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.3100</td>\n",
       "      <td>9.5</td>\n",
       "      <td>184.85</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.55</td>\n",
       "      <td>391.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   visit  fraction_relevant  iteration  item_id  reward  total_reward  \\\n",
       "0      0             0.3000        9.5   187.60    0.30          0.30   \n",
       "1      1             0.2750        9.5   176.40    0.25          0.55   \n",
       "2      2             0.2500        9.5   183.80    0.20          0.75   \n",
       "3      3             0.2875        9.5   184.60    0.40          1.15   \n",
       "4      4             0.3100        9.5   184.85    0.40          1.55   \n",
       "\n",
       "   visitor_id  \n",
       "0      280.45  \n",
       "1      445.70  \n",
       "2      473.60  \n",
       "3      450.35  \n",
       "4      391.70  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thompson_avg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's summarize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We'll plot the percentage of recommendations that were \"liked\" (i.e., rated 5) by the users as a function of time (i.e., as more and more recommendations are made by each algorithm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c099ad5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,10))\n",
    "\n",
    "# add a line for the optimal value -- 0.5575 for Star Wars (from exploration noteboook)\n",
    "ax.axhline(y=0.5575, color='k', linestyle=':', linewidth=2.5)\n",
    "\n",
    "for (avg_results_df, style) in [(thompson_avg, 'tab:brown'),\n",
    "                                (eps_greedy_05_avg, 'b-'),\n",
    "                                (eps_greedy_10_avg, 'b--'),\n",
    "                                (ab_test_1k_avg, 'r-'),\n",
    "                                (ab_test_5k_avg, 'r--')]:\n",
    "    \n",
    "    ax.plot(avg_results_df.visit, avg_results_df.fraction_relevant, style, linewidth=3.5)\n",
    "\n",
    "ax.set_title('Percentage of Liked Recommendations')\n",
    "ax.set_xlabel('Recommendation #')\n",
    "ax.set_ylabel('% of Recs Liked')\n",
    "\n",
    "ax.set_xticks(range(0,22000,5000))\n",
    "ax.set_ylim(0.2, 0.6)\n",
    "ax.set_yticks(np.arange(0.2, 0.7, 0.1))\n",
    "\n",
    "# rescale the y-axis tick labels to show them as a percentage\n",
    "ax.set_yticklabels((ax.get_yticks()*100).astype(int))\n",
    "\n",
    "ax.legend(['Optimal (Star Wars)',\n",
    "           'Thompson Sampling',\n",
    "           '$\\epsilon$ = 0.05',\n",
    "           '$\\epsilon$ = 0.10',\n",
    "           'A/B Test (1k Recs)',\n",
    "           'A/B Test (5k Recs)'\n",
    "          ],\n",
    "          loc='lower right'\n",
    "         )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('output/images/pct_liked_recs.png', transparent=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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