Reproduce Amazon Video Games Final Tables and Plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "import ast\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_NAME = 'amazon/video_games/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_baseline = pd.read_csv(\"tables/amazon/video_games/amazon_video_games_test_result.csv\", index_col=None, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_baseline = df_baseline[['model', 'R-Precision', 'NDCG', 'MAP@5',  'MAP@10', 'MAP@20', 'Precision@5', 'Precision@10', 'Precision@20', 'Recall@5', 'Recall@10', 'Recall@20']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "remaining_metrics = ['R-Precision', 'NDCG', 'MAP@5',  'MAP@10', 'MAP@20', 'Precision@5', 'Precision@10', 'Precision@20', 'Recall@5', 'Recall@10', 'Recall@20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for metric in remaining_metrics:\n",
    "    df_baseline[metric] = df_baseline[metric].map(lambda x: ast.literal_eval(x)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_baseline = df_baseline.sort_values(by='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_baseline.to_csv('tables/amazon/digital_music/results.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Recommendation Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tables/amazon/video_games/final_general/mmp_final.csv']\n"
     ]
    }
   ],
   "source": [
    "folder_path = 'tables/amazon/video_games/final_general'\n",
    "    \n",
    "csv_files = [join(folder_path, f) for f in listdir(folder_path) \n",
    "             if isfile(join(folder_path, f)) and f.endswith('.csv')]\n",
    "print(csv_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = []\n",
    "\n",
    "for filename in csv_files:\n",
    "    df = pd.read_csv(filename, index_col=None, header=0)\n",
    "    df_list.append(df)\n",
    "\n",
    "df = pd.concat(df_list, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>alpha</th>\n",
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       "      <th>corruption</th>\n",
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       "      <th>lambda</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>...</th>\n",
       "      <th>Precision@20</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@50</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>Recall@10</th>\n",
       "      <th>Recall@15</th>\n",
       "      <th>Recall@20</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@50</th>\n",
       "      <th>metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMP</td>\n",
       "      <td>Cosine</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.4</td>\n",
       "      <td>300</td>\n",
       "      <td>10</td>\n",
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       "      <td>0.001</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0069, 0.0002]</td>\n",
       "      <td>[0.012, 0.0004]</td>\n",
       "      <td>[0.0045, 0.0001]</td>\n",
       "      <td>[0.0179, 0.0009]</td>\n",
       "      <td>[0.0538, 0.0017]</td>\n",
       "      <td>[0.0667, 0.0019]</td>\n",
       "      <td>[0.0775, 0.002]</td>\n",
       "      <td>[0.0362, 0.0014]</td>\n",
       "      <td>[0.1241, 0.0025]</td>\n",
       "      <td>['R-Precision', 'NDCG', 'Precision', 'Recall',...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  model similarity  alpha  batch_size  corruption  epoch  iteration  \\\n",
       "0   MMP     Cosine    1.0          32         0.4    300         10   \n",
       "\n",
       "   key_dimension  lambda  learning_rate  ...      Precision@20  \\\n",
       "0              3   0.001        0.00005  ...  [0.0069, 0.0002]   \n",
       "\n",
       "       Precision@5      Precision@50       R-Precision         Recall@10  \\\n",
       "0  [0.012, 0.0004]  [0.0045, 0.0001]  [0.0179, 0.0009]  [0.0538, 0.0017]   \n",
       "\n",
       "          Recall@15        Recall@20          Recall@5         Recall@50  \\\n",
       "0  [0.0667, 0.0019]  [0.0775, 0.002]  [0.0362, 0.0014]  [0.1241, 0.0025]   \n",
       "\n",
       "                                              metric  \n",
       "0  ['R-Precision', 'NDCG', 'Precision', 'Recall',...  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "remaining_metrics = ['R-Precision', 'NDCG', 'MAP@5',  'MAP@10', 'MAP@20', 'Precision@5', 'Precision@10', 'Precision@20', 'Recall@5', 'Recall@10', 'Recall@20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for metric in remaining_metrics:\n",
    "    df[metric] = df[metric].map(lambda x: ast.literal_eval(x)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>similarity</th>\n",
       "      <th>alpha</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>corruption</th>\n",
       "      <th>epoch</th>\n",
       "      <th>iteration</th>\n",
       "      <th>key_dimension</th>\n",
       "      <th>lambda</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>...</th>\n",
       "      <th>Precision@20</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@50</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>Recall@10</th>\n",
       "      <th>Recall@15</th>\n",
       "      <th>Recall@20</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@50</th>\n",
       "      <th>metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMP</td>\n",
       "      <td>Cosine</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.4</td>\n",
       "      <td>300</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.012</td>\n",
       "      <td>[0.0045, 0.0001]</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>[0.0667, 0.0019]</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>[0.1241, 0.0025]</td>\n",
       "      <td>['R-Precision', 'NDCG', 'Precision', 'Recall',...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  model similarity  alpha  batch_size  corruption  epoch  iteration  \\\n",
       "0   MMP     Cosine    1.0          32         0.4    300         10   \n",
       "\n",
       "   key_dimension  lambda  learning_rate  ...  Precision@20  Precision@5  \\\n",
       "0              3   0.001        0.00005  ...        0.0069        0.012   \n",
       "\n",
       "       Precision@50  R-Precision Recall@10         Recall@15 Recall@20  \\\n",
       "0  [0.0045, 0.0001]       0.0179    0.0538  [0.0667, 0.0019]    0.0775   \n",
       "\n",
       "   Recall@5         Recall@50  \\\n",
       "0    0.0362  [0.1241, 0.0025]   \n",
       "\n",
       "                                              metric  \n",
       "0  ['R-Precision', 'NDCG', 'Precision', 'Recall',...  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>model</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>MAP@20</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Precision@20</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "      <th>Recall@20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMP</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>0.0775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model  R-Precision    NDCG   MAP@5  MAP@10  MAP@20  Precision@5  \\\n",
       "0   MMP       0.0179  0.0519  0.0153  0.0128  0.0103        0.012   \n",
       "\n",
       "   Precision@10  Precision@20  Recall@5  Recall@10  Recall@20  \n",
       "0        0.0092        0.0069    0.0362     0.0538     0.0775  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by='NDCG', ascending=False)[['model', 'R-Precision', 'NDCG', 'MAP@5',  'MAP@10', 'MAP@20', 'Precision@5', 'Precision@10', 'Precision@20', 'Recall@5', 'Recall@10', 'Recall@20']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>MAP@20</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Precision@20</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "      <th>Recall@20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AutoRec</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.0273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BPR</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0111</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0513</td>\n",
       "      <td>0.0819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CDAE</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CML</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.0326</td>\n",
       "      <td>0.0533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NCE-PLRec</td>\n",
       "      <td>0.0194</td>\n",
       "      <td>0.0569</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>0.0861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLRec</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0514</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.0524</td>\n",
       "      <td>0.0772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POP</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PureSVD</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0281</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VAE-CF</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>0.0562</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.0571</td>\n",
       "      <td>0.0851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WRMF</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>0.0783</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.0814</td>\n",
       "      <td>0.1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMP</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>0.0775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  R-Precision    NDCG   MAP@5  MAP@10  MAP@20  Precision@5  \\\n",
       "7    AutoRec       0.0031  0.0181  0.0037  0.0036  0.0032       0.0039   \n",
       "9        BPR       0.0148  0.0541  0.0127  0.0111  0.0094       0.0107   \n",
       "3       CDAE       0.0035  0.0162  0.0036  0.0032  0.0028       0.0033   \n",
       "1        CML       0.0118  0.0379  0.0098  0.0081  0.0067       0.0074   \n",
       "5  NCE-PLRec       0.0194  0.0569  0.0169  0.0141  0.0114       0.0134   \n",
       "4      PLRec       0.0177  0.0514  0.0150  0.0124  0.0100       0.0117   \n",
       "0        POP       0.0024  0.0112  0.0021  0.0019  0.0019       0.0017   \n",
       "8    PureSVD       0.0132  0.0408  0.0116  0.0098  0.0079       0.0093   \n",
       "2     VAE-CF       0.0182  0.0562  0.0149  0.0125  0.0102       0.0119   \n",
       "6       WRMF       0.0230  0.0783  0.0198  0.0171  0.0143       0.0166   \n",
       "0        MMP       0.0179  0.0519  0.0153  0.0128  0.0103       0.0120   \n",
       "\n",
       "   Precision@10  Precision@20  Recall@5  Recall@10  Recall@20  \n",
       "7        0.0032        0.0025    0.0104     0.0169     0.0273  \n",
       "9        0.0088        0.0071    0.0323     0.0513     0.0819  \n",
       "3        0.0025        0.0023    0.0085     0.0132     0.0248  \n",
       "1        0.0059        0.0049    0.0208     0.0326     0.0533  \n",
       "5        0.0104        0.0077    0.0395     0.0596     0.0861  \n",
       "4        0.0090        0.0068    0.0351     0.0524     0.0772  \n",
       "0        0.0019        0.0017    0.0046     0.0098     0.0174  \n",
       "8        0.0072        0.0054    0.0281     0.0423     0.0609  \n",
       "2        0.0092        0.0071    0.0379     0.0571     0.0851  \n",
       "6        0.0134        0.0104    0.0518     0.0814     0.1233  \n",
       "0        0.0092        0.0069    0.0362     0.0538     0.0775  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total = df_baseline.append(df.head(1))\n",
    "df_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total.to_csv('tables/amazon/video_games/results.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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