Reproduce ML1M 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 = 'ml1m'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_baseline = pd.read_csv(\"tables/ml1m/movielens1m_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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Recommendation Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tables/ml1m/final_general/mmp_final.csv']\n"
     ]
    }
   ],
   "source": [
    "folder_path = 'tables/ml1m/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": 9,
   "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": 10,
   "metadata": {
    "scrolled": true
   },
   "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>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>0.3</td>\n",
       "      <td>300</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.1112, 0.0029]</td>\n",
       "      <td>[0.1302, 0.0047]</td>\n",
       "      <td>[0.0946, 0.0021]</td>\n",
       "      <td>[0.1014, 0.0026]</td>\n",
       "      <td>[0.0762, 0.003]</td>\n",
       "      <td>[0.1057, 0.0036]</td>\n",
       "      <td>[0.1325, 0.004]</td>\n",
       "      <td>[0.0418, 0.0022]</td>\n",
       "      <td>[0.2568, 0.0055]</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         0.3    300         10   \n",
       "\n",
       "   key_dimension   lambda  learning_rate  ...      Precision@20  \\\n",
       "0              3  0.00001        0.00005  ...  [0.1112, 0.0029]   \n",
       "\n",
       "        Precision@5      Precision@50       R-Precision        Recall@10  \\\n",
       "0  [0.1302, 0.0047]  [0.0946, 0.0021]  [0.1014, 0.0026]  [0.0762, 0.003]   \n",
       "\n",
       "          Recall@15        Recall@20          Recall@5         Recall@50  \\\n",
       "0  [0.1057, 0.0036]  [0.1325, 0.004]  [0.0418, 0.0022]  [0.2568, 0.0055]   \n",
       "\n",
       "                                              metric  \n",
       "0  ['R-Precision', 'NDCG', 'Precision', 'Recall',...  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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": 13,
   "metadata": {
    "scrolled": true
   },
   "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>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>0.3</td>\n",
       "      <td>300</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1112</td>\n",
       "      <td>0.1302</td>\n",
       "      <td>[0.0946, 0.0021]</td>\n",
       "      <td>0.1014</td>\n",
       "      <td>0.0762</td>\n",
       "      <td>[0.1057, 0.0036]</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>[0.2568, 0.0055]</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         0.3    300         10   \n",
       "\n",
       "   key_dimension   lambda  learning_rate  ...  Precision@20  Precision@5  \\\n",
       "0              3  0.00001        0.00005  ...        0.1112       0.1302   \n",
       "\n",
       "       Precision@50  R-Precision Recall@10         Recall@15 Recall@20  \\\n",
       "0  [0.0946, 0.0021]       0.1014    0.0762  [0.1057, 0.0036]    0.1325   \n",
       "\n",
       "   Recall@5         Recall@50  \\\n",
       "0    0.0418  [0.2568, 0.0055]   \n",
       "\n",
       "                                              metric  \n",
       "0  ['R-Precision', 'NDCG', 'Precision', 'Recall',...  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>0</th>\n",
       "      <td>MMP</td>\n",
       "      <td>0.1014</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.1344</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.1227</td>\n",
       "      <td>0.1302</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.1112</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.1325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  model  R-Precision    NDCG   MAP@5  MAP@10  MAP@20  Precision@5  \\\n",
       "0   MMP       0.1014  0.1791  0.1344    0.13  0.1227       0.1302   \n",
       "\n",
       "   Precision@10  Precision@20  Recall@5  Recall@10  Recall@20  \n",
       "0        0.1225        0.1112    0.0418     0.0762     0.1325  "
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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>10</th>\n",
       "      <td>AutoRec</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.1674</td>\n",
       "      <td>0.1254</td>\n",
       "      <td>0.1202</td>\n",
       "      <td>0.1131</td>\n",
       "      <td>0.1194</td>\n",
       "      <td>0.1115</td>\n",
       "      <td>0.1029</td>\n",
       "      <td>0.0377</td>\n",
       "      <td>0.0690</td>\n",
       "      <td>0.1215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BPR</td>\n",
       "      <td>0.0933</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.1192</td>\n",
       "      <td>0.1153</td>\n",
       "      <td>0.1102</td>\n",
       "      <td>0.1141</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.0692</td>\n",
       "      <td>0.1228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CDAE</td>\n",
       "      <td>0.0941</td>\n",
       "      <td>0.1588</td>\n",
       "      <td>0.1297</td>\n",
       "      <td>0.1234</td>\n",
       "      <td>0.1155</td>\n",
       "      <td>0.1226</td>\n",
       "      <td>0.1143</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>0.1079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CML</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1767</td>\n",
       "      <td>0.1356</td>\n",
       "      <td>0.1299</td>\n",
       "      <td>0.1229</td>\n",
       "      <td>0.1297</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>0.1114</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0718</td>\n",
       "      <td>0.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCE-PLRec</td>\n",
       "      <td>0.0991</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.1181</td>\n",
       "      <td>0.1248</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.1358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NCE-SVD</td>\n",
       "      <td>0.0756</td>\n",
       "      <td>0.1409</td>\n",
       "      <td>0.0966</td>\n",
       "      <td>0.0930</td>\n",
       "      <td>0.0882</td>\n",
       "      <td>0.0927</td>\n",
       "      <td>0.0872</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.0619</td>\n",
       "      <td>0.1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PLRec</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>0.1704</td>\n",
       "      <td>0.1248</td>\n",
       "      <td>0.1198</td>\n",
       "      <td>0.1132</td>\n",
       "      <td>0.1197</td>\n",
       "      <td>0.1116</td>\n",
       "      <td>0.1032</td>\n",
       "      <td>0.0401</td>\n",
       "      <td>0.0719</td>\n",
       "      <td>0.1279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POP</td>\n",
       "      <td>0.0736</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.1168</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1031</td>\n",
       "      <td>0.1085</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>0.0934</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>0.0841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PureSVD</td>\n",
       "      <td>0.0920</td>\n",
       "      <td>0.1644</td>\n",
       "      <td>0.1212</td>\n",
       "      <td>0.1161</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0.1079</td>\n",
       "      <td>0.0998</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>0.1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>VAE-CF</td>\n",
       "      <td>0.0892</td>\n",
       "      <td>0.1634</td>\n",
       "      <td>0.1066</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.1006</td>\n",
       "      <td>0.1054</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>0.0688</td>\n",
       "      <td>0.1207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WRMF</td>\n",
       "      <td>0.0970</td>\n",
       "      <td>0.1788</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.1193</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.1198</td>\n",
       "      <td>0.1129</td>\n",
       "      <td>0.1053</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>0.0749</td>\n",
       "      <td>0.1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMP</td>\n",
       "      <td>0.1014</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.1344</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.1227</td>\n",
       "      <td>0.1302</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.1112</td>\n",
       "      <td>0.0418</td>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.1325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        model  R-Precision    NDCG   MAP@5  MAP@10  MAP@20  Precision@5  \\\n",
       "10    AutoRec       0.0945  0.1674  0.1254  0.1202  0.1131       0.1194   \n",
       "2         BPR       0.0933  0.1675  0.1192  0.1153  0.1102       0.1141   \n",
       "7        CDAE       0.0941  0.1588  0.1297  0.1234  0.1155       0.1226   \n",
       "5         CML       0.1000  0.1767  0.1356  0.1299  0.1229       0.1297   \n",
       "4   NCE-PLRec       0.0991  0.1790  0.1310  0.1250  0.1181       0.1248   \n",
       "3     NCE-SVD       0.0756  0.1409  0.0966  0.0930  0.0882       0.0927   \n",
       "1       PLRec       0.0953  0.1704  0.1248  0.1198  0.1132       0.1197   \n",
       "0         POP       0.0736  0.1225  0.1168  0.1099  0.1031       0.1085   \n",
       "6     PureSVD       0.0920  0.1644  0.1212  0.1161  0.1098       0.1160   \n",
       "9      VAE-CF       0.0892  0.1634  0.1066  0.1045  0.1006       0.1054   \n",
       "8        WRMF       0.0970  0.1788  0.1235  0.1193  0.1140       0.1198   \n",
       "0         MMP       0.1014  0.1791  0.1344  0.1300  0.1227       0.1302   \n",
       "\n",
       "    Precision@10  Precision@20  Recall@5  Recall@10  Recall@20  \n",
       "10        0.1115        0.1029    0.0377     0.0690     0.1215  \n",
       "2         0.1094        0.1016    0.0375     0.0692     0.1228  \n",
       "7         0.1143        0.1035    0.0350     0.0618     0.1079  \n",
       "5         0.1221        0.1114    0.0400     0.0718     0.1265  \n",
       "4         0.1170        0.1071    0.0424     0.0775     0.1358  \n",
       "3         0.0872        0.0803    0.0344     0.0619     0.1096  \n",
       "1         0.1116        0.1032    0.0401     0.0719     0.1279  \n",
       "0         0.0999        0.0934    0.0237     0.0434     0.0841  \n",
       "6         0.1079        0.0998    0.0383     0.0693     0.1230  \n",
       "9         0.1008        0.0942    0.0376     0.0688     0.1207  \n",
       "8         0.1129        0.1053    0.0411     0.0749     0.1344  \n",
       "0         0.1225        0.1112    0.0418     0.0762     0.1325  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_total = df_baseline.append(df)\n",
    "df_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total.to_csv('tables/ml1m/results.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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