Reproduce 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 = 'beer'\n",
    "DATASET_NAME = 'CD'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Recommendation Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if DATASET_NAME == 'beer':\n",
    "    folder_path = 'tables/beer_rating_final'\n",
    "else:\n",
    "    folder_path = 'tables/CDsVinyl_rating_final'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tables/CDsVinyl_rating_final/CDsVinyl_final_result1.csv',\n",
       " 'tables/CDsVinyl_rating_final/CDsVinyl_final_result2.csv',\n",
       " 'tables/CDsVinyl_rating_final/CDsVinyl_final_result5.csv',\n",
       " 'tables/CDsVinyl_rating_final/CDsVinyl_final_result4.csv',\n",
       " 'tables/CDsVinyl_rating_final/CDsVinyl_final_result3.csv']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_files = [join(folder_path, f) for f in listdir(folder_path) \n",
    "             if isfile(join(folder_path, f)) and f.endswith('.csv')]\n",
    "csv_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>model</th>\n",
       "      <th>rank</th>\n",
       "      <th>beta</th>\n",
       "      <th>lambda_l2</th>\n",
       "      <th>lambda_keyphrase</th>\n",
       "      <th>lambda_latent</th>\n",
       "      <th>lambda_rating</th>\n",
       "      <th>topK</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>epoch</th>\n",
       "      <th>...</th>\n",
       "      <th>Precision@15</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0474, 0.0017]</td>\n",
       "      <td>[0.0414, 0.0014]</td>\n",
       "      <td>[0.0716, 0.0033]</td>\n",
       "      <td>[0.0263, 0.0008]</td>\n",
       "      <td>[0.0719, 0.0037]</td>\n",
       "      <td>[0.1366, 0.0057]</td>\n",
       "      <td>[0.1687, 0.0062]</td>\n",
       "      <td>[0.1945, 0.0066]</td>\n",
       "      <td>[0.0892, 0.0046]</td>\n",
       "      <td>[0.2957, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0467, 0.0017]</td>\n",
       "      <td>[0.0415, 0.0015]</td>\n",
       "      <td>[0.0703, 0.0033]</td>\n",
       "      <td>[0.0263, 0.0008]</td>\n",
       "      <td>[0.072, 0.0037]</td>\n",
       "      <td>[0.1342, 0.0057]</td>\n",
       "      <td>[0.1666, 0.0062]</td>\n",
       "      <td>[0.1949, 0.0066]</td>\n",
       "      <td>[0.0884, 0.0047]</td>\n",
       "      <td>[0.2969, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0475, 0.0017]</td>\n",
       "      <td>[0.0416, 0.0015]</td>\n",
       "      <td>[0.0715, 0.0033]</td>\n",
       "      <td>[0.0264, 0.0008]</td>\n",
       "      <td>[0.0727, 0.0037]</td>\n",
       "      <td>[0.1346, 0.0056]</td>\n",
       "      <td>[0.1692, 0.0062]</td>\n",
       "      <td>[0.1956, 0.0066]</td>\n",
       "      <td>[0.0898, 0.0046]</td>\n",
       "      <td>[0.2968, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0468, 0.0017]</td>\n",
       "      <td>[0.0412, 0.0014]</td>\n",
       "      <td>[0.0708, 0.0032]</td>\n",
       "      <td>[0.0262, 0.0008]</td>\n",
       "      <td>[0.0724, 0.0038]</td>\n",
       "      <td>[0.1342, 0.0056]</td>\n",
       "      <td>[0.1671, 0.0062]</td>\n",
       "      <td>[0.1942, 0.0066]</td>\n",
       "      <td>[0.0888, 0.0046]</td>\n",
       "      <td>[0.2948, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0472, 0.0017]</td>\n",
       "      <td>[0.0417, 0.0014]</td>\n",
       "      <td>[0.0707, 0.0032]</td>\n",
       "      <td>[0.0263, 0.0008]</td>\n",
       "      <td>[0.0727, 0.0038]</td>\n",
       "      <td>[0.1343, 0.0057]</td>\n",
       "      <td>[0.1684, 0.0062]</td>\n",
       "      <td>[0.1961, 0.0066]</td>\n",
       "      <td>[0.0886, 0.0046]</td>\n",
       "      <td>[0.2968, 0.0076]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  rank    beta  lambda_l2  lambda_keyphrase  lambda_latent  \\\n",
       "0  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "1  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "2  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "3  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "4  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "\n",
       "   lambda_rating  topK  learning_rate  epoch  ...      Precision@15  \\\n",
       "0              1   NaN         0.0001    600  ...  [0.0474, 0.0017]   \n",
       "1              1   NaN         0.0001    600  ...  [0.0467, 0.0017]   \n",
       "2              1   NaN         0.0001    600  ...  [0.0475, 0.0017]   \n",
       "3              1   NaN         0.0001    600  ...  [0.0468, 0.0017]   \n",
       "4              1   NaN         0.0001    600  ...  [0.0472, 0.0017]   \n",
       "\n",
       "       Precision@20       Precision@5      Precision@50       R-Precision  \\\n",
       "0  [0.0414, 0.0014]  [0.0716, 0.0033]  [0.0263, 0.0008]  [0.0719, 0.0037]   \n",
       "1  [0.0415, 0.0015]  [0.0703, 0.0033]  [0.0263, 0.0008]   [0.072, 0.0037]   \n",
       "2  [0.0416, 0.0015]  [0.0715, 0.0033]  [0.0264, 0.0008]  [0.0727, 0.0037]   \n",
       "3  [0.0412, 0.0014]  [0.0708, 0.0032]  [0.0262, 0.0008]  [0.0724, 0.0038]   \n",
       "4  [0.0417, 0.0014]  [0.0707, 0.0032]  [0.0263, 0.0008]  [0.0727, 0.0038]   \n",
       "\n",
       "          Recall@10         Recall@15         Recall@20          Recall@5  \\\n",
       "0  [0.1366, 0.0057]  [0.1687, 0.0062]  [0.1945, 0.0066]  [0.0892, 0.0046]   \n",
       "1  [0.1342, 0.0057]  [0.1666, 0.0062]  [0.1949, 0.0066]  [0.0884, 0.0047]   \n",
       "2  [0.1346, 0.0056]  [0.1692, 0.0062]  [0.1956, 0.0066]  [0.0898, 0.0046]   \n",
       "3  [0.1342, 0.0056]  [0.1671, 0.0062]  [0.1942, 0.0066]  [0.0888, 0.0046]   \n",
       "4  [0.1343, 0.0057]  [0.1684, 0.0062]  [0.1961, 0.0066]  [0.0886, 0.0046]   \n",
       "\n",
       "          Recall@50  \n",
       "0  [0.2957, 0.0076]  \n",
       "1  [0.2969, 0.0076]  \n",
       "2  [0.2968, 0.0076]  \n",
       "3  [0.2948, 0.0076]  \n",
       "4  [0.2968, 0.0076]  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\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>model</th>\n",
       "      <th>rank</th>\n",
       "      <th>beta</th>\n",
       "      <th>lambda_l2</th>\n",
       "      <th>lambda_keyphrase</th>\n",
       "      <th>lambda_latent</th>\n",
       "      <th>lambda_rating</th>\n",
       "      <th>topK</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>epoch</th>\n",
       "      <th>...</th>\n",
       "      <th>Precision@15</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0474, 0.0017]</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.0716</td>\n",
       "      <td>[0.0263, 0.0008]</td>\n",
       "      <td>0.0719</td>\n",
       "      <td>0.1366</td>\n",
       "      <td>[0.1687, 0.0062]</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.0892</td>\n",
       "      <td>[0.2957, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0467, 0.0017]</td>\n",
       "      <td>0.0415</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>[0.0263, 0.0008]</td>\n",
       "      <td>0.0720</td>\n",
       "      <td>0.1342</td>\n",
       "      <td>[0.1666, 0.0062]</td>\n",
       "      <td>0.1949</td>\n",
       "      <td>0.0884</td>\n",
       "      <td>[0.2969, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0475, 0.0017]</td>\n",
       "      <td>0.0416</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>[0.0264, 0.0008]</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.1346</td>\n",
       "      <td>[0.1692, 0.0062]</td>\n",
       "      <td>0.1956</td>\n",
       "      <td>0.0898</td>\n",
       "      <td>[0.2968, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0468, 0.0017]</td>\n",
       "      <td>0.0412</td>\n",
       "      <td>0.0708</td>\n",
       "      <td>[0.0262, 0.0008]</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.1342</td>\n",
       "      <td>[0.1671, 0.0062]</td>\n",
       "      <td>0.1942</td>\n",
       "      <td>0.0888</td>\n",
       "      <td>[0.2948, 0.0076]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>600</td>\n",
       "      <td>...</td>\n",
       "      <td>[0.0472, 0.0017]</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.0707</td>\n",
       "      <td>[0.0263, 0.0008]</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>[0.1684, 0.0062]</td>\n",
       "      <td>0.1961</td>\n",
       "      <td>0.0886</td>\n",
       "      <td>[0.2968, 0.0076]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  rank    beta  lambda_l2  lambda_keyphrase  lambda_latent  \\\n",
       "0  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "1  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "2  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "3  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "4  E-CDE-VAE   200  0.0001     0.0001             0.001          0.001   \n",
       "\n",
       "   lambda_rating  topK  learning_rate  epoch  ...      Precision@15  \\\n",
       "0              1   NaN         0.0001    600  ...  [0.0474, 0.0017]   \n",
       "1              1   NaN         0.0001    600  ...  [0.0467, 0.0017]   \n",
       "2              1   NaN         0.0001    600  ...  [0.0475, 0.0017]   \n",
       "3              1   NaN         0.0001    600  ...  [0.0468, 0.0017]   \n",
       "4              1   NaN         0.0001    600  ...  [0.0472, 0.0017]   \n",
       "\n",
       "  Precision@20 Precision@5      Precision@50 R-Precision  Recall@10  \\\n",
       "0       0.0414      0.0716  [0.0263, 0.0008]      0.0719     0.1366   \n",
       "1       0.0415      0.0703  [0.0263, 0.0008]      0.0720     0.1342   \n",
       "2       0.0416      0.0715  [0.0264, 0.0008]      0.0727     0.1346   \n",
       "3       0.0412      0.0708  [0.0262, 0.0008]      0.0724     0.1342   \n",
       "4       0.0417      0.0707  [0.0263, 0.0008]      0.0727     0.1343   \n",
       "\n",
       "          Recall@15 Recall@20  Recall@5         Recall@50  \n",
       "0  [0.1687, 0.0062]    0.1945    0.0892  [0.2957, 0.0076]  \n",
       "1  [0.1666, 0.0062]    0.1949    0.0884  [0.2969, 0.0076]  \n",
       "2  [0.1692, 0.0062]    0.1956    0.0898  [0.2968, 0.0076]  \n",
       "3  [0.1671, 0.0062]    0.1942    0.0888  [0.2948, 0.0076]  \n",
       "4  [0.1684, 0.0062]    0.1961    0.0886  [0.2968, 0.0076]  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.groupby('model', \n",
    "           as_index=False)['R-Precision', 'NDCG', 'MAP@5',  'MAP@10', 'MAP@20', \n",
    "                           'Precision@5', 'Precision@10', 'Precision@20', \n",
    "                           'Recall@5', 'Recall@10', 'Recall@20', \n",
    "                           ].agg({'R-Precision': 'mean', 'NDCG': 'mean', \n",
    "                                  'MAP@5': 'mean', 'MAP@10': 'mean', \n",
    "                                  'MAP@20': 'mean', 'Precision@5': 'mean',\n",
    "                                  'Precision@10': 'mean', 'Precision@20': 'mean', \n",
    "                                  'Recall@5': 'mean', 'Recall@10': 'mean',\n",
    "                                  'Recall@20': 'mean'}).to_csv(folder_path+'/final_results.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Explanation Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tables/CDsVinyl_explanation_final/CDsVinyl_final_explanation_result1.csv',\n",
       " 'tables/CDsVinyl_explanation_final/CDsVinyl_final_explanation_result2.csv',\n",
       " 'tables/CDsVinyl_explanation_final/CDsVinyl_final_explanation_result5.csv',\n",
       " 'tables/CDsVinyl_explanation_final/CDsVinyl_final_explanation_result3.csv',\n",
       " 'tables/CDsVinyl_explanation_final/CDsVinyl_final_explanation_result4.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if DATASET_NAME == 'beer':\n",
    "    folder_path = 'tables/beer_explanation_final'\n",
    "else:\n",
    "    folder_path = 'tables/CDsVinyl_explanation_final'\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",
    "\n",
    "csv_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MAP@5'] = df['MAP@5'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['MAP@10'] = df['MAP@10'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['MAP@15'] = df['MAP@15'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['MAP@20'] = df['MAP@20'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "\n",
    "df['NDCG@5'] = df['NDCG@5'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['NDCG@10'] = df['NDCG@10'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['NDCG@15'] = df['NDCG@15'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['NDCG@20'] = df['NDCG@20'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "\n",
    "df['Precision@5'] = df['Precision@5'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['Precision@10'] = df['Precision@10'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['Precision@15'] = df['Precision@15'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['Precision@20'] = df['Precision@20'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "\n",
    "df['Recall@10'] = df['Recall@10'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['Recall@15'] = df['Recall@15'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['Recall@20'] = df['Recall@20'].apply(lambda accuracy: ast.literal_eval(accuracy)[0])\n",
    "df['Recall@5'] = df['Recall@5'].apply(lambda accuracy: ast.literal_eval(accuracy)[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",
       "        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>model</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>MAP@15</th>\n",
       "      <th>MAP@20</th>\n",
       "      <th>NDCG@5</th>\n",
       "      <th>NDCG@10</th>\n",
       "      <th>NDCG@15</th>\n",
       "      <th>NDCG@20</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Precision@15</th>\n",
       "      <th>Precision@20</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "      <th>Recall@15</th>\n",
       "      <th>Recall@20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E-CDE-VAE</td>\n",
       "      <td>0.6152</td>\n",
       "      <td>0.53176</td>\n",
       "      <td>0.47576</td>\n",
       "      <td>0.43314</td>\n",
       "      <td>0.52604</td>\n",
       "      <td>0.64608</td>\n",
       "      <td>0.70708</td>\n",
       "      <td>0.74154</td>\n",
       "      <td>0.52282</td>\n",
       "      <td>0.4083</td>\n",
       "      <td>0.33766</td>\n",
       "      <td>0.28596</td>\n",
       "      <td>0.48606</td>\n",
       "      <td>0.68958</td>\n",
       "      <td>0.80982</td>\n",
       "      <td>0.88384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model   MAP@5   MAP@10   MAP@15   MAP@20   NDCG@5  NDCG@10  NDCG@15  \\\n",
       "0  E-CDE-VAE  0.6152  0.53176  0.47576  0.43314  0.52604  0.64608  0.70708   \n",
       "\n",
       "   NDCG@20  Precision@5  Precision@10  Precision@15  Precision@20  Recall@5  \\\n",
       "0  0.74154      0.52282        0.4083       0.33766       0.28596   0.48606   \n",
       "\n",
       "   Recall@10  Recall@15  Recall@20  \n",
       "0    0.68958    0.80982    0.88384  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.groupby('model', \n",
    "                as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', \n",
    "                                'NDCG@5', 'NDCG@10', 'NDCG@15', 'NDCG@20', \n",
    "                                'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', \n",
    "                                'Recall@5', 'Recall@10', 'Recall@15', 'Recall@20'\n",
    "                                ].agg({'MAP@5': 'mean', 'MAP@10': 'mean',\n",
    "                                       'MAP@15': 'mean', 'MAP@20': 'mean',\n",
    "                                       'NDCG@5': 'mean', 'NDCG@10': 'mean',\n",
    "                                       'NDCG@15': 'mean', 'NDCG@20': 'mean',\n",
    "                                       'Precision@5': 'mean', 'Precision@10': 'mean',\n",
    "                                       'Precision@15': 'mean', 'Precision@20': 'mean', \n",
    "                                       'Recall@5': 'mean', 'Recall@10': 'mean',\n",
    "                                       'Recall@15': 'mean', 'Recall@20': 'mean'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['model', 'NDCG@5', 'NDCG@10', 'NDCG@20', \n",
    "    'MAP@5', 'MAP@10', 'MAP@20', \n",
    "    'Precision@5', 'Precision@10', 'Precision@20', \n",
    "    'Recall@5', 'Recall@10', 'Recall@20']].to_csv(folder_path+'/final_explanation_results.csv', index=False)"
   ]
  }
 ],
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