schema_matching_NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Schema matching using NLP methods "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn.manifold import TSNE\n",
    "from gensim.models import Phrases\n",
    "import gensim.downloader as api\n",
    "from nltk import download\n",
    "from fuzzywuzzy import fuzz\n",
    "from collections import defaultdict\n",
    "from difflib import SequenceMatcher\n",
    "import logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aux functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "part of https://www.analyticsinsight.net/company-names-standardization-using-a-fuzzy-nlp-approach/\n",
    "\"\"\"\n",
    "\n",
    "def text2int(textnum, numwords={}):\n",
    "    if not numwords:\n",
    "        units = [\n",
    "        \"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\",\n",
    "        \"nine\", \"ten\", \"eleven\", \"twelve\", \"thirteen\", \"fourteen\", \"fifteen\",\n",
    "        \"sixteen\", \"seventeen\", \"eighteen\", \"nineteen\",\n",
    "        ]\n",
    "\n",
    "        tens = [\"\", \"\", \"twenty\", \"thirty\", \"forty\", \"fifty\", \"sixty\", \"seventy\", \"eighty\", \"ninety\"]\n",
    "\n",
    "        scales = [\"hundred\", \"thousand\", \"million\", \"billion\", \"trillion\"]\n",
    "\n",
    "        numwords[\"and\"] = (1, 0)\n",
    "        for idx, word in enumerate(units):  numwords[word] = (1, idx)\n",
    "        for idx, word in enumerate(tens):       numwords[word] = (1, idx * 10)\n",
    "        for idx, word in enumerate(scales): numwords[word] = (10 ** (idx * 3 or 2), 0)\n",
    "\n",
    "    ordinal_words = {'first':1, 'second':2, 'third':3, 'fifth':5, 'eighth':8, 'ninth':9, 'twelfth':12}\n",
    "    ordinal_endings = [('ieth', 'y'), ('th', '')]\n",
    "\n",
    "    # textnum = textnum.replace('-', ' ')\n",
    "\n",
    "    current = result = 0\n",
    "    curstring = \"\"\n",
    "    onnumber = False\n",
    "    for word in textnum.split():\n",
    "        if word in ordinal_words:\n",
    "            scale, increment = (1, ordinal_words[word])\n",
    "            current = current * scale + increment\n",
    "            if scale > 100:\n",
    "                result += current\n",
    "                current = 0\n",
    "            onnumber = True\n",
    "        else:\n",
    "            for ending, replacement in ordinal_endings:\n",
    "                if word.endswith(ending):\n",
    "                    word = \"%s%s\" % (word[:-len(ending)], replacement)\n",
    "\n",
    "            if word not in numwords:\n",
    "                if onnumber:\n",
    "                    curstring += repr(result + current) + \" \"\n",
    "                curstring += word + \" \"\n",
    "                result = current = 0\n",
    "                onnumber = False\n",
    "            else:\n",
    "                scale, increment = numwords[word]\n",
    "\n",
    "                current = current * scale + increment\n",
    "                if scale > 100:\n",
    "                    result += current\n",
    "                    current = 0\n",
    "                onnumber = True\n",
    "\n",
    "    if onnumber:\n",
    "        curstring += repr(result + current)\n",
    "\n",
    "    return curstring\n",
    "\n",
    "\n",
    "def clean_special_characters(txt):\n",
    "    seps = [\" \", \";\", \":\", \".\", \",\", \"*\", \"#\", \"@\", \"|\", r\"/\", r\"\\\\\", \"-\", \"_\", \"?\", \"%\", \"!\", \"^\", \"+\", \"=\", \"(\", \")\", r\"'\", \"&\", \"<\", \">\"]\n",
    "    default_sep = seps[0]\n",
    "    \n",
    "    special_words = [\n",
    "        ('street|saint', 'st'),\n",
    "        ('and', '')\n",
    "    ]\n",
    "\n",
    "    for sep in seps[1:]:\n",
    "        txt = txt.replace(sep, default_sep)\n",
    "    \n",
    "    for pattern, repl in special_words:\n",
    "        txt = re.sub(r'\\b' + pattern + r'\\b', repl, txt)\n",
    "    \n",
    "    txt = txt.replace(' s ', \" \")\n",
    "    re.sub(' +', \" \", txt)\n",
    "    temp_list = [i.strip() for i in txt.split(default_sep)]\n",
    "    temp_list = [i for i in temp_list if i]\n",
    "    return \" \".join(temp_list)\n",
    "\n",
    "\n",
    "def clean_stopword(txt):\n",
    "    temp_list = txt.split()\n",
    "    stopwords_list = stopwords.words('english')\n",
    "    temp_list = [i for i in temp_list if i not in stopwords_list]\n",
    "    return \" \".join(temp_list)\n",
    "\n",
    "\n",
    "def data_cleaning(data, nameCol='Name', dropForeign=False):\n",
    "    data = data.dropna(subset=[nameCol])\n",
    "    data = data.rename_axis('idx').reset_index()\n",
    "    data['nonAscii_count'] = data[nameCol].apply(lambda x: sum([not c.isascii() for c in x]))\n",
    "    if dropForeign:\n",
    "        data = data[data.nonAscii_count == 0]\n",
    "    else:\n",
    "        pass\n",
    "    data = data.drop('nonAscii_count', axis=1)\n",
    "    data_clean = data.copy()\n",
    "    data_clean['name_clean'] = data_clean[nameCol].apply(lambda x: x.strip().lower())\n",
    "    data_clean['name_clean'] = data_clean['name_clean'].apply(clean_special_characters)\n",
    "    data_clean['name_clean'] = data_clean['name_clean'].apply(clean_stopword)\n",
    "    # data_clean['name_clean'] = data_clean['name_clean'].apply(text2int)\n",
    "    data_clean['name_clean'] = data_clean['name_clean'].apply(str.strip)\n",
    "\n",
    "    return data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_dfs(to_concat=False):\n",
    "    \"\"\"\n",
    "\n",
    "    :param to_clean:\n",
    "    :param to_concat:\n",
    "    :return: if to_concat- return concated df, else- return 2 dfs, rating & loc\n",
    "    \"\"\"\n",
    "    rating_df = pd.read_csv('/data/tripAdvisor_data.csv')\n",
    "    rating_df_clean = data_cleaning(rating_df, nameCol='name').drop_duplicates('name_clean')\n",
    "\n",
    "    loc_df = pd.read_csv('/data/Attractions.csv')\n",
    "    loc_df = loc_df[loc_df.AddressRegion == 'Dublin']\n",
    "    loc_df = loc_df[['Name', \"Longitude\", 'Latitude']]\n",
    "    loc_df = loc_df.drop_duplicates(['Name'])\n",
    "    loc_df = loc_df.rename(columns={'Name': 'name'})\n",
    "    loc_df_clean = data_cleaning(loc_df, nameCol='name').drop_duplicates('name_clean')\n",
    "\n",
    "    if to_concat:\n",
    "        loc_names = loc_df.copy()\n",
    "        loc_names['df'] = 'loc'\n",
    "        rating_names = rating_df.copy()\n",
    "        rating_names['df'] = 'rating'\n",
    "        all_attractions_names = pd.concat([loc_names, rating_names], sort=False)\n",
    "        df = data_cleaning(all_attractions_names, nameCol='name', dropForeign=False).drop_duplicates('name_clean')\n",
    "        return df, rating_df_clean, loc_df_clean\n",
    "\n",
    "    return rating_df_clean, loc_df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df, rating_df, loc_df = read_dfs(to_concat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(638, 564)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rating_df), len(loc_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_joined_names(rating_df, loc_df):\n",
    "    joined = pd.merge(rating_df, loc_df, on='name_clean', suffixes=['_rating', '_loc'])\n",
    "\n",
    "    rating_df = rating_df.set_index('name_clean', drop=False).drop(index=joined['name_clean']).set_index('idx', drop=False)\n",
    "    loc_df = loc_df.set_index('name_clean', drop=False).drop(index=joined['name_clean']).set_index('idx', drop=False)\n",
    "\n",
    "    return rating_df, loc_df, joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Similarity:\n",
    "    def similarity(self, texts1, texts2) -> np.ndarray:\n",
    "        sim = np.zeros((len(texts1), len(texts2)))\n",
    "                       \n",
    "        for i, x in enumerate(texts1):\n",
    "            for j, y in enumerate(texts2):\n",
    "                sim[i][j] = self.single_similarity(x, y)\n",
    "        \n",
    "        return sim\n",
    "    \n",
    "    def single_similarity(self, text1, text2) -> float:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# exact match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExactSimilarity(Similarity):\n",
    "    def single_similarity(self, text1, text2) -> float:\n",
    "        if text1 == text2:\n",
    "            return 1.0\n",
    "        \n",
    "        return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exact_similarity = ExactSimilarity()\n",
    "exact_sim = exact_similarity.similarity(loc_df.name_clean, rating_df.name_clean)\n",
    "exact_sim = exact_sim * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ngrams(string, n=3):\n",
    "    string = re.sub(r'[,-./]|\\sBD', r'', string)\n",
    "    ngrams = zip(*[string[i:] for i in range(n)])\n",
    "    return [''.join(ngram) for ngram in ngrams]\n",
    "\n",
    "\n",
    "class TFIDFSimilarity(Similarity):\n",
    "    def __init__(self, names1, names2):\n",
    "        self.vectorizer = TfidfVectorizer(analyzer=ngrams)\n",
    "        joined = pd.concat([names1, names2])\n",
    "        self.vectorizer.fit_transform(joined)\n",
    "    \n",
    "    def similarity(self, names1, names2):\n",
    "        X1 = self.vectorizer.transform(names1)\n",
    "        X2 = self.vectorizer.transform(names2)\n",
    "        similarity = X1.dot(X2.T)\n",
    "        return similarity.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_similarity = TFIDFSimilarity(loc_df.name_clean, rating_df.name_clean)\n",
    "tfidf_sim = tfidf_similarity.similarity(loc_df.name_clean, rating_df.name_clean)\n",
    "tfidf_sim = tfidf_sim * 100 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code for creating Heat Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_max_i_for_j = match_i_j_greedy(tfidf_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98\n",
      "4\n"
     ]
    },
    {
     "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>rating_name</th>\n",
       "      <th>loc_name</th>\n",
       "      <th>score</th>\n",
       "      <th>loc_idxs</th>\n",
       "      <th>rating_idxs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>kilmainham gaol museum</td>\n",
       "      <td>kilmainham gaol</td>\n",
       "      <td>88.039731</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>post general post office</td>\n",
       "      <td>general post office</td>\n",
       "      <td>91.571589</td>\n",
       "      <td>68</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>james clarence mangan bust</td>\n",
       "      <td>james clarence mangan</td>\n",
       "      <td>89.306374</td>\n",
       "      <td>182</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>sporting emporium casino dublin</td>\n",
       "      <td>sporting emporium casino</td>\n",
       "      <td>91.845682</td>\n",
       "      <td>307</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         rating_name                  loc_name      score  \\\n",
       "39            kilmainham gaol museum           kilmainham gaol  88.039731   \n",
       "58          post general post office       general post office  91.571589   \n",
       "144       james clarence mangan bust     james clarence mangan  89.306374   \n",
       "206  sporting emporium casino dublin  sporting emporium casino  91.845682   \n",
       "\n",
       "     loc_idxs  rating_idxs  \n",
       "39         43            4  \n",
       "58         68          114  \n",
       "144       182          471  \n",
       "206       307          134  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_names = []\n",
    "rating_names = []\n",
    "scores = []\n",
    "loc_idxs = []\n",
    "rating_idxs = []\n",
    "\n",
    "for j, i in tfidf_max_i_for_j.items():\n",
    "#     if all_sims_max[i,j] > 0.7:\n",
    "    loc_names.append(loc_df.iloc[i].name_clean)\n",
    "    rating_names.append(rating_df.iloc[j].name_clean)\n",
    "    scores.append(tfidf_sim[i,j])\n",
    "    loc_idxs.append(i)\n",
    "    rating_idxs.append(j)\n",
    "\n",
    "tfidf_res_df = pd.DataFrame(list(zip(rating_names, loc_names, scores, loc_idxs, rating_idxs)),\n",
    "                            columns =['rating_name', 'loc_name', 'score', \"loc_idxs\", \"rating_idxs\"])\n",
    "tfidf_res_df = tfidf_res_df[tfidf_res_df.score > 87]\n",
    "print(len(tfidf_res_df))\n",
    "tfidf_res_df = tfidf_res_df[tfidf_res_df.rating_name!=tfidf_res_df.loc_name]#.head(20)\n",
    "print(len(tfidf_res_df))\n",
    "tfidf_res_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[88.03973144,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        , 91.57158883,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        , 89.30637368,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        , 91.84568164]])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_idxs = tfidf_res_df.loc_idxs.to_list()\n",
    "rating_idxs = tfidf_res_df.rating_idxs.to_list() \n",
    "\n",
    "heat_map_scores = np.zeros(shape=(len(loc_idxs), len(rating_idxs)))\n",
    "print(heat_map_scores.shape)\n",
    "for idx1, i in enumerate(loc_idxs):\n",
    "    for idx2, j in enumerate(rating_idxs):\n",
    "        heat_map_scores[idx1][idx2] = tfidf_sim[i][j]\n",
    "heat_map_scores   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loc = loc_df.name_clean.iloc[loc_idxs].to_list()\n",
    "rating = rating_df.name_clean.iloc[rating_idxs].to_list() \n",
    "\n",
    "tfidf_array = heat_map_scores.round(2) #[:10, :10]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(tfidf_array, )\n",
    "\n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(len(rating)))\n",
    "ax.set_yticks(np.arange(len(loc)))\n",
    "# ... and label them with the respective list entries\n",
    "ax.set_xticklabels(rating, fontsize=14)\n",
    "ax.set_yticklabels(loc, fontsize=14)\n",
    "\n",
    "# Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(loc)):\n",
    "    for j in range(len(rating)):\n",
    "        text = ax.text(j, i, tfidf_array[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"w\", fontsize=16)\n",
    "\n",
    "ax.set_title(\"HeatMap for tfidf & cosing similarity\")\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(20, 20)\n",
    "plt.show() \n",
    "plt.savefig(fname='heatmap.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-213-cafe3d831250>, line 26)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;36m  File \u001B[1;32m\"<ipython-input-213-cafe3d831250>\"\u001B[1;36m, line \u001B[1;32m26\u001B[0m\n\u001B[1;33m    ax = plt.gca()/\u001B[0m\n\u001B[1;37m                   ^\u001B[0m\n\u001B[1;31mSyntaxError\u001B[0m\u001B[1;31m:\u001B[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def heatmap(data, row_labels, col_labels, ax=None,\n",
    "            cbar_kw={}, cbarlabel=\"\", **kwargs):\n",
    "    \"\"\"\n",
    "    Create a heatmap from a numpy array and two lists of labels.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data\n",
    "        A 2D numpy array of shape (N, M).\n",
    "    row_labels\n",
    "        A list or array of length N with the labels for the rows.\n",
    "    col_labels\n",
    "        A list or array of length M with the labels for the columns.\n",
    "    ax\n",
    "        A `matplotlib.axes.Axes` instance to which the heatmap is plotted.  If\n",
    "        not provided, use current axes or create a new one.  Optional.\n",
    "    cbar_kw\n",
    "        A dictionary with arguments to `matplotlib.Figure.colorbar`.  Optional.\n",
    "    cbarlabel\n",
    "        The label for the colorbar.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to `imshow`.\n",
    "    \"\"\"\n",
    "\n",
    "    if not ax:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    # Plot the heatmap\n",
    "    im = ax.imshow(data, **kwargs)\n",
    "\n",
    "    # Create colorbar\n",
    "    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n",
    "    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n",
    "\n",
    "    # We want to show all ticks...\n",
    "    ax.set_xticks(np.arange(data.shape[1]))\n",
    "    ax.set_yticks(np.arange(data.shape[0]))\n",
    "    # ... and label them with the respective list entries.\n",
    "    ax.set_xticklabels(col_labels, fontsize=16)\n",
    "    ax.set_yticklabels(row_labels, fontsize=16)\n",
    "\n",
    "    # Let the horizontal axes labeling appear on top.\n",
    "    ax.tick_params(top=True, bottom=False,\n",
    "                   labeltop=True, labelbottom=False)\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Turn spines off and create white grid.\n",
    "    for edge, spine in ax.spines.items():\n",
    "        spine.set_visible(False)\n",
    "\n",
    "    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n",
    "    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n",
    "    ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n",
    "    ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "    return im, cbar\n",
    "\n",
    "\n",
    "def annotate_heatmap(im, data=None, valfmt=\"{x:.2f}\",\n",
    "                     textcolors=[\"black\", \"white\"],\n",
    "                     threshold=None, **textkw):\n",
    "    \"\"\"\n",
    "    A function to annotate a heatmap.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    im\n",
    "        The AxesImage to be labeled.\n",
    "    data\n",
    "        Data used to annotate.  If None, the image's data is used.  Optional.\n",
    "    valfmt\n",
    "        The format of the annotations inside the heatmap.  This should either\n",
    "        use the string format method, e.g. \"$ {x:.2f}\", or be a\n",
    "        `matplotlib.ticker.Formatter`.  Optional.\n",
    "    textcolors\n",
    "        A list or array of two color specifications.  The first is used for\n",
    "        values below a threshold, the second for those above.  Optional.\n",
    "    threshold\n",
    "        Value in data units according to which the colors from textcolors are\n",
    "        applied.  If None (the default) uses the middle of the colormap as\n",
    "        separation.  Optional.\n",
    "    **kwargs\n",
    "        All other arguments are forwarded to each call to `text` used to create\n",
    "        the text labels.\n",
    "    \"\"\"\n",
    "\n",
    "    if not isinstance(data, (list, np.ndarray)):\n",
    "        data = im.get_array()\n",
    "\n",
    "    # Normalize the threshold to the images color range.\n",
    "    if threshold is not None:\n",
    "        threshold = im.norm(threshold)\n",
    "    else:\n",
    "        threshold = im.norm(data.max())/2.\n",
    "\n",
    "    # Set default alignment to center, but allow it to be\n",
    "    # overwritten by textkw.\n",
    "    kw = dict(horizontalalignment=\"center\",\n",
    "              verticalalignment=\"center\")\n",
    "    kw.update(textkw)\n",
    "\n",
    "    # Get the formatter in case a string is supplied\n",
    "    if isinstance(valfmt, str):\n",
    "        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)\n",
    "\n",
    "    # Loop over the data and create a `Text` for each \"pixel\".\n",
    "    # Change the text's color depending on the data.\n",
    "    texts = []\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[1]):\n",
    "            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n",
    "            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw, fontsize=18)\n",
    "            texts.append(text)\n",
    "\n",
    "    return texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "im, cbar = heatmap(tfidf_array, loc, rating, ax=ax,\n",
    "                   cmap=\"YlGn\", cbarlabel=\"harvest [t/year]\")\n",
    "texts = annotate_heatmap(im, valfmt=\"{x:.1f} \")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(20, 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# difflib sequenceMatcher similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class difflib_sequenceMatcher(Similarity):\n",
    "    def single_similarity(self, text1, text2) -> float:\n",
    "        return SequenceMatcher(None, text1, text2).ratio() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difflib_similarity = difflib_sequenceMatcher()\n",
    "difflib_sim = difflib_similarity.similarity(loc_df.name_clean, rating_df.name_clean)\n",
    "difflib_sim = difflib_sim * 100 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fuzz similarity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class fuzzSimilarity(Similarity):\n",
    "    def single_similarity(self, text1, text2) -> float:\n",
    "        s1 = fuzz.token_set_ratio(text1, text2) + 0.000000000001\n",
    "        s2 = fuzz.partial_ratio(text1, text2) + 0.000000000001\n",
    "        return 2*s1*s2 / (s1+s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.000000000001"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz_similarity = fuzzSimilarity()\n",
    "fuzz_sim = fuzz_similarity.similarity(loc_df.name_clean, rating_df.name_clean)\n",
    "fuzz_sim.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# wmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "\n",
    "\n",
    "class WMDSimilarity(Similarity):\n",
    "    def __init__(self, path):\n",
    "        self.model = api.load(path)\n",
    "        download('stopwords')  # Download stopwords list.\n",
    "        self.stop_words = stopwords.words('english')\n",
    "        \n",
    "    def single_similarity(self, text1, text2) -> float:\n",
    "        text1 = [w for w in text1.split() if w not in self.stop_words]\n",
    "        text2 = [w for w in text2.split() if w not in self.stop_words]\n",
    "        return self.model.wmdistance(text1, text2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "WMD_similarity = WMDSimilarity('word2vec-google-news-300')\n",
    "WMD_sim = WMD_similarity.similarity(loc_df.name_clean, rating_df.name_clean)\n",
    "# WMD_sim = WMD_sim * 100 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.62720937 3.63210605 3.40152698 ... 4.29350613 3.64186834 3.56554277]\n",
      " [3.88706532 3.81934316 3.87214337 ... 4.60459798 3.85758903 3.95710408]\n",
      " [4.07020182 4.08179004 4.160716   ... 1.9446218  4.0802799  3.86596785]\n",
      " ...\n",
      " [2.78502895 3.91806414 3.85363462 ... 4.42044687 3.68473934 3.65420797]\n",
      " [2.38662472 3.61088371 4.00876352 ... 4.66743193 3.91289412 4.1179045 ]\n",
      " [3.03743592 3.21784404 4.26908209 ... 4.65148439 4.19434736 4.28736437]]\n"
     ]
    }
   ],
   "source": [
    "print(WMD_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.958060264587402"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WMD_sim[WMD_sim == np.inf] = -1\n",
    "WMD_sim.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ..., inf, inf, inf],\n",
       "       [ 0.,  0.,  0., ..., inf, inf, inf],\n",
       "       [ 0.,  0.,  0., ..., inf, inf, inf],\n",
       "       ...,\n",
       "       [inf, inf, inf, ..., inf, inf, inf],\n",
       "       [inf, inf, inf, ..., inf, inf, inf],\n",
       "       [inf, inf, inf, ..., inf, inf, inf]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WMD_sim.sort(axis=0)\n",
    "WMD_sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code for creating similarity graph "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_closestwords_tsnescatterplot(model, word):\n",
    "    \n",
    "    arr = np.empty((0,100), dtype='f')\n",
    "    word_labels = [word]\n",
    "\n",
    "    # get close words\n",
    "    close_words = model.similar_by_word(word)\n",
    "    \n",
    "    # add the vector for each of the closest words to the array\n",
    "    arr = np.append(arr, np.array([model[word]]), axis=0)\n",
    "    for wrd_score in close_words:\n",
    "        wrd_vector = model[wrd_score[0]]\n",
    "        word_labels.append(wrd_score[0])\n",
    "        arr = np.append(arr, np.array([wrd_vector]), axis=0)\n",
    "        \n",
    "    # find tsne coords for 2 dimensions\n",
    "    tsne = TSNE(n_components=2, random_state=0)\n",
    "    np.set_printoptions(suppress=True)\n",
    "    Y = tsne.fit_transform(arr)\n",
    "\n",
    "    x_coords = Y[:, 0]\n",
    "    y_coords = Y[:, 1]\n",
    "    # display scatter plot\n",
    "    plt.scatter(x_coords, y_coords)\n",
    "\n",
    "    for label, x, y in zip(word_labels, x_coords, y_coords):\n",
    "        plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')\n",
    "    plt.xlim(x_coords.min()+0.00005, x_coords.max()+0.00005)\n",
    "    plt.ylim(y_coords.min()+0.00005, y_coords.max()+0.00005)\n",
    "    plt.show()\n",
    "    \n",
    "# display_closestwords_tsnescatterplot(WMD_similarity.model, word=loc_df.name_clean[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = []\n",
    "for df in [loc_df.name_clean, rating_df.name_clean]:\n",
    "    for name in df:\n",
    "        sent = []\n",
    "        for word in name.split():\n",
    "            sent.append(word)\n",
    "        sentences.append(sent)\n",
    "\n",
    "\n",
    "bigram_transformer = Phrases(sentences)\n",
    "# # Apply the trained MWE detector to a corpus, using the result to train a Word2vec model.\n",
    "model = Word2Vec(bigram_transformer[sentences], min_count=1, size=100)\n",
    "\n",
    "\n",
    "display_closestwords_tsnescatterplot(model, 'sculpture') #loc_df.name_clean[10].split()[1])\n",
    "display_closestwords_tsnescatterplot(model, 'st')\n",
    "display_closestwords_tsnescatterplot(model, 'catedral')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# aggregate similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_sims = np.stack([exact_sim, tfidf_sim, difflib_sim, fuzz_sim]) #, WMD_sim])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_sims_max = all_sims.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 564, 638)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sims.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.000000000001"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sims_max.max() #sanety check "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(564, 638)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_sims_max.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_i_j_greedy(similarity):\n",
    "    max_j_for_i = similarity.argmax(axis=1)\n",
    "    max_i_for_j = defaultdict()\n",
    "\n",
    "    for i, j in enumerate(max_j_for_i):\n",
    "        if j not in max_i_for_j:\n",
    "            max_i_for_j[j] = i\n",
    "        else:\n",
    "            current_sim = similarity[max_i_for_j[j], j]\n",
    "            if current_sim < similarity[i, j]:\n",
    "                max_i_for_j[j] = i\n",
    "                \n",
    "    return max_i_for_j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_i_for_j = match_i_j_greedy(all_sims_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "286\n"
     ]
    }
   ],
   "source": [
    "loc_names = []\n",
    "rating_names = []\n",
    "scores = []\n",
    "\n",
    "for j, i in max_i_for_j.items():\n",
    "#     if all_sims_max[i,j] > 0.7:\n",
    "    loc_names.append(loc_df.iloc[i].name_clean)\n",
    "    rating_names.append(rating_df.iloc[j].name_clean)\n",
    "    scores.append(all_sims_max[i,j])\n",
    "\n",
    "agg_res_df = pd.DataFrame(list(zip(rating_names, loc_names, scores)), columns =['agg_rating_name', 'agg_loc_name', 'agg_score'])\n",
    "# agg_res_df = agg_res_df[agg_res_df.agg_score > 70]\n",
    "print(len(agg_res_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## take exact mathces "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124\n"
     ]
    },
    {
     "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>agg_rating_name</th>\n",
       "      <th>agg_loc_name</th>\n",
       "      <th>agg_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>racing tours ireland</td>\n",
       "      <td>racing tours ireland</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>patrick kavanagh statue</td>\n",
       "      <td>patrick kavanagh</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>monument oscar wilde</td>\n",
       "      <td>oscar wilde</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>molly malone statue</td>\n",
       "      <td>molly malone</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>james joyce statue</td>\n",
       "      <td>james joyce</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>spire</td>\n",
       "      <td>spire</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>temple bar</td>\n",
       "      <td>temple bar cultural trust temple bar cultural ...</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>st patrick cathedral</td>\n",
       "      <td>st patrick cathedral</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>st mary pro cathedral</td>\n",
       "      <td>st mary pro cathedral</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>george salmon statue</td>\n",
       "      <td>george salmon</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             agg_rating_name  \\\n",
       "0       racing tours ireland   \n",
       "166  patrick kavanagh statue   \n",
       "165     monument oscar wilde   \n",
       "164      molly malone statue   \n",
       "162       james joyce statue   \n",
       "..                       ...   \n",
       "54                     spire   \n",
       "52                temple bar   \n",
       "49      st patrick cathedral   \n",
       "65     st mary pro cathedral   \n",
       "282     george salmon statue   \n",
       "\n",
       "                                          agg_loc_name  agg_score  \n",
       "0                                 racing tours ireland      100.0  \n",
       "166                                   patrick kavanagh      100.0  \n",
       "165                                        oscar wilde      100.0  \n",
       "164                                       molly malone      100.0  \n",
       "162                                        james joyce      100.0  \n",
       "..                                                 ...        ...  \n",
       "54                                               spire      100.0  \n",
       "52   temple bar cultural trust temple bar cultural ...      100.0  \n",
       "49                                st patrick cathedral      100.0  \n",
       "65                               st mary pro cathedral      100.0  \n",
       "282                                      george salmon      100.0  \n",
       "\n",
       "[124 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_res_df_100 = agg_res_df[agg_res_df.agg_score >99]\n",
    "print(len(agg_res_df_100))\n",
    "agg_res_df_100.sort_values('agg_score') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## hand-choose from 70%-99% mathces "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing mistakes: 55\n",
      "after removing mistakes: 26\n"
     ]
    },
    {
     "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>agg_rating_name</th>\n",
       "      <th>agg_loc_name</th>\n",
       "      <th>agg_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>james joyce bust</td>\n",
       "      <td>james joyce museum</td>\n",
       "      <td>84.894118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>steine</td>\n",
       "      <td>stena line</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ambassador event centre</td>\n",
       "      <td>ambassador theatre</td>\n",
       "      <td>78.048780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>blessington st park basin</td>\n",
       "      <td>blessington st basin dublin secret garden</td>\n",
       "      <td>84.260355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>blanchardstown centre</td>\n",
       "      <td>leisureplex blanchardstown</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>griffith bridge</td>\n",
       "      <td>griffith park</td>\n",
       "      <td>76.496732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>seapoint beach</td>\n",
       "      <td>seapoint blue flag beach 2019</td>\n",
       "      <td>88.268156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>dun laoghaire rathdown library</td>\n",
       "      <td>dun laoghaire rathdown heritage society</td>\n",
       "      <td>83.988095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>king inns</td>\n",
       "      <td>kings inns</td>\n",
       "      <td>94.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>rathmichael church</td>\n",
       "      <td>rathmichael lodge</td>\n",
       "      <td>74.786667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>victorian chapel</td>\n",
       "      <td>ardgillan castle victorian gardens</td>\n",
       "      <td>73.469388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>freemason hall</td>\n",
       "      <td>freemasons hall</td>\n",
       "      <td>96.551724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>charles parnell monument</td>\n",
       "      <td>charles stewart parnell</td>\n",
       "      <td>71.319444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>jim larkin statue</td>\n",
       "      <td>james larkin</td>\n",
       "      <td>71.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>sphere within sphere</td>\n",
       "      <td>sphere sphere</td>\n",
       "      <td>82.533103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>william smith brien statue</td>\n",
       "      <td>william smith brien monument</td>\n",
       "      <td>84.497041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>st kevins park</td>\n",
       "      <td>st kevin oratory</td>\n",
       "      <td>71.986111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>ha penny flea market</td>\n",
       "      <td>ha penny laugh</td>\n",
       "      <td>75.881579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>william butler yeats memorial</td>\n",
       "      <td>yeats life works william butler yeats</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>island bridge</td>\n",
       "      <td>island ferries</td>\n",
       "      <td>75.470199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>tourist office dublin</td>\n",
       "      <td>dun laoghaire community tourist office</td>\n",
       "      <td>72.925170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>séamus ennis arts centre</td>\n",
       "      <td>seamus ennis arts centre</td>\n",
       "      <td>96.989691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>powerscourt house</td>\n",
       "      <td>powerscourt theatre</td>\n",
       "      <td>80.472050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>gormleys fine art</td>\n",
       "      <td>gormley fine art</td>\n",
       "      <td>96.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>revolution 1916</td>\n",
       "      <td>1916 ride revolution rise rebels bus tour hidd...</td>\n",
       "      <td>84.393064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>roe co distillery</td>\n",
       "      <td>roe co whiskey distillery</td>\n",
       "      <td>83.040936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    agg_rating_name  \\\n",
       "33                 james joyce bust   \n",
       "50                           steine   \n",
       "53          ambassador event centre   \n",
       "74        blessington st park basin   \n",
       "90            blanchardstown centre   \n",
       "96                  griffith bridge   \n",
       "107                  seapoint beach   \n",
       "112  dun laoghaire rathdown library   \n",
       "115                       king inns   \n",
       "120              rathmichael church   \n",
       "127                victorian chapel   \n",
       "129                  freemason hall   \n",
       "138        charles parnell monument   \n",
       "142               jim larkin statue   \n",
       "144            sphere within sphere   \n",
       "145      william smith brien statue   \n",
       "152                  st kevins park   \n",
       "156            ha penny flea market   \n",
       "198   william butler yeats memorial   \n",
       "221                   island bridge   \n",
       "226           tourist office dublin   \n",
       "241        séamus ennis arts centre   \n",
       "249               powerscourt house   \n",
       "262               gormleys fine art   \n",
       "268                 revolution 1916   \n",
       "285               roe co distillery   \n",
       "\n",
       "                                          agg_loc_name  agg_score  \n",
       "33                                  james joyce museum  84.894118  \n",
       "50                                          stena line  75.000000  \n",
       "53                                  ambassador theatre  78.048780  \n",
       "74           blessington st basin dublin secret garden  84.260355  \n",
       "90                          leisureplex blanchardstown  80.000000  \n",
       "96                                       griffith park  76.496732  \n",
       "107                      seapoint blue flag beach 2019  88.268156  \n",
       "112            dun laoghaire rathdown heritage society  83.988095  \n",
       "115                                         kings inns  94.736842  \n",
       "120                                  rathmichael lodge  74.786667  \n",
       "127                 ardgillan castle victorian gardens  73.469388  \n",
       "129                                    freemasons hall  96.551724  \n",
       "138                            charles stewart parnell  71.319444  \n",
       "142                                       james larkin  71.875000  \n",
       "144                                      sphere sphere  82.533103  \n",
       "145                       william smith brien monument  84.497041  \n",
       "152                                   st kevin oratory  71.986111  \n",
       "156                                     ha penny laugh  75.881579  \n",
       "198              yeats life works william butler yeats  82.000000  \n",
       "221                                     island ferries  75.470199  \n",
       "226             dun laoghaire community tourist office  72.925170  \n",
       "241                           seamus ennis arts centre  96.989691  \n",
       "249                                powerscourt theatre  80.472050  \n",
       "262                                   gormley fine art  96.969697  \n",
       "268  1916 ride revolution rise rebels bus tour hidd...  84.393064  \n",
       "285                          roe co whiskey distillery  83.040936  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_res_df_70_99 = agg_res_df[(agg_res_df.agg_score <99) & (agg_res_df.agg_score >70)]\n",
    "print('before removing mistakes:', len(agg_res_df_70_99))\n",
    "agg_res_df_70_99.sort_values('agg_score') \n",
    "\n",
    "agg_idxs_to_remove = [117, 197, 16, 181, 256, 169, 146, 73, 176, 113, 140, 191, 9, 186, 265, 97, 199, 14, 151, 245, 102, 182, 273, 218, 243, 105, 136, 184, 233]\n",
    "not_sure = [142, 152, 226, 127, 50, 96, 53, 249]\n",
    "agg_res_df_70_99 = agg_res_df_70_99.drop(index=agg_idxs_to_remove)\n",
    "print('after removing mistakes:', len(agg_res_df_70_99))\n",
    "agg_res_df_70_99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# combine final result table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>rating_name</th>\n",
       "      <th>loc_name</th>\n",
       "      <th>agg_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>racing tours ireland</td>\n",
       "      <td>racing tours ireland</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>macau casino club dublin</td>\n",
       "      <td>macau casino club</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gallery photography</td>\n",
       "      <td>gallery photography</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vaults live</td>\n",
       "      <td>vaults live</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sandycove beach</td>\n",
       "      <td>sandycove beach</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>séamus ennis arts centre</td>\n",
       "      <td>seamus ennis arts centre</td>\n",
       "      <td>96.989691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>powerscourt house</td>\n",
       "      <td>powerscourt theatre</td>\n",
       "      <td>80.472050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>gormleys fine art</td>\n",
       "      <td>gormley fine art</td>\n",
       "      <td>96.969697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>revolution 1916</td>\n",
       "      <td>1916 ride revolution rise rebels bus tour hidd...</td>\n",
       "      <td>84.393064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>roe co distillery</td>\n",
       "      <td>roe co whiskey distillery</td>\n",
       "      <td>83.040936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rating_name  \\\n",
       "0        racing tours ireland   \n",
       "2    macau casino club dublin   \n",
       "3         gallery photography   \n",
       "4                 vaults live   \n",
       "6             sandycove beach   \n",
       "..                        ...   \n",
       "241  séamus ennis arts centre   \n",
       "249         powerscourt house   \n",
       "262         gormleys fine art   \n",
       "268           revolution 1916   \n",
       "285         roe co distillery   \n",
       "\n",
       "                                              loc_name   agg_score  \n",
       "0                                 racing tours ireland  100.000000  \n",
       "2                                    macau casino club  100.000000  \n",
       "3                                  gallery photography  100.000000  \n",
       "4                                          vaults live  100.000000  \n",
       "6                                      sandycove beach  100.000000  \n",
       "..                                                 ...         ...  \n",
       "241                           seamus ennis arts centre   96.989691  \n",
       "249                                powerscourt theatre   80.472050  \n",
       "262                                   gormley fine art   96.969697  \n",
       "268  1916 ride revolution rise rebels bus tour hidd...   84.393064  \n",
       "285                          roe co whiskey distillery   83.040936  \n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_df = pd.concat([\n",
    "                        agg_res_df_100,\n",
    "                        agg_res_df_70_99\n",
    "                        ])\n",
    "matches_df = matches_df.rename(columns={'agg_loc_name': 'loc_name', 'agg_rating_name':'rating_name'})\n",
    "len(matches_df)\n",
    "matches_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>loc_name</th>\n",
       "      <th>rating_name</th>\n",
       "      <th>agg_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.265696</td>\n",
       "      <td>53.341171</td>\n",
       "      <td>macau casino club</td>\n",
       "      <td>macau casino club dublin</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.137493</td>\n",
       "      <td>53.374989</td>\n",
       "      <td>bull island</td>\n",
       "      <td>bull island</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.267419</td>\n",
       "      <td>53.342900</td>\n",
       "      <td>dublin castle</td>\n",
       "      <td>dublin castle</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.267149</td>\n",
       "      <td>53.343922</td>\n",
       "      <td>dublin city hall</td>\n",
       "      <td>city hall</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6.372209</td>\n",
       "      <td>53.288411</td>\n",
       "      <td>civic theatre</td>\n",
       "      <td>civic theatre</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>-6.277493</td>\n",
       "      <td>53.343579</td>\n",
       "      <td>vaults live</td>\n",
       "      <td>vaults live</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-6.284198</td>\n",
       "      <td>53.343248</td>\n",
       "      <td>guinness storehouse</td>\n",
       "      <td>guinness storehouse</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>-6.270211</td>\n",
       "      <td>53.352507</td>\n",
       "      <td>14 henrietta st</td>\n",
       "      <td>14 henrietta st</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>-6.276364</td>\n",
       "      <td>53.337285</td>\n",
       "      <td>dublin liberties distillery</td>\n",
       "      <td>dublin liberties distillery</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-6.285661</td>\n",
       "      <td>53.343638</td>\n",
       "      <td>roe co whiskey distillery</td>\n",
       "      <td>roe co distillery</td>\n",
       "      <td>83.040936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Longitude   Latitude                     loc_name  \\\n",
       "0    -6.265696  53.341171            macau casino club   \n",
       "1    -6.137493  53.374989                  bull island   \n",
       "2    -6.267419  53.342900                dublin castle   \n",
       "3    -6.267149  53.343922             dublin city hall   \n",
       "4    -6.372209  53.288411                civic theatre   \n",
       "..         ...        ...                          ...   \n",
       "145  -6.277493  53.343579                  vaults live   \n",
       "146  -6.284198  53.343248          guinness storehouse   \n",
       "147  -6.270211  53.352507              14 henrietta st   \n",
       "148  -6.276364  53.337285  dublin liberties distillery   \n",
       "149  -6.285661  53.343638    roe co whiskey distillery   \n",
       "\n",
       "                     rating_name   agg_score  \n",
       "0       macau casino club dublin  100.000000  \n",
       "1                    bull island  100.000000  \n",
       "2                  dublin castle  100.000000  \n",
       "3                      city hall  100.000000  \n",
       "4                  civic theatre  100.000000  \n",
       "..                           ...         ...  \n",
       "145                  vaults live  100.000000  \n",
       "146          guinness storehouse  100.000000  \n",
       "147              14 henrietta st  100.000000  \n",
       "148  dublin liberties distillery  100.000000  \n",
       "149            roe co distillery   83.040936  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_merged = pd.merge(loc_df.rename(columns={'name_clean':'loc_name'}), matches_df, on='loc_name')\n",
    "loc_merged.drop(columns=['idx', 'name'], inplace=True)\n",
    "loc_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>loc_name</th>\n",
       "      <th>rating_name</th>\n",
       "      <th>agg_score</th>\n",
       "      <th>rating</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-6.265696</td>\n",
       "      <td>53.341171</td>\n",
       "      <td>macau casino club</td>\n",
       "      <td>macau casino club dublin</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>506</td>\n",
       "      <td>CASINOS &amp; GAMBLING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.137493</td>\n",
       "      <td>53.374989</td>\n",
       "      <td>bull island</td>\n",
       "      <td>bull island</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>74</td>\n",
       "      <td>NATURE &amp; PARKS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.267419</td>\n",
       "      <td>53.342900</td>\n",
       "      <td>dublin castle</td>\n",
       "      <td>dublin castle</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>43</td>\n",
       "      <td>SIGHTS &amp; LANDMARKS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.267149</td>\n",
       "      <td>53.343922</td>\n",
       "      <td>dublin city hall</td>\n",
       "      <td>city hall</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>82</td>\n",
       "      <td>MUSEUMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-6.372209</td>\n",
       "      <td>53.288411</td>\n",
       "      <td>civic theatre</td>\n",
       "      <td>civic theatre</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>161</td>\n",
       "      <td>CONCERTS &amp; SHOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>-6.277493</td>\n",
       "      <td>53.343579</td>\n",
       "      <td>vaults live</td>\n",
       "      <td>vaults live</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>CONCERTS &amp; SHOWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-6.284198</td>\n",
       "      <td>53.343248</td>\n",
       "      <td>guinness storehouse</td>\n",
       "      <td>guinness storehouse</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>MUSEUMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>-6.270211</td>\n",
       "      <td>53.352507</td>\n",
       "      <td>14 henrietta st</td>\n",
       "      <td>14 henrietta st</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>MUSEUMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>-6.276364</td>\n",
       "      <td>53.337285</td>\n",
       "      <td>dublin liberties distillery</td>\n",
       "      <td>dublin liberties distillery</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>52</td>\n",
       "      <td>FOOD &amp; DRINK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-6.285661</td>\n",
       "      <td>53.343638</td>\n",
       "      <td>roe co whiskey distillery</td>\n",
       "      <td>roe co distillery</td>\n",
       "      <td>83.040936</td>\n",
       "      <td>48</td>\n",
       "      <td>FOOD &amp; DRINK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Longitude   Latitude                     loc_name  \\\n",
       "0    -6.265696  53.341171            macau casino club   \n",
       "1    -6.137493  53.374989                  bull island   \n",
       "2    -6.267419  53.342900                dublin castle   \n",
       "3    -6.267149  53.343922             dublin city hall   \n",
       "4    -6.372209  53.288411                civic theatre   \n",
       "..         ...        ...                          ...   \n",
       "145  -6.277493  53.343579                  vaults live   \n",
       "146  -6.284198  53.343248          guinness storehouse   \n",
       "147  -6.270211  53.352507              14 henrietta st   \n",
       "148  -6.276364  53.337285  dublin liberties distillery   \n",
       "149  -6.285661  53.343638    roe co whiskey distillery   \n",
       "\n",
       "                     rating_name   agg_score  rating            category  \n",
       "0       macau casino club dublin  100.000000     506  CASINOS & GAMBLING  \n",
       "1                    bull island  100.000000      74      NATURE & PARKS  \n",
       "2                  dublin castle  100.000000      43  SIGHTS & LANDMARKS  \n",
       "3                      city hall  100.000000      82             MUSEUMS  \n",
       "4                  civic theatre  100.000000     161    CONCERTS & SHOWS  \n",
       "..                           ...         ...     ...                 ...  \n",
       "145                  vaults live  100.000000      30    CONCERTS & SHOWS  \n",
       "146          guinness storehouse  100.000000       9             MUSEUMS  \n",
       "147              14 henrietta st  100.000000      17             MUSEUMS  \n",
       "148  dublin liberties distillery  100.000000      52        FOOD & DRINK  \n",
       "149            roe co distillery   83.040936      48        FOOD & DRINK  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.merge(loc_merged, rating_df.rename(columns={'name_clean':'rating_name'}), on='rating_name', suffixes=['_loc', '_rating'])\n",
    "final_df.drop(columns=['idx', 'name'], inplace=True)\n",
    "final_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('rating_loc_attractions.csv', index=True)  "
   ]
  }
 ],
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