{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import clear_output, Markdown, Math\n",
    "import ipywidgets as widgets\n",
    "import os\n",
    "import unicodedata as uni\n",
    "import numpy as np\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from nltk.corpus import wordnet\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* read in table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>code</th>\n",
       "      <th>character</th>\n",
       "      <th>description</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>126980</td>\n",
       "      <td>🀄</td>\n",
       "      <td>MAHJONG TILE RED DRAGON</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>129525</td>\n",
       "      <td>🧵</td>\n",
       "      <td>SPOOL OF THREAD</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>129526</td>\n",
       "      <td>🧶</td>\n",
       "      <td>BALL OF YARN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>127183</td>\n",
       "      <td>🃏</td>\n",
       "      <td>PLAYING CARD BLACK JOKER</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>129296</td>\n",
       "      <td>🤐</td>\n",
       "      <td>ZIPPER-MOUTH FACE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    code character               description  Unnamed: 4\n",
       "0           0  126980         🀄   MAHJONG TILE RED DRAGON         NaN\n",
       "1           1  129525         🧵           SPOOL OF THREAD         NaN\n",
       "2           2  129526         🧶              BALL OF YARN         NaN\n",
       "3           3  127183         🃏  PLAYING CARD BLACK JOKER         NaN\n",
       "4           4  129296         🤐         ZIPPER-MOUTH FACE         NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.read_csv('../Tools/emoji_descriptions.csv')\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* todo: read in a lot of messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\"Hello, this is a testing message\", \"this is a very sunny day today, i am very happy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmed_messages = []\n",
    "for m in messages:\n",
    "    words = word_tokenize(m)\n",
    "    sm = []\n",
    "    for w in words:\n",
    "        sm.append(ps.stem(w))\n",
    "    stemmed_messages.append(sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['hello', ',', 'thi', 'is', 'a', 'test', 'messag'],\n",
       " ['thi',\n",
       "  'is',\n",
       "  'a',\n",
       "  'veri',\n",
       "  'sunni',\n",
       "  'day',\n",
       "  'today',\n",
       "  ',',\n",
       "  'i',\n",
       "  'am',\n",
       "  'veri',\n",
       "  'happi']]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stemmed_messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1027, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* compare words to emoji descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_sentence(sentence):\n",
    "    tokenized_sentence = word_tokenize(sentence)\n",
    "    n = len(tokenized_sentence)\n",
    "    l = table.shape[0]\n",
    "    matrix_list = []\n",
    "    \n",
    "    for index, row in table.iterrows():\n",
    "        emoji_tokens = word_tokenize(row['description'])\n",
    "        m = len(emoji_tokens)\n",
    "\n",
    "        mat = np.zeros(shape=(m,n))\n",
    "        for i in range(len(emoji_tokens)):\n",
    "            for j in range(len(tokenized_sentence)):\n",
    "                syn1 = wordnet.synsets(emoji_tokens[i])\n",
    "                if len(syn1) == 0:\n",
    "                    continue\n",
    "                w1 = syn1[0]\n",
    "                #print(j, tokenized_sentence)\n",
    "                syn2 = wordnet.synsets(tokenized_sentence[j])\n",
    "                if len(syn2) == 0:\n",
    "                    continue\n",
    "                w2 = syn2[0]\n",
    "                val = w1.wup_similarity(w2)\n",
    "                if val is None:\n",
    "                    continue\n",
    "                mat[i,j] = val\n",
    "        #print(row['character'], mat)\n",
    "        matrix_list.append(mat)\n",
    "            \n",
    "    return matrix_list\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = evaluate_sentence(\"car soccer surf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* building a lookup table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup = {}\n",
    "for index, row in table.iterrows():\n",
    "    lookup[index] = row['character']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "summed = np.argsort([-np.sum(x) for x in result])\n",
    "max_val = np.argsort([-np.max(x) for x in result])\n",
    "avg =  np.argsort([-np.mean(x) for x in result])\n",
    "\n",
    "t = 0.7\n",
    "threshold = np.argsort([-len(np.where(x>t)[0]) for x in result])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# 🏉⚾🎳🔥🏐🎱💏🧾🚗🚘"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def print_best_results(sorted_indices, n=10):\n",
    "    print([lookup[x] + \" -- \" + table.iloc[]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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