LDA-TopicModeling .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import os, sys, glob\n",
    "import re\n",
    "from ast import literal_eval\n",
    "from datetime import datetime\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "from IPython.display import display\n",
    "\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "from collections import Counter\n",
    "from gensim.corpora.dictionary import Dictionary\n",
    "import gensim\n",
    "from gensim.test.utils import datapath\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data from google cloud storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#read files\n",
    "def read_gs(path, id_name):\n",
    "    dev_file = tf.gfile.Glob(path)[0]\n",
    "    df = pd.read_csv(tf.gfile.Open(dev_file, mode='rb'), \n",
    "                     escapechar='\\\\', \n",
    "                     error_bad_lines=False, \n",
    "                     header=0, \n",
    "                     dtype={'original_id': str})\n",
    "    df.rename(columns={'original_id': id_name}, inplace=True)\n",
    "    \n",
    "    return df\n",
    "\n",
    "path = 'gs://dmp_recommendation_dev/resources/prv'\n",
    "shop_dir = path + '/prv_model_mimi/cms_shop/*.csv'\n",
    "shopDf = read_gs(shop_dir, \"shop_id\")\n",
    "# path = 'gs://directory/'\n",
    "# shop_dir = path + '/*.csv'\n",
    "# shopDf = read_gs(shop_dir, \"shop_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7799\n"
     ]
    }
   ],
   "source": [
    "#replace column with another column according to condition given\n",
    "shopDf = shopDf[shopDf['tags_th'] != \"['null']\"]\n",
    "shopDf['tags_th'] = np.where(shopDf['tags_th'] == '[]', shopDf['tags_en'], shopDf['tags_th'])\n",
    "shopDf['title_th'] = np.where(shopDf['title_th'].isnull(), shopDf['title_en'], shopDf['title_th'])\n",
    "\n",
    "tags_en = shopDf['tags_en']\n",
    "tags_th = shopDf['tags_th']\n",
    "\n",
    "def literal(i):\n",
    "    return literal_eval(i.replace(\"['\",\"[\\\"\").replace(\"']\", \"\\\"]\").replace(\"','\", \"\\\",\\\"\"))\n",
    "\n",
    "tags = tags_th.map(literal)\n",
    "print (len(tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def doc_lens(doc):\n",
    "    \n",
    "    document_lengths = np.array(list(map(len, doc)))\n",
    "    print(\"The average number of words in a document is: {}.\".format(np.mean(document_lengths)))\n",
    "    print(\"The minimum number of words in a document is: {}.\".format(min(document_lengths)))\n",
    "    print(\"The maximum number of words in a document is: {}.\".format(max(document_lengths)))\n",
    "    \n",
    "    return document_lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average number of words in a document is: 8.793947942043852.\n",
      "The minimum number of words in a document is: 1.\n",
      "The maximum number of words in a document is: 44.\n",
      "There are 22 documents with over 30 words.\n"
     ]
    }
   ],
   "source": [
    "document_lengths = doc_lens(tags)\n",
    "shorten_length = 30\n",
    "print(\"There are {} documents with over {} words.\".format(sum(document_lengths > shorten_length), shorten_length))\n",
    "shorter_documents = document_lengths[document_lengths <= shorten_length]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove word that ends with s\n",
    "#### Don't want to apply stemming and lematization because it effects shop's name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_word(documents):\n",
    "#     pattern_s = \"([^e+u+s\\s])s$\"\n",
    "    pattern_s = \"([^s\\s])s$\"\n",
    "    for i in documents:\n",
    "        for j, item in enumerate(i):\n",
    "            item = item.lower()\n",
    "            if re.search(re.compile(pattern_s), item) != None:\n",
    "                item = item[:-1]\n",
    "            i[j] = item\n",
    "\n",
    "    return documents\n",
    "\n",
    "tags = filter_word(tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenize the text using nltk's word tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/mimi/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "\n",
    "documents = []\n",
    "for tag in tags:\n",
    "    g = []\n",
    "    for word in tag:\n",
    "        #Tokenize a string to split off punctuation other than periods\n",
    "        words = nltk.word_tokenize(word)\n",
    "        for current_word in words:\n",
    "            current_word = current_word.lower()\n",
    "            g.append(current_word)\n",
    "    documents.append(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average number of words in a document is: 9.357866393127324.\n",
      "The minimum number of words in a document is: 1.\n",
      "The maximum number of words in a document is: 52.\n"
     ]
    }
   ],
   "source": [
    "document_lengths = doc_lens(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mimi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "\n",
    "ax.set_title(\"Distribution of number of words\", fontsize=16)\n",
    "ax.set_xlabel(\"Number of words\")\n",
    "sns.distplot(document_lengths, bins=50, ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 61 documents with over 30 words.\n"
     ]
    }
   ],
   "source": [
    "exclude_len = 30\n",
    "print(\"There are {} documents with over {} words.\".format(sum(document_lengths > exclude_len), exclude_len))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mimi/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "\n",
    "ax.set_title(\"Distribution of number of words\", fontsize=16)\n",
    "ax.set_xlabel(\"Number of words\")\n",
    "sns.distplot(shorter_documents);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show two-word document\n",
    "The shorter documents will probably be harder to classify since we'll have less words to cling to. LDA for example tries to find topics in documents, but if the documents are so short, perhaps it will find it hard to really find a topic in a two-word document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['kanchanaburi', 'กาญจนบุรี'],\n",
       " ['phuket', 'ภูเก็ต'],\n",
       " ['ซีคอน', 'บางแค'],\n",
       " ['ยโสธร', 'ร้านอาหาร'],\n",
       " ['chiangmai', 'เชียงใหม่'],\n",
       " ['สนามบินสมุย', 'สมุย'],\n",
       " ['ฟรีเมนูเซ็ต', 'สนามบิน'],\n",
       " ['ฟรีเมนูเซ็ต', 'สนามบิน'],\n",
       " ['คิงเพาเวอร์ออนไลน์', 'ช้อปปิ้งออนไลน์'],\n",
       " ['กันและกัน2017'],\n",
       " ['buddhamonthon', 'พุทธมณฑล'],\n",
       " ['kamphaengphet', 'กำแพงเพชร'],\n",
       " ['เคลือบแก้ว'],\n",
       " ['truebonu', 'ทรูโบนัส'],\n",
       " ['สนามบินสมุย', 'สมุย'],\n",
       " ['tak', 'ตาก']]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in documents if len(i) <= 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total number of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72982\n",
      "5130\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a1de9b940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flatten = lambda l : [item for sublist in l for item in sublist]\n",
    "allword = flatten(documents)\n",
    "print (len(allword))\n",
    "print (len(set(allword)))\n",
    "word = 300\n",
    "most_common = Counter(allword).most_common(word)\n",
    "least_common = Counter(allword).most_common()[-word-1:-1]\n",
    "x = [i[0] for i in most_common]\n",
    "y = [i[1] for i in most_common]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "ax.set_title(\"Distribution of word frequency\", fontsize=16)\n",
    "ax.set_xlabel(\"word\")\n",
    "sns.barplot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From above chart, we choose more than 3 frequency of word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lenghts of total word 68302\n",
      "Lengths unique word 1092\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "frequency = defaultdict(int)\n",
    "for text in documents:\n",
    "    for token in text:\n",
    "        frequency[token] += 1\n",
    "        \n",
    "documents1 = [[token for token in text if frequency[token] >= 3 ] for text in documents]\n",
    "flat_doc1 = flatten(documents1)\n",
    "print ('Lenghts of total word {}'.format(len(flat_doc1)))\n",
    "print ('Lengths unique word {}'.format(len(set(flat_doc1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove word less than 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/mimi/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "\n",
    "documents_cha = []\n",
    "for tag in documents:\n",
    "    g = []\n",
    "    for word in tag:\n",
    "        if len(word) >= 3 or word == \"ยา\" or word == \"ชา\":\n",
    "            g.append(word)\n",
    "    documents_cha.append(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lenghts of total word 67343\n",
      "Lengths unique word 1039\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "frequency = defaultdict(int)\n",
    "for text in documents_cha:\n",
    "    for token in text:\n",
    "        frequency[token] += 1\n",
    "        \n",
    "documents_freq = [[token for token in text if frequency[token] >= 3] for text in documents_cha]\n",
    "\n",
    "flat_doc1 = flatten(documents_freq)\n",
    "print ('Lenghts of total word {}'.format(len(flat_doc1)))\n",
    "print ('Lengths unique word {}'.format(len(set(flat_doc1))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove unwanted word those are Thailand province, district, and road in both Thai and English that are contained in the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n"
     ]
    }
   ],
   "source": [
    "province = np.load('province.npy')\n",
    "print (len(province))\n",
    "district = np.load('district.npy')\n",
    "road = np.load('road.npy')\n",
    "exclude_word = list(province) + list(district) + list(road)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dictionary and Corpus needed for LDA -Topic Modeling\n",
    "\n",
    "Dictionary is a unique id for each word in the document.\n",
    "\n",
    "Corpus is a mapping of (word_id, word_frequency).\n",
    "\n",
    "Remove exclude from dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Dictionary\n",
    "dictionary = Dictionary.from_documents(documents_freq)\n",
    "# ignore words that appear in less than 3 documents or more than 80% documents\n",
    "dictionary.filter_extremes(no_below=3, no_above=0.9)\n",
    "dictionary.compactify()\n",
    "# remove exclude word from dictionary \n",
    "del_ids = [k for k,v in dictionary.items() if v in exclude_word]\n",
    "dictionary.filter_tokens(bad_ids=del_ids)\n",
    "dictionary.compactify()\n",
    "corpus = [dictionary.doc2bow(doc) for doc in documents]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the cleansed words frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleansed_words_df = pd.DataFrame.from_dict(dictionary.token2id, orient='index')\n",
    "cleansed_words_df.rename(columns={0: 'id'}, inplace=True)\n",
    "cleansed_words_df['count'] = list(map(lambda id_: dictionary.dfs.get(id_), cleansed_words_df.id))\n",
    "cleansed_words_df.drop(['id'], axis=1, inplace=True)\n",
    "cleansed_words_df.sort_values('count', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_frequency_barplot(df, nr_top_words=50):\n",
    "    \"\"\" df should have a column named count.\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(1,1,figsize=(20,5))\n",
    "    sns.barplot(list(range(nr_top_words)), df['count'].values[:nr_top_words], palette='hls', ax=ax)\n",
    "    \n",
    "    ax.set_xticks(list(range(nr_top_words)))\n",
    "    ax.set_xticklabels(df.index[:nr_top_words], fontsize=14, rotation=90)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "ax = word_frequency_barplot(cleansed_words_df)\n",
    "ax.set_title(\"Document Frequencies (Number of documents a word appears in)\", fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tf-idf score\n",
    "Tf-idf reflects how important a word is to a document in a collection or corpus.\n",
    "\n",
    "The higher the Tf-idf score (weight), the rarer the term and vice versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tfidf_model = gensim.models.TfidfModel(corpus, id2word=dictionary)\n",
    "low_value = 0.1\n",
    "low_value_words = []\n",
    "for bow in corpus:\n",
    "    low_value_words += [id for id, value in tfidf_model[bow] if value < low_value]\n",
    "\n",
    "#filter value tfidf > 0.9 (unique words that we don't want to feed in LDA)\n",
    "dictionary.filter_tokens(bad_ids=low_value_words)\n",
    "corpus = [dictionary.doc2bow(doc) for doc in documents]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building LDA Model\n",
    "In addition to the corpus and dictionary, you need to provide the number of topics as well.\n",
    "LDA model is built with 5 different topics where each topic is a combination of keywords and each keyword contributes a certain weightage to the topic.\n",
    "\n",
    "The weights reflect how important a keyword is to that topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the optimal number of topics for LDA\n",
    "Build many LDA models with different values of number of topics and pick the one that gives the highest coherence value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        model= gensim.models.ldamodel.LdaModel(corpus=corpus, \n",
    "                                           num_topics=num_topics,  \n",
    "                                           id2word=dictionary,\n",
    "                                            )\n",
    "        \n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "\n",
    "    return model_list, coherence_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot coherence score to choose the best number of topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting graph\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Topics = 2  has Coherence Value of 0.4767\n",
      "Num Topics = 5  has Coherence Value of 0.4763\n",
      "Num Topics = 8  has Coherence Value of 0.4355\n",
      "Num Topics = 11  has Coherence Value of 0.4623\n",
      "Num Topics = 14  has Coherence Value of 0.4587\n",
      "Num Topics = 17  has Coherence Value of 0.4684\n"
     ]
    }
   ],
   "source": [
    "start=2\n",
    "limit=20\n",
    "step=3\n",
    "corpus = corpus\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, corpus=corpus, \n",
    "                                                        texts=documents, start=start, limit=limit, step=step)\n",
    "# Print the coherence scores\n",
    "print ('Plotting graph')\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()\n",
    "\n",
    "for m, cv in zip(x, coherence_values):\n",
    "    round_cv = round(cv, 4)\n",
    "    print(\"Num Topics =\", m, \" has Coherence Value of\", round_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choose number of topic that have the best coherence score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim.test.utils import datapath\n",
    "\n",
    "# optimal_model = gensim.models.ldamodel.LdaModel.load(\"lda.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(4,\n",
       "  '0.068*\"tour\" + 0.050*\"hotel\" + 0.049*\"travel\" + 0.047*\"resort\" + 0.046*\"ร้านค้าออนไลน์\" + 0.043*\"package\" + 0.041*\"online\" + 0.039*\"room\" + 0.032*\"accommodation\" + 0.031*\"traveling\"'),\n",
       " (2,\n",
       "  '0.098*\"ช้อปปิ้งมอลล์\" + 0.097*\"คอมมิวนิตี้มอลล์\" + 0.093*\"ผ่อนคลาย\" + 0.048*\"พักผ่อน\" + 0.043*\"บันเทิง\" + 0.032*\"ของฝาก\" + 0.032*\"กิ๊ฟช็อป\" + 0.032*\"ของที่ระลึก\" + 0.022*\"พิซซ่า\" + 0.021*\"อิตาเลียน\"'),\n",
       " (3,\n",
       "  '0.128*\"อาหารไทย-อีสาน\" + 0.102*\"ฟิวชั่น\" + 0.065*\"บริการ\" + 0.031*\"ปิ้งย่าง\" + 0.029*\"บาร์บีคิว\" + 0.027*\"ยากินิกุ\" + 0.025*\"การศึกษา\" + 0.025*\"เรียน\" + 0.025*\"ความรู้\" + 0.022*\"โรงเรียน\"'),\n",
       " (0,\n",
       "  '0.037*\"อาหารญี่ปุ่น\" + 0.033*\"health\" + 0.032*\"ซูชิ\" + 0.029*\"เครื่องเรือน\" + 0.029*\"เฟอร์นิเจอร์\" + 0.029*\"ของตกแต่ง\" + 0.029*\"ตกแต่งบ้าน\" + 0.029*\"ข้าวปั้น\" + 0.029*\"ซาชิมิ\" + 0.029*\"ปลาดิบ\"'),\n",
       " (9,\n",
       "  '0.093*\"แต่งรถ\" + 0.091*\"ตกแต่งรถ\" + 0.085*\"สุกี้\" + 0.083*\"ชาบู\" + 0.083*\"สุกี้ยากี้\" + 0.036*\"อุปกรณ์กีฬา\" + 0.033*\"ออกกำลังกาย\" + 0.029*\"แอ่วหนาวอิ่มเหนือ\" + 0.029*\"อิ่มทั่วไทย\" + 0.024*\"อิ่มแซ่บ\"'),\n",
       " (7,\n",
       "  '0.108*\"coffee\" + 0.066*\"eating\" + 0.062*\"dining\" + 0.051*\"cafe\" + 0.034*\"international\" + 0.030*\"beverage\" + 0.030*\"drink\" + 0.029*\"bean\" + 0.027*\"คาเฟ่\" + 0.026*\"ร้านกาแฟ\"'),\n",
       " (1,\n",
       "  '0.043*\"กล้อง\" + 0.043*\"ถ่ายภาพ\" + 0.043*\"กล้องถ่ายรูป\" + 0.037*\"สัตว์เลี้ยง\" + 0.034*\"steak\" + 0.027*\"shabu\" + 0.026*\"the\" + 0.026*\"ช้อปปิ้งมอลล์\" + 0.026*\"คอมมิวนิตี้มอลล์\" + 0.024*\"suki\"'),\n",
       " (5,\n",
       "  '0.152*\"สินค้าไอที\" + 0.152*\"แก็ดเจ็ต\" + 0.055*\"เครื่องประดับ\" + 0.054*\"ตกแต่ง\" + 0.048*\"ห้างสรรพสินค้า\" + 0.048*\"ศูนย์การค้า\" + 0.047*\"เบเกอรี่\" + 0.042*\"เค้ก\" + 0.039*\"ขนมปัง\" + 0.022*\"bakery\"'),\n",
       " (6,\n",
       "  '0.162*\"ก๋วยเตี๋ยว\" + 0.155*\"ราเมน\" + 0.155*\"ราเมง\" + 0.050*\"สเต็ก\" + 0.048*\"ศูนย์บริการ\" + 0.047*\"ดูแลรถ\" + 0.045*\"car\" + 0.018*\"service\" + 0.014*\"noodle\" + 0.010*\"nakhon\"'),\n",
       " (8,\n",
       "  '0.140*\"ขนม\" + 0.137*\"ของกินเล่น\" + 0.038*\"store\" + 0.038*\"buying\" + 0.031*\"brand\" + 0.025*\"leading\" + 0.023*\"ของใช้เด็ก\" + 0.018*\"fashion\" + 0.014*\"clothe\" + 0.012*\"cosmetic\"')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = model_list[3]\n",
    "#save model\n",
    "model.save('lda.model')\n",
    "model_topics = model.show_topics(formatted=True)\n",
    "print (len(model_topics))\n",
    "model_topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute Model Perplexity and Coherance Score\n",
    "The higher coherance Score, the better model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -5.371567931907914\n",
      "\n",
      "Coherence Score:  0.4623075202394309\n"
     ]
    }
   ],
   "source": [
    "# Compute Perplexity\n",
    "print('\\nPerplexity: ', model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=model, texts=documents, dictionary=dictionary, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the topics keywords\n",
    "A good topic model should have non-overlapping cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el831402998484669847438632308\"></div>\n",
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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el831402998484669847438632308\", ldavis_el831402998484669847438632308_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el831402998484669847438632308\", ldavis_el831402998484669847438632308_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el831402998484669847438632308\", ldavis_el831402998484669847438632308_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim\n",
    "lda_display = pyLDAvis.gensim.prepare(model, corpus, dictionary)\n",
    "pyLDAvis.display(lda_display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look into LDA result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "shopDf['group'] = [max(model[i], key=lambda x:x[1])[0] for i in corpus]\n",
    "shopDf['prob'] = [max(model[i], key=lambda x:x[1])[1] for i in corpus]\n",
    "shopDf['tags_other'] = [max(model[i], key=lambda x:x[1])[0] if (max(model[i], key=lambda x:x[1])[1] > 0.6) else 99 for i in corpus ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def result(tag_group, column, shopDf):\n",
    "    \n",
    "    result = shopDf[shopDf[column] == tag_group][['merchant_id', 'title_th', 'tags_th']]\n",
    "    print ('Number of result is...\\n{}'.format(result.count()))\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of result is...\n",
      "merchant_id    223\n",
      "title_th       223\n",
      "tags_th        223\n",
      "dtype: int64\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>merchant_id</th>\n",
       "      <th>title_th</th>\n",
       "      <th>tags_th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>PD4X44Eb8l3D</td>\n",
       "      <td>จิระผ้าม่าน (จ.เชียงใหม่)</td>\n",
       "      <td>['ของตกแต่ง','ช้อปปิ้ง','ซื้อของ','ตกแต่งบ้าน','ตลาดนัด','ร้านค้า','เครื่องเรือน','เชียงใหม่','เฟอร์นิเจอร์']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>j289mmRGAnv1</td>\n",
       "      <td>จันทร์อาดีโคมไฟ</td>\n",
       "      <td>['กรุงเทพมหานคร','ของตกแต่ง','ช้อปปิ้ง','ซื้อของ','ตกแต่งบ้าน','ตลาดนัด','ร้านค้า','เครื่องเรือน','เฟอร์นิเจอร์']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>OWBMBBNprXvZ</td>\n",
       "      <td>ทาโกะจัง คาเฟ่ (จ.เชียงใหม่)</td>\n",
       "      <td>['กินข้าว','ข้าวปั้น','ซาชิมิ','ซูชิ','ปลาดิบ','ร้านอาหาร','อาหารญี่ปุ่น','เชียงใหม่']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>EvyNBBoKrozb</td>\n",
       "      <td>โน้ตไม้พาเลท เพชรบูรณ์ (จ.เพชรบูรณ์)</td>\n",
       "      <td>['ของตกแต่ง','ช้อปปิ้ง','ซื้อของ','ตกแต่งบ้าน','ตลาดนัด','ร้านค้า','เครื่องเรือน','เพชรบูรณ์','เฟอร์นิเจอร์']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>aqGB11mnD50y</td>\n",
       "      <td>TAZITA Spa and Art Design</td>\n",
       "      <td>['beauty','health','healthy','physical','relax','relaxation','ความงาม','ทาสิทา','ทาสิทาสปาแอนด์ดีไซน์','บิวตี้','ผ่อนคลาย','พักผ่อน','ร่างกาย','สุขภาพ','เสริมสวย']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      merchant_id                              title_th  \\\n",
       "47   PD4X44Eb8l3D  จิระผ้าม่าน (จ.เชียงใหม่)              \n",
       "55   j289mmRGAnv1  จันทร์อาดีโคมไฟ                        \n",
       "135  OWBMBBNprXvZ  ทาโกะจัง คาเฟ่ (จ.เชียงใหม่)           \n",
       "243  EvyNBBoKrozb  โน้ตไม้พาเลท เพชรบูรณ์ (จ.เพชรบูรณ์)   \n",
       "281  aqGB11mnD50y  TAZITA Spa and Art Design              \n",
       "\n",
       "                                                                                                                                                                 tags_th  \n",
       "47   ['ของตกแต่ง','ช้อปปิ้ง','ซื้อของ','ตกแต่งบ้าน','ตลาดนัด','ร้านค้า','เครื่องเรือน','เชียงใหม่','เฟอร์นิเจอร์']                                                        \n",
       "55   ['กรุงเทพมหานคร','ของตกแต่ง','ช้อปปิ้ง','ซื้อของ','ตกแต่งบ้าน','ตลาดนัด','ร้านค้า','เครื่องเรือน','เฟอร์นิเจอร์']                                                    \n",
       "135  ['กินข้าว','ข้าวปั้น','ซาชิมิ','ซูชิ','ปลาดิบ','ร้านอาหาร','อาหารญี่ปุ่น','เชียงใหม่']                                                                               \n",
       "243  ['ของตกแต่ง','ช้อปปิ้ง','ซื้อของ','ตกแต่งบ้าน','ตลาดนัด','ร้านค้า','เครื่องเรือน','เพชรบูรณ์','เฟอร์นิเจอร์']                                                        \n",
       "281  ['beauty','health','healthy','physical','relax','relaxation','ความงาม','ทาสิทา','ทาสิทาสปาแอนด์ดีไซน์','บิวตี้','ผ่อนคลาย','พักผ่อน','ร่างกาย','สุขภาพ','เสริมสวย']  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = result(0, 'tags_other', shopDf)\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting top threshold of probability of each word assigned to a topic or select top n word "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def title_exact_match(data):\n",
    "\n",
    "    for cate in SHOP.keys():\n",
    "        for val in SHOP[cate]:\n",
    "            if re.search(r'\\b{0}\\b'.format(val.lower()), data.lower()):\n",
    "                return cate\n",
    "    return 99\n",
    "\n",
    "def title_some_match(data):\n",
    "\n",
    "    for cate in SHOP.keys():\n",
    "        for val in SHOP[cate]:\n",
    "            if val.lower() in data.lower():\n",
    "                return cate\n",
    "    return 99\n",
    "\n",
    "def percent_trash():\n",
    "    shopDf['group'] = shopDf['tags_th'].map(title_exact_match)\n",
    "    #replace bin 99 with group that have matched word\n",
    "    shopDf['group'] = np.where(shopDf['group'] == 99, shopDf['tags_th'].map(title_some_match), shopDf['group'])\n",
    "    extra_bin = shopDf[shopDf['group'] == 99]['merchant_id'].count()\n",
    "    prop_trash = extra_bin / total_doc \n",
    "    \n",
    "    return prop_trash"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### top n word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "trash_word = []\n",
    "total_doc = shopDf['merchant_id'].count()\n",
    "range_word = range(1, 10)\n",
    "for topword in range_word:\n",
    "    keyword = []\n",
    "    for t in range(len(model_topics)):\n",
    "        wordlist = (model.show_topic(t, topword))\n",
    "        keyword.append([x for x,_ in wordlist])\n",
    "    SHOP = dict(enumerate(keyword, start=1))\n",
    "\n",
    "    prop_trash = percent_trash()\n",
    "    trash_word.append(prop_trash)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### top n threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "keyword = []\n",
    "trash_threshold = []\n",
    "threshold = [0.04, 0.03, 0.02]\n",
    "for i in threshold:\n",
    "    for t in range(len(model_topics)):\n",
    "        wordlist = (model.show_topic(t, topn=20))\n",
    "        keyword.append([x for x, y in wordlist if y > i])\n",
    "    SHOP = dict(enumerate(keyword, start=1))\n",
    "\n",
    "    prop_trash = percent_trash()\n",
    "    trash_threshold.append(prop_trash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'threshold')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_range = range(1, 10)\n",
    "f = plt.figure(figsize=(10,3))\n",
    "ax = f.add_subplot(121)\n",
    "ax2 = f.add_subplot(122)\n",
    "ax.plot(word_range, trash_word)\n",
    "ax2.plot(threshold, trash_threshold)\n",
    "ax.set_xlabel('word range', fontsize=15)\n",
    "ax.set_ylabel('trash proportion', fontsize=15)\n",
    "ax2.set_xlabel('threshold', fontsize=15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}