Mini Project 02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re, nltk, spacy, gensim\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "# Sklearn\n",
    "from sklearn.decomposition import LatentDirichletAllocation, TruncatedSVD\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from pprint import pprint\n",
    "\n",
    "# Plotting tools\n",
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>body</th>\n",
       "      <th>post_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ابنتي  يالسبيعي</td>\n",
       "      <td>\\n\\nابنتي  يالسبيعيابنتي بدأت تكتشف غريزتها ال...</td>\n",
       "      <td>31172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>لماذا أنا طيب ؟</td>\n",
       "      <td>\\n\\nلماذا أنا طيب ؟لماذا أنا طيب ؟ لماذا إذا أ...</td>\n",
       "      <td>31196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>خمسه وعشرون سنه من المعاناه فهل من منقذ قبل فو...</td>\n",
       "      <td>\\n\\nخمسه وعشرون سنه من المعاناه فهل من منقذ قب...</td>\n",
       "      <td>31214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>00فن الإنصات00</td>\n",
       "      <td>\\n\\n00فن الإنصات00كيف أتعلم فن الانصات\\n\\r\\nرأ...</td>\n",
       "      <td>31225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>البكاء .... يادكتور سبيعي</td>\n",
       "      <td>\\n\\nالبكاء .... يادكتور سبيعيالسلام عليكم ورحم...</td>\n",
       "      <td>31234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>طائر يسجد لله</td>\n",
       "      <td>\\n\\nطائر يسجد للهطائر يسجد لله ((منقول))\\n\\r\\n...</td>\n",
       "      <td>31235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>معلومات خفيفة وغريبة نوعاً ما</td>\n",
       "      <td>\\n\\nمعلومات خفيفة وغريبة نوعاً مامعلومات خفيفة...</td>\n",
       "      <td>31236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>فتاة يخرج أنفها المسك عند تغسيلها</td>\n",
       "      <td>\\n\\nفتاة يخرج أنفها المسك عند تغسيلهافتاة يخرج...</td>\n",
       "      <td>31237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>الموسيقى ..اسمع وتمتع</td>\n",
       "      <td>\\n\\nالموسيقى ..اسمع وتمتعليش تبي تسمع نسيت أنه...</td>\n",
       "      <td>31252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>عــلاج الحســد000لايفوتكم 000مهم جدا000</td>\n",
       "      <td>\\n\\nعــلاج الحســد000لايفوتكم 000مهم جدا000بسم...</td>\n",
       "      <td>31257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Unnamed: 0  id                                              title  \\\n",
       "0           0   1                                    ابنتي  يالسبيعي   \n",
       "1           1   2                                    لماذا أنا طيب ؟   \n",
       "2           2   3  خمسه وعشرون سنه من المعاناه فهل من منقذ قبل فو...   \n",
       "3           3   4                                     00فن الإنصات00   \n",
       "4           4   5                          البكاء .... يادكتور سبيعي   \n",
       "5           5   6                                      طائر يسجد لله   \n",
       "6           6   7                      معلومات خفيفة وغريبة نوعاً ما   \n",
       "7           7   8                  فتاة يخرج أنفها المسك عند تغسيلها   \n",
       "8           8   9                              الموسيقى ..اسمع وتمتع   \n",
       "9           9  10            عــلاج الحســد000لايفوتكم 000مهم جدا000   \n",
       "\n",
       "                                                body  post_id  \n",
       "0  \\n\\nابنتي  يالسبيعيابنتي بدأت تكتشف غريزتها ال...    31172  \n",
       "1  \\n\\nلماذا أنا طيب ؟لماذا أنا طيب ؟ لماذا إذا أ...    31196  \n",
       "2  \\n\\nخمسه وعشرون سنه من المعاناه فهل من منقذ قب...    31214  \n",
       "3  \\n\\n00فن الإنصات00كيف أتعلم فن الانصات\\n\\r\\nرأ...    31225  \n",
       "4  \\n\\nالبكاء .... يادكتور سبيعيالسلام عليكم ورحم...    31234  \n",
       "5  \\n\\nطائر يسجد للهطائر يسجد لله ((منقول))\\n\\r\\n...    31235  \n",
       "6  \\n\\nمعلومات خفيفة وغريبة نوعاً مامعلومات خفيفة...    31236  \n",
       "7  \\n\\nفتاة يخرج أنفها المسك عند تغسيلهافتاة يخرج...    31237  \n",
       "8  \\n\\nالموسيقى ..اسمع وتمتعليش تبي تسمع نسيت أنه...    31252  \n",
       "9  \\n\\nعــلاج الحســد000لايفوتكم 000مهم جدا000بسم...    31257  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Data/posts.csv', encoding='utf-8',error_bad_lines=False);\n",
    "docs=data['body'].drop_duplicates().values.tolist()\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "STOPWORDS = set(stopwords.words('english')+stopwords.words('arabic'))\n",
    "\n",
    "tobespace_re = r'[\\/\\\\(){}\\[\\]\\|\\\"\\'،…—.;,-=+$%^&*:÷×؛~٪؟?!_#@]'\n",
    "arabic_diacritics = re.compile(\"\"\"\n",
    "                             ّ    | # Tashdid\n",
    "                             َ    | # Fatha\n",
    "                             ً    | # Tanwin Fath\n",
    "                             ُ    | # Damma\n",
    "                             ٌ    | # Tanwin Damm\n",
    "                             ِ    | # Kasra\n",
    "                             ٍ    | # Tanwin Kasr\n",
    "                             ْ    | # Sukun\n",
    "                             ـ     # Tatwil/Kashida\n",
    "                         \"\"\", re.VERBOSE)\n",
    "\n",
    "def normalize(text):\n",
    "    text = re.sub(\"[إأآ]\", \"ا\", text)\n",
    "    text = re.sub(\"ى\", \"ي\", text)\n",
    "    text = re.sub(\"[ؤئ]\", \"ء\", text)\n",
    "    text = re.sub(\"هة\", \"ه\", text)\n",
    "    text = re.sub(\"[ة]\", \"ه\", text)\n",
    "    text = re.sub(\"گ\", \"ك\", text)\n",
    "    text = re.sub(arabic_diacritics, '', text)\n",
    "    return text\n",
    "\n",
    "def clean(text):\n",
    "    text = re.sub(r'http\\S+', '', text)    #لإزالة الروابط\n",
    "    text = re.sub(r'\\S+@\\S+\\s?', '', text)  #لإزالة البريد الإلكتروني  \n",
    "    text = re.sub(r'@\\S+', '', text)       #لإزالة المنشنات\n",
    "    text = re.sub(r'pic.\\S+', '', text)    #لإزالة الصور\n",
    "#     stemmer = isri.ISRIStemmer()\n",
    "#     text = \" \".join(stemmer.stem(w) for w in text.split() if ( (w not in STOPWORDS) and (len(w)>3) ))\n",
    "    text = \" \".join(w for w in text.split() if ( (w not in STOPWORDS) and (len(w)>3) ))\n",
    "    text = normalize(text)\n",
    "    text = re.sub(r'(.)\\1+', r'\\1\\1', text)         # إزالة الأحرف المكررة أكثر من مرتين\n",
    "    text = re.sub(r'(.)(.)(\\1\\2)+', r'\\1\\2', text)  #إزالة ثنائية الأحرف المكررة\n",
    "    text = re.sub(tobespace_re, ' ', text)          # الإستبدال بفراغ\n",
    "    text = re.sub(r'\\s+', ' ', text)                #إزالة الفراغات المكررة \n",
    "    text = text.lower()\n",
    "#     clean = [w for w in text.split()]\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_docs = [clean(doc) for doc in docs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Create the Document-Word matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LDA topic model algorithm requires a document word matrix as the main input.\n",
    "\n",
    "You can create one using CountVectorizer. In the below code, I have configured the CountVectorizer to consider words that has occurred at least 10 times (min_df), remove built-in english stopwords, convert all words to lowercase, and a word can contain numbers and alphabets of at least length 3 in order to be qualified as a word.\n",
    "\n",
    "So, to create the doc-word matrix, you need to first initialise the CountVectorizer class with the required configuration and then apply fit_transform to actually create the matrix.\n",
    "\n",
    "Since most cells contain zeros, the result will be in the form of a sparse matrix to save memory.\n",
    "\n",
    "If you want to materialize it in a 2D array format, call the todense() method of the sparse matrix like its done in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(#ngram_range=(1,2),\n",
    "                             min_df=10)\n",
    "\n",
    "data_vectorized = vectorizer.fit_transform(cleaned_docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Check the Sparsicity\n",
    "\n",
    "Sparsicity is nothing but the percentage of non-zero datapoints in the document-word matrix, that is data_vectorized.\n",
    "\n",
    "Since most cells in this matrix will be zero, I am interested in knowing what percentage of cells contain non-zero values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Materialize the sparse data\n",
    "data_dense = data_vectorized.todense()\n",
    "\n",
    "# Compute Sparsicity = Percentage of Non-Zero cells\n",
    "print(\"Sparsicity: \", ((data_dense > 0).sum()/data_dense.size)*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sparsicity: -0.3%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Build LDA model with sklearn\n",
    "\n",
    "Everything is ready to build a Latent Dirichlet Allocation (LDA) model. Let’s initialise one and call fit_transform() to build the LDA model.\n",
    "\n",
    "For this example, I have set the n_topics as 20 based on prior knowledge about the dataset. Later we will find the optimal number using grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
      "                          evaluate_every=-1, learning_decay=0.7,\n",
      "                          learning_method='online', learning_offset=10.0,\n",
      "                          max_doc_update_iter=100, max_iter=10,\n",
      "                          mean_change_tol=0.001, n_components=20, n_jobs=-1,\n",
      "                          perp_tol=0.1, random_state=100, topic_word_prior=None,\n",
      "                          total_samples=1000000.0, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "# Build LDA Model\n",
    "lda_model = LatentDirichletAllocation(n_components=20,           # Number of topics\n",
    "                                      max_iter=10,               # Max learning iterations\n",
    "                                      learning_method='online',   \n",
    "                                      random_state=100,          # Random state\n",
    "                                      batch_size=128,            # n docs in each learning iter\n",
    "                                      evaluate_every = -1,       # compute perplexity every n iters, default: Don't\n",
    "                                      n_jobs = -1,               # Use all available CPUs\n",
    "                                     )\n",
    "lda_output = lda_model.fit_transform(data_vectorized)\n",
    "\n",
    "print(lda_model)  # Model attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,    \n",
    "             evaluate_every=-1, learning_decay=0.7,   \n",
    "             learning_method='online', learning_offset=10.0,   \n",
    "             max_doc_update_iter=100, max_iter=10, mean_change_tol=0.001,    \n",
    "             n_components=10, n_jobs=-1, n_topics=20, perp_tol=0.1,    \n",
    "             random_state=100, topic_word_prior=None,   \n",
    "             total_samples=1000000.0, verbose=0)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. Diagnose model performance with perplexity and log-likelihood\n",
    "\n",
    "A model with higher log-likelihood and lower perplexity (exp(-1. * log-likelihood per word)) is considered to be good. Let’s check for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Likelihood:  -16785441.244628094\n",
      "Perplexity:  7151.39355975934\n",
      "{'batch_size': 128,\n",
      " 'doc_topic_prior': None,\n",
      " 'evaluate_every': -1,\n",
      " 'learning_decay': 0.7,\n",
      " 'learning_method': 'online',\n",
      " 'learning_offset': 10.0,\n",
      " 'max_doc_update_iter': 100,\n",
      " 'max_iter': 10,\n",
      " 'mean_change_tol': 0.001,\n",
      " 'n_components': 20,\n",
      " 'n_jobs': -1,\n",
      " 'perp_tol': 0.1,\n",
      " 'random_state': 100,\n",
      " 'topic_word_prior': None,\n",
      " 'total_samples': 1000000.0,\n",
      " 'verbose': 0}\n"
     ]
    }
   ],
   "source": [
    "# Log Likelyhood: Higher the better\n",
    "print(\"Log Likelihood: \", lda_model.score(data_vectorized))\n",
    "\n",
    "# Perplexity: Lower the better. Perplexity = exp(-1. * log-likelihood per word)\n",
    "print(\"Perplexity: \", lda_model.perplexity(data_vectorized))\n",
    "\n",
    "# See model parameters\n",
    "pprint(lda_model.get_params())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a different note, perplexity might not be the best measure to evaluate topic models because it doesn’t consider the context and semantic associations between words. This can be captured using topic coherence measure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11. How to GridSearch the best LDA model?\n",
    "\n",
    "The most important tuning parameter for LDA models is n_components (number of topics). In addition, I am going to search learning_decay (which controls the learning rate) as well.\n",
    "\n",
    "Besides these, other possible search params could be learning_offset (downweigh early iterations. Should be > 1) and max_iter. These could be worth experimenting if you have enough computing resources.\n",
    "\n",
    "the grid search constructs multiple LDA models for all possible combinations of param values in the param_grid dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "             estimator=LatentDirichletAllocation(batch_size=128,\n",
       "                                                 doc_topic_prior=None,\n",
       "                                                 evaluate_every=-1,\n",
       "                                                 learning_decay=0.7,\n",
       "                                                 learning_method='batch',\n",
       "                                                 learning_offset=10.0,\n",
       "                                                 max_doc_update_iter=100,\n",
       "                                                 max_iter=10,\n",
       "                                                 mean_change_tol=0.001,\n",
       "                                                 n_components=10, n_jobs=None,\n",
       "                                                 perp_tol=0.1,\n",
       "                                                 random_state=None,\n",
       "                                                 topic_word_prior=None,\n",
       "                                                 total_samples=1000000.0,\n",
       "                                                 verbose=0),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'learning_decay': [0.5, 0.7, 0.9],\n",
       "                         'n_components': [5, 8, 10, 15, 20, 25]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define Search Param\n",
    "search_params = {'n_components': [5, 8, 10, 15, 20, 25], 'learning_decay': [.5, .7, .9]}\n",
    "\n",
    "# Init the Model\n",
    "lda = LatentDirichletAllocation()\n",
    "\n",
    "# Init Grid Search Class\n",
    "model = GridSearchCV(lda, param_grid=search_params)\n",
    "\n",
    "# Do the Grid Search\n",
    "model.fit(data_vectorized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearchCV(cv=None, error_score='raise',   \n",
    "       estimator=LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,   \n",
    "             evaluate_every=-1, learning_decay=0.7, learning_method=None,   \n",
    "             learning_offset=10.0, max_doc_update_iter=100, max_iter=10,   \n",
    "             mean_change_tol=0.001, n_components=10, n_jobs=1,   \n",
    "             n_topics=None, perp_tol=0.1, random_state=None,    \n",
    "             topic_word_prior=None, total_samples=1000000.0, verbose=0),   \n",
    "       fit_params=None, iid=True, n_jobs=1,   \n",
    "       param_grid={'n_topics': [5, 8, 10, 15, 20, 25], 'learning_decay': [0.5, 0.7, 0.9]},   \n",
    "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',   \n",
    "       scoring=None, verbose=0)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12. How to see the best topic model and its parameters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model's Params:  {'learning_decay': 0.9, 'n_components': 5}\n",
      "Best Log Likelihood Score:  -5748064.402447553\n",
      "Model Perplexity:  6773.365855242588\n"
     ]
    }
   ],
   "source": [
    "# Best Model\n",
    "best_lda_model = model.best_estimator_\n",
    "\n",
    "# Model Parameters\n",
    "print(\"Best Model's Params: \", model.best_params_)\n",
    "\n",
    "# Log Likelihood Score\n",
    "print(\"Best Log Likelihood Score: \", model.best_score_)\n",
    "\n",
    "# Perplexity\n",
    "print(\"Model Perplexity: \", best_lda_model.perplexity(data_vectorized))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13. Compare LDA Model Performance Scores\n",
    "\n",
    "Plotting the log-likelihood scores against num_topics, clearly shows number of topics = 10 has better scores. And learning_decay of 0.7 outperforms both 0.5 and 0.9.\n",
    "\n",
    "This makes me think, even though we know that the dataset has 20 distinct topics to start with, some topics could share common keywords. For example, ‘alt.atheism’ and ‘soc.religion.christian’ can have a lot of common words. Same with ‘rec.motorcycles’ and ‘rec.autos’, ‘comp.sys.ibm.pc.hardware’ and ‘comp.sys.mac.hardware’, you get the idea.\n",
    "\n",
    "To tune this even further, you can do a finer grid search for number of topics between 10 and 15. But I am going to skip that for now.\n",
    "\n",
    "So the bottom line is, a lower optimal number of distinct topics (even 10 topics) may be reasonable for this dataset. I don’t know that yet. But LDA says so. Let’s see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get Log Likelyhoods from Grid Search Output\n",
    "n_topics = [5, 8, 10, 15, 20, 25]\n",
    "results = pd.DataFrame(model.cv_results_)\n",
    "\n",
    "mts=results['mean_test_score']\n",
    "log_likelyhoods_5 = mts[:6]\n",
    "log_likelyhoods_7 = mts[6:12]\n",
    "log_likelyhoods_9 = mts[12:]\n",
    "\n",
    "# Show graph\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(n_topics, log_likelyhoods_5, label='0.5')\n",
    "plt.plot(n_topics, log_likelyhoods_7, label='0.7')\n",
    "plt.plot(n_topics, log_likelyhoods_9, label='0.9')\n",
    "plt.title(\"Choosing Optimal LDA Model\")\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Log Likelyhood Scores\")\n",
    "plt.legend(title='Learning decay', loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14. How to see the dominant topic in each document?\n",
    "\n",
    "To classify a document as belonging to a particular topic, a logical approach is to see which topic has the highest contribution to that document and assign it.\n",
    "\n",
    "In the table below, I’ve greened out all major topics in a document and assigned the most dominant topic in its own column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
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       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow1_col3\" class=\"data row1 col3\" >0.07</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow1_col4\" class=\"data row1 col4\" >0.1</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow1_col5\" class=\"data row1 col5\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row2\" class=\"row_heading level0 row2\" >Doc2</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow2_col0\" class=\"data row2 col0\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow2_col1\" class=\"data row2 col1\" >0.1</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow2_col2\" class=\"data row2 col2\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow2_col3\" class=\"data row2 col3\" >0.31</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow2_col4\" class=\"data row2 col4\" >0.58</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow2_col5\" class=\"data row2 col5\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row3\" class=\"row_heading level0 row3\" >Doc3</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow3_col0\" class=\"data row3 col0\" >0.15</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow3_col1\" class=\"data row3 col1\" >0.07</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow3_col2\" class=\"data row3 col2\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow3_col3\" class=\"data row3 col3\" >0.67</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow3_col4\" class=\"data row3 col4\" >0.11</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow3_col5\" class=\"data row3 col5\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row4\" class=\"row_heading level0 row4\" >Doc4</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow4_col0\" class=\"data row4 col0\" >0.13</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow4_col1\" class=\"data row4 col1\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow4_col2\" class=\"data row4 col2\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow4_col3\" class=\"data row4 col3\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow4_col4\" class=\"data row4 col4\" >0.86</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow4_col5\" class=\"data row4 col5\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row5\" class=\"row_heading level0 row5\" >Doc5</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow5_col0\" class=\"data row5 col0\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow5_col1\" class=\"data row5 col1\" >0.61</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow5_col2\" class=\"data row5 col2\" >0.27</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow5_col3\" class=\"data row5 col3\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow5_col4\" class=\"data row5 col4\" >0.12</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row6\" class=\"row_heading level0 row6\" >Doc6</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow6_col0\" class=\"data row6 col0\" >0.11</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow6_col1\" class=\"data row6 col1\" >0.08</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow6_col2\" class=\"data row6 col2\" >0.2</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow6_col3\" class=\"data row6 col3\" >0.62</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow6_col4\" class=\"data row6 col4\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow6_col5\" class=\"data row6 col5\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row7\" class=\"row_heading level0 row7\" >Doc7</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow7_col0\" class=\"data row7 col0\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow7_col1\" class=\"data row7 col1\" >0.21</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow7_col2\" class=\"data row7 col2\" >0.79</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow7_col3\" class=\"data row7 col3\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow7_col4\" class=\"data row7 col4\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow7_col5\" class=\"data row7 col5\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row8\" class=\"row_heading level0 row8\" >Doc8</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow8_col0\" class=\"data row8 col0\" >0.23</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow8_col1\" class=\"data row8 col1\" >0.38</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow8_col2\" class=\"data row8 col2\" >0.19</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow8_col3\" class=\"data row8 col3\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow8_col4\" class=\"data row8 col4\" >0.19</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow8_col5\" class=\"data row8 col5\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row9\" class=\"row_heading level0 row9\" >Doc9</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow9_col0\" class=\"data row9 col0\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow9_col1\" class=\"data row9 col1\" >0.75</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow9_col2\" class=\"data row9 col2\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow9_col3\" class=\"data row9 col3\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow9_col4\" class=\"data row9 col4\" >0.24</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow9_col5\" class=\"data row9 col5\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row10\" class=\"row_heading level0 row10\" >Doc10</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow10_col0\" class=\"data row10 col0\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow10_col1\" class=\"data row10 col1\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow10_col2\" class=\"data row10 col2\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow10_col3\" class=\"data row10 col3\" >0.65</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow10_col4\" class=\"data row10 col4\" >0.34</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow10_col5\" class=\"data row10 col5\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row11\" class=\"row_heading level0 row11\" >Doc11</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow11_col0\" class=\"data row11 col0\" >0.03</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow11_col1\" class=\"data row11 col1\" >0.03</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow11_col2\" class=\"data row11 col2\" >0.36</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow11_col3\" class=\"data row11 col3\" >0.03</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow11_col4\" class=\"data row11 col4\" >0.55</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow11_col5\" class=\"data row11 col5\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row12\" class=\"row_heading level0 row12\" >Doc12</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow12_col0\" class=\"data row12 col0\" >0.01</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow12_col1\" class=\"data row12 col1\" >0.01</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow12_col2\" class=\"data row12 col2\" >0.3</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow12_col3\" class=\"data row12 col3\" >0.01</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow12_col4\" class=\"data row12 col4\" >0.66</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow12_col5\" class=\"data row12 col5\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row13\" class=\"row_heading level0 row13\" >Doc13</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow13_col0\" class=\"data row13 col0\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow13_col1\" class=\"data row13 col1\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow13_col2\" class=\"data row13 col2\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow13_col3\" class=\"data row13 col3\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow13_col4\" class=\"data row13 col4\" >0.98</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow13_col5\" class=\"data row13 col5\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefblevel0_row14\" class=\"row_heading level0 row14\" >Doc14</th>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow14_col0\" class=\"data row14 col0\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow14_col1\" class=\"data row14 col1\" >0</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow14_col2\" class=\"data row14 col2\" >0.07</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow14_col3\" class=\"data row14 col3\" >0.19</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow14_col4\" class=\"data row14 col4\" >0.74</td>\n",
       "                        <td id=\"T_ddffcee4_2e2c_11ea_a955_ef7993bddefbrow14_col5\" class=\"data row14 col5\" >4</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x145812364c8>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Document - Topic Matrix\n",
    "lda_output = best_lda_model.transform(data_vectorized)\n",
    "\n",
    "# column names\n",
    "topicnames = [\"Topic\" + str(i) for i in range(best_lda_model.n_components)]\n",
    "\n",
    "# index names\n",
    "docnames = [\"Doc\" + str(i) for i in range(len(docs))]\n",
    "\n",
    "# Make the pandas dataframe\n",
    "df_document_topic = pd.DataFrame(np.round(lda_output, 2), columns=topicnames, index=docnames)\n",
    "\n",
    "# Get dominant topic for each document\n",
    "dominant_topic = np.argmax(df_document_topic.values, axis=1)\n",
    "df_document_topic['dominant_topic'] = dominant_topic\n",
    "\n",
    "# Styling\n",
    "def color_green(val):\n",
    "    color = 'green' if val > .1 else 'black'\n",
    "    return 'color: {col}'.format(col=color)\n",
    "\n",
    "def make_bold(val):\n",
    "    weight = 700 if val > .1 else 400\n",
    "    return 'font-weight: {weight}'.format(weight=weight)\n",
    "\n",
    "# Apply Style\n",
    "df_document_topics = df_document_topic.head(15).style.applymap(color_green).applymap(make_bold)\n",
    "df_document_topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15. Review topics distribution across documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topic_distribution = df_document_topic['dominant_topic'].value_counts().reset_index(name=\"Num Documents\")\n",
    "df_topic_distribution.columns = ['Topic Num', 'Num Documents']\n",
    "df_topic_distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "16. How to visualize the LDA model with pyLDAvis?\n",
    "\n",
    "The pyLDAvis offers the best visualization to view the topics-keywords distribution.\n",
    "\n",
    "A good topic model will have non-overlapping, fairly big sized blobs for each topic. This seems to be the case here. So, we are good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyLDAvis\\_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
     "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_el1039613982556401369442003369\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1039613982556401369442003369_data = {\"mdsDat\": {\"x\": [132.98162841796875, -39.95067596435547, 198.1978759765625, 83.9782485961914, -27.38692855834961], \"y\": [-144.2368927001953, -152.33432006835938, 9.348442077636719, 139.44256591796875, 14.535013198852539], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [28.545210425424578, 23.601760665708348, 23.582995931309085, 13.833810990206363, 10.436221987351631]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", 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       "\n",
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       "}\n",
       "\n",
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       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el1039613982556401369442003369\", ldavis_el1039613982556401369442003369_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_el1039613982556401369442003369\", ldavis_el1039613982556401369442003369_data);\n",
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       "    // 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",
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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=                x           y  topics  cluster       Freq\n",
       "topic                                                    \n",
       "4      132.981628 -144.236893       1        1  28.545210\n",
       "1      -39.950676 -152.334320       2        1  23.601761\n",
       "3      198.197876    9.348442       3        1  23.582996\n",
       "0       83.978249  139.442566       4        1  13.833811\n",
       "2      -27.386929   14.535013       5        1  10.436222, topic_info=      Category          Freq    Term         Total  loglift  logprob\n",
       "6420   Default  43746.000000    الله  43746.000000  30.0000  30.0000\n",
       "24694  Default   4513.000000    وسلم   4513.000000  29.0000  29.0000\n",
       "6806   Default   3185.000000  المراه   3185.000000  28.0000  28.0000\n",
       "11495  Default   3133.000000   تعالي   3133.000000  27.0000  27.0000\n",
       "16123  Default   6579.000000   عليكم   6579.000000  26.0000  26.0000\n",
       "...        ...           ...     ...           ...      ...      ...\n",
       "11862   Topic5    421.877248    تقول   1588.051173   0.9343  -6.1482\n",
       "7024    Topic5    879.656350  المصدر  14899.818948  -0.5697  -5.4134\n",
       "21517   Topic5    862.816978  نفساني  15603.590058  -0.6352  -5.4327\n",
       "2847    Topic5    408.045780    الام   1762.526014   0.7968  -6.1815\n",
       "2548    Topic5    347.277256   الارض   1455.423958   0.8270  -6.3428\n",
       "\n",
       "[452 rows x 6 columns], token_table=       Topic      Freq    Term\n",
       "term                          \n",
       "3          5  0.989173   allah\n",
       "48         5  0.989397    know\n",
       "52         5  0.989050    life\n",
       "54         5  0.989510    like\n",
       "66         5  0.989275    need\n",
       "...      ...       ...     ...\n",
       "27663      2  0.022191    يمكن\n",
       "27663      3  0.573791    يمكن\n",
       "27663      4  0.072384    يمكن\n",
       "27663      5  0.073969    يمكن\n",
       "27729      4  0.955614  ينتظرك\n",
       "\n",
       "[1158 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 2, 4, 1, 3])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "panel = pyLDAvis.sklearn.prepare(best_lda_model, data_vectorized, vectorizer, mds='tsne')\n",
    "panel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "17. How to see the Topic’s keywords?\n",
    "\n",
    "The weights of each keyword in each topic is contained in lda_model.components_ as a 2d array. The names of the keywords itself can be obtained from vectorizer object using get_feature_names().\n",
    "\n",
    "Let’s use this info to construct a weight matrix for all keywords in each topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aa</th>\n",
       "      <th>al</th>\n",
       "      <th>align</th>\n",
       "      <th>allah</th>\n",
       "      <th>also</th>\n",
       "      <th>arab</th>\n",
       "      <th>arabia</th>\n",
       "      <th>arabic</th>\n",
       "      <th>aurorix</th>\n",
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       "      <th>ييسر</th>\n",
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       "  </thead>\n",
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       "      <th>Topic0</th>\n",
       "      <td>10.916225</td>\n",
       "      <td>0.880723</td>\n",
       "      <td>7.349556</td>\n",
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       "      <td>0.202504</td>\n",
       "      <td>0.200002</td>\n",
       "      <td>0.200134</td>\n",
       "      <td>...</td>\n",
       "      <td>3.739760</td>\n",
       "      <td>0.492539</td>\n",
       "      <td>0.203905</td>\n",
       "      <td>0.205828</td>\n",
       "      <td>0.200697</td>\n",
       "      <td>0.200964</td>\n",
       "      <td>1.581385</td>\n",
       "      <td>0.201055</td>\n",
       "      <td>6.274209</td>\n",
       "      <td>0.206668</td>\n",
       "    </tr>\n",
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       "      <th>Topic1</th>\n",
       "      <td>0.202463</td>\n",
       "      <td>15.901111</td>\n",
       "      <td>2.590175</td>\n",
       "      <td>0.201149</td>\n",
       "      <td>0.201077</td>\n",
       "      <td>0.200558</td>\n",
       "      <td>1.400958</td>\n",
       "      <td>1.716943</td>\n",
       "      <td>0.200057</td>\n",
       "      <td>0.200409</td>\n",
       "      <td>...</td>\n",
       "      <td>26.665265</td>\n",
       "      <td>17.576326</td>\n",
       "      <td>0.200850</td>\n",
       "      <td>13.871351</td>\n",
       "      <td>0.203120</td>\n",
       "      <td>43.144940</td>\n",
       "      <td>0.203397</td>\n",
       "      <td>0.200925</td>\n",
       "      <td>9.189317</td>\n",
       "      <td>19.835229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic2</th>\n",
       "      <td>0.202367</td>\n",
       "      <td>4.671780</td>\n",
       "      <td>4.828725</td>\n",
       "      <td>59.198236</td>\n",
       "      <td>20.198687</td>\n",
       "      <td>9.583683</td>\n",
       "      <td>0.553415</td>\n",
       "      <td>16.282918</td>\n",
       "      <td>0.200002</td>\n",
       "      <td>15.198506</td>\n",
       "      <td>...</td>\n",
       "      <td>1.759620</td>\n",
       "      <td>63.251656</td>\n",
       "      <td>5.551595</td>\n",
       "      <td>53.002900</td>\n",
       "      <td>0.206251</td>\n",
       "      <td>0.204266</td>\n",
       "      <td>16.705718</td>\n",
       "      <td>15.695852</td>\n",
       "      <td>0.202730</td>\n",
       "      <td>0.201556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic3</th>\n",
       "      <td>0.201347</td>\n",
       "      <td>4.340512</td>\n",
       "      <td>9.283409</td>\n",
       "      <td>0.200267</td>\n",
       "      <td>0.200143</td>\n",
       "      <td>2.814787</td>\n",
       "      <td>2.510216</td>\n",
       "      <td>2.594426</td>\n",
       "      <td>0.200700</td>\n",
       "      <td>0.200632</td>\n",
       "      <td>...</td>\n",
       "      <td>31.269247</td>\n",
       "      <td>229.526768</td>\n",
       "      <td>4.287529</td>\n",
       "      <td>7.769381</td>\n",
       "      <td>23.154906</td>\n",
       "      <td>0.249624</td>\n",
       "      <td>1.264654</td>\n",
       "      <td>0.200038</td>\n",
       "      <td>2.407460</td>\n",
       "      <td>0.441119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic4</th>\n",
       "      <td>12.477598</td>\n",
       "      <td>0.205874</td>\n",
       "      <td>14.948135</td>\n",
       "      <td>0.200118</td>\n",
       "      <td>0.200092</td>\n",
       "      <td>0.200742</td>\n",
       "      <td>6.335403</td>\n",
       "      <td>0.203208</td>\n",
       "      <td>16.199239</td>\n",
       "      <td>0.200319</td>\n",
       "      <td>...</td>\n",
       "      <td>68.566108</td>\n",
       "      <td>175.152710</td>\n",
       "      <td>6.756122</td>\n",
       "      <td>185.150540</td>\n",
       "      <td>11.235026</td>\n",
       "      <td>0.200207</td>\n",
       "      <td>1.244846</td>\n",
       "      <td>7.702130</td>\n",
       "      <td>1.926284</td>\n",
       "      <td>21.315428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27921 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               aa         al      align      allah       also      arab  \\\n",
       "Topic0  10.916225   0.880723   7.349556   0.200230   0.200000  0.200229   \n",
       "Topic1   0.202463  15.901111   2.590175   0.201149   0.201077  0.200558   \n",
       "Topic2   0.202367   4.671780   4.828725  59.198236  20.198687  9.583683   \n",
       "Topic3   0.201347   4.340512   9.283409   0.200267   0.200143  2.814787   \n",
       "Topic4  12.477598   0.205874  14.948135   0.200118   0.200092  0.200742   \n",
       "\n",
       "          arabia     arabic    aurorix       best  ...       يومي       يوميا  \\\n",
       "Topic0  0.200008   0.202504   0.200002   0.200134  ...   3.739760    0.492539   \n",
       "Topic1  1.400958   1.716943   0.200057   0.200409  ...  26.665265   17.576326   \n",
       "Topic2  0.553415  16.282918   0.200002  15.198506  ...   1.759620   63.251656   \n",
       "Topic3  2.510216   2.594426   0.200700   0.200632  ...  31.269247  229.526768   \n",
       "Topic4  6.335403   0.203208  16.199239   0.200319  ...  68.566108  175.152710   \n",
       "\n",
       "          يوميات       يومين      يوميه       يونس      يونيو       يووه  \\\n",
       "Topic0  0.203905    0.205828   0.200697   0.200964   1.581385   0.201055   \n",
       "Topic1  0.200850   13.871351   0.203120  43.144940   0.203397   0.200925   \n",
       "Topic2  5.551595   53.002900   0.206251   0.204266  16.705718  15.695852   \n",
       "Topic3  4.287529    7.769381  23.154906   0.249624   1.264654   0.200038   \n",
       "Topic4  6.756122  185.150540  11.235026   0.200207   1.244846   7.702130   \n",
       "\n",
       "            يياس       ييسر  \n",
       "Topic0  6.274209   0.206668  \n",
       "Topic1  9.189317  19.835229  \n",
       "Topic2  0.202730   0.201556  \n",
       "Topic3  2.407460   0.441119  \n",
       "Topic4  1.926284  21.315428  \n",
       "\n",
       "[5 rows x 27921 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Topic-Keyword Matrix\n",
    "df_topic_keywords = pd.DataFrame(best_lda_model.components_)\n",
    "\n",
    "# Assign Column and Index\n",
    "df_topic_keywords.columns = vectorizer.get_feature_names()\n",
    "df_topic_keywords.index = topicnames\n",
    "\n",
    "# View\n",
    "df_topic_keywords.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "18. Get the top 15 keywords each topic\n",
    "\n",
    "From the above output, I want to see the top 15 keywords that are representative of the topic.\n",
    "\n",
    "The show_topics() defined below creates that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Word 0</th>\n",
       "      <th>Word 1</th>\n",
       "      <th>Word 2</th>\n",
       "      <th>Word 3</th>\n",
       "      <th>Word 4</th>\n",
       "      <th>Word 5</th>\n",
       "      <th>Word 6</th>\n",
       "      <th>Word 7</th>\n",
       "      <th>Word 8</th>\n",
       "      <th>Word 9</th>\n",
       "      <th>Word 10</th>\n",
       "      <th>Word 11</th>\n",
       "      <th>Word 12</th>\n",
       "      <th>Word 13</th>\n",
       "      <th>Word 14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Topic 0</th>\n",
       "      <td>المراه</td>\n",
       "      <td>نفساني</td>\n",
       "      <td>الحياه</td>\n",
       "      <td>المصدر</td>\n",
       "      <td>الحب</td>\n",
       "      <td>الرجل</td>\n",
       "      <td>عندما</td>\n",
       "      <td>الله</td>\n",
       "      <td>الناس</td>\n",
       "      <td>لماذا</td>\n",
       "      <td>تكون</td>\n",
       "      <td>كانت</td>\n",
       "      <td>نفسك</td>\n",
       "      <td>الانسان</td>\n",
       "      <td>السعاده</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 1</th>\n",
       "      <td>الله</td>\n",
       "      <td>وسلم</td>\n",
       "      <td>تعالي</td>\n",
       "      <td>رسول</td>\n",
       "      <td>المصدر</td>\n",
       "      <td>نفساني</td>\n",
       "      <td>فقال</td>\n",
       "      <td>الناس</td>\n",
       "      <td>اللهم</td>\n",
       "      <td>الصلاه</td>\n",
       "      <td>الجنه</td>\n",
       "      <td>القران</td>\n",
       "      <td>الدنيا</td>\n",
       "      <td>محمد</td>\n",
       "      <td>النبي</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 2</th>\n",
       "      <td>كانت</td>\n",
       "      <td>المصدر</td>\n",
       "      <td>نفساني</td>\n",
       "      <td>قالت</td>\n",
       "      <td>اللي</td>\n",
       "      <td>اليوم</td>\n",
       "      <td>انها</td>\n",
       "      <td>واحد</td>\n",
       "      <td>عليها</td>\n",
       "      <td>وكان</td>\n",
       "      <td>الشاب</td>\n",
       "      <td>البيت</td>\n",
       "      <td>وبعد</td>\n",
       "      <td>الفتاه</td>\n",
       "      <td>وقال</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 3</th>\n",
       "      <td>يكون</td>\n",
       "      <td>المصدر</td>\n",
       "      <td>نفساني</td>\n",
       "      <td>الانسان</td>\n",
       "      <td>الاخرين</td>\n",
       "      <td>تكون</td>\n",
       "      <td>النفسيه</td>\n",
       "      <td>الناس</td>\n",
       "      <td>الطفل</td>\n",
       "      <td>خلال</td>\n",
       "      <td>النفسي</td>\n",
       "      <td>يمكن</td>\n",
       "      <td>النفس</td>\n",
       "      <td>الله</td>\n",
       "      <td>ايضا</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 4</th>\n",
       "      <td>الله</td>\n",
       "      <td>نفساني</td>\n",
       "      <td>المصدر</td>\n",
       "      <td>عليكم</td>\n",
       "      <td>ورحمه</td>\n",
       "      <td>وانا</td>\n",
       "      <td>وبركاته</td>\n",
       "      <td>نفسي</td>\n",
       "      <td>انني</td>\n",
       "      <td>السلام</td>\n",
       "      <td>اريد</td>\n",
       "      <td>الان</td>\n",
       "      <td>المنتدي</td>\n",
       "      <td>والله</td>\n",
       "      <td>انها</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Word 0  Word 1  Word 2   Word 3   Word 4  Word 5   Word 6 Word 7  \\\n",
       "Topic 0  المراه  نفساني  الحياه   المصدر     الحب   الرجل    عندما   الله   \n",
       "Topic 1    الله    وسلم   تعالي     رسول   المصدر  نفساني     فقال  الناس   \n",
       "Topic 2    كانت  المصدر  نفساني     قالت     اللي   اليوم     انها   واحد   \n",
       "Topic 3    يكون  المصدر  نفساني  الانسان  الاخرين    تكون  النفسيه  الناس   \n",
       "Topic 4    الله  نفساني  المصدر    عليكم    ورحمه    وانا  وبركاته   نفسي   \n",
       "\n",
       "        Word 8  Word 9 Word 10 Word 11  Word 12  Word 13  Word 14  \n",
       "Topic 0  الناس   لماذا    تكون    كانت     نفسك  الانسان  السعاده  \n",
       "Topic 1  اللهم  الصلاه   الجنه  القران   الدنيا     محمد    النبي  \n",
       "Topic 2  عليها    وكان   الشاب   البيت     وبعد   الفتاه     وقال  \n",
       "Topic 3  الطفل    خلال  النفسي    يمكن    النفس     الله     ايضا  \n",
       "Topic 4   انني  السلام    اريد    الان  المنتدي    والله     انها  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show top n keywords for each topic\n",
    "def show_topics(vectorizer=vectorizer, lda_model=lda_model, n_words=20):\n",
    "    keywords = np.array(vectorizer.get_feature_names())\n",
    "    topic_keywords = []\n",
    "    for topic_weights in lda_model.components_:\n",
    "        top_keyword_locs = (-topic_weights).argsort()[:n_words]\n",
    "        topic_keywords.append(keywords.take(top_keyword_locs))\n",
    "    return topic_keywords\n",
    "\n",
    "topic_keywords = show_topics(vectorizer=vectorizer, lda_model=best_lda_model, n_words=15)        \n",
    "\n",
    "# Topic - Keywords Dataframe\n",
    "df_topic_keywords = pd.DataFrame(topic_keywords)\n",
    "df_topic_keywords.columns = ['Word '+str(i) for i in range(df_topic_keywords.shape[1])]\n",
    "df_topic_keywords.index = ['Topic '+str(i) for i in range(df_topic_keywords.shape[0])]\n",
    "df_topic_keywords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "19. How to predict the topics for a new piece of text?\n",
    "\n",
    "Assuming that you have already built the topic model, you need to take the text through the same routine of transformations and before predicting the topic.\n",
    "\n",
    "For our case, the order of transformations is:\n",
    "\n",
    "sent_to_words() –> lemmatization() –> vectorizer.transform() –> best_lda_model.transform()\n",
    "\n",
    "You need to apply these transformations in the same order. So to simplify it, let’s combine these steps into a predict_topic() function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['الله', 'نفساني', 'المصدر', 'عليكم', 'ورحمه', 'وانا', 'وبركاته', 'نفسي', 'انني', 'السلام', 'اريد', 'الان', 'المنتدي', 'والله', 'انها']\n"
     ]
    }
   ],
   "source": [
    "# Define function to predict topic for a given text document.\n",
    "#  nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "def predict_topic(text):\n",
    "    # Step 1: Clean with clean\n",
    "    mytext_2 = [clean(text)]\n",
    "\n",
    "    # Step 2: Vectorize transform\n",
    "    mytext_3 = vectorizer.transform(mytext_2)\n",
    "\n",
    "    # Step 3: LDA Transform\n",
    "    topic_probability_scores = best_lda_model.transform(mytext_3)\n",
    "    topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), :].values.tolist()\n",
    "    return topic, topic_probability_scores\n",
    "\n",
    "# Predict the topic\n",
    "mytext = [\"أعاني من الخوف و الوحدة \"]\n",
    "topic, prob_scores = predict_topic(text = mytext[0])\n",
    "print(topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20. How to cluster documents that share similar topics and plot?\n",
    "\n",
    "You can use k-means clustering on the document-topic probabilioty matrix, which is nothing but lda_output object. Since our best model has 5 clusters, I’ve set n_clusters=5 in KMeans().\n",
    "\n",
    "Alternately, you could avoid k-means and instead, assign the cluster as the topic column number with the highest probability score.\n",
    "\n",
    "We now have the cluster number. But we also need the X and Y columns to draw the plot.\n",
    "\n",
    "For the X and Y, you can use SVD on the lda_output object with n_components as 2. SVD ensures that these two columns captures the maximum possible amount of information from lda_output in the first 2 components. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component's weights: \n",
      " [[ 0.1   0.1   0.07  0.15  0.98]\n",
      " [ 0.33  0.82  0.16  0.41 -0.19]]\n",
      "Perc of Variance Explained: \n",
      " [0.3  0.23]\n"
     ]
    }
   ],
   "source": [
    "# Construct the k-means clusters\n",
    "from sklearn.cluster import KMeans\n",
    "clusters = KMeans(n_clusters=5, random_state=100).fit_predict(lda_output)\n",
    "\n",
    "# Build the Singular Value Decomposition(SVD) model\n",
    "svd_model = TruncatedSVD(n_components=2)  # 2 components\n",
    "lda_output_svd = svd_model.fit_transform(lda_output)\n",
    "\n",
    "# X and Y axes of the plot using SVD decomposition\n",
    "x = lda_output_svd[:, 0]\n",
    "y = lda_output_svd[:, 1]\n",
    "\n",
    "# Weights for the 15 columns of lda_output, for each component\n",
    "print(\"Component's weights: \\n\", np.round(svd_model.components_, 2))\n",
    "\n",
    "# Percentage of total information in 'lda_output' explained by the two components\n",
    "print(\"Perc of Variance Explained: \\n\", np.round(svd_model.explained_variance_ratio_, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the X, Y and the cluster number for each document.\n",
    "\n",
    "Let’s plot the document along the two SVD decomposed components. The color of points represents the cluster number (in this case) or topic number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Segregation of Topic Clusters')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.scatter(x, y, c=clusters)\n",
    "plt.xlabel('Component 2')\n",
    "plt.xlabel('Component 1')\n",
    "plt.title(\"Segregation of Topic Clusters\", )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "21. How to get similar documents for any given piece of text?\n",
    "\n",
    "Once you know the probaility of topics for a given document (using predict_topic()), compute the euclidean distance with the probability scores of all other documents.\n",
    "\n",
    "The most similar documents are the ones with the smallest distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "\n",
    "def similar_documents(text, doc_topic_probs, documents = docs, top_n=5, verbose=False):\n",
    "    topic, x  = predict_topic(text)\n",
    "    dists = euclidean_distances(x.reshape(1, -1), doc_topic_probs)[0]\n",
    "    doc_ids = np.argsort(dists)[:top_n]\n",
    "    if verbose:        \n",
    "        print(\"Topic KeyWords: \", topic)\n",
    "        print(\"Topic Prob Scores of text: \", np.round(x, 1))\n",
    "        print(\"Most Similar Doc's Probs:  \", np.round(doc_topic_probs[doc_ids], 1))\n",
    "    return doc_ids, np.take(documents, doc_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic KeyWords:  ['الله', 'نفساني', 'المصدر', 'عليكم', 'ورحمه', 'وانا', 'وبركاته', 'نفسي', 'انني', 'السلام', 'اريد', 'الان', 'المنتدي', 'والله', 'انها']\n",
      "Topic Prob Scores of text:  [[0.  0.  0.  0.  0.8]]\n",
      "Most Similar Doc's Probs:   [[0.  0.  0.  0.  0.8]]\n",
      "\n",
      " ['\\n\\nالعلاج بالطاقةما هي تجاربكم معه وهل هو مُجدي؟\\r\\n\\t\\n\\n\\n\\n\\n\\nالمصدر: نفساني\\n\\n\\n']\n"
     ]
    }
   ],
   "source": [
    "# Get similar documents\n",
    "mytext = \" أولادي بعيدين عني أنا أشعر بالوحدة\"\n",
    "doc_ids, simdocuments = similar_documents(text=mytext, doc_topic_probs=lda_output, documents = docs, top_n=1, verbose=True)\n",
    "print('\\n', simdocuments)"
   ]
  }
 ],
 "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": "3.7.3"
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 },
 "nbformat": 4,
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}