modeling_text_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='files/thresher.png' width=300 align='left'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evann Smith, Ph.D."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Senior Data Scientist</b> <br />\n",
    "evann@thresher.io <br />\n",
    "http://www.evannsmith.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling Text Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised vs. Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised\n",
    "We have training data! <br />\n",
    "<b>Thresher</b> can get you there quickly :)\n",
    "\n",
    "- Answering a specific question\n",
    "- Hypothesis testing\n",
    "- Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised\n",
    "We have NO training data.\n",
    "\n",
    "- Exploration\n",
    "- Discovery\n",
    "- Data Mining\n",
    "- Hypothesis Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import string\n",
    "import re\n",
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas(desc='progress-bar')\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from gensim import corpora, models\n",
    "from tabulate import tabulate\n",
    "import pyLDAvis.gensim\n",
    "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data\n",
    "- We'll use the Movie Reviews corpus built into NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import movie_reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data\n",
    "- `movie_reviews` is a nltk plain text corpus reader, which will help you conveniently read in the documents in the nltk corpus (make sure that you have run `nltk.download()` to have access to the data!).\n",
    "- Reviews are categorized as either positive or negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>plot : two teen couples go to a church party ,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neg</td>\n",
       "      <td>the happy bastard's quick movie review \\ndamn ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neg</td>\n",
       "      <td>it is movies like these that make a jaded movi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>neg</td>\n",
       "      <td>\" quest for camelot \" is warner bros . ' firs...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neg</td>\n",
       "      <td>synopsis : a mentally unstable man undergoing ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                                               text\n",
       "0      neg  plot : two teen couples go to a church party ,...\n",
       "1      neg  the happy bastard's quick movie review \\ndamn ...\n",
       "2      neg  it is movies like these that make a jaded movi...\n",
       "3      neg   \" quest for camelot \" is warner bros . ' firs...\n",
       "4      neg  synopsis : a mentally unstable man undergoing ..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = [(movie_reviews.categories(fileid)[0], movie_reviews.raw(fileid)) for fileid in movie_reviews.fileids()]\n",
    "data = pd.DataFrame(reviews, columns=['category', 'text'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neg    1000\n",
       "pos    1000\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the Data\n",
    "- Lowercase\n",
    "- Tokenize\n",
    "- Remove stop words and punctuation\n",
    "- Remove words with fewer than 2 characters\n",
    "- Stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stop = set(stopwords.words('english') + list(string.punctuation))\n",
    "stemmer = PorterStemmer()\n",
    "re_punct = re.compile('[' + ''.join(string.punctuation) + ']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    try:\n",
    "        text = text.lower()\n",
    "        tokens = word_tokenize(text)\n",
    "        tokens = [t for t in tokens if not t in stop]\n",
    "        tokens = [re.sub(re_punct, '', t) for t in tokens]\n",
    "        tokens = [t for t in tokens if len(t) > 2]\n",
    "        tokens = [stemmer.stem(t) for t in tokens]\n",
    "        if len(tokens) == 0:\n",
    "            return None\n",
    "        else:\n",
    "            return ' '.join(tokens)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "progress-bar: 100%|██████████| 2000/2000 [00:34<00:00, 57.95it/s]\n"
     ]
    }
   ],
   "source": [
    "data['tokens'] = data['text'].progress_map(preprocess)\n",
    "data = data[data['tokens'].notnull()]\n",
    "data.reset_index(inplace=True)\n",
    "data.drop('index', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 reviews\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>text</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>plot : two teen couples go to a church party ,...</td>\n",
       "      <td>plot two teen coupl church parti drink drive g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neg</td>\n",
       "      <td>the happy bastard's quick movie review \\ndamn ...</td>\n",
       "      <td>happi bastard quick movi review damn y2k bug g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neg</td>\n",
       "      <td>it is movies like these that make a jaded movi...</td>\n",
       "      <td>movi like make jade movi viewer thank invent t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>neg</td>\n",
       "      <td>\" quest for camelot \" is warner bros . ' firs...</td>\n",
       "      <td>quest camelot warner bro first featurelength f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neg</td>\n",
       "      <td>synopsis : a mentally unstable man undergoing ...</td>\n",
       "      <td>synopsi mental unstabl man undergo psychothera...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                                               text  \\\n",
       "0      neg  plot : two teen couples go to a church party ,...   \n",
       "1      neg  the happy bastard's quick movie review \\ndamn ...   \n",
       "2      neg  it is movies like these that make a jaded movi...   \n",
       "3      neg   \" quest for camelot \" is warner bros . ' firs...   \n",
       "4      neg  synopsis : a mentally unstable man undergoing ...   \n",
       "\n",
       "                                              tokens  \n",
       "0  plot two teen coupl church parti drink drive g...  \n",
       "1  happi bastard quick movi review damn y2k bug g...  \n",
       "2  movi like make jade movi viewer thank invent t...  \n",
       "3  quest camelot warner bro first featurelength f...  \n",
       "4  synopsi mental unstabl man undergo psychothera...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('{} reviews'.format(len(data)))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning for Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We'll focus on Support Vector Machines (SVMs), which is a type of discriminant model for classification and regression\n",
    "- They try to find a combination of samples to build a plane maximizing the margin between classes.\n",
    "- SVMs are a workhorse in supervised classification.\n",
    "<br /><br />\n",
    "\n",
    "Advantages:\n",
    "- Effective in high dimensional spaces.\n",
    "- Effective in cases where number of dimensions is greater than the number of samples.\n",
    "- Memory efficient (because it uses a subset of training points in the decision function).\n",
    "- Versatile (different Kernel functions can be specified for the decision function). \n",
    "\n",
    "Disadvantages:\n",
    "- If the number of features is <b>much</b> greater than the number of samples, performances is likely to be poor.\n",
    "- SVMs do not directly provide probability estimates, these are calculated using cross-validation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Words into numbers\n",
    "To model text, we need to <b>engineer features</b>. \n",
    "\n",
    "- We're going to start with a <b>bag of words</b> approach. (More on other approaches later.)\n",
    "    - Tokenizing: an integer ID for each unique token\n",
    "    - Counting: the number of occurrences of each unique token in each document\n",
    "    - Normalizing: diminish the importance of words that appear in the majority of documents\n",
    "\n",
    "\n",
    "- We use term frequency-inverse document frequency (tfidf) to normalize and weight our feature representations:\n",
    "\n",
    "For a term, t, and document, d:\n",
    "\n",
    "     tfidf(t,d) = tf(t,d) x idf(t)\n",
    "\n",
    "where tf(t,d) is the frequency of the term in that document, and the idf of the term is:\n",
    "\n",
    "     idf(t) = log [(1 + n(d)) / (1 + df(d,t))] + 1\n",
    "\n",
    "where n(d) is the number of total documents and df(d,t) is the number of documents that contain t.\n",
    "\n",
    "Finally we normalize the document vectors by the Euclidean norm to account for differing document lengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "texts = data.tokens.tolist()\n",
    "y = data.category.tolist()\n",
    "vectorizer = TfidfVectorizer(min_df=5, max_df=0.8)\n",
    "X = vectorizer.fit_transform(texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test/Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "- With text data, the number of features (words) is almost always much larger than the number of samples (documents).\n",
    "- We want to select the <i>most discriminant</i> features for classification.\n",
    "- We use Chi2, which calculates the Chi square statistic for each word, relative to class. Chi2 measures the dependence between stochastic variables, so words with a low Chi2 are likely to be independent of class and thus not useful for classifcation. We want to keep the words with the highest Chi2 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9719 total features prior to selection\n"
     ]
    }
   ],
   "source": [
    "total_features = len(vectorizer.get_feature_names())\n",
    "print('{} total features prior to selection'.format(total_features))\n",
    "ch2 = SelectKBest(chi2, k=500)\n",
    "X_train = ch2.fit_transform(X_train, y_train)\n",
    "X_test = ch2.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.83 ms, sys: 1.14 ms, total: 8.97 ms\n",
      "Wall time: 8.1 ms\n",
      "Accuracy: 0.818\n"
     ]
    }
   ],
   "source": [
    "classifier = LinearSVC()\n",
    "%time classifier.fit(X_train, y_train)\n",
    "print('Accuracy: {}'.format(round(classifier.score(X_test, y_test), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 66.9 ms, sys: 1.44 ms, total: 68.3 ms\n",
      "Wall time: 70.5 ms\n",
      "Accuracy: 0.742\n"
     ]
    }
   ],
   "source": [
    "classifier = RandomForestClassifier()\n",
    "%time classifier.fit(X_train, y_train)\n",
    "print('Accuracy: {}'.format(round(classifier.score(X_test, y_test), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.4 ms, sys: 7.97 ms, total: 24.4 ms\n",
      "Wall time: 24.7 ms\n",
      "Accuracy: 0.695\n"
     ]
    }
   ],
   "source": [
    "classifier = GaussianNB()\n",
    "%time classifier.fit(X_train.toarray(), y_train)\n",
    "print('Accuracy: {}'.format(round(classifier.score(X_test.toarray(), y_test), 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br /><br />\n",
    "## Unsupervised Analysis for Discovery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latent Dirichlet Allocation (LDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Generative statistical model \n",
    "- Expains similarlites between observations as function of unobserved groups\n",
    "\n",
    "<br />\n",
    "\n",
    "For text data:\n",
    "- Documents are a mixture of topics\n",
    "    - Topics are assumed to have a sparse Dirichlet prior (capturing the intuition that documents only cover a few topics and topics only use a small set of words frequently)\n",
    "- Topics are a distribution of words\n",
    "\n",
    "<br />\n",
    "\n",
    "Topics are <b>latent structures</b>, which we try to backwards infer from the collection of documents that could have generated them:\n",
    "- Decide the length of the document, N\n",
    "- Choose a topic mixture (e.g., 1/3 about zombies and 2/3 about romance) from a Dirichlet distribution with K fixed topics\n",
    "- Generate each word in the document by:\n",
    "    - Pick a topic according to the distribution sampled above\n",
    "    - Pick a word according to the topic's distribution of words\n",
    "\n",
    "<br />\n",
    "\n",
    "To learn the topics and words (collapsed Gibbs Sampling):\n",
    "- Randomly assign each word in each document to a topic K\n",
    "    - This gives you (bad) topic representations of all documents and word distributions of all topics\n",
    "- Improve for each word in each document by:\n",
    "    - Assuming that all topic assignments except for the current word are correct and update the assignment of the current word using the generative model\n",
    "    - Rinse, repeat until you reach a \"steady state\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the Data\n",
    "- Use `TfidfVectorizer` to remove non-informative words\n",
    "- Convert to a `gensim` corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "texts = data.tokens.tolist()\n",
    "vectorizer = TfidfVectorizer(min_df=5, max_df=0.8)\n",
    "X = vectorizer.fit_transform(texts)\n",
    "vocab = vectorizer.vocabulary_\n",
    "idf = vectorizer.idf_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.2625142 ,  4.99048442,  5.89335213,  6.52196079,  2.23383371])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(idf, [0, 25, 50, 75, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vocab_keep = np.where(idf >= np.percentile(idf, 50))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5007 words in the vocabulary\n"
     ]
    }
   ],
   "source": [
    "vocab = [k for k,v in vocab.items() if v in vocab_keep]\n",
    "print('{} words in the vocabulary'.format(len(vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "texts = [[token for token in tokens.split() if token in vocab] for tokens in data.tokens]\n",
    "id2word = corpora.Dictionary(texts)\n",
    "corpus = [id2word.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 2), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 2), (8, 1), (9, 1), (10, 1), (11, 1)]\n"
     ]
    }
   ],
   "source": [
    "print(corpus[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpora.MmCorpus.serialize('reviews.mm', corpus)\n",
    "id2word.save('reviews.dict')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validated LDA\n",
    "The existential question: <b>How many topics?</b>\n",
    "\n",
    "- Theoretical validation\n",
    "    - No substitute for substantive expertise\n",
    "- Empirical validation on held-out data\n",
    "    - We want to minimize perplexity, which is a decreasing function of the log-likelihood on unseen documents. (The higher the log-liklihood on unseen documents, the better the model.)\n",
    "\n",
    "<b>CAVEAT 1:</b> The statistically optimal empirical solution may not be the theoretically optimal solution. Estimating the optimal mixture of distributions is not the same thing as estimating the number of \"cohesive and well-separated from other data\" ideas (Baudry et al. 2010; also see Biernacki, 2000). <br />\n",
    "<br />\n",
    "<b>CAVEAT 2:</b> Perplexity is not strongly correlated with human judgement (Chang et al., 2009)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ldaCV(n_topics, corpus, id2word, test_size=0.2, sample=None):\n",
    "    if not isinstance(n_topics, list):\n",
    "        n_topics = [n_topics]\n",
    "    obs = len(corpus)\n",
    "    corpus = np.array(corpus)\n",
    "    \n",
    "    if sample is not None:\n",
    "        if sample < 1:\n",
    "            sample_idx = random.sample(range(obs), int(obs*sample))\n",
    "        else:\n",
    "            sample_idx = random.sample(range(obs), int(sample))\n",
    "        corpus = corpus[sample_idx]\n",
    "    \n",
    "    train, test = train_test_split(corpus, test_size=test_size, random_state=12345)\n",
    "    perplexities = []\n",
    "    \n",
    "    for n in n_topics:\n",
    "        print('{} topics'.format(n))\n",
    "        model = models.ldamodel.LdaModel(train, num_topics=n, id2word=id2word, random_state=12345)\n",
    "        perplexity = model.log_perplexity(test)       \n",
    "        print(' - Perplexity: {}'.format(round(perplexity, 3)))\n",
    "        perplexities.append((n, perplexity))\n",
    "    \n",
    "    return perplexities    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 topics\n",
      " - Perplexity: -10.71\n",
      "10 topics\n",
      " - Perplexity: -12.466\n",
      "15 topics\n",
      " - Perplexity: -13.793\n",
      "20 topics\n",
      " - Perplexity: -14.884\n",
      "25 topics\n",
      " - Perplexity: -15.751\n",
      "30 topics\n",
      " - Perplexity: -16.538\n",
      "35 topics\n",
      " - Perplexity: -17.201\n",
      "40 topics\n",
      " - Perplexity: -17.814\n",
      "45 topics\n",
      " - Perplexity: -18.233\n",
      "50 topics\n",
      " - Perplexity: -18.747\n",
      "CPU times: user 2min 3s, sys: 1.07 s, total: 2min 4s\n",
      "Wall time: 2min 10s\n"
     ]
    }
   ],
   "source": [
    "%time p = ldaCV(list(range(5, 55, 5)), corpus, id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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5pKysjIqKSoqKjgWqgReBvwDv0afPo7z/fmm6ASVJa5SzAhFjPCPGeDHw+OqOCyEcCfQD\nfpmrLMo/NTXVlJePBqaQ3AfxFXbf/WRKSorZdddkzghJUv5K9THOEMJQ4CfAToDnrbuR4uJiZs26\nk/r6ehoaGigpKaG0tJS334aJE2H8eLj6ajjkkLSTSpJWJbUCEULoDdwE/DDG+HIIwQLRDZWWllJa\nuuKSxQYbwJ//DEcdBYceCs8/D6edBiGkGFKS9ClZFYgQwrnASas5JAJDYoyZNnzcz4GnYow1yz++\nxa+rNXXqVPr167fStqqqKqqqqtry5cpjvXrBr38N22wDp54Kzz0HV10FvXunnUySCkdNTQ01NTUr\nbVvSgesIhBhj2w8OYRNgkzUctjDG+HGzr/kWcGGMceMWn/UIMLz5JpJ7Mj4GzokxntFKhpHA/Pnz\n5zNy5Mg2Z1dhuumm5DLGbrvBH/4ALTqjJCkLdXV1jBo1CmBUjLGuPZ+V1RmIGOMiYFF7vmEzBwDr\nNvv9TiRPY+wKLOyg76ECd9BBsNVWsP/+sOuuMGNGsjCXJClduZwHon8IYQQwECgKIYxoeq0PEGN8\nNsb41PIX8CzJWYh/xhjfyFUuFZ499oD774e3306mwX7UB34lKXW5nAfiTKAOmAb0bXpfB4xazde0\n/XqKupWhQ2HePNhyS/jSl2DWrLQTSVL3lst5IA6NMRat4tVy+sHlx9/btP+tXGVSYdtiC/jrX5Mz\nEvvskzzmKUlKh2thqKD07Qu33QaHH568Tj0VsrgPWJLUQVKdSEpaGz17wmWXJY95/uhHyWOe11wD\n66yTdjJJ6j4sECpIIcCJJ8LAgXDwwfDyy8mZieLitJNJUvfgJQwVtIkTk3UzHn8cdtklORshSco9\nC4QK3q67woMPwgcfJI95/uMfaSeSpK7PAqEuoawsKRGf+xzsvjv85S9pJ5Kkrs0CoS7jM5+Bu++G\nigrYbz+4/PK0E0lS12WBUJey3npw661wzDFw9NHJjZbLlqWdSpK6Hp/CUJdTVAQXXZQ85jl1arIk\n+A03QJ8+aSeTpK7DMxDqso47LlnB8447oLwcFnXUMnCSJAuEurb994d77oFnnoGdd4bGxrQTSVLX\nYIFQlzd6dLIQFyQl4qGH0s0jSV2BBULdwqBB8MADyeOeX/5yMmulJGntWSDUbWy6aTJr5T77wNe+\nBhdfnHYiSSpcFgh1K336wM03ww9/CMcfn7yWLk07lSQVHh/jVLfTowecd14ya+Uxx8ALL0B1dTKH\nhCSpbTwDoW7rqKPg9tuhthb23BNefz3tRJJUOCwQ6ta+8hW4995kFc+dd4ZMJu1EklQYLBDq9nbc\nMXnMs3fvpETcf3+yPZPJMHPmTOrr69MNKEl5yAIhkdwP8cAD8IUvwNixke23P5fBgwdTWVlJWVkZ\n48ZNYPHixWnHlKS8YYGQmhQXJ/dDbLzxX1mw4GRgPvACUM2cOfOoqpqcckJJyh8+hSE18/zzGf71\nr7HAY8DIpq2TWLo0Uls7hfr6ekpLS1NMKEn5wTMQUjONjY1ABPq12LMXAA0NDZ0dSZLykgVCambQ\noEFN7+5rsWcz4BwGDCjp5ESSlJ8sEFIzZWVlVFRUUlR0LFANvAj8jhDOIYQfMXlyKU8+mXJIScoD\nFgiphZqaasrLRwNTgAHAZPbeex733PMuH34Io0bBhRfCsmUpB5WkFHkTpdRCcXExs2bdSX19PQ0N\nDZSUlHxy4+T8+XDKKXDCCXDHHXDddTBgQLp5JSkNnoGQWlFaWsr48eNXeuqiTx+44AKYOxcaGpJ5\nI6qrIcYUg0pSCiwQ0lrYc0947DHYd1+YMgUmToRFi9JOJUmdxwIhraWNNoIbb4Tp0+Huu5OzEbNm\npZ1KkjqHBUJqp69/HR5/HLbbDsaPh6OPhnfeSTuVJOWWBULqAFttBTNnwmWXwbXXwg47wEMPpZ1K\nknLHAiF1kBDgqKPgkUeSyxu77AKnnw4ffZR2MknqeBYIqYMNHpwsCf7Tn8LZZ8OYMfDMM2mnkqSO\nZYGQcqBXL5g2DR58EN56K7mkcemlPu4pqeuwQEg59D//k1zS+M534JhjYNw4eOWVtFNJUvtZIKQc\nW289uOSS5BHPxx+H4cOTRz8lqZBZIKROUlEBTzwB5eXwjW/A5Mnwn/+knUqS1o4FQupEG28Mt9yS\nTH/9l78kk0/NnZt2KknKngVC6mQhwKRJyeWMsrLkjMTxx8N776WdTJLazgIhpaR/f7jrLrjoIrjy\nymSZ8Lq6tFNJUttYIKQU9egBxx2XFIc+feCLX4Sf/Qw+/jjtZJK0ehYIKQ8MHQrz5sFJJyUTUO22\nGzQ2pp1KklpngZDyRO/eycyVf/sbvP46jBgBV1/t5FOS8pMFQsozY8bAo48mN1oefjjsuy+8+mra\nqSRpZRYIKQ/17QtXXQV33AEPP5w87nnbbSv2ZzIZZs6cSX19fXohJXVrFggpj+2zTzL51K67wgEH\nwEEHfUB5+YEMHjyYyspKysrKGDduAosXL047qqRuxgIh5bnNNoM//hGuvRamT1/K3LkXALOBF4Bq\n5syZR1XV5JRTSupuLBBSAQgBxozJsHTpUKAPsBfQH5jE0qUXU1s7w8sZkjqVBUIqEI2NjcDzwAct\n9lQA0NDQ0NmRJHVjFgipQAwaNKjp3X0t9mwG/IG+fQd3ciJJ3VnOCkQI4ZQQwv0hhHdCCG+u5rhD\nQggLQgjvhRBeDSFckqtMUiErKyujoqKSoqJjgWrgRaCaEL5N7957MGHCtlx2GSxdmnJQSd1CLs9A\n9AKmA1e0dkAI4QTgLOBnwFCgHKjNYSapoNXUVFNePhqYAgwAprD33q/xzDM9OOgg+P73YZdd4LHH\nUg4qqcvrmasPjjGeARBC+Naq9ocQNiIpDxNijH9ttuuJXGWSCl1xcTGzZt1JfX09DQ0NlJSUUFpa\nCiQLck2Zkkw+NWoU/OAHcNppsN56KYeW1CWleQ/EXkAA+ocQngohvBhCuCWEsHWKmaSCUFpayvjx\n4z8pD8vtsgs88gicfnqyyufw4TB7djoZJXVtaRaIbYEi4GTgWOBrwMbAXSGEnJ0Zkbq63r3h1FPh\n8cdhm22goiKZFvv119NOJqkryeof6hDCucBJqzkkAkNijJk2fFyPpu9/TIxxbtPnVwGvAl8G7lrd\nF0+dOpV+/fqttK2qqoqqqqo2fGup6ysthTlz4MYb4YQT4POfh/PPh0MPTeaVkNS11dTUUFNTs9K2\nJUuWdNjnh5jFUn8hhE2ATdZw2MIY48fNvuZbwIUxxo1bfNYhwG+B/jHGV5ptfxU4Ncb421YyjATm\nz58/n5EjR7Y5u9SdvfFGck/EDTfA7rsn62wM9qlPqdupq6tj1KhRAKNijHXt+aysLmHEGBfFGDNr\neH285k8C4P6mXz/5ayyEsDGwKclsOZI6yKabwvXXJ2ckXn4ZttsOzjgDPmg5J5UktVEu54HoH0IY\nAQwEikIII5pe6wPEGOuBPwMXhxB2DiEMB64HngLuyVUuqTsbOzZ5xPPEE+Hss2H77eG+lvNSSVIb\n5PImyjOBOmAa0LfpfR0wqtkxU4CHgL+QlIb3gfExRqfCkXJk3XWT8vDII7DxxskljcMOgzdbne5N\nkj4tZwUixnhojLFoFa/7mh3z3xjjd2OMm8QYN4sxfj3G+HKuMklaYfhw+Nvf4Ior4Pe/hyFD4Kab\nIIvboiR1Y66FIXVjPXrA974HTz+dnImYNAnGj4dnn007maR8Z4GQxJZbwvTp8Je/JGVi2DA47zz4\n6KO0k0nKVxYISZ+YMAGefDI5K3HyybDjjvDww2mnkpSPLBCSVtK3L1xwQVIcevaE0aPhmGPgrbfS\nTiYpn1ggJK3SqFHw0EPwy1/CtdfC0KFw++1pp5KULywQklrVsydMnZpc1thhB9h//+T10ktpJ5OU\nNguEpDUaOBD+/Ge49dbkrMTQoXDJJbDUGVukbssCIalNQoADD0ye0pg8GY49FsaMgQULVhyTyWSY\nOXMm9fX16QWV1CksEJKy0q8fXH453H8/vPNOcq/Esce+x157HcDgwYOprKykrKyMceMmsHjx4rTj\nSsoRC4SktTJmDNTVwZlnwmWXFTFnzgXAXOAFoJo5c+ZRVTU55ZSScsUCIWmt9e4NBx6YYdmyocA6\nwJ5Af2ASS5deTG3tDC9nSF2UBUJSuzQ2NgKNwMct9uwHbEpDQ0Pnh5KUcxYISe0yaNCgpnct1wVf\nB1jIjBk7OQmV1AVZICS1S1lZGRUVlRQVHQtUAy8C1fToMZTPfW42V1+9CYMGwUUXwfvvpxxWUoex\nQEhqt5qaasrLRwNTgAHAFPbaazB1dXvS0ABf/Sr84AdQVpbMavlxy6sdkgqOBUJSuxUXFzNr1p1k\nMhlmzJhBJpNh1qw7KS4uZuut4eqr4amnknU1vv1t2G47uO02iDHt5JLWlgVCUocpLS1l/PjxlJaW\nfmrf4MHJkuF//ztsvTUccEBSKO6+O4WgktrNAiGpU+24I8yeDXPnJr8fOxb23hvmz083l6TsWCAk\npWLPPWHePPjjH5PFuXbcESZOhGeeSTuZpLawQEhKTQjJ6p6PPQbXXJMUimHD4LvfdcVPKd9ZICSl\nrmdPOPRQyGTg/PPh9tuhpAROPBEWLUo7naRVsUBIyht9+sDxx0NjI/z4x3DllbDttnD22fDf/6ad\nTlJzFghJeWfDDeH002HhwuSxz7POgkGD4NJL4cMP004nCSwQkvLYZpvBhRcmlzYqK+G445LHQW+8\nEZYuTTud1L1ZICTlvYEDkxksH3sMtt8eDj44+fWOO5yMSkqLBUJSwRg2LJnB8sEHYdNNYd99Yddd\n4b6W63hJyjkLhKSCs3wGy9raZIGu3XdPLnE8+mjayaTuwwIhqSCFkMxg+fe/wy23QEMD7LADHHRQ\n8l5SblkgJBW0Hj2SGSyffBJ+/Wu4914YMgSOPBJeeWXFcZlMhpkzZ1JfX59eWKkLsUBI6hJ69Upm\nsGxogJ/9LDkrUVICxx//HmPHfoPBgwdTWVlJWVkZ48ZNYPHixWlHlgqaBUJSl7LuuskMlgsXwgkn\nwKWXBu6++yqgDngRqGbOnHlUVU1OOalU2CwQkrqkjTaCgw/OsHTp54DXgB2ArYFJLF16KbW1M7yc\nIbWDBUJSl9XY2EhSHtZrsWci8CMee+zZzg8ldREWCEld1qBBg5retZwoohE4i8MOG8vZZ8OSJZ0c\nTOoCLBCSuqyysjIqKiopKjoWqGb5PRBFRTuz227fZfLkIs4+O5npcto0ePPNlANLBcQCIalLq6mp\nprx8NDAFGABMobx8NLfffhGXXALPPpss2PW//wuf+xyccgq88Ua6maVCYIGQ1KUVFxcza9adZDIZ\nZsyYQSaTYdasOykuLgZgyy3hggvgueeSuSN+9avkjMSJJ8Jrr6WbXcpnFghJ3UJpaSnjx4+ntLR0\nlfs/8xn4xS+SIjF1Klx1VXJG4vjjV56QSlLCAiFJzWy6KZx9Njz/PPz4x3D99bDttnD00fDCC2mn\nk/KHBUKSVqG4OLmx8rnn4LTT4Oabk5ktjzgiuW9C6u4sEJK0Gv36JTdWPv88nHNOspx4aWly46WL\ndqk7s0BIUhv07ZvcWPncc8kTGzNnwuDBMGUK/POfaaeTOp8FQpKysN56yU2WCxfCxRfDX/8KQ4fC\nN78JTzyRdjqp81ggJGktrLsufP/7yWWMK66AefPgC1+Ar30NHn007XRS7lkgJKkd1lknubGyvh5+\n+1tYsAB22AH23Rf+/ve000m5Y4GQpA7Qq1dyY+U//wk33ACZDOy0E4wfDw8+mHY6qeNZICSpA/Xs\nmdxY+eSTUFOTzB0xZgyUl8N9Ldf0kgqYBUKScqCoKLmx8vHH4fe/T9bX2H132GMPmDsXYlxxbCaT\nYebMmdTX16eWV8qWBUKScqhHj+TGykcegT/9Cf773+RsxK67wq23vk1FxQQGDx5MZWUlZWVljBs3\ngcWLF6cdW1ojC4QkdYIQVtxYOWMGLFsGEyduwOzZZwH3AC8A1cyZM4+qqskpp5XWzAIhSZ0ohOTG\nyuuuywDlwNbAHkB/YBJLl95Abe0ML2co7+WsQIQQTgkh3B9CeCeE8GYrx4wOIdwdQlgcQngzhDAr\nhLBdrjIVgmsvAAAN/UlEQVRJUr5YuLARmAt80GLPBOAZfvrTwGOPrXyvhJRPcnkGohcwHbhiVTtD\nCBsAM4GFwE7ALsDbwKwQQlEOc0lS6gYNGtT0ruWjGXOBvzFr1jaMGAFDhiSLeT3+uGVC+SVnBSLG\neEaM8WLg8VYOKQM2BKbFGOtjjE8DZwCbAwNzlUuS8kFZWRkVFZUUFR0LVAMvAtUUFU2kouKPvP56\nETNmJI+AXnIJbLddMmX2tGnJI6JS2tK8B+IpkruGvhNC6BVCWBc4rGn7cynmkqROUVNTTXn5aGAK\nMACYQnn5aGpqqundO7lX4ppr4LXX4M474YtfTNbfGD4chg2DM86Ap55K+T9C3VZqBSLG+B4wDjgE\neI/k8sXeQGWMcVlauSSpsxQXFzNr1p1kMhlmzJhBJpNh1qw7KS4uXum43r2hshKuuy4pE3fcATvu\nCBdckBSJ4cPhzDNdFVSdK8QsLqqFEM4FTlrNIREYEmPMNPuabwEXxhg3bvFZ/YD5wN+AS4CewA+B\nIcCOMcaWdxYt/7qRwPzddtuNfv36rbSvqqqKqqqqNv/3SFIh++ADmD0bpk9P5ph4++1kQa+JE+Hr\nX0+WG1f3VVNTQ01NzUrblixZwn3JlKijYox17fn8bAvEJsAmazhsYYzx42Zf01qB+B7J/Q9bNtvW\nC1gMfDvGOL2VDCOB+fPnz2fkyJFtzi5JXdn770NtLdx664oJq7bbbkWZKCtLO6HyQV1dHaNGjYIO\nKBA9szk4xrgIWNSeb9hMD6DlpYrY9HJ+CknKQp8+sN9+yeu995IyMX06nHsu/OQnsP32K8pESUna\nadUV5HIeiP4hhBEkT1QUhRBGNL3WbzqkFtgohHBZCOHzIYRhwLXARyTTskmS1sK668JXvwo33QT/\n/jf84Q/w+c/DOedAaSmMHAk//zk0NqadVIUslz/pnwnUAdOAvk3v64BRADHGRpIZU4YDDwD3AlsA\nFTHG13KYS5K6jXXXhQMOSFYGff31ZGGv0lI466zkTMSOO8IvfgELF676613oS63J6h6IfOA9EJLU\nfu+8k6zJMX168ojoe+8lZWL5ZY4NN3yTgw6aQm3tjE++pqKikpqa6k89JaLC0ZH3QHivgSR1Q+uv\nnxSFW29NLnPccgsMHJjMernNNrDttv9m9uxRwG240JdWxQIhSd3c+usnZx5+//ukTFxwwb9YsuRJ\nYjwN+CorFvr6lQt96RMWCEnSJ/r2hc9//lHga8CrLfZ+EzidefNe6vxgyjsWCEnSSlYs9HVviz0N\nwAkceuge7L9/MonVMucN7rYsEJKklbS+0NcYxo49lEsuCTQ2QkVFMtvlL38JizpqhiAVDAuEJOlT\nWlvo69Zbr+bII2HBAvi//4OddoJTToHPfhYOOQQeeshlx7sLC4Qk6VPWtNBXCLDLLvC738FLLyUr\ng957L4weDaNGwdVXJ4+KquuyQEiSWlVaWsr48eMpLS1t9ZjNNoOTToKGhmROic9+Fo44ArbaCo49\n1iXHuyoLhCSpQxQVJcuO33EHPPssfP/7yfwSw4bBHnskk1Z9+GHaKdVRLBCSpA43cGCy9saLLybT\naMcI3/gGDBiQLO71wgtpJ1R7WSAkSTnTuzd885vJ/RGPPw4HHgi/+lUy2+W++8KsWT4KWqgsEJKk\nTjF8OFx6KbzyClxxRXJ2Yvz4ZHGv886DN95IO6GyYYGQJHWqvn3h8MOhrg4eeCB5muO002DrrWHK\nlGSbj4LmPwuEJCkVIcDOO8MNNySPgp511opCsf32cOWV8PbbaadUaywQkqTUbbopnHgi1Ncn90Vs\nsw0cfXTySOjRR8MTT6x8fCaTYebMmS7slSILhCQpb/TokUyRffvt8NxzcNxx8Mc/whe+AF/6Elx9\n9X/Ze+/9GDx4MJWVlZSVlTFu3AQWL16cdvRuxwIhScpL/fsnlzVeeCGZQ6JnTzj88L7cdddvgCeA\nl4Fq5syZR1XV5JTTdj8WCElSXuvVC77+dbjqqgwwBFgEDAO2AiaxdOnF1NbO8HJGJ7NASJIKQmNj\nI/BPYP0We3YHoKGhobMjdWsWCElSQRg0aFDTu/ta7LkXgJKSkk7N091ZICRJBaGsrIyKikqKio4F\nqoEXgWqKio6joqJytQt+qeNZICRJBaOmppry8tHAFGAAMIXy8tHU1FSnnKz76Zl2AEmS2qq4uJhZ\ns+6kvr6ehoYGSkpKPPOQEguEJKnglJaWWhxS5iUMSZKUNQuEJEnKmgVCkiRlzQIhSZKyZoGQJElZ\ns0BIkqSsWSAkSVLWLBCSJClrFghJkpQ1C4QkScqaBUKSJGXNAiFJkrJmgZAkSVmzQEiSpKxZICRJ\nUtYsEJIkKWsWCEmSlDULhCRJypoFQpIkZc0CIUmSsmaBkCRJWbNASJKkrFkgJElS1iwQkiQpaxYI\nSZKUNQuEJEnKmgWim6ipqUk7QkFy3LLnmK0dxy17jlm6clIgQggDQwi/CSEsDCG8G0KoDyGcHkLo\n1eK4/iGEO0MI74QQXg0hnBdCsNTkgP+jrR3HLXuO2dpx3LLnmKWrZ44+9/NAAL4LNALDgd8A6wE/\nAmgqCjOAV4DRwFbAjcCHwE9ylEuSJHWAnPy0H2OsjTF+J8Y4N8b4XIzxL8D5wAHNDqsgKRqTYoyP\nxxhrgZ8CR4cQclVsJElSB+jMywUbAW82+/1o4PEY4xvNttUC/YBhnZhLkiRlqVN+0g8hlADfB05o\ntnkL4LUWh77WbN+CVj6uD8DTTz/dkRG7vCVLllBXV5d2jILjuGXPMVs7jlv2HLPsNfu3s097PyvE\nGNt+cAjnAiet5pAIDIkxZpp9zWeBvwJ3xxiPaLb9KmBAjHF8s23rAu8A45suaawqw0HA79ocWpIk\ntTQpxnhTez4g2zMQ5wPXruGYhcvfhBC2Au4G/q95eWjyKvA/LbZt3mxfa2qBScBzwPtryCJJklbo\nA3yO5N/SdsnqDERWH5ycebgb+DswJbb4RiGEccAdwJbL74MIIRwO/AL4TIzxo5wEkyRJ7ZaTAtF0\n5uFe4FngEGDp8n0xxteajukBPELyGOdJwJbADcCvY4w/7fBQkiSpw+SqQHwLuKblZiDGGIuaHdcf\nuALYg+Teh+uAk2OMyzo8lCRJ6jA5u4QhSZK6LqeNliRJWbNASJKkrBVUgQghHB1CeDaE8F4IYV4I\noeVjoN1aCOFLIYQ/hxBeDiEsCyHsu4pjzgwhvNK0yNldTZN8dVshhJNDCA+HEN4KIbwWQrgthFC2\niuMct2ZCCN8LISwIISxpej3Q9GRV82Mcs9UIIfy46f/TC1psd9yaCSFMaxqn5q+nWhzjmLUQQtgq\nhHBjCOGNpnFZEEIY2eKYdo1bwRSIEMI3gF8C04AdSGaqrA0hbJpqsPyyPvAocBTJpF4rCSGcRDIj\n6OHATiQ3rtaGEHp3Zsg88yXgEuCLQDnQC5jdNKkZ4Li14kWSp6dGAqNIHtn+cwhhKDhma9L0w8/h\ntJhx13Fr1RMk8wRt0fTadfkOx+zTQggbAfcDH5CsOzUE+AGwuNkx7R+3GGNBvIB5wMXNfh+Al4Af\npZ0tH1/AMmDfFtteAaY2+/2GwHvAxLTz5ssL2LRp7HZ13LIeu0XAoY7ZGsepL/AMsCdwD3BBs32O\n26fHaxpQt5r9jtmnx+TnwL1rOKbd41YQZyBCCL1IfsqZu3xbTP6L5wA7p5WrkIQQtiFp7s3H8C3g\nIRzD5jYiOXvzJjhubRFC6BFC+CawDnCfY7ZGlwF3xBjvbr7RcVut0qZLs40hhOqmKQAcs9Z9BfhH\nCGF606XZuhDCYct3dtS4FUSBIPmpsIhVL761RefHKUhbkPzD6Bi2IoQQgItIpl5ffo3VcWtFCGF4\nCOFtktOkV5H85NKIY9aqpqK1PXDyKnY7bqs2j2RCwgrge8A2JEV1fRyz1mwLHElypmtvkvmWfhVC\nmNK0v0PGrVNW45QKxOXAUGCXtIMUiH8CI4B+wIHAzSGE3dONlL9CCFuTFNTy6FT9bRZXXljxiRDC\nw8DzwESSP4P6tB7Aw3HFrM4LQgjDSQrYjR35TQrBGyTTYW/eYvvmrH7hLa3wKsl9I47hKoQQLgUq\ngT1ijP9qtstxa0WM8eMY48IY4yMxxlNJTn8eiWPWmlHAZkBdCOGjEMJHwO7AcSGED0l++nPc1iDG\nuATIACX4Z601/wKebrHtaWBA0/sOGbeCKBBNbX0+MHb5tqbTzWOBB9LKVUhijM+S/MFoPoYbkjx9\n0K3HsKk87Ad8Ocb4QvN9jltWegBFjlmr5gBfILmEMaLp9Q+gGhgRY1yI47ZGIYS+JOXhFf+step+\nYHCLbYNJztx03N9rad8tmsVdpROBd4GDgc+TXHNdBGyWdrZ8eZE8xjmC5C+oZcDxTb/v37T/R01j\n9hWSv8huB+qB3mlnT3HMLid5tOlLJO17+atPs2Mct0+P28+axmwgMBw4F/iIpIQ5Zm0fx5ZPYThu\nnx6j/wV2a/qzNga4i+RszSaOWatjtiPJvUknA4OAg4C3gW925J+11P9DsxyUo4DnSB41eRDYMe1M\n+fQiOR26jORyT/PXNc2OOZ3k8Z13SdaDL0k7d8pjtqrxWgoc3OI4x23l8fgNsLDp/8VXgdnAno5Z\n1uN4d/MC4bitcoxqSB7Zfw94AbgJ2MYxW+O4VQKPNY3Jk8C3V3FMu8bNxbQkSVLWCuIeCEmSlF8s\nEJIkKWsWCEmSlDULhCRJypoFQpIkZc0CIUmSsmaBkCRJWbNASJKkrFkgJElS1iwQkiQpaxYISZKU\ntf8HpFkfce+uHrkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104259630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = zip(*p)\n",
    "plt.plot(x, y)\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Full Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = models.ldamodel.LdaModel(corpus, num_topics=20, id2word=id2word, random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('reviews_20_lda.model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Top Topic Words\n",
    "- View the top 5 words associated with 10 random topics.\n",
    "- The number is the weight of the word it is multiplying (i.e., how much does this word influence the particular topic). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(15,\n",
       "  '0.006*\"mulan\" + 0.005*\"boiler\" + 0.005*\"spencer\" + 0.005*\"metro\" + 0.005*\"josi\"'),\n",
       " (5,\n",
       "  '0.013*\"flubber\" + 0.007*\"carlito\" + 0.006*\"gladiat\" + 0.006*\"bulworth\" + 0.006*\"gorilla\"'),\n",
       " (19,\n",
       "  '0.010*\"anaconda\" + 0.008*\"ordel\" + 0.006*\"dolor\" + 0.005*\"hewitt\" + 0.005*\"lambeau\"'),\n",
       " (8,\n",
       "  '0.009*\"pauli\" + 0.004*\"crawford\" + 0.004*\"shyamalan\" + 0.004*\"cindi\" + 0.004*\"mccabe\"'),\n",
       " (14,\n",
       "  '0.017*\"shrek\" + 0.008*\"8mm\" + 0.007*\"fiona\" + 0.007*\"hammer\" + 0.006*\"frequenc\"'),\n",
       " (13,\n",
       "  '0.006*\"insurrect\" + 0.006*\"robocop\" + 0.006*\"felix\" + 0.005*\"dora\" + 0.005*\"judith\"'),\n",
       " (11,\n",
       "  '0.009*\"farrelli\" + 0.008*\"iren\" + 0.005*\"1900\" + 0.005*\"raimi\" + 0.004*\"mermaid\"'),\n",
       " (3,\n",
       "  '0.010*\"bulworth\" + 0.009*\"sonni\" + 0.007*\"margaret\" + 0.007*\"hamlet\" + 0.007*\"apostl\"'),\n",
       " (12,\n",
       "  '0.007*\"robocop\" + 0.005*\"col\" + 0.005*\"kelley\" + 0.005*\"sherri\" + 0.005*\"beau\"'),\n",
       " (0,\n",
       "  '0.018*\"mulan\" + 0.011*\"whale\" + 0.004*\"melvin\" + 0.003*\"mamet\" + 0.003*\"buster\"')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.print_topics(num_topics=10, num_words=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query the Model With New Text\n",
    "- Credit to http://christop.club/2014/05/06/using-gensim-for-lda/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query = \"I love the movie Shrek. The donkey is hilarious. It is much better than the old Disney films.\"\n",
    "query = preprocess(query).split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(4866, 1), (4867, 1)]\n"
     ]
    }
   ],
   "source": [
    "query = id2word.doc2bow(query)\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most relevant: (14, 0.68333333318392886)\n",
      "Least relevant: (19, 0.01666666666666667)\n"
     ]
    }
   ],
   "source": [
    "topic_relevance = list(sorted(model[query], key=lambda x: -x[1]))\n",
    "print('Most relevant: {}'.format(topic_relevance[0]))\n",
    "print('Least relevant: {}'.format(topic_relevance[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.017*\"shrek\" + 0.008*\"8mm\" + 0.007*\"fiona\" + 0.007*\"hammer\" + 0.006*\"frequenc\" + 0.006*\"donkey\" + 0.006*\"farquaad\" + 0.005*\"snuff\" + 0.005*\"bowfing\" + 0.004*\"muppet\"'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.print_topic(topic_relevance[0][0]) # most related"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.010*\"anaconda\" + 0.008*\"ordel\" + 0.006*\"dolor\" + 0.005*\"hewitt\" + 0.005*\"lambeau\" + 0.005*\"bruckheim\" + 0.004*\"ripley\" + 0.004*\"crab\" + 0.004*\"carlito\" + 0.004*\"gellar\"'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.print_topic(topic_relevance[-1][0]) # least related"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Model\n",
    "- http://nbviewer.jupyter.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb#topic=0&lambda=1&term="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "id2word = corpora.Dictionary.load('reviews.dict')\n",
    "corpus = corpora.MmCorpus('reviews.mm')\n",
    "model = models.ldamodel.LdaModel.load('reviews_20_lda.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": 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",
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       "\n",
       "<div id=\"ldavis_el2769545220077922799687473\"></div>\n",
       "<script type=\"text/javascript\">\n",
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\"helgenberg\", \"helgenberg\", \"helgenberg\", \"helgenberg\", \"helgenberg\", \"henson\", \"henson\", \"henson\", \"henson\", \"henson\", \"hereaft\", \"hereaft\", \"hereaft\", \"hereaft\", \"hereaft\", \"hereaft\", \"hereaft\", \"herman\", \"herman\", \"herman\", \"herman\", \"herman\", \"herman\", \"herman\", \"herman\", \"herman\", \"herman\", \"heterosexu\", \"heterosexu\", \"heterosexu\", \"heterosexu\", \"heterosexu\", \"heterosexu\", \"heterosexu\", \"hewitt\", \"hewitt\", \"hewitt\", \"hewitt\", \"hewitt\", \"hewitt\", \"hewitt\", \"hewitt\", \"highland\", \"highland\", \"highland\", \"highland\", \"hiphop\", \"hiphop\", \"hiphop\", \"hiphop\", \"hiphop\", \"hisher\", \"hisher\", \"hisher\", \"hisher\", \"hitler\", \"hitler\", \"hitler\", \"hitler\", \"hitler\", \"hockey\", \"hockey\", \"hockey\", \"hockey\", \"hockey\", \"hockey\", \"hockey\", \"hogan\", \"hogan\", \"hogan\", \"hogan\", \"hogan\", \"homer\", \"homer\", \"homer\", \"homer\", \"homer\", \"homer\", \"homer\", \"homer\", \"honour\", \"honour\", \"honour\", \"honour\", \"honour\", \"honour\", \"hose\", \"hose\", \"hose\", \"hose\", \"hose\", \"hottest\", \"hottest\", \"hottest\", \"hottest\", \"howi\", \"howi\", \"howi\", \"howi\", \"howi\", \"hugo\", \"hugo\", \"hugo\", \"hugo\", \"hugo\", \"hugo\", \"hugo\", \"hugo\", \"hun\", \"hun\", \"hun\", \"hun\", \"hush\", \"hush\", \"hush\", \"hush\", \"hush\", \"hush\", \"hush\", \"hush\", \"hush\", \"hustler\", \"hustler\", \"hustler\", \"hustler\", \"hustler\", \"hustler\", \"hustler\", \"huston\", \"huston\", \"huston\", \"huston\", \"huston\", \"hyam\", \"hyam\", \"hyam\", \"hyam\", \"hyam\", \"hyam\", \"hyam\", \"hyam\", \"ichabod\", \"ichabod\", \"ichabod\", \"ichabod\", \"id4\", \"id4\", \"id4\", \"id4\", \"id4\", \"id4\", \"id4\", \"id4\", \"id4\", \"identif\", \"identif\", \"identif\", \"identif\", \"identif\", \"identif\", \"implement\", \"implement\", \"implement\", \"inc\", \"inc\", \"inc\", \"inc\", \"inch\", \"inch\", \"inch\", 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\"janet\", \"janet\", \"janet\", \"janet\", \"janet\", \"jawbreak\", \"jawbreak\", \"jawbreak\", \"jawbreak\", \"jawbreak\", \"jeann\", \"jeann\", \"jeann\", \"jeann\", \"jeann\", \"jeann\", \"jeann\", \"jenni\", \"jenni\", \"jenni\", \"jenni\", \"jenni\", \"jenni\", \"jenni\", \"jenni\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jeopardi\", \"jericho\", \"jericho\", \"jericho\", \"jericho\", \"jericho\", \"jericho\", \"jerom\", \"jerom\", \"jerom\", \"jerom\", \"jerom\", \"jerom\", \"jerom\", \"jerom\", \"jess\", \"jess\", \"jess\", \"jess\", \"jess\", \"jess\", \"jess\", \"jess\", \"jess\", \"jess\", \"jill\", \"jill\", \"jill\", \"jill\", \"jill\", \"joker\", \"joker\", \"joker\", \"joker\", \"joker\", \"joker\", \"joker\", \"joker\", \"josi\", \"josi\", \"josi\", \"joss\", \"joss\", \"joss\", \"joss\", \"joss\", \"jovovich\", \"jovovich\", \"jovovich\", \"jovovich\", \"jovovich\", \"jovovich\", 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\"lebowski\", \"lebowski\", \"lebowski\", \"leder\", \"leder\", \"leder\", \"leder\", \"leder\", \"leguizamo\", \"leguizamo\", \"leguizamo\", \"leguizamo\", \"leguizamo\", \"leguizamo\", \"leguizamo\", \"leguizamo\", \"lemmon\", \"lemmon\", \"lemmon\", \"lemmon\", \"lemmon\", \"lemmon\", \"lemmon\", \"lemmon\", \"lemmon\", \"lenni\", \"lenni\", \"lenni\", \"lenni\", \"leon\", \"leon\", \"leon\", \"leon\", \"leon\", \"leon\", \"leon\", \"leon\", \"leon\", \"lerner\", \"lerner\", \"lerner\", \"lerner\", \"lerner\", \"lester\", \"lester\", \"lester\", \"lester\", \"lester\", \"levi\", \"levi\", \"levi\", \"levi\", \"levi\", \"levi\", \"levi\", \"levi\", \"levi\", \"levi\", \"levi\", \"levin\", \"levin\", \"levin\", \"levin\", \"levinson\", \"levinson\", \"levinson\", \"liaison\", \"liaison\", \"liaison\", \"liaison\", \"liaison\", \"liaison\", \"liaison\", \"liaison\", \"liaison\", \"liar\", \"liar\", \"liar\", \"liar\", \"liar\", \"liar\", \"libbi\", \"libbi\", \"libbi\", \"libbi\", \"libbi\", \"libbi\", \"libbi\", \"libbi\", \"libbi\", \"lifeform\", \"lifeform\", \"lifeform\", \"lifeform\", \"lin\", \"lin\", \"lin\", \"lin\", \"lin\", \"lin\", \"lin\", \"lindo\", \"lindo\", \"lindo\", \"lindo\", \"lindo\", \"lindo\", \"lindo\", \"lindo\", \"lindo\", \"lindo\", \"liotta\", \"liotta\", \"liotta\", \"liotta\", \"liotta\", \"lithgow\", \"lithgow\", \"lithgow\", \"lithgow\", \"lithgow\", \"liveli\", \"liveli\", \"liveli\", \"liz\", \"liz\", \"liz\", \"liz\", \"liz\", \"liz\", \"liz\", \"liz\", \"lolita\", \"lolita\", \"lolita\", \"lolita\", \"lolita\", \"lolita\", \"lolita\", \"lombardo\", \"lombardo\", \"lombardo\", \"lombardo\", \"lombardo\", \"lombardo\", \"lombardo\", \"lookalik\", \"lookalik\", \"lookalik\", \"lookalik\", \"lookalik\", \"looney\", \"looney\", \"looney\", \"looney\", \"louis\", \"louis\", \"louis\", \"louis\", \"louis\", \"luhrmann\", \"luhrmann\", \"luhrmann\", \"lui\", \"lui\", \"lui\", \"lui\", \"lui\", \"lui\", \"lui\", \"lui\", \"lui\", 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\"mama\", \"mama\", \"mamet\", \"mamet\", \"mamet\", \"mamet\", \"mamet\", \"mamet\", \"mamet\", \"marceau\", \"marceau\", \"marceau\", \"marceau\", \"marceau\", \"marci\", \"marci\", \"marci\", \"marci\", \"marci\", \"marg\", \"marg\", \"marg\", \"marg\", \"margaret\", \"margaret\", \"margaret\", \"margaret\", \"margaret\", \"marti\", \"marti\", \"marti\", \"marti\", \"marti\", \"marti\", \"marti\", \"marti\", \"marvin\", \"marvin\", \"marvin\", \"marvin\", \"marvin\", \"marvin\", \"marvin\", \"marvin\", \"maul\", \"maul\", \"maul\", \"maul\", \"maul\", \"maul\", \"maureen\", \"maureen\", \"maureen\", \"maureen\", \"mauric\", \"mauric\", \"mauric\", \"mauric\", \"mauric\", \"mauric\", \"mauric\", \"mauric\", \"mauric\", \"mccabe\", \"mccabe\", \"mccabe\", \"mccabe\", \"mccabe\", \"mccabe\", \"mcdermott\", \"mcdermott\", \"mcdermott\", \"mcdermott\", \"mcdermott\", \"mctiernan\", \"mctiernan\", \"mctiernan\", \"mctiernan\", \"mctiernan\", \"mctiernan\", \"meatier\", \"meatier\", \"meatier\", \"meatier\", \"meatloaf\", \"meatloaf\", \"meatloaf\", \"meatloaf\", \"melvin\", \"melvin\", \"melvin\", \"melvin\", \"melvin\", \"melvin\", \"mercuri\", \"mercuri\", \"mercuri\", \"mercuri\", \"mercuri\", \"mercuri\", \"mercuri\", \"mercuri\", \"mercuri\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"mermaid\", \"messeng\", \"messeng\", \"messeng\", \"messeng\", \"messeng\", \"messeng\", \"messeng\", \"messeng\", \"messeng\", \"messeng\", \"meteor\", \"meteor\", \"meteor\", \"meteor\", \"meteor\", \"meteor\", \"meteor\", \"meteor\", \"meteor\", \"metro\", \"metro\", \"metro\", \"metro\", \"metro\", \"metro\", \"mia\", \"mia\", \"mia\", \"mia\", \"mia\", \"mia\", \"mia\", \"mia\", \"mia\", \"mia\", \"mib\", \"mib\", \"mib\", \"mib\", \"mib\", \"mib\", \"mib\", \"midget\", \"midget\", \"midget\", \"midget\", \"milton\", \"milton\", \"milton\", \"milton\", \"milton\", \"milton\", 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       "\n",
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       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
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       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "reviews_vis = pyLDAvis.gensim.prepare(model, corpus, id2word)\n",
    "pyLDAvis.display(reviews_vis)"
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   "source": [
    "<br />\n",
    "## Semi Supervised Analysis to Exploit Semantic Relationships"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "There are two big \"classes\" of approaches:\n",
    "- Count-based (bag of words):\n",
    "    - Each word is represented with a single number (\"one-hot\" representation)\n",
    "    - Very fast, easy to understand (and often outperform more complex solutions on small to medium data)\n",
    "    - Treats documents as \"bags of words\"\n",
    "        - Loses semantic and syntactic context\n",
    "        - Bad with words that are rare in the training set\n",
    "\n",
    "<br />\n",
    "- Word embeddings:\n",
    "    - Each word is represented by a dense vector of continuous values\n",
    "    - Seems to capture semantic and syntactic relationships, though we don't fully understand why (Goldberg and Levy 2014)\n",
    "        - The hypothesis is that words in similar contexts have similar meanings\n",
    "    - Requires a lot of training data\n",
    "    - Bad with words that are rare across the entire corpus"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "There are many implementations:\n",
    "- Stanford's GloVE (https://nlp.stanford.edu/projects/glove/)\n",
    "- Facebook FastText (https://github.com/facebookresearch/fastText)\n",
    "- Word2Vec (Mikolov et al. 2013)\n",
    "<br /><br />\n",
    "\n",
    "Some evidence suggests FastText may be better at syntactic relationships, while Word2Vec may be better at semantic relationships. <br />\n",
    "\n",
    "Additional evidence emphasizes the importance of domain specific embeddings (Hamilton et al., 2016). (\"Soft\" means something very different when talking about fuzzy animals vs. sports.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word2Vec and Doc2Vec\n",
    " - Train a simple neural network with a single hidden layer\n",
    " - Use the weights of the hidden layer as “word vectors”\n",
    "\n",
    "<br />\n",
    "\n",
    "<b>Word2Vec</b> (as we use it) captures context through skipgrams, built from sequences of text in a document:\n",
    "- Uses a hidden layer to produce a projection of a word in context\n",
    "- Weights are optimized based on context words, embedding the distribution over context words in the representation of the center word\n",
    "\n",
    "<img src='files/skip-gram.png' width=\"500\" align='center'>\n",
    "http://zhangbanger.github.io/2015/12/13/allen-ai-challenge-part-3.html\n",
    "\n",
    "<br />\n",
    "<b>Doc2Vec</b> jointly estimates word vectors and document vectors (Le and Mikolov, 2014)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prep the Data\n",
    "- Tag the documents and create the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 2000\n"
     ]
    }
   ],
   "source": [
    "y = []\n",
    "doc_vectors = []\n",
    "for i, row in data.iterrows():\n",
    "    doc_vectors.append(TaggedDocument(row['tokens'].split(), ['doc_' + str(i)]))\n",
    "    y.append(row['category'])\n",
    "print(len(y), len(doc_vectors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Word Embeddings (The Unsupervised Part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def shuffle_docs(docs):\n",
    "    random.shuffle(docs)\n",
    "    return docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Epoch 1\n",
      "Epoch 2\n",
      "Epoch 3\n",
      "Epoch 4\n",
      "Epoch 5\n",
      "Epoch 6\n",
      "Epoch 7\n",
      "Epoch 8\n",
      "Epoch 9\n",
      "Epoch 10\n",
      "Epoch 11\n",
      "Epoch 12\n",
      "Epoch 13\n",
      "Epoch 14\n",
      "Epoch 15\n",
      "Epoch 16\n",
      "Epoch 17\n",
      "Epoch 18\n",
      "Epoch 19\n"
     ]
    }
   ],
   "source": [
    "token_count = sum([len(doc_vector) for doc_vector in doc_vectors])\n",
    "model = Doc2Vec(size=100, window=10, min_count=1, workers=4)\n",
    "model.build_vocab(doc_vectors)\n",
    "for epoch in range(20):\n",
    "    print('Epoch {}'.format(epoch))\n",
    "    model.train(shuffle_docs(doc_vectors), total_examples=token_count, epochs=1)\n",
    "d2v = {d: vec for d, vec in zip(model.docvecs.offset2doctag, model.docvecs.doctag_syn0)}\n",
    "X = []\n",
    "for d in range(len(doc_vectors)):\n",
    "    X.append(d2v['doc_' + str(d)])\n",
    "X = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('reviews_doc2vec.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Doc2Vec.load('reviews_doc2vec.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d2v = {d: vec for d, vec in zip(model.docvecs.offset2doctag, model.docvecs.doctag_syn0)}\n",
    "X = []\n",
    "for d in range(len(doc_vectors)):\n",
    "    X.append(d2v['doc_' + str(d)])\n",
    "X = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('decent', 0.5797518491744995),\n",
       " ('mcgann', 0.4261823296546936),\n",
       " ('dolittl', 0.42150282859802246),\n",
       " ('niftiest', 0.39561766386032104),\n",
       " ('poor', 0.39115533232688904),\n",
       " ('perfectli', 0.38975512981414795),\n",
       " ('backandforth', 0.3866485357284546),\n",
       " ('amaz', 0.3853611946105957),\n",
       " ('tough', 0.38419878482818604),\n",
       " ('miniatur', 0.3829454779624939)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('good')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('terribl', 0.5605968832969666),\n",
       " ('cruddi', 0.5057984590530396),\n",
       " ('horrif', 0.44955626130104065),\n",
       " ('lastminut', 0.4449557662010193),\n",
       " ('hokey', 0.4345216751098633),\n",
       " ('corni', 0.42026379704475403),\n",
       " ('coloss', 0.4185634255409241),\n",
       " ('jawdroppingli', 0.4184514284133911),\n",
       " ('gobetween', 0.4174378514289856),\n",
       " ('ruin', 0.4078148305416107)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('horribl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='files/word2vecqueen.png' width=\"500\" align='center'>\n",
    "http://blog.christianperone.com/2016/01/voynich-manuscript-word-vectors-and-t-sne-visualization-of-some-patterns/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('actress', 0.4704681932926178),\n",
       " ('nuanc', 0.40951240062713623),\n",
       " ('talent', 0.37572893500328064),\n",
       " ('tolkan', 0.3689042329788208),\n",
       " ('judd', 0.3588447868824005),\n",
       " ('gena', 0.353010892868042),\n",
       " ('fraser', 0.3452267646789551),\n",
       " ('bitingli', 0.3444317877292633),\n",
       " ('onscreen', 0.3393170237541199),\n",
       " ('ashley', 0.3369916081428528)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(positive=['woman', 'actor'], negative=['man'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test the Classifier (The Supervised Part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.802\n"
     ]
    }
   ],
   "source": [
    "classifier = LinearSVC()\n",
    "classifier.fit(x_train, y_train)\n",
    "print('Accuracy: {}'.format(round(classifier.score(x_test, y_test), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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