Chapters5-6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 5: Author profiling as a Machine Learning task\n",
    "\n",
    "## Step 1: Get the data\n",
    "\n",
    "Explore the NLTK's interface to the Gutenberg project collection of texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['austen-emma.txt',\n",
       " 'austen-persuasion.txt',\n",
       " 'austen-sense.txt',\n",
       " 'bible-kjv.txt',\n",
       " 'blake-poems.txt',\n",
       " 'bryant-stories.txt',\n",
       " 'burgess-busterbrown.txt',\n",
       " 'carroll-alice.txt',\n",
       " 'chesterton-ball.txt',\n",
       " 'chesterton-brown.txt',\n",
       " 'chesterton-thursday.txt',\n",
       " 'edgeworth-parents.txt',\n",
       " 'melville-moby_dick.txt',\n",
       " 'milton-paradise.txt',\n",
       " 'shakespeare-caesar.txt',\n",
       " 'shakespeare-hamlet.txt',\n",
       " 'shakespeare-macbeth.txt',\n",
       " 'whitman-leaves.txt']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.corpus import gutenberg\n",
    "\n",
    "gutenberg.fileids()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: you can use `gutenberg.sents` to access individual sentences. \n",
    "\n",
    "Let's select some authors and get some of their works into the training set. Some of the largest number of works in this collection are for *Jane Austen* and *William Shakespeare*, so for the rest of the chapter let's stick with those:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']'], ['VOLUME', 'I'], ...]\n",
      "11499\n"
     ]
    }
   ],
   "source": [
    "author1_train = gutenberg.sents('austen-emma.txt') + gutenberg.sents('austen-persuasion.txt')\n",
    "print (author1_train)\n",
    "print (len(author1_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['[', 'Sense', 'and', 'Sensibility', 'by', 'Jane', 'Austen', '1811', ']'], ['CHAPTER', '1'], ...]\n",
      "4999\n"
     ]
    }
   ],
   "source": [
    "author1_test = gutenberg.sents('austen-sense.txt')\n",
    "print (author1_test)\n",
    "print (len(author1_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['[', 'The', 'Tragedie', 'of', 'Julius', 'Caesar', 'by', 'William', 'Shakespeare', '1599', ']'], ['Actus', 'Primus', '.'], ...]\n",
      "5269\n"
     ]
    }
   ],
   "source": [
    "author2_train = gutenberg.sents('shakespeare-caesar.txt') + gutenberg.sents(\n",
    "    'shakespeare-hamlet.txt')\n",
    "print (author2_train)\n",
    "print (len(author2_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['[', 'The', 'Tragedie', 'of', 'Macbeth', 'by', 'William', 'Shakespeare', '1603', ']'], ['Actus', 'Primus', '.'], ...]\n",
      "1907\n"
     ]
    }
   ],
   "source": [
    "author2_test = gutenberg.sents('shakespeare-macbeth.txt')\n",
    "print (author2_test)\n",
    "print (len(author2_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Finally, let's check if the two authors produce markedly different texts: estimate the average number of characters per word, number of words per sentence, and *diversity* of author's vocabulary – average number of times each word occurs in a text by the author:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 25 26 austen-emma.txt\n",
      "5 26 17 austen-persuasion.txt\n",
      "5 28 22 austen-sense.txt\n",
      "4 12 9 shakespeare-caesar.txt\n",
      "4 12 8 shakespeare-hamlet.txt\n",
      "4 12 7 shakespeare-macbeth.txt\n"
     ]
    }
   ],
   "source": [
    "def statistics(gutenberg_data):\n",
    "    for work in gutenberg_data:\n",
    "        num_chars = len(gutenberg.raw(work))\n",
    "        num_words = len(gutenberg.words(work))\n",
    "        num_sents = len(gutenberg.sents(work))\n",
    "        num_vocab = len(set(w.lower() for w in gutenberg.words(work)))\n",
    "        print(round(num_chars/num_words), # average word length in characters\n",
    "             round(num_words/num_sents), # average sentence length in words\n",
    "             round(num_words/num_vocab), # average number of times each word occurs uniquely\n",
    "             work)\n",
    "        \n",
    "gutenberg_data = ['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt',\n",
    "                 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt']\n",
    "statistics(gutenberg_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Set additional test data\n",
    "\n",
    "To fairly test generalization behavior, let's set additional test data from within the same set of works as we are training the algorithm on. By comparing the algorithm's performance on the set of sentences coming from the same literary works to its performances on a different set of works, you will be able to tell how well the algorithm generalizes above the words it has seen in the training data.\n",
    "\n",
    "First, put the sentences with the author labels together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size = 16768 sentences\n"
     ]
    }
   ],
   "source": [
    "all_sents = [(sent, \"austen\") for sent in author1_train]\n",
    "all_sents += [(sent, \"shakespeare\") for sent in author2_train]\n",
    "print (f\"Dataset size = {str(len(all_sents))} sentences\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, shuffle the data and split it keeping the proportion of the author-speficic data consistent across the training and the same-data testing set. Let's call the test set coming from the same data `pre-test`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "values = [author for (sent, author) in all_sents]\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "strat_train_set = []\n",
    "strat_pretest_set = []\n",
    "for train_index, pretest_index in split.split(all_sents, values):\n",
    "    strat_train_set = [all_sents[index] for index in train_index]\n",
    "    strat_pretest_set = [all_sents[index] for index in pretest_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that the proportions are kept the same across the two data portions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Category     Overall   Stratified train  Stratified pretest \n",
      " austen       0.685771  0.685776          0.685748           \n",
      " shakespeare  0.314229  0.314224          0.314252           \n"
     ]
    }
   ],
   "source": [
    "def cat_proportions(data, cat):\n",
    "    count = 0\n",
    "    for item in data:\n",
    "        if item[1]==cat:\n",
    "            count += 1\n",
    "    return float(count) / float(len(data))\n",
    "\n",
    "categories = [\"austen\", \"shakespeare\"]\n",
    "rows = []\n",
    "rows.append([\"Category\", \"Overall\", \"Stratified train\", \"Stratified pretest\"])\n",
    "for cat in categories:\n",
    "    rows.append([cat, f\"{cat_proportions(all_sents, cat):.6f}\", \n",
    "                f\"{cat_proportions(strat_train_set, cat):.6f}\",\n",
    "                f\"{cat_proportions(strat_pretest_set, cat):.6f}\"])\n",
    "\n",
    "columns = zip(*rows)\n",
    "column_widths = [max(len(item) for item in col) for col in columns]\n",
    "for row in rows:\n",
    "    print(''.join(' {:{width}} '.format(row[i], width=column_widths[i]) \n",
    "                  for i in range(0, len(row))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now also initialize the test set in the same way, by adding author labels to the sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Category     Overall   Stratified train  Stratified pretest  Test     \n",
      " austen       0.685771  0.685776          0.685748            0.723863 \n",
      " shakespeare  0.314229  0.314224          0.314252            0.276137 \n"
     ]
    }
   ],
   "source": [
    "test_set = [(sent, \"austen\") for sent in author1_test]\n",
    "test_set += [(sent, \"shakespeare\") for sent in author2_test]\n",
    "\n",
    "rows = []\n",
    "rows.append([\"Category\", \"Overall\", \"Stratified train\", \"Stratified pretest\", \"Test\"])\n",
    "for cat in categories:\n",
    "    rows.append([cat, f\"{cat_proportions(all_sents, cat):.6f}\", \n",
    "                f\"{cat_proportions(strat_train_set, cat):.6f}\",\n",
    "                f\"{cat_proportions(strat_pretest_set, cat):.6f}\",\n",
    "                f\"{cat_proportions(test_set, cat):.6f}\"])\n",
    "\n",
    "columns = zip(*rows)\n",
    "column_widths = [max(len(item) for item in col) for col in columns]\n",
    "for row in rows:\n",
    "    print(''.join(' {:{width}} '.format(row[i], width=column_widths[i]) \n",
    "                  for i in range(0, len(row))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Run a benchmark model\n",
    "\n",
    "Naive Bayes model from Chapter 2 with words as features can be used as a reasonable approach to set up the benchmark result. Let's first extract the word features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414\n",
      "{'Pol': True, '.': True}\n",
      "{'And': True, 'as': True, 'to': True, 'my': True, 'father': True, ',': True, 'I': True, 'really': True, 'should': True, 'not': True, 'have': True, 'thought': True, 'that': True, 'he': True, 'who': True, 'has': True, 'kept': True, 'himself': True, 'single': True, 'so': True, 'long': True, 'for': True, 'our': True, 'sakes': True, 'need': True, 'be': True, 'suspected': True, 'now': True, '.': True}\n"
     ]
    }
   ],
   "source": [
    "def get_features(text): \n",
    "    features = {}\n",
    "    word_list = [word for word in text]\n",
    "    for word in word_list:\n",
    "        features[word] = True\n",
    "    return features\n",
    "\n",
    "train_features = [(get_features(sents), label) for (sents, label) in strat_train_set]\n",
    "pretest_features = [(get_features(sents), label) for (sents, label) in strat_pretest_set]\n",
    "\n",
    "print(len(train_features))\n",
    "print(train_features[0][0])\n",
    "print(train_features[100][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now train NLTK's Naive Bayes classifier on the training data and test it on the pretest data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size = 13414 sentences\n",
      "Pretest set size = 3354 sentences\n",
      "Accuracy on the training set = 0.9786789920978083\n",
      "Accuracy on the pretest set = 0.9636255217650567\n",
      "Most Informative Features\n",
      "                    been = True           austen : shakes =    257.7 : 1.0\n",
      "                    King = True           shakes : austen =    197.1 : 1.0\n",
      "                    thou = True           shakes : austen =    191.3 : 1.0\n",
      "                    Lord = True           shakes : austen =     61.2 : 1.0\n",
      "                    doth = True           shakes : austen =     60.4 : 1.0\n",
      "                       d = True           shakes : austen =     58.9 : 1.0\n",
      "                   quite = True           austen : shakes =     55.6 : 1.0\n",
      "                     Tis = True           shakes : austen =     51.6 : 1.0\n",
      "                     She = True           austen : shakes =     43.2 : 1.0\n",
      "                   think = True           austen : shakes =     39.9 : 1.0\n",
      "                    back = True           austen : shakes =     34.4 : 1.0\n",
      "                     has = True           austen : shakes =     34.2 : 1.0\n",
      "                  father = True           austen : shakes =     32.3 : 1.0\n",
      "                  coming = True           austen : shakes =     29.5 : 1.0\n",
      "                  moment = True           austen : shakes =     29.1 : 1.0\n",
      "                 looking = True           austen : shakes =     28.6 : 1.0\n",
      "                       l = True           shakes : austen =     28.4 : 1.0\n",
      "                    mind = True           austen : shakes =     28.3 : 1.0\n",
      "                     far = True           austen : shakes =     26.1 : 1.0\n",
      "                   years = True           austen : shakes =     25.8 : 1.0\n",
      "                   known = True           austen : shakes =     25.5 : 1.0\n",
      "                  mother = True           austen : shakes =     25.5 : 1.0\n",
      "                     Nor = True           shakes : austen =     25.5 : 1.0\n",
      "                carriage = True           austen : shakes =     24.9 : 1.0\n",
      "                  hardly = True           austen : shakes =     24.9 : 1.0\n",
      "                   party = True           austen : shakes =     24.5 : 1.0\n",
      "                     ere = True           shakes : austen =     24.0 : 1.0\n",
      "                     few = True           austen : shakes =     23.7 : 1.0\n",
      "                 account = True           austen : shakes =     23.7 : 1.0\n",
      "                    poor = True           austen : shakes =     23.0 : 1.0\n",
      "                 feeling = True           austen : shakes =     22.8 : 1.0\n",
      "                     she = True           austen : shakes =     22.7 : 1.0\n",
      "                   among = True           austen : shakes =     22.1 : 1.0\n",
      "                 brother = True           austen : shakes =     21.8 : 1.0\n",
      "                  assure = True           austen : shakes =     21.2 : 1.0\n",
      "                 Brother = True           shakes : austen =     21.1 : 1.0\n",
      "                    seen = True           austen : shakes =     20.6 : 1.0\n",
      "              afterwards = True           austen : shakes =     19.7 : 1.0\n",
      "                 manners = True           austen : shakes =     19.7 : 1.0\n",
      "                    Mark = True           shakes : austen =     19.6 : 1.0\n",
      "                 whether = True           austen : shakes =     19.1 : 1.0\n",
      "                    care = True           austen : shakes =     18.5 : 1.0\n",
      "                    mean = True           austen : shakes =     18.5 : 1.0\n",
      "                       3 = True           shakes : austen =     18.2 : 1.0\n",
      "                       4 = True           shakes : austen =     18.2 : 1.0\n",
      "                 Letters = True           shakes : austen =     18.2 : 1.0\n",
      "               beginning = True           austen : shakes =     17.6 : 1.0\n",
      "                 husband = True           austen : shakes =     17.6 : 1.0\n",
      "                 company = True           austen : shakes =     17.3 : 1.0\n",
      "                 imagine = True           austen : shakes =     17.3 : 1.0\n"
     ]
    }
   ],
   "source": [
    "from nltk import NaiveBayesClassifier, classify\n",
    "\n",
    "print (f\"Training set size = {str(len(train_features))} sentences\")\n",
    "print (f\"Pretest set size = {str(len(pretest_features))} sentences\")\n",
    "# train the classifier\n",
    "classifier = NaiveBayesClassifier.train(train_features)\n",
    "\n",
    "print (f\"Accuracy on the training set = {str(classify.accuracy(classifier, train_features))}\")\n",
    "print (f\"Accuracy on the pretest set = {str(classify.accuracy(classifier, pretest_features))}\")    \n",
    "# check which words are most informative for the classifier\n",
    "classifier.show_most_informative_features(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this performance to the performance on the new test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set size = 6906 sentences\n",
      "Accuracy on the test set = 0.895742832319722\n"
     ]
    }
   ],
   "source": [
    "test_features = [(get_features(sents), label) for (sents, label) in test_set]\n",
    "print (f\"Test set size = {str(len(test_features))} sentences\")\n",
    "print (f\"Accuracy on the test set = {str(classify.accuracy(classifier, test_features))}\")    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the accuracy across the three datasets with bar charts using `matplotlib`. This will help you better understand the differences in accuracy scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Y+CXftF+3br706vqGuACBuu684/V3vF54I+7JbPHn1Xz7CRTDH5QfiVwEXB8t/8+85h4Zrg4tjJYgtkOfBP48tKOo7mqajbJnwJbkjy12/wO4D8YvJT92iQ7u3X3Yfv9GLg6yQEMziTf0l33j8AHux/MvsJ198mrqluT/CXw+SRPAR4B3sjgO99Lu2dDFfD27pAPMVg+e5jBd9LNrrv7VEhJapDLMpLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUoP8Bf3Wl1oGCN38AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.legend(*args, **kwargs)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "a = [\"Train\", \"Pretest\", \"Test\"]\n",
    "index = range(len(a))\n",
    "b = [97.87, 96.36, 89.57] # Accuracy scores for the datasets\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "axes = plt.gca()\n",
    "# Let's set 68 as the lower bound as the majority class baseline is at 68.58 for the original set\n",
    "axes.set_ylim([68,100])\n",
    "\n",
    "ax.bar(index, b, color=['#0A40A4', '#61A4F6', '#DB025B'])\n",
    "plt.xticks(index, a) \n",
    "\n",
    "plt.show()\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.axes.Axes.plot\n",
    "matplotlib.pyplot.plot\n",
    "matplotlib.axes.Axes.legend\n",
    "matplotlib.pyplot.legend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply a different classifier – `DecisionTreeClassifier` – to this task. This classifier will struggle with this high number of features (over 13K words), so let's try to narrow the number of features down. A useful heuristics is to take into account the words that are neither too frequent (e.g., occur in all or most texts) nor too rare (because they will make for very sparse and therefore not very useful features). Let's first estimate how often each word occurs across all texts, i.e. estimate their *document frequencies*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13553\n",
      "Counter({'.': 9108, ',': 7126, 'to': 4382, 'the': 4119, 'and': 3996, 'of': 3823, 'a': 3078, 'I': 2967, 'in': 2473, 'not': 2450, ';': 2411, 'was': 2317, 'it': 2269, 'be': 2149, 'that': 1949, '\"': 1932, 'you': 1884, 'her': 1877, \"'\": 1702, 'had': 1595, 'for': 1582, 'she': 1542, 'with': 1489, 'is': 1480, 'but': 1439, 'as': 1381, 'he': 1372, 'have': 1316, 'his': 1241, '.\"': 1238, 'at': 1178, 'very': 1150, 'all': 1106, 's': 1097, 'him': 1071, 'so': 1028, 'Mr': 1009, 'my': 990, 'could': 957, 'on': 904, 'would': 886, '--': 863, 'me': 856, '?': 856, ':': 853, 'been': 844, 'by': 800, 'were': 791, 'no': 785, 'this': 742, 'which': 725, 'Mrs': 716, 'She': 713, 'do': 709, 'will': 706, '-': 700, '!': 687, 'from': 677, 'must': 672, 'any': 666, 'Emma': 657, 'more': 616, 'The': 613, 'or': 610, 'them': 607, 'He': 598, 'what': 590, 'an': 590, 'are': 586, 'they': 577, 'And': 563, 'much': 561, 'there': 551, 'your': 535, 'It': 531, 'said': 530, 'one': 517, 'than': 514, ',\"': 513, 'Miss': 506, 'such': 506, 'am': 493, 'good': 484, 'should': 481, 'know': 470, 'did': 470, 'if': 467, 'But': 451, 'when': 442, 'well': 441, 'being': 435, 'their': 427, 'only': 423, 'now': 422, 'little': 416, '.--': 403, 'might': 397, 'You': 396, 'think': 395, 'Anne': 382, 'thing': 380, 'we': 379, 'who': 370, 'say': 368, 'never': 367, 'every': 363, 'time': 362, 'Harriet': 359, 'shall': 352, 'most': 352, 'man': 345, 'herself': 344, 'some': 341, 'too': 336, 'great': 331, 'can': 331, 'own': 328, 'Weston': 324, 'see': 319, 'Knightley': 312, 'nothing': 310, 'other': 309, 'quite': 305, 'before': 303, 'like': 300, 'may': 294, 'd': 293, 'out': 293, 'how': 291, 'Elton': 290, 'then': 288, 'about': 281, 'again': 279, 'first': 279, 'come': 272, 'soon': 269, 'Ham': 267, 'always': 265, 'made': 265, 'has': 264, 'thought': 262, '?\"': 257, 'Oh': 255, 'father': 249, 'What': 248, 'without': 244, 'day': 241, 'They': 238, 'though': 237, 'two': 234, 'make': 233, 'Woodhouse': 232, 'into': 230, 'sure': 228, '!--': 226, 'Jane': 225, 'go': 225, 'A': 224, 'Captain': 220, 'That': 220, 'young': 218, 'Lord': 217, 'better': 215, 'Elliot': 212, 'ever': 211, 'up': 211, 'friend': 211, 'done': 211, 'away': 209, 'haue': 208, 'dear': 208, 'indeed': 206, 'our': 205, 'long': 201, 'There': 199, 'To': 198, 'If': 198, 'way': 196, 'No': 195, 'last': 194, 'just': 192, 'give': 192, '(': 192, 'Fairfax': 186, 'himself': 186, 'My': 186, 'This': 186, 'many': 185, 'having': 184, 'seemed': 184, 'after': 183, 'home': 180, 'over': 180, '!\"': 175, 'rather': 174, 'yet': 174, 'body': 173, 'Wentworth': 170, 'take': 166, 'How': 166, 'enough': 165, 'wish': 164, 'Churchill': 164, 'cannot': 162, 'hope': 161, 'Lady': 160, 'moment': 160, 'off': 160, 'Sir': 159, 'heard': 158, 'felt': 157, 'mind': 156, 'really': 156, 'happy': 154, 'upon': 154, 'while': 154, 'Frank': 154, 'same': 151, 'going': 151, 'another': 151, 'woman': 151, 'here': 150, 'still': 149, 'even': 149, 'found': 148, 'however': 147, 'came': 147, 'us': 145, 'look': 145, 'room': 145, 'We': 144, 'once': 144, 'house': 144, 'let': 143, 'does': 142, 'tell': 142, 'morning': 141, 'In': 141, 'feelings': 139, 'those': 139, 'King': 136, 'party': 135, 'life': 135, 'knew': 134, 'its': 134, 'half': 134, 'place': 133, 'saw': 133, 'thou': 132, 'Hartfield': 131, 'few': 131, 'till': 131, '?--': 131, 'For': 130, 'something': 130, 'poor': 127, 'present': 126, 'As': 126, 'word': 126, 'Caesar': 124, 'sort': 123, 'where': 123, 'old': 123, 'Charles': 122, 'right': 121, 'almost': 121, 'Brutus': 121, 'both': 121, 'believe': 120, 'Smith': 120, 'Bru': 119, 'Russell': 119, 'best': 119, 'these': 118, 'part': 118, 'certainly': 118, 'evening': 117, 'Bates': 117, 'pleasure': 116, 'So': 116, 'world': 116, 'gone': 116, 'family': 115, 'love': 115, 'heart': 115, 'ought': 115, 'people': 115, 'seen': 114, 'Enter': 114, 'feel': 114, ')': 113, 'back': 113, 'When': 112, 'Her': 112, 'idea': 111, 'speak': 111, 'doubt': 108, 'together': 108, 'acquaintance': 108, 'deal': 107, 'looked': 107, 'Walter': 106, 'perhaps': 106, ';--': 106, 'whole': 106, 'cried': 105, 'myself': 105, 'else': 105, 'Highbury': 104, 'left': 104, 'possible': 104, 'least': 103, 'hear': 103, 'Well': 103, 'men': 102, 'put': 102, 'between': 102, 'set': 101, 'hour': 100, 'subject': 100, 'Yes': 100, 'down': 100, 'manner': 100, 'Mary': 99, 'often': 99, 'Musgrove': 99, 'friends': 98, 'answer': 97, 'visit': 97, 'coming': 97, 'hand': 97, 'get': 96, 'true': 96, 'name': 95, 'Why': 95, 'nor': 95, 'sister': 95, 'night': 95, 'looking': 94, 'others': 94, 'letter': 94, 'eyes': 94, 'short': 93, 'Bath': 92, 'vs': 92, 'want': 92, 'side': 91, 'ill': 91, 'Louisa': 91, 'three': 90, 'told': 90, 'since': 90, 'wife': 90, 'selfe': 88, 't': 87, 'His': 87, 'spirits': 87, 'suppose': 86, 'perfectly': 86, 'far': 86, 'Do': 86, 'opinion': 85, 'change': 85, 'Let': 85, 'years': 85, 'things': 84, 'O': 84, 'known': 84, 'general': 84, 'mother': 84, 'whom': 84, '`': 84, 'therefore': 83, 'under': 83, 'given': 83, 'against': 83, 'happiness': 83, 'lady': 82, 'able': 82, 'replied': 82, 'carriage': 82, 'next': 82, 'obliged': 82, 'hardly': 82, 'immediately': 81, 'wanted': 81, 'Cassi': 80, 'walk': 80, 'find': 80, 'thee': 80, 'talked': 79, 'daughter': 79, 'head': 79, 'bad': 79, 'passed': 79, 'towards': 79, 'mine': 78, '--\"': 78, 'Hor': 78, 'thinking': 78, 'account': 78, 'John': 77, 'ready': 77, 'words': 77, 'point': 77, 'state': 77, 'thy': 76, 'glad': 76, 'understand': 76, 'brought': 76, 'each': 76, 'Elizabeth': 76, 'person': 76, 'either': 75, 'course': 75, 'talk': 75, 'feeling': 75, 'interest': 75, 'kind': 74, 'among': 73, 'began': 73, 'comfort': 73, 'near': 73, 'through': 73, 'end': 73, 'taken': 72, 'brother': 72, 'Now': 71, 'Hamlet': 71, 'children': 71, 'longer': 71, 'rest': 71, 'situation': 70, 'With': 70, 'leave': 70, 'stay': 70, 'assure': 70, 'less': 70, 'matter': 70, 'marry': 70, 'full': 69, 'attention': 69, 'walked': 69, 'satisfied': 69, 'Then': 69, 'agreeable': 69, 'pretty': 68, 'Cassius': 68, 'making': 68, 'went': 67, 'Randalls': 67, 'call': 66, 'Perry': 66, 'seeing': 66, 'cold': 66, 'character': 66, 'equal': 66, 'gave': 65, 'afterwards': 65, 'themselves': 65, 'beyond': 65, 'manners': 65, 'extremely': 64, 'sir': 64, 'return': 64, 'because': 64, 'Martin': 64, 'whether': 63, 'afraid': 63, 'whose': 63, 'morrow': 63, 'within': 62, 'attachment': 62, 'fine': 62, 'business': 62, 'care': 61, 'sorry': 61, 'married': 61, 'mean': 61, 'wonder': 60, 'question': 60, 'spoke': 60, 'wished': 60, 'minutes': 59, 'saying': 59, 'Antony': 59, 'read': 59, 'dare': 59, 'heere': 59, 'death': 59, 'vpon': 59, 'talking': 59, 'husband': 58, 'hath': 58, 'speake': 58, 'nobody': 58, 'conversation': 58, 'door': 58, 'days': 58, 'Ile': 58, 'nature': 58, 'natural': 58, 'Kellynch': 58, 'face': 58, 'beginning': 58, 'called': 57, 'strong': 57, 'Such': 57, 'imagine': 57, 'degree': 57, 'Henrietta': 57, 'company': 57, 'usual': 57, 'loue': 57, 'dinner': 56, 'speaking': 56, 'doing': 56, 'power': 56, 'ladies': 56, 'use': 56, 'used': 56, 'Benwick': 56, 'sense': 56, 'particularly': 56, 'likely': 56, 'child': 56, 'eye': 56, 'appeared': 56, 'Not': 56, 'Uppercross': 55, 'regard': 55, 'remember': 55, 'comes': 55, 'walking': 55, 'received': 55, 'exactly': 55, 'thoughts': 55, 'meet': 55, 'Is': 55, 'object': 55, 'Your': 55, 'met': 55, 'pleased': 55, 'superior': 55, 'voice': 54, 'new': 54, 'consequence': 54, 'Where': 54, 'Come': 54, 'means': 54, 'Harville': 54, 'determined': 54, 'directly': 53, 'vp': 53, 'early': 53, 'bring': 53, 'farther': 53, 'Isabella': 52, 'reason': 52, 'shew': 52, 'taste': 52, 'bear': 52, 'help': 51, 'sit': 51, 'proper': 51, 'different': 51, 'took': 51, 'Very': 51, 'already': 51, 'Of': 51, 'returned': 51, 'yourself': 51, 'sight': 51, 'thus': 51, 'Will': 51, 'open': 51, 'need': 50, 'neither': 50, 'hoped': 50, 'common': 50, 'Good': 50, 'respect': 50, 'Qu': 50, 'wrong': 50, 'Lyme': 50, 'asked': 49, 'giving': 49, 'forward': 49, 'meant': 49, 'th': 49, 'got': 49, 'Goddard': 49, 'At': 49, 'worse': 49, 'real': 49, 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'discerning': 2, 'unfelt': 2, 'sour': 2, 'contract': 2, 'pregnant': 2, 'asleep': 2, 'Schoole': 2, 'runne': 2, 'Hilts': 2, 'holiday': 2, 'masters': 2, 'culture': 2, 'earn': 2, 'forgets': 2, 'Cobler': 2, 'desiring': 2, 'unconvinced': 2, 'final': 2, 'satin': 2, 'overthrow': 2, 'portraits': 2, 'perfections': 2, 'driuen': 2, 'desperate': 2, 'mount': 2, 'Cheeke': 2, 'yon': 2, 'Walkes': 2, 'Morne': 2, 'aduice': 2, 'dew': 2, 'parishes': 2, 'indescribable': 2, 'incomprehensible': 2, 'Conditions': 2, 'guest': 2, 'Stirre': 2, 'Iigge': 2, 'Prythee': 2, 'tale': 2, 'strangest': 2, 'brighter': 2, 'shy': 2, 'impediment': 2, 'amuses': 2, 'knee': 2, 'torment': 2, 'polished': 2, 'ceases': 2, 'playfulness': 2, 'definition': 2, 'Verie': 2, 'orchestra': 2, 'clerks': 2, 'Actor': 2, 'stays': 2, 'nicely': 2, 'seeking': 2, 'assistant': 2, 'abode': 2, 'fearfully': 2, 'contriving': 2, 'remoue': 2, 'Author': 2, 'phoo': 2, 'intellectual': 2, 'sign': 2, 'greeue': 2, 'tardie': 2, 'Knocke': 2, 'droppes': 2, 'Clifton': 2, 'closing': 2, 'host': 2, 'Rewards': 2, 'equall': 2, 'Chasticement': 2, 'obedience': 2, 'conjugal': 2, 'states': 2, 'bounded': 2, 'fainted': 2, 'reasoned': 2, 'advised': 2, 'sworne': 2, 'wittier': 2, 'beast': 2, 'urged': 2, 'forwards': 2, 'flew': 2, 'disagreement': 2, 'affording': 2, 'stept': 2, 'Seruice': 2, 'purchased': 2, 'fatal': 2, 'cliffs': 2, 'tide': 2, 'romantic': 2, 'Charmouth': 2, 'expedition': 2, 'Tents': 2, 'Maiden': 2, 'Courtiers': 2, 'glasse': 2, 'Schollers': 2, 'obseru': 2, 'incumbent': 2, 'seruants': 2, 'sly': 2, 'Rest': 2, 'curtailed': 2, 'expediency': 2, 'grandson': 2, 'regulations': 2, 'reductions': 2, 'disapprobation': 2, 'pestilent': 2, 'Canopy': 2, 'heauenly': 2, 'honourably': 2, 'Tunbridge': 2, 'dated': 2, 'July': 2, 'swore': 2, 'streetes': 2, 'Vnckle': 2, 'liued': 2, 'writer': 2, 'medium': 2, 'interfering': 2, 'somthing': 2, 'accuse': 2, 'lingering': 2, 'softening': 2, 'Least': 2, 'pitteous': 2, 'owe': 2, 'Drown': 2, 'courteous': 2, 'vigour': 2, 'secondly': 2, 'cuts': 2, 'obscure': 2, 'undue': 2, 'results': 2, 'reasonably': 2, 'Pin': 2, 'lodged': 2, 'Pass': 2, 'likenesses': 2, 'Send': 2, 'Hetty': 2, 'prized': 2, 'singular': 2, 'weighing': 2, 'speedy': 2, 'Brigden': 2, 'indifferently': 2, 'examine': 2, 'audible': 2, 'vice': 2, 'Note': 2, 'weary': 2, 'booke': 2, 'fetched': 2, 'Wherefore': 2, 'brauery': 2, 'saile': 2, 'Precepts': 2, 'aboord': 2, 'Character': 2, 'Mountaines': 2, 'vilde': 2, 'Skill': 2, 'praying': 2, 'conjectures': 2, 'foolishly': 2, 'abused': 2, 'wilful': 2, 'follies': 2, 'subduing': 2, 'remind': 2, 'sobering': 2, 'sharing': 2, 'contrition': 2, 'flight': 2, 'Rascall': 2, 'muddy': 2, 'damn': 2, 'Haile': 2, 'train': 2, 'expressly': 2, 'Batchellor': 2, 'glowed': 2, 'staring': 2, 'extended': 2, 'Naturall': 2, 'Iustice': 2, 'bleede': 2, 'dyest': 2, 'hero': 2, 'runs': 2, 'Youth': 2, 'pew': 2, 'favouring': 2, 'fierie': 2, 'deerely': 2, 'K': 2, 'witty': 2, 'Creatures': 2, 'Scoene': 2, 'proclaim': 2, 'mischievous': 2, 'construction': 2, 'nut': 2, 'storms': 2, 'bell': 2, 'vtter': 2, 'travelled': 2, 'affayres': 2, 'cheering': 2, 'cooling': 2, 'enforced': 2, 'medical': 2, 'OF': 2, 'wag': 2, 'sanction': 2, 'Birmingham': 2, 'Ape': 2, 'presenting': 2, 'observances': 2, 'clothed': 2, 'egg': 2, 'dogs': 2, 'unanswered': 2, 'sympathetic': 2, 'Accordingly': 2, 'perverse': 2, 'infection': 2, 'equalled': 2, 'apartments': 2, 'unprepared': 2, 'missing': 2, 'chatted': 2, 'feebleness': 2, 'shabby': 2, 'detailed': 2, 'commanding': 2, 'select': 2, 'distrusted': 2, 'repressed': 2, 'boat': 2, 'nonsensical': 2, 'foretell': 2, 'umbrella': 2, 'dishonour': 2, 'imply': 2, 'feete': 2, 'Volt': 2, 'Recorder': 2, 'nodded': 2, 'closely': 2, 'Drachmaes': 2, 'seuenty': 2, 'fiue': 2, 'Conspirator': 2, 'perceiving': 2, 'solemnity': 2, 'Gowne': 2, 'brilliancy': 2, 'keen': 2, 'Controuersie': 2, 'lists': 2, 'stepped': 2, 'tryall': 2, 'blows': 2, 'rambling': 2, 'protected': 2, 'hesitations': 2, 'vision': 2, 'sucke': 2, 'renting': 2, 'unpolished': 2, 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'receives': 2, 'clocke': 2, 'abide': 2, 'playfully': 2, 'prickt': 2, 'Alicia': 2, 'attribute': 2, 'signifie': 2, 'library': 2, 'Element': 2, 'terribly': 2, 'labours': 2, 'boast': 2, 'lodging': 2, 'sweetbread': 2, 'asparagus': 2, 'glory': 2, 'aspect': 2, 'Hitherto': 2, 'heaven': 2, 'Gowland': 2, 'misinterpreted': 2, 'candlelight': 2, 'sheets': 2, 'Soft': 2, 'unexpectedly': 2, 'plainer': 2, 'saucy': 2, 'presiding': 2, 'eats': 2, 'militia': 2, 'courses': 2, 'dish': 2, 'Mistris': 2, 'repulsed': 2, 'Messengers': 2, 'advance': 2, 'Aunt': 2, 'Uncle': 2, 'pensive': 2, 'Bastard': 2, 'suppresse': 2, 'Nephewes': 2, 'prejudice': 2, 'deliberately': 2, 'Whale': 2, 'meals': 2, 'Seeing': 2, 'bursts': 2, 'oppressed': 2, 'humanity': 2, 'cough': 2, 'Gifts': 2, 'Poysoner': 2, 'awhile': 2, 'loath': 2, 'surmised': 2, 'rugged': 2, 'peeuish': 2, 'sence': 2, 'discussed': 2, 'deciding': 2, 'ashore': 2, 'Argall': 2, 'Tyrants': 2, 'Countries': 2, 'Foe': 2, 'revive': 2, 'Obedience': 2, 'eighteen': 2, 'instruction': 2, 'Point': 2, 'sinke': 2, 'gipsies': 2, 'confirmation': 2, 'wildenesse': 2, 'wonted': 2, 'dancer': 2, 'pearls': 2, 'blew': 2, 'Mountaine': 2, 'combined': 2, 'eternall': 2, 'Tale': 2, 'secrets': 2, 'underrated': 2, 'vndertake': 2, 'audibly': 2, 'bedroom': 2, 'pressure': 2, 'poetry': 2, 'ioynt': 2, 'indisposition': 2, 'ioyes': 2, 'chanc': 2, 'dies': 2, 'Plebeians': 2, 'development': 2, 'kisse': 2, 'Roome': 2, 'etc': 2, '_Now_': 2, 'dishes': 2, 'ribbon': 2, 'partially': 2, 'converse': 2, 'shed': 2, 'odde': 2, 'Foyles': 2, 'effusion': 2, 'Voltemand': 2, 'prouidence': 2, 'simply': 2, 'nowhere': 2, 'sheep': 2, 'browes': 2, 'bids': 2, 'Saue': 2, 'virtue': 2, 'insolence': 2, 'atonement': 2, 'screen': 2, 'negligent': 2, 'oppression': 2, 'significant': 2, 'dialogue': 2, 'adoration': 2, 'debate': 2, 'cal': 2, 'hardy': 2, 'Clownes': 2, 'bearers': 2, 'profusion': 2, 'vnbraced': 2, 'drank': 2, 'recount': 2, 'iealous': 2, 'plots': 2, 'indiscretion': 2, 'Camell': 2, 'gossip': 2, 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'poetical': 1, 'inexhaustible': 1, 'swathing': 1, 'hearer': 1, 'clouts': 1, 'camp': 1, 'pluckes': 1, 'Contraction': 1, 'rapsidie': 1, 'Religion': 1, 'ouercame': 1, 'Conquerors': 1, 'Relatiue': 1, 'Leaps': 1, 'unwillingly': 1, 'Dancing': 1, 'tastes': 1, 'couples': 1, 'exceptions': 1, 'countenances': 1, 'springing': 1, 'beaming': 1, 'fortified': 1, 'Nights': 1, 'Sit': 1, 'assaile': 1, 'Gent': 1, 'Core': 1, 'Passions': 1, 'ruins': 1, 'Vs': 1, 'Contaminate': 1, 'Robbers': 1, 'Formost': 1, 'infuse': 1, 'Masterly': 1, 'especiall': 1, 'possessor': 1, 'rendred': 1, 'streete': 1, 'dullest': 1, 'literature': 1, 'affable': 1, 'worthies': 1, 'Sailors': 1, 'vessel': 1, 'batten': 1, 'Moore': 1, 'performances': 1, 'smirked': 1, 'aimable': 1, 'Tweakes': 1, 'Nose': 1, 'Firebrands': 1, 'Brands': 1, 'Penelope': 1, 'Stands': 1, 'hides': 1, 'wrongs': 1, 'deafer': 1, 'consistently': 1, 'undesignedly': 1, '_told_': 1, 'Hie': 1, 'Post': 1, 'Ministring': 1, 'howling': 1, 'churlish': 1, 'strictest': 1, 'injunctions': 1, 'Sanitie': 1, 'prosperously': 1, 'happinesse': 1, 'Langham': 1, 'successively': 1, 'Instances': 1, 'accrue': 1, 'twisted': 1, 'thoughtfully': 1, 'surprizes': 1, 'faints': 1, 'sage': 1, 'prophane': 1, 'Requiem': 1, 'swound': 1, 'Abdy': 1, 'learne': 1, 'affraid': 1, 'Plum': 1, 'wrinkled': 1, 'Slanders': 1, 'Satyricall': 1, 'Hammes': 1, 'gray': 1, 'plentifull': 1, 'Beards': 1, 'Amber': 1, 'Gumme': 1, 'hundreds': 1, 'Understanding': 1, 'parson': 1, 'bespoke': 1, 'Sixteen': 1, ',\"--\"': 1, 'quaintly': 1, 'assault': 1, 'sauagenes': 1, 'taints': 1, 'Incontinencie': 1, 'vnreclaim': 1, 'Iemme': 1, 'Brooch': 1, 'moode': 1, 'pittied': 1, 'importunate': 1, 'clean': 1, 'Anything': 1, 'defied': 1, 'dashing': 1, '_introduction_': 1, 'auspices': 1, 'reputed': 1, 'snatched': 1, 'killing': 1, 'unselfish': 1, 'smoothed': 1, 'awkwardnesses': 1, 'reproached': 1, 'Citizens': 1, 'Writings': 1, 'tending': 1, 'obscurely': 1, 'wherein': 1, 'Hands': 1, 'teasing': 1, 'reserving': 1, 'Full': 1, 'untowardly': 1, 'bookish': 1, 'uninterrupted': 1, 'gratifications': 1, 'chairs': 1, 'speculate': 1, 'winters': 1, 'Wall': 1, 'expell': 1, 'flaw': 1, 'patch': 1, 'Exeter': 1, 'Orator': 1, 'Vtterance': 1, 'notch': 1, 'secondary': 1, 'incongruity': 1, 'raving': 1, 'landed': 1, 'complaisant': 1, 'inconsiderable': 1, 'patronage': 1, 'smallness': 1, 'dividing': 1, 'kinds': 1, 'mode': 1, 'entitling': 1, 'leuell': 1, 'guiltlesse': 1, 'trimming': 1, 'complimented': 1, 'hopelessness': 1, 'borders': 1, 'pick': 1, 'protestations': 1, 'Apologies': 1, 'effusions': 1, 'heavens': 1, 'Desires': 1, 'Greetings': 1, 'sympathise': 1, 'betraying': 1, 'inadequate': 1, 'extinction': 1, 'routine': 1, 'Tanner': 1, 'fluently': 1, 'slavery': 1, 'craues': 1, 'Sting': 1, 'Adder': 1, 'warie': 1, 'mysteriously': 1, 'conceald': 1, 'treble': 1, 'hap': 1, 'requite': 1, 'eleuen': 1, 'Platforme': 1, 'fam': 1, 'Tricks': 1, 'Tenures': 1, 'scarlet': 1, 'Cobham': 1, 'kite': 1, 'Bella': 1, 'sighes': 1, 'transplanted': 1, 'splendour': 1, 'enuy': 1, 'revolting': 1, 'manoeuvres': 1, 'duplicity': 1, 'lecture': 1, 'widowhood': 1, 'Confesse': 1, 'Repent': 1, 'Weedes': 1, 'Compost': 1, 'Willes': 1, 'Legacie': 1, 'Sacred': 1, 'Memory': 1, 'Napkins': 1, 'dip': 1, 'Bequeathing': 1, 'unforgiving': 1, 'receipt': 1, 'Paradox': 1, 'dissipation': 1, 'tease': 1, 'concession': 1, 'joins': 1, 'fork': 1, 'tricks': 1, 'search': 1, 'waterflie': 1, 'Showt': 1, 'Vol': 1, 'Exceedingly': 1, 'soultry': 1, 'blowing': 1, 'bleak': 1, 'blown': 1, 'insinuations': 1, 'discriminating': 1, 'coast': 1, 'Former': 1, 'provocations': 1, 'blockhead': 1, 'palme': 1, 'vnhatch': 1, 'Comrade': 1, 'vnproportion': 1, 'hoopes': 1, 'vnfledg': 1, 'swelled': 1, 'nought': 1, 'laine': 1, 'Scul': 1, 'Heres': 1, 'Whats': 1, 'lawfull': 1, 'appease': 1, 'Gentlewoman': 1, 'mixt': 1, 'Elements': 1, 'olive': 1, 'Mender': 1, 'Extremely': 1, 'inadvertence': 1, 'wantonly': 1, 'captiously': 1, 'catastrophe': 1, 'judgements': 1, 'canvassed': 1, 'concussion': 1, 'vnluckily': 1, 'feast': 1, 'declension': 1, 'waile': 1, 'raues': 1, 'whereon': 1, 'Lightnesse': 1, 'Thence': 1, 'Sadnesse': 1, 'Saile': 1, 'compelled': 1, 'Finding': 1, 'Court': 1, 'Atlantic': 1, 'Cork': 1, 'Neptune': 1, 'executor': 1, 'anguish': 1, 'heaped': 1, 'corresponding': 1, 'Monument': 1, 'Strengthen': 1, 'disguised': 1, 'Seldom': 1, 'impute': 1, '_letting_': 1, 'Wil': 1, 'Sundaies': 1, 'Herbe': 1, 'handle': 1, 'Wickedness': 1, 'coile': 1, 'shuffel': 1, 'packs': 1, 'unbroken': 1, 'refreshments': 1, 'waiters': 1, 'hangers': 1, 'Leagues': 1, 'indiscreet': 1, 'Beene': 1, 'encountred': 1, 'discuss': 1, 'prevalence': 1, 'beckoning': 1, 'deepely': 1, 'widely': 1, 'victims': 1, 'plaid': 1, 'Vniuersity': 1, 'feeles': 1, 'avert': 1, 'shortness': 1, 'profess': 1, 'objecting': 1, ',--`': 1, 'temporary': 1, 'gloom': 1, 'Wed': 1, 'hadst': 1, 'Quoth': 1, 'tumbled': 1, 'Ought': 1, 'Leade': 1, 'cameos': 1, 'engravings': 1, 'medals': 1, 'corals': 1, 'shells': 1, 'Books': 1, 'drawers': 1, 'cabinets': 1, 'Comerce': 1, 'Ibbotsons': 1, 'Pella': 1, 'Sardians': 1, 'bushel': 1})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "words = []\n",
    "\n",
    "def extract_words(text, words):\n",
    "    words += set([word for word in text])\n",
    "    return words\n",
    "\n",
    "for (sents, label) in strat_train_set:\n",
    "    words = extract_words(sents, words)\n",
    "\n",
    "#print(len(set(words))) # =13553\n",
    "counts = Counter(words)\n",
    "print(len(set(counts))) # =13553\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize how many words occur in more than a certain percentage of documents. To convert raw document counts into percentages of the total, you need to divide the number of documents in which a word occurs (in `counts`) by the total number of documents in `strat_train_set`. This total equals $13414$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0%-0.0125% texts: 13355 words\n",
      "0.0125%-0.025% texts: 84 words\n",
      "0.025%-0.0375% texts: 33 words\n",
      "0.0375%-0.05% texts: 22 words\n",
      "0.05%-0.1% texts: 32 words\n",
      "0.1%-0.15% texts: 13 words\n",
      "0.15%-0.2% texts: 6 words\n",
      "0.2%-0.25% texts: 2 words\n",
      "0.25%-0.3% texts: 2 words\n",
      "0.3%-0.35% texts: 2 words\n",
      "0.5%-0.55% texts: 1 words\n",
      "0.65%-0.70% texts: 1 words\n"
     ]
    }
   ],
   "source": [
    "from numpy import arange\n",
    "\n",
    "percentages = {}\n",
    "maximum = float(13414)\n",
    "\n",
    "# Let's explore the document frequency bands\n",
    "for item in counts.items():\n",
    "    perc = float(item[1])/maximum\n",
    "    for freq in arange(0.00, 0.05, 0.0125):\n",
    "        if perc>=freq and perc<=freq+0.0125:\n",
    "            freq_range = str(freq)[:6] + \"%-\" + str(freq+0.0125)[:6] + \"%\"\n",
    "            percentages[freq_range] = percentages.get(freq_range, 0) + 1\n",
    "    for freq in arange(0.05, 1.00, 0.05):\n",
    "        if perc>=freq and perc<=freq+0.05:\n",
    "            freq_range = str(freq)[:4] + \"%-\" + str(freq+0.05)[:4] + \"%\"\n",
    "            percentages[freq_range] = percentages.get(freq_range, 0) + 1\n",
    "\n",
    "# Print out these frequency bands\n",
    "for key in sorted(percentages.keys()):\n",
    "    print(key + \" texts: \" + str(percentages.get(key)) + \" words\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize word document frequencies. For that, let's first arrange the word frequencies in texts in descending order and assign each word a rank: for example, the most frequent word that occurs in most texts would get a rank of 1, the second most frequent word would get a rank of 2, and so on. Next, let's plot these ranks against the total number of occurrences of each word in documents: for example, the most frequent word (full stop, \".\") with rank 1 occurs in $9108$ texts, the word with rank 2 (comma, \",\") occurs in $7126$ in total, and so on. Let's also select a word, for instance, \"happy\" and get its rank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "# Based on https://www.digitalocean.com/community/tutorials/how-to-graph-word-frequency-using-matplotlib-with-python-3\n",
    "def visualize(word_doc_map, word):\n",
    "    sorted_map = (sorted(word_doc_map.items(), key=operator.itemgetter(1)))[::-1]\n",
    "    occurrences = []\n",
    "    ranks = []\n",
    "    word_rank = 0\n",
    "    word_frequency = 0\n",
    "\n",
    "    # Find the rank and overall document frequency of all words\n",
    "    rank = 1\n",
    "    for item in sorted_map:\n",
    "        if (item[0] == word):\n",
    "            word_rank = rank\n",
    "            word_frequency = item[1]\n",
    "        ranks.append(rank)\n",
    "        rank += 1\n",
    "        occurrences.append(item[1])\n",
    "\n",
    "    # Plot word frequences against their ranks\n",
    "    plt.title(\"Word document frequencies\")\n",
    "    plt.ylabel(\"Total number of document occurrences\")\n",
    "    plt.xlabel(\"Word ranks (rank of word \\\"\" + word + \"\\\" is \" + str(word_rank) + \")\")\n",
    "    # Logarithms help present the frequency/rank information concisely\n",
    "    plt.plot(ranks, occurrences)\n",
    "    plt.scatter(\n",
    "        [word_rank],\n",
    "        [word_frequency],\n",
    "        color=\"red\",\n",
    "        marker=\"x\",\n",
    "        s=100,\n",
    "        label=word\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "visualize(counts, \"happy\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Frequencies drop very fast, as this graph shows. You can apply logarithmic function to the absolute frequency values to smooth the curve and make the changes in frequencies clearer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "# Based on https://www.digitalocean.com/community/tutorials/how-to-graph-word-frequency-using-matplotlib-with-python-3\n",
    "def visualize(word_doc_map, word):\n",
    "    sorted_map = (sorted(word_doc_map.items(), key=operator.itemgetter(1)))[::-1]\n",
    "    occurrences = []\n",
    "    ranks = []\n",
    "    word_rank = 0\n",
    "    word_frequency = 0\n",
    "\n",
    "    # Find the rank and overall document frequency of all words\n",
    "    rank = 1\n",
    "    for item in sorted_map:\n",
    "        if (item[0] == word):\n",
    "            word_rank = rank\n",
    "            word_frequency = item[1]\n",
    "        ranks.append(rank)\n",
    "        rank += 1\n",
    "        occurrences.append(item[1])\n",
    "\n",
    "    # Plot word frequences against their ranks\n",
    "    plt.title(\"Word document frequencies (log)\")\n",
    "    plt.ylabel(\"Total number of document occurrences\")\n",
    "    plt.xlabel(\"Word ranks (rank of word \\\"\" + word + \"\\\" is \" + str(word_rank) + \")\")\n",
    "    # Logarithms help present the frequency/rank information concisely\n",
    "    plt.loglog(ranks, occurrences, basex=10)\n",
    "    plt.scatter(\n",
    "        [word_rank],\n",
    "        [word_frequency],\n",
    "        color=\"red\",\n",
    "        marker=\"x\",\n",
    "        s=100,\n",
    "        label=word\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "visualize(counts, \"happy\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These graphs exemplify *Zipf's law* – an empirical law formulated by George Kingsley Zipf, which states that the frequency of any word is inversely proportional to its rank in the frequency table. Originally, it states that the most frequent word will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, and so on. The particular proportion is a rough estimate and depends on the data (for instance, in this case, you are looking into document rather than total word frequencies, and the ratio between the first and the second ranks is not exactly 1/2), but what matters is that the rank-frequency distribution is an inverse relation. In plain terms, this means that a small amount of very frequent words will occur in most documents, and a much larger amount of words (so-called long tail of the distribution) will be seen very rarely.\n",
    "\n",
    "One of the problems with this distribution for clasifiers like Decision Trees is that such rare words add to the complexity of the algorithm but not help classification. Here is your first example of feature selection practices: let's filter out rare words and consider as features only the words that occur with in certain proportion or number of documents. The code below uses a minimum frequency threshold of $200$ documents for the word to be considered as a feature, and a maximum frequency threshold of $20\\%$ of the texts. Feel free to modify these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166\n",
      "Accuracy on the training set = 0.8099746533472492\n",
      "Accuracy on the pretest set = 0.7960644007155635\n",
      "Accuracy on the test set = 0.8066898349261512\n"
     ]
    }
   ],
   "source": [
    "from nltk import DecisionTreeClassifier\n",
    "\n",
    "maximum = float(13414)\n",
    "\n",
    "selected_words = []\n",
    "for item in counts.items():\n",
    "    count = float(item[1])\n",
    "    if count > 200 and count/maximum < 0.2:\n",
    "        selected_words.append(item[0])\n",
    "print(len(selected_words))\n",
    "\n",
    "def get_features(text, selected_words): \n",
    "    features = {}\n",
    "    word_list = [word for word in text]\n",
    "    for word in word_list:\n",
    "        if word in selected_words:\n",
    "            features[word] = True\n",
    "    return features\n",
    "\n",
    "train_features = [(get_features(sents, selected_words), label) for (sents, label) \n",
    "                  in strat_train_set]\n",
    "pretest_features = [(get_features(sents, selected_words), label) for (sents, label) \n",
    "                    in strat_pretest_set]\n",
    "test_features = [(get_features(sents, selected_words), label) for (sents, label) \n",
    "                 in test_set]\n",
    "\n",
    "\n",
    "classifier = DecisionTreeClassifier.train(train_features)\n",
    "\n",
    "print (f\"Accuracy on the training set = {str(classify.accuracy(classifier, train_features))}\")\n",
    "print (f\"Accuracy on the pretest set = {str(classify.accuracy(classifier, pretest_features))}\")    \n",
    "print (f\"Accuracy on the test set = {str(classify.accuracy(classifier, test_features))}\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's visualize these accuracy scores. Note that despite the results being overall lower than those you achieved with the benchmark model, they are consistent across all three datasets, which shows that the classifier generalizes well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.legend(*args, **kwargs)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [\"Train\", \"Pretest\", \"Test\"]\n",
    "index = range(len(a))\n",
    "b = [81.00, 79.64, 80.79] # Accuracy scores for the datasets\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "axes = plt.gca()\n",
    "# Let's set 68 as the lower bound as the majority class baseline is at 68.58 for the original set\n",
    "axes.set_ylim([68,100])\n",
    "\n",
    "ax.bar(index, b, color=['#0A40A4', '#61A4F6', '#DB025B'])\n",
    "plt.xticks(index, a) \n",
    "\n",
    "plt.show()\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.axes.Axes.plot\n",
    "matplotlib.pyplot.plot\n",
    "matplotlib.axes.Axes.legend\n",
    "matplotlib.pyplot.legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 6: Linguistic feature engineering for author profiling\n",
    "\n",
    "\n",
    "## Step 4: Run a different model with a different set of features \n",
    "\n",
    "**Feature types 1 and 2 – simple statistics over the length of words**: Take average number of words per sentence and average number of characters per words as features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5714285714285716\n",
      "7.0\n"
     ]
    }
   ],
   "source": [
    "#average word length in characters\n",
    "def avg_number_chars(text):\n",
    "    total_chars = 0.0\n",
    "    for word in text:\n",
    "        total_chars += len(word)\n",
    "    return float(total_chars)/float(len(text))\n",
    "\n",
    "#length in terms of words\n",
    "def number_words(text):\n",
    "    return float(len(text))\n",
    "\n",
    "print(avg_number_chars([\"Not\", \"so\", \"happy\", \",\", \"yet\", \"much\", \"happyer\"]))\n",
    "print(number_words([\"Not\", \"so\", \"happy\", \",\", \"yet\", \"much\", \"happyer\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Represent all data sets with their feature sets. You will need to initialize a feature set for each text and map it to the author. In addition, let's switch to numerical representation of author labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414 13414\n",
      "3354 3354\n",
      "6906 6906\n"
     ]
    }
   ],
   "source": [
    "def initialize_dataset(source):\n",
    "    all_features = []\n",
    "    targets = []\n",
    "    for (sent, label) in source:\n",
    "        feature_list=[]\n",
    "        feature_list.append(avg_number_chars(sent))\n",
    "        feature_list.append(number_words(sent))\n",
    "        all_features.append(feature_list)\n",
    "        if label==\"austen\": targets.append(0)\n",
    "        else: targets.append(1)\n",
    "    return all_features, targets\n",
    "\n",
    "train_data, train_targets = initialize_dataset(strat_train_set)\n",
    "pretest_data, pretest_targets = initialize_dataset(strat_pretest_set)\n",
    "test_data, test_targets = initialize_dataset(test_set)\n",
    "\n",
    "print (len(train_data), len(train_targets))\n",
    "print (len(pretest_data), len(pretest_targets))\n",
    "print (len(test_data), len(test_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now apply classification with the `sklearn`'s `Decision Trees` classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "text_clf = DecisionTreeClassifier(random_state=42)\n",
    "text_clf.fit(train_data, train_targets)  \n",
    "predicted = text_clf.predict(pretest_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run evaluation including the following metrics: *accuracy*, *confusion matrix*, *precision*, *recall* and *F1*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7975551580202743\n",
      "[[2133  167]\n",
      " [ 512  542]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.93      0.86      2300\n",
      "           1       0.76      0.51      0.61      1054\n",
      "\n",
      "    accuracy                           0.80      3354\n",
      "   macro avg       0.79      0.72      0.74      3354\n",
      "weighted avg       0.79      0.80      0.78      3354\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "\n",
    "def evaluate(predicted, targets):\n",
    "    print(np.mean(predicted == targets))\n",
    "    print(metrics.confusion_matrix(targets, predicted))\n",
    "    print(metrics.classification_report(targets, predicted))\n",
    "    \n",
    "evaluate(predicted, pretest_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8049522154648132\n",
      "[[4605  394]\n",
      " [ 953  954]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.92      0.87      4999\n",
      "           1       0.71      0.50      0.59      1907\n",
      "\n",
      "    accuracy                           0.80      6906\n",
      "   macro avg       0.77      0.71      0.73      6906\n",
      "weighted avg       0.80      0.80      0.79      6906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predicted = text_clf.predict(test_data)\n",
    "evaluate(predicted, test_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks like a much more generalizable set of features – the performance on both set is very close ($0.7973$ vs. $0.8050$). Besides, it contains only $2$ features as opposed to over $13K$ words! However, now the performance is much lower than with words. Let's try and improve it further. Let's visualize these results with `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adapted from: https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/barchart.html\n",
    "pretestAcc = (96.36, 79.72)\n",
    "testAcc = (89.57, 80.49)\n",
    "\n",
    "ind = np.arange(len(pretestAcc))  # the x locations for the groups\n",
    "width = 0.2  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, pretestAcc, width, label='Pretest', color='#61A4F6')\n",
    "rects2 = ax.bar(ind + width/2, testAcc, width, label='Test', color='#DB025B')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Accuracy scores')\n",
    "ax.set_ylim([68,100])\n",
    "ax.set_title('Scores by feature set and data set')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('Benchmark', 'F1-2'))\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature type 3 – count of stopwords**: Add `spaCy` functionality and see how only a handful of frequent words are distributed in texts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "from spacy.lang.en.stop_words import STOP_WORDS\n",
    "\n",
    "nlp = spacy.load('en_core_web_md')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a very general method that can be applied to any type of words\n",
    "def word_counts(text):\n",
    "    counts = {}\n",
    "    for word in text:\n",
    "        counts[word.lower()] = counts.get(word.lower(), 0) + 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's augment our feature set with the counts of stopwords only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414 13414\n",
      "3354 3354\n",
      "6906 6906\n"
     ]
    }
   ],
   "source": [
    "def initialize_dataset(source):\n",
    "    all_features = []\n",
    "    targets = []\n",
    "    for (sent, label) in source:\n",
    "        feature_list=[]\n",
    "        feature_list.append(avg_number_chars(sent))\n",
    "        feature_list.append(number_words(sent))\n",
    "        counts = word_counts(sent)\n",
    "        for word in STOP_WORDS:\n",
    "            if word in counts.keys():\n",
    "                feature_list.append(counts.get(word))\n",
    "            else:\n",
    "                feature_list.append(0)        \n",
    "        all_features.append(feature_list)\n",
    "        if label==\"austen\": targets.append(0)\n",
    "        else: targets.append(1)\n",
    "    return all_features, targets\n",
    "\n",
    "train_data, train_targets = initialize_dataset(strat_train_set)\n",
    "pretest_data, pretest_targets = initialize_dataset(strat_pretest_set)\n",
    "test_data, test_targets = initialize_dataset(test_set)\n",
    "\n",
    "print (len(train_data), len(train_targets))\n",
    "print (len(pretest_data), len(pretest_targets))\n",
    "print (len(test_data), len(test_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now train and test on both pretest and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8127608825283243\n",
      "[[1967  333]\n",
      " [ 295  759]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.86      0.86      2300\n",
      "           1       0.70      0.72      0.71      1054\n",
      "\n",
      "    accuracy                           0.81      3354\n",
      "   macro avg       0.78      0.79      0.78      3354\n",
      "weighted avg       0.81      0.81      0.81      3354\n",
      "\n",
      "0.8087170576310455\n",
      "[[4225  774]\n",
      " [ 547 1360]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.85      0.86      4999\n",
      "           1       0.64      0.71      0.67      1907\n",
      "\n",
      "    accuracy                           0.81      6906\n",
      "   macro avg       0.76      0.78      0.77      6906\n",
      "weighted avg       0.82      0.81      0.81      6906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "text_clf = DecisionTreeClassifier(random_state=42)\n",
    "text_clf.fit(train_data, train_targets)  \n",
    "predicted = text_clf.predict(pretest_data)\n",
    "evaluate(predicted, pretest_targets)\n",
    "\n",
    "predicted = text_clf.predict(test_data)\n",
    "evaluate(predicted, test_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a slight improvement in accuracy of about $0.005$ (or $0.5\\%$), up to $0.812$-$0.815$ and $0.806$-$0.81$. However, what is most interesting about this feature is that there is now a more considerable improvement in performance metrics on the minority class (*shakespeare*): on the pretest set recall rises from $0.51$ to $0.72$ and F1 from $0.61$ to $0.71$ – a whole of $10$ points; on the test set the improvement in recall is from $0.50$ to $0.71$ and in F1 from $0.59$ to $0.67$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretestAcc = (96.36, 79.72, 81.18) # use the last accuracy score as the last value\n",
    "testAcc = (89.57, 80.49, 80.96) # use the last accuracy score as the last value\n",
    "\n",
    "ind = np.arange(len(pretestAcc))  # the x locations for the groups\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, pretestAcc, width, label='Pretest', color='#61A4F6')\n",
    "rects2 = ax.bar(ind + width/2, testAcc, width, label='Test', color='#DB025B')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Accuracy scores')\n",
    "ax.set_ylim([68,100])\n",
    "ax.set_title('Scores by feature set and data set')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('Benchmark', 'F1-2', 'F1-3'))\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature type 4 – proportion of stopwords**: Estimate what proportion of words in sentence are stopwords:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414 13414\n",
      "3354 3354\n",
      "6906 6906\n"
     ]
    }
   ],
   "source": [
    "def proportion_words(text, wordlist):\n",
    "    count = 0\n",
    "    for word in text:\n",
    "        if word.lower() in wordlist:\n",
    "            count += 1\n",
    "    return float(count)/float(len(text))\n",
    "\n",
    "def initialize_dataset(source):\n",
    "    all_features = []\n",
    "    targets = []\n",
    "    for (sent, label) in source:\n",
    "        feature_list=[]\n",
    "        feature_list.append(avg_number_chars(sent))\n",
    "        feature_list.append(number_words(sent))\n",
    "        counts = word_counts(sent)\n",
    "        for word in STOP_WORDS:\n",
    "            if word in counts.keys():\n",
    "                feature_list.append(counts.get(word))\n",
    "            else:\n",
    "                feature_list.append(0)        \n",
    "        feature_list.append(proportion_words(sent, STOP_WORDS))\n",
    "        all_features.append(feature_list)\n",
    "        if label==\"austen\": targets.append(0)\n",
    "        else: targets.append(1)\n",
    "    return all_features, targets\n",
    "\n",
    "train_data, train_targets = initialize_dataset(strat_train_set)\n",
    "pretest_data, pretest_targets = initialize_dataset(strat_pretest_set)\n",
    "test_data, test_targets = initialize_dataset(test_set)\n",
    "\n",
    "print (len(train_data), len(train_targets))\n",
    "print (len(pretest_data), len(pretest_targets))\n",
    "print (len(test_data), len(test_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, train and test on both pretest and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8106738223017292\n",
      "[[1985  315]\n",
      " [ 320  734]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.86      0.86      2300\n",
      "           1       0.70      0.70      0.70      1054\n",
      "\n",
      "    accuracy                           0.81      3354\n",
      "   macro avg       0.78      0.78      0.78      3354\n",
      "weighted avg       0.81      0.81      0.81      3354\n",
      "\n",
      "0.8124818997972777\n",
      "[[4275  724]\n",
      " [ 571 1336]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87      4999\n",
      "           1       0.65      0.70      0.67      1907\n",
      "\n",
      "    accuracy                           0.81      6906\n",
      "   macro avg       0.77      0.78      0.77      6906\n",
      "weighted avg       0.82      0.81      0.81      6906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "text_clf = DecisionTreeClassifier(random_state=42)\n",
    "text_clf.fit(train_data, train_targets)  \n",
    "predicted = text_clf.predict(pretest_data)\n",
    "evaluate(predicted, pretest_targets)\n",
    "\n",
    "predicted = text_clf.predict(test_data)\n",
    "evaluate(predicted, test_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see an even further small improvement: $0.812$-$0.815$ and $0.815$-$0.819$. Moreover, performance on both pretest and test sets is very similar now. However, overall perfomance is still not as good as what you've got with words, so let's keep going. Let's plot these values, too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretestAcc = (96.36, 79.72, 81.22) # use the last accuracy score as the last value\n",
    "testAcc = (89.57, 80.49, 81.83) # use the last accuracy score as the last value\n",
    "\n",
    "ind = np.arange(len(pretestAcc))  # the x locations for the groups\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, pretestAcc, width, label='Pretest', color='#61A4F6')\n",
    "rects2 = ax.bar(ind + width/2, testAcc, width, label='Test', color='#DB025B')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Accuracy scores')\n",
    "ax.set_ylim([68,100])\n",
    "ax.set_title('Scores by feature set and data set')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('Benchmark', 'F1-2', 'F1-4'))\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature type 5 – proportion of words of specific parts of speech**: Just like you added proportion of stopwords, add proportions of words of specific parts of speech. For that, first, preprocess the sentences and for each of them keep a dictionary mapping each sentence to its `spaCy`'s representation with all language-related fields (this might take some time due to processing run by `spaCy`, so let's add some code to track how many sentences have been processed):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000 texts processed\n",
      "4000 texts processed\n",
      "6000 texts processed\n",
      "8000 texts processed\n",
      "10000 texts processed\n",
      "12000 texts processed\n",
      "Dataset processed\n",
      "2000 texts processed\n",
      "Dataset processed\n",
      "2000 texts processed\n",
      "4000 texts processed\n",
      "6000 texts processed\n",
      "Dataset processed\n"
     ]
    }
   ],
   "source": [
    "def preprocess(source):\n",
    "    source_docs = {}\n",
    "    index = 0\n",
    "    for (sent, label) in source:\n",
    "        text = \" \".join(sent)\n",
    "        source_docs[text] = nlp(text)\n",
    "        if index>0 and (index%2000)==0:\n",
    "            print(str(index) + \" texts processed\")\n",
    "        index += 1\n",
    "    print(\"Dataset processed\")\n",
    "    return source_docs\n",
    "\n",
    "train_docs = preprocess(strat_train_set)\n",
    "pretest_docs = preprocess(strat_pretest_set)\n",
    "test_docs = preprocess(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now add the PoS distributions as features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414 13414\n",
      "3354 3354\n",
      "6906 6906\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "pos_list = [\"C\", \"D\", \"E\", \"F\", \"I\", \"J\", \"M\", \"N\", \"P\", \"R\", \"T\", \"U\", \"V\", \"W\"]\n",
    "\n",
    "def pos_counts(text, source_docs, pos_list):\n",
    "    pos_counts = {}\n",
    "    doc = source_docs.get(\" \".join(text))\n",
    "    tags = []\n",
    "    for word in doc:\n",
    "        tags.append(str(word.tag_)[0])\n",
    "    counts = Counter(tags)\n",
    "    for pos in pos_list:\n",
    "        if pos in counts.keys():\n",
    "            pos_counts[pos] = counts.get(pos)\n",
    "        else: pos_counts[pos] = 0\n",
    "    return pos_counts\n",
    "\n",
    "def initialize_dataset(source, source_docs):\n",
    "    all_features = []\n",
    "    targets = []\n",
    "    for (sent, label) in source:\n",
    "        feature_list=[]\n",
    "        feature_list.append(avg_number_chars(sent))\n",
    "        feature_list.append(number_words(sent))\n",
    "        counts = word_counts(sent)\n",
    "        for word in STOP_WORDS:\n",
    "            if word in counts.keys():\n",
    "                feature_list.append(counts.get(word))\n",
    "            else:\n",
    "                feature_list.append(0)        \n",
    "        feature_list.append(proportion_words(sent, STOP_WORDS))\n",
    "        p_counts = pos_counts(sent, source_docs, pos_list)\n",
    "        for pos in p_counts.keys():\n",
    "            feature_list.append(float(p_counts.get(pos))/float(len(sent)))\n",
    "        all_features.append(feature_list)\n",
    "        if label==\"austen\": targets.append(0)\n",
    "        else: targets.append(1)\n",
    "    return all_features, targets\n",
    "\n",
    "train_data, train_targets = initialize_dataset(strat_train_set, train_docs)\n",
    "pretest_data, pretest_targets = initialize_dataset(strat_pretest_set, pretest_docs)\n",
    "test_data, test_targets = initialize_dataset(test_set, test_docs)\n",
    "\n",
    "print (len(train_data), len(train_targets))\n",
    "print (len(pretest_data), len(pretest_targets))\n",
    "print (len(test_data), len(test_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, as before, train, test and evaluate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8208109719737626\n",
      "[[1999  301]\n",
      " [ 300  754]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87      2300\n",
      "           1       0.71      0.72      0.72      1054\n",
      "\n",
      "    accuracy                           0.82      3354\n",
      "   macro avg       0.79      0.79      0.79      3354\n",
      "weighted avg       0.82      0.82      0.82      3354\n",
      "\n",
      "0.8284100781928757\n",
      "[[4326  673]\n",
      " [ 512 1395]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88      4999\n",
      "           1       0.67      0.73      0.70      1907\n",
      "\n",
      "    accuracy                           0.83      6906\n",
      "   macro avg       0.78      0.80      0.79      6906\n",
      "weighted avg       0.83      0.83      0.83      6906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "text_clf = DecisionTreeClassifier(random_state=42)\n",
    "text_clf.fit(train_data, train_targets)  \n",
    "predicted = text_clf.predict(pretest_data)\n",
    "evaluate(predicted, pretest_targets)\n",
    "\n",
    "predicted = text_clf.predict(test_data)\n",
    "evaluate(predicted, test_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An improvement with this feature reaches $0.82$-$0.83$ on both sets.\n",
    "\n",
    "For convenience, let's pack up the datasets initialization, training, testing and evaluation into a method, since we don't change any code in this bit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414 13414\n",
      "3354 3354\n",
      "6906 6906\n",
      "\n",
      "0.8208109719737626\n",
      "[[1999  301]\n",
      " [ 300  754]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87      2300\n",
      "           1       0.71      0.72      0.72      1054\n",
      "\n",
      "    accuracy                           0.82      3354\n",
      "   macro avg       0.79      0.79      0.79      3354\n",
      "weighted avg       0.82      0.82      0.82      3354\n",
      "\n",
      "0.8284100781928757\n",
      "[[4326  673]\n",
      " [ 512 1395]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.87      0.88      4999\n",
      "           1       0.67      0.73      0.70      1907\n",
      "\n",
      "    accuracy                           0.83      6906\n",
      "   macro avg       0.78      0.80      0.79      6906\n",
      "weighted avg       0.83      0.83      0.83      6906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def run():\n",
    "    train_data, train_targets = initialize_dataset(strat_train_set, train_docs)\n",
    "    pretest_data, pretest_targets = initialize_dataset(strat_pretest_set, pretest_docs)\n",
    "    test_data, test_targets = initialize_dataset(test_set, test_docs)\n",
    "\n",
    "    print (len(train_data), len(train_targets))\n",
    "    print (len(pretest_data), len(pretest_targets))\n",
    "    print (len(test_data), len(test_targets))\n",
    "    print ()\n",
    "    \n",
    "    text_clf = DecisionTreeClassifier(random_state=42)\n",
    "    text_clf.fit(train_data, train_targets)  \n",
    "    predicted = text_clf.predict(pretest_data)\n",
    "    evaluate(predicted, pretest_targets)\n",
    "\n",
    "    predicted = text_clf.predict(test_data)\n",
    "    evaluate(predicted, test_targets)\n",
    "    \n",
    "run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the improvements in the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretestAcc = (96.36, 79.72, 81.22, 83.10) # use the last accuracy score as the last value\n",
    "testAcc = (89.57, 80.49, 81.83, 82.54) # use the last accuracy score as the last value\n",
    "\n",
    "ind = np.arange(len(pretestAcc))  # the x locations for the groups\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, pretestAcc, width, label='Pretest', color='#61A4F6')\n",
    "rects2 = ax.bar(ind + width/2, testAcc, width, label='Test', color='#DB025B')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Accuracy scores')\n",
    "ax.set_ylim([68,100])\n",
    "ax.set_title('Scores by feature set and data set')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('Benchmark', 'F1-2', 'F1-4', 'F1-5'))\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add further linguistic feature – for instance, suffixes that are already stored in the `docs`.\n",
    "\n",
    "**Feature type 6 – count selected suffixes**: As the number of suffixes will be quite large (smaller than the number of words, though), let's set a cutoff point to, e.g. the top $40\\%$ of the suffixes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "577\n",
      "[',', '.', 'the', 'and', 'to', 'ing', 'of', 'her', '\"', 'a', 'i', 'hat', 'it', 'in', ';', 'was', 'not', 'she', 'you', 'his', 'uld', 'be', 'he', 'ere', 'had', \"'\", 'as', '--', 'all', 'ion', 'but', 'for', 'ith', 'ery', 'is', 'ave', 'ent', 'ill', 'nce', 'ght', 'ter', 'at', 'my', 'our', 'so', 'him', 'een', '?', 's', 'uch', 'ore', 'ome', 'mr', 'ver', 'are', 'ted', 'one', ':', 'ble', 'ell', 'no', 'on', 'now', 'any', 'ust', 'by', 'me', '!', 'hen', 'hey', 'out', 'ess', 'ich', '-', 'do', 'ure', 'mrs', 'ain', 'rom', 'elf', 'red', 'or', 'ood', 'if', 'mma', 'use', 'aid', 'hem', 'ton', 'ely', 'sed', 'own', 'est', 'man', 'an', 'ost', 'ake', 'ers', 'ear', 'nly', '_', 'we', 'iss', 'ned', 'ugh', '.--', 'lly', 'eir', 'ied', 'end', 'am', 'han', 'ons', 'did', 'ame', 'can', 'tle', 'ite', 'ose', 'ate', 'tly', 'how', 'ant', 'ard', 'ast', 'ong', 'ive', 'ity', 'who', 'ine', 'ved', 'eat', 'ect', 'iet', 'nne', 'ded', 'ink', 'way', 'ord', 'ous', 'ays', 'ime', 'ked', 'ady', 'say', 'age', 'ike', 'old', 'ies', 'eed', 'ngs', 'too', 'd', 'ind', 'les', 'ade', 'der', 'aue', 'see', 'med', 'ort', 'ile', 'day', 'rst', 'ley', 'rth', 'its', 'ace', 'may', 'art', 'ove', 'ody', 'let', 'ice', 'nds', 'ane', 'ful', 'led', 'ish', 'oon', 'has', 'und', 'yes', 'nts', 'ven', 'tes', 'ath', 'pon', 'rds', 'rry', 'hed', 'ged', 'ary', 'ead', 'son', 'ple', 'nto', 'two', 'mes', 'ily', 'go', 'ife', 'ten', 'ung', 'ook', 'ise', 'ner', 'ius', 'ral', 'sir', 'iot', 'dly', 'rly', 'rse', 'ank', 'oor', 'ger', 'rty', 'up', '(', 'ide', ')', 'men', 'ree', 'low', 'ves', 'ced', 'new', 'oth', 'yet', 'fax', 'res', 'wer', 'eal', 'ses', 'oes', 'sse', 'ars', 'ase', 'ces', 'eld', 'ual', 'ppy', 'ope', 'oom', 'ole', 'ond', 'elt', 'hou', 'ken', 'per', 'off', 'bly', 'alk', 'ese', 'lay', 'ick', 'us', 'sit', 'unt', 'thy', 'tus', 'oke', 'why', 'iue', 'ire', 'nor', 'alf', 'saw', 'sar', 'ack', 'nge', 'kes', 'wed', 'int', 'oh', 'eet', 'hee', 'ach', 'get', 'eak', 'ber', 'dge', 'few', 'lls', 'row', 'air', 'aps', 'lfe', 'urs', 'rld', 'wne', 'nes', 'uth', 'tch', 'ull', 'eve', 'rue', 'arm', 'o', 'ury', 'eel', 'cke', 'lse', 'urn', 'ene', 'set', '`', 'ety', 'dea', 'oue', 'ubt', 'nse', 'try', 'eft', 'put', 'ues', 'uer', 'vs', 'ins', 'hts', 'ude', 'ped', 'eth', 'tty', 'ony', 'isa', 't', 'nst', 'oss', 'mer', 'ept', 'ued', 'ans', 'hom', 'uen', 'ext', 'act', 'far', 'don', 'lar', 'rge', 'sly', 'nke', 'hes', 'ors', 'ret', 'ren', 'bad', 'nch', 'ohn', 'gly', 'lad', 'une', 'rit', 'ory', 'igh', 'tis', 'ior', 'hin', 'pen', 'eep', 'rve', 'nty', 'ild', 'gan', 'wes', 'tay', 'hew', 'tin', 'rke', 'ial', 'ges', 'oft', 'fer', 'rne', 'yed', 'des', 'ote', 'nal', 'ray', 'uty', 'ert', 'ste', 'nde', 'den', 'ets', 'ean', 'ows', 'ncy', 'cle', 'hus', 'hip', 'cts', 'ist', 'lla', 'tta', 'mon', 'got', 'hly', 'ews', 'eye', 'th', 'ize', 'ule', 'vp', 'lth', 'epe', 'ete', 'met', 'ult', 'yme', 'rts', 'lle', 'pes', 'st', 'eem', 'uck', 'fit', 'ute', 'ply', 'nel', 'vil', 'doe', 'eek', 'ier', 'elp', 'gin', 'oks', 'lor', 'ths', 'tie', 'bed', 'asy', 'ens', 'ech', 'val', 'gue', 'ass', 'ale', 'dow', 'acy', 'eme', 'zed', 'rew', 'ark', 'ape', 'irl', 'rer', 'ool', 'uit', 'mpt', 'lty', 'yle', 'irs', 'ece', 'rms', 'uct', 'xed', 'ern', 'iew', 'ago', 'ney', 'oad', 'afe', 'ask', 'rme', 'rch', 'rre', 'ork', 'ief', 'oms', 'xon', 'nay', 'rie', 'cal', 'nks', 'boy', 'joy', 'eks', 'ods', 'sts', 'god', 'l', 'mbe', 'sat', 'als', 'uce', 'bey', 'lue', 'ler', 'lan', 'apa', 'ush', 'lis', 'orm', 'ock', 'sea', 'gle', '&', 'dle', 'ait', 'gth', 're', 'due', 'oin', 'oof', 'sin', 'ims', 'top', 'ems', 'law', 'die', 'uly', 'tio', 'tal', 'eas', 'sad', 'mit', 'ian', 'mad', 'lso', 'fts', 'nth', 'oud', 'run', 'wee', 'gun', 'lts', 'usy', 'sic', 'gry', 'iam', 'ees', 'aes', 'els', 'iod', 'ror', 'esh', 'ume', 'rce', 'tor', 'bid', 'lye', 'eds', 'awn', 'gge', 'ker', 'lke', 'raw', 'ha', 'oot', 'lds', 'ser', 'odd', 'ska', 'rls', 'aim', 'dit', 'mly', 'pay', 'sty', 'erd', 'ilt', 'ede', 'dom', 'ils', 'tic', 'tea', 'lia', 'box', 'bit', 'net', 'cut', 'oat', 'sal', 'ash']\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "def select_suffixes(cutoff):\n",
    "    all_suffixes = []\n",
    "    for doc in train_docs.values():\n",
    "        for word in doc:\n",
    "            all_suffixes.append(str(word.suffix_).lower())\n",
    "    counts = Counter(all_suffixes)\n",
    "    sorted_counts = sorted(counts.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    selected_suffixes = []\n",
    "    for i in range(0, round(len(counts)*cutoff)):\n",
    "        selected_suffixes.append(sorted_counts[i][0])\n",
    "    return selected_suffixes\n",
    "    \n",
    "selected_suffixes = select_suffixes(0.4)\n",
    "print(len(selected_suffixes))\n",
    "print(selected_suffixes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def suffix_counts(text, source_docs, suffix_list):\n",
    "    suffix_counts = {}\n",
    "    doc = source_docs.get(\" \".join(text))\n",
    "    suffixes = []\n",
    "    for word in doc:\n",
    "        suffixes.append(str(word.suffix_))\n",
    "    counts = Counter(suffixes)\n",
    "    for suffix in suffix_list:\n",
    "        if suffix in counts.keys():\n",
    "            suffix_counts[suffix] = counts.get(suffix)\n",
    "        else: suffix_counts[suffix] = 0\n",
    "    return suffix_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414 13414\n",
      "3354 3354\n",
      "6906 6906\n",
      "\n",
      "0.9543828264758497\n",
      "[[2218   82]\n",
      " [  71  983]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.97      2300\n",
      "           1       0.92      0.93      0.93      1054\n",
      "\n",
      "    accuracy                           0.95      3354\n",
      "   macro avg       0.95      0.95      0.95      3354\n",
      "weighted avg       0.95      0.95      0.95      3354\n",
      "\n",
      "0.9501882421083117\n",
      "[[4815  184]\n",
      " [ 160 1747]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.97      4999\n",
      "           1       0.90      0.92      0.91      1907\n",
      "\n",
      "    accuracy                           0.95      6906\n",
      "   macro avg       0.94      0.94      0.94      6906\n",
      "weighted avg       0.95      0.95      0.95      6906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def initialize_dataset(source, source_docs):\n",
    "    all_features = []\n",
    "    targets = []\n",
    "    for (sent, label) in source:\n",
    "        feature_list=[]\n",
    "        feature_list.append(avg_number_chars(sent))\n",
    "        feature_list.append(number_words(sent))\n",
    "        counts = word_counts(sent)\n",
    "        for word in STOP_WORDS:\n",
    "            if word in counts.keys():\n",
    "                feature_list.append(counts.get(word))\n",
    "            else:\n",
    "                feature_list.append(0)        \n",
    "        feature_list.append(proportion_words(sent, STOP_WORDS))\n",
    "        p_counts = pos_counts(sent, source_docs, pos_list)\n",
    "        for pos in p_counts.keys():\n",
    "            feature_list.append(float(p_counts.get(pos))/float(len(sent)))\n",
    "        s_counts = suffix_counts(sent, source_docs, selected_suffixes)\n",
    "        for suffix in s_counts.keys():\n",
    "            feature_list.append(float(s_counts.get(suffix))/float(len(sent)))\n",
    "        all_features.append(feature_list)\n",
    "        if label==\"austen\": targets.append(0)\n",
    "        else: targets.append(1)\n",
    "    return all_features, targets\n",
    "\n",
    "run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feature brings the largest improvement: up to $0.954$-$0.956$ and $0.952$-$0.954$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretestAcc = (96.36, 79.72, 81.22, 83.10, 95.47) # use the last accuracy score as the last value\n",
    "testAcc = (89.57, 80.49, 81.83, 82.54, 95.34) # use the last accuracy score as the last value\n",
    "\n",
    "ind = np.arange(len(pretestAcc))  # the x locations for the groups\n",
    "width = 0.3  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, pretestAcc, width, label='Pretest', color='#61A4F6')\n",
    "rects2 = ax.bar(ind + width/2, testAcc, width, label='Test', color='#DB025B')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Accuracy scores')\n",
    "ax.set_ylim([68,100])\n",
    "ax.set_title('Scores by feature set and data set')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('Benchmark', 'F1-2', 'F1-4', 'F1-5', 'F1-6'))\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Finally, let's collect specific (non-overlapping) vocabularies per each author.\n",
    "\n",
    "**Feature type 7 – count words that are specific for each author**: First collect the set of words that is unique for each author (i.e., the words that only Shakespeare or only Austen uses) and then count their occurrences across the data sets. You can introduce a cutoff as before and only use, e.g., top $50\\%$ of the words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4435\n",
      "['\"', 'have', '--', '.\"', 'mr', 'mrs', 'emma', 'miss', 'than', '.--', ',\"', 'only', 'every', 'never', 'harriet', 'anne', 'herself', 'own', 'weston', 'knightley', 'elton', 'again', 'always', 'soon', '!--', '?\"', 'captain', 'jane', 'woodhouse', 'dear', 'elliot', 'ever', 'up', 'just', 'having', 'give', 'himself', 'fairfax', 'over', 'upon', 'seemed', 'wentworth', '!\"', 'churchill', 'however', '?--', 'even', 'felt', 'really', 'frank', 'us', 'its', 'room', 'half', 'feelings', 'hartfield', 'certainly', 'charles', 'smith', 'bates', 'russell', 'believe', 'love', 'family', 'evening', 'feel', 'walter', ';--', 'hear', 'looked', 'idea', 'deal', 'acquaintance', 'myself', 'highbury', 'down', 'between', 'musgrove', 'mary', 'hour', 'subject', '`', 'louisa', 'perfectly', 'suppose', 'under', 'general', 'obliged', 'happiness', 'able', 'wanted', 'replied', 'given', 'john', 'talked', 'passed', 'elizabeth', '--\"', 'understand', 'nobody', 'leave', 'kind', 'less', 'interest', 'near', 'attention', 'situation', 'randalls', 'martin', 'agreeable', 'walked', 'perry', 'chapter', 'afraid', 'equal', 'extremely', 'gave', 'themselves', 'attachment', 'natural', 'kellynch', 'business', 'henrietta', 'wished', 'usual', 'talking', 'door', 'called', 'minutes', 'conversation', 'used', 'benwick', 'days', 'child', 'degree', 'appeared', 'object', 'uppercross', 'isabella', 'use', 'particularly', 'pleased', 'received', 'harville', 'superior', 'means', 'different', 'lyme', 'admiral', 'goddard', 'took', 'colonel', 'son', 'help', 'returned', 'yourself', 'forward', 'hoped', 'asked', 'giving', 'air', 'cole', 'real', 'added', 'society', 'handsome', 'croft', 'expected', 'believed', 'continued', 'year', 'settled', 'kindness', 'girl', 'four', 'weather', 'anything', 'supposed', 'five', 'appearance', 'entirely', 'engaged', 'anxious', 'fond', 'probably', 'turned', 'campbell', 'engagement', 'london', 'lived', 'hours', 'circumstances', 'taylor', 'delighted', 'pain', 'advantage', 'week', 'warm', 'curiosity', 'delightful', 'donwell', 'decided', 'comfortable', 'evil', 'dixon', 'loved', 'everything', 'understanding', 'style', 'persuaded', 'conduct', 'sat', 'amiable', 'easy', 'generally', 'news', 'looks', 'completely', 'self', 'understood', 'fair', 'tone', 'knowing', 'dancing', 'mentioned', \",'\", 'obliging', 'plan', 'regret', 'favour', 'living', 'serious', ',--', 'wanting', 'fact', 'aware', 'agreed', 'convinced', 'especially', 'disposed', ',)', 'pass', \".'\", 'knows', 'influence', 'happened', 'elegant', 'hayter', 'actually', 'papa', 'wallis', 'gratitude', 'extraordinary', 'observed', 'interesting', 'marrying', 'says', 'justice', 'pity', 'merely', 'absolutely', 'cheerful', 'observe', 'attentions', 'distress', 'clever', 'trying', 'compliment', 'rooms', 'abbey', 'judge', 'charming', 'instead', 'spite', 'enscombe', 'join', 'proof', 'waiting', 'summer', 'whenever', 'calling', 'difficulty', 'seems', 'recollect', 'vain', 'certain', '),', 'live', 'staying', 'useful', 'hint', 'claims', 'greatest', 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'popillius', 'enuy', 'disclos', 'stretcht', 'gray', 'beards', 'witnesse', 'serue', 'vantage', 'witchcraft', 'grosse', 'charme', 'foure', 'houres', 'crack', 'pith', 'ancestors', 'belou', 'spectacle', 'peeces', 'terme', 'anticke', 'deckt', 'steale', 'cannon', 'hangers', 'moneth', 'yoake', 'couch', 'hounds', 'shal', 'accidentall', 'brest', 'termes', 'pastime', 'wel', 'auoyd', 'whet', 'serpent', 'storme', 'region', 'nightly', 'toyles', 'affrighted', 'clap', 'blasted', 'iudgements', 'forgetfull', 'stab', 'breefe', 'liege', 'shrewdly', 'mutes', 'saies', 'strawes', 'assay', 'gall', 'blowne', 'heauie', 'purging', 'worthinesse', 'vnderneath', 'builds', 'mason', 'shipwright', 'shold', 'ensigne', 'whit', 'distract', 'rapiers', 'bondage', 'visitation', 'flint', 'parchment', 'skinnes', 'tride', 'ripe', 'ioyne', 'flash', 'rotten', 'horrid', 'bleeding', 'butchers', 'priam', 'imperiall', 'blowes', 'vnknowne', 'councell', 'rebellious', 'louers', 'wast', 'kites', 'cerimony', 'fantasie', 'pawse', 'trash', 'scourge', 'speede', 'toy', 'guildenstern', 'stomacke', 'mourn', 'tyrant', 'griefes', 'pittie', 'comicall', 'tragicall', 'beguile', 'buzze', 'strucke', 'mooue', 'fauours', 'keene', 'interim', 'testament', 'dardanius', 'stoope', 'bribes', 'ophel', 'passeth', 'trappings', 'carde', 'equiuocation', 'vndoe', 'picked', 'kibe', 'threatning', 'bisson', 'rheume', 'clout', 'lanke', 'teamed', 'blanket', 'villany', 'cups', 'kettle', 'cannoneer', 'cannons', 'cauerne', 'maske', 'antike', 'heraulds', 'shelfe', 'coynage', 'bodilesse', 'drownes', 'blench', 'ioyfully', 'lowlynesse', 'climber', 'vpward', 'attaines', 'vpmost', 'scorning', 'ascend', 'til', 'priests', 'stayes', 'physicke', 'prolongs', 'dispos', 'seduc', 'israel', 'greekes', 'madman', 'lyest', 'skies', 'vnnumbred', 'vnknowing', 'circumscrib', 'yeelding', 'bargaine', 'iuggel', 'rant', 'offendendo', 'argues', 'ro', 'rebellion', 'gyant', 'meate', 'earnes', 'southerly', 'hawke', 'handsaw', 'apron', 'shouted', 'trap', 'melancholly', 'abuses', 'damne', 'menace', 'pretors', 'chayre', 'hoorded', 'extorted', 'wombe', 'woodcocke', 'sprindge', 'treacherie', 'kingly', 'beauer', 'cum', 'alijs', 'sect', 'boorded', 'cleare', 'shippe', 'outlarie', 'smelt', 'yorick', 'iest', 'gorge', 'vntrod', 'validitie', 'fruite', 'vnripe', 'stickes', 'vnshaken', 'mellow', 'purpled', 'reeke', 'smoake', 'speedier', 'smels', 'primall', 'marrie', 'bang', 'proceede', 'sutors', 'accoutred', 'recame', 'offends', 'robustious', 'pery', 'wig', 'pated', 'tatters', 'ragges', 'groundlings', 'capeable', 'inexplicable', 'whipt', 'termagant', 'outherod', 'herod', 'petitions', 'cabin', 'scarft', 'grop', 'withdrew', 'vnseale', 'knauery', 'denmarks', 'englands', 'hoo', 'bugges', 'goblins', 'superuize', 'leasure', 'bated', 'grinding', 'foolerie', 'digest', 'venom', 'spleene', 'scholler', 'cautell', 'besmerch', 'vnuallued', 'sanctity', 'iumpe', 'polake', 'arriued', 'affabilitie', 'erebus', 'dimme', 'preuention', 'feauer', 'spaine', 'lustre', 'bookes', 'feeble', 'maiesticke', 'sorrowes', 'spies', 'battalians', 'ladie', 'insupportable', 'losse', 'quake', 'returneth', 'conuerted', 'stopp', 'beere', 'barrell', 'quintus', 'reueale', 'ouerlook', 'fac', 'raines', 'doue', 'tarry', 'prethee', 'whilest', 'maisters', 'amb', 'cimber', 'preferre', 'calender', 'challenger', 'chidden', 'ferret', 'crost', 'oration', 'russet', 'clad', 'easterne', 'abler', 'oct', 'signall', 'flashes', 'rore', 'baudry', 'barre', 'libertie', 'candied', 'pompe', 'crooke', 'hindges', 'faining', 'rerule', 'goodman', 'deluer', 'salutation', 'beckens', 'rossius', 'fishmonger', 'bait', 'falshood', 'windlesses', 'assaies', 'indirections', 'tyrannie', 'muddied', 'vnwholsome', 'whispers', 'greenly', 'hugger', 'mugger', 'interre', 'vnskilfull', 'iudicious', 'reway', 'theater', 'globe', 'ruddy', 'buffets', 'nunnerie', 'showts', 'clamors', 'maintains', 'nutshell', 'sodainely', 'pub', 'plucking', 'intrailes', 'feele', 'infaith', 'mood', 'scanter', 'entreatments', 'expectansie', 'mould', 'obseruers', 'inuites', 'discouery', 'secricie', 'moult', 'feather', 'forgone', 'sterrill', 'promontory', 'maiesticall', 'roofe', 'fretted', 'golden', 'congregation', 'vapours', 'drawne', 'heape', 'gastly', 'mowes', 'ducates', 'sterne', 'exits', 'saint', 'patricke', 'remaster', 'reform', 'arriu', 'tenure', 'cutpurse', 'empire', 'brau', 'con', 'roate', 'sayst', 'tut', 'bosomes']\n"
     ]
    }
   ],
   "source": [
    "def unique_vocabulary(label1, label2, cutoff):\n",
    "    voc1 = []\n",
    "    voc2 = []\n",
    "    for (sent, label) in strat_train_set:\n",
    "        if label==label1:\n",
    "            for word in sent:\n",
    "                voc1.append(word.lower())\n",
    "        elif label==label2:\n",
    "            for word in sent:\n",
    "                voc2.append(word.lower())    \n",
    "    counts1 = Counter(voc1)\n",
    "    sorted_counts1 = sorted(counts1.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    counts2 = Counter(voc2)\n",
    "    sorted_counts2 = sorted(counts2.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    unique_voc = []\n",
    "    for i in range(0, round(len(sorted_counts1)*cutoff)):\n",
    "        if not sorted_counts1[i][0] in counts2.keys():\n",
    "            unique_voc.append(sorted_counts1[i][0])\n",
    "    for i in range(0, round(len(sorted_counts2)*cutoff)):\n",
    "        if not sorted_counts2[i][0] in counts1.keys():\n",
    "            unique_voc.append(sorted_counts2[i][0])\n",
    "    return unique_voc\n",
    "    \n",
    "unique_voc = unique_vocabulary(\"austen\", \"shakespeare\", 0.5)\n",
    "print(len(unique_voc))\n",
    "print(unique_voc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_counts(text, unique_voc):\n",
    "    unique_counts = {}\n",
    "    words = []\n",
    "    for word in text:\n",
    "        words.append(word.lower())\n",
    "    counts = Counter(words)\n",
    "    for word in unique_voc:\n",
    "        if word in counts.keys():\n",
    "            unique_counts[word] = counts.get(word)\n",
    "        else: unique_counts[word] = 0\n",
    "    return unique_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13414 13414\n",
      "3354 3354\n",
      "6906 6906\n",
      "\n",
      "0.9633273703041145\n",
      "[[2237   63]\n",
      " [  60  994]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97      2300\n",
      "           1       0.94      0.94      0.94      1054\n",
      "\n",
      "    accuracy                           0.96      3354\n",
      "   macro avg       0.96      0.96      0.96      3354\n",
      "weighted avg       0.96      0.96      0.96      3354\n",
      "\n",
      "0.9643788010425717\n",
      "[[4881  118]\n",
      " [ 128 1779]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.98      4999\n",
      "           1       0.94      0.93      0.94      1907\n",
      "\n",
      "    accuracy                           0.96      6906\n",
      "   macro avg       0.96      0.95      0.96      6906\n",
      "weighted avg       0.96      0.96      0.96      6906\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def initialize_dataset(source, source_docs):\n",
    "    all_features = []\n",
    "    targets = []\n",
    "    for (sent, label) in source:\n",
    "        feature_list=[]\n",
    "        feature_list.append(avg_number_chars(sent))\n",
    "        feature_list.append(number_words(sent))\n",
    "        counts = word_counts(sent)\n",
    "        for word in STOP_WORDS:\n",
    "            if word in counts.keys():\n",
    "                feature_list.append(counts.get(word))\n",
    "            else:\n",
    "                feature_list.append(0)        \n",
    "        feature_list.append(proportion_words(sent, STOP_WORDS))\n",
    "        p_counts = pos_counts(sent, source_docs, pos_list)\n",
    "        for pos in p_counts.keys():\n",
    "            feature_list.append(float(p_counts.get(pos))/float(len(sent)))\n",
    "        s_counts = suffix_counts(sent, source_docs, selected_suffixes)\n",
    "        for suffix in s_counts.keys():\n",
    "            feature_list.append(float(s_counts.get(suffix))/float(len(sent)))\n",
    "        u_counts = unique_counts(sent, unique_voc)\n",
    "        for word in u_counts.keys():\n",
    "            feature_list.append(u_counts.get(word))\n",
    "        all_features.append(feature_list)\n",
    "        if label==\"austen\": targets.append(0)\n",
    "        else: targets.append(1)\n",
    "    return all_features, targets\n",
    "\n",
    "run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final and best result: $0.95$-$0.96$ on the pretest and $0.95$-$96$ on the test set – i.e., almost identical! What is more, the performance on both classes, majority as well as minority, is now also almost identical – F1 of $0.97$ and $0.94$ on pretest, and $0.97$ and $0.92$ on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the last accuracy score as the last value\n",
    "pretestAcc = (96.36, 79.72, 81.22, 83.10, 95.47, 96.27)\n",
    "# use the last accuracy score as the last value\n",
    "testAcc = (89.57, 80.49, 81.83, 82.54, 95.34, 95.82)\n",
    "\n",
    "ind = np.arange(len(pretestAcc))  # the x locations for the groups\n",
    "width = 0.3  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind - width/2, pretestAcc, width, label='Pretest', color='#61A4F6')\n",
    "rects2 = ax.bar(ind + width/2, testAcc, width, label='Test', color='#DB025B')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Accuracy scores')\n",
    "ax.set_ylim([68,100])\n",
    "ax.set_title('Scores by feature set and data set')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('Benchmark', 'F1-2', 'F1-4', 'F1-5', 'F1-6', 'F1-7'))\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}