SVMs+Author CategoricalNB Ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "from ast import literal_eval\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, cohen_kappa_score, f1_score , confusion_matrix\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.svm import LinearSVC, NuSVC\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "import seaborn as sns\n",
    "\n",
    "from germeval import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vectorizer =  pickle.load(open(\"vectorizer_without_num.pickle\",'rb'))\n",
    "# Text with the numbers removed\n",
    "X_train = pickle.load(open(\"X_train_without_num.pickle\", \"rb\"))\n",
    "X_test = pickle.load(open(\"X_test_without_num.pickle\", \"rb\"))\n",
    "y_train = pickle.load( open(\"y_train_without_num.pickle\", \"rb\"))\n",
    "y_test = pickle.load(open(\"y_test_without_num.pickle\", \"rb\"))\n",
    "le_mapping = pickle.load(open(\"le_mapping.pickle\", \"rb\")) # LabelEncoder Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model(clf, X_train,y_train):\n",
    "    clf.fit(X_train, y_train)\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16929, 638284)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4233, 638284)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 27.6 s, sys: 1min 24s, total: 1min 51s\n",
      "Wall time: 2min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "X_full = np.concatenate((X_train.toarray(),X_test.toarray()))\n",
    "Y_full = np.concatenate((y_train,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del X_test\n",
    "del X_train\n",
    "del y_train\n",
    "del y_test\n",
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear]CPU times: user 2min 15s, sys: 38.2 s, total: 2min 53s\n",
      "Wall time: 2min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# data = X_train.toarray(), X_test.toarray(), y_train, y_test\n",
    "lsvcclf = LinearSVC(random_state=42,verbose=1)\n",
    "clf = build_model(lsvcclf,X_full,Y_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%%time\n",
    "# Pickle the model\n",
    "with open('svm_tfidf_clf.pickle', 'wb') as handle:\n",
    "pickle.dump(clf, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load from the pickled model\n",
    "with open('svm_tfidf_clf.pickle', 'rb') as handle:\n",
    "    clf = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "validation_data = pd.read_csv(\"../../data/validation_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def combine_title_desc(row):\n",
    "    \"\"\"Combines the Title and Description fields in the given row\n",
    "    and returns the combined result\"\"\"\n",
    "    return str(row[\"title\"]) + \" \" + str(row[\"description\"])\n",
    "    #return str(row[\"title\"]) + \" \" + str(row[\"description\"]) + \" \" + str(row[\"author\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "validation_tf_idf = vectorizer.transform(validation_data.apply(lambda row: combine_title_desc(row),axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2079, 638284)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_tf_idf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = clf._predict_proba_lr(validation_tf_idf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "       195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
       "       208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n",
       "       221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n",
       "       234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n",
       "       247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,\n",
       "       260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,\n",
       "       273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,\n",
       "       286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,\n",
       "       299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,\n",
       "       312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,\n",
       "       325, 326, 327, 328, 329])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_full' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-118-d0f0dd22cb21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mX_full\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mY_full\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_full' is not defined"
     ]
    }
   ],
   "source": [
    "del X_full\n",
    "del Y_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1696"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_counts = pd.read_csv(\"class_count_predictions_from_svc.csv\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getClasses(class_probs,class_count):\n",
    "    arr = np.array(class_probs)\n",
    "#     print(\"===\")\n",
    "#     print(class_probs)\n",
    "#     print(class_count)\n",
    "#     print(arr.argsort()[-1 * class_count:][::-1])\n",
    "    return arr.argsort()[-1 * class_count:][::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing missing category maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "book_df = load_data()\n",
    "book_df = repeat_categories(book_df)\n",
    "\n",
    "map_id = max(le_mapping.values())\n",
    "for sample in book_df[\"categories\"]:\n",
    "    for category in sample:\n",
    "        if category not in le_mapping:\n",
    "            map_id += 1\n",
    "            le_mapping[category] = map_id\n",
    "            \n",
    "book_df[\"cat_map\"] = book_df[\"category\"].apply(lambda x: le_mapping[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinominal Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Hans-Martin Lübking'],\n",
       " ['Cate Tiernan'],\n",
       " ['Titus Müller'],\n",
       " ['Dalai Lama'],\n",
       " ['John Hart'],\n",
       " ['Marc Levy'],\n",
       " ['Paolo Bacigalupi'],\n",
       " ['Anne Østby'],\n",
       " ['Karin Slaughter'],\n",
       " ['Tom Segev'],\n",
       " ['Jeffery Deaver'],\n",
       " ['Clemens Berger'],\n",
       " ['Jesper Juul'],\n",
       " ['T.C. Boyle'],\n",
       " ['Selahattin Demirtaş'],\n",
       " ['Hannelore Hoger'],\n",
       " ['Susan Pinker'],\n",
       " ['Zoé Armbruster'],\n",
       " ['Stephen Leather'],\n",
       " ['Tim Jürgens',\n",
       "  'Philipp Köster',\n",
       "  '11 Freunde Verlags GmbH & Co. KG Tim Jürgens'],\n",
       " ['Michael Köhlmeier'],\n",
       " ['Susan Forward', 'Donna Frazier Glynn'],\n",
       " ['Monica Murphy'],\n",
       " ['Loretta Napoleoni'],\n",
       " ['David Brooks'],\n",
       " ['James P. Hogan'],\n",
       " ['Helene Tursten'],\n",
       " ['John Burnside'],\n",
       " ['Katrina Leno'],\n",
       " ['David Drake'],\n",
       " ['Franz Schuier'],\n",
       " ['David F. Ross'],\n",
       " ['Jeri Taylor'],\n",
       " ['Yvonne C. Heiler'],\n",
       " ['Paolo Bacigalupi'],\n",
       " ['Laura Griffin'],\n",
       " ['Uwe Christiansen'],\n",
       " ['Arthur Miller'],\n",
       " ['Cecilie Enger'],\n",
       " ['Peter Wawerzinek'],\n",
       " ['Christoph Borchelt'],\n",
       " ['Marcel Reich-Ranicki'],\n",
       " ['Bahar Yilmaz'],\n",
       " ['Dorothee Meyer-Kahrweg', 'Hans Sarkowicz'],\n",
       " ['José Carreras', 'Màrius Carol'],\n",
       " ['Adina Rishe Gewirtz'],\n",
       " ['Richard Laymon'],\n",
       " ['L.A. Banks'],\n",
       " ['Joachim Bauer'],\n",
       " ['Harlan Coben'],\n",
       " ['J. R. Ward'],\n",
       " ['Kathy Stinson'],\n",
       " ['Stephan Heinrich Nolte'],\n",
       " ['Sasha Wagstaff'],\n",
       " ['Tessa Korber'],\n",
       " ['Emma Wildes'],\n",
       " ['Brian Herbert', 'Kevin J. Anderson'],\n",
       " ['Sabine Raml'],\n",
       " ['Stephan Valentin'],\n",
       " ['Burhan Sönmez'],\n",
       " ['Reinhold Lackner'],\n",
       " ['Andy Haller'],\n",
       " ['Jürgen Roth'],\n",
       " ['Charlotte Guillain'],\n",
       " ['François Truffaut'],\n",
       " ['Herbert Günther'],\n",
       " ['Carolin Kebekus'],\n",
       " ['Usch Luhn', 'Steffi Korda'],\n",
       " ['Henning Mankell'],\n",
       " ['Andreas Gruber'],\n",
       " ['Ruediger Dahlke'],\n",
       " ['Bernd Brucker'],\n",
       " ['Jonathan Kellerman'],\n",
       " ['K.B. Wagers'],\n",
       " ['Atze Schröder'],\n",
       " ['Alex Berenson'],\n",
       " ['Rainer Bielfeldt', 'Otto Senn'],\n",
       " ['Petra Morsbach'],\n",
       " ['Rachel Harris'],\n",
       " ['Andrea Vanoni'],\n",
       " ['Bronnie Ware'],\n",
       " ['Stella Gemmell'],\n",
       " ['Sandra Brown'],\n",
       " ['Pierre Lota'],\n",
       " ['Naomi Alderman'],\n",
       " ['Alexander McCall Smith'],\n",
       " ['Georg Vetten'],\n",
       " ['S. Quinn'],\n",
       " ['John Scalzi'],\n",
       " ['Noah Gordon'],\n",
       " ['Katherine Webb'],\n",
       " ['Jeaniene Frost'],\n",
       " ['Carola Kleinschmidt'],\n",
       " ['Chris Harrison'],\n",
       " ['Ulrich Janßen', 'Ulla Steuernagel'],\n",
       " ['Christopher Moore'],\n",
       " ['Anne Perry'],\n",
       " ['David Chang'],\n",
       " ['Ingo Siegner'],\n",
       " ['Martin Buber', 'Ran HaCohen'],\n",
       " ['Felix Anschütz', 'Nico Degenkolb', 'Krischan Dietmaier', 'Thomas Neumann'],\n",
       " ['Thomas Arnold',\n",
       "  'Benedikt Dolzer',\n",
       "  'Judith Lurweg',\n",
       "  'Lucia Pfeiffer',\n",
       "  'Christian Schröder',\n",
       "  'Florian Sobetzko',\n",
       "  'Klaus Vellguth'],\n",
       " ['Otto de Kat'],\n",
       " ['Eleanor Prescott'],\n",
       " ['Paul Cleave'],\n",
       " ['Kevin Way Jeter'],\n",
       " ['Robert A. Heinlein'],\n",
       " ['Sven Siedenberg'],\n",
       " ['nan'],\n",
       " ['Enid Blyton'],\n",
       " ['nan'],\n",
       " ['Christian Wilhelm Echter'],\n",
       " ['Barbara von Bellingen'],\n",
       " ['Proschat Madani'],\n",
       " ['Tracy Kidder'],\n",
       " ['Jimi Hendrix'],\n",
       " ['Mark Twain'],\n",
       " ['Lou Paget'],\n",
       " ['Diverse'],\n",
       " ['Tiffany Baker'],\n",
       " ['Ingeborg Feustel'],\n",
       " ['Norbert Pautner'],\n",
       " ['Helmut Vorndran'],\n",
       " ['John L. Campbell'],\n",
       " ['Fadumo Korn'],\n",
       " ['Doris Cramer'],\n",
       " ['Jan Costin Wagner'],\n",
       " ['Nora Roberts'],\n",
       " ['Jason Shulman'],\n",
       " ['Joachim Penzel'],\n",
       " ['Dieter Hildebrandt'],\n",
       " ['Sylvia Day'],\n",
       " ['Andreas Gruber'],\n",
       " ['J. M. Dillard'],\n",
       " ['Sophie Andresky'],\n",
       " ['Susan Elizabeth Phillips'],\n",
       " ['nan'],\n",
       " ['Anna Mansell'],\n",
       " ['Catherine MacPhail'],\n",
       " ['Timothy Ferriss'],\n",
       " ['Mark Twain'],\n",
       " ['Kami Garcia', 'Margaret Stohl'],\n",
       " ['Jürgen Schmieder'],\n",
       " ['Julie Plec'],\n",
       " ['Lisa J. Smith'],\n",
       " ['Theresia Graw'],\n",
       " ['Marcel Reich-Ranicki', 'Thomas Anz'],\n",
       " ['Lydie Salvayre'],\n",
       " ['George R.R. Martin'],\n",
       " ['Josef Wilfling'],\n",
       " ['Ingo Siegner'],\n",
       " ['James Barclay'],\n",
       " ['Nina Blazon'],\n",
       " ['Hans Conrad Zander'],\n",
       " ['Peter V. Brett'],\n",
       " ['John Stephens'],\n",
       " ['Christian Gerlach'],\n",
       " ['Johann Wolfgang von Goethe', 'Hermann Hesse', 'Elizabeth von Arnim'],\n",
       " [\"Alessandro D'Avenia\"],\n",
       " ['Robert Silverberg'],\n",
       " ['Lisa Ballantyne'],\n",
       " ['E L James'],\n",
       " ['Annelie Wendeberg'],\n",
       " ['Kathy Reichs'],\n",
       " ['Ingo Siegner'],\n",
       " ['Luis Sellano'],\n",
       " ['Fearne Cotton'],\n",
       " ['Ingo Siegner'],\n",
       " ['Stan Nicholls'],\n",
       " ['Harlan Coben'],\n",
       " ['Dietmar Bittrich'],\n",
       " ['Alexandra Ivy'],\n",
       " ['John Grisham'],\n",
       " ['Amy Bratley'],\n",
       " ['Agatha Christie'],\n",
       " ['Charles Chadwick'],\n",
       " ['Michael McCollum'],\n",
       " ['Jonathan Stroud'],\n",
       " ['Scott Turow'],\n",
       " ['Ingo Siegner'],\n",
       " ['Brian Herbert', 'Kevin J. Anderson'],\n",
       " ['Christiane zu Salm'],\n",
       " ['Nathaniel Hawthorne'],\n",
       " ['Carly Phillips'],\n",
       " ['Brendan Simms'],\n",
       " ['nan'],\n",
       " ['Alexandra Potter'],\n",
       " ['Jorge Bucay'],\n",
       " ['Mandy Baggot'],\n",
       " ['Willie Nelson', 'David Ritz'],\n",
       " ['Håkan Nesser'],\n",
       " ['John Vornholt'],\n",
       " ['Stefan Limmer'],\n",
       " ['Louise Voss', 'Mark Edwards'],\n",
       " ['Stuart MacBride'],\n",
       " ['Peter Ensikat'],\n",
       " ['Santo Cilauro', 'Tom Gleisner', 'Rob Sitch'],\n",
       " ['Hunter S. Thompson'],\n",
       " ['J. R. Ward'],\n",
       " ['Hans Neuenfels'],\n",
       " ['Frank Schirrmacher', 'Jakob Augstein'],\n",
       " ['Albrecht Beutelspacher'],\n",
       " ['Tom McCarthy'],\n",
       " ['Caroline Graham'],\n",
       " ['Kathy Reichs'],\n",
       " ['Helmut Merklein'],\n",
       " ['Glenda Larke'],\n",
       " ['Martin Buber',\n",
       "  'Francesco Ferrari',\n",
       "  'Stefano Franchini',\n",
       "  'Massimiliano De Villa'],\n",
       " ['Katarzyna Bonda'],\n",
       " ['Jeffery Deaver'],\n",
       " ['David Campany', 'Hripsimé Visser', 'Nan Goldin'],\n",
       " ['Marc Beise'],\n",
       " ['Miroslav Volf'],\n",
       " ['Michael J. Sullivan'],\n",
       " ['Wim Luijpers'],\n",
       " ['Sibylle Rieckhoff'],\n",
       " ['Mary Higgins Clark'],\n",
       " ['Frode Granhus'],\n",
       " ['Gillian Anderson', 'Jennifer Nadel'],\n",
       " ['Michael Pollan'],\n",
       " ['Roger MacBride Allen'],\n",
       " ['Jörg Zittlau'],\n",
       " ['Mo Hayder'],\n",
       " ['Oliver Noelle'],\n",
       " ['Andreas Kossert'],\n",
       " ['Robert Ludlum'],\n",
       " ['Eckhard Frick SJ', 'Bruno Lautenschlager SJ'],\n",
       " ['Evie Blake'],\n",
       " ['John Scalzi'],\n",
       " ['Hans Wagner'],\n",
       " ['Faye Kellerman'],\n",
       " ['Anna Todd'],\n",
       " ['Rebecca James'],\n",
       " ['Suren Zormudjan'],\n",
       " ['Thomas Mann'],\n",
       " ['Peter Hess'],\n",
       " ['David S. Goyer', 'Michael Cassutt'],\n",
       " ['Torsten Fink'],\n",
       " ['Sue Stratford'],\n",
       " ['Dean Koontz'],\n",
       " ['Maria Rossbauer'],\n",
       " ['Lianne Pot'],\n",
       " ['Tove Jansson'],\n",
       " ['Volker Weidermann'],\n",
       " ['Kate Wilhelm'],\n",
       " ['Mari Jungstedt'],\n",
       " ['Greg Bear'],\n",
       " ['Susanne Kliem'],\n",
       " ['Steve Alten'],\n",
       " ['Hanns-Josef Ortheil'],\n",
       " ['Peter Heimdall'],\n",
       " ['Monica Murphy'],\n",
       " ['Matthias Wittekindt'],\n",
       " ['Arthur Benjamin', 'Michael Shermer'],\n",
       " ['Geneva Lee'],\n",
       " ['Agatha Christie'],\n",
       " ['Jena Pincott'],\n",
       " ['Lea Ackermann', 'Mary Kreutzer', 'Alicia Allgäuer'],\n",
       " ['Kôji Suzuki'],\n",
       " ['Robin Palmer'],\n",
       " ['James Oswald'],\n",
       " ['Jutta Allmendinger'],\n",
       " ['Tiziano Terzani'],\n",
       " ['Ingo Siegner'],\n",
       " ['Robert Low'],\n",
       " ['Annie Proulx'],\n",
       " ['J. Kenner'],\n",
       " ['Jenny Lawson'],\n",
       " ['Iris Hanika'],\n",
       " ['Franck Goddio', 'David Fabre'],\n",
       " ['Ulrich Drüner'],\n",
       " ['Volker Mehl', 'Christina Raftery'],\n",
       " ['Marianne Berglund'],\n",
       " ['Wulfing von Rohr', 'Jutta Fuezi'],\n",
       " ['Christine Janson'],\n",
       " ['Margot Käßmann'],\n",
       " ['Peter Gaymann'],\n",
       " ['Markus Lutteman'],\n",
       " ['Daniela Wattenbach'],\n",
       " ['Miriam Kalliwoda'],\n",
       " ['Dr. Oetker'],\n",
       " ['Janet Chapman'],\n",
       " ['Jess McConkey'],\n",
       " ['Kathy Benjamin'],\n",
       " ['Heike Gade'],\n",
       " ['Giles Kristian'],\n",
       " ['Andrew Britton'],\n",
       " ['Stephen King'],\n",
       " ['Günter de Bruyn'],\n",
       " ['Iris Bosold', 'Peter Kliemann'],\n",
       " ['Jalid Sehouli'],\n",
       " ['Jonas Herlin'],\n",
       " ['Karl Ove Knausgård'],\n",
       " ['Johanna Lindsey'],\n",
       " ['Beth Kery'],\n",
       " ['Franz Brandl'],\n",
       " ['Felix Anschütz', 'Nico Degenkolb', 'Krischan Dietmaier', 'Thomas Neumann'],\n",
       " ['Jan Guillou'],\n",
       " ['Chade-Meng Tan'],\n",
       " ['Clive Cussler'],\n",
       " ['Homer'],\n",
       " ['Florian Rauch', 'Nicole Rinder', 'Tita Kern'],\n",
       " ['Amelie Fried', 'Peter Probst'],\n",
       " ['Sabine Zett'],\n",
       " ['Juli Zeh'],\n",
       " ['Miles Cameron'],\n",
       " ['Christoph Hardebusch'],\n",
       " ['Peter Scholl-Latour'],\n",
       " ['Nora Roberts'],\n",
       " ['Martin Buber'],\n",
       " ['Sascha Mamczak', 'Wolfgang Jeschke'],\n",
       " ['Univ. Doz. Dr. med. Maximilian Ledochowski'],\n",
       " ['Joachim Köhler'],\n",
       " ['Terry Goodkind'],\n",
       " ['Steve Jenkins', 'Derek Walter', 'Caprice Crane'],\n",
       " ['Greg Behrendt', 'Liz Tuccillo'],\n",
       " ['Ian Rankin'],\n",
       " ['Wladimir Kaminer'],\n",
       " ['Christian Meier'],\n",
       " ['Henry James'],\n",
       " ['Peter V. Brett'],\n",
       " ['Steven Erikson'],\n",
       " ['John Grisham'],\n",
       " ['Walter Kempowski'],\n",
       " ['Douglas Adams', 'Mark Carwardine'],\n",
       " ['James Rollins', 'Rebecca Cantrell'],\n",
       " ['Enid Blyton'],\n",
       " ['Maria Konnikova'],\n",
       " ['Nora Roberts'],\n",
       " ['Fiona Harrison'],\n",
       " ['Sophie Bush'],\n",
       " ['Mary Higgins Clark'],\n",
       " ['Royce Buckingham'],\n",
       " ['Sharon Bolton'],\n",
       " ['Harry Harrison'],\n",
       " ['Anke Precht'],\n",
       " ['Simon Scarrow'],\n",
       " ['Diane Carey'],\n",
       " ['Carrie Vaughn'],\n",
       " ['David Levithan'],\n",
       " ['Joe Kirschvink', 'Peter Ward'],\n",
       " ['Jon Skovron'],\n",
       " ['Gilbert Keith Chesterton'],\n",
       " ['Lothar Seiwert'],\n",
       " ['Homer'],\n",
       " ['Markus Lanz'],\n",
       " ['Markus Roßmann', 'Bernd Neumann'],\n",
       " ['Alex Thomas'],\n",
       " ['Peter Wohlleben'],\n",
       " ['Dieter Wartenweiler'],\n",
       " ['Ingo Siegner'],\n",
       " ['Dietrich Bonhoeffer'],\n",
       " ['J. M. Dillard'],\n",
       " ['Howard Jacobson'],\n",
       " ['Olaf Gersemann'],\n",
       " ['Bettina Alberti'],\n",
       " ['Lisa J. Smith'],\n",
       " ['Timothy Zahn'],\n",
       " ['Antonia Edwards'],\n",
       " ['Megan Kreiner'],\n",
       " ['Mary Higgins Clark'],\n",
       " ['Claire Winter'],\n",
       " ['Jan-Christof Scheibe'],\n",
       " ['Prof. Dr. Eran Segal', 'Prof. Dr. Eran Elinav'],\n",
       " ['Simone van der Vlugt'],\n",
       " ['Louise Hay'],\n",
       " ['Usch Luhn'],\n",
       " ['Axel Mengewein'],\n",
       " ['Dr. David Perlmutter'],\n",
       " ['nan'],\n",
       " ['Luigi Pirandello'],\n",
       " ['Cecily von Ziegesar'],\n",
       " ['Alexander Deeg', 'David Plüss'],\n",
       " ['Jamie Thomson'],\n",
       " ['S. Quinn'],\n",
       " ['Richard J. Evans'],\n",
       " ['Marina Caba Rall'],\n",
       " ['Jonathan Stroud'],\n",
       " ['Thomas Bernhard'],\n",
       " ['Sabine Schonert-Hirz'],\n",
       " ['11 Freunde Verlag'],\n",
       " ['Ayelet Waldman'],\n",
       " ['William Dietz'],\n",
       " ['Hanna Lindberg'],\n",
       " ['Alex Thomas'],\n",
       " ['Stephan Lermer'],\n",
       " ['Mark Barnes'],\n",
       " ['Antonio Damasio'],\n",
       " ['nan'],\n",
       " ['Bjarne Schilling', 'Gorm Vølver'],\n",
       " ['Ross Antony', 'Sabine Zett'],\n",
       " ['Georg Frhr. von Gayl', 'Christa Brand'],\n",
       " ['Richard Laymon'],\n",
       " ['Stephen King'],\n",
       " ['Bernhard Hennen', 'Robert Corvus'],\n",
       " ['Eva-Maria Zurhorst', 'Wolfram Zurhorst'],\n",
       " ['Alan Dean Foster'],\n",
       " ['Alyson Noël'],\n",
       " ['Annie Sloan'],\n",
       " ['Ulla Lena Lundberg'],\n",
       " ['Nate Silver'],\n",
       " ['Marjorie M. Liu'],\n",
       " ['Micaela Jary'],\n",
       " ['Emmy Laybourne'],\n",
       " ['Enid Blyton'],\n",
       " ['Fritz Bachmann', 'Walter Duwe', 'Friedrich Haase', 'Traugott Kuhlo'],\n",
       " ['Lisa Yee'],\n",
       " ['Thomas Gordon'],\n",
       " ['Deborah Harkness'],\n",
       " ['Jan Anderson', 'Christian Koch'],\n",
       " ['Hedwig Kellner'],\n",
       " ['Sharon Bolton'],\n",
       " ['C.J. Box'],\n",
       " ['Amitav Ghosh'],\n",
       " ['Thomas Enger'],\n",
       " ['Petra Morsbach'],\n",
       " ['Patricia Schröder'],\n",
       " ['Boris Koch'],\n",
       " ['Clive Barker'],\n",
       " ['Alexi Zentner'],\n",
       " ['Stefanie Kasper'],\n",
       " ['Nora Roberts'],\n",
       " ['Martina Kink'],\n",
       " ['Siri Kolu'],\n",
       " ['Martin Nusch'],\n",
       " ['Sandra Ingerman'],\n",
       " ['Walter Kempowski', 'Walter Adler'],\n",
       " ['Arthur Conan Doyle'],\n",
       " ['William Horwood'],\n",
       " ['Tine Ratig', 'Hannah Wilhelm'],\n",
       " ['Frank R. Tallis'],\n",
       " ['Vera Griebert-Schröder', 'Franziska Muri'],\n",
       " ['Tracy Chevalier'],\n",
       " ['Werner Toporski'],\n",
       " ['Oliver Versch', 'Martin Nusch', 'Ingo Zamperoni'],\n",
       " ['Adam Soboczynski'],\n",
       " ['Simone Elkeles'],\n",
       " ['Stefan Klein'],\n",
       " ['William Gibson', 'John Shirley'],\n",
       " ['Amber Dusick'],\n",
       " ['Kate Wilhelm'],\n",
       " ['James P. Hogan'],\n",
       " ['Sandra Runge'],\n",
       " ['Nataša Dragnić'],\n",
       " ['Catherine Aurel'],\n",
       " ['Paul McAuley'],\n",
       " ['Emily Giffin'],\n",
       " ['Tom Clancy'],\n",
       " ['Timothy Zahn'],\n",
       " ['Michael Jan Friedman'],\n",
       " ['Mirja Winkelmann'],\n",
       " ['nan'],\n",
       " ['Horst Gorski'],\n",
       " ['Diane Duane'],\n",
       " ['Steve White', 'David Weber'],\n",
       " ['Dagmar Knecht'],\n",
       " ['Meg Cabot'],\n",
       " ['Sofja Tolstaja'],\n",
       " ['Thomas Bernhard'],\n",
       " ['Jean-Philippe Blondel'],\n",
       " ['Michaela Axt-Gadermann'],\n",
       " ['Karina Halle'],\n",
       " ['Claire'],\n",
       " ['Doris Kutschbach'],\n",
       " ['Robert Gernhardt'],\n",
       " ['Frank Fabian'],\n",
       " ['Georg Haddenbach'],\n",
       " ['nan'],\n",
       " ['Brigitte Blobel'],\n",
       " ['nan'],\n",
       " ['Franziska Sperr'],\n",
       " ['Meg Leder'],\n",
       " ['John Lydon'],\n",
       " ['Pamela Druckerman'],\n",
       " ['Doreen Virtue', 'Robert Reeves'],\n",
       " ['Heinz-Richard Heinemann'],\n",
       " ['Gilbert Keith Chesterton'],\n",
       " ['Carl Hoffman'],\n",
       " ['Elana Rosenbaum'],\n",
       " ['Johanna Paungger', 'Thomas Poppe'],\n",
       " ['M. Agejew'],\n",
       " ['Jessica Sorensen'],\n",
       " ['Anna Barnes'],\n",
       " ['Jan Andersen'],\n",
       " ['Martin Bucer'],\n",
       " ['Tobias O. Meißner'],\n",
       " ['Patrick Mauriès'],\n",
       " ['Helen Fielding'],\n",
       " ['J. R. Ward'],\n",
       " ['Johanna Handschmann'],\n",
       " ['Lisa J. Smith'],\n",
       " ['Britta Teckentrup'],\n",
       " ['Titus Müller'],\n",
       " ['Jes Tyng-Yee Lim', 'Julie A. Lim'],\n",
       " ['Heike Höfler'],\n",
       " ['Alan Bradley'],\n",
       " ['Jessica Sorensen'],\n",
       " ['Rebecca Sky'],\n",
       " ['Amanda Quick'],\n",
       " ['Karen M. McManus'],\n",
       " ['Frederick Forsyth'],\n",
       " ['Gisa Klönne'],\n",
       " ['Sarah Jane Arnold'],\n",
       " ['Annie Proulx'],\n",
       " ['Sara Raasch'],\n",
       " ['Kristina Ohlsson'],\n",
       " ['Roselies Taube', 'Hella Thomas', 'Susanne Witschkowski'],\n",
       " ['Martin Cruz Smith'],\n",
       " ['Lynn Weingarten'],\n",
       " ['M. Leighton'],\n",
       " ['Giorgio Panariello'],\n",
       " ['Robert Ludlum'],\n",
       " ['Trudi Canavan'],\n",
       " ['nan'],\n",
       " ['J.L. Bourne'],\n",
       " ['Holly Black'],\n",
       " ['S.P. Somtow'],\n",
       " ['Piet Klocke'],\n",
       " ['Vanessa Kullmann'],\n",
       " ['Stephen Baxter'],\n",
       " ['Dr. Ruth K. Westheimer'],\n",
       " ['Mike Nicol'],\n",
       " ['Andreas Englisch'],\n",
       " ['Franziska Gehm'],\n",
       " ['Michael Köhlmeier'],\n",
       " ['Pinchas Lapide', 'Viktor E. Frankl'],\n",
       " ['Gabrielle Williams'],\n",
       " ['Meg Cabot'],\n",
       " ['Alison Tyler'],\n",
       " ['Barbara Bartos-Höppner'],\n",
       " ['Wolfgang Pehnt'],\n",
       " ['Jeffery Deaver'],\n",
       " ['Grace Paley'],\n",
       " ['Federica de Cesco'],\n",
       " ['Tim Collins'],\n",
       " ['nan'],\n",
       " ['Barbara Hambly'],\n",
       " ['Willigis Jäger OSB'],\n",
       " ['Hans Wagner'],\n",
       " ['Julie Plec'],\n",
       " ['Walter Moers', 'Anja Dollinger'],\n",
       " ['Poul Anderson'],\n",
       " ['Anna Schädlich', 'Susanne Schädlich'],\n",
       " ['Johannes Kottjé'],\n",
       " ['Vanna Vannuccini'],\n",
       " ['nan'],\n",
       " ['Birgit Schlieper'],\n",
       " ['Ulrich Strunz'],\n",
       " ['George R.R. Martin'],\n",
       " ['Hal Clement'],\n",
       " ['Gianrico Carofiglio'],\n",
       " ['Gail Carriger'],\n",
       " ['Hans Christian Andersen'],\n",
       " ['Stefanie Lohaus', 'Tobias Scholz'],\n",
       " ['Paul Cleave'],\n",
       " ['Robert Sheckley'],\n",
       " ['Orson Scott Card'],\n",
       " ['Haruki Murakami'],\n",
       " ['Ruediger Dahlke'],\n",
       " ['Michaela Seul'],\n",
       " ['Mary Higgins Clark'],\n",
       " ['Julia Heilmann', 'Thomas Lindemann'],\n",
       " ['Ella Montgomery'],\n",
       " ['George R.R. Martin'],\n",
       " ['Karin Wahlberg'],\n",
       " ['Harald Lesch', 'Jörn Müller'],\n",
       " ['Wolf Schreiner'],\n",
       " ['Susanne Mierau'],\n",
       " ['Martin Nusch'],\n",
       " ['Bettina Schümann'],\n",
       " ['Ali McNamara'],\n",
       " ['Rachel Kramer Bussel'],\n",
       " ['Murasaki Shikibu'],\n",
       " ['Cathy Woodman'],\n",
       " ['Alexej Rakitin'],\n",
       " ['Lauren Weisberger'],\n",
       " ['Stefanie Zweig'],\n",
       " ['Martin Nusch'],\n",
       " ['Annie Barrows'],\n",
       " ['Franz Werfel'],\n",
       " ['Michael J. Sullivan'],\n",
       " ['Salman Rushdie'],\n",
       " ['Monica McCarty'],\n",
       " ['Carola Kleinschmidt'],\n",
       " ['Jude Watson'],\n",
       " ['Michael Kleeberg'],\n",
       " ['Robert Wilson'],\n",
       " ['Thomas Sonnenburg', 'Simone Winkelmann'],\n",
       " ['Susan Juby'],\n",
       " ['Alexander Neubacher'],\n",
       " ['Sally Kempton'],\n",
       " ['Eleonore Jacobi'],\n",
       " ['Wilhelm Goldmann Verlag GmbH'],\n",
       " ['Kevin J. Anderson'],\n",
       " ['Robert Galbraith'],\n",
       " ['Simone Neumann'],\n",
       " ['Hanns-Josef Ortheil'],\n",
       " ['J.D. Robb'],\n",
       " ['Johannes Bobrowski', 'Eberhard Haufe'],\n",
       " ['Ingo Siegner'],\n",
       " ['John Connell'],\n",
       " ['Nadja Hermann'],\n",
       " ['Harriet Quick'],\n",
       " ['Lucie Marshall'],\n",
       " ['Tess Gerritsen'],\n",
       " ['Mary Higgins Clark'],\n",
       " ['Bettina Obrecht'],\n",
       " ['Sara Raasch'],\n",
       " ['Tessa Korber'],\n",
       " ['Udo Weigelt'],\n",
       " ['Christian Schwarz'],\n",
       " ['Katie Kirby'],\n",
       " ['Teja Fiedler', 'Marc Goergen'],\n",
       " ['Jaiya Hanauer', 'Jon Hanauer'],\n",
       " ['Frank Witzel'],\n",
       " ['Ian Whates'],\n",
       " ['Lorna Byrne', 'Bettina Lemke'],\n",
       " ['Luise Schorn-Schütte'],\n",
       " ['Richard Burton', 'Chris Williams'],\n",
       " ['Ulrich Strunz'],\n",
       " ['Robert C. Solomon'],\n",
       " ['Johanna Paungger', 'Thomas Poppe'],\n",
       " ['Alexander Kern'],\n",
       " ['Harry Rowohlt', 'David Sedaris', 'David Lodge'],\n",
       " ['Serdar Somuncu'],\n",
       " ['Thomas Vašek'],\n",
       " ['Matti Y. Joensuu'],\n",
       " ['Joachim Bernd Vollmer'],\n",
       " ['Rainer Hank'],\n",
       " ['Oscar de Muriel'],\n",
       " ['Brandon Sanderson'],\n",
       " ['Wolfgang Jeschke'],\n",
       " ['Paul Cleave'],\n",
       " ['Howard Weinstein'],\n",
       " ['Michael Jürgs'],\n",
       " ['Nina Garcia'],\n",
       " ['Vivian Dittmar'],\n",
       " ['Manfred Gortz'],\n",
       " ['Stephen King'],\n",
       " ['Charlotte Bronte'],\n",
       " ['Helga Föger'],\n",
       " ['Birgit Hasselbusch', 'Stefan Grothoff'],\n",
       " ['Rachel Gibson'],\n",
       " ['Roger Pihl'],\n",
       " ['Horst Evers'],\n",
       " ['Michael Grumley'],\n",
       " ['Hans-Martin Lübking'],\n",
       " ['Naomi Novik'],\n",
       " ['Erik Axl Sund'],\n",
       " ['Prof. Dr. Thomas Kretschmar', 'Martin Tzschaschel'],\n",
       " ['Wolfgang Hohlbein'],\n",
       " ['Anja Janotta'],\n",
       " ['Kester Schlenz'],\n",
       " ['Jan Moran'],\n",
       " ['Burkhard Spinnen'],\n",
       " ['Oliver Geyer'],\n",
       " ['Donna Tartt'],\n",
       " ['Brian Staveley'],\n",
       " ['Richard David Precht'],\n",
       " ['Michaela Karl'],\n",
       " ['Stefan Wilfert'],\n",
       " ['Vadim Tschenze', 'Dani Felber'],\n",
       " ['Homer'],\n",
       " ['Thomas Greanias'],\n",
       " ['Bernhard Aichner'],\n",
       " ['Gisbert Haefs'],\n",
       " ['Andreas Pietsch', 'Barbara Stollberg-Rilinger'],\n",
       " ['Diana Faust'],\n",
       " ['Karen Swan'],\n",
       " ['Paul McAuley'],\n",
       " ['Karsten Brandt'],\n",
       " ['Raymond Feist'],\n",
       " ['Richard Morgan'],\n",
       " ['Simon Scarrow', 'T. J. Andrews'],\n",
       " ['Deborah Crombie'],\n",
       " ['Thomas Cathcart', 'Daniel Klein'],\n",
       " ['Wladimir Kaminer', 'Axel Hacke', 'Håkan Nesser'],\n",
       " ['Shabnam Arzt', 'Wolfgang Arzt'],\n",
       " ['Dennis L. McKiernan'],\n",
       " ['Kristin Halbrook'],\n",
       " ['Raoul Schrott'],\n",
       " ['James Corey'],\n",
       " ['Anne Berest'],\n",
       " ['Thea Dorn'],\n",
       " ['Sharon Bolton'],\n",
       " ['Kurt Langbein'],\n",
       " ['Thorsten Schröder'],\n",
       " ['Andrea Richter'],\n",
       " ['Tamara Dietl'],\n",
       " ['Thomas Weiß'],\n",
       " ['Nuno Mendes'],\n",
       " ['Anna Enquist'],\n",
       " ['Hans Greissing', 'Charlotte Rogers'],\n",
       " ['Christopher Hitchens'],\n",
       " ['Gundula Fuchsberger'],\n",
       " ['Liz Balfour'],\n",
       " ['Anne Jones'],\n",
       " ['Serdar Somuncu'],\n",
       " ['Manfred Theisen'],\n",
       " ['Dirk Hempel'],\n",
       " ['Wolfgang Hohlbein'],\n",
       " ['Hademar Bankhofer'],\n",
       " ['Tenzin Wangyal Rinpoche'],\n",
       " ['P.C. Cast'],\n",
       " ['Harald Lesch', 'Wilhelm Vossenkuhl'],\n",
       " ['Ulf Torreck'],\n",
       " ['Karen Clarke'],\n",
       " ['Gerhart Hauptmann'],\n",
       " ['Steven Erikson'],\n",
       " ['Usch Luhn'],\n",
       " ['Peter Stepan'],\n",
       " ['Ute Krause'],\n",
       " ['Enid Blyton'],\n",
       " ['Dagmar Trodler'],\n",
       " ['Lynn Raven'],\n",
       " ['Meg Cabot'],\n",
       " ['Enid Blyton'],\n",
       " ['Sara Shepard'],\n",
       " ['Jon Stock'],\n",
       " ['Ingo Siegner'],\n",
       " ['Uwe Birnstein'],\n",
       " ['Neale Donald Walsch'],\n",
       " ['Geneva Lee'],\n",
       " ['Terry Brooks'],\n",
       " ['Annette Roeder'],\n",
       " ['S.C. Stephens'],\n",
       " ['NEON', 'Marc Schürmann'],\n",
       " ['Rudy Rucker'],\n",
       " ['Charles Dickens'],\n",
       " ['Ingeborg Bachmann'],\n",
       " ['Kalayna Price'],\n",
       " ['Fabian Lenk'],\n",
       " ['Robert Charles Wilson'],\n",
       " ['Angelika Felenda'],\n",
       " ['Simon Scarrow', 'T. J. Andrews'],\n",
       " ['Ajahn Brahm'],\n",
       " ['Ernst Piper'],\n",
       " ['Jean M. Auel'],\n",
       " ['Tanja Heitmann'],\n",
       " ['Melanie Raabe'],\n",
       " ['Stephanie Laurens'],\n",
       " ['Alan Bradley'],\n",
       " ['Tobias Haberl'],\n",
       " ['Donald Ray Pollock'],\n",
       " ['nan'],\n",
       " ['Celeste Bradley'],\n",
       " ['Paul Cleave'],\n",
       " ['Annette Roeder'],\n",
       " ['Heike Schwarz', 'Sara Appelhagen', 'Kirsten Witte'],\n",
       " ['Martha Conway'],\n",
       " ['Einar Kárason'],\n",
       " ['J. R. Ward'],\n",
       " ['António Lobo Antunes'],\n",
       " ['Tina Georgescu', 'Razvan Georgescu'],\n",
       " ['Royce Buckingham'],\n",
       " ['Sara Raasch'],\n",
       " ['Dr. Michael Mosley', 'Peta Bee'],\n",
       " ['Vina Jackson'],\n",
       " ['N.K. Jemisin'],\n",
       " ['Elizabeth Craft', 'Shea Olsen'],\n",
       " ['Julie Kagawa'],\n",
       " ['Clive Cussler'],\n",
       " ['David Brooks'],\n",
       " ['Sandra Ingerman'],\n",
       " ['Ingo Siegner'],\n",
       " ['Markus Reiter'],\n",
       " ['Pierre Franckh'],\n",
       " ['Jeffery Deaver'],\n",
       " ['James Swallow'],\n",
       " ['Wolf Funfack'],\n",
       " ['Ulrich Strunz'],\n",
       " ['Britta Sabbag', 'Maite Kelly'],\n",
       " ['Sara Nović'],\n",
       " ['Werner Eberwein'],\n",
       " ['Shelly Kaldunski'],\n",
       " ['Mitchell Hogan'],\n",
       " ['Colin Taylor'],\n",
       " ['Günter M. Ziegler'],\n",
       " ['Harald Martenstein'],\n",
       " ['Claudia Dornieden', 'Stefan Rieß'],\n",
       " ['René Freund'],\n",
       " ['Adam Hamdy'],\n",
       " ['Michael Moss'],\n",
       " ['John Cleese'],\n",
       " ['Jeanne Siaud-Facchin'],\n",
       " ['Keri Arthur'],\n",
       " ['Giuseppe Furno'],\n",
       " ['Ursula James'],\n",
       " ['Jenny Erpenbeck'],\n",
       " ['Courtney Roulston'],\n",
       " ['Willi Fährmann'],\n",
       " ['Nina Grunenberg'],\n",
       " ['Melda Akbas'],\n",
       " ['Sharon Salzberg', 'Robert Thurman'],\n",
       " ['Ruth Rendell'],\n",
       " ['nan'],\n",
       " ['James Patterson', 'David Ellis'],\n",
       " ['nan'],\n",
       " ['Bora Ćosić'],\n",
       " ['Gisela Anna Erler'],\n",
       " ['Richard David Precht'],\n",
       " ['Dr. med. Rosemarie Unshelm'],\n",
       " ['Richard Yates'],\n",
       " ['Petra Durst-Benning'],\n",
       " ['Denise Mina'],\n",
       " ['Tad Williams'],\n",
       " ['Tami Hoag'],\n",
       " ['Karl Ove Knausgård'],\n",
       " ['Eberhard Krüger'],\n",
       " ['Meg Cabot'],\n",
       " ['Michael Marcus Thurner'],\n",
       " ['Wolf Jobst Siedler'],\n",
       " ['Galina Schatalova'],\n",
       " ['Ellen Sandberg'],\n",
       " ['Abby Hanlon'],\n",
       " ['Jörg Zittlau'],\n",
       " ['Rebekka Reinhard'],\n",
       " ['Harlan Ellison'],\n",
       " ['Diverse'],\n",
       " ['Karl Ove Knausgård'],\n",
       " ['Meg Cabot'],\n",
       " ['Hollis Seamon'],\n",
       " ['Jana Lukas'],\n",
       " ['Marc Levy'],\n",
       " ['Irvin D. Yalom'],\n",
       " ['J. R. Ward'],\n",
       " ['David Zinczenko'],\n",
       " ['Isabel Schupp'],\n",
       " ['Holger Reiners'],\n",
       " ['Michael Jürgs'],\n",
       " ['Michael McCollum'],\n",
       " ['Julie Lescault'],\n",
       " ['Christine Feehan'],\n",
       " ['Anna Rosendahl'],\n",
       " ['Paul Hoffman'],\n",
       " ['Stephen King'],\n",
       " ['Barbara Ann Brennan'],\n",
       " ['Edo Popović'],\n",
       " ['Catherine Alliott'],\n",
       " ['Nora Roberts'],\n",
       " ['Heinrich Heine',\n",
       "  'Matthias Claudius',\n",
       "  'Joachim Ringelnatz',\n",
       "  'Hoffmann von Fallersleben',\n",
       "  'Karel Jaromir Erben'],\n",
       " ['A. J. Rich'],\n",
       " ['Martin Cruz Smith'],\n",
       " ['Anke Greifeneder'],\n",
       " ['Edward Follis', 'Douglas Century'],\n",
       " ['Jens Westerbeck'],\n",
       " ['Jonathan Stroud'],\n",
       " ['Katarzyna Mol-Wolf'],\n",
       " ['Antonella Boralevi'],\n",
       " ['Victoria Hislop'],\n",
       " ['Giuliano Belfiore', 'Francesco Sbano'],\n",
       " ['Ernst Ulrich von Weizsäcker', 'Anders Wijkman'],\n",
       " ['Sam Jolig'],\n",
       " ['Adelheid Ohlig'],\n",
       " ['Hermine König', 'Karl Heinz König', 'Karl Joseph Klöckner'],\n",
       " ['Hermine König', 'Karl Heinz König'],\n",
       " ['Andreas Wirsching'],\n",
       " ['Axel Dammler'],\n",
       " ['Frank Rehfeld'],\n",
       " ['Adam Nevill'],\n",
       " ['Brandon Sanderson'],\n",
       " ['Lia Riley'],\n",
       " ['Stefan Lukschy'],\n",
       " ['Christian Cantrell'],\n",
       " ['Jane Austen'],\n",
       " ['Chandran Nair'],\n",
       " ['Jules Verne'],\n",
       " ['Deborah Chester'],\n",
       " ['Lena Klassen'],\n",
       " ['Mo Hayder'],\n",
       " ['Alan Parks'],\n",
       " ['Frank Swain'],\n",
       " ['Stephanie Grimm'],\n",
       " ['Wolfgang Jeschke'],\n",
       " ['Rajiv Parti', 'Paul Perry'],\n",
       " ['Dennis E. Taylor'],\n",
       " ['Jane Shemilt'],\n",
       " ['Pierre Grimbert'],\n",
       " ['Boris Koch'],\n",
       " ['Janne Teller'],\n",
       " ['Chris Carter', 'James Peisker'],\n",
       " ['Clive Cussler', 'Graham Brown'],\n",
       " ['Neal Stephenson'],\n",
       " ['Andrew Blum'],\n",
       " ['Jens Andersen'],\n",
       " ['Michael McBride'],\n",
       " ['Maria von Welser'],\n",
       " ['Søren Kierkegaard'],\n",
       " ['nan'],\n",
       " ['Manfred Theisen'],\n",
       " ['Noah Hawley'],\n",
       " ['Susanne Goga'],\n",
       " ['Lena Andersson'],\n",
       " ['Michelle Callahan'],\n",
       " ['Jesper Juul'],\n",
       " ['Edward St Aubyn'],\n",
       " ['Henning Boëtius'],\n",
       " ['Marion Grillparzer'],\n",
       " ['Hans Joachim Schellnhuber'],\n",
       " ['Martin Nusch'],\n",
       " ['Waltraut Engelberg'],\n",
       " ['Katherine Mansfield'],\n",
       " ['Jonathan Holt'],\n",
       " ['Eric Kandel'],\n",
       " ['Fiona McIntosh'],\n",
       " ['Christoph Peters'],\n",
       " ['Linda Lovelace'],\n",
       " ['Sally Beauman'],\n",
       " ['Christopher Paolini'],\n",
       " ['Hardy Schober'],\n",
       " ['Michael Robotham'],\n",
       " ['Ulla Hahn'],\n",
       " ['Martin Schwarz Lausten', 'Johannes Schilling'],\n",
       " ['Ulrich Sonnenschein'],\n",
       " ['Gina Schad'],\n",
       " ['Italo Svevo'],\n",
       " ['Chrisanna Northrup', 'Pepper Schwartz', 'James Witte'],\n",
       " ['Nora Roberts', 'J.D. Robb'],\n",
       " ['Catherine Ponder'],\n",
       " ['Enid Blyton'],\n",
       " ['Sigrid Meggendorfer'],\n",
       " ['Norbert Hummelt'],\n",
       " ['Ulla Hahn'],\n",
       " ['Annie Darling'],\n",
       " [\"Caragh O'Brien\"],\n",
       " ['Charles Todd'],\n",
       " ['Bernhard Wolff'],\n",
       " ['Karl Pilny'],\n",
       " ['Alastair Reynolds'],\n",
       " ['Thich Nhat Hanh'],\n",
       " ['Dana Reinhardt'],\n",
       " ['Jane Corry'],\n",
       " ['Ursel Scheffler'],\n",
       " ['Tom Wood'],\n",
       " ['Vonda N. McIntyre'],\n",
       " ['Hilary Boyd'],\n",
       " ['Maud Lethielleux'],\n",
       " ['Marc Ritter', 'Tom Ising'],\n",
       " ['Enid Blyton'],\n",
       " ['Christian Schwarz'],\n",
       " ['Anna Simons'],\n",
       " ['Stephanie Laurens'],\n",
       " ['Gemma Burgess'],\n",
       " ['Robert Muchamore'],\n",
       " ['Catherine Cookson'],\n",
       " ['Vina Jackson'],\n",
       " ['Sara Bilotti'],\n",
       " ['Carolyn J. Cherryh'],\n",
       " ['John Lewis Gaddis'],\n",
       " ['Philip Kiefer'],\n",
       " ['Henning Mankell'],\n",
       " ['Andrés Pascual'],\n",
       " ['James A. Moore'],\n",
       " ['Jon Skovron'],\n",
       " ['Helmut Krausser'],\n",
       " ['Adam Hamdy'],\n",
       " ['Sidney Sheldon'],\n",
       " ['Kelley York'],\n",
       " ['Peter Vollmer'],\n",
       " ['Stephen Greenblatt'],\n",
       " ['Robin Cook'],\n",
       " ['Frank Baer'],\n",
       " ['Michael Scott', 'Melanie Ruth Rose'],\n",
       " ['Janet Evanovich'],\n",
       " ['Michael Winterhoff'],\n",
       " ['Richard Mason'],\n",
       " ['Horst A. Friedrichs', 'Simon Crompton', 'Toby Egelnick'],\n",
       " ['Coreene Callahan'],\n",
       " ['Franz Alt'],\n",
       " ['Elizabeth Graver'],\n",
       " ['Jan Costin Wagner'],\n",
       " ['Julia Breitenöder'],\n",
       " ['Luca Veste'],\n",
       " ['Hajar Taddigs'],\n",
       " ['James Patterson'],\n",
       " ['Dagmar Knecht'],\n",
       " ['David Stubbs'],\n",
       " ['Usch Luhn'],\n",
       " ['Jane McGonigal'],\n",
       " ['Daniel Defoe'],\n",
       " ['Alberto Villoldo', 'Dr. David Perlmutter'],\n",
       " ['Stefan Zweig'],\n",
       " ['Mark Billingham'],\n",
       " ['Linda Howard'],\n",
       " ['Jack Vance'],\n",
       " ['Laurie Frankel'],\n",
       " ['David Hair'],\n",
       " ['Julie Cohen'],\n",
       " ['Élisa Géhin'],\n",
       " ['Cornelia Funke'],\n",
       " ['Dalai Lama'],\n",
       " ['Konstantin Wecker'],\n",
       " ['Deana Zinßmeister'],\n",
       " ['Jan Dochhorn'],\n",
       " ['Bettina Belitz'],\n",
       " ['Yvonne Hofstetter'],\n",
       " ['Anna Todd'],\n",
       " ['Veronica Roth'],\n",
       " ['Halldór Gudmundsson'],\n",
       " ['Aurélie Bastian'],\n",
       " ...]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(val_data_authors.values) + list(book_df['author'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21169, 10283), (21169,))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_df[\"author\"] = book_df[\"author\"].apply(lambda x: [y.strip() for y in str(x).split(',')])\n",
    "val_data_authors = validation_data[\"author\"].apply(lambda x: [y.strip() for y in str(x).split(',')])\n",
    "\n",
    "mlb = MultiLabelBinarizer()\n",
    "mlb.fit(list(val_data_authors.values) + list(book_df['author'].values))\n",
    "X = mlb.transform(book_df[\"author\"])\n",
    "y = book_df['cat_map']\n",
    "# y2 = book_df['top_level']\n",
    "\n",
    "clf = MultinomialNB()\n",
    "clf.fit(X, y)\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = mlb.transform(val_data_authors)\n",
    "pred_prob = clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "## Categorical Naive Bayes\n",
    "\n",
    "book_df = repeat_authors(book_df)\n",
    "\n",
    "X = book_df['author'].values.reshape((-1,1))\n",
    "y = book_df['cat_map']\n",
    "\n",
    "%%time\n",
    "clf = CategoricalNB()\n",
    "clf.fit(X, y)\n",
    "\n",
    "accuracy_score(y, clf.predict(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2079, 330)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are seven more classes missed out on the SVM predictions. The average value for each prediction on the remaining 330 probabilities will be added for the remaining 7 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "seven_dummy = np.repeat(predictions.mean(axis=1).reshape(-1,1), 7, axis=1)\n",
    "preds = np.hstack((predictions, seven_dummy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ENSEMBLING STEP\n",
    "predictions = preds * pred_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "le_mapping_inv = {v: k for k, v in le_mapping.items()}\n",
    "\n",
    "def reverseMap(number):\n",
    "    return le_mapping_inv[number]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_prob_list = [i.tolist() for i in predictions]\n",
    "validation_data[\"class_probs\"] = y_prob_list\n",
    "validation_data[\"class_count\"] = class_counts.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# validation_data[\"predictions\"]  = \n",
    "validation_data[\"class_predictions_e\"] = validation_data.apply(lambda x:getClasses(x[\"class_probs\"],x[\"class_count\"]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "validation_data[\"class_predictions\"] = validation_data[\"class_predictions_e\"].apply(lambda x: [reverseMap(i) for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def formatOutput(isbn,class_predictions):\n",
    "    classes = [\"\\t\"+str(i) for i in class_predictions]\n",
    "    return str(isbn) + \"\".join(classes) + \"\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "validation_data[\"class_predictions_top_level\"] = validation_data[\"class_predictions\"].apply(lambda cat_paths: [x.split(\" > \")[0] for x in cat_paths])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>description</th>\n",
       "      <th>author</th>\n",
       "      <th>published_date</th>\n",
       "      <th>isbn</th>\n",
       "      <th>class_probs</th>\n",
       "      <th>class_count</th>\n",
       "      <th>class_predictions_e</th>\n",
       "      <th>class_predictions</th>\n",
       "      <th>class_predictions_top_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Selbstständig werden im Glauben</td>\n",
       "      <td>Die Konfirmandenzeit wird für Jugendliche beso...</td>\n",
       "      <td>Hans-Martin Lübking</td>\n",
       "      <td>2013-07-22</td>\n",
       "      <td>9783579074030</td>\n",
       "      <td>[1.6924214503943732e-06, 5.923485481108555e-07...</td>\n",
       "      <td>1</td>\n",
       "      <td>[171]</td>\n",
       "      <td>[Literatur &amp; Unterhaltung &gt; Romane &amp; Erzählungen]</td>\n",
       "      <td>[Literatur &amp; Unterhaltung]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Das Buch der Schatten - Schwarze Seelen</td>\n",
       "      <td>Als Morgan von einer prophetischen Vision heim...</td>\n",
       "      <td>Cate Tiernan</td>\n",
       "      <td>2013-06-10</td>\n",
       "      <td>9783570380093</td>\n",
       "      <td>[1.6413662804066912e-06, 5.925976887299815e-07...</td>\n",
       "      <td>1</td>\n",
       "      <td>[171]</td>\n",
       "      <td>[Literatur &amp; Unterhaltung &gt; Romane &amp; Erzählungen]</td>\n",
       "      <td>[Literatur &amp; Unterhaltung]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     title  \\\n",
       "0          Selbstständig werden im Glauben   \n",
       "1  Das Buch der Schatten - Schwarze Seelen   \n",
       "\n",
       "                                         description               author  \\\n",
       "0  Die Konfirmandenzeit wird für Jugendliche beso...  Hans-Martin Lübking   \n",
       "1  Als Morgan von einer prophetischen Vision heim...         Cate Tiernan   \n",
       "\n",
       "  published_date           isbn  \\\n",
       "0     2013-07-22  9783579074030   \n",
       "1     2013-06-10  9783570380093   \n",
       "\n",
       "                                         class_probs  class_count  \\\n",
       "0  [1.6924214503943732e-06, 5.923485481108555e-07...            1   \n",
       "1  [1.6413662804066912e-06, 5.925976887299815e-07...            1   \n",
       "\n",
       "  class_predictions_e                                  class_predictions  \\\n",
       "0               [171]  [Literatur & Unterhaltung > Romane & Erzählungen]   \n",
       "1               [171]  [Literatur & Unterhaltung > Romane & Erzählungen]   \n",
       "\n",
       "  class_predictions_top_level  \n",
       "0  [Literatur & Unterhaltung]  \n",
       "1  [Literatur & Unterhaltung]  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "submissions = validation_data.apply(lambda x:formatOutput(x[\"isbn\"],x[\"class_predictions_top_level\"]),axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submissions = np.insert(submissions,0,\"subtask_a\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission_format = \"\".join(submissions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission_file = open(\"../../data/answers_svm_multinomialNB_ensemble.txt\",'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission_file.writelines(submission_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets check the confusion matrix (Do not run this , this will take long time and will not yield good results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "array = confusion_matrix(y_pred,y_test,labels=list(le_mapping.values()))\n",
    "df_cm = pd.DataFrame(array, index = list(le_mapping.keys()),\n",
    "                  columns = list(le_mapping.keys()))\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(df_cm, annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inv_map = {v: k for k, v in le_mapping.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_top = [inv_map[i].split(\" > \")[0] for i in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test_top = [inv_map[i].split(\" > \")[0] for i in y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confarray = confusion_matrix(y_test_top, y_pred_top,labels=list(set(y_test_top)))\n",
    "confarray.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conf = pd.DataFrame(confarray,columns=list(set(y_test_top)))\n",
    "conf.index = list(set(y_test_top))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc35a92390>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(conf, annot=True,fmt=\"0.5g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8235294117647058"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred_top,y_test_top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  36,    0,    0,    1,    0,    0,    3,    1],\n",
       "       [   1,  144,   11,    0,    0,    1,   35,   14],\n",
       "       [   0,    8,  123,    2,    0,    9,    0,   28],\n",
       "       [   0,    3,    1,  687,    2,   83,   19,    9],\n",
       "       [   0,    0,    2,    2,   19,    0,    2,    8],\n",
       "       [   2,    8,   19,   77,    3, 1697,   22,  123],\n",
       "       [   3,   46,    4,   14,    2,    5,  363,   59],\n",
       "       [   0,    2,   13,    5,    3,   35,   57,  417]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_pred_top,y_test_top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deeplearning_36",
   "language": "python",
   "name": "deeplearning_36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}