Assignment 7 - NLP, entity extraction and visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework Assignment 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:** Select a book and construct a network of people and locations (20pt). Visualize the network using Cytoscape or Gephi (20pt).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from tqdm import tqdm\n",
    "from collections import Counter\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ORG': {'  ': 506,\n",
       "  'thror': 4,\n",
       "  'smaug': 36,\n",
       "  'moria': 6,\n",
       "  'orcrist': 4,\n",
       "  'goblins’': 5,\n",
       "  'eagles': 17,\n",
       "  'carrock': 9,\n",
       "  'mirkwood': 6,\n",
       "  'attercop': 6,\n",
       "  'elvenking': 12,\n",
       "  'bard': 4},\n",
       " 'PERSON': {'bilbo': 526,\n",
       "  '  ': 718,\n",
       "  'thorin': 215,\n",
       "  'elrond': 25,\n",
       "  'baggins': 74,\n",
       "  'gandalf': 178,\n",
       "  'kili': 37,\n",
       "  'fili': 47,\n",
       "  'dori': 37,\n",
       "  'bombur': 35,\n",
       "  'dale': 33,\n",
       "  'smaug': 37,\n",
       "  'william': 27,\n",
       "  'bert': 28,\n",
       "  'tom': 21,\n",
       "  'gollum': 81,\n",
       "  'beorn': 62,\n",
       "  'bard': 24,\n",
       "  'dain': 27},\n",
       " 'WORK_OF_ART': {'  ': 24, 'the master': 9, 'farewell': 6},\n",
       " 'EVENT': {},\n",
       " 'CARDINAL': {'one': 128,\n",
       "  'two': 64,\n",
       "  'three': 23,\n",
       "  'four': 9,\n",
       "  'thirteen': 7,\n",
       "  'twelve': 8,\n",
       "  'six': 9,\n",
       "  'half': 15,\n",
       "  'ten': 4,\n",
       "  'fourteen': 6,\n",
       "  'hundreds': 8,\n",
       "  'fifteen': 6},\n",
       " 'LAW': {},\n",
       " 'NORP': {'dwalin': 6},\n",
       " 'LOC': {'mountain': 97,\n",
       "  'misty mountains': 6,\n",
       "  'east': 20,\n",
       "  'mirkwood': 12,\n",
       "  'west': 15,\n",
       "  'south': 21,\n",
       "  'north': 23,\n",
       "  'the running river': 6,\n",
       "  'the long lake': 4,\n",
       "  'the mountain': 5,\n",
       "  'the misty mountains': 5,\n",
       "  'the lonely mountain': 9,\n",
       "  'mountains': 5,\n",
       "  'the moon': 4,\n",
       "  'lake': 21,\n",
       "  'the forest river': 4,\n",
       "  'forest river': 4,\n",
       "  'running river': 4},\n",
       " 'DATE': {'today': 7,\n",
       "  'tomorrow': 8,\n",
       "  'next day': 6,\n",
       "  'days': 11,\n",
       "  'those days': 4,\n",
       "  'one day': 11,\n",
       "  'may': 10,\n",
       "  'june': 6,\n",
       "  'all day': 6,\n",
       "  'the day': 5,\n",
       "  'summer': 4,\n",
       "  'that day': 4,\n",
       "  'midsummer': 5,\n",
       "  'autumn': 8,\n",
       "  'the days': 5,\n",
       "  'winter': 8},\n",
       " 'FAC': {'the front gate': 9, 'the great goblin': 4, 'gate': 9},\n",
       " 'ORDINAL': {'first': 76,\n",
       "  'second': 12,\n",
       "  'third': 11,\n",
       "  'fourteenth': 7,\n",
       "  '’ll': 7},\n",
       " 'PRODUCT': {'wargs': 14},\n",
       " 'GPE': {'thrain': 16,\n",
       "  'balin': 46,\n",
       "  'dwalin': 11,\n",
       "  'bifur': 9,\n",
       "  'bombur': 23,\n",
       "  'wood': 9,\n",
       "  'lake-town': 5},\n",
       " 'LANGUAGE': {'english': 4, 'oin': 5},\n",
       " 'QUANTITY': {},\n",
       " 'TIME': {'morning': 16,\n",
       "  'evening': 6,\n",
       "  'the night': 10,\n",
       "  'the morning': 5,\n",
       "  'night': 14,\n",
       "  'tonight': 9,\n",
       "  'last night': 5,\n",
       "  'that night': 4,\n",
       "  'midnight': 6}}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp = spacy.load('en_core_web_lg')\n",
    "book_path = \"./the hobbit.txt\"\n",
    "\n",
    "def get_entites_dict_from_text(text):\n",
    "    entities_dict= {}\n",
    "    #using spaCy to get entities\n",
    "    doc = nlp(text)\n",
    "    for entity in doc.ents:\n",
    "        label = entity.label_\n",
    "        e = entity.text.lower()\n",
    "        if  label not in entities_dict:\n",
    "            entities_dict[label] = {}\n",
    "        if e not in entities_dict[label]:\n",
    "            entities_dict[label][e] = 0\n",
    "        entities_dict[label][e] += 1\n",
    "    return entities_dict\n",
    "\n",
    "def get_book_entities(path, person_min_times, other_entities_min_times=3):\n",
    "    txt = open(path,\"r\", encoding=\"utf8\", errors=\"ignore\").read()\n",
    "    txt = txt.replace(\"\\n\", \" \")\n",
    "    doc = nlp(txt)\n",
    "    d = get_entites_dict_from_text(txt) \n",
    "    entities_dict = {}\n",
    "    for k in d.keys():\n",
    "        min_times = other_entities_min_times\n",
    "        if k == \"PERSON\":\n",
    "            min_times = person_min_times\n",
    "        entity_dict = {k:v for k,v in d[k].items() if v>min_times}\n",
    "        entities_dict[k] = entity_dict\n",
    "    return entities_dict\n",
    "\n",
    "entities_dict = get_book_entities(book_path, 20)\n",
    "entities_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2525/2525 [00:26<00:00, 95.23it/s] \n"
     ]
    }
   ],
   "source": [
    "def get_links(txt):\n",
    "    links_set = set()\n",
    "    doc = nlp(txt)\n",
    "    l = [entity.text.lower().strip() for entity in doc.ents if entity.label_ == \"PERSON\" or entity.label_ == \"LOC\"]\n",
    "    for e1 in l:\n",
    "        for e2 in l:\n",
    "            if e1 == e2 or len(e1) < 2 or len(e2)< 2:\n",
    "                continue\n",
    "            if e1 > e2:\n",
    "                e1, e2 = e2, e1 # switch order\n",
    "            links_set.add((e1,e2))\n",
    "    return links_set\n",
    "\n",
    "\n",
    "txt = open(book_path).read()\n",
    "paragraphs_list = txt.split(\"\\n\\n\")\n",
    "links_dict = {}\n",
    "links_list = []    \n",
    "for para in tqdm(paragraphs_list):\n",
    "    # for each paragraph each link counts only once\n",
    "    links_list += list(get_links(para))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('bilbo', 'gandalf'), 46),\n",
       " (('bilbo', 'thorin'), 35),\n",
       " (('bilbo', 'gollum'), 27),\n",
       " (('fili', 'kili'), 20),\n",
       " (('baggins', 'bilbo'), 19),\n",
       " (('baggins', 'gandalf'), 14),\n",
       " (('gandalf', 'thorin'), 14),\n",
       " (('bilbo', 'fili'), 13),\n",
       " (('bilbo', 'dori'), 13),\n",
       " (('bilbo', 'kili'), 12),\n",
       " (('mountain', 'thorin'), 12),\n",
       " (('balin', 'bilbo'), 12),\n",
       " (('bifur', 'bofur'), 11),\n",
       " (('baggins', 'thorin'), 10),\n",
       " (('fili', 'thorin'), 10),\n",
       " (('bilbo', 'bombur'), 9),\n",
       " (('bilbo', 'mountain'), 9),\n",
       " (('beorn', 'gandalf'), 9),\n",
       " (('arkenstone', 'thorin'), 9),\n",
       " (('dori', 'nori'), 8),\n",
       " (('beorn', 'bilbo'), 8),\n",
       " (('dain', 'thorin'), 8),\n",
       " (('kili', 'thorin'), 7),\n",
       " (('bert', 'william'), 7),\n",
       " (('bert', 'tom'), 7),\n",
       " (('elrond', 'gandalf'), 7),\n",
       " (('dori', 'ori'), 6),\n",
       " (('bofur', 'nori'), 6),\n",
       " (('dori', 'fili'), 6),\n",
       " (('dori', 'kili'), 6),\n",
       " (('bofur', 'dori'), 6),\n",
       " (('bilbo', 'smaug'), 6),\n",
       " (('bifur', 'bilbo'), 5),\n",
       " (('balin', 'kili'), 5),\n",
       " (('balin', 'fili'), 5),\n",
       " (('bifur', 'ori'), 5),\n",
       " (('bifur', 'dori'), 5),\n",
       " (('bifur', 'fili'), 5),\n",
       " (('bofur', 'ori'), 5),\n",
       " (('bofur', 'kili'), 5)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Counter(links_list)\n",
    "c.most_common(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Name: \\nType: Graph\\nNumber of nodes: 26\\nNumber of edges: 48\\nAverage degree:   3.6923'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = nx.Graph()\n",
    "for e,count in dict(c).items():\n",
    "    if count < 5:\n",
    "        # only links that appeared at least 5 times\n",
    "        continue\n",
    "    v1,v2 = e\n",
    "    g.add_edge(v1,v2, weight=count)\n",
    "nx.info(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "nx.draw_kamada_kawai(g, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.write_graphml_lxml(g, \"hobbit.graphml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = \"./hobbit network.PNG\", width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2:** Use the [Seinfeld Text corpus](https://www.kaggle.com/luongleanstocode/seinfeld-text-corpus)\n",
    "to create a weighted social networks of Seinfeld characters. \n",
    "Visualize the number of lines each character of the show said (only for the main characters) (20pt).\n",
    "Create a classifier that given a line of text can predict\n",
    " if the line was said by Jerry, Elaine, George, or Kramer (30pt).\n",
    "Evaluate your classifier (10pt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "import spacy\n",
    "import turicreate as tc\n",
    "from afinn import Afinn\n",
    "import en_core_web_lg\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./seinfeld_corpus.txt\") as f:\n",
    "    seinfeld_corpus = f.read()\n",
    "entities_dict = get_entites_dict_from_text(seinfeld_corpus[:999999]) # sample beacuse of error with more text\n",
    "main_characters = [(k,v) for k, v in entities_dict['PERSON'].items() if v > 300]\n",
    "characters_speech_count = {}\n",
    "for character in main_characters:\n",
    "    current_character = character[0].upper()\n",
    "    characters_speech_count[current_character] = len(re.findall( r'' + current_character +':', seinfeld_corpus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f17b75bd208>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "character_speech_df = pd.DataFrame(list(characters_speech_count.items()), columns=['character', 'speech_count'])\n",
    "sns.barplot(data=character_speech_df, x='character', y='speech_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 64919/64919 [08:24<00:00, 128.72it/s]\n"
     ]
    }
   ],
   "source": [
    "dialoges = re.split('\\n', seinfeld_corpus)\n",
    "def get_links(txt):\n",
    "    links_set = set()\n",
    "    doc = nlp(txt)\n",
    "    l = [entity.text.lower().strip() for entity in doc.ents if entity.label_ == \"PERSON\" or entity.label_ == \"LOC\"]\n",
    "    for e1 in l:\n",
    "        for e2 in l:\n",
    "            if e1 == e2 or len(e1) < 2 or len(e2)< 2:\n",
    "                continue\n",
    "            if e1 > e2:\n",
    "                e1, e2 = e2, e1 # switch order\n",
    "            links_set.add((e1,e2))\n",
    "    return links_set\n",
    "\n",
    "\n",
    "links_list = []\n",
    "for dialogue in tqdm(dialoges):\n",
    "    if dialogue != '':\n",
    "        # for each dialogue each link counts only once\n",
    "        links_list += list(get_links(dialogue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('george', 'jerry'), 1649),\n",
       " (('elaine', 'jerry'), 1582),\n",
       " (('jerry', 'kramer'), 985),\n",
       " (('elaine', 'george'), 791),\n",
       " (('elaine', 'kramer'), 480),\n",
       " (('george', 'kramer'), 428),\n",
       " (('george', 'susan'), 216),\n",
       " (('jerry', 'newman'), 205),\n",
       " (('elaine', 'peterman'), 147),\n",
       " (('kramer', 'newman'), 129),\n",
       " (('helen', 'jerry'), 109),\n",
       " (('helen', 'morty'), 103),\n",
       " (('jerry', 'morty'), 100),\n",
       " (('frank', 'george'), 63),\n",
       " (('jerry', 'susan'), 62),\n",
       " (('george', 'wilhelm'), 62),\n",
       " (('jerry', 'leo'), 59),\n",
       " (('elaine', 'puddy'), 53),\n",
       " (('elaine', 'pitt'), 50),\n",
       " (('elaine', 'lippman'), 48),\n",
       " (('george', 'kruger'), 44),\n",
       " (('elaine', 'susan'), 43),\n",
       " (('bania', 'jerry'), 43),\n",
       " (('frank', 'kramer'), 38),\n",
       " (('elaine', 'newman'), 37),\n",
       " (('brett', 'elaine'), 36),\n",
       " (('jerry', 'jerry seinfeld'), 35),\n",
       " (('george', 'monk'), 35),\n",
       " (('george', 'steinbrenner'), 34),\n",
       " (('david', 'elaine'), 33),\n",
       " (('elaine', 'monk'), 32),\n",
       " (('jerry', 'uncle leo'), 31),\n",
       " (('elaine', 'kevin'), 31),\n",
       " (('george', 'peterman'), 30),\n",
       " (('jerry', 'mike'), 29),\n",
       " (('george', 'ross'), 29),\n",
       " (('estelle', 'frank'), 29),\n",
       " (('elaine', 'helen'), 28),\n",
       " (('bob', 'elaine'), 28),\n",
       " (('jerry', 'tony'), 28)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Counter(links_list)\n",
    "c.most_common(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Name: \\nType: Graph\\nNumber of nodes: 24\\nNumber of edges: 34\\nAverage degree:   2.8333'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = nx.Graph()\n",
    "for e,count in dict(c).items():\n",
    "    if count < 30:\n",
    "        # only links that appeared at least 5 times\n",
    "        continue\n",
    "    v1,v2 = e\n",
    "    g.add_edge(v1,v2, weight=count)\n",
    "nx.info(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/home/laadan/anaconda3/envs/BigData/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "nx.draw_kamada_kawai(g, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>line</th>\n",
       "      <th>character</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Do you know what this is all about? Do you kn...</td>\n",
       "      <td>JERRY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(pointing at George's shirt) See, to me, that...</td>\n",
       "      <td>JERRY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You do of course try on, when you buy?</td>\n",
       "      <td>JERRY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oh, you don't recall?</td>\n",
       "      <td>JERRY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Well, senator, I'd just like to know, what yo...</td>\n",
       "      <td>JERRY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                line character\n",
       "0   Do you know what this is all about? Do you kn...     JERRY\n",
       "1   (pointing at George's shirt) See, to me, that...     JERRY\n",
       "2             You do of course try on, when you buy?     JERRY\n",
       "3                              Oh, you don't recall?     JERRY\n",
       "4   Well, senator, I'd just like to know, what yo...     JERRY"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seinfeld_df = pd.DataFrame(columns=['line', 'character'])\n",
    "for character in main_characters:\n",
    "    current_character = character[0].upper()\n",
    "    all_sentences = re.findall(r'' + current_character + ': .*\\n', seinfeld_corpus)\n",
    "    for sentence in all_sentences:\n",
    "        seinfeld_df = seinfeld_df.append({'line': sentence.replace(current_character + ':', '').replace('\\n', ''), 'character': current_character}, ignore_index=True)\n",
    "seinfeld_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "afinn = Afinn()\n",
    "nlp = en_core_web_lg.load()\n",
    "seinfeld_sf = tc.SFrame(seinfeld_df)\n",
    "seinfeld_sf['words_dict'] = tc.text_analytics.count_words(seinfeld_sf['line'])\n",
    "seinfeld_sf['words_dict_no_stop'] = seinfeld_sf['words_dict'].dict_trim_by_keys(tc.text_analytics.stop_words(lang='en'), exclude=True)\n",
    "seinfeld_sf['words_2grams'] = tc.text_analytics.count_ngrams(seinfeld_sf['line'], n=2, method='word')\n",
    "seinfeld_sf['words_2grams'] = seinfeld_sf['words_2grams'].dict_trim_by_keys(tc.text_analytics.stop_words(lang='en'), exclude=True)\n",
    "seinfeld_sf['words_3grams'] = tc.text_analytics.count_ngrams(seinfeld_sf['line'], n=3, method='word')\n",
    "seinfeld_sf['words_3grams'] = seinfeld_sf['words_3grams'].dict_trim_by_keys(tc.text_analytics.stop_words(lang='en'), exclude=True)\n",
    "seinfeld_sf['afinn'] = seinfeld_sf['line'].apply(lambda t: afinn.score(t))\n",
    "seinfeld_sf['blob'] = seinfeld_sf['line'].apply(lambda t: TextBlob(t).sentiment.polarity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = []\n",
    "for i in tqdm(range(len(seinfeld_sf))):\n",
    "    text = seinfeld_sf[i][\"line\"]\n",
    "    l.append(nlp(text).vector)\n",
    "seinfeld_sf['embbeddings'] = l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">line</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">character</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">words_dict</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">words_dict_no_stop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> (pointing at George&#x27;s<br>shirt) See, to me, that ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;your&#x27;: 1, &#x27;with&#x27;: 1,<br>&#x27;live&#x27;: 1, &#x27;like&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;live&#x27;: 1, &#x27;mother&#x27;: 1,<br>&#x27;land&#x27;: 1, &#x27;high&#x27;: 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> You do of course try on,<br>when you buy? ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;buy&#x27;: 1, &#x27;do&#x27;: 1,<br>&#x27;when&#x27;: 1, &#x27;of&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;buy&#x27;: 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Oh, you don&#x27;t recall?</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;recall&#x27;: 1, &#x27;t&#x27;: 1,<br>&#x27;don&#x27;: 1, &#x27;oh&#x27;: 1, &#x27;y ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;recall&#x27;: 1, &#x27;don&#x27;: 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Well, senator, I&#x27;d just<br>like to know, what you ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;it&#x27;: 1, &#x27;when&#x27;: 1,<br>&#x27;you&#x27;: 2, &#x27;what&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;senator&#x27;: 1, &#x27;knew&#x27;: 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Can you relax, it&#x27;s a<br>cup of coffee. Claire is ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;professional&#x27;: 1,<br>&#x27;claire&#x27;: 1, &#x27;of&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;professional&#x27;: 1,<br>&#x27;claire&#x27;: 1, &#x27;cup&#x27;: 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Well, there&#x27;s this uh,<br>woman might be comin&#x27; ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;be&#x27;: 1, &#x27;in&#x27;: 1,<br>&#x27;might&#x27;: 1, &#x27;this&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;comin&#x27;: 1, &#x27;uh&#x27;: 1,<br>&#x27;woman&#x27;: 1} ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Oh, she&#x27;s really great.<br>I mean, she&#x27;s got like a ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;much&#x27;: 1, &#x27;know&#x27;: 1,<br>&#x27;is&#x27;: 1, &#x27;talking&#x27;: 2, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;talking&#x27;: 2, &#x27;pretty&#x27;:<br>1, &#x27;great&#x27;: 1, &#x27;warmth&#x27;: ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Oh, nothing happened,<br>you know, but is was ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;was&#x27;: 1, &#x27;nothing&#x27;: 1,<br>&#x27;oh&#x27;: 1, &#x27;is&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;happened&#x27;: 1, &#x27;great&#x27;:<br>1} ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Yeah.</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;yeah&#x27;: 1}</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;yeah&#x27;: 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"> Yeah, well...not<br>exactly. I mean, she ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">JERRY</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;together&#x27;: 1, &#x27;get&#x27;: 1,<br>&#x27;we&#x27;: 1, &#x27;maybe&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;seminar&#x27;: 1, &#x27;yeah&#x27;: 1,<br>&#x27;called&#x27;: 1, &#x27;ll&#x27;: 1, ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">words_2grams</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">words_3grams</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">afinn</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">blob</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">embbeddings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;with your&#x27;: 1, &#x27;live<br>with&#x27;: 1, &#x27;like you&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;live with your&#x27;: 1,<br>&#x27;like you live&#x27;: 1, &#x27;you ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.13272727272727275</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-6.27667829e-02<br>2.22127602e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;do of&#x27;: 1, &#x27;you do&#x27;: 1,<br>&#x27;of course&#x27;: 1, &#x27;course ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;when you buy&#x27;: 1, &#x27;do<br>of course&#x27;: 1, &#x27;you do ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-0.05372993  0.17371196<br>-0.24236836 -0.12451249 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;t recall&#x27;: 1, &#x27;don t&#x27;:<br>1, &#x27;oh you&#x27;: 1, &#x27;you ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;don t recall&#x27;: 1, &#x27;oh<br>you don&#x27;: 1, &#x27;you don ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-6.16721287e-02<br>2.45086119e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;and when&#x27;: 1, &#x27;you<br>knew&#x27;: 2, &#x27;knew and&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;you knew it&#x27;: 1, &#x27;when<br>you knew&#x27;: 1, &#x27;and when ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[ 1.50005659e-02<br>2.97425896e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;a professional&#x27;: 1, &#x27;is<br>a&#x27;: 1, &#x27;cup of&#x27;: 1, &#x27;of ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;a professional<br>waitress&#x27;: 1, &#x27;is a ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[ 1.16294005e-03<br>1.90382332e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;be comin&#x27;: 1, &#x27;might<br>be&#x27;: 1, &#x27;there s&#x27;: 1, ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;be comin in&#x27;: 1, &#x27;might<br>be comin&#x27;: 1, &#x27;well t ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-4.62472700e-02<br>2.32114941e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;much better&#x27;: 1, &#x27;know<br>obviously&#x27;: 1, &#x27;you ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;know obviously much&#x27;:<br>1, &#x27;you know obviously&#x27;: ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.26071428571428573</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-8.93691480e-02<br>3.00025612e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;was great&#x27;: 1, &#x27;is<br>was&#x27;: 1, &#x27;oh nothing&#x27; ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;is was great&#x27;: 1,<br>&#x27;nothing happened you&#x27;: ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-9.35519934e-02<br>3.83491427e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{}</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{}</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-8.96296576e-02<br>1.53486669e-01 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;ll get&#x27;: 1, &#x27;maybe we&#x27;:<br>1, &#x27;seminar and&#x27;: 1, &#x27;a ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{&#x27;we ll get&#x27;: 1, &#x27;maybe<br>we ll&#x27;: 1, &#x27;ll get ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.3125</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-4.24613431e-02<br>3.07772070e-01 ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 9 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tline\tstr\n",
       "\tcharacter\tstr\n",
       "\twords_dict\tdict\n",
       "\twords_dict_no_stop\tdict\n",
       "\twords_2grams\tdict\n",
       "\twords_3grams\tdict\n",
       "\tafinn\tfloat\n",
       "\tblob\tfloat\n",
       "\tembbeddings\tndarray\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------------------------------+-----------+-------------------------------+\n",
       "|              line             | character |           words_dict          |\n",
       "+-------------------------------+-----------+-------------------------------+\n",
       "|  (pointing at George's shi... |   JERRY   | {'your': 1, 'with': 1, 'li... |\n",
       "|  You do of course try on, ... |   JERRY   | {'buy': 1, 'do': 1, 'when'... |\n",
       "|      Oh, you don't recall?    |   JERRY   | {'recall': 1, 't': 1, 'don... |\n",
       "|  Well, senator, I'd just l... |   JERRY   | {'it': 1, 'when': 1, 'you'... |\n",
       "|  Can you relax, it's a cup... |   JERRY   | {'professional': 1, 'clair... |\n",
       "|  Well, there's this uh, wo... |   JERRY   | {'be': 1, 'in': 1, 'might'... |\n",
       "|  Oh, she's really great. I... |   JERRY   | {'much': 1, 'know': 1, 'is... |\n",
       "|  Oh, nothing happened, you... |   JERRY   | {'was': 1, 'nothing': 1, '... |\n",
       "|              Yeah.            |   JERRY   |          {'yeah': 1}          |\n",
       "|  Yeah, well...not exactly.... |   JERRY   | {'together': 1, 'get': 1, ... |\n",
       "+-------------------------------+-----------+-------------------------------+\n",
       "+-------------------------------+-------------------------------+\n",
       "|       words_dict_no_stop      |          words_2grams         |\n",
       "+-------------------------------+-------------------------------+\n",
       "| {'live': 1, 'mother': 1, '... | {'with your': 1, 'live wit... |\n",
       "|           {'buy': 1}          | {'do of': 1, 'you do': 1, ... |\n",
       "|    {'recall': 1, 'don': 1}    | {'t recall': 1, 'don t': 1... |\n",
       "|   {'senator': 1, 'knew': 2}   | {'and when': 1, 'you knew'... |\n",
       "| {'professional': 1, 'clair... | {'a professional': 1, 'is ... |\n",
       "| {'comin': 1, 'uh': 1, 'wom... | {'be comin': 1, 'might be'... |\n",
       "| {'talking': 2, 'pretty': 1... | {'much better': 1, 'know o... |\n",
       "|  {'happened': 1, 'great': 1}  | {'was great': 1, 'is was':... |\n",
       "|          {'yeah': 1}          |               {}              |\n",
       "| {'seminar': 1, 'yeah': 1, ... | {'ll get': 1, 'maybe we': ... |\n",
       "+-------------------------------+-------------------------------+\n",
       "+-------------------------------+-------+----------------------+\n",
       "|          words_3grams         | afinn |         blob         |\n",
       "+-------------------------------+-------+----------------------+\n",
       "| {'live with your': 1, 'lik... |  -2.0 | -0.13272727272727275 |\n",
       "| {'when you buy': 1, 'do of... |  0.0  |         0.0          |\n",
       "| {'don t recall': 1, 'oh yo... |  0.0  |         0.0          |\n",
       "| {'you knew it': 1, 'when y... |  2.0  |         0.0          |\n",
       "| {'a professional waitress'... |  0.0  |         0.1          |\n",
       "| {'be comin in': 1, 'might ... |  0.0  |         0.0          |\n",
       "| {'know obviously much': 1,... |  13.0 | 0.26071428571428573  |\n",
       "| {'is was great': 1, 'nothi... |  3.0  |         0.8          |\n",
       "|               {}              |  1.0  |         0.0          |\n",
       "| {'we ll get': 1, 'maybe we... |  1.0  |       -0.3125        |\n",
       "+-------------------------------+-------+----------------------+\n",
       "+-------------------------------+\n",
       "|          embbeddings          |\n",
       "+-------------------------------+\n",
       "| [-6.27667829e-02  2.221276... |\n",
       "| [-0.05372993  0.17371196 -... |\n",
       "| [-6.16721287e-02  2.450861... |\n",
       "| [ 1.50005659e-02  2.974258... |\n",
       "| [ 1.16294005e-03  1.903823... |\n",
       "| [-4.62472700e-02  2.321149... |\n",
       "| [-8.93691480e-02  3.000256... |\n",
       "| [-9.35519934e-02  3.834914... |\n",
       "| [-8.96296576e-02  1.534866... |\n",
       "| [-4.24613431e-02  3.077720... |\n",
       "+-------------------------------+\n",
       "[10 rows x 9 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train, test = seinfeld_sf.random_split(0.75)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n",
      "PROGRESS: The following methods are available for this type of problem.\n",
      "PROGRESS: BoostedTreesClassifier, RandomForestClassifier, DecisionTreeClassifier, LogisticClassifier\n",
      "PROGRESS: The returned model will be chosen according to validation accuracy.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Boosted trees classifier:</pre>"
      ],
      "text/plain": [
       "Boosted trees classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 29432</pre>"
      ],
      "text/plain": [
       "Number of examples          : 29432"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 4</pre>"
      ],
      "text/plain": [
       "Number of classes           : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 5</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 288744</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 288744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.730709     | 0.420767          | 0.418697            | 1.347354          | 1.353523            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.730709     | 0.420767          | 0.418697            | 1.347354          | 1.353523            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 1.446109     | 0.432217          | 0.420013            | 1.319641          | 1.332106            |</pre>"
      ],
      "text/plain": [
       "| 2         | 1.446109     | 0.432217          | 0.420013            | 1.319641          | 1.332106            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 2.139970     | 0.442036          | 0.424621            | 1.298506          | 1.315488            |</pre>"
      ],
      "text/plain": [
       "| 3         | 2.139970     | 0.442036          | 0.424621            | 1.298506          | 1.315488            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 2.832049     | 0.448356          | 0.430546            | 1.280625          | 1.304805            |</pre>"
      ],
      "text/plain": [
       "| 4         | 2.832049     | 0.448356          | 0.430546            | 1.280625          | 1.304805            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 3.556606     | 0.453894          | 0.440421            | 1.264978          | 1.294858            |</pre>"
      ],
      "text/plain": [
       "| 5         | 3.556606     | 0.453894          | 0.440421            | 1.264978          | 1.294858            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 6.964416     | 0.477134          | 0.442396            | 1.215149          | 1.274318            |</pre>"
      ],
      "text/plain": [
       "| 10        | 6.964416     | 0.477134          | 0.442396            | 1.215149          | 1.274318            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Random forest classifier:</pre>"
      ],
      "text/plain": [
       "Random forest classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 29432</pre>"
      ],
      "text/plain": [
       "Number of examples          : 29432"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 4</pre>"
      ],
      "text/plain": [
       "Number of classes           : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 5</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 288744</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 288744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.702553     | 0.414447          | 0.419355            | 1.292989          | 1.300563            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.702553     | 0.414447          | 0.419355            | 1.292989          | 1.300563            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 1.384271     | 0.425387          | 0.431205            | 1.286200          | 1.295787            |</pre>"
      ],
      "text/plain": [
       "| 2         | 1.384271     | 0.425387          | 0.431205            | 1.286200          | 1.295787            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 2.061249     | 0.429295          | 0.426596            | 1.283549          | 1.297569            |</pre>"
      ],
      "text/plain": [
       "| 3         | 2.061249     | 0.429295          | 0.426596            | 1.283549          | 1.297569            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 2.698353     | 0.429498          | 0.433180            | 1.283642          | 1.296395            |</pre>"
      ],
      "text/plain": [
       "| 4         | 2.698353     | 0.429498          | 0.433180            | 1.283642          | 1.296395            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 3.338439     | 0.427528          | 0.430546            | 1.283749          | 1.296592            |</pre>"
      ],
      "text/plain": [
       "| 5         | 3.338439     | 0.427528          | 0.430546            | 1.283749          | 1.296592            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 6.653715     | 0.429125          | 0.432521            | 1.282194          | 1.298445            |</pre>"
      ],
      "text/plain": [
       "| 10        | 6.653715     | 0.429125          | 0.432521            | 1.282194          | 1.298445            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Decision tree classifier:</pre>"
      ],
      "text/plain": [
       "Decision tree classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 29432</pre>"
      ],
      "text/plain": [
       "Number of examples          : 29432"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 4</pre>"
      ],
      "text/plain": [
       "Number of classes           : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 5</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 288744</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 288744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training Accuracy | Validation Accuracy | Training Log Loss | Validation Log Loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.733688     | 0.420767          | 0.418697            | 1.288623          | 1.309699            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.733688     | 0.420767          | 0.418697            | 1.288623          | 1.309699            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
      ],
      "text/plain": [
       "Logistic regression:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 29432</pre>"
      ],
      "text/plain": [
       "Number of examples          : 29432"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 4</pre>"
      ],
      "text/plain": [
       "Number of classes           : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 5</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 288744</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 288744"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients      : 866235</pre>"
      ],
      "text/plain": [
       "Number of coefficients      : 866235"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy | Validation Accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0         | 4        | 0.250000  | 0.796045     | 0.464053          | 0.403555            |</pre>"
      ],
      "text/plain": [
       "| 0         | 4        | 0.250000  | 0.796045     | 0.464053          | 0.403555            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 7        | 1.250000  | 6.242457     | 0.703452          | 0.432521            |</pre>"
      ],
      "text/plain": [
       "| 1         | 7        | 1.250000  | 6.242457     | 0.703452          | 0.432521            |"
      ]
     },
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       "<pre>| 2         | 8        | 1.250000  | 11.128696    | 0.813264          | 0.431205            |</pre>"
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       "| 2         | 8        | 1.250000  | 11.128696    | 0.813264          | 0.431205            |"
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       "<pre>| 3         | 9        | 1.250000  | 16.411131    | 0.868069          | 0.417380            |</pre>"
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       "| 3         | 9        | 1.250000  | 16.411131    | 0.868069          | 0.417380            |"
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       "<pre>| 4         | 10       | 1.250000  | 21.276484    | 0.886994          | 0.397630            |</pre>"
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       "| 4         | 10       | 1.250000  | 21.276484    | 0.886994          | 0.397630            |"
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       "<pre>| 6         | 12       | 1.250000  | 31.068775    | 0.904458          | 0.381830            |</pre>"
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       "| 6         | 12       | 1.250000  | 31.068775    | 0.904458          | 0.381830            |"
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       "<pre>| 7         | 13       | 1.250000  | 35.972559    | 0.914481          | 0.396972            |</pre>"
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       "| 7         | 13       | 1.250000  | 35.972559    | 0.914481          | 0.396972            |"
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       "<pre>| 8         | 14       | 1.250000  | 41.070646    | 0.917539          | 0.403555            |</pre>"
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       "| 8         | 14       | 1.250000  | 41.070646    | 0.917539          | 0.403555            |"
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       "<pre>| 9         | 15       | 1.250000  | 46.190871    | 0.921548          | 0.393022            |</pre>"
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       "| 9         | 15       | 1.250000  | 46.190871    | 0.921548          | 0.393022            |"
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      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Model selection based on validation accuracy:\n",
      "PROGRESS: ---------------------------------------------\n",
      "PROGRESS: BoostedTreesClassifier          : 0.4423963133640553\n",
      "PROGRESS: RandomForestClassifier          : 0.4325213956550362\n",
      "PROGRESS: DecisionTreeClassifier          : 0.4186965108624095\n",
      "PROGRESS: LogisticClassifier              : 0.39302172481895986\n",
      "PROGRESS: ---------------------------------------------\n",
      "PROGRESS: Selecting BoostedTreesClassifier based on validation set performance.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.4045613580406894,\n",
       " 'auc': 0.6071047544208094,\n",
       " 'confusion_matrix': Columns:\n",
       " \ttarget_label\tstr\n",
       " \tpredicted_label\tstr\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 16\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |    JERRY     |      KRAMER     |   97  |\n",
       " |    ELAINE    |      ELAINE     |   97  |\n",
       " |    JERRY     |      GEORGE     |   71  |\n",
       " |    GEORGE    |      KRAMER     |   92  |\n",
       " |    JERRY     |      JERRY      |  2629 |\n",
       " |    GEORGE    |      ELAINE     |   55  |\n",
       " |    JERRY     |      ELAINE     |   49  |\n",
       " |    GEORGE    |      JERRY      |  1659 |\n",
       " |    KRAMER    |      GEORGE     |   84  |\n",
       " |    ELAINE    |      JERRY      |  1301 |\n",
       " +--------------+-----------------+-------+\n",
       " [16 rows x 3 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.,\n",
       " 'f1_score': 0.26329788301245816,\n",
       " 'log_loss': 1.2951079488752653,\n",
       " 'precision': 0.4196324140898925,\n",
       " 'recall': 0.30979514095760313,\n",
       " 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \tclass\tint\n",
       " \n",
       " Rows: 400004\n",
       " \n",
       " Data:\n",
       " +-----------+-----+-----+------+------+-------+\n",
       " | threshold | fpr | tpr |  p   |  n   | class |\n",
       " +-----------+-----+-----+------+------+-------+\n",
       " |    0.0    | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   1e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   2e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   3e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   4e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   5e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   6e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   7e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   8e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " |   9e-05   | 1.0 | 1.0 | 1582 | 6135 |   0   |\n",
       " +-----------+-----+-----+------+------+-------+\n",
       " [400004 rows x 6 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls = tc.classifier.create(train, features=['words_2grams','words_3grams', 'afinn','words_dict_no_stop', 'embbeddings'], target=\"character\")\n",
    "cls.evaluate(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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