Classificador_NaiveBayes_Mandalorian.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projeto 1 - Ciência dos Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nome: Letícia Coêlho Barbosa\n",
    "\n",
    "Nome: Matheus Silva Melo de Oliveira"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contextualização\n",
    "\n",
    "------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O produto escolhido para a classificação foi a tão falada e renomada série do Universo de Star Wars,<em><b>The Mandalorian</b></em> atualmente vinculado à Disney e transmitido no serviço de assinatura Disney+.\n",
    "\n",
    "<br>\n",
    "\n",
    "A classificação foi realizada com intuito de analisar caso internautas ou possíveis consumidores da série comentam sobre esta na rede social Twitter. O critério utilizado para relevância foi caso o usuário fizesse algum comentário que remetia algum laço de intensidade em relação à série, positivo ou não, como elogiando algum capítulo, sentindo saudade da série ou criticando algum aspecto relevante à obra. Tweets que falavam apenas de aspectos tangentes à esse certame, como elogios ou comentários a atores da obra, listagens no qual à série se enquadra e vagas menções sobre a série foram consideradas como irrelevantes nessa classificação.\n",
    "\n",
    "<em>Alguns dos principais tópicos contidos nos tweets</em>\n",
    "- Elogiando episódio final da segunda temporada\n",
    "- Elogiando o personagem principal da obra, Mandaloriano ou o personagem em CGI, apeliadado de \"Baby Yoda\"\n",
    "- Elogios à série, com advérbios que remetem falta da série, que está em hiato, ou elogios à história\n",
    "- Comentários alheios a atores e atrizes da obra, como Pedro Pascal\n",
    "- Comparações com outras obras, principalmente Wandavision, do mesmo serviço de streaming Disney+\n",
    "\n",
    "<br>\n",
    "\n",
    "<center><img src=\"mandalorian_assets/mandalorian.gif\" width=500 style=\"float: center; margin: 0px 0px 10px 10px\"></center>\n",
    "<center><b>Cena da série do Disney+</b></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bibliotecas Utilizadas\n",
    "\n",
    "------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso não possuir bibliotecas utilizadas descomentar célula abaixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "# !pip install seaborn\n",
    "# !pip install emoji\n",
    "# !pip install pysinonimos\n",
    "# !pip install nltk\n",
    "# !pip install sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carregando algumas bibliotecas essenciais:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carregando algumas bibliotecas para limpeza:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "import emoji\n",
    "import nltk\n",
    "import pysinonimos.sinonimos as sinom\n",
    "from emoji import UNICODE_EMOJI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carregando demais bibliotecas de uso específico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "from math import ceil\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirmando diretório de trabalho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esperamos trabalhar no diretório\n",
      "C:\\Users\\Leticia\\Documents\\INSPER\\2_Semestre\\C_Dados\\Projeto 1\\TheMandalorian\n"
     ]
    }
   ],
   "source": [
    "print('Esperamos trabalhar no diretório')\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Banco de Dados\n",
    "\n",
    "------------------------------------------------------------\n",
    "\n",
    "Carregando a base de dados com os tweets classificados como relevantes e não relevantes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'mandalorian_assets/mandalorian.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Treinamento</th>\n",
       "      <th>Relevancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>executivo da disney defendeu demissão de gina ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@daredvevil nao seriooo melhor dia 😭 volta the...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>preciso terminar mandalorian logo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>toda vez que eu vejo o baby yoda usando a forç...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jon fraveau eu te amo obrigado por fazer the m...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Treinamento  Relevancia\n",
       "0  executivo da disney defendeu demissão de gina ...           0\n",
       "1  @daredvevil nao seriooo melhor dia 😭 volta the...           1\n",
       "2                  preciso terminar mandalorian logo           0\n",
       "3  toda vez que eu vejo o baby yoda usando a forç...           0\n",
       "4  jon fraveau eu te amo obrigado por fazer the m...           0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_excel(filename)\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Teste</th>\n",
       "      <th>Relevancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>the mandalorian é uma série com dinheiro e tec...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>agora comecei mandalorian confiando novamente ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@fepazo1 @gabriel_ns1999 @joelmsimmons @jurand...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>eu assisti 3 minutos de mandalorian e emocionei</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rt @anakinwar: já ta                          ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Teste  Relevancia\n",
       "0  the mandalorian é uma série com dinheiro e tec...           1\n",
       "1  agora comecei mandalorian confiando novamente ...           1\n",
       "2  @fepazo1 @gabriel_ns1999 @joelmsimmons @jurand...           1\n",
       "3    eu assisti 3 minutos de mandalorian e emocionei           1\n",
       "4  rt @anakinwar: já ta                          ...           0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_excel(filename, sheet_name = 'Teste')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classificador automático de sentimento\n",
    "\n",
    "------------------------------------------------------------\n",
    "\n",
    "Iremos nesse passo de definição das funções de limpeza seguir os seguintes passos:\n",
    "* Limpar itens comuns e pontuações, como \"@\" e '!';\n",
    "* Retirar espaços entre emojis e textos, assim como substituir os emojis por suas transcrições utilizando sua descrição em seu próprio UNICODE.\n",
    "* Retirar preposições do texto, visto que essas raramente agregam algum sentimento de intensidade em relação ao produto e são meramente necessárias ao sentido da frase especificada;\n",
    "* Tornar, caso tenha-se, palavras com letras maiúsculas em letras minúsculas para evitar diferenciações entre palavras iguais como \"gosto\" e \"Gosto\";\n",
    "* Dividindo as palavras em listas para análise probalística de cada palavra de pertencer ao respectivo grupo (nota-se que aqui foram excluídos termos como 'rt' e 'https' que nao agregam na análise).\n",
    "* Funções de divisão e concatenção de palavras;\n",
    "* Função de aplicação, que junta os fragmentos de palavras separados com os emojis transcritos, aplicando simultaneamente todas as funções anteriores garantindo uma limpeza efetiva conforme os critérios avaliados justos pelo grupo.\n",
    "* Função de retorno de série com as palavras que apareceram em cada tweet, importante para formação relativa de cada palavra na base de dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Definindo Funções\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Limpando os tweets retirando pontos desnecessarios\n",
    "def limpeza(tweet):\n",
    "    '''\n",
    "    Limpando sinais, arrombas, aspas e outras pontuações.\n",
    "    '''\n",
    "    punctuation = '[”@\\-/!.:?;,''\"]'             \n",
    "    pattern = re.compile(punctuation)\n",
    "    text_subbed = re.sub(pattern,'', tweet)\n",
    "    return text_subbed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def separa_emoji(tweet):\n",
    "    '''\n",
    "    Essa função separa os emojis juntos, além de transcreve-los\n",
    "    para suas respectivas strings descritivas, optimizando um pouco o processamento.\n",
    "    Nota-se que alguns emojis não usados não estão no Unicode da língua portuguesa,\n",
    "    adotamos assim o de língua inglesa como alternativa de transcrição.\n",
    "    '''\n",
    "    modified=' '.join(emoji.get_emoji_regexp().split(tweet))\n",
    "    modified=modified.split()\n",
    "    for i,emoji1 in enumerate(modified):\n",
    "        if emoji1 in UNICODE_EMOJI['pt']:\n",
    "            modified[i]=UNICODE_EMOJI['pt'][emoji1].replace(':','')\n",
    "        elif emoji1 in UNICODE_EMOJI['en']:\n",
    "            modified[i]=UNICODE_EMOJI['en'][emoji1].replace(':','')\n",
    "        else:\n",
    "            continue\n",
    "    modified=' '.join(modified)\n",
    "        \n",
    "    return modified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Leticia\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "#Armazenando listas com preposições na língua portuguesa\n",
    "nltk.download('stopwords')\n",
    "prep = nltk.corpus.stopwords.words('portuguese')\n",
    "prep.append('')\n",
    "\n",
    "# Define função que aplica remoção de preposições\n",
    "def limpa_preposicao(lista):\n",
    "    '''\n",
    "    Retira preposições, conjunções e outras palavras auxiliares do tweet\n",
    "    '''\n",
    "    sem_prep=[]     \n",
    "    for palavra in lista: \n",
    "        if palavra in prep: \n",
    "            continue\n",
    "        else:\n",
    "            sem_prep.append(palavra)\n",
    " \n",
    "    return sem_prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformando tweets em lower case:\n",
    "def lower(tweet):\n",
    "    '''\n",
    "    Torna lowercase todas as palavras, evitando diferenciação\n",
    "    entre maiúsculo e minúsculo.\n",
    "    '''\n",
    "    return tweet.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#retira escritas comuns em tweets, mas irrelevantes para análise de dados\n",
    "def tweet_to_frase(lista):\n",
    "    '''\n",
    "    Retirando aparatos básicos de um tweet, no caso o rt ou imagens\n",
    "    '''\n",
    "    for i,word in enumerate(lista):\n",
    "        if 'rt' in word or 'https' in word: \n",
    "            lista[i]=lista[i].replace(word,'null')\n",
    "    new_list=[x for x in lista if x!='null']\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transformando lista em string\n",
    "def list_to_string(lista):\n",
    "    return ','.join(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transformando string em lista\n",
    "def string_to_list(string):\n",
    "    return string.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aplicando todas as funções acima no df:\n",
    "def aplica(tweet):\n",
    "    \"Função de aplicação das funções anteriores\"\n",
    "    retira_pontuacao=limpeza(lower(tweet))\n",
    "    transcreve_emoji=string_to_list(separa_emoji(retira_pontuacao))\n",
    "    tweet_limpo=list_to_string(tweet_to_frase(limpa_preposicao(transcreve_emoji)))\n",
    "                                    \n",
    "    #retornando uma string limpa e com emojis transcritos\n",
    "    return tweet_limpo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frases_to_words (Serie_frases):\n",
    "    \"\"\"\n",
    "    Função de transcrição de todos os tweets relevantes\n",
    "    ou irrelevantes\n",
    "    \"\"\"\n",
    "    texto=''\n",
    "    \n",
    "    for palavra in Serie_frases:\n",
    "        texto+=str(palavra)                        #Criando uma string gigante com todas as palavras das frases\n",
    "    \n",
    "    lista_palavras=texto.split(',')                #Criando lista das palavras contidas na variável texto\n",
    "    return pd.Series(lista_palavras)               #Retornando uma série de palavras contida nas frases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Montando um Classificador Naive-Bayes\n",
    "\n",
    "\n",
    "Aqui iremos definir parâmetros necessários para a alimentação do modelo, configurando assim os tweets classificados como relevantes ou irrelevantes em listas e Series, e guardando seus valores e comprimentos e variáveis para usos em nossos modelos probabílisticos mais a frente.\n",
    "\n",
    "Considerando apenas as mensagens da planilha Treinamento:\n",
    "\n",
    "<div id=\"captacao\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transformando palavras em variáveis categóricas:\n",
    "train['Treinamento'] = train['Treinamento'].astype('category')\n",
    "test['Teste'] = test['Teste'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aplicando funções de limpeza e certificação no dataframe de treinamento da base de dados:\n",
    "train['Clean']=train['Treinamento'].apply(aplica)\n",
    "test['Clean']=test['Teste'].apply(aplica)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separando dados de treino por relevância:\n",
    "train_relevantes=train[train['Relevancia']==1]\n",
    "train_irrelevantes=train[train['Relevancia']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Serie de palavras relevantes e irrelevantes no conjunto treino:\n",
    "palavras_r= frases_to_words(train_relevantes['Clean'])\n",
    "palavras_i= frases_to_words(train_irrelevantes['Clean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequências relativas das palavras:\n",
    "rel_relevantes=palavras_r.value_counts()\n",
    "rel_irrelevantes=palavras_i.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Guardando uma lista as palavras pertencentes aos tweets irrelevantes:\n",
    "list_i=palavras_i.tolist()\n",
    "\n",
    "#Guardando uma lista as palavras pertencentes aos tweets relevantes:\n",
    "list_r=palavras_r.tolist()\n",
    "\n",
    "#Guardando em uma lista todas as palavras possíveis na base de dados (SEM REPETIÇÕES):\n",
    "elementos_nao_repetidos=set(list_i+list_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Série representante do conjunto universo de todas as palavras:\n",
    "#total=palavras_i+palavras_r\n",
    "total=pd.Series(list_i+list_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Frequência relativa das palavras no total:\n",
    "rel_total=total.value_counts(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probabilidades Iniciais "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para entendermos melhor as probabilidade utilizadas no classificador Naive Bayes iremos  primeiramente calcular a probabilidade de palavras relevantes e irrelevantes aparecerem em nosso conjunto universo, onde os elementos do conjunto universo é representado pela soma de elementos do conjunto palavras irrelevantes e elementos do conjunto palavras relevantes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P_{relevante}= \\frac{N° Palavras_{relevantes}}{N° Total_{palavras}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P_{irrelevante}= \\frac{N° Palavras_{irrelevantes}}{N° Total_{palavras}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pela Teoria complementar de conjuntos, temos que:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P_{relevante}+ P_{irrelevante}=1 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transformando em código:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Probabilidade tweet ser relevante ou irrelevante:\n",
    "\n",
    "P_r=len(list_r)/len(total)\n",
    "P_i=len(list_i)/len(total)\n",
    "\n",
    "#Garantindo a verdade acima\n",
    "assert P_r+P_i==1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naïve Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos como objetivo desse trabalho construir um algoritimo que consiga tomar a decisão de agrupar um tweet (texto) como um assunto relevante ou não sobre sobre o tema que escolhemos. Naive Bayes é um classificador probabilístico que utiliza o Teorema de Bayes para categorizar textos baseado na frequência das palavras usadas e será a ferramenta utilizada para obtermos a classificação que desejamos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conceitos Inicias:\n",
    "Se chamarmos A e B de eventos em nosso conjunto universo denominado $\\Omega$, temos:\n",
    " \n",
    "$P(A) \\rightarrow$ Probabilidade do evento A ocorrer\n",
    " \n",
    "$P(A^c) \\rightarrow$ Probabilidade do evento A não ocorrer\n",
    " \n",
    "$P(B) \\rightarrow$ Probabilidade do evento B ocorrer\n",
    " \n",
    "$P(B^c) \\rightarrow$ Probabilidade do evento B não ocorrer\n",
    " \n",
    "$P(A \\cap B) \\rightarrow$ Probabilidade do evento A e B ocorrer\n",
    " \n",
    "$P(A|B) \\rightarrow$ Probabilidade do evento A ocorrer dado que B ocorreu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trazendo para o nosso problema os Eventos que temos são:\n",
    "\n",
    "$P(R) \\rightarrow $ Probabilidade de um Tweet ser relevante.\n",
    "\n",
    "$P(I)  \\ ou \\ P(R^c)\\rightarrow$ Probabilidade de um Tweet ser irrelevante.\n",
    "\n",
    "$P(tweet) \\rightarrow$ Probabilidade de determinado Tweet ocorrer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos dizer também que as probabilidades que o nosso classificador precisa encontrar são: \"Dado um tweet qual a probabilidade dele ser relevante?\" e \"Dado um tweet qual a probabilidade dele ser irrelevante?\".\n",
    "\n",
    " \n",
    "\n",
    "Ou matematicamente representado por:\n",
    "$P(R|tweet)$ e $P(I|tweet)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"mandalorian_assets/DiagramaExemplo.png\" width=\"300\" height=\"200\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As probabilidade $P(R|tweet)$ ou $P(tweet|R)$ , podem ser interpretadas como uma probabilidade de \"peso\" em um novo conjunto universo. Exemplificando, nos exemplos dados anteriromente temos como novos conjuntos universos o conjunto tweet e conjunto relevância, respectivamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falando no português brasileiro temos a seguinte interpretação da \"probabilidade de peso\": \"Qual a probabilidade de encontrarmos elementos que pertencem ao conjunto A e B no conjunto B\"\n",
    "$$P(A|B) = \\frac{P(A \\cap B)}{P(B)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou seja:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$P(A|B) = \\frac{N° elementos(A \\cap B)}{N° elementos(B)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando o mesmo raciocínio para as probabilidades que estamos buscando, temos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(R|tweet)= \\frac{P(tweet \\cap R)}{P(tweet)} $$\n",
    "$$ P(tweet|R)= \\frac{P(tweet \\cap R)}{P(R)} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogamente:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(I|tweet)= \\frac{P(tweet \\cap I)}{P(tweet)} $$\n",
    "$$ P(tweet|I)= \\frac{P(tweet \\cap I)}{P(I)} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A probabilidade $P(tweet \\cap R)$ pode ser representada de duas formas como mostra o diagrama exemplo e as equações acima:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(tweet \\cap R) = P(R|tweet) P(tweet) $$\n",
    "$$ P(tweet \\cap R) = P(tweet|R) P(R) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A probabilidade $P(tweet \\cap I)$ pode ser representada de duas formas como mostra o diagrama exemplo e as equações acima:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(tweet \\cap I) = P(I|tweet) P(tweet) $$\n",
    "$$ P(tweet \\cap I) = P(tweet|I) P(I) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por meio dessa igualdade , encontramos a probabilidade utilizada por Naive Bayes para classificar um texto como relevante ou não:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(R|tweet)= \\frac{P(tweet|R) P(R)}{P(tweet)} $$\n",
    "$$ P(I|tweet)= \\frac{P(tweet|I) P(I)}{P(tweet)} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essas duas probabilidades acima são utilizadas pelo classificador Naive Bayes para julgar o tweet como relevante ou não."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim, a **Classificação do tweet** se dará conforme abaixo:\n",
    "\n",
    "$\\quad \\Rightarrow$ Se $P(R|tweet) > P(I|tweet)$, o tweet será classificado como **Relevante**.\n",
    "\n",
    "$\\quad \\Rightarrow$ Se $P(R|tweet) < P(I|tweet)$, o tweet será classificado como **Irrelevante**.\n",
    "\n",
    "*Obs: o calculo do P(tweet) não precisa ser calculado pois é \"cortado\" em ambos os lados da inequação*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Já sabemos como o classificador Naive Bayes decide por meio da probabilidade se um tweet é ou não relevante, mas ainda possuimos um termo que precisamos calcular para chegarmos nessa decisão: $P(tweet|R)$ e $P(tweet|I)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A ingenuidade do classificador Naive Bayes: Independência de palavras!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chegamos agora na parte em que nosso classificador comete um grande erro de ingenuidade. Para entendermos melhor esse erro cometido daremos um pequeno exemplo de como a independência de conjuntos é aplicada em um texto.\n",
    "Considere a seguinte frase \"eu gosto de chocolate\", dissemos de forma simplificada que a probabilidade dessa frase ocorrer seria a mesma probabilidade da frase \"de chocolate eu gosto\", ou qualquer outra frase formada pela combinação dessa palavras.\n",
    " \n",
    "A ingenuidade por trás dessa abordagem é que consideramos que a probabilidade de uma palavra ocorrer na frase não influencia na probabilidade de outra ocorrer. Em formula podemos dizer que a probabilidade da frase citada é:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$P(frase)=P(\"eu\").P(\"gosto\").P(\"de\").P(\"chocolate\")$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trazendo agora para a probabilidade que queremos calcular, a probabilidade de $P(tweet|R)$ ou $P(tweet|R)$ podem ser representadas,de forma análoga, considerando essa independência entre as palavras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(tweet|R) = P(palavra_1|R)P(palavra_2|R)...P(palavra_n|R)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(tweet|I) = P(palavra_1|R)P(palavra_2|R)...P(palavra_n|R)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Substituindo nas duas probabilidades mostradas anteriormente e utilizadas em Naive Bayes, temos de forma mais completa:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ P(R|tweet) = \\frac{P(palavra_1|R)P(palavra_2|R)...P(palavra_n|R) P(R)}{P(tweet)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$P(I|tweet)=\\frac{P(palavra_1|I)P(palavra_2|I)...P(palavra_n|I) P(I)}{P(tweet)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suavização de Laplace: E se a palavra não estiver no conjunto universo? P(palavra|Universo)=0?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso apareça uma palavra estranha ou que não esteja na base de dados, precisamos amplificar nosse leque de equações utilizando um recurso matemático conhecido como <b><em>Suavização de Laplace</em></b>, que consiste em basicamente \"incluir\" a nova palavra no âmbito das probabilidades relativas de uma determinada palavra ser classificada como Relevante ou Irrelevante.\n",
    "\n",
    "Para realizar tal recurso, devemos analisar quantas vezes a palavra analisada aparece no absolutamente na comparação em questão, seja relevante ou irrelavante, somar uma unidade (que evita o <b>0</b>) no numerador, e somar a quantidade de \"possíveis palavras no denominador\", isto é a quantidade de palavras únicas pertencentes ao dataset de tweets relevantes ou irrelevantes. Matematicamente teremos:\n",
    "\n",
    "$$P(palavra1|R) = \\frac{F_{AR}+1}{P_{R}+P_p}$$\n",
    "\n",
    "$$P(palavra1|I) = \\frac{F_{AI}+1}{P_{I}+P_p}$$\n",
    "\n",
    "Onde: \n",
    "\n",
    "$ F_{AR}$: Frequência absoluta da palavra na categoria relevante \n",
    "\n",
    "$ F_{AI}$: Frequência absoluta da palavra na categoria irrelevante \n",
    "    \n",
    "$P_{R}$: Todas as palavras pertencentes aos tweets rotulados como relevantes\n",
    "    \n",
    "$P_{I}$: Todas as palavras pertencentes aos tweets rotulados como irrelevantes\n",
    "\n",
    "$P_p$: Todas as palavras possíveis na base de dados de treinamento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com essa consideração temos agora as probabilidades $P(tweet|R)$ e $P(tweet|I)$ adequadas para receber palavras novas em seu universo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feita a análise teórica, vamos implantar o modelo:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calcula a frequência absoluta da palavra no respectivo conjunto de dados\n",
    "def freq_absoluta(palavra,freq_absoluta_conjunto):\n",
    "    \"\"\"\n",
    "    Conta quantas vezes determinada palavra apareceu \n",
    "    na respectiva categoria, seja relevante ou irrelevante\n",
    "    \"\"\"\n",
    "\n",
    "    try:\n",
    "        return freq_absoluta_conjunto[palavra]\n",
    "    \n",
    "    except:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aplicando a suavização de laplace\n",
    "def laplace(frequencia_absoluta,palavras_pertecentes_a_classe):\n",
    "    \"\"\"\"\"\n",
    "    Retorna a probabilidade independente da palavra analisada pertencer ou não\n",
    "    a determinada classe\n",
    "    \"\"\"\n",
    "    return (frequencia_absoluta+1)/(len(palavras_pertecentes_a_classe)+len(elementos_nao_repetidos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def P_tweet_r(tweet):\n",
    "    '''\n",
    "    Calcula a probabilidade do tweet ser relevante fazendo uso das funções\n",
    "    definidas acima\n",
    "    '''\n",
    "    prob_tweet=1 #Cont\n",
    "    quebra_tweet=tweet.split(',')\n",
    "    for palavra in quebra_tweet:\n",
    "        fa=freq_absoluta(palavra,rel_relevantes)\n",
    "        prob_palavra=laplace(fa,list_r)\n",
    "        prob_tweet*=prob_palavra\n",
    "           \n",
    "    return prob_tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def P_tweet_i (tweet):\n",
    "    '''\n",
    "    Calcula a probabilidade do tweet ser irrelevante fazendo uso das funções\n",
    "    definidas acima\n",
    "    '''\n",
    "    prob_tweet=1 #Cont\n",
    "    quebra_tweet=tweet.split(',')\n",
    "    for palavra in quebra_tweet:\n",
    "        fa=freq_absoluta(palavra,rel_irrelevantes)\n",
    "        prob_palavra=laplace(fa,list_i)\n",
    "        prob_tweet*=prob_palavra\n",
    "           \n",
    "    return prob_tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando Nossa função de retorno Naive_Bayes:\n",
    "def Naive_Bayes (tweet):\n",
    "    '''\n",
    "    Julga por meio de probabilidade a relevância ou não de um tweet\n",
    "    '''\n",
    "    if(P_tweet_r(tweet)*P_r > P_tweet_i(tweet)*P_i):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Vendo a eficácia do modelo no conjunto de treinamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando uma nova coluna para armazenar resultado da nossa função Naïve_Bayes\n",
    "train['Classificador']=train.Clean.apply(Naive_Bayes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Treinamento</th>\n",
       "      <th>Relevancia</th>\n",
       "      <th>Clean</th>\n",
       "      <th>Classificador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>executivo da disney defendeu demissão de gina ...</td>\n",
       "      <td>0</td>\n",
       "      <td>executivo,disney,defendeu,demissão,gina,carano...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@daredvevil nao seriooo melhor dia 😭 volta the...</td>\n",
       "      <td>1</td>\n",
       "      <td>daredvevil,nao,seriooo,melhor,dia,rosto_choran...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>preciso terminar mandalorian logo</td>\n",
       "      <td>0</td>\n",
       "      <td>preciso,terminar,mandalorian,logo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>toda vez que eu vejo o baby yoda usando a forç...</td>\n",
       "      <td>0</td>\n",
       "      <td>toda,vez,vejo,baby,yoda,usando,força,the,manda...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jon fraveau eu te amo obrigado por fazer the m...</td>\n",
       "      <td>0</td>\n",
       "      <td>jon,fraveau,amo,obrigado,fazer,the,mandalorian...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Treinamento  Relevancia  \\\n",
       "0  executivo da disney defendeu demissão de gina ...           0   \n",
       "1  @daredvevil nao seriooo melhor dia 😭 volta the...           1   \n",
       "2                  preciso terminar mandalorian logo           0   \n",
       "3  toda vez que eu vejo o baby yoda usando a forç...           0   \n",
       "4  jon fraveau eu te amo obrigado por fazer the m...           0   \n",
       "\n",
       "                                               Clean  Classificador  \n",
       "0  executivo,disney,defendeu,demissão,gina,carano...              0  \n",
       "1  daredvevil,nao,seriooo,melhor,dia,rosto_choran...              1  \n",
       "2                  preciso,terminar,mandalorian,logo              1  \n",
       "3  toda,vez,vejo,baby,yoda,usando,força,the,manda...              0  \n",
       "4  jon,fraveau,amo,obrigado,fazer,the,mandalorian...              1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A acurácia do modelo foi de:  92.2 %\n"
     ]
    }
   ],
   "source": [
    "#Porcentagem de acerto de acerto no conjunto de Treino:\n",
    "\n",
    "tp=train.loc[(train['Classificador']==1)&(train['Relevancia']==1),:].shape[0]\n",
    "tn=train.loc[(train['Classificador']==0)&(train['Relevancia']==0),:].shape[0]\n",
    "print('A acurácia do modelo foi de: ',100*(tp+tn)/train.shape[0],'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Utilizando matriz de confusão\n",
    "\n",
    "* Com esse recurso gráfico podemos vizualizar a distribuição de Falsos Positivos, Falsos Negativos, Verdadeiros Positivos e Verdadeiros Negativos.\n",
    "\n",
    "* Iremos utilizar a biblioteca Seaborn que nos auxiliará com um mapa de calor que irá tornar a matriz de Confusão ainda mais clara\n",
    "<br>\n",
    "<img src=\"mandalorian_assets/confusion_matrix.png\" width=500>\n",
    "\n",
    "<center><b>Figura 1 - Matriz de confusão que ilustra graficamente as porcentagens sugeridas</b></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15d60c9e5b0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vendo a classificação efetiva\n",
    "df_cm=pd.crosstab(train.Relevancia,train.Classificador,normalize=True)\n",
    "plt.figure(figsize = (9,7))\n",
    "plt.title('Matriz de Confusão para Treino \\n em porcentagem\\n',fontdict={'fontsize': 14})\n",
    "sn.heatmap(df_cm, annot=True, annot_kws={\"size\":30},fmt='.1%',cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Verificando a performance do Classificador\n",
    "\n",
    "Iremos agora \"apresentar\" ao nosso modelo uma nova configuração de tweets, aos quais nçao estavam disponíveis na base de treinamento. Esperamos dessa forma que o modelo seja capaz de generalizar bem a essas situações reconhecendo palavras presentes na base de treinamento e se adaptando a novos contextos ou palavras estranhas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aplicando no dataframe de teste\n",
    "test['Classificador']=test.Clean.apply(Naive_Bayes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A acurácia do modelo foi de: 76.00%\n"
     ]
    }
   ],
   "source": [
    "tp=test.loc[(test['Classificador']==1)&(test['Relevancia']==1),:].shape[0]\n",
    "tn=test.loc[(test['Classificador']==0)&(test['Relevancia']==0),:].shape[0]\n",
    "print(f'A acurácia do modelo foi de: {100*(tp+tn)/test.shape[0]:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15d60c9ed30>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cm= pd.crosstab(test.Relevancia,test.Classificador,normalize=True)\n",
    "plt.figure(figsize = (9,7))\n",
    "plt.title('Matriz de Confusão para Teste \\n em porcentagem\\n',fontdict={'fontsize': 14})\n",
    "sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 30},fmt='.1%',cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Concluindo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vimos nas matrizes de confusão, e nas próprias acurácias,o modelo teve uma perfomance razoável, acerta corretamente cerca de 75% das situações.\n",
    "\n",
    "Todavia, grande parte dos erros obtidos no modelo advém da média porcentagem de Falsos Positivos, isto é, tweets que são irrelevantes e foram categorizados erroneamente como relevantes pelo modelo.\n",
    "\n",
    "Isso retoma o fato de que como o Classificador é ingênuo, e desconsidera a dependência entre as palavras, muitos casos como de ambiguidade, dupla negação, sarcasmos ou ironias podem ser classificados erroneamente, visto que ele analisará matematicamente a probabilidade das palavras de constituição do tweet e não seu sentido. \n",
    "\n",
    "Outro fator que colabora para esse índice é o tamanho da base de dados que para fins de um classificador efetivo teria de ser maior, principalmente para uma maior variedade de palavras tal qual melhor assimilação de padrões que possam aumentar a probabilidade de determinadas palavras e termos, aumentando consideravelmente a capacidade de generalização do modelo para com novos tweets e situações.\n",
    "\n",
    "Vale-se destacar que como esse classificador tem modelagem quase que estritamente matemática, sem muitas considerações semânticas e humanas, sua perfomance foi considerável dentro do possível, e com isso, agregando fatores e melhorias abaixo, sua perfomance tende a melhorar conseguindo cumprir com extrema eficácia sua proposição esperada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Por que a perfomance entre Treino e Teste variou tanto? \n",
    "\n",
    "Outro fenômeno possivelmente que ocorreu no modelo é conhecido por <b> Overfitting.</b>\n",
    "\n",
    "O modelo, apesar de conseguir criar um padrão de classificação ideal para a base de treinamento é incapaz de generalizar idealmente para novas amostras, exemplificando podemos dizer que o nosso modelo \"acostuma-se\" com certos padrões de repetição encontrados na base  de treinamento,tendendo a julgar de maneira repetitiva tweets que considera semelhante, diminuindo sua capacidade de abstração a novas situações. Isso ocorre, dentre outros motivos, devido ao tamanho da base de dados escolhida (relativamente pequena), a não <b>lematização das palavras</b> e pela não substiuição de sinônimos ou expressões adjuntas, que geram probabilidades diferentes para possíveis mesmo sentidos ou sentimentos expressos.\n",
    "\n",
    "Esse fato explica por que tivemos uma diferença percentual de quase 15% entre a porcentagem de acertos para o Treinamento em relação aos Testes. \n",
    "\n",
    "Dessa forma,o Classificador foi satisfatório em sua análise. Melhorias realizadas poderiam ser:\n",
    "- Cross-Fold\n",
    "- Dicionário de sinônimos\n",
    "- Lematização das palavras\n",
    "- Base de dados de treinamento com maior quantidade de tweets\n",
    "\n",
    "\n",
    "<img src=\"mandalorian_assets/overfitting.png\" width=600 >\n",
    "<center><b>Figura 1 - Situação descrita acima, na qual devido ao pequeno tamanho do dataset,<br> a disparidade entre os erros relativos é grande</b></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Por que não posso alimentar minha base de Treinamento automaticamente usando o próprio classificador?\n",
    "\n",
    "Como o modelo, nas condições aqui descritas, se restringe a meramente ferramentas matemáticas e probabíslisticas, ignorando assim quase que completamente fatores linguísticos, humanos e culturais dos enunciadores dos tweets, ele se torna incapaz de medir e abstrair de maneira correta sobre diferentes contextos de enunciação, prejudicando assim a capacidade de generalização deste caso ele fosse a fonte de alimentação da base de dados. \n",
    "\n",
    "Além disso, o modelo está sujeito ao viés da classificação manual realizada para os rótulos de comparação, durante a classificação manual que realizamos observamos o quão subjetivo pode ser essa analise, tendo sido necessário discussão para o consenso no rótulo de determinados tweets. Dessa forma, utilizar o modelo como ferramenta de alimentação acarretaria em uma enorme expansão desse viés, o que novamente prejudicaria sua generalização e tenderia a diminuir sua acurácia em novos contextos ou frases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sarcasmo e dupla negação\n",
    "\n",
    "Como dito anteriormente o classifiador Naive Bayes é ingênuo por não levar em consideração a relação entre as palavras, considerando apenas a probabilidade das mesmas pertencerem ou não a determinada categoria. Portanto sacarmos e sentimentos do autor do tweet não são levados em consideração.\n",
    "\n",
    "Em termos práticos, é basicamente observar como o classificador rotularia a situação abaixo, onde solicitaríamos a classificação de uma frase nitidamente sarcástica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A Frase é classificada como Relevante\n"
     ]
    }
   ],
   "source": [
    "frase_sarcasmo = 'Mandalorian é tão legal quanto andar de ônibus lotado'\n",
    "\n",
    "if Naive_Bayes(aplica(frase_sarcasmo))==1:\n",
    "    print('A Frase é classificada como Relevante')\n",
    "else:\n",
    "    print('A Frase é classificada como Irrelevante')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A Frase é classificada como Irrelevante\n"
     ]
    }
   ],
   "source": [
    "dupla_neg = 'Mandalorian não é nada fantástico como dizem'\n",
    " \n",
    "if Naive_Bayes(aplica(dupla_neg))==1:\n",
    "    print('A Frase é classificada como Relevante')\n",
    "else:\n",
    "    print('A Frase é classificada como Irrelevante')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como visto no exemplo, frases que possuem palavras que geram sentimento, de intensidade positiva ou negativa, ou apresentam palavras sarcásticas, ambíguas ou irônicas, serão provavelmnete classificadas de forma inadequada pelo Modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Novos cenários para um classificador Naïve-Bayes\n",
    "\n",
    "Como já citado inúmeras vezes nesse arquivo, o modelo é baseado quase que estritamente em ferramentas matemáticas, por tanto ele acaba por não ser extramemente funcional em contextos cuja a associação de palavras e sentido de frases sejam relevantes para sua classificação. Nesse certame, contextos mais simples, nos quais dependem apenas de palavras-chaves ou pequenas indicações, o classificador Naïve Bayes seria de grande eficácia. Um bom exemplo desse uso, poderia ser uma plataforma de streaming ou vídeos, dentre os quais, de acordo com as palavras mais digitadas pelo usuário em sua busca ou de acordo pelas palavras presentes em, por exemplo, títulos ou categorias desses vídeos, o modelo pudesse abstrair e recomendar com maior eficácia temas parecidos ou ligados a essas procuras, mantendo o usuário mais conectado na plataforma devido a maior sugestão de temas de seu interesse.\n",
    "\n",
    "Outra aplicação em que o classificado Naive Bayes teria grande serventia seria quanto a diagnósticos médicos, julgando a probabilidade de um exame ter dado um Falso Positivo ou Negativo. O mesmo provavelmente apresentaria bom desempenho pelos mesmos motivos do exemplo anterior, diagnóstico de teste possuem palavras chaves e estão , em sua grande maioria, alheios a ambiguidades, assim poderiamos responder perguntas como: \"se uma nova pessoa realizar o teste e receber um resultado positivo, qual a probabilidade dela realmente possuir a doença?\", e sugerir a realização de um novo exame dependendo da resposta obtida."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Aperfeiçoamento:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorias Intermediárias\n",
    "\n",
    "*Criando categorias intermediárias baseadas na probabilidade relativa estipulada pelo modelo*\n",
    "\n",
    "Dentro das duas categorias que criamos (Relevante e Irrelevante) podemos encontras algumas subcategorias (Muito Irrelevante, Irrelevante, Neutro, Reletante e Muito Relevante).\n",
    "\n",
    "Considere nesse exemplo que o tweet A e o tweet B foram classificados como relevante. Nessa nova classificação passariamos a considerar que caso a probabilidade de um tweet A ser relevante seja significadamente maior do que a probabilidade de um tweet B ser relevante, poderiamos dividir a \"Relevância\" em mais dois rótulos: tweet A passaria a pertencer a subcategoria \"Muito Relevante\" e o tweet B estaria na subcategoria \"Relevante\", ambos dentro da categoria Relevância. Racicioníneo análogo aplica-se aos subconjuntos \"Muito Irrelevante\" e \"Irrelevante\".\n",
    "\n",
    "\n",
    "Caso tivessemos um tweet C que possui uma diferença de probabilidade ($P(R|tweetC)-P(I|tweetC)$) próxima de zero, poderiamos subclassifica-lo como um tweet \"Neutro\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo novas funções utilizadas para categorias intermediárias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Naive_Bayes_new_categories(tweet):\n",
    "    \"\"\"\n",
    "    Retorna a diferença entre as probabilidades que serão usadas mais \n",
    "    tarde para a reclassificação de acordo com o grau estabelecido\n",
    "    \"\"\"\n",
    "    return P_tweet_r(tweet)*P_r -P_tweet_i(tweet)*P_i\n",
    "\n",
    "def Separando_em_categorias(prob):\n",
    "    \n",
    "    '''\n",
    "    Função recebe um valor de probabilidade. \n",
    "    Retorna rótulo Muito Irrelevante, Irrelevante, Neutro, Relevante ou Muito Relevante\n",
    "    '''\n",
    "    \n",
    "    #Criando uma lista ordenada com os valores de probabilidade em ordem.\n",
    "    prob_lista=test_categorie_degree.Prob.sort_values().tolist()\n",
    "    \n",
    "    #Contador que busca ponto de virada do sinal na lista ordenada de probabilidade.\n",
    "    troca_sinal=0\n",
    "    \n",
    "    #Posição ultimo elemento da lista:\n",
    "    pos_ultimo=len(prob_lista)\n",
    "    \n",
    "    for i, value in enumerate(prob_lista):\n",
    "            \n",
    "            if(value<0):           \n",
    "                continue\n",
    "            else:\n",
    "                #Posição em que os valores mudam para positivo na lista ordenada de probabilidade\n",
    "                troca_sinal=i                 \n",
    "                break\n",
    "    \n",
    "    #Probabilidade recebida interpretada como neutra para valores próximos de 0\n",
    "    if prob in prob_lista[troca_sinal-5:troca_sinal+10]:\n",
    "         return 'Neutro'                       \n",
    "    \n",
    "    #Na classificação Relevante,Irrelevante buscamos metade das parcelas restantes do intervalo anterior e posterior \n",
    "    #ao neutro para a subdivisão: (Relevante, Muito relevante) e (Irrelevante, Muito irrelevante)\n",
    "    \n",
    "    else:    \n",
    "        \n",
    "        # Para a condição em que a probabilidade é negativa podemos dividi-la em 2 Subgrupos\n",
    "        if prob < 0:\n",
    "            \n",
    "            #Primeira parcela de valores negativos (Valores mais negativos)\n",
    "            if prob in prob_lista[0:ceil((troca_sinal-5)/2)]:\n",
    "                return 'Muito Irrelevante'\n",
    "            \n",
    "            #Segunda parcela de valores negativos (Valores menos negativos)\n",
    "            return 'Irrelevante'\n",
    "        \n",
    "        # Para a condição em que a probabilidade é positiva podemos dividi-la em 2 Subgrupos\n",
    "        else:\n",
    "            \n",
    "            #Primeira parcela de valores positivos (Valores menos positivos)\n",
    "            if prob in prob_lista[(troca_sinal+10):ceil((troca_sinal+10)+(pos_ultimo-(troca_sinal+10))/2)]:\n",
    "                return 'Relevante'\n",
    "            \n",
    "            #Segunda parcela de valores positivos (Valores mais positivos)\n",
    "            return 'Muito Relevante'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copiando conjunto teste e aplicando nova classificação:\n",
    "test_categorie_degree=test.copy()\n",
    "test_categorie_degree['Prob']=test_categorie_degree['Clean'].apply(Naive_Bayes_new_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Teste</th>\n",
       "      <th>Relevancia</th>\n",
       "      <th>Clean</th>\n",
       "      <th>Classificador</th>\n",
       "      <th>Grau de Relevancia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>the mandalorian é uma série com dinheiro e tec...</td>\n",
       "      <td>1</td>\n",
       "      <td>the,mandalorian,série,dinheiro,tecnicamente,bo...</td>\n",
       "      <td>1</td>\n",
       "      <td>Relevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>agora comecei mandalorian confiando novamente ...</td>\n",
       "      <td>1</td>\n",
       "      <td>agora,comecei,mandalorian,confiando,novamente,...</td>\n",
       "      <td>0</td>\n",
       "      <td>Irrelevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@fepazo1 @gabriel_ns1999 @joelmsimmons @jurand...</td>\n",
       "      <td>1</td>\n",
       "      <td>fepazo1,gabriel_ns1999,joelmsimmons,jurandirfi...</td>\n",
       "      <td>0</td>\n",
       "      <td>Neutro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>eu assisti 3 minutos de mandalorian e emocionei</td>\n",
       "      <td>1</td>\n",
       "      <td>assisti,3,minutos,mandalorian,emocionei</td>\n",
       "      <td>1</td>\n",
       "      <td>Muito Relevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rt @anakinwar: já ta                          ...</td>\n",
       "      <td>0</td>\n",
       "      <td>anakinwar,ta,mãe,chorando,final,dnv,garota,the...</td>\n",
       "      <td>1</td>\n",
       "      <td>Relevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>tô com saudade de the mandalorian, acho que vo...</td>\n",
       "      <td>1</td>\n",
       "      <td>tô,saudade,the,mandalorian,acho,vou,ver,tudo,novo</td>\n",
       "      <td>1</td>\n",
       "      <td>Muito Relevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>meu deus pedro pascal é o mandalorian 😱😱😱😱😱😱😱</td>\n",
       "      <td>1</td>\n",
       "      <td>deus,pedro,pascal,mandalorian,rosto_gritando_d...</td>\n",
       "      <td>1</td>\n",
       "      <td>Relevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>@39marukin @ghostjacobs tem. assisti lá.\\nagor...</td>\n",
       "      <td>1</td>\n",
       "      <td>39marukin,ghostjacobs,assisti,lá,agora,finalme...</td>\n",
       "      <td>1</td>\n",
       "      <td>Relevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>@ip_mandalorian @tmarretamma ficou parecendo o...</td>\n",
       "      <td>0</td>\n",
       "      <td>ip_mandalorian,tmarretamma,ficou,parecendo,peq...</td>\n",
       "      <td>0</td>\n",
       "      <td>Irrelevante</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>@tebaldopaladino e nem acabamos a maratona de ...</td>\n",
       "      <td>1</td>\n",
       "      <td>tebaldopaladino,acabamos,maratona,star,wars,ma...</td>\n",
       "      <td>1</td>\n",
       "      <td>Muito Relevante</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Teste  Relevancia  \\\n",
       "0    the mandalorian é uma série com dinheiro e tec...           1   \n",
       "1    agora comecei mandalorian confiando novamente ...           1   \n",
       "2    @fepazo1 @gabriel_ns1999 @joelmsimmons @jurand...           1   \n",
       "3      eu assisti 3 minutos de mandalorian e emocionei           1   \n",
       "4    rt @anakinwar: já ta                          ...           0   \n",
       "..                                                 ...         ...   \n",
       "220  tô com saudade de the mandalorian, acho que vo...           1   \n",
       "221      meu deus pedro pascal é o mandalorian 😱😱😱😱😱😱😱           1   \n",
       "222  @39marukin @ghostjacobs tem. assisti lá.\\nagor...           1   \n",
       "223  @ip_mandalorian @tmarretamma ficou parecendo o...           0   \n",
       "224  @tebaldopaladino e nem acabamos a maratona de ...           1   \n",
       "\n",
       "                                                 Clean  Classificador  \\\n",
       "0    the,mandalorian,série,dinheiro,tecnicamente,bo...              1   \n",
       "1    agora,comecei,mandalorian,confiando,novamente,...              0   \n",
       "2    fepazo1,gabriel_ns1999,joelmsimmons,jurandirfi...              0   \n",
       "3              assisti,3,minutos,mandalorian,emocionei              1   \n",
       "4    anakinwar,ta,mãe,chorando,final,dnv,garota,the...              1   \n",
       "..                                                 ...            ...   \n",
       "220  tô,saudade,the,mandalorian,acho,vou,ver,tudo,novo              1   \n",
       "221  deus,pedro,pascal,mandalorian,rosto_gritando_d...              1   \n",
       "222  39marukin,ghostjacobs,assisti,lá,agora,finalme...              1   \n",
       "223  ip_mandalorian,tmarretamma,ficou,parecendo,peq...              0   \n",
       "224  tebaldopaladino,acabamos,maratona,star,wars,ma...              1   \n",
       "\n",
       "    Grau de Relevancia  \n",
       "0            Relevante  \n",
       "1          Irrelevante  \n",
       "2               Neutro  \n",
       "3      Muito Relevante  \n",
       "4            Relevante  \n",
       "..                 ...  \n",
       "220    Muito Relevante  \n",
       "221          Relevante  \n",
       "222          Relevante  \n",
       "223        Irrelevante  \n",
       "224    Muito Relevante  \n",
       "\n",
       "[225 rows x 5 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_categorie_degree['Grau de Relevancia']= test_categorie_degree.Prob.apply(Separando_em_categorias)\n",
    "test_categorie_degree=test_categorie_degree.drop('Prob',axis=1)\n",
    "test_categorie_degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Muito Relevante      67\n",
       "Relevante            67\n",
       "Irrelevante          38\n",
       "Muito Irrelevante    38\n",
       "Neutro               15\n",
       "Name: Grau de Relevancia, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_categorie_degree['Grau de Relevancia'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### Qualidade do Classificador a partir de novas separações dos tweets entre Treinamento e Teste\n",
    "\n",
    "Iremos agora fazer diferentes separações da base de dados em treino e teste de maneira aleatória e variada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Primeiramente iremos hospedar novamente a base de dados;\n",
    "- Concatenar ambos os Dataframe de Teste e Treinamento em um só;\n",
    "- Aplicar a função de limpeza no Dataframe resultante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Juntando os tweets em um unico DF\n",
    "test_split = pd.read_excel(filename, sheet_name = 'Teste')\n",
    "test_split = test_split.rename(columns={'Teste':'Total'})\n",
    "test_split.head()\n",
    "\n",
    "train_split = pd.read_excel(filename, sheet_name = 'Treinamento')\n",
    "train_split = train_split.rename(columns={'Treinamento':'Total'})\n",
    "\n",
    "#Concatenando as tabelas\n",
    "data_set=pd.concat([train_split,test_split])\n",
    "\n",
    "#Limpando os tweets\n",
    "data_set['Clean']=data_set['Total'].apply(aplica)\n",
    "data_set.head()\n",
    "\n",
    "#garatindo que a concatenação manteve toda a base de tweets\n",
    "assert test.shape[0]+train.shape[0]==data_set.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ajustando funções e parâmetros\n",
    "- Iremos agora ajustar as funções de cálculo de probabilidade e classificação para que elas se adequem ao loop de repetição, principalmente ajustando as variáveis globais;\n",
    "- Para tal, ajustaremos alguns parâmetros como a quantidade de palavras possíveis (vinda do treinamento que será splittado aleatoriamente a cada loop) e as listas de relevantes e irrelevantes de cada loop, para que mudem de acordo com a repetição, alterando os elementos de seu conjunto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "palavras_r_split=palavras_r\n",
    "palavras_i_split=palavras_i\n",
    "\n",
    "def count_palavras_novas(tweet):\n",
    "    '''\n",
    "    Conta o número de palavras novas por tweet\n",
    "    '''\n",
    "    \n",
    "    count_nova=0    # Quantas vezes palavras novsa aparecem no tweet\n",
    "    \n",
    "    total_palavras=pd.concat([palavras_r_split,palavras_i_split])  #Concatena serie de palavras relevantes e irrelevantes.\n",
    "    total_palavras=total_palavras.value_counts()                #Quantidade de cada palavras no conjunto total de palavras.\n",
    "    \n",
    "    quebra_tweet=tweet.split(',')\n",
    "\n",
    "    for palavra in quebra_tweet:\n",
    "        fa=freq_absoluta(palavra,total_palavras)                \n",
    "        if fa==0:                                       #Quando a palavra não é identificada na lista de palavras totais. \n",
    "            count_nova+=1                               #Somar palavras novas no contador.\n",
    "        else:\n",
    "            continue\n",
    "    return count_nova"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Utilizaremos aqui o módulo sklearn que é próprio para análises de Machine Learning\n",
    "\n",
    "Nele iremos importar a função \n",
    "\n",
    ">**train_test_split**\n",
    "\n",
    "Essa função basicamente divide o conjunto de dados de Treinamento e teste em 4 variáveis, conhecidas como, respectivamente,\n",
    "atributos de Treinamento, atributos de Teste, rótulos de Treino e rótulos de Teste, e em seus parâmetros internos podemos dividir a quantidade relativa de teste/treino que desejamos.\n",
    "Adotaremos a preposição de 30% da base de dados para treino, emulando de maneira próxima a proporção utilizada no dataset original.\n",
    " Nota-se também que como não alteraremos o parâmetros <em>random_state</em> a função sempre irá dividir a base de dados diferentemente em cada repetição, atingindo o princípio esperado de Validação Cruzada deste item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "lista_hist=[]\n",
    "lista_proporcao_r=[]\n",
    "numero_palavras_novas=[]\n",
    "\n",
    "for i in range(0,100):\n",
    "    \n",
    "    X_train,X_test,y_train,y_test=train_test_split(data_set[['Clean','Relevancia']],data_set.Relevancia,\n",
    "    test_size=0.3)\n",
    "\n",
    "    train_relevantes_new=X_train[X_train['Relevancia']==1]\n",
    "    train_irrelevantes_new=X_train[X_train['Relevancia']==0]\n",
    "\n",
    "    palavras_r_split= frases_to_words(train_relevantes_new['Clean'])\n",
    "    palavras_i_split= frases_to_words(train_irrelevantes_new['Clean'])\n",
    "    \n",
    "    rel_relevantes=palavras_r_split.value_counts()\n",
    "    rel_irrelevantes=palavras_i_split.value_counts()\n",
    "    \n",
    "    prop=palavras_r_split.shape[0]/(palavras_r_split.shape[0]+palavras_i_split.shape[0])\n",
    "    \n",
    "    list_i=palavras_i_split.tolist()\n",
    "    list_r=palavras_r_split.tolist()\n",
    "\n",
    "    total=pd.Series(list_i+list_r)\n",
    "\n",
    "    P_r=len(list_r)/len(total)\n",
    "    P_i=len(list_i)/len(total)\n",
    "    assert P_r+P_i==1\n",
    "\n",
    "    elementos_nao_repetidos=set(list_i+list_r)\n",
    "\n",
    "    X_test['Classificador']=X_test.Clean.apply(Naive_Bayes)\n",
    "    X_test['Palavras Novas']=X_test.Clean.apply(count_palavras_novas)\n",
    "\n",
    "    verdadeiro_positivo=X_test.loc[(X_test['Classificador']==1)&(X_test['Relevancia']==1),:].shape[0]\n",
    "    verdadeiro_negativo=X_test.loc[(X_test['Classificador']==0)&(X_test['Relevancia']==0),:].shape[0]\n",
    "    score=(verdadeiro_positivo+verdadeiro_negativo)/X_test.shape[0]\n",
    "    \n",
    "    lista_hist.append(score*100)\n",
    "    lista_proporcao_r.append(prop*100)\n",
    "    numero_palavras_novas.append(sum(X_test['Palavras Novas'].tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scores para o Modelo com cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "faixa=np.arange(55,80,0.6)\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist(lista_hist, bins=faixa, edgecolor='black', density=False)\n",
    "plt.title('Scores para o Modelo com cross-validation')\n",
    "plt.ylabel('Frequência')\n",
    "plt.xlabel('Porcentagem de acerto(%)')\n",
    "plt.xlim(60,80)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score máximo:\t77.06\n",
      "Score mínimo:\t59.63\n",
      "Score médio:\t68.94\n"
     ]
    }
   ],
   "source": [
    "mean=sum(lista_hist)/len(lista_hist)\n",
    "mean\n",
    "max(lista_hist)\n",
    "print(f'Score máximo:\\t{max(lista_hist):.2f}\\nScore mínimo:\\t{min(lista_hist):.2f}\\nScore médio:\\t{mean:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vendo o histograma acima, averigua-se uma grande variabilidade de porcentagem de acertos para configurações diversas de conjunto Treinamento/Teste. Caso realizassemos essa configuração somente uma vez, tal qual foi feita para esse projeto, teriamos uma grande chance de obtermos altos percentuais de acertos assim como de obtermos percentuais mais baixos, conforme ilustrado graficamente. \n",
    "Dessa forma ressalta-se as desvantagens em se dividir a base de dados uma única vez, visto que nem sempre essa partição retornará o melhor desempenho na classifição dos tweets. \n",
    "Assim, conclue-se que sempre que possível, realizar o processo de Validação Cruzada, é um fator de grande estímulo para o melhor desempenho do modelo, dado que é possível ver a melhor configuração e adequa-la a situação requerida.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*OBS: Para salvar a melhor configuração do modelo que obteve a melhor porcentagem de acerto pode-se usar o módulo:*\n",
    "> <a href=https://docs.python.org/3/library/pickle.html>Pickle</a> \n",
    "\n",
    "Ele salva a conjuntura do modelo em um arquivo JSON, que pode depois ser guardado fora do loop de repetição, para assim certificar o melhor desempenho do modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proporção palavras relevantes e sua relação com os scores obtidos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dada as novas separações entre treinamento e teste, a proporção de tweets relevantes e irrelevantes pelo total, muda. Dessa forma analisamos qual a correlação dessa mudança com os scores (verdadeiros positivos/verdadeiros negativos). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.scatter(lista_proporcao_r,lista_hist, edgecolor='black', alpha=0.8)\n",
    "plt.title('Proporcao palavras relevantes x Score')\n",
    "plt.xlabel('% palavras relevantes')\n",
    "plt.ylabel('% Score')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3475"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista_proporcao_r=pd.Series(lista_proporcao_r)\n",
    "lista_hist=pd.Series(lista_hist)\n",
    "\n",
    "lista_hist.corr(lista_proporcao_r).round(decimals=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vimos pelo gráfico e pelo valor resultante da correlação, temos uma associação do tipo fraca/moderada negativa. Observamos também que a proporções de palavras relevantes encontra-se entre 44% e 49% majoritariamente, valores próximos ao data set original que foi utilizado para o treinamento do modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantidade de palavras novas e sua relação com os scores obtidos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dada as novas separações entre treinamento e teste, a quantidade de palavras novas obtidas em cada dataset varia. Dessa forma analisamos qual a correlação entre o número de palavras novas com os scores (verdadeiros positivos/verdadeiros negativos)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.scatter(numero_palavras_novas,lista_hist, edgecolor='black', alpha=0.8)\n",
    "plt.title('Quantidade de palavras novas x Score')\n",
    "plt.xlabel('Número Palavras Novas')\n",
    "plt.ylabel('% Score')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.09254498223014396"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numero_palavras_novas=pd.Series(numero_palavras_novas)\n",
    "\n",
    "lista_hist.corr(numero_palavras_novas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando o gráfico e o valor obtido da correlação, vemos que não há relação entre as duas variáveis, sendo sua associação classificada como fraca negativa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Melhorias para o Modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando uma agregação probabilística de Sinônimos\n",
    "\n",
    "Muitas palavras dentro da base de dados representam o mesmo conjunto de sentimentos ou tem grau de intensidade parecido, todavia terão diferentes probabilidades pelo Classificador devido a falta da consideração de Sinonímia entre estas. \n",
    "\n",
    "Criando uma função dicionário, muito parecida com a abaixo, que usa a biblioteca Psynonimos, seria possível, analisar se determinada palavra pertence a um conjunto de palavras sinônimas e associar a probabilidade desse conjunto pertecenter de maneira igual ao dataset. Dessa forma, o grau de classificação ficaria com maior possibilidade de acerto, visto que reduziria a classificação independente siginificativamente de palavras pouco usadas, mas com mesmo sentido de algumas mais comuns.\n",
    "\n",
    "Para mais informações sobre esse módulo conferir em: https://github.com/diegofsousa/pysinonimos\n",
    "\n",
    "Basicamente após a captação das palavras via base de dados de treinamento após \n",
    "os processos de transcrição realizados [nesta etapa](#captacao), iriamos reduzir o conglomerado de palavras para um conglomerado de sinônimos, agregados ou em uma string ou em uma lista, garantindo assim que palavras com mesmo sentido tenham mesma probabilidade estatística, reduzindo erros advindos da comunalidade ou não de palavras análogas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O número de vezes que contamos a \"ver\" ou algum sinônimo    na base de dados foi 2\n",
      "O número de vezes que contamos a \"mandalorian\" ou algum sinônimo    na base de dados foi 1\n",
      "O número de vezes que contamos a \"traz\" ou algum sinônimo    na base de dados foi 0\n",
      "O número de vezes que contamos a \"felicidade\" ou algum sinônimo    na base de dados foi 3\n"
     ]
    }
   ],
   "source": [
    "#Suponha que nosso armazenamento de palavras relevantes obtidas da base de treinamento seja o seguinte:\n",
    "palavras_r=pd.Series(['assistir','mandalorian','gosto','muito','deixa',\n",
    "                      'muita','alegria','star','wars','alegria','assistir'])\n",
    "\n",
    "#Assim, o tweet a ser analisado seja o seguinte:\n",
    "tweet=\"Ver mandalorian me traz felicidade\"\n",
    "\n",
    "#Como podemos averiguar, as palavras \"Ver\" e \"felicidade\" tem os sinônimos \"assistir\" e \"alegria\"\n",
    "#na base de dados. Toda via, como são diferentes, apesar de seu sentido, sua probabilidade será suavizada\n",
    "#por Laplace, visto que é uma palavra nova, e dessa forma, iremos agregar uma probabilidade diferente \n",
    "#a \"felicidade\" e \"alegria\" mesmo estas tendo mesmo sentido e intensidade em grande parte dos contextos\n",
    "\n",
    "#Para contornar esse problema, podemos usar o modulo Psynonimos, que gera uma lista de sinônimos\n",
    "#para algumas palavras, e analisando essa lista, podemos associar algumas palavras a outras existentes\n",
    "#aumentando assim a eficácia do classificador, que agora não mais irá atribuir diferentes probabilidades\n",
    "#para palavras sinônimas, mas com grau de comunalidade diferente\n",
    "\n",
    "#Limpando o tweet\n",
    "tweet_limpo=aplica(tweet)\n",
    "\n",
    "#Modificando a função de contagem da palavra para associar sinonimos\n",
    "def freq_absoluta_sin(palavra,lista):\n",
    "    \"\"\"\n",
    "    Conta quantas vezes determinada palavra apareceu \n",
    "    na respectiva categoria, seja relevante ou irrelevante\n",
    "    \"\"\"\n",
    "    nova=sinom.Search(palavra).synonyms()   #Lista com sinônimos da palavra, se estes existirem\n",
    "    count=0\n",
    "    for i in range(0,len(lista)):\n",
    "        if palavra==lista[i]:\n",
    "            count+=1   \n",
    "        else:\n",
    "            if nova!=404:                 #Condição de existência da lista de sinônimo\n",
    "                if lista[i] in nova:      #Se a palavra da da base de treino tem algum sinônimo a palavra analisada\n",
    "                    count+=1              #Esse também é considerado como parte da probabilidade  \n",
    "                else:\n",
    "                    continue\n",
    "            else:                       #Não existe lista de sinônimos\n",
    "                continue                 #Caso da palavra que não possui sequer sinônimos na base de treinamento\n",
    "    return count\n",
    "\n",
    "for palavra in tweet_limpo.split(','):\n",
    "    print(f'O número de vezes que contamos a \"{palavra}\" ou algum sinônimo\\\n",
    "    na base de dados foi {freq_absoluta_sin(palavra,palavras_r)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Como podemos ver acima, mesmo que as palavras \"ver\" e \"felicidade\" não existirem na base de treino, seus sinônimos, respectivamente (\"assistir\") e (\"gosto\" e \"alegria\"), pertencem a base e por possuírem sentidos iguais, estas palavras contribuem para a frequência absoluta na classificação gerando, dessa forma, uma classificação mais efetiva da frase.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adotando uma lematização entre as palavras\n",
    "\n",
    "Para a língua Inglesa, por exemplo, já existem módulos prontos, dentro da própria biblioteca <b>ntlk</b> que conseguem seperar blocos de palavras e conseguir assim extrair sentimento e intensidade desses, gerando assim uma dependência entre as palavras, intensificando a probabilidade correta de um tweet.\n",
    "\n",
    "Dessa forma, o nosso objetivo seria a implantação de uma função ou programa que de maneira automatizada conseguisse aprender sobre o grau de associação entre as palavras de acordo com seu rótulo. Para isso seriam necessários uma maior base de dados além de uma classificação mais refinada, com critérios e categorias mais elaboradas que possam assim melhorar a qualidade e interpretação do modelo pela lematização e grau de associação entre palavras próximas.\n",
    "\n",
    "Mais sobre esse processo e algoritmos em desevolvimento para essa captação associativa entre as palavras pode ser visto em:\n",
    "https://lars76.github.io/2018/05/08/portuguese-lemmatizers.html#4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aumentando o tamanho e atualização da base de dados com Web Scrapping\n",
    "\n",
    "Apesar de já possuírmos uma quantidade razóavel para constituir o modelo, para mantê-lo sempre o máximo eficaz e atualizado, seria necessário uma busca e atualização constante de tweets diretamente da plataforma. \n",
    "\n",
    "Com esse fator, poderíamos simultaneamente treinar o Classificador com novas situações em relação ao produto, como por exemplo a introdução de um novo personagem ou a impressão do público sobre uma nova temporada, tal qual evitar erros de Overfitting e Underfitting gerados pela constância de tamanho da base de dados.\n",
    "\n",
    "Para realizar esse fato, seria necessário solicitar uma nova licensa, mais ampla, ao Twitter para que o processo de tweets seja mais constante em menores períodos de tempo, tal qual mais eficaz de acordo com a busca, uma busca por termos mais efetivos.\n",
    "\n",
    "Uma boa maneira de optimizar esse processo é além do uso do módulo Tweepy, fazer uso da biblioteca Beatifulsoap. Para mais informações, veja o artigo Indiano que explica um pouco mais: https://towardsdatascience.com/web-scraping-for-beginners-beautifulsoup-scrapy-selenium-twitter-api-f5a6d0589ea6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Referências"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Naive Bayes and Text Classification](https://arxiv.org/pdf/1410.5329.pdf)  **Mais completo**\n",
    "\n",
    "[A practical explanation of a Naive Bayes Classifier](https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/) **Mais simples**"
   ]
  }
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