ToxicCommentsAnalysis.ipynb

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    "# Business Overview\n",
    "## Context\n",
    "Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments.\n",
    "\n",
    "The Conversation AI team, a research initiative founded by Jigsaw and Google (both a part of Alphabet) are working on tools to help improve online conversation. One area of focus is the study of negative online behaviors, like toxic comments (i.e. comments that are rude, disrespectful or otherwise likely to make someone leave a discussion). \n",
    "\n",
    "## Problem\n",
    "So far they’ve built a range of publicly available models served through the Perspective API, including toxicity. But the current models still make errors, and they don’t allow users to select which types of toxicity they’re interested in finding (e.g. some platforms may be fine with profanity, but not with other types of toxic content).\n",
    "\n",
    "## Objective\n",
    "Build a multi-headed model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. We'll be using a dataset of comments from Wikipedia’s talk page edits. Improvements to the current model will hopefully help online discussion become more productive and respectful.\n",
    "\n",
    "\n",
    "# Technical Overview\n",
    "## Problem\n",
    "Given a set of comments as input, our model has to predict in which of the following 6 classes each comment is classified:\n",
    "- Toxic\n",
    "- Severe toxic\n",
    "- Obscene\n",
    "- Threat\n",
    "- Insult\n",
    "- Identity_hate\n",
    "\n",
    "From the train data set, the model has to learn how to classify each comment. Knowing that, the model applies its learning on the test data set to predict the classifications of each comment. \n",
    "\n",
    "## Class Properties Assumptions\n",
    "According to the classes, we cannot answer with exactitude what are the properties that define each class. What are the differences between a comment classified as toxic versus severe toxic? Normally, we would have asked the question to the person that create those classes.\n",
    "\n",
    "Using the definition of each class, we assume the following definitions:\n",
    "- Toxic: Only few parts of the comment are using words like fuck, suck or any other similar word.\n",
    "- Severe toxic: All parts of the comment is toxic and the ratio between bad words like fuck, suck or similar word and the number of words is closer to 1.\n",
    "- Obscene: All comments containg exagerated abominations or have sexual content.\n",
    "- Threat: All comments containing menace (severe or simply a warning) to other people or wishing some punishments like insisting on banning someone.\n",
    "- Insult: All comments insulting other people like telling that someone is stupid, imbecile or any other similar insults.\n",
    "- Identity_hate: All comments having racist content, sexual orientation hate or religion hate.\n",
    "\n",
    "These definitions could be used as a set of rules to follow to increase the accuracy of the classification of comments.\n",
    "\n",
    "## Mathematical Problem Definition\n",
    "Let $T = \\{x_1,x_2,\\ldots,x_n\\}$ be the multiset of texts where every text $x_i$ is defined by a multiset of words $\\{w_{i,1}, w_{i,2}, \\ldots, w_{i,k_i}\\}$. Note that the position of the texts in $X$ does not matter.\n",
    "\n",
    "We note \n",
    "$\\begin{equation} \\label{eq:ExplainedVariableMatrix}\n",
    "Y =\\begin{bmatrix}\n",
    "\t    y_{1,1}      & y_{1,2}      & \\ldots & y_{1,m} \\\\\n",
    "\t    y_{2,1} & y_{2,2} & \\ldots & y_{2,m} \\\\\n",
    "\t    \\vdots       & \\vdots        & \\ddots & \\vdots \\\\\n",
    "\t    y_{n,1}  & y_{n,2}  & \\ldots & y_{n,m}\n",
    "\t\\end{bmatrix}\n",
    "\\end{equation}$\n",
    "the matrix of binary output values (explained variables) $y_{i,j} \\in \\{0,1\\}$ for $1 \\leq i \\leq n$ and $1 \\leq j \\leq m$.\n",
    "\n",
    "Since the goal is to estimate $Y$ because we are not supposed to know $y_{i,j}$, we note \n",
    "$\\begin{equation} \\label{eq:EstimatorMatrix}\n",
    "\\widehat{Y} =\\begin{bmatrix}\n",
    "\t    \\widehat{y}_{1,1}      & \\widehat{y}_{1,2}      & \\ldots & \\widehat{y}_{1,m} \\\\\n",
    "\t    \\widehat{y}_{2,1} & \\widehat{y}_{2,2} & \\ldots & \\widehat{y}_{2,m} \\\\\n",
    "\t    \\vdots       & \\vdots        & \\ddots & \\vdots \\\\\n",
    "\t    \\widehat{y}_{n,1}  & \\widehat{y}_{n,2}  & \\ldots & \\widehat{y}_{n,m}\n",
    "\t\\end{bmatrix}\n",
    "\\end{equation}$\n",
    "the estimator matrix of $Y$ where $\\widehat{y}_{i,j} \\in [0,1]$ because we want to give a probability.\n",
    "\n",
    "Between the estimated and the true values, there is generally a bias that we note\n",
    "$\\begin{equation} \\label{eq:BiasMatrix}\n",
    "\\epsilon =\\begin{bmatrix}\n",
    "\t    \\epsilon_{1,1}      & \\epsilon_{1,2}      & \\ldots & \\epsilon_{1,m} \\\\\n",
    "\t    \\epsilon_{2,1} & \\epsilon_{2,2} & \\ldots & \\epsilon_{2,m} \\\\\n",
    "\t    \\vdots       & \\vdots        & \\ddots & \\vdots \\\\\n",
    "\t    \\epsilon_{n,1}  & \\epsilon_{n,2}  & \\ldots & \\epsilon_{n,m}\n",
    "\t\\end{bmatrix}\n",
    "\\end{equation}$\n",
    "where $\\epsilon_{i,j} \\in [-1, 1]$ because the bias may be negative or positive. If $y_{i,j} = 1$ and the model estimated $\\widehat{y}_{i,j} = 0.971$, then the bias is positive because $\\epsilon_{i,j} = 1 - 0.97 = 0.03$. However, if $y_{i,j} = 0$ and $\\widehat{y}_{i,j} = 0.12$, then the bias is negative because $\\epsilon_{i,j} = 0 - 0.12 = -0.12$.\n",
    "\n",
    "We deduce the vectored equation \n",
    "$\\begin{equation}\n",
    "\tY = \\widehat{Y} + \\epsilon\n",
    "\\end{equation}$\n",
    "where the operator $+$ is the element-wise matrix addition.\n",
    "\n",
    "Let $f : T \\longrightarrow \\mathbb{M}_{n \\times m}([0,1])$ be a model defined by $f(x) = \\widehat{Y}$ where the notation $\\mathbb{M}_{n \\times m}([0,1])$ means the set of matrix $n$ by $m$ for which each element is a real number in $[0,1]$.\n",
    "\n",
    "The goal is to find a model $f$ such that the bias $\\epsilon$ is minimized when $f$ is applied on $x$. Obtaining $\\epsilon = \\mathbf{0}_{n \\times m}$ means that the model $f$ predict perfectly how the texts will be classified.\n",
    "\n",
    "## Evaluation Metric\n",
    "We use the __Receiver Operating Characteristic Area Under Curve (ROCAUC)__ function to evaluate the score of the column $i$ noted $R(Y_i, \\hat{Y}_i)$. To obtain the multi-class ROCAUC final score, we define the cost function as\n",
    "$$S(Y, \\hat{Y}) = \\frac{1}{m} \\sum_{i = 1}^m R(Y_i, \\hat{Y}_i)$$\n",
    "where $0 \\leq R(Y_i, \\hat{Y}_i) \\leq 1$ is the ROCAUC function.\n",
    "\n",
    "The goal is to maximize $S(Y, \\hat{Y})$ where $S(Y, \\hat{Y}) = 1$ means that the model is perfect. \n",
    "\n",
    "\n",
    "# Data Acquisition\n",
    "The goal is to get the train and test data sets and extract basic information before starting the data exploratory phase.\n",
    "\n",
    "## Data Source\n",
    "The data come from Kaggle and are downloaded __[here](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data)__.\n",
    "\n",
    "## Dataset Basic Information\n",
    "We need to know how much data do we have in our data sets to help us determining a list of algorithms that will suit better to solve the problem.\n",
    "- Number of rows\n",
    "- Number of columns\n",
    "- Percentage: number of rows of a data set / total number of rows of test + train sets * 100\n",
    "- Average, Min and Max length of comments (number of words and number of characters)"
   ]
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     "text": [
      "Train set - Number of rows:  159571\n",
      "Train set - Number of columns:  8\n",
      "\n",
      "Test set - Number of rows:  153164\n",
      "Test set - Number of columns:  2\n",
      "\n",
      "Number of output classes:  6\n",
      "Output classes:  ['toxic' 'severe_toxic' 'obscene' 'threat' 'insult' 'identity_hate']\n",
      "The test set contains  48.98 % of the overall data sets.\n",
      "Number of unique words:  470340\n",
      "\n",
      "Length of the smallest comment:  6\n",
      "Length of the largest comment:  5000\n",
      "Average length of comments:  394.0732213246768\n",
      "Standard deviation of the length of comments:  590.7202819048919\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "import seaborn as graph\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import re\n",
    "import time\n",
    "\n",
    "from string import punctuation\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "#from nltk.corpus import stopwords\n",
    "#from nltk import word_tokenize\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "#from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, roc_curve\n",
    "\n",
    "from PlotUtils import *\n",
    "from Model import *\n",
    "from NewFeatureGenerator import *\n",
    "from DataExploratory import *\n",
    "\n",
    "## Remove the scientific notation.\n",
    "np.set_printoptions(suppress=True)\n",
    "pandas.set_option('display.float_format', lambda x: '%.9f' % x)\n",
    "\n",
    "\n",
    "train = pandas.read_csv(\"dataset/train.csv\")\n",
    "train_number_of_rows = len(train.index)\n",
    "train_number_of_cols = len(train.columns)\n",
    "print(\"Train set - Number of rows: \", train_number_of_rows)\n",
    "print(\"Train set - Number of columns: \", train_number_of_cols)\n",
    "\n",
    "test = pandas.read_csv(\"dataset/test.csv\")\n",
    "test_number_of_rows = len(test.index)\n",
    "test_number_of_cols = len(test.columns)\n",
    "print(\"\\nTest set - Number of rows: \", test_number_of_rows)\n",
    "print(\"Test set - Number of columns: \", test_number_of_cols)\n",
    "\n",
    "number_of_classes = train_number_of_cols - test_number_of_cols\n",
    "print(\"\\nNumber of output classes: \", number_of_classes)\n",
    "\n",
    "output_classes = train.columns.values[test_number_of_cols:]\n",
    "print(\"Output classes: \", output_classes)\n",
    "\n",
    "train_input = train[train.columns[0:test_number_of_cols]]\n",
    "train_output = train[output_classes]\n",
    "\n",
    "percentage_test = round(test_number_of_rows / (test_number_of_rows + train_number_of_rows) * 100, 2)\n",
    "print(\"The test set contains \", percentage_test, \"% of the overall data sets.\")\n",
    "\n",
    "words_frequency = GetWordFrequency(train.comment_text)\n",
    "print(\"Number of unique words: \", len(words_frequency))\n",
    "\n",
    "lengths = train.comment_text.str.len()\n",
    "print(\"\\nLength of the smallest comment: \", lengths.min())\n",
    "print(\"Length of the largest comment: \", lengths.max())\n",
    "print(\"Average length of comments: \", lengths.mean())\n",
    "print(\"Standard deviation of the length of comments: \", lengths.std())"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Applicable Algorithms\n",
    "The goal is to find a list of algorithms that can be applied to our problem.\n",
    "- We know the possible outputs where each comment has to be classified hence we seek for a supervised algorithm.\n",
    "- As the problem states, the output is a multi-classification which explain why the multi-class AUROC metric has been chosen.\n",
    "- We have to analyse comments (texts) that is part of the Natural Language Processing (NLP). \n",
    "- We have 159571 comments in the train set which is a small data set.\n",
    "\n",
    "According to those points, supervised algorithms for classification related to the NLP are mostly suitable to solve the problem. However, regression algorithms for classification should work because we can extract features from the comments. This will be explained with more details in the Feature Extraction section of this document.\n",
    "\n",
    "We will try the following algorithms:\n",
    "- Naive Bayes Classification (NB): https://web.stanford.edu/class/cs124/lec/naivebayes.pdf\n",
    "- Support Vector Machine (SVM): https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/\n",
    "- Maximum Entropy Classifier (http://www.kamalnigam.com/papers/maxent-ijcaiws99.pdf)\n",
    "- Recurrent Convolutional Neural Networks (http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploratory\n",
    "The goal is to explore the training and test sets to understand and visualize how the comments are written and how they are classified.\n",
    "\n",
    "Comments can be written the following ways knowing that emotions are generally a strong factor that influances how people write their comments:\n",
    "- Normal sentences without any of the previous ways specified above\n",
    "- Lower case or upper case. We need to check if this has influance on the classification\n",
    "- Invisible characters like tabulations, carriage returns, spaces \n",
    "- Words with repeated characters (e.g. suckkkkkkkk)\n",
    "- Abusive usage of puctuation like !?\n",
    "- Unicode special characters like Chinese characters or any other languages using caligraphic characters\n",
    "- Words with grammar, spelling or typing errors like the word \"btich\" which should be \"bitch\"\n",
    "- Abbreviations \n",
    "- Words written as they sound (e.g. \"you\" is written \"u\")\n",
    "- Bad words hidden like \"sh*t\"\n",
    "- Other special characters (e.g. &%?/\"!)\n",
    "- Words with same meaning (e.g. fuck, fucking, fucker)\n",
    "- Numbers\n",
    "\n",
    "## Not Applicable or Null Comments\n",
    "Let's check if there are comments containing NA or being empty.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of empty comments in train set:  0\n",
      "Number of empty comments in test set:  0\n",
      "Number of NA in comments of train set:  0\n",
      "Number of NA in comments of test set:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of empty comments in train set: \", len(train[train['comment_text'].isnull()]))\n",
    "print(\"Number of empty comments in test set: \", len(test[test['comment_text'].isnull()]))\n",
    "\n",
    "train.comment_text.fillna(\"unknown\", inplace=True)\n",
    "test.comment_text.fillna(\"unknown\", inplace=True)\n",
    "print(\"Number of NA in comments of train set: \", len(train[train['comment_text'] == 'unknown']))\n",
    "print(\"Number of NA in comments of test set: \", len(test[test['comment_text'] == 'unknown']))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We conclude that there are neither NA nor null values in both data sets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification of Comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Number of comments for each class: \n",
      " toxic            15294\n",
      "severe_toxic      1595\n",
      "obscene           8449\n",
      "threat             478\n",
      "insult            7877\n",
      "identity_hate     1405\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'toxic'),\n",
       " Text(0,0,'severe_toxic'),\n",
       " Text(0,0,'obscene'),\n",
       " Text(0,0,'threat'),\n",
       " Text(0,0,'insult'),\n",
       " Text(0,0,'identity_hate')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fccfc6e4f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes_frequency = pandas.Series.from_array(train[output_classes].sum())\n",
    "print(\"\\n Number of comments for each class: \\n\", classes_frequency)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "ax = classes_frequency.plot(kind='bar')\n",
    "ax.set_title(\"Bad Comments Frequency\")\n",
    "ax.set_xlabel(\"Classes\")\n",
    "ax.set_ylabel(\"Frequency\")\n",
    "ax.set_xticklabels(output_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assumptions\n",
    "1. Upper case words are mostly subject to bad comments.\n",
    "2. Abusive invisible characters are there to dissimulate bad comments. This is a way to write an inappropriate comment and try to bypass the algorithm to not get blocked.\n",
    "3. Words containing abusive repeated characters means frustration and are mostly bad.\n",
    "4. Abusive usage of exclamation marks normally means that people are angry and the risks of containing bad comment are high.\n",
    "5. Special characters like 'sh*t' hide bad words and are mostly in bad comments.\n",
    "6. Smaller comments (less than 10 words) are mostly bad comments because people may write sentences like 'Fuck u'.\n",
    "7. Foreign languages using caligraphic unicode characters (e.g. Chinese characters) are used to hide bad comments because they use their language to not be recognized.\n",
    "8. Numbers are used to dissimulate a bad word like \"5h1t\" but it should be a rare case.\n",
    "9. Repeated words or sentences are mostly used in bad comments.\n",
    "\n",
    "## Feature Extraction\n",
    "Let's add new features to the train set according to the assumptions to show if those assumptions are true or false. To prove if they are true or false, we will check the correlation between the new features and the classes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>identity_hate</th>\n",
       "      <th>insult</th>\n",
       "      <th>obscene</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>threat</th>\n",
       "      <th>toxic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>comment_length</th>\n",
       "      <td>-0.013646812</td>\n",
       "      <td>-0.045052165</td>\n",
       "      <td>-0.042944845</td>\n",
       "      <td>0.010131476</td>\n",
       "      <td>-0.008010929</td>\n",
       "      <td>-0.054470061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_capitals</th>\n",
       "      <td>0.053576196</td>\n",
       "      <td>0.075944701</td>\n",
       "      <td>0.081690524</td>\n",
       "      <td>0.143410057</td>\n",
       "      <td>0.033581380</td>\n",
       "      <td>0.091174923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_capitals_length</th>\n",
       "      <td>0.093725789</td>\n",
       "      <td>0.170383907</td>\n",
       "      <td>0.182451738</td>\n",
       "      <td>0.169242883</td>\n",
       "      <td>0.055596134</td>\n",
       "      <td>0.220777185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exclamation_marks</th>\n",
       "      <td>0.006004905</td>\n",
       "      <td>0.027010069</td>\n",
       "      <td>0.024361887</td>\n",
       "      <td>0.060577965</td>\n",
       "      <td>0.034202172</td>\n",
       "      <td>0.037336753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_exclamation_marks_length</th>\n",
       "      <td>0.039348852</td>\n",
       "      <td>0.102696574</td>\n",
       "      <td>0.108243169</td>\n",
       "      <td>0.093135949</td>\n",
       "      <td>0.037812460</td>\n",
       "      <td>0.132070566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>question_marks</th>\n",
       "      <td>-0.000516349</td>\n",
       "      <td>0.004858674</td>\n",
       "      <td>0.005404492</td>\n",
       "      <td>-0.004540922</td>\n",
       "      <td>-0.003949309</td>\n",
       "      <td>0.027820112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_question_marks_length</th>\n",
       "      <td>0.000794396</td>\n",
       "      <td>0.010765304</td>\n",
       "      <td>0.014391032</td>\n",
       "      <td>0.000715929</td>\n",
       "      <td>-0.005940034</td>\n",
       "      <td>0.031023651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carriage_returns</th>\n",
       "      <td>0.002479304</td>\n",
       "      <td>-0.008844677</td>\n",
       "      <td>-0.002749310</td>\n",
       "      <td>0.019784200</td>\n",
       "      <td>-0.000248372</td>\n",
       "      <td>-0.010433779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_carriage_returns_length</th>\n",
       "      <td>0.026409986</td>\n",
       "      <td>0.074650203</td>\n",
       "      <td>0.080759894</td>\n",
       "      <td>0.049831116</td>\n",
       "      <td>0.021215112</td>\n",
       "      <td>0.082218584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>special_chars</th>\n",
       "      <td>0.013767673</td>\n",
       "      <td>0.007175059</td>\n",
       "      <td>0.015846232</td>\n",
       "      <td>0.016189399</td>\n",
       "      <td>0.002421710</td>\n",
       "      <td>0.017097496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spaces</th>\n",
       "      <td>-0.014837901</td>\n",
       "      <td>-0.044126775</td>\n",
       "      <td>-0.042686597</td>\n",
       "      <td>0.008349258</td>\n",
       "      <td>-0.006922824</td>\n",
       "      <td>-0.052289450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_spaces_length</th>\n",
       "      <td>0.007553259</td>\n",
       "      <td>0.021009786</td>\n",
       "      <td>0.026067308</td>\n",
       "      <td>0.006839244</td>\n",
       "      <td>0.024987473</td>\n",
       "      <td>0.037167527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_special_chars_length</th>\n",
       "      <td>0.014501906</td>\n",
       "      <td>0.039657637</td>\n",
       "      <td>0.047283493</td>\n",
       "      <td>0.034813186</td>\n",
       "      <td>0.005767416</td>\n",
       "      <td>0.053784492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all_words</th>\n",
       "      <td>-0.014493434</td>\n",
       "      <td>-0.043641788</td>\n",
       "      <td>-0.042207139</td>\n",
       "      <td>0.008452379</td>\n",
       "      <td>-0.006688447</td>\n",
       "      <td>-0.052443987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_words</th>\n",
       "      <td>-0.032796145</td>\n",
       "      <td>-0.080959611</td>\n",
       "      <td>-0.080942159</td>\n",
       "      <td>-0.048376582</td>\n",
       "      <td>-0.020278764</td>\n",
       "      <td>-0.096255759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio_all_vs_unique_words</th>\n",
       "      <td>0.010632406</td>\n",
       "      <td>0.043348220</td>\n",
       "      <td>0.042754548</td>\n",
       "      <td>-0.027049537</td>\n",
       "      <td>-0.004020403</td>\n",
       "      <td>0.056490752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                identity_hate       insult      obscene  \\\n",
       "comment_length                   -0.013646812 -0.045052165 -0.042944845   \n",
       "number_of_capitals                0.053576196  0.075944701  0.081690524   \n",
       "ratio_capitals_length             0.093725789  0.170383907  0.182451738   \n",
       "exclamation_marks                 0.006004905  0.027010069  0.024361887   \n",
       "ratio_exclamation_marks_length    0.039348852  0.102696574  0.108243169   \n",
       "question_marks                   -0.000516349  0.004858674  0.005404492   \n",
       "ratio_question_marks_length       0.000794396  0.010765304  0.014391032   \n",
       "carriage_returns                  0.002479304 -0.008844677 -0.002749310   \n",
       "ratio_carriage_returns_length     0.026409986  0.074650203  0.080759894   \n",
       "special_chars                     0.013767673  0.007175059  0.015846232   \n",
       "spaces                           -0.014837901 -0.044126775 -0.042686597   \n",
       "ratio_spaces_length               0.007553259  0.021009786  0.026067308   \n",
       "ratio_special_chars_length        0.014501906  0.039657637  0.047283493   \n",
       "all_words                        -0.014493434 -0.043641788 -0.042207139   \n",
       "unique_words                     -0.032796145 -0.080959611 -0.080942159   \n",
       "ratio_all_vs_unique_words         0.010632406  0.043348220  0.042754548   \n",
       "\n",
       "                                severe_toxic       threat        toxic  \n",
       "comment_length                   0.010131476 -0.008010929 -0.054470061  \n",
       "number_of_capitals               0.143410057  0.033581380  0.091174923  \n",
       "ratio_capitals_length            0.169242883  0.055596134  0.220777185  \n",
       "exclamation_marks                0.060577965  0.034202172  0.037336753  \n",
       "ratio_exclamation_marks_length   0.093135949  0.037812460  0.132070566  \n",
       "question_marks                  -0.004540922 -0.003949309  0.027820112  \n",
       "ratio_question_marks_length      0.000715929 -0.005940034  0.031023651  \n",
       "carriage_returns                 0.019784200 -0.000248372 -0.010433779  \n",
       "ratio_carriage_returns_length    0.049831116  0.021215112  0.082218584  \n",
       "special_chars                    0.016189399  0.002421710  0.017097496  \n",
       "spaces                           0.008349258 -0.006922824 -0.052289450  \n",
       "ratio_spaces_length              0.006839244  0.024987473  0.037167527  \n",
       "ratio_special_chars_length       0.034813186  0.005767416  0.053784492  \n",
       "all_words                        0.008452379 -0.006688447 -0.052443987  \n",
       "unique_words                    -0.048376582 -0.020278764 -0.096255759  \n",
       "ratio_all_vs_unique_words       -0.027049537 -0.004020403  0.056490752  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc99decb00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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omW3+lWRIStejAwO9aooHZ8dxHKfLU84KYdXAg7PjOI7T5XFVqkWQjqhDJbS7V3z9RqkqDUgaFedWO47j1AVlnEpVFTxzTimxIMpxwN05SlU1RVJ9/fx0HMcBGhrTEXST4sE5IqkbcA1B+KEHcCZBWWQPwgTzCcBehJrRVxCG0D9jZr/OsvH/gFsJJS57AD81s7ckvUWoSb09YXJ6I/B94EEzO1nS9sDvCSpWMwjiFpcAG8QpWxMI86FPkHQssG885L1m9gdJNxImxm8CrAYcUEQ1axSh4tha8VxPBX4GrA78AJhMHgWsWADlP4TSpR9n2bsNeIhQNa0kZSzHcZxqUW/PnOvL28qyP/BhVJ7aA7gQOAE4h1AP+24ze5ugQHJ4VBxZUVL/LBv9COpW2xEkFTPTrdYgSFJuARxDkF8cSAiKEKQoD4g1vr8AdgQuACx7ypakNYARhJKfWwP7SPp23NzLzHYkVPM6qJ1z7WNmO0U/fpr1fjeKK2D9N8efE4D3okJVR5SxHMdxqkJjz+6JlzSQDi/SwWBga0mZ+by9CUpMBwMH0jbPVxn1JTM7CED6RkzqI+C3MTtdFng+rp+ZUYqSNBt43szmS8r8OPoEuEZSd0I2+3gBHzciSFfOj7aeBr4btz0ZX98n/AgoxoT4+iFtQhYfEdRTMgpYhxF6B5bLsx8EZZbVaKu5fR9wpaTvEIqdJFHGchzHqQr11q1dX95Wlibg7KjStK2ZDYg6xcsRfsQsEdsVE80+E/hXzDqzM84FFJsywTWLG4Cjol7yfUXs56pjZYQ6co/R3nPh+QXeN1BcAasp631fQjf8VtBhZSzHcZyq0NCtW+IlDXhwbuM5YHcIiiKSzpG0L/AqQezi3Njuf5K2iO2ul7ROlo2+wFtx0NQe5KhHFWFpYLKkZQiKVoXUsV4EBknqHrPsLeK6clJUASuLOwlC41dI6t1BZSzHcZyqUG+jtdPhRTr4CzA71kv9ByFYnwycZWZ3AWvHmtXHAhdJegr4PIptZ7ga+CPwMCF4DZG0Q4Jj/x+hC/16Qk3sUwhZck9Jd2Uamdm7hEFr4wjd2NeZ2XsdP+W8tKeA9Q2x6/o2wnP5jihjOY7jVIXGxsbESxpwVSqnJnz95ay6+uI1fvV5rV0oGS/fWXmal1y+1i6UTENrsSdz6aT7SgM6PYVzyqmHJL7nrPr762s+ZdQHhHVRJN1DeHaczRdmtnst/HEcx6kljT3qK9zVl7dOYsxseK19KEa3Lz6otQslMf+lMbV2oWRam+bW2oWSaF1qufYbpYxua9ef8MWs+/5UaxdKZtkjzuu0jbQ8S06KB2fHcRyny+PB2XEcx3FSxiJXIUzSanEUM5IujVWsqoaLTpS8/7uSliyTL7vFGuBI+qQcNh3HcSpBQ2Nj4iUNlJQ5S2ows9wRb0OBJYEJZjaybJ7VEBedSMxxwBgWLE7iOI6TOtJSljMp7XoraQSwE7AK8IaktQmiDlcTqlmNAuZJmky4WR8FTAFuJNSM7gEcU0SIIVdw4jTgKWA8LjpRMdEJSSsT5lX3BJqBQ81ssqQ3gXuBLeP1+CGwMqH2dhNhjvU2cd+BwIOShkWb58dtnwK7xkImjuM4NSctGXFSknrbH9gBeMnMBhNuwGea2XRCEB5tZn/Pan8soQb0dsBIQrArRK7gxKWxbKaLTlRWdOIs4CIzGwZcCvwurl8TuNnMBhHqg28I/Ar4Sywvuhjh4twCTAN2jp9XH+BWMxsY32+YwAfHcZyq0NitW+IlDSTN8yea2RxJfWIFrSag2Oz7TYGzAcxskqS1irRdSHBCUk8ze1ySi04svB+UR3RicDgNnUrIyKfH9TMz1ziez9LAOoSKZxB6ITbPYy97v6lxP8dxnFTQVUdrN0kaQni+PMTM5sVAV4hcgYZiP0UyghO359mWLToxg2SiE1fFwVy7xPVJRCd+aGavSrq8iP1qi070ASZlbS8kOvGkmT0maTPCOd8k6QQzK/QjI9ve3mb2YRH/Mn410Hauhars5NvPcRwnFdRbcC7F277AlBiYdwO6x4FT+QQaJgLbAUgaSHgmWoiFBCfiexedqKzoxHOE64SkoZL2L9L2Ldqy9J2z1ue7To7jOKmj3kZrl+LFo8AASU8AIohDXEkYuHWipAOy2o4GNpE0hhBcjy1iN1dw4klJS+GiE5UWnRgF7BE/z9MJn2MhRgOHS3qUkBE3x/Vjgack9U1wPMdxnJrR2LN74iUNuPCF0y6S1gOWMbOnJe0HbGdmP++MzfkfLDQlL9V4+c7K01iX5TvbG2KSPuq0fGenH5N9efvvE99zltjv1Jo/lqvaT4Q4LWndPJt2NrM51fKjGtRadCL2MpyfZ9OdZnZlB0zOAq6W1Eroyj64M/45juNUm4bGdIzCTkrVgnP2NKCuTq1FJ8xsArBtGe1Npm3EvOM4Tv3hwdlx2mden/7tN0oRjUMOrLULJVPzfrkSaW7sUWsXSqa5/SapY8m9Fpk8aUFSMtArKR6cHcdxnC5PQxmLi0i6hFDMqhU41swmZm1bjDB4eF0z27SAiXapr58SjuM4jtMRuvdMvhQh1vwYEKsoHkKoXJnNBYTpxJ3Cg3NKkbRhrPiFpDsk9a61TxlcgcpxnHqjjPOchxH0B4jTeZeN038znEKYFtwpPDinl+HAdwDMbN+0jGhPoTKW4zhO+zR2S74Upx9t5Y6J77/RMzCzYtUzE+PPnMtA/NV0D0EUYgzwU8KziPXNbLakCwlV0m4hR4HLzMZIOoig5tUEvAxcBfwCmC7pY0KhlvUJIh030FbF7JB4nJsIVby+C7xoZocW8bUjKl2DgeOBbwHZSmDfI6hh7UAohrIpoVTrlWZ2YwcupeM4TmWo3GjtiiQsnjmXh58QFLu2Av5H4frTCylwxfUnAHvG/ScRKn49BJwSp0VlOBO4PqpPXUGo8gVBrvI3wGbAD2Ip0kJ0RKULgsrUjpmBD7Eq2FUECc2ehPrkgwlTrupv2K3jOF2aMnZrf8CCyn8rE4SMyooH5/KwDvBsfD+2SLvBhJKZY4G7iQpcwO3A3ySNBB4o0oW9aZb9x2mrof2mmU2LNbk/oLgi1Ewze83MvgIyKl1zaPsuZFS6xhFqjWfKNr1sZl/H942EEqnnm9lkM/sMeF3SfcA+wM1Fju84jlN9yjQgjFAiei8ASRsDH5jZrHK768G5PDTQli1n1Jmys+dMJplR4No2LgPMrMnMziU8Y24ExkgqVMcwWxmrkCoWFO9mSaLSdVTUbr4va322KtZSwL8JXe8ZOzsT9Ke/RxkGQziO45SThm7dEi/FMLNngOejHsRlwJGSRkj6EUDUIPgXsJ6kVyQd0hF//ZlzeXiN0E38V8KzXICZwEqS3iZ0G79ImwLX7ZJWAEYCpwJnAaPM7GJJ6wL9Ka72dTswhAUlJctFrkrXv/O0mWFmv5J0U9SefgTYzcwuA16Q9HyefRzHcWpHGYuQmNnJOateztq2PWXAg3N5uIXQLf0EbepOlxMySAP+G9f9BRgaf3F1IwTkFkmzgPGSvgDeBl4iKF9dFrdlOA24PgbEJsKAsHI/382odL1FqM99OuF5dj5GEs73YWBwlPn8mpB9O47jpIc6K9/pqlRlRtKSwCtmtnqtfUkzc+bOrasvXmPzvFq70OWpx/Kd9UiP2R/X2oWS6bH8ap0eET3vuXsT33N6bLFHzaeMeubcBZG0G3Bcnk2jzexv1fbHcRyn5tRZbW3PnJ2aMHfOnLr64jXM/7r9RimjoSV3rF+6aem5RK1dKJn5dfUtDvSaNa3WLpRMjxXX6HQmO//FhxJ/Wt032skzZ8dxHMepOHWWOXtwdhzHcbo85VSlqgYenB3HcZyuT52N1q5pni9pNUmbx/eXSlqjlv5k6KqKUHGi/IVl8mMpSTvE96MkHVUOu47jOBWhfMIXVaFqmbOkBjPLfSA/FFgSmGBmI6vlSwKGEwp8vG5m+9bamQwpU4TamCB48XCtHXEcx2mPhu71NVWvosFZ0ghgJ2AV4A1JaxOKZlxNKA05CpgnaTJh6s9RwBTgRoIAQw/gGDN7oYD9hdSgzKy/pHdxRaiiSDqSIMTRAtxrZhdJGkWoECbg28BIM3tQ0knAfoQCKd2BiwnFSpaS9Ho0uZGkB+J+x5rZQ+354DiOUzUa6mtAWDW87U8IIC9F1aJtgDPNbDohCI82s79ntT8WeDYqN40ELiliO6kaFLgi1DfExwd7xfbbAHtKWi1uXtXMfkD4HA6X1Ifw42UQcASwbWx3AXCnmV0T/1467ncMWTW3HcdxUkFDY/IlBVTDi4lR9ahPLFv5ILB8kfbfKC+Z2SRgrSJtk6pBgStCZbM5MCCex+OErHz1uO2p+Pp+PJe1gP+Y2Rwz+wiYQH4y+02l+DVwHMepOq0NjYmXNFCNZ85NkoYQgs0QM5snaXaR9tnKSxC6aguRTw0KiitC3Z5j41xJtxEyyTGStkngVyUVoX5oZq9KujxrfSFFqHuinZ2jdNn+wEGEnopiNAH/NLPDs1dKGprjY0NcWrLWFeqdyN3PcRwnPaQk6CalWt72BabEwLwb0D1mrcWUl5A0kPC8uBAZNShoU4OCNkWoboRuY2hThELSCpLOkdQo6WxCd/fFBBGH9hShoHqKUPmERWeY2a+ADyUdJml1SceY2QtmdgJt2XYxnge2k7S4pAZJo4uMSH8XWF9SD0nLE3oQIP81chzHSScNDcmXFFCt4PwoMCCqNomg1nQlIRieKOmArLajgU0kjQHOIzz7LMQtwGbR7kZZ6zOKUPewoCLU7Ni1/g/gydgVnVGEeoyQFWYrQg3LsnkacFD0awRBrancZBShricoQp0CrFSg7UjCQLFWgiLUM5IeJ4EilJlNJjxzf4LwWGBaoe782JX9Z0J39uj42gy8AOwj6YTEZ+c4jlMjWrt1T7ykgS5TW9vVoCpHHHX/Z0LX9X8Ig9Pe74xNr61deby2duXx2trVoSy1tae+mry29irr1Dx9TsdPhHaQdAWwbp5NOxcZwFUT0qAIVYHr1Y/wWOBr4LbOBmbHcZyqU2fPnLtM5uzUF3O/+rKuvnjzaltMr0N0S8mzs6R0nzuj1i6UTHPvYjMn00lTc1396wGw9BK9O585f/hG8sx5pQE1/+epi8zZcRzHcTpDWqZIJcWDs+M4jtP1qbPgXF/edjEk7STpiBT4sVuc2uY4jtM1qTPhCw/OVSRbuCIKgTxkZldW+9h5OI78c6pLseE4jpNa6q1CmA8I6wSSehCEL/oTxCp+Rpi/vTjQGzjazCZIegP4J/ApQRjia2AFgvjH+mZ2gqSLCQVTegBXm9l1kjaM9mcQiqCsYGYj8olWFPEx+9jXx6UnYa7yoYSCKtdG+4cAfzazTeO+kwiV00bl+LwVoQSrgAvM7PoojjE8+vQPMzun2LXzAWGVxweEVR4fEFYdyjEgrOmzDxKfeM8+K9f8n6f+7jjp4qeEAh5bEgLcbsC1UbjiZOCk2K4H8JCZnRX//tzMfpQxImkx4N1sYZC46XSCSMh2xNrX7YhW5CP72GcBF5nZMEIRkt+Z2S3ANGBnFiwTmku2zxsQAvEewNFx3QnAloQa5p8XseM4jlN9XPhikWJjQkUvzOwOgujEcElPESp8ZZfSnFDgPWY2l/zCIOtk7BOkJqG4aEUhMscbDIyK4h+nkKzUZz6fx5tZM23iGBDERB4FDgNuK8Gu4zhO5amz4OyjtTtHMwv+wBkJTDWzn0jaFLgwa1tTgfcUEQbJFp1ozdp3IdGKdmjKet3bzD4s0C632ydbfjLb54VELszsiKjX/WNgrKTN8wh7OI7j1ITWxvoKd+n4iVC/TCQEVSTtApwKvBW3DSfBIKtIIWGQt2gTmtg5vpYiWpHLc4SuaCQNlbR/XJ8RsZgJrBjt9iM8H28XSUtLOi3KYZ4JfEZQz3Icx0kHnjkvUtwBbB/1l+cRlLGukrQv8EdgX0kHJ7DzKHBSFPD4B23CIL8HrpP0K4KAx9JmNllSRrSimTAgLGlJzlHAnyTtR8iSR8T1Ywl6zNtGXyYCLwMvJjFqZl9IWl7SBIJO9TNRZ9pxHCcdlHGApKRLCAN4W4FjzWxi1rbtgXMI9+f18KeGAAAgAElEQVQHssYalYSP1k4xUTLzKzP7t6RTgIb2RkHXCz5au/L4aO3K46O1q0M5Rmt//eWsxCfea4lvFTxefAz5azPbRdI6wA1mNihr+/+AHYGpwDjgcDP7X6n+euacbr4Grpc0B/iKMH1qISRtThiAlsud1ZpH7TiOk2bKOH95GHAvgJm9KmlZSUuZ2UxJawKfmdkUAEkPxPYenLsSZvYisFmCdhMIXdKO4zhOPsoXnPsRxv5kmB7XzYyv07O2fUzCsTu5eHB2akK3Lz+ttQsl0f39kn/41pzWprm1dqEkGldcvdYulE5rS/ttUkZvG19rF0pn0J6dNtFCxR7zFDPc4YN6cHYcx3G6PC3lG1/1ASFDzrAy8GGBbavEdSVTf6NcHMdxHKdEWktY2uFhQpVGJG0MfGBmswDM7F1gKUmrS+oO7BLbl0xNgrOk1eIgJiRdGktSVtuHRU4RStK2ku4uo7094+sISRe2195xHKdWtLQmX4phZs8Az8eKjpcBR8Z7YKa88RHA7cCThEG5r3fE34p3a0f1pdzTHQosCUwws5GV9iHXj4wiVDWOm3vsPBwHjKF4Xev2bFQdSasD+wF/rbErjuM47VLOacNmdnLOqpeztj0BDKKTVGSes6QRwE6E/vY3gLWJaksEVaOJhKIdxxOC01HAFOBGYJnY9hgze6GA/Vw1qIOAWYSiIK4IVWAutKRtgaPMbC9Jw+P1nw9MMrPj4+eWz/5PgBPjZzSdUNN7b0Kd7z8Ck4EfAL2A78T9bih0XQDmTZ+cmh8aSWjwAWEVpx4HhDUvtUKtXSidOhwQ1nPQnp0ezfXZrK8S33P6fGvxmhcJqGS3dn9gB+ClbLUlM5tOCMKjzezvWe2PBZ6NCkwjgUuK2M6nBrUirgiVSBFK0pKEUqNDzWwIsKqkLfPZl9QInEuofrZ3PG+AC4BxsVwnhM87s98x7fngOI5TTZpbky9poJLBeWIsK5lPbSkfmxLKSGJmk4C1irRdQA0qFtr4GFeESqoItR6wGvCv6M8AQnDNZ78vMNPMPjKzL4HHCtjM7Dc1yy/HcZxU0NramnhJA5V85txURG0pH60sOCesW5G2uWpQ4IpQpShCNQHPm9mO2Stjt3au/ezrkM/Pgn45juOkhXqbkV7p0dqF1JYyKkjZTAS2g29qSr9SxO4CalCSfhOP5YpQyRShDFhH0grRxhmSVinQ9lNguViirjdtlcjyfYaO4zippLU1+ZIGKh2cHwUGRLUl0aa2NB44UdIBWW1HA5tIGgOcR3gGXYg7gCWiGtRIwsCtm4HjJD1G6AbuV4IiVD4ffw9cKOlfhC7zZjObTHhe/ATwLOG5dymKUHvE45xOuAbQpgjVjTZFqLMpQREKWF7ShHjtnm1PEcrMviJctwckPU3oYs87UT5m4GcRpgX8GZhE6Ll4Fdg4qrM4juOkmnJNpaoWrkpVgK6sCFUqkvYCxpjZZ/HHyhlxrl+H8dHalcdHa1ceH61dHcoxWnvyZ7MT33NW67NkzR/NpbpbUtIVwLp5Nu1cQsbaUepWEUrSacRu/xwONrN3OmBycWCMpC8Jo+87FZgdx3GqTb3loZ45OzXh65mf1dcXr7HY+MR00tA8r9YudHlaeiQdcpIeGpu+rLULJdNzmRU6ncm+80lyPec1+hbWc64Wqc6cHcdxHKcc1Fc24MHZcRzHWQRIy0CvpLgqVZ0i6XuSziiyfZSko0q0WfI+juM49UC9TaXyzLlOMbOXgJdq7UfaBDkcx3Hy0ZyWqJsQD85VItbgvpUwR7g7YU7zOoQSoKsCl5jZnyRtDZxDEAaZAhxmZk2SRgNbECpx/YJQPCUjYnE8oToYwANmVjCjzvKnP2F+eDfgPUK9coCNJD1AKIJyrJk9lM++pFHAGsC3Jf0Q+AtB+KIXcGQh0RLHcZxa4N3aTiH2Ah6JQhrHEqZqrQfsTpj29PsoMnEZsLuZDQU+AvaWtD2wqpkNBH4D7JNju5UgfDEQGCGpvQphEAqdXGxmWxMKkGSqoS1tZj8giFf8oh37vcxsK2AY8H4UHTmAoKDlOI6TGrxb2ynEw8DfJC1DEKqYRlB1mg98IulzgujGAOAeSQBLAJ8QpDczQh9PAE9E+ccMcwiVxuYTMuo+CfzZmFiFzcxOBJC0M6FaGSwoYFHIfkaQYzzhx8VVwD3V1Mp2HMdJQkudjdf2zLlKmNkrwHcJZTDPJahCZV//jMDEVDPbNi6bmdn55Bf6AL7pnj4O2Clmru8ldKmQzQUELNqx3xTP7cN4bvcAR8QiKI7jOKnBM2cnL5L2Bd42s3slfQI8ALwlqRuwLOHZ86ex7bpm9j9JRwPjCPW2TwYukLQRcChwVzTdF/jYzGZL2pgg/ZhE9CMjHnKnpDMJ9cLz0a792O3ew8welPQ/4Iok18RxHKdatKQl6ibEg3P1eB24KkpSNgMnATsQguxawG/NrEXSIcCfJDURngVfY2ZfS9pd0pPR1i9p04N+CZgd9ajHA1cTgmOme7oQp8fj/BKYDJwBbJWnXRL7bwK3SjqJkP2fnuiKOI7jVIl5zfUVnL18Z42I2snrm9kJtfalFnj5zsrj5Tsrj5fvrA7lKN/57HvJ7zkD+/fx8p1O5Yi61A/n2WRmdni1/XEcx6kV9dat7ZmzUxM8c648njlXHs+cq0M5Mucn3/408T1n6zWX88zZWTT5smGxWrtQEp/Naa61Cx2gV60dKIlVe7fU2oWSmV+HE14mz62v/z3IrxtcKvWWOXtwdhzHcbo88+qsRJgHZ8dxHKfL0+zB2XEcx3HShXdrO47jOE7KqLNpzh6cuzolqGEdQBC7mAf8z8x+LqkHQbmqPzAXOIhQE/waYE2gB3CamY2RdBBwFKGk58tmdmQVT9NxHKcolcyc473yRsK9shk42MzezmnTB7gdmGVme7Vns/6GGjqlklQNqzewc1SZkqQNCDKS08xsS+BaYDdgf+DDaG8P4NJ4nBOAPeP+kyTV3xwTx3G6LM0trYmXDrA/MCPe/84m6CfkcgXweFKDnjl3fZKoYfUFZsR2EGYuLEdQrnoMwMzuAJB0JbC1pEypz96x2Mntcf9bgdvNbE61TtBxHKc9Kjxaexhwc3z/KHBDnjaHEqR5N82zbSE8c+7iJFTDagD+D9jHzIYAz8Vt+ZSrmoCzs5SzBphZk5mdCwyP7cdIWg7HcZyU0NLamnjpAP2A6QBm1gK0xqTlG8xsdikGPXPu4iRUw5oPzDezaZJWBTYjKE9llKvukrQLsCEhcO8O3C5pBWAkcCpwFjDKzC6WtC7h2cun1TxXx3GcQrSUKXOWdCghC85mi5y/O11hzINz1yeJGtankh6RNBH4L3A+cAmhW3t7SeMIA8V+CnwEDI0qVd0IAblF0ixgvKQvgLcJalaO4zipoFyjtc3sOuC67HWSbiRkzy/HwWENZtbUmeN4cO7imNkLwOaZv6Ma1lu5alhmNiJn14vi60F5zOb+asTMzgPO64yvjuM4laLC85wfBvYG/gXsSgkDvwrhwdlxHMfp8sxrrmjt9juB70t6ijAjZgSApJOBccAE4GVgCaCPpFeAE8zsoUIGXZXKqQlfz/6irr54DfPqb/B5w/xO9apVnZbFl621CyXTMOeLWrtQMo11+F3uvtKATj/Dvfq59xLfcw7for+rUjmO4zhOpfHynY7jOI6TMprrLDj7POcCSFpN0ubx/aWS1qi1T51F0uqSJpXR3l7xdVtJd5fLruM4TrmpcIWwsuPBGZCU7/nCUOIoZzMbaWbvVNerdBMn2B9Xaz8cx3GSUG/BeZHt1o5TinYCVgHekLQ2QcjhauA+YBQwT9JkQhA6CphCKG6+TGx7TJyqlM9+ruDEgcB28Zg1F52QtDVwTjzmFOAwYHC000IQx7jbzM6QtD2hhvY04DXgE2B5YANJVwB/AZaWdCeh9OddZnZmez44juNUi6b5FR2tXXYW9cy5P6Egx0tmNhjYBjjTzKYTgvBoM/t7VvtjgWej6MNIQqGOQuQKTqwU16dFdOIyYHczG0ooLLJ3XL85YRrAIODouO4PwE+AHQmFSQAuAMzMfhn/FnBwzn6O4zipwDPn+mKimc2R1CdWvGoiZISF2JSgOIKZTZK0VpG2CwhOmNn4mJ3XXHRC0orAAOCeeMwlCNnwVOAFM/sqtsvs0t/MXozrHiD/9yZ7v5pPQ3Acx8kmLUE3KYt6cG6SNISQxQ4xs3mxzGUhWlmwZmq3Qg3N7BVJ3yVk5udKyqiUFBKd+G6sbX1/3FZMdOL2nPXnSrqNkK2PkbSNmRWra90ETDWzbbNXStqWUGe7GIW+4e3t5ziOUzPqLTgv6t3aEDLXKTEw7wZ0j9loCwv/eJlIeG6MpIHAK4WMRsGJ9c3sXoIwREYmbJCkbpL6kkx0Akm7SPoNbaITSFpB0jmSGiWdTejuvhgYT+iuL4iZfR5trBtfj5a0YZFdpklaO4pl7BDX5bs+juM4qcS7teuPR4GTJD0B/CMuVwJ3ADdJmp7VdjTwJ0ljCD9sig28yhWcOIagXPIu6RCdOCSeSxPwAWGg2aACbU8F7gHeAV6N5/Mh0FPSXYTM33EcJ7WkJegmxct3VpE4Qnz9XNGJtCNpB+B1M3tX0tWE5+Z/7oxNL99Zebx8Z+Xx8p3VoRzlO4+775XE95yLd1+/5uNmPHPuJHEq0bp5Nu3c3sCsSiHp54SR3bmcYmbjO2CygTDgbBYhe/eCI47j1BWeOTtOAjxzrjyeOVcez5yrQzky56P++u/E95zL99zQM2dn0aSheV6tXSiJxjq8CTfMm1trF0qjzr4TAC3fWqHWLpRMw6yPa+1CTai32toenB3HcZwuT711a3twdhzHcbo8Xr6zAqRRIUrS9ySdUWT7KElHlWiz5H1KRdKNknYpk60NJX0nvh8raf1y2HUcxyk3zS0tiZc0kLrMWVKDmeX2PwwFlgQmmNnIGri1EGb2EsnmE1eUAterWgwHJhHmdDuO46QW79buAClTiFpIrcnMmiSNJhQRmQ/8glBZ7Cgz20vS8cCP4+EeMLOCGXWWT/0JqlPdgPcIRUYANor1q78NHGtmD+WzL2kUsAbwbUk/JChD9YrLkYWuRdbxu5Ff4Wos8AjhB1FfYFdCwZFbCZXHno6+/CBeh+mSMiNMfiJpM6APsJuZTW7vOjiO41SDegvOaerWTotC1EJqTVEycVUzGwj8Btgnx34rsCUwEBghaakE53s2cLGZbU2o0JUp77m0mf2AUFHsF+3Y7xWVqIYB78da2QcASYaQFlK4AphpZsOABwnZ8U7AYvH8HwdWMbP/AA8R5k5PiPtNidcts5/jOE4qmN/SmnhJA2kKzhNj0Y6MQtSDtK8QNRaCQhShHGYhHgYOknQRIaA9G9ePM7P5ZvYJ8Hk8XkataSwhu16FUErz6XisJ8zsdzn250RfHidkm30SnG+2zRPN7Lm4/qn4OhVYuh37maA4nlCz+ypgLTN7KMHxBwN7xPO8mzaFK4An4+v70Yd1Mr4CD1BY5CKf747jODXHa2t3nDQoRLWQX63peAr8kInd08cBG5nZbEkFxTByyKc6BQsGvoZ27DfF8/swnt92wBGSBprZme0cP6/CVZSJXMCHuDTHv1tJpkxV80n8juM4GXy0dueotULUp7F9rlpT9rE2kpQt9NAX+DgGzo0J3fM9aZ9s1akzY9d5Ptq1H/fd3sweBo7OOr9iLKRwVaTtW1k2d6Dts3BlKsdx6oJ6y5zTFpwfBQZEhSjRphA1HjhR0gFZbUcDm0SFqPMIz5IL8TpweWx7erQJbQpRYwgKUS20qTU9CWwFmJk9Abwa110GXJVl+yVgduyKP4AwiO2KBOd6OnBYVJ1ag9BlnY8k9t8Efhu7qG8GLkhw/L9k2f0HbV3Z+bgfWErSU8DWxB8xcZ/LJA1LcDzHcZyaUW/BeZGtrV2vClG1QFIfYDsz+6ukVYDHzGztzths+uKTuvriNX75afuNUka9le9s6bVkrV0omXos39n94zdq7ULJdFtj404/Jht22ZOJ7zmPHbN1zR/LdakuybQpRMUu+YfzbDIzO7yOjjsL+LGkXxN6W37VGf8cx3GqTUtKMuKkLLKZs1NbPHOuPJ45Vx7PnKtDOTLnbS8Zl/ieM/ZXQzxzdhZNZjcuXmsXSuKzHr1q7ULp9Ki1A6Wxau/6Gk0LMK+h/m6hb/UuNus0neTrDi2V5gqO1pbUg1CPoz9hZsvBZvZ2Tpt9gOMJA2kfM7PfFrOZtgFhjuM4jlN2WluSLx1gf2BGLAp1NnBu9kZJiwPnA9sDg4DtM7OCCuHB2XEcx+nytLa2Jl46wDDgb/H9o4SKjt9gZl8BG5rZzKiF8CmwXDGDHpy7CJLelbRkOVWnEhzzqFjj23EcJ9W0tLQmXjpAP2A6QJyS25pVcZG4/gsASRsAqwPPUoT6e2Di1IwaK2A5juN0mNYyjdaWdChwaM7qLXL+zjugTNIA4M/A/mY2r9hxPDjXIVH44g5gcaA3oSpYkv22B/Y0syMk7U8QrdhA0kqEL8we5FH6kvQG8DzwmKS3CSIZ0whqVW9LWpoSVbEcx3GqSXNzeQaEmdl1wHXZ6yTdSMieX46DwxrMrCmnzf8D7gV+EiWHi+Ld2vXJisC1sQb4ycBJCfd7hiC4AeGZyMcxsG5JqFBWSOlrTeAsM7uWMNDhQDP7PqG0KHRMFctxHKdqtLa0Jl46wMPA3vH9ruSv+Hg9cETSxMUz5/rkY2B4FOToBXyZZCcz+0rS13HkYH/CAIYtCMH5b8AJhJGGmNkkSZk5F1+a2X/j+9XN7OX4fhwhcx9PkNy8CrgnoSqW4zhO1ShXt3YB7gS+H0scfw2MAJB0MuE++Smh9PGZUVwIgmTw3xc2FfDgXJ+MJKhn/UTSpsCFJez7FCHTnUUYkPBDQjZ9EoWVvrK7Z7L7hhqhw6pYjuM4VaOSFcLMrBk4OM/687L+LKm4g3dr1yd9CUpRAMNJpoKVYRyh+3oC8DIhc/4qPh9JovQ1VYEGYNvYtiOqWI7jOFWjwlOpyo4H5/rkZuA4SY8Rgmw/kusnPw1sA4yPowWXjOsgmdLXb4G7CUpWU+O6jqhiOY7jVI0KFyEpO15b26kJn836qq6+eJ/Nba61C12euizf2VhKp1U6mDyz6AyeVLJuv6U6Xet6/RPuT3zPeeXCXby2tlN+JK1GyGBzGWdmp1fbH8dxnFpT4QFhZceDcxfEzCYTnwenla/m1VeW9O6M+lJ4qkeaW+tPXKTPYvV1wwd45ePZtXahZNbtt1SnbbTUWS+xB2fHcRyny+OZs+M4juOkjHoLzj5aO2VI2knSEbX2IymSLpQ0otZ+OI7jFKPCwhdlxzPnlJH26loufuE4Tj3SUqba2tXCg3OViNnl+mZ2gqQlCQU+5gNXE2qx9iIIce+Z1e6PwEDAgHWAvYDTgbvN7P4oDbmXmY2QdCRB8LsFuNfMLirgx6HAcmb2B0m/AQaZ2a6SBgE/B34H3EAobNICHEKoHHYLMBu4ItbjPgl4H5gDvBJHiN8KNBO+Vwea2Xvlun6O4zidIS0ZcVK8W7u2dAdeM7NtgHcIZTUBkLQeoXrX5sBvgA0IQXIhJK1BCNxbEQqM7BmDZT7GEQI+wCa0FS/JiF+cCVwfRSyuAEbF7RsDBwL3A+dEX3cDMvW39wIeiaIZxwIrJTh/x3GcqtDa0px4SQMenGvPk/H1fWDprPXrABPMrDVOjXq7iI3NgQGE4Po48C2CmPdCmNkbwKqx/GYP4DVJ3yEE57GE0ptjY/PHgY3i+7fM7FNgOWCWmX0cK4xlqos9DBwk6SKgl5kVFRJ3HMepJvUWnL1bu3pkZ709st7Pz3rfkPO+NU+7fHaagH+a2eEJfXkd2Bl4jVD+czDQz8wmS8oWv8h0bWeOkfErn/jFK1H8YgfgXEk3mFm+QiiO4zhVJy1BNykenKvHTNq6erdK0P414PiY4a4KfKeIneeBP0QpyDnApcDJZjangO1xwHHA/xHELm4AMpKQGfGL24EhwKScfT8Flpa0DEGqcktgvKR9gbfN7F5JnwA/Jn+VMsdxnKrTMq+p/UYpwru1q8djgKI4xNosmH0uhJn9h6AaNYEgQvFa3HQLcIKkh4B5se1kQkB+giADOa1IYIYQnIcSxC/ej/6MjdtOI3RPjyFoki5Q7tPMWgjPoccRBDAyQf114PK43+nAlcXOz3Ecp5rUW7e2C1/UCZImEUZmv1trX8rB+5/Nrqsv3mufFvut45SDVZeux/Kd9df5+Pi7M2rtQsn8eMOVOy1EsfI+Vya+53xw5xEufOFUBkmbA+fn2XSnmXlW6zjOIkVaMuKkeHCuE8xs0xLbTyDl4heO4zjVwoOz4yRg+R7z22+UIvr1/rjWLnR5pvdcvdYulMwSPepv2M5eS7xfaxc6wMqdttDiwdlxHMdx0kXL/Poare3B2XEcx+nytDbXV+ZcF30yklaLA5yQdGksV1kOu+9KWlLSjbFOdU2R1E/S1bX2IymSdpF0Y639cBzHaY96m0qVusy5gOrRUGBJQjnLkTVwqyqY2TQgaZWvquOKVI7j1CtpCbpJSUVwjopNOwGrAG9IWptQmvJq4D5C0Yt5kiYTKlsdBUwBbgSWiW2PMbMXCthfCrgDWBzoDRwdRzMn8e27wCVmNjT+fTrwOTAj+tEEvGxmRxbYf3WCitSm8e9JBJGIUcAHBPGJ1YADgM8ybSX9BDgxnucs4CFC6c4FlK3MbHVJWxPEKObF9oeZ2UIPWCStBfzRzHaWNBh4AOhD6EF5iVBH+xpgTYJK1mlm9rCkN4B/Ap9KupdQ+esz4K1otwdBkWqluN/paZe+dBxn0aLegnOaurX7E+oyv2RmgwnqSmea2XRCEB5tZn/Pan8s8GxUQRoJXFLE9orAtVFp6WSC3GEizOxlYOVYrhKCEtNfgROAPc1sK2CSpN5JbWbRy8x2BEYDB2VWxpKdZxPKaO4GrEsBRarIZcDu8QfER8DeBc7lTeD/RftbAi8C6wHfI1Qi2w+Ya2ZDgOHA5XHXHsBDZnYWQVJylJkNI8hDQlDM6hvVtXYkBHzHcZzU0NrSknhJA2kKzhNjyck+kp4BHgSWL9L+G/UkM5tEm3RhPj4Ghkt6ilCYY7kSffsHsFOUYZxrZlMJtaf/Jmkk8EA75TILUUiRajlgppl9YmbzaVN+WghJKxIUqe6JpUG3I/RAFOI/hDrdmxMkIQeRR5HKzD4AvpaUCbSZnoZ1gWfi+7Hx9TXgW5JuITyCuKPI8R3HcapOy/ymxEsaSFNwbpI0hHBzHxKz3K+LtM9WTwLoVqTtSGBqzHKP6IBv9wC70pY1Y2bnErLLRmCMpEIBPzfjrYQi1VQz2zYum5lZvspgGcYS9JwXJ0hCDqRNyzn3mranSpVRpPoq2rka+AFwXZHjO47jVJ2WlubESxpIU3AG6AtMMbN5knYDukvKBIjc5+MZ9SQkDQReacfuW/H9cELQKYVnCRnjD4G7JTVKOhv40MwuBsYTuuXzMRNYUVKDpH7AtxMc7xNgGUl9JHWnrdLXQopUZvY5gKR14+vRkjYsYnsc8BPgTTP7hNA7sbyZTWHBa7oq0GJmuYV4jZBhk9V2Y2B/M3uK8ONn3QTn6DiOUzVam5sTL2kgbcH5UWCApCcAEbqTryQEvxMlHZDVdjSwSVRBOo/wDLoQNwPHSXqM0D3bT9LBSZ2KI5SfAZY2s8lRmWkWQSrxMULG+VKBfT+P5zWR8Bz5xYTHO52Q5d4PvBE3FVK2OgT4k6QnCUHbitg2QvAcH1d9DrwZ398BdJP0eHyfb+T474HzJT1AW0b/DnBgPP4jwAXtnaPjOE41qeRUKkk9JN0m6SlJ4yStmafNaZLGS3pW0qnt2XRVqjpA0oWEkdk31tqXcvH1rBl19cXr9sXUWrvQ5Zm+5Oq1dqFklu5V7GlaOlnsg3/X2oWSafz25p1WiVpss18kvufMnXhVSceT9FNgczM7UtIOwCFmtk/W9tWBC8xsb0ndCON0hsSxPXlJxVSqciHpCvJ3qe7c3oCtONjr5jybxpnZ6XnW5+7/c2D/PJtOMbPxedZXjPgo4OE8m8zMUjuP2nEcp1JUeKDXMNrix6PADdkbo9RvZhbNsoRez5nFDHrm7DiO4zidQNLDwK/j1FskTQG+nVtvQtJoYF/geDO7tZjNLpU5O47jOE4lkXQocGjO6i1y/s7bLW5mx0oaBYyV9LSZvVPoOB6cHcdxHCchZnYdOdNFo8ZAP+DlWDGxITtrjrNf+pnZRDP7XNLTwGaEwbR5SdtobcdxHMepNx6m7ZnyroS6EdksD1whqXscELYJ8Hoxg545O47jOE7nuBP4fqxC+TUwAkDSyYRBxeMl3UOo9tgI3G9meaffZvABYY7jOI6TMrxb23Ecx3FShgdnx3Ecx0kZHpydiiJpJ0lH5KybFCvmlGJnqVh5B0knSxoU3+/ZAZ/GSlo/YdsNJX2nA8dY6Lw7YGP1qP+NpG0krdAZewWO8W7UBl9kkbSapM1L3Od7ks4ocZ89JY2IFf/KQvb/RSUp9f9M0n2V8mVRwQeEORXFzB4qk6mNCXrfD5vZefBNSbz9iEphFWI4MIl2RlbmUsbzzvAz4EKC/KlTBEkNsT59UoYCS9Imi9oucTBP0QE9OT6tTviu3t9Ou1J9/+b/ooR9SqIj/2dmtnul/FlU8AFhTkWRNAJYn6AENoggyrEhQX6zCbg+bmsGDjWzyZLeBO4lSFnOIKiB/QdYCjgLGAzcTVDA2hy4HDgI+J6ZzZK0FXCcmQ0v4NNY4DnCPMM+0ZcPgJuAVYHewBnAewQhj+kEcZFewDnAPGAKcFhuBaCc896FMIXiLeC7wItmdmjMdH4PzAE+Ag4ArgXuNrP7Je0C7AWMiud5CnAXQQBlTzObXOyaFyLOv7YG4ukAAAdRSURBVLwGWDOey2nx7zsI13o+8CPCdb6V8Jl0Bw7Muj79gbmE6z0ty14P4DQzGxOv7yOEoNcX2DV+rmcDWxPkXW8HfpxzjNOybQGfApeY2dDo/+kEoZZHCZ95K0GAZgSwDHALMJugUz6DBJ+VpOUJojTzgOMJ807/j1BecRbwU8L3bwszO0rSgQRxmTuAo8xsL0k/AY6J+1xsZnfmOc4/Cd/VPxL+H3oRdNUvMLMbJL0B/DOe8/Xk/784Pl4zCBryZ0j6b/y8zjKza3KPWw6yfL8c+B7hWveI5zyL8F0ZBKxBGLU8iKDY11fSRoTPowV4xsx+XQkfuyLere1UgzUJAXULQqBRXH8WcJGZDQMuBX6X1f5mMxtEqEO7IUHp6s6cG9AFhGkKZwB/IwQW4uuf2/FpSrzpP0jIjvsAj5jZNoQb4Blm9h/gIUJ99AnAZcDucb+PaJvXWIxNgN8Qfgj8QNIywFGE8n1DCDf5QlrgAJjZI4Qs7eCOBubIfsDceNzhhJstwEtmtjXwPEFOdC/CtdiOoPa2EiFITTOzLQk/JHYj1JL/MLbbg/AZZpgZP9cHgeGStgb6x+s7FDgVeDzrGAfl2oqlEFeO1wza9NT/CBwe7T8MHBm3bwwcaGb/IOFnZWbTgRuB0Wb2d4La3a+jnvw44Fgzu4WgBrcxQRv+5Mz+kr5F+CGxDbAj+evrQ/yuApMJP3CGx/M8Jm7vATxkZmdR+P+ilfAjaiAwQtJS5P+/KDcZ31uBZ+NnNJLww+kNwmf8M6I6oJnNy9r3MsJntSVBOreQtK6Tg3drO9VgQ+BfUWpziqS34/rBhJveqYRsanpcP9PMMtI57wNLJzjGzcAf4utQ4LfttH8qvk4lBMfPgU0lHUb4lb9AwJS0IjAAuEcSwBIE3e32eNPMpkUbH8RzuQu4StJtwO1mNi3arDSbEmRIMbMPJH1NqGqUKZgwgRBkrgL+FoPi3XGO5kEEyVLM7I54PlcCW8eeCoDeUXQF4Mn4+j7hWg4GBsasGuAr4KD4vPtuYOUCtv4B7CTpGcIPi6nx+fC18Zr1ImS+AG+Z2aed+KwA1jWz5+L7xwnSrRCC6BOEHpkZWZ/XOsBrUVhnDpCkO3e8mTVLmsqC3+1Mt3qh/4s5hM9vPqFHok/CcyoXmxJkbzGzSZLWiuvPJczffdnMns7ZR5n/ZTM7qGqedgE8ODvVoIE27Wlo67FpAvY2sw9z2s/P+btd+TYz+7ek5SUNI2SCc9vZJfsYDYSMpw+h27UP4TlzNk3A1JhRlcJC52Jm/7+9swmt6gri+K+fFC24aKgbS939sYqlttXuRFeCSgQLCm4EF12JWKSLgK2bGhBEECEItUWk9QMVIS4shvgFptLiB4Sm00VLwVU3llZdJKAuZo65ud6o72HSR5jfJnkn996Zd847b875z9zcI5J+xHdO/ZI+xXclhddatPG8PGRiX76Oj0vV9kMzG5b0Pp7L7JX0LS6v1pW2UeBrMztabYzAVe/fUeCQmfVWjptVbOC7yZ6Ga53GlYYuxnOe94EV1dxs5EWLbN3uWNUp/QO+wPgPmFc7pqlfnkW9bwpV/yfMi9hxfg58YGZ3JQ23aPNFUP/8lOdlzor2uQ3nPGhoS56DlLWT6eAW8KGkl+JLpjyI/BoeoJC0UtJkkiD4JK8vJuttPwB9wPdt+NgF/Bm7+/X4F/NjG2Z2J/x8L35ulbS4DTtI2gmMhRR5DH/M6b+4fAye06zT9P5b5WdgRfjwTlzzH3xBAi6XjkjaCCwyszO4/PxRnFtyv2sk9eDj1x1tb0va/RTb14C1kl6W9IakczUbY5Nc6ye8f1bjO2zwz9OqOHZjLMge08ZYVft2uNwJACwHfpH0KrAHVxW6NfFOg9/chN6M93VeUtNispXxa5oXXcDfEZiX4IuZsniY6k1WsVH9/HwClAVCL64w/CVpQ+3cXyUti3MOSVowxb7OGDI4J9PBH3hB1xBeCFWqXHcB6yRdxif30557fR3YIGlHpW0EWCJpX7w+gVfdDrbh4yk8eFzE5cPbkr7E5dn9EQC2AN9JuoIHUGvDDnjecUDSAF4odg4vZtoRQWus4ZxLwElJC9u0Cb4QeEXShfi9PNt7YfiyGC/u+R04IGkQH5e+OH62pEt4vvEw3t93Q3LuZ1zKfgIzu4rLxEO4PDxUs7G+6VqxO74KzKnk27cBPeHLZuBGg8lWxmoI+ELSJly+3h1+fYznTLcD/WZ2G68fKLl6zOwennMewCXnbyapth7Bc+L7Gv5WZxdPzoubjPfPJuAgXmjVNC9eNMX3t/BF9iCRX47A+66ZncX7YWfkwgvbgL3yf2t5x8xGptDPGUVWayczhsgXzzOzr555cJIkSQeTOedkRhB50fmMS6NLcSmyznEz65tG15IOIIrLmu4FNjP7rKE9Sf5XcuecJEmSJB1G5pyTJEmSpMPI4JwkSZIkHUYG5yRJkiTpMDI4J0mSJEmHkcE5SZIkSTqMR87XTMxLYwWSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc99def0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Assumption 1      \n",
    "train['comment_length'] = [comment_length for comment_length in comment_lengths(train['comment_text'])]\n",
    "\n",
    "train['number_of_capitals'] = [capitals for capitals in comments_capitals_counter(train['comment_text'])]\n",
    "\n",
    "train['ratio_capitals_length'] = [ratio for ratio in ratio_feature_vs_length(train['number_of_capitals'], train['comment_length'])]\n",
    "\n",
    "\n",
    "## Assumption 2\n",
    "train['carriage_returns'] = [carriage_returns for carriage_returns in comments_chars_counter(train['comment_text'], '\\n\\r')]\n",
    "\n",
    "train['ratio_carriage_returns_length'] = [ratio for ratio in ratio_feature_vs_length(train['carriage_returns'], train['comment_length'])]\n",
    "\n",
    "train['spaces'] = [spaces for spaces in comments_char_counter(train['comment_text'], ' ')]\n",
    "\n",
    "train['ratio_spaces_length'] = [ratio for ratio in ratio_feature_vs_length(train['spaces'], train['comment_length'])]\n",
    "\n",
    "\n",
    "## Assumption 3\n",
    "\n",
    "\n",
    "## Assumption 4\n",
    "train['exclamation_marks'] = [exclamation_marks for exclamation_marks in comments_char_counter(train['comment_text'], '!')]\n",
    "\n",
    "train['ratio_exclamation_marks_length'] = [ratio for ratio in ratio_feature_vs_length(train['exclamation_marks'], train['comment_length'])]\n",
    "\n",
    "train['question_marks'] = [question_marks for question_marks in comments_char_counter(train['comment_text'], '?')]\n",
    "\n",
    "train['ratio_question_marks_length'] = [ratio for ratio in ratio_feature_vs_length(train['question_marks'], train['comment_length'])]\n",
    "\n",
    "\n",
    "## Assumption 5\n",
    "train['special_chars'] = [special_chars for special_chars in comments_chars_counter(train['comment_text'], '#*$')]\n",
    "\n",
    "train['ratio_special_chars_length'] = [ratio for ratio in ratio_feature_vs_length(train['special_chars'], train['comment_length'])]\n",
    "\n",
    "\n",
    "## Assumption 9\n",
    "train['all_words'] = [all_words for all_words in all_words_counter(train['comment_text'])]\n",
    "\n",
    "train['unique_words'] = [unique_words for unique_words in unique_words_counter(train['comment_text'])]\n",
    "\n",
    "train['ratio_all_vs_unique_words'] = [ratio for ratio in ratio_feature_vs_length(train['unique_words'], train['all_words'])]\n",
    "\n",
    "\n",
    "\n",
    "new_features = ['comment_length', 'number_of_capitals', 'ratio_capitals_length', \n",
    "                'exclamation_marks', 'ratio_exclamation_marks_length', 'question_marks', 'ratio_question_marks_length',\n",
    "                'carriage_returns', 'ratio_carriage_returns_length', 'special_chars', 'spaces',\n",
    "                'ratio_spaces_length', 'ratio_special_chars_length', 'all_words', 'unique_words', \n",
    "                'ratio_all_vs_unique_words']\n",
    "\n",
    "rows = [{column:train[feature].corr(train[column]) for column in output_classes} for feature in new_features]\n",
    "train_correlations = pandas.DataFrame(rows, index=new_features)\n",
    "\n",
    "\n",
    "## Print a data frame in a more esthetic table.\n",
    "display(train_correlations)\n",
    "\n",
    "## Visualize the correlation with a heatmap.\n",
    "graph.heatmap(train_correlations, vmin=-0.3, vmax=0.3, center=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let $-1 \\leq r(x,y) \\leq 1$ be the correlation coefficient where $x$ is the new feature and $y$ the class. If $x$ increases, $y$ increases if and only if $r(x,y) > 0$. If $x$ increases, $y$ decreases if and only if $r(x,y) < 0$. In our case, if the value of the new feature is increased, we want to know if each class $y$ will be most suitable to be classified. Therefore, we search for all new feature $x_i$ such that $r(x_i, y_j) > 0$ (mostly closest to 1).\n",
    "\n",
    "According to the heatmap and corrolation table, we see that the following new features are subject to bad comments in order of importance:\n",
    "- ratio between the number of capital letters and the length of comments;\n",
    "- ratio between the number of exclamation marks and the length of comments;\n",
    "- Number of exclamation marks;\n",
    "- Special characters like '*\\$#';\n",
    "- ratio between the number of special characters and the length of comments;\n",
    "- ratio between the number of carriage returns and the length of comments;\n",
    "- ratio between the number of words and the number of unique words in each comment.\n",
    "\n",
    "However, we also search for features $x_i$ such that $r(x_i, y_j) < 0$. Indeed, we assume that if the comment length decreases, then the probability of having a bad comment increases. In that case, we search for $r(x_i, y_j) < 0$. This assumption holds for the following new features for the same reason as the comment length:\n",
    "- Small comment length;\n",
    "- Number of unique words\n",
    "- Number of words per comment\n",
    "\n",
    "Therefore, we drop features that are useless (mostly new features $x_i$ such that $r(x_i, y_j) \\approx 0$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_features = ['spaces', 'special_chars', 'question_marks', 'ratio_special_chars_length']\n",
    "new_features = [feature for feature in new_features if feature not in remove_features]\n",
    "train.drop(remove_features, axis=1, inplace=True, errors = 'ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship Between Classes\n",
    "We want to check if there is a strong relationship between the classes. The correlation table should show a stronger relationship if $R^2$ is closer to $1$. This could reduce the number of classes because if we find a strong correlation between 2 classes (A and B), we know that we can create a new class in function of A and B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
       "      <th>insult</th>\n",
       "      <th>identity_hate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>toxic</th>\n",
       "      <td>1.000000000</td>\n",
       "      <td>0.308619097</td>\n",
       "      <td>0.676514523</td>\n",
       "      <td>0.157058409</td>\n",
       "      <td>0.647518127</td>\n",
       "      <td>0.266009382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>severe_toxic</th>\n",
       "      <td>0.308619097</td>\n",
       "      <td>1.000000000</td>\n",
       "      <td>0.403014456</td>\n",
       "      <td>0.123601289</td>\n",
       "      <td>0.375807151</td>\n",
       "      <td>0.201600209</td>\n",
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       "    <tr>\n",
       "      <th>obscene</th>\n",
       "      <td>0.676514523</td>\n",
       "      <td>0.403014456</td>\n",
       "      <td>1.000000000</td>\n",
       "      <td>0.141179025</td>\n",
       "      <td>0.741272436</td>\n",
       "      <td>0.286866868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threat</th>\n",
       "      <td>0.157058409</td>\n",
       "      <td>0.123601289</td>\n",
       "      <td>0.141179025</td>\n",
       "      <td>1.000000000</td>\n",
       "      <td>0.150022409</td>\n",
       "      <td>0.115128331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insult</th>\n",
       "      <td>0.647518127</td>\n",
       "      <td>0.375807151</td>\n",
       "      <td>0.741272436</td>\n",
       "      <td>0.150022409</td>\n",
       "      <td>1.000000000</td>\n",
       "      <td>0.337736254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>identity_hate</th>\n",
       "      <td>0.266009382</td>\n",
       "      <td>0.201600209</td>\n",
       "      <td>0.286866868</td>\n",
       "      <td>0.115128331</td>\n",
       "      <td>0.337736254</td>\n",
       "      <td>1.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    toxic  severe_toxic     obscene      threat      insult  \\\n",
       "toxic         1.000000000   0.308619097 0.676514523 0.157058409 0.647518127   \n",
       "severe_toxic  0.308619097   1.000000000 0.403014456 0.123601289 0.375807151   \n",
       "obscene       0.676514523   0.403014456 1.000000000 0.141179025 0.741272436   \n",
       "threat        0.157058409   0.123601289 0.141179025 1.000000000 0.150022409   \n",
       "insult        0.647518127   0.375807151 0.741272436 0.150022409 1.000000000   \n",
       "identity_hate 0.266009382   0.201600209 0.286866868 0.115128331 0.337736254   \n",
       "\n",
       "               identity_hate  \n",
       "toxic            0.266009382  \n",
       "severe_toxic     0.201600209  \n",
       "obscene          0.286866868  \n",
       "threat           0.115128331  \n",
       "insult           0.337736254  \n",
       "identity_hate    1.000000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fccfc6e4710>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fccfc6e46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_output_correlations = train_output.corr()\n",
    "display(train_output_correlations)\n",
    "graph.heatmap(train_output_correlations, vmin=0, vmax=1, center=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the heatmap and the corrolation table, \n",
    "- Obscene and Insult ($r = 0.741$)\n",
    "- Obscene and Toxic ($r = 0.677$)\n",
    "- Insult and Toxic ($r = 0.648$)\n",
    "\n",
    "have the strongest relationships. However, the relation does not seem to be enough strong to create a relation between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Set Cleaning\n",
    "The goal is to remove noisy words because these words will hinder an accurate prediction.\n",
    "\n",
    "- Comments contain noise like the words \"the\", \"a\", \"I\", \"in\", \"do\" and many others where their frequency should be the highest among the words used in the English language.\n",
    "- We need to remove punctuations because they cause noise. For example, words \"hello.\" and \"hello\" would have been counted as 2 words. Removing the puctuation (the \".\") gives one word \"hello\" with frequency 2 instead of one.\n",
    "- Remove usernames and IP addresses.\n",
    "- Remove invisible characters like carriage returns.\n",
    "- Set comments to lower case.\n",
    "- Remove non ascii characters (e.g. Chinese characters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of unique words:  173662\n",
      "the     496539\n",
      "to      297244\n",
      "i       240473\n",
      "of      224348\n",
      "and     224004\n",
      "you     218607\n",
      "a       217150\n",
      "is      176326\n",
      "that    160816\n",
      "it      148592\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train.comment_text = clean_text(train.comment_text)\n",
    "test.comment_text = clean_text(test.comment_text)\n",
    "\n",
    "words_frequency = GetWordFrequency(train.comment_text)\n",
    "\n",
    "print(\"\\nNumber of unique words: \", len(words_frequency))\n",
    "print(words_frequency[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Models\n",
    "The goal is to cross-validate our models on the train set to check if they predict accurately or not with the help of the roc-auc score and confusion matrices.\n",
    "\n",
    "### Cross-Validation\n",
    "We choose to split the train set in the following way:\n",
    "- 50% of the train set is used as the training set\n",
    "- 50% of the train set is used as the testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Cross-validation: \n",
    "## X_train: 50% of train comments\n",
    "## X_test: 50% of train output classes\n",
    "## y_train: 50% of train comments used as test\n",
    "## y_test: 50% of the train comments used as test\n",
    "X_train, X_test, y_train, y_test = train_test_split(train.comment_text, \n",
    "                                                    train[output_classes], \n",
    "                                                    random_state=45, \n",
    "                                                    test_size=0.50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Classifier Model\n",
    "The goal is to apply the Naive Bayes classifier model on the training set and cross-validate with half of its data. However, this model has 2 major weaknesses:\n",
    "- The model assumes that each word are independent between each other. This simplifies the problem but the assumption may be false because the context of the sentence may change completly the sense of words. For example: \"Fuck yeah! I'm so happy now :-)\". The word \"Fuck\" is used but knowing the context, the comment is not toxic at all.\n",
    "- The model does not take in consideration puctuations, capital letters or invisible characters which could be good predictors.\n",
    "\n",
    "### Predictions\n",
    "Note that predictions returned are \n",
    "- probabilities for each class;\n",
    "- 0 or 1 for each class where the threshold is set to $0.5$. If the probability $p \\geq 0.5$, then return 1, otherwise 0.\n",
    "\n",
    "For example, if the probability calculated is $p = 0.65$ that a comment is toxic, then the value 1 is returned for toxic class and 0 for non-toxic class because $0.65 > 0.5$. To get the confusion matrix, we need to get binary values (0 or 1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  toxic\n",
      "ROC AUC:  0.9478904760648847\n",
      "\n",
      "Class:  severe_toxic\n",
      "ROC AUC:  0.9731141037114117\n",
      "\n",
      "Class:  obscene\n",
      "ROC AUC:  0.9571639687466613\n",
      "\n",
      "Class:  threat\n",
      "ROC AUC:  0.956515713340408\n",
      "\n",
      "Class:  insult\n",
      "ROC AUC:  0.9562186390297754\n",
      "\n",
      "Class:  identity_hate\n",
      "ROC AUC:  0.9512101481489038\n",
      "\n",
      "Multi-class ROC AUC:  0.9570188415070074\n",
      "\n",
      "Execution time:  9.688201665878296 seconds\n"
     ]
    }
   ],
   "source": [
    "#words = ['the', 'to', 'i', 'of', 'and', 'you', 'a', 'is', 'that', 'it']\n",
    "start_time = time.time()\n",
    "vectorizer = TfidfVectorizer(norm='l1')\n",
    "X_train_vect = vectorizer.fit_transform(X_train)\n",
    "\n",
    "X_test_vect = vectorizer.transform(X_test)\n",
    "\n",
    "classifier = MultinomialNB(alpha=0.005)\n",
    "\n",
    "auc_roc_nb, predictions, predictions_int = calculate_predictions(classifier, X_train_vect, X_test_vect, y_train, y_test, output_classes)\n",
    "end_time = time.time()\n",
    "total_time_nb = end_time - start_time\n",
    "print(\"\\nExecution time: \", total_time_nb, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[72121    44]\n",
      " [ 6593  1028]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc99e01b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  Non toxic       0.92      1.00      0.96     72165\n",
      "      toxic       0.96      0.13      0.24      7621\n",
      "\n",
      "avg / total       0.92      0.92      0.89     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79000     4]\n",
      " [  778     4]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97dd7c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  precision    recall  f1-score   support\n",
      "\n",
      "Non severe_toxic       0.99      1.00      1.00     79004\n",
      "    severe_toxic       0.50      0.01      0.01       782\n",
      "\n",
      "     avg / total       0.99      0.99      0.99     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75562    32]\n",
      " [ 3786   406]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97e59208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "Non obscene       0.95      1.00      0.98     75594\n",
      "    obscene       0.93      0.10      0.18      4192\n",
      "\n",
      "avg / total       0.95      0.95      0.93     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79564     0]\n",
      " [  222     0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97ea5ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non threat       1.00      1.00      1.00     79564\n",
      "     threat       0.00      0.00      0.00       222\n",
      "\n",
      "avg / total       0.99      1.00      1.00     79786\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[75925    40]\n",
      " [ 3639   182]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97f49160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non insult       0.95      1.00      0.98     75965\n",
      "     insult       0.82      0.05      0.09      3821\n",
      "\n",
      "avg / total       0.95      0.95      0.93     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79111     1]\n",
      " [  668     6]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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x0Zqcc/zXl23BVpKv9v8KL734Yt5ltEnV3vNojes8zieZW+VKkkM0dXxx+q9OJB+wkCypWK+56YMXFzyu7z3NB/aLiPcbbL+wweMWf6IRMVXSlyQNBp6PiLktvKRh3QeT9D4GpF+nNNh+PvBuRAxsqZZ6O+x3cbGbVqQ5z11Bl21PzLuMzMycXN0X0HXuCHMb/p9WRTqX8UlZ7eGR+am6EfFvkjGHY9Km54C+kjpK6kjSw3iulF0C20uqkbQuyWEpSGaQ3AdA0iBJBzezj8UsHZwN224mWc/3phJqq7cq8GbaOxpBOi5T/x4RMTOtc/P060mFZ6aZWeWr9sNWrXWdx89JBo+JiLeAscCjJIepromIacXuKD0E9QLwFHAh8Hz61LnAPpIeI5lh8qlmdvN3krGW0wva/gFsJ+kX6eNbSaY6ntDwxUW4HdhL0iPAHOAdSWeTfL+Xpz2aI4Hfp7Ng9icJRTOrEtV+kWBNYxdjGaTjFWtFxDl51wLQZdsTq/oH5cNWla0dHLYq+VN92/MmlPX/7HPnDKqIBPHcVo2Q9DuSdYL3Th/vBPy0kU1viYirWrE0M6sQFdSJKIvDoxERcUSDx88AA/OpxswqUSUdgiqHw8PMLANVnh0ODzOzLLjnYWZmJavy7HB4mJllwT0PMzMrWZVnh8PDzCwL7nmYmVnJqjw7HB5mZllwz8PMzEpW5dnh8DAzy0JWPQ9JA4HbgJfSphdIpk+6kWQto/eBQyNinqRDgFNIZvQeGxHXSloOuA5Yl2TV1VER8YakrUlmEq8DpkbEcc3V0Vqz6pqZtSsZz6r7aEQMTG8nkaybdGVEDABeA46Q1A04GxhCMr3SqZJWIVlv6OOI6A9cBIxJ93kZcHJE9AN6SBrWXAEODzOzDLTyeh4DgbvT+/eQBEYfYHJEfBIRc4AngH7AYJKltgHGA/0kdQLWj4jJDfbRJB+2MjPLQMYD5ptLuptkpdLzgG4RMS997gNgDaAXS5bjbrQ9IhZLqkvbZjaybZMcHmZmGcgwO14lCYxbSVZSfZgvfpY3t4R3se0tVu/DVmZmFSQi3o2IWyKiLiJeB/4FrCypS7pJb+C99Nar4KVLtaeD5zUkg+w9G9m2SQ4PM7MMZDVgLukQSeem91cDVgN+D4xINxkBjAMmATtKWknSCiTjHROBB4D90m33Ah6OiAXAK5L6p+3D0300yYetzMwykOFhq7uBmyU9QXJq7vHAc8ANko4BpgHXR8QCSWcA95OcfnteRHwi6RZgqKTHgXnAyHS/pwBXS6oFJkXE+OaKcHiYmWWgNqP0iIhPSXoMDQ1tZNs/A39u0LYIGNXIti8DA4qtw+FhZpYBX2FuZmYl89xWZmZWstrqzg6Hh5lZFtzzMDOzklV5djg8zMyyUNPyRdoVzeFhZpaBdj/mkV6+vnpEvCNpK2Br4PaImJ15dWZmFaraxzyKmZ7kemBnSb2BO4AvkywkYmZmTWjlKdlbXTHh0TsibgUOAH4dEd8nmQbYzMyaUFtTU9atUhQz5rG8pBpgX+DItG2F7EoyM6t8FZQDZSmm5/EI8AnwfkT8U9IpQGRalZlZhct4GdrctRgeEXEGsE5E7J823QUclWlVZmYVrt2PeaSLoH8jvX8T8CCNz+hoZmbtRDFjHmcDe6Uh0gHYFriX5MwrMzNrRCUNfpejmDGP2RHxH2BP4MaImAUsyrYsM7PKVlPmrVIUEx6dJX0P2AN4SNLGQI9syzIzq2ztfsAcOJpkMfRRETEX2B34QaZVmZlVuNqa8m6VosUxj4h4iWRt23pjgZuAh7Iqysys0lVSL6IcxcxtdShwKUuuKl+Mg8PMrFlVnh1FnW31HZL5rP5EMmj+LWBWlkWZmVW6au95FDPm8UlE/AvoEBGfRcTVwKiM6zIzq2jtfswDWCzpG8B0SecCLwFrZVqVmVmFc88jOUw1jWTQfM308UlZFmVmVumq/TqPJnsekuqD5T/pDeDYzCsyM6sC1X6FeXOHrRYCdSwJw7r0a016v0OGdZmZVbQqz46mwyMiijmkZWZmjchyzENSF+BF4AKSSyduJPmD/n3g0IiYJ+kQkuGGxcDYiLg2XVb8OmBdkmmmRkXEG5K2Bq4i6RhMjYjjWqqhmFl1B0i6vuDxg5J2Ke1bNTNrXzKekv0s4KP0/vnAlRExAHgNOEJSN5JJbYcAA4FTJa0CHAx8HBH9gYuAMek+LgNOjoh+QI90ItxmFdO7GEOSbvWOKXhDMzNrRFbL0EraFNgM+GvaNBC4O71/D0lg9AEmR8QnETEHeALoBwwG7ky3HQ/0k9QJWD8iJjfYR/PfXxH/BjUR8Vr9g4h4A8+qa2bWrAx7Hj8HTit43C0i5qX3PwDWAHoBHxZss1R7RCwmOUzVC5jZyLbNKuY6j7cl/YRkOdpaktl1pxfxOjMzW4YkHQY8FhFvSWpsk6bip5T2oiKsmPAYBZwOHE+SUk/iWXVb3YxJv8q7hMy1h+/R2o+MBsz3BDaQNJzkYu15wCxJXdLDU72B99Jbr4LX9QaeLmj/v3TwvIZkkL1ng23fa6mQYmbVnQtcWMQ3ZWZmqSxOV42IA+rvpzN+vAXsDIwA/pB+HQdMAq6RtBLJZRf9SM68WhHYD7ifZDnxhyNigaRXJPWPiMeB4UCLf8n5dFwzswy04mJQ5wCHS5pIMvv59Wkv5AySkBgPnBcRnwC3AB0kPQ6cAJyZ7uMUYIykJ4DXI2J8i99fXV1dS9tYGzB7fnX/oLp2qmH2/Or9Fmsraca7MnTuCHMX5l1Fdjp3LH3mkFP+8kpZv9CX7b1pRfyyFNXzkNRT0g7pffdWzMxaUO2z6hZzkeBBJAMt16VNv5J0ZJZFmZlVOq9hnpxPvDVLzhk+nWRdczMza0K773mQLAY1u/5BOhAzP7uSzMwqX8bTk+SumOs8/iPpcKCLpO2AA/jilYtmZtZAtU/JXkzP41hgR6A7cA3QBTgqy6LMzCpdbZm3SlHMRYIfAye2Qi1mZlWjyjseLYeHpOksWQjqcxGxTiYVmZlVgWo/bFXMmEf/gvudSKb07ZJNOWZm1aHKs6Oow1bTGjS9Kul+4BfZlGRmVvkq6bTbchRz2GpQg6a1gQ2zKcfMrDr4sBX8qOB+HfBfkjOwzMysCVWeHUWFx/ciYkrmlZiZVZFqP2xVzGnFP8u8CjMzqyjF9DymSXqEZHLEz6cliYizsyrKzKzS1ZQ+i3tFKSY83kxvZmZWpGo/bNVkeEg6JCJuiojzWrMgM7NqUO3h0dyYh9fsMDMrU7Wv51HMYSszMytRtfc8mguPnSW93Uh7DVDnua3MzJpWQZ2IsjQXHs8BB7ZWIWZm1aQ9X2E+t5F5rczMrAjt+bDVM61WhZlZlanyjkfT4RERP2jNQszMqkmtLxI0M7NStdueh5mZlS+LMQ9JXYHrgNWBzsAFwP8BNwIdgPeBQyNinqRDgFOAxcDYiLhW0nLp69cFFgGjIuINSVsDV5HMnD41Io5rqZZKWm/dzKxi1NbUlHVrwV7AlIj4KrA/cClwPnBlRAwAXgOOkNQNOBsYAgwETpW0CnAw8HFE9AcuAsak+70MODki+gE9JA1rqRD3PMzMMpDFYauIuKXg4drAOyThUL/G0j3A6UAAkyPiEwBJTwD9SJYRvyHddjzwO0mdgPUjYnLBPoYA9zVXi3seZmYZyKjnAYCkJ4GbSQ5LdYuIeelTHwBrAL2ADwteslR7RCwmOUzVC5jZyLbNf39FVWpmZiWpqSnvVoyI2Bn4BvAH+MJpXU3toZT2oqpweJiZVQhJO0haByAinicZevhUUpd0k97Ae+mtV8FLl2pPB89rSAbZezaybbMcHmZmGagt89aCAcBpAJJWB1YgGbsYkT4/AhgHTAJ2lLSSpBVIxjsmAg8A+6Xb7gU8HBELgFck9U/bh6f7aPH7MzOzZSyjKdl/A6wmaSLwV+AE4Bzg8LRtFeD6iJgDnAHcTxIu56WD57cAHSQ9nr72zHS/pwBj0oH11yNifIvfX11dXan/JpaD2fOr+wfVtVMNs+dX77dYW+UTHXXuCHMX5l1Fdjp3LP1y8RumTC/rF/qwHdauiF8Wn6prZpaB9jyrrpmZlam6o8PhYWaWiSrveDg8zMyyUEnrkZfD4WFmloFqP5XV4WFmlgH3PMzMrGTVHR0ODzOzTLjnYWZmJfOYh5mZlazaex7VHo7WhvzpjzfRZ4dt6PeVHRj3t7+yYMECRh52CLv068PgwYOZOTNZUuDcs3/I4IH92XWXnbn05z/NuWprzh9vvomdttua7bffnvv+9te8y2lTasq8VQr3PKxVzJgxgzEXns/jT09h1qxZXHT+Obz99jRWXXVVrrvhJv5w3W958vGJrLfBBjz26CNMePQJFi9ezA7bbMnB3zqMXr16tfwm1qpmzJjBxReex5OTnmXh3Fmc9aNzGPa1PfMuq82o8o6Hw8Nax8MTxrProMF0796d7t27c8VVY9lnr69x1tnnAnD00Ucze34d777zDvPmzWPevHksWrSI2tpaunbtmm/x1qgJD41n0KAhdO/enc4rd+fK34zNu6Q2pbai+hGl82EraxXTpr3F7Dlz2G/43gwdtAsPT3iIt6e9xQP338ceQ3flwAMP5KOPPqL3Wmsx4pv7s+nG67HZxutx9LHHs+KKK+ZdvjVi2rS3mD17Nt/c9xsMGDCAhyc8lHdJ1ooqKjwk7SHpuAZtUyStV+J+VpS0W3r/DEl90/sjmn9lo/t6RNKWRW67laRNSn2PalBXV8dHM2bwx1tv5+rf/p5jjz6CxYsXs/EmYtyDD7Plllvy85+O4c033uCuO2/npVdeZ+rLrzL26qv48MMPW34Da3V1dXV89NEM/nTbHVx33XUcfdQovMTDElkuQ9sWVFR4RMS4iLhqGexqO2C3dJ8/join0gA6aBnsuznDgXYZHquttjp9+valY8eObLDhhnRfoTu1tbX0H/BVAHbffXf+8fLLPPvsZHbccSe6du1Kjx492GLLL/PSiy/kXL01ZvXVVucrfXemY8eObLjhhnTv3t1BX6CmzP8qRUWNeUgaCWwJdAL6ApHeR9KawLXp40XAURHxtqTXgLtIlmH8GNgTuBJYUdI/gZ2BPwPHATtJOgc4DNgmIj5Nl2Y8LSKGN1PaoZJ2JFnF6xsk6/9eD6wNdAHOA6YBxwIfSvoAWB64GFgATAe+HRHz/+d/pDZqyJDdOPqoUXz39B8wc+ZMZn02iyOPOpoHHxjHYYeP4tlnn2XjTTZhww034spf/ZLFixezaNEiXnrxBdZbb/28y7dGDB66G0cfOZLvfu8HzJgxk1mzZrHqqqvmXVabUUm9iHJUVM8jtQHJB34fkiUUlbZfAFwSEYOBy4AfFWx/Q0T0BVYGtgJ+BtwSEYUjfD8DHo2I84A7gX3T9n2Bm1uoaXpEDALuI+ldrAI8GBG7APuTLAH5Asm6wGdGxDPA5cDe6ev+zZJ1havSmr17s+/wEQwc0Jd9v/E1Lrn0ck446RQeGHcfQ3YdwF133cV3v3cG2263PYOHDGXIrgPYfchARo46kvXWd3i0Rb1792bf4d9kl35fYdiwYVx62a+ora3Ej5Rs1FJT1q1SVFTPI7UVcH9ELAamS3ojbd8ZkKSzgA5Aff/5vxExNb3/DtCjiPe4AfhJ+nUQ8MMWtn88/fou0BOYCewg6dvA4rTtc+nC9RsDd0gC6Ab8p7k36Nyx8pcyPemEYznphGO/0HbH7bcttd3FF54PF57fWmXZ/+DE44/hxOOPybuMNqnaex6VGB41JB/I9er/1JkP7BcR7zfYvuHKyi3+SCNiqqQvSRoMPB8Rc1t4SeF71AAHk/Q+BqRfpzTYfj7wbkQMbKmWesn60NU7GOk1zCtbO1jDvGTVHh6V2Mf8P2B7STWS1iU5LAUwCdgHQNIgSQc3s4/FLB2cDdtuBq4CbiqjxlWBN9Pe0QjScZn694iImWmdm6dfT5K0VRnvY2ZtVLUPmFdieLwBvAA8BVwIPJ+2nwvsI+kx4Jz0+ab8HThA0ukFbf8AtpP0i/TxrcAKwIQyarwd2EvSI8Ac4B1JZwMTgcvTHs2RwO8lTQT6kwz+m1mVqK0p71YpanxeduPZtAziAAAQbElEQVTS8Yq1IuKcvGsBmD2/un9QPmxV2drBYauSf4ATXplR1i/0oE17VsQvSyWOeWRO0u+A9YC908c7AY3N0HfLMrruxMyqTLWPebjnUSHc86hs7nlUtnJ6Ho/ER2X9Qg/UKhXxy+Keh5lZBrL8e0HST0nO5uwIjAEmAzeSXKbwPnBoRMyTdAhwCsnJOmMj4lpJywHXAeuSXFA9KiLekLQ1yUlCdcDUiDiOZlTigLmZWZuX1dlWknYFvpxe+LwHyUXR5wNXRsQA4DXgCEndgLOBIcBA4FRJq5BcSvBxRPQHLiIJH9L9nBwR/YAekoY1V4fDw8wsAxlOjDiRJTNSfExykfFA4O607R6SwOgDTI6ITyJiDvAEyTRNg0lm0QAYD/ST1AlYPyImN9hHkxweZmYZyGolwYhYGBGz0odHAn8DukXEvLTtA2ANoBdLZtpotD29Fq0ubZvZyLZN8piHmVkGajM+3UrS3iThsRvwasFTTb1xKe0tFu+eh5lZhZG0O8mce8Mi4hNglqQu6dO9SWb2fo+kR0FT7engeQ3JIHvPRrZtksPDzCwDWR22ktSDZBbwr0fER2nzeJKpkEi/jiOZsmlHSStJWoFkvGMi8ABLxkz2Ah6OiAXAK+kSFJDMDj6uuTp82MrMLAvZHbU6gGT+vFvTWbkBDgeukXQMydpB10fEAklnAPeTjGucFxGfSLoFGCrpcWAeMDLdxynA1ZJqgUkRMb65InyRYIXwRYKVzRcJVrZyLhKc9PonZf1C99mwR0X8srjnYWaWgWqfnsThYWaWgSrPDoeHmVkmqjw9HB5mZhmopIWdyuHwMDPLgMc8zMysZFWeHQ4PM7NMVHl6ODzMzDLgMQ8zMyuZxzzMzKxkVZ4dDg8zs0xUeXo4PMzMMuAxDzMzK1m1j3l4PQ8zMyuZex5mZhmo8o6Hw8PMLBNVnh4ODzOzDHjA3MzMSlbtA+YODzOzDFR5djg8zMwyUeXp4fAwM8uAxzzMzKxkHvMwM7OSVXl2ODzMzDJR5enh8DAzy4DHPMzMrGQe8zAzs5JlmR2StgLuBH4REVdIWhu4EegAvA8cGhHzJB0CnAIsBsZGxLWSlgOuA9YFFgGjIuINSVsDVwF1wNSIOK65GjyrrplZFmrKvLVAUjfgUuDBgubzgSsjYgDwGnBEut3ZwBBgIHCqpFWAg4GPI6I/cBEwJt3HZcDJEdEP6CFpWHN1ODzMzDJQU+Z/RZgHfB34V0HbQODu9P49JIHRB5gcEZ9ExBzgCaAfMJik1wIwHugnqROwfkRMbrCPJjk8zMwyUFNT3q0lEbEwIuY2aO4WEfPS+x8AawC9gA8LtlmqPSIWkxym6gXMbGTbJnnMw8wsAzmOlzf11qW0t1i+ex5mZpVvlqQu6f3ewHvprVfBNku1p4PnNSSD7D0b2bZJDg8zsyxkNGDehPHAiPT+CGAcMAnYUdJKklYgGe+YCDwA7JduuxfwcEQsAF6R1D9tH57uo+lvr66uruxqrfXMnl/dP6iunWqYPb96v8Xa2uo+6b9zR5i7MO8qstO5Y+kf69NmzCvrF3rdnss3+16SvgJcA6wGLAQ+AnYnOf22MzCN5PTbBZK+CXyPZFzjVxFxk6QO6es3Jhl8HxkR0yVtDlxN0qmYFBGnNVeHw6NCODwqm8OjspUTHm9/VF54rLNK8+HRVnjA3MwsAxWRAP8Dh4eZWQY8PYmZmZWhutPD4WFmlgH3PMzMrGRVnh0Oj0rRtVO1/x2TnHFllauzP02+oNr/j/WP28wsA14MyszMSlfd2eHwMDPLQpVnh8PDzCwLHvMwM7OSeczDzMxKV93Z4fAwM8tClWeHw8PyIakjyZoCvSPi55K2BCJdV8DM2jgvBmV5+S2wDUsWpRkI3JBbNVYSSR0lHSTp9PTxlumqdJbKag3ztsLhYXlZOyJ+AMwGiIgrgDXzLclK4PBvQU2Z/1UKh4flpZOklUhWOEPSZsDy+ZZkJXD4t6Daex4e87C8jAYmABtL+kfadmSO9VhpHP7tnHselpe5EbEdsCHQNyL84VNZ6sN/xzT87wCaXfO6vXHPw2wZkrQRIOBiSWeQntGYnn11ObBeftVZCeZGxHaSVgPmR8THknbNu6i2pJLGL8rh8LDW1gXYAVgN2L+gfTFwbh4FWfEc/sWrpF5EORwe1qoi4gXgBUm3R8SLhc9JOiunsqx4Dv8iVXl2ODwsN+tIug5YJX3cCXgHuDC3iqxFDv8SVHl6ODwsL+cA3yS5NmDf9P5nuVZkpXD4t6Daxzx8tpXlZU5EvAXURsSMiLgaOCLnmqx49eH/DrAjcAFwRa4VtTE+28osG+9IOgx4TtIfgDeBnjnXZMWbExFvSaqNiBnA1ZImAH/Iu7C2ooJyoCwOD8vL4cDKwM3AwSTBsVeuFVkpHP4tqfL0cHhYXtYAhgM9SP43qwFGAufnWJMVz+Hfgmof83B4WF7uBsYB7+ZdiJXF4d+CShq/KIfDw/IyMyJG512Elc3h34LOHau761FTV1eXdw3WjkjaPL17GPA28DiwsP75iHg5j7qsNJIeiojBeddh+XF4WKuS9HAzT9dFxKBWK8ZK5vC3eg4Pa3MkHZNe92FtjMPf6jk8rM2RNMEfQpXL4d8++Apza4uqeqCxHTgg7wIsew4Pa4vcHa5sDv92wOFhZsuaw78dcHhYW+S/XM3aOF8kaLmQdDtwE3BvRMxv8PT3cyjJlh2Hfzvgnofl5VJgZ+BpSddK+vzsqoiYnF9ZVgxJt0saLqlTI087/NsBn6pruUuD4yqgG/Bb4OcR4YWh2jBJ/UgW8RoEPAfcFBET8q3KWpPDw3IhqRvJh89BwJeAPwK3AsOAAyNiSI7lWQkc/u2TxzwsL/8H3AGcka6LXe8aSX1zqsmK1Ej4/4Yl4f8XwOFf5RwelpcbIuIL03dLuiQivhsRR+ZVlBXN4d/O+bCVtSpJw0n+Wt0FeLTgqeWAbSNivTzqstJIOrup8M+rJmtdDg9rdZLWA64AflbQvBj4R0T8J5eirCgOf6vn8LBWJalPREyStCeNXIkcEX/LoSwrgcPfwGMe1voGApOAbzbyXB3g8GjDCsK//uyqQjvhn1+74fCwVhURP0nvPhER1xQ+J+m0HEqy0gzE4W84PKyVSRoK7AbsL2mTgqeWA/YnufLc2iiHv9VzeFhrexpYQHI9wEsF7YuBaxp9hbUZDn+r5/CwVhURnwKPSPoy8GWgB0sm0uuZW2FWLIe/AQ4Py8+9wMrAuwVtdcBj+ZRjxXD4Wz2Hh+Vl5YjYOe8irGwO/3bO4WF5eVzSFhHxUsubWhvk8G/nfJGg5ULSq8AGwH9JjqHXAHURsVquhVlRJP0UuN7h3345PMysZA5/c3hYLiStBZxNcvhjP0kHAk9FxLScSzOzIngZWsvLNcCdQP1fqh8A1+VWjZVE0lqSxkq6LX18oKR1867LWo/Dw/LSISLuI7k+gHQJU/8+Vg6Hfzvn/1ktLwvS5Us7SFpd0rHAnLyLsqI5/Ns5n6preTkSuABYFRhHMtneqFwrslJ8IfxJlqR1+LcjDg9rVZLWKXhYvxJdDckFZsu1fkVWJod/O+fwsNZ2O0lQdAIEvAF0ANYHngf65FeatcThb/UcHtaqImJHAEk3Al+PiHfSx+uy5MPI2i6HvwEe4LL8bFIfHADp9R0b51iPFSEidoyInYAXgI0jYouI2BTYBHgl3+qsNbnnYXmZJOkZkmPli4EdgKn5lmQlWCr8JTn82xFfYW65kbQZsDnJMfOIiBdyLsmKJOmXQF+WhP/2wAsRcVyuhVmr8WEra1WSjkm//ozk7Jw+wE7Aoelke9aG1f/8gPnAdGAPYE/g38CnedVlrc+Hray1vZV+fTHPIqxsb6VfX0xvd+dXiuXJh63MzKxkPmxlZmYlc3iYmVnJPOZhuZC0DXAY0IPkbCsAIuKI3Ioys6I5PCwvNwGXA++0tKGZtT0OD8vL9Ii4Ou8izKw8PtvKciHpIpL5kSYCC+vbI+JvuRVlZkVzz8Pysmb6dd+CtjrA4WFWAdzzsNxIWh/YBlgEPBcR03MuycyK5FN1LReSvgfcBgwEvgb8RZLnRTKrED5sZXnZB+gTEYsAJHUEHgWuyrUqMyuKex6WlxqS2VjrLSYZ8zCzCuCeh+XlFuBZSU+R/BHzFWBsviWZWbE8YG65kbQesC1Jj+O5dDVBM6sADg9rVZIOa+75iLihtWoxs/L5sJW1tppG2joCxwJrAQ4PswrgnoflStIBwBnAXcDPI+KznEsysyK452G5kLQrcBHwLLB7RHyQc0lmVgKHh7UqSVsCPwZmAYdGxOs5l2RmZfBhK2tVkhYCL5P0OAp/+WqAOq/nYVYZ3POw1rZh3gWY2f/OPQ8zMyuZpycxM7OSOTzMzKxkHvOwqpJOeRLAU2nTcsA04PiI+LjMfR4F9I+IkZL+BHw3It5tYtudgX9FxBtF7rsjsCAiGrt4srHt64DlImJhixubZcg9D6tGH0bEwPTWD3gXOKtwA0lFfVg3FBEHNhUcqVHABuXs26ySuOdh7cFjwDEAkt4imdF3Y2C4pP2Bk0hOFf4QOCoiZkg6HjgemA68V7+j9PVDgDeAy4Ed0qcuIVmLfT9gJ0mnAq8Bvwa6AisAoyNivCQBfwBmAw83VrCkLsDvgXXSpjMj4tGC51dP99EB6AH8MiJuSK+jGQvMS9/3/Ij4q6QfA4PS9neBwyNiXin/iGaF3POwqiapAzAcmFjQ/GpEDJe0NvBDYEhE9AceAUZL6gFcAHw1IoYBqzay60OA1SPiK8AewEjgbuB5ksNaE0gWtrokIgYB3wCuSQ9TnQP8LiK+CkxtovTTgekRsTNwOHBUg+fXAK5M9/114NK0/dvAXyJiV2Av4EuSVgZOAPpGxACSqWBWb+7fzawl7nlYNfqSpEfS+7UkwfGLguefTL/2JfkQvj/pDLA88CawEfBWRMxIt3uYZK31Qn1IwoZ0LGVPgHQ/9XYFuks6J328AFgN+DIwJm2b0MT30Id0VcWIeBU4tMHz7wM/kHQ6yRrwPdP224HrJK0L3AtcHxF1ku4HHpV0J3BLRLzTxPuaFcXhYdXow4gY2Mzz89Ov84BnIuLrhU9K2oEvrnLYoZF91NFyz30eMDwi/tNg/4WrKDa272L2fyFJD+ogSSsAnwJExGPpoavBJL2hbwEHR8Q3JW1KEnKPShoREc+3UL9Zk3zYytqzySTjE70AJO0naW/gdWADSSulH/SDG3ntkySHq5DUQ9IkSZ1IQmG5dJvHgf3TbVaVdFna/jJJrweS8ZPGFO5/A0kPNXh+deCl9P4hwGJJy0s6CVgrIu4BjgT6pK8/NSJeiYhLgDuArVv+5zFrmsPD2q2IeA84GbhX0mMkH7ZPR8RMkhl/JwJ/Ad5q5OW3Am9KehJ4ELg0Iuan96+WNBz4DrCvpInA31hyiOp84Pj0UJJIBtobuhxYOX3tTSQ9jUJXAOdLmkAy8P4QcDPwCvBHSQ8DfyWZ7v4dYFtJz6QhtD7J4S2zsnl6EjMzK5l7HmZmVjKHh5mZlczhYWZmJXN4mJlZyRweZmZWMoeHmZmVzOFhZmYlc3iYmVnJ/h8OVY931CwUUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97fa2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Non identity_hate       0.99      1.00      1.00     79112\n",
      "    identity_hate       0.86      0.01      0.02       674\n",
      "\n",
      "      avg / total       0.99      0.99      0.99     79786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_confusion_matrix_and_plot(y_test, predictions_int, output_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area Under Curve ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97ffff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_auc_roc(output_classes, y_test, predictions, auc_roc_nb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine Model\n",
    "\n",
    "\n",
    "### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  toxic\n",
      "ROC AUC:  0.9727658434636912\n",
      "\n",
      "Class:  severe_toxic\n",
      "ROC AUC:  0.9848012163196502\n",
      "\n",
      "Class:  obscene\n",
      "ROC AUC:  0.9876500839180662\n",
      "\n",
      "Class:  threat\n",
      "ROC AUC:  0.9907157012474744\n",
      "\n",
      "Class:  insult\n",
      "ROC AUC:  0.9791394620310014\n",
      "\n",
      "Class:  identity_hate\n",
      "ROC AUC:  0.9752703825519649\n",
      "\n",
      "Multi-class ROC AUC:  0.9817237815886415\n",
      "\n",
      "Execution time:  16.545900583267212 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "vectorizer = TfidfVectorizer(sublinear_tf=True, norm='l2')\n",
    "X_train_vect = vectorizer.fit_transform(X_train)\n",
    "\n",
    "X_test_vect = vectorizer.transform(X_test)\n",
    "\n",
    "svm = LinearSVC(C=0.12)\n",
    "classifier = CalibratedClassifierCV(svm)\n",
    "\n",
    "auc_roc_svm, predictions, predictions_int = calculate_predictions(classifier, X_train_vect, X_test_vect, y_train, y_test, output_classes)\n",
    "end_time = time.time()\n",
    "total_time_svm = end_time - start_time\n",
    "print(\"\\nExecution time: \", total_time_svm, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[71326   839]\n",
      " [ 2369  5252]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc99e412e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  Non toxic       0.97      0.99      0.98     72165\n",
      "      toxic       0.86      0.69      0.77      7621\n",
      "\n",
      "avg / total       0.96      0.96      0.96     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[78830   174]\n",
      " [  564   218]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97f0a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  precision    recall  f1-score   support\n",
      "\n",
      "Non severe_toxic       0.99      1.00      1.00     79004\n",
      "    severe_toxic       0.56      0.28      0.37       782\n",
      "\n",
      "     avg / total       0.99      0.99      0.99     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75171   423]\n",
      " [ 1118  3074]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97e89eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "Non obscene       0.99      0.99      0.99     75594\n",
      "    obscene       0.88      0.73      0.80      4192\n",
      "\n",
      "avg / total       0.98      0.98      0.98     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79502    62]\n",
      " [  161    61]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97e38080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non threat       1.00      1.00      1.00     79564\n",
      "     threat       0.50      0.27      0.35       222\n",
      "\n",
      "avg / total       1.00      1.00      1.00     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75358   607]\n",
      " [ 1503  2318]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97eb6978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non insult       0.98      0.99      0.99     75965\n",
      "     insult       0.79      0.61      0.69      3821\n",
      "\n",
      "avg / total       0.97      0.97      0.97     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[78982   130]\n",
      " [  461   213]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc980ccb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Non identity_hate       0.99      1.00      1.00     79112\n",
      "    identity_hate       0.62      0.32      0.42       674\n",
      "\n",
      "      avg / total       0.99      0.99      0.99     79786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_confusion_matrix_and_plot(y_test, predictions_int, output_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " oh god  why don t you just fuck of and die of gaids   \n",
      "\n",
      "\n",
      " grandma terri should burn alive and be shot    get shot asshole g mau    fuck grandma terri in her old ass  grandma terri  i hope you die soon  fuck you in the eye  i hate you  i want to shoot your ass  grandma terri                 grandma terri  you are shit    grandma terri  i wish you die    die and go to hell  grandma terri            \n",
      "\n",
      "\n",
      " lo er   no one likes you  go kill yourself\n",
      "\n",
      "\n",
      " they will die and none of them will block me again  hell hell hell hell hell crap \n",
      "\n",
      "\n",
      " fuck off mexican bastard  i will work endelessly to have your wikipedia account banned indefinitely  you have made my life a misery and i hope you die \n",
      "\n",
      "\n",
      " important note i would also like to point out that i hate you aswell and  hope you die  best regards \n",
      "\n",
      "\n",
      " do you know what i say gas the mother fucking jews   anhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleachanhero kill yourself drink bleac\n",
      "\n",
      "\n",
      " you shrew  your blocks are a joke  much like your life  hope you die a painful death  or live in america the rest of your  equally bad things  \n",
      "\n",
      "\n",
      " please kill yourself \n",
      "\n",
      "\n",
      "     is something wrong with you  you fucking freak  why don t you fuck off  do the world a favor and kill yourself you disgusting piece of shit  fuck off and stop making trouble \n",
      "\n",
      "\n",
      " you f      white propaganda chiefs  go and die  down with america \n",
      "\n",
      "\n",
      " page go die you stupid arsewhole automated filter\n",
      "\n",
      "\n",
      " you are a fucking piece of shit   die and rot in  hell\n",
      "\n",
      "\n",
      "     fuck you  eat shit and die \n",
      "\n",
      "\n",
      " you can t kill the party soap  you can t kill the party \n",
      "\n",
      "\n",
      " re   fuck off you piece of shit  do yourself a favor and kill yourself  the world doesn t need anymore freaks \n",
      "\n",
      "\n",
      " die you piece of ordure   you do not deserve to be on this planet               \n",
      "\n",
      "\n",
      " i m scared brrrr     i m gonna die now\n",
      "\n",
      "\n",
      " help me i feel like im going to kill myself   h\n",
      "\n",
      "\n",
      "     kill yourself   i mean just look at you you fucking faggot  you are indian  you lose  kill yourself  with love swagfuckingtastic       \n",
      "\n",
      "\n",
      " go suck a cock bitches  fuck wikipedia  my ip changes in like  a  couple of hours anyway  fuck you go you cocker cocksuer mohterfuck unlce suckefing you go fuck your mom you dirty little ass fuck bitch i will kill you i konw where you live i will rape you yoru fucking ass\n",
      "\n",
      "\n",
      " you are retarded    screw you for deleting my article  you are retarded  go kill yourself in a barrel full of shit \n",
      "\n",
      "\n",
      " you are a raging faggot   kill yourself \n",
      "\n",
      "\n",
      " you are a faggot  i prey you get cancer and die a painful death \n",
      "\n",
      "\n",
      " muslim scum go die soon will you\n",
      "\n",
      "\n",
      " grandma terri  die in trash  i fucking hate you grandma terri  with all my heart  soul  mind  and strength  rot in trash grandma terri  die now  fuck you in the ass             \n",
      "\n",
      "\n",
      " eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga  eat shit and die bitch ass nigga\n",
      "\n",
      "\n",
      " i reckon you should die\n",
      "\n",
      "\n",
      " fuck you   fuck you motherfucker     go die in a hole        \n",
      "\n",
      "\n",
      " if a girl ever sees you naked  she will cry for laughing so hard    you should just kill yourself \n",
      "\n",
      "\n",
      " zhanzhao is a motherfucking pussy indian chinese bastard  http   twitter com phoenixreporter   go to the above address and hack and destroy it   and guys let s find out zhanzhao s email and kill him  die you asian bastard \n",
      "\n",
      "\n",
      " californiaalibaba is a bitch go fuck yourself you bitch ass motherfucker  don t you ever in your fucking life threatening me you fucking piece of shit you  you don t know me i will fucking kill your ass you punk pussy bitch  tell your mama to suck my black dick \n",
      "\n",
      "\n",
      " plan that will kill millions of innocent people\n",
      "\n",
      "\n",
      " gay everyone is e mailing me about deep shadow and you  gay or straight  like two lovebirds  how sweet  who are you darre                   loser  gay ass bastard die die die commit suicide you jerk loser  motherfucker block me  ya block me  you big jerk  too scared to fight  ur gonna block me  ust dont wanna fight do you  come  i live in maryland      i hate darrenhusted \n",
      "\n",
      "\n",
      " his is your last warning stop violating multiple wikipedia policys and stop being an american retard  do humanity a favour and kill yourself            \n",
      "\n",
      "\n",
      " kill yourself  fuckin  bastard \n",
      "\n",
      "\n",
      " help me i feel like im going to kill myself and hurt some animals i care for\n",
      "\n",
      "\n",
      "     your turn   help i feel like im going to kill myself               preceding unsigned comment added by                   \n",
      "\n",
      "\n",
      "    go die you worthless cocksucking faggot   \n",
      "\n",
      "\n",
      " no she did not die that is so stupid \n",
      "\n",
      "\n",
      " honestly    you need to crawl under a rock and die you fat bastard    \n",
      "\n",
      "\n",
      " your way to kill the gore\n",
      "\n",
      "\n",
      " original research must die where s the delete button \n",
      "\n",
      "\n",
      " fuck off and die you old timer\n",
      "\n",
      "\n",
      "   as for asking someone to die  only a fucking moron like you would take that to mean   i m going to kill you    why would i commit a crime over a loser like wookitty  you gotta be kidding me  try again with your silly little threats  \n",
      "\n",
      "\n",
      " don t care  come kill me c mon     \n",
      "\n",
      "\n",
      " you should die  from cancer \n",
      "\n",
      "\n",
      " do humanity a favour   and kill yourself  you nazi bias yank piece of shit               \n",
      "\n",
      "\n",
      " god  i just hate them so much  lets hope they die \n",
      "\n",
      "\n",
      " italic textbold textwe re all gonna die          aaarrrgggh   \n",
      "\n",
      "\n",
      " you are a useless prick  die of aids\n",
      "\n",
      "\n",
      " go and fall off a cliff and die you pathetic bastard \n",
      "\n",
      "\n",
      " hey how ya doin fuck face  did u think you were safe  huh  did ya  skank  you are a ho  you need death  and nothing else  i really dislike your existance  i think that you are a piece of shit  i will come  to your home  and kill you  and any other man lover you have hiding there  you should perish you skank  you are more worthless than the dog shit in the backyard  if you think that you can come get me  as god as my witness i will conduct a crusade against your kind  why the fuck would you change the lci food fight article  i really hope you die in a hole somewhere  i will continue to come back  until you fucking rot in hell  you are no longer needed on earth  or wikipedia  so make like the homosexual you are  and anal fuck yourself until you drown in a cesspool of your own bodily fluids  i really hope you perish soon  end your life any way you want  here are some starting ideas  hang yourself  shoot yourself in  the head with a shotgun  whatever you wish  hell  even light yourself on fire and hang yourself  just dont make too much of a mess  as no one will want to spend that much time on a worthless piece of shit like you  go die  commit suicide  terminate your existance  all of the above  that would make me happy  i hope to god that you end your life by tomorrow  or even do it in the sunrise  when i get blocked for this  i will know that you aread it  thank you  you are no longer needed   thank you  management  god \n",
      "\n",
      "\n",
      " give me liberty or give me death       live free or die trying  \n",
      "\n",
      "\n",
      " go  to   die   go  to   die\n",
      "\n",
      "\n",
      " what a rude and nasty human being you are  making people s lives a misery on wikipedia  go and die you piece of shit \n",
      "\n",
      "\n",
      " the jungle baby your guna die \n",
      "\n",
      "\n",
      " i hope you die    find a pit and jump into it        \n",
      "\n",
      "\n",
      " hello  eat shit and die you cunt\n",
      "\n",
      "\n",
      " now its your turn you should of mined ur own bussiness   help im going to kill myslf\n",
      "\n",
      "\n",
      " die   please why dont u do us all a favioe and kill yourslef\n",
      "\n",
      "\n",
      " i would rather die than be unblocked \n"
     ]
    }
   ],
   "source": [
    "for y_true, y_pred, comment in zip(y_test['threat'], predictions_int[:, 3], X_test):\n",
    "    if(y_true == 0 and y_pred == 1):\n",
    "        print(\"\\n\\n\", comment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area Under Curve ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc980f4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_auc_roc(output_classes, y_test, predictions, auc_roc_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Gradient Descent Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  toxic\n",
      "ROC AUC:  0.9721863294719462\n",
      "\n",
      "Class:  severe_toxic\n",
      "ROC AUC:  0.9850974880225559\n",
      "\n",
      "Class:  obscene\n",
      "ROC AUC:  0.9856148858925353\n",
      "\n",
      "Class:  threat\n",
      "ROC AUC:  0.9889524881323936\n",
      "\n",
      "Class:  insult\n",
      "ROC AUC:  0.9775659746884425\n",
      "\n",
      "Class:  identity_hate\n",
      "ROC AUC:  0.9754108324958972\n",
      "\n",
      "Multi-class ROC AUC:  0.9808046664506285\n",
      "\n",
      "Execution time:  10.7253999710083 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "vectorizer = TfidfVectorizer(sublinear_tf=True)\n",
    "X_train_vect = vectorizer.fit_transform(X_train)\n",
    "\n",
    "X_test_vect = vectorizer.transform(X_test)\n",
    "\n",
    "classifier = SGDClassifier(loss='log', penalty='l2', alpha=0.000004)\n",
    "\n",
    "auc_roc_sgd, predictions, predictions_int = calculate_predictions(classifier, X_train_vect, X_test_vect, y_train, y_test, output_classes)\n",
    "end_time = time.time()\n",
    "total_time_sgd = end_time - start_time\n",
    "print(\"\\nExecution time: \", total_time_sgd, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[71549   616]\n",
      " [ 2646  4975]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97e4d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  Non toxic       0.96      0.99      0.98     72165\n",
      "      toxic       0.89      0.65      0.75      7621\n",
      "\n",
      "avg / total       0.96      0.96      0.96     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[78856   148]\n",
      " [  583   199]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97ea00b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  precision    recall  f1-score   support\n",
      "\n",
      "Non severe_toxic       0.99      1.00      1.00     79004\n",
      "    severe_toxic       0.57      0.25      0.35       782\n",
      "\n",
      "     avg / total       0.99      0.99      0.99     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75262   332]\n",
      " [ 1317  2875]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97f31550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "Non obscene       0.98      1.00      0.99     75594\n",
      "    obscene       0.90      0.69      0.78      4192\n",
      "\n",
      "avg / total       0.98      0.98      0.98     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79536    28]\n",
      " [  179    43]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97ead048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non threat       1.00      1.00      1.00     79564\n",
      "     threat       0.61      0.19      0.29       222\n",
      "\n",
      "avg / total       1.00      1.00      1.00     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75406   559]\n",
      " [ 1601  2220]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc980919b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non insult       0.98      0.99      0.99     75965\n",
      "     insult       0.80      0.58      0.67      3821\n",
      "\n",
      "avg / total       0.97      0.97      0.97     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79033    79]\n",
      " [  520   154]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97f3d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Non identity_hate       0.99      1.00      1.00     79112\n",
      "    identity_hate       0.66      0.23      0.34       674\n",
      "\n",
      "      avg / total       0.99      0.99      0.99     79786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_confusion_matrix_and_plot(y_test, predictions_int, output_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area Under Curve ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97f25710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_auc_roc(output_classes, y_test, predictions, auc_roc_sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extreme Gradient Boosting Tree Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  toxic\n",
      "ROC AUC:  0.7643629160393477\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  severe_toxic\n",
      "ROC AUC:  0.84526378346475\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  obscene\n",
      "ROC AUC:  0.7728880049902986\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  threat\n",
      "ROC AUC:  0.8093787096885231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  insult\n",
      "ROC AUC:  0.7685245703570873\n",
      "\n",
      "Class:  identity_hate\n",
      "ROC AUC:  0.7347624657436417\n",
      "\n",
      "Multi-class ROC AUC:  0.7825300750472749\n",
      "\n",
      "Execution time:  56.283751487731934 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc98066438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "X_train, X_test, y_train, y_test = train_test_split(train[new_features], \n",
    "                                                    train[output_classes], \n",
    "                                                    random_state=45, \n",
    "                                                    test_size=0.50)\n",
    "\n",
    "params = {\n",
    "    'n_estimators': 300,\n",
    "    'objective': 'binary:logistic',\n",
    "    'learning_rate': 0.1,\n",
    "    'subsample': 0.5,\n",
    "    'colsample_bytree': 0.5,\n",
    "    'eval_metric': 'auc',\n",
    "    'seed': 1024\n",
    "}\n",
    "\n",
    "classifier = xgb.XGBClassifier(**params)\n",
    "\n",
    "auc_roc_xgb, predictions, predictions_int = calculate_predictions(classifier, X_train, X_test, y_train, y_test, output_classes)\n",
    "end_time = time.time()\n",
    "total_time_xgb = end_time - start_time\n",
    "print(\"\\nExecution time: \", total_time_xgb, \"seconds\")\n",
    "\n",
    "xgb.plot_importance(classifier)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[71538   627]\n",
      " [ 6583  1038]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc99dfdcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  Non toxic       0.92      0.99      0.95     72165\n",
      "      toxic       0.62      0.14      0.22      7621\n",
      "\n",
      "avg / total       0.89      0.91      0.88     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[78971    33]\n",
      " [  753    29]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97e55860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  precision    recall  f1-score   support\n",
      "\n",
      "Non severe_toxic       0.99      1.00      1.00     79004\n",
      "    severe_toxic       0.47      0.04      0.07       782\n",
      "\n",
      "     avg / total       0.99      0.99      0.99     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75348   246]\n",
      " [ 3976   216]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97e45c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "Non obscene       0.95      1.00      0.97     75594\n",
      "    obscene       0.47      0.05      0.09      4192\n",
      "\n",
      "avg / total       0.92      0.95      0.93     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79561     3]\n",
      " [  220     2]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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CXVNZmXv/pezetnW2u9cJq165eaOydj/ZgbOP75b1/lPHn79R2e5tW7N729YMPUOXrBVSo80bM+HeewrdjLxbvS6345LeA98UV6EMI9xKOgpYS+gBp/9WGxJWq4AQVCtk+s1Xte83aT/7uvviig1mtiNwHtDO3Vea2RvZfgiAvfv+oSa7x86qV26mcbszC92MvFk+Z+OTU1I0qp97kEuypAfwvP+t5e6fAA8Dp0ZFrwAdzKy+mdUn9LRfyaKqMmp2wplNSK9gZt3M7DhCj3tpFLz3JKRhKk4guqRSJGGSnkLZVMmyPwM7ALj7h8AY4DlCyuQ2d5+fRR3/BfY0sxuzfM+rgF5m9jxhoplZwKvASjObCfQnzGVwC/AyIRd/QbYfSETqvjzNRlhnpHTNaPYatzsz0b8spVDiqxhSKI3q13wOhz2HPZvTv9mXr+wWiyiutIGIJFacetO5UAAXkcRKePxWABeR5FIPXEQkphIevxXARSS51AMXEYmphMdvBXARSS71wEVEYirh8VsBXESSK+k9cM07KSISU+qBi0hiJbwDrgAuIsmV9BSKAriIJFY+AriZdQXuB96Mil4H/gTcRVg8ZjEwwN3XmFl/YAhhyuox7j7WzBoA4wjTWZcCg9z9fTPbAxhNWDPhNXc/PVNblAMXkcTK43zgz7l71+hxFmHhmlHu3gV4FxhsZk2AK4HuQFfgXDNrARwHfOHunYHhwIiozpHAOe7eCWhmZodmaoQCuIgk1iacD7wrMDF6/ighaO8LzHH3Fe6+CphBWI/3QOChaN/JQCczawjs5O5zNqijWkqhiEhi5TEFvpuZTSSsqXs10MTd10TblgLbAq34di3eSsvdvczMyqOy5ZXsWy0FcBFJrDwNYr5DCNr3ATsDU/huLK3qTWtSnlXDFcBFJLHyEb/dfREwIXr5npktAdqbWeMoVbId8HH0aJV26HbAC2nl/4kGNFOEgc+WG+z7caa2KAcuIolVkkrl9KiOmfU3s6ui51sDWwN3AH2iXfoAkwgLq7c3s+Zm1pSQ/54GPA30jfY9HJji7muBeWbWOSrvHdVRLfXARSSx8pQDnwjcY2YzCJcNngG8AtxpZqcC84Hx7r7WzC4GniJcGni1u68wswnAQWY2HVgDDIzqHQLcamYlwGx3n5ypIQrgIpJY+ciBu/uXhJ7zhg6qZN8HgAc2KCsFBlWy71tAl5q0RQFcRBKrJNk3YiqAi0hy6VZ6EZGYSnj8VgAXkeRKZXc5dWwpgItIYiU9B67rwEVEYipjDzy6U2gbd//IzH4O7AH8y92/znvrRES+h6QPYmbTAx8PdDSz7YAHgZ8R5rIVEanT8jidbJ2QTQDfzt3vA44BbnH3iwgzcImI1Gn5uJW+LslmEHMzM0sBRwEnRWVN89ckEZHaEaNYnJNseuBTgRXAYnd/28yGAJ7XVomI1IJNuKBDQWQM4O5+MdDG3ftFRQ8DJ+e1VSIitaDoc+DRumxHRM/vBv5N5RO5iIjUKUnPgWeTQrkSeCIK5PWAdsDZeW2ViEgtSOX4iItsAvjX7v4p0AO4y91XAqX5bZaIyPdX9DlwoJGZXQgcAjxjZrsAzfLbLBGR768kldsjLrIJ4L8hrM82yN1XAwcDv8trq0REakHSe+AZrwN39zcJS/1UGAPcDTyTr0aJiNSGGMXinGQzF8oA4Aa+vfuyDAVvEYmBOPWmc5HNnZhnE+Y/+SdhIPN4YGU+GyUiUhvilM/ORTY58BXuvgSo5+5fufutVLIgp4hIXVP0OXCgzMyOABaa2VXAm8D2eW2ViEgtiE8ozk02PfDjgfmEgczW0euz8tkoERHJrMoeuJlVBPdPowfAaXlvkYhILYnTbfG5qC6Fsg4o59u/Qsqjn6noeb08tktE5HtLePyuOoC7u9bLFJFYy+eApJk1Bt4AriFcWn0XoWO7GBjg7mvMrD8h/VwGjHH3sdEyleOAHQnTkgxy9/fNbA9gNKGD/Jq7n56pDdnMRtjFzManvf63mf2yZh9VRGTTy/N0spcDn0fPhwGj3L0L8C4w2MyaECYD7A50Bc41sxbAccAX7t4ZGA6MiOoYCZzj7p2AZtEEgtXKppc9gnCGqXBq2huKiNRZ+ZpO1sx+DPwEeDwq6gpMjJ4/Sgja+wJz3H2Fu68CZgCdgAOBh6J9JwOdzKwhsJO7z9mgjuo/Xxa/g5S7v1vxwt3fR7MRikgM5LEH/mfgvLTXTdx9TfR8KbAt0ApYlrbPRuXuXkZImbQClleyb7WyuQ58gZn9kbC0WglhVsKFWRyXOMvn3FzoJuRdMXxGKR75yIGb2QnA8+7+oZlV+rZVNacG5Vk1PJse+CDgS+AMQvpkEXBKNpWLiBRSSY6PDHoAfc3sBcLyklcAK6NBTQizt34cPVqlHbdReTSgmSIMfLasZN9qZTMb4Wrg95n2ExGpa/LRA3f3YyqeR3enfwh0BPoA/4h+TgJmA7eZWXPCZdmdCFekbAH0BZ4iLE85xd3Xmtk8M+vs7tOB3sBfM7VFlwqKSGJtwgUdhgInmtk0wsyt46OBy4sJgXoycLW7rwAmAPXMbDrwW+CSqI4hwAgzmwG85+6TM71pqry8PNM+Elm9jkT/shrVh9XrCt0KyUUxfHeN6td8apPzJs7L6d/sDUf8OBa3AGXVAzezlma2d/RcvXYRiYWkz0aYzY08xwIvEO4cAvirmZ2Uz0aJiNQGrYkZrnXcg2+vZ7yAsE6miEidluc7MQsu2wUdvq54ESXmv8lfk0REake+7sSsK7K5kedTMzsRaGxmewLH8N27i0RE6qSkD9hl8/lOA9oDmwO3AY0JF6+LiEgBZXMjzxfAmZugLSIitSpG2ZCcZAzgZrYQNr7+2d3b5KVFIiK1JE757FxkkwPvnPa8IWEqxMZV7CsiUmckPH5nlUKZv0HRO2b2FHBjfpokIlI74nRNdy6ySaF026BoB+D/8tMcEZHaoxRKmCqxQjnwP7Q6vYjEQMLjd1YB/EJ3n5v3loiI1LKkp1CyuQ78ury3QkQkD1I5/hcX2fTA55vZVMKEVutvoXf3K/PVKBGR2pD0Hng2AfyD6CEiEitFG8DNrL+73+3uV2/KBomI1JY4ze2di+py4JrzW0RiLenzgWeTQhERiaWEd8CrDeAdzWxBJeUpoFxzoYhIXVfMN/K8Avx6UzVERKS2xSkdkovqAvjqSuZBERGJjYR3wKsN4C9uslaIiORBSYxuyslFlVehuPvvNmVDRESkZnQViogkVjGnUEREYq2YBzFFRGItH5cRmtkPgHHANkAj4BrgP8BdQD1gMTDA3deYWX9gCFAGjHH3sWbWIDp+R6AUGOTu75vZHsBowrTdr7n76Znaks1shCIisZRK5fbI4HBgrrvvD/QDbgCGAaPcvQvwLjDYzJoAVwLdga7AuWbWAjgO+MLdOwPDgRFRvSOBc9y9E9DMzA7N1BD1wEUksfLRA3f3CWkvdwA+IgToioVuHgUuAByY4+4rAMxsBtCJsK7wndG+k4HbzawhsJO7z0mrozvwZHVtUQ9cRBIrTz1wAMxsJnAPIUXSxN3XRJuWAtsCrYBlaYdsVO7uZYSUSStgeSX7VksBXEQSqyTHRzbcvSNwBPAP+M4F51WdAmpSntVpRAFcRBIrlUrl9KiOme1tZm0A3P1VQir6SzNrHO2yHfBx9GiVduhG5dGAZoow8Nmykn2rpQAuIomVyvGRQRfgPAAz2wZoSshl94m29wEmAbOB9mbW3MyaEvLf04Cngb7RvocDU9x9LTDPzDpH5b2jOqqlQUwRSaw8zUb4N2CsmU0DGgO/BeYCd5rZqcB8YLy7rzWzi4GnCHnuq919hZlNAA4ys+nAGmBgVO8Q4FYzKwFmu/vkTA1JlZeX1/JnS67V60j0L6tRfVi9rtCtkFwUw3fXqH7NJza5+6WPcvo323+v7WNxC5B64CKSWLqVXkQkppK+JqYCuIgkVtKv0lAAF5HEUg9cRCSmkh2+k/8XhohIYqkHLiKJpRSKiEhMJT3FoAAuIomV9B540k9QsoFLL76I/Tt3oNN+7Xn4oQdZuHAhhx3cnYO67U/37t1ZsmQJAPfeczed9mtPl477Mu72sQVutVTn0osvokOHb79T+Vae5kKpM9QDLyLPTZ3Cm2+8znPTZ/HZZ5+xX/t27L//AQwcfDL9jvk1Y28dxU0jb+CyK4YyYvgwps18kYYNG9K5Q3uO6HUULVq0KPRHkA1UfKezZs1i0SfhO+11VO9CN6vOSHgHXAG8mHTq3IW99m4PQPPmzfn6q6+4fuRNNGnSBICtttqKOXNfZs6Ls9lrr/Y0a9YMgA4dOjFr5gx69Dy8YG2XylX2nZaWllKvXr0Ct6xuKIlVf7rmlEIpIvXr16dp06YAjLt9LAcfchjNmjWjfv36lJaWMmrUKI459jiWLFnClltttf64rbbemiWLFxeq2VKNyr5TBe9v5XNFnrogVj1wM+sDbA781N0vqKU6twD2c/ena6O+OHh04iOMu2Msjz0ZPnJpaSmDBw6gW7duHNDtQP557z3f2V8zVtZ9jzzy3e9UgpR64HWDmf0IODaL/Wr6je0J/CqXNsXRv59+ij+OGM4jjz25PkXym5MG0bbtLgwdOhSA1q1b88knS9Yf8/GiRWzbunVB2iuZ/fvppxg+/LvfqQRJ74HHZj5wM3sc2Af4K/BTYDNgV+A6d7/dzN4BHgc+A8ZGj4ZAKXCyuy8ws/OBflGVT7j71Wb2JrAFcI27j6muDXGfD3zFihV079qFx5+azNZbbw2Eq02enzqF0WNuWz+n9KpVq9i73c+Y8cJc6tevT4d99mT6rDkKDnVQxXf6zDOT2aLF1oVuTl7lMh/4pDeX5fRv9pDdt4pFGI9TCuU64ExgAdAD6Ai0BSYAtwMNgEnuPsnMxgLXu/tkMzsMuAI4hbAqRidCUH/fzG6M6v1ppuAN0LAelMTia63cnf+awGeffcoJx/VbX7ZgwQKaN2/OId27ArDbbrtxyy238Mdrr+XIHgeTSqW4auhQtmmp4F0XVXyn/fp9+53eeeedtGnTpoCtqn25LlYRp950LuIUwNPNcvdSM1sEpEeWF6OfHQEzs8uBesCyqHwVMBVYB2wJ1Oi6uG9Kv0+TC++Ewb/hhMG/qXJ7RQ989Tro2etoevY6ev22pK/2ElcV3+mGK/Lo+woUwOum9P8907+ib9J+9nX39ZdOmNmOhIVI27n7SjN7I//NFJFCSvogZpwCeBnZt3c20AsYbWbdgFaAA0uj4L0nsCMhR16TekUkRuKc8sxGbK5CAf5LuGLkxiz2vQroZWbPA0OBWcCrwEozmwn0B24FbgFeBo4xs1q5LFFE6o5Ujv/FRWyuQqkL4n4VSibFsLJ5UhXDd5fLVShT/LOc/s0eYC1jEcXj1AMXEZE0yv2KSGLFKR2SCwVwEUmspA9iKoCLSGLlqwduZn8CuhBi6AhgDnAX4b6TxcAAd19jZv2BIYSr3ca4+1gzawCMI1wJVwoMcvf3zWwPYDThhsPX3P30TO1QDlxEEisfc6GY2QHAz9y9A3AIMBIYBoxy9y7Au8BgM2sCXAl0B7oC55pZC+A44At37wwMJ5wAiOo5x907Ac3M7NBMn08BXEQSK08r8kwD+kbPvwCaEAL0xKjsUULQ3heY4+4r3H0VMIMwlceBwEPRvpOBTmbWENjJ3edsUEe1FMBFJLFKUqmcHtVx93XuvjJ6eRLwBNDE3ddEZUuBbQk3EC5LO3SjcncvI6RMWgHLK9m3WsqBi0hi5XMM08yOJATwXwHvZPG2NSnPqunqgYtIcuUph2JmBwOXAYe6+wrCXd6No83bAR9Hj1Zph21UHg1opggDny0r2bdaCuAiklj5uJXezJoRpqHu6e6fR8WTgT7R8z7AJMKcTO3NrLmZNSXkv6cBT/NtDv1wYIq7rwXmmVnnqLx3VEe1lEIRkcTK03SyxxCmo77PzCrKTgRuM7NTgfnAeHdfa2YXA08R8txXu/sKM5sAHGRm04E1wMCojiHArWZWAsx298mZGqK5UGpAc6FIXVUM310uc6HMeX9FTv9m2+/cLBa3AKkHLiLJFYswnDsFcBFJLM2FIiISU1pSTUQkphIscX2gAAAKsklEQVQevxXARSTBEh7BdR24iEhMqQcuIomlQUwRkZjSIKaISEwlPH4rgItIgiU8giuAi0hiKQcuIhJTyoGLiMRUwuO3AriIJFjCI7gCuIgklnLgIiIxpRy4iEhMJTx+K4CLSIIlPIIrgItIYikHLiISU8qBi4jEVMLjtwK4iCRYwiO4FnQQEYkp9cBFJLE0iCkiElMaxBQRial8xW8z+znwEHCju99sZjsAdwH1gMXAAHdfY2b9gSFAGTDG3ceaWQNgHLAjUAoMcvf3zWwPYDRQDrzm7qdnaody4CKSXKkcH9UwsybADcC/04qHAaPcvQvwLjA42u9KoDvQFTjXzFoAxwFfuHtnYDgwIqpjJHCOu3cCmpnZoZk+ngK4iCRWKsf/MlgD9ASWpJV1BSZGzx8lBO19gTnuvsLdVwEzgE7AgYTeO8BkoJOZNQR2cvc5G9RRLQVwEUmsVCq3R3XcfZ27r96guIm7r4meLwW2BVoBy9L22ajc3csIKZNWwPJK9q2WcuAiklgFGsOs6m1rUp5V09UDF5HkykMOvAorzaxx9Hw74OPo0Sptn43KowHNFGHgs2Ul+1ZLAVxEEitPOfDKTAb6RM/7AJOA2UB7M2tuZk0J+e9pwNNA32jfw4Ep7r4WmGdmnaPy3lEd1X++8vLyXBpblFavI9G/rEb1YfW6QrdCclEM312j+jWPrAs+X5PTv9k2LTar8r3MbD/gNmBrYB3wOXAw4dLARsB8wqWBa83saOBCQp77r+5+t5nVi47fhTAgOtDdF5rZbsCthI71bHc/L1M7FcBrQAFc6qpi+O5yCeALcwzgO1QTwOsSDWKKSGLpTkwRkdhKdgRXABeRxFIPXEQkphIevxXAayKXQZS4aaT/I2JL393G1AMXEYmppM8Hrht5RERiSj1wEUmuZHfAFcBFJLkSHr8VwEUkuTSIKSISU0kfxFQAF5HkSnb81lUoxczMTq6kLOMMaCJxsemmAy8M9cCLkJkdBPwK6Gdmu6ZtagD0IyzYKnWYmZ3s7rdtUHaeu+u7S6McuCTRC8Ba4FDgzbTyMuDvBWmRZEUn35pRDlwSx92/BKYCP41WCmkRbdoMGEUIEFI36eRbA+qBS2KZ2RXAIMJafAuANoQVQaSO0slX0mkQs7gd5u47Ay+7+8+AA4jXGE7Rik6+rwGvA48Dc4FXC9qoOiiVyu0RFwrgxS1lZiVAfTNr7O4vAx0K3SjJik6+WdiEixoXhAJ4cbsPOBu4G/iPmU0DVha2SZIlnXyzkPQeuBY1FgDMrA2wJfCqu5cVuj1Sveh6/TJgNXAe8AnwlbsfUtCG1TFfri7LKcBt3qgkFmFcg5hFzMy2B64Efujufc2sI/AZML+wLZNM0q/3NrMniE6+hWtRHRWLMJw7pVCK223AQ8DW0eulwLiCtUayZmbbm9kYM7vf3RcAuwI7FLpddY1y4JJk9dz9ScKf4rj7s+j/ibjQyVf0j7XIrTWzbkA9M9vGzE4DVhW6UZIVnXyzkPRBTOXAi9tJwDWE/OkkYDbhxh6p+75z8gWOQiffjcQoFudEAby4DXb3jWYklFjQyTcbCY/gCuDFrWU0OdIc4JuKQnf/unBNkizp5JuFOA1I5kIBvLj1JPzpna4c2LkAbZGa0ck3C40bJDuC60YekRgys7cJU8imK49ur5cioQBexMxsIOFW+makZQsVBETiQSmU4nYhIYWyqNANkZrRyVdAAbzYvePubxe6EZITnXxFAbwYmdl1hMHKNWY2C5gFrKvY7u4XFaptkjWdfEUBvEi9Ef18s5JtGhSpw3TylXQK4EXI3ccDmNnl7v779G1mdn1hWiVZqjj5/g94InpeTsiD3wAogBcRBfAiZGa9gWOBX5rZz9M2NQDaAecXpGGSjS+JvjvgOULgLid8d1qMo8joMsIiZWY/Am4GrksrLgP+6+6fFqRRkhV9d1JBAVxEJKY0/aSISEwpgIuIxJQGMYuYmf0COIGN7+YbXLBGiUjWFMCL293ATcBHhW6IiNScAnhxW+jutxa6ESKSG12FUsTMbDjQEJjGd+/me6LKg0SkzlAPvLi1jn6mL+pQzrd3+IlIHaYeeJEzs52AXwClwCvuvrDATRKRLOkywiJmZhcC9wNdgcOAR8zs9II2SkSyphRKcesF7OvupQBmVp8wv8bogrZKRLKiHnhxSxHm0KhQhqaTFYkN9cCL2wTgpWhe6RJgP2BMYZskItnSIGaRi2a2a0foeb/i7vML2yIRyZYCeBEysxOq2+7ud26qtohI7pRCKU6pSsrqA6cB2wMK4CIxoB64YGbHABcDDwN/dvevCtwkEcmCeuBFzMwOAIYDLwEHu/vSAjdJRGpAAbwImdlPgWsJaygOcPf3CtwkEcmBUihFyMzWAW8Ret7p/wOkgHLNBy4SD+qBF6f/K3QDROT7Uw9cRCSmdCu9iEhMKYCLiMSUcuBS66Lb8x2YFRU1AOYDZ7j7FznWeTLQ2d0Hmtk/gfPdfVEV+3YElrj7+1nWXR9Y6+6V3eBU2f7lQAN3X5dxZ5E8Ug9c8mWZu3eNHp2ARcDl6TuYWVYBc0Pu/uuqgndkELBzLnWLxIl64LKpPA+cCmBmHxJmQtwF6G1m/YCzCJcxLgNOdvfPzOwM4AxgIfBxRUXR8d2B94GbgL2jTdcT1vbsC+xjZucC7wK3AD8AmgKXuvtkMzPgH8DXwJTKGmxmjYE7gDZR0SXu/lza9m2iOuoBzYC/uPud0XX2Y4A10fsOc/fHzexaoFtUvgg40d3X1OSXKJJOPXDJOzOrB/QmLJ5c4R13721mOwCXAd3dvTMwFbjUzJoB1wD7u/uhwJaVVN0f2Mbd9wMOAQYCE4FXCSmWZwmLU1zv7t2AI4DbopTJUOB2d98feK2Kpl8ALHT3jsCJwMkbbN8WGBXV3RO4ISo/BXjE3Q8ADge2MrMfAr8FOrh7F8K0BdtU93sTyUQ9cMmXrcxsavS8hBC8b0zbPjP62YEQCJ8KnWI2Az4A2gIfuvtn0X5TCGt3ptuXEPCJcus9AKJ6KhwAbG5mQ6PXa4GtgZ8BI6KyZ6v4DPsSrU7k7u8AAzbYvhj4nZldQFhTtGVU/i9gnJntCDwGjHf3cjN7CnjOzB4CJrj7R1W8r0hWFMAlX5a5e9dqtn8T/VwDvOjuPdM3mtnefHe1oHqV1FFO5r8i1wC93f3TDepPX42osrqzqf/3hL8kjjWzpsCXAO7+fJRGOZDwV8HxwHHufrSZ/ZhwonnOzPq4+6sZ2i9SJaVQpNDmEPLVrQDMrK+ZHQm8B+xsZs2jYHtgJcfOJKROMLNmZjbbzBoSAnODaJ/pQL9ony3NbGRU/hah9w8hn16Z9Pp3NrNnNti+DfBm9Lw/UGZmm5nZWcD27v4ocBKwb3T8ue4+z92vBx4E9sj86xGpmgK4FJS7fwycAzxmZs8TAt4L7r6cMFPiNOAR4MNKDr8P+MDMZgL/Bm5w92+i57eaWW/gbOAoM5sGPMG36ZJhwBlRWsMIg58bugn4YXTs3YQed7qbgWFm9ixhMPQZ4B5gHnCvmU0BHidM1fsR0M7MXoxOBDsRUi0iOdOt9CIiMaUeuIhITCmAi4jElAK4iEhMKYCLiMSUAriISEwpgIuIxJQCuIhITCmAi4jE1P8DlWbBYSjItXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9816edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non threat       1.00      1.00      1.00     79564\n",
      "     threat       0.40      0.01      0.02       222\n",
      "\n",
      "avg / total       1.00      1.00      1.00     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75765   200]\n",
      " [ 3677   144]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc981313c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non insult       0.95      1.00      0.98     75965\n",
      "     insult       0.42      0.04      0.07      3821\n",
      "\n",
      "avg / total       0.93      0.95      0.93     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79103     9]\n",
      " [  673     1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc98149278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Non identity_hate       0.99      1.00      1.00     79112\n",
      "    identity_hate       0.10      0.00      0.00       674\n",
      "\n",
      "      avg / total       0.98      0.99      0.99     79786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_confusion_matrix_and_plot(y_test, predictions_int, output_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area Under Curve ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9801f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_auc_roc(output_classes, y_test, predictions, auc_roc_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Class:  toxic\n",
      "ROC AUC:  0.7192869325936122\n",
      "\n",
      "Class:  severe_toxic\n",
      "ROC AUC:  0.768016529254694\n",
      "\n",
      "Class:  obscene\n",
      "ROC AUC:  0.7226592818086858\n",
      "\n",
      "Class:  threat\n",
      "ROC AUC:  0.6875783832699021\n",
      "\n",
      "Class:  insult\n",
      "ROC AUC:  0.7166232424321501\n",
      "\n",
      "Class:  identity_hate\n",
      "ROC AUC:  0.6589500934407532\n",
      "\n",
      "Multi-class ROC AUC:  0.7121857437999662\n",
      "\n",
      "Execution time:  857.8475501537323 seconds\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "X_train, X_test, y_train, y_test = train_test_split(train[new_features], \n",
    "                                                    train[output_classes], \n",
    "                                                    random_state=45, \n",
    "                                                    test_size=0.50)\n",
    "params = {\n",
    "    'n_estimators': 700,\n",
    "    'criterion': 'entropy'\n",
    "}\n",
    "\n",
    "classifier = RandomForestClassifier(**params)\n",
    "\n",
    "auc_roc_rf, predictions, predictions_int = calculate_predictions(classifier, X_train, X_test, y_train, y_test, output_classes)\n",
    "end_time = time.time()\n",
    "total_time_rf = end_time - start_time\n",
    "print(\"\\nExecution time: \", total_time_rf, \"seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc980c1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = classifier.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.yticks(range(len(indices)), [new_features[i] for i in indices])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[70909  1256]\n",
      " [ 6416  1205]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc981dee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "  Non toxic       0.92      0.98      0.95     72165\n",
      "      toxic       0.49      0.16      0.24      7621\n",
      "\n",
      "avg / total       0.88      0.90      0.88     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[78936    68]\n",
      " [  752    30]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9816e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  precision    recall  f1-score   support\n",
      "\n",
      "Non severe_toxic       0.99      1.00      0.99     79004\n",
      "    severe_toxic       0.31      0.04      0.07       782\n",
      "\n",
      "     avg / total       0.98      0.99      0.99     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75042   552]\n",
      " [ 3904   288]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9800ada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "Non obscene       0.95      0.99      0.97     75594\n",
      "    obscene       0.34      0.07      0.11      4192\n",
      "\n",
      "avg / total       0.92      0.94      0.93     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79557     7]\n",
      " [  220     2]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc98089c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non threat       1.00      1.00      1.00     79564\n",
      "     threat       0.22      0.01      0.02       222\n",
      "\n",
      "avg / total       1.00      1.00      1.00     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[75474   491]\n",
      " [ 3605   216]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc981bdc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " Non insult       0.95      0.99      0.97     75965\n",
      "     insult       0.31      0.06      0.10      3821\n",
      "\n",
      "avg / total       0.92      0.95      0.93     79786\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[79060    52]\n",
      " [  673     1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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rUe2946zHPH4KXCxp0Z+ipPWBoSQXowwADpa0Yfr0chGxG3AFyRf4UiRtRvKFvwPJlZFKn7oQ+HVEDCaZl/6ctH0D4KaI6EsyA+WWwC+BURFxXb1d/xJ4PCLOB/5KMukY6c9bm/mckyNiEMnVnkNIQuQfEbETcBDJlZ0vk1zuf2ZEPAdcCeyTvu4DFk8XUBV6rroi02Z8uujxqL8/z9nHfpNnbv0JE95+nweeepUv5szjnN/exT3XfB+AS669n09mf8FzL0/knakf89jIH3Ld+d/m1J/dBsCPfnUHF5y0N4/84VTenvIRj46JXD5bW/etvfZm3IsvMHCnfhyw395c8dtruPbaa3lp3IvsOriGXQfXcPKJJ+RdZu6qfcwj07OtIuJ1SS8CB9dr3gZ4NiLmw6I55rdKn3sy/TmFJBwashkwJiIWApMlvZW275jsTmeTrKZVNxXxJxExvt5+ixllvQn4efpzEEkINuWp9Oe7JJOLzQB6S/ouyQpe9SccQ9IawMbAnZIAugIfNfUGz99+Fl/baK0iSm9dPh931VJt22y6Nj/49qCl2v9wyVD+cMnQpdofG/nDpdr6br1hg/uw7HVeeQXuu/eeL7Xtu9cyOTpdVaq959ESp+peQDK3ytUkh2hq+fL0X51IvmAhWVKxTlPTBy+s97iu9zQXODAipi6x/fwlHjf7NxoR4yWtJmkw8FJEfNHMS5as+zCS3seA9OfzS2w/F3g3Imqaq6VO7wMvKXbTivT5uKvoss2JeZeRmRljlw7RatK5A3yx5P9pVaRzGd+U1R4emZ+qGxEfkIw5HJs2jQP6SuogqQNJD2NcKbsEtpNUkLQuyWEpSGaQ3BdA0iBJTZ0ruJClg3PJtltJ1vO9pYTa6qwKvJ32jvYnHZepe4+ImJHWuVn686T6Z6aZWeWr9sNWLXWdx69IBo+JiInAdcDjJIepro+IScXuKD0E9TLwT+Ai4KX0qfOAfSU9QTLD5D+b2M2LJGMtp9dr+zewraTfpI9vI5nq+JElX1yEO4C9JD0GfA5MkXQuyee9Mu3RHA38IZ0Fsz9JKJpZlchqVt3WolBbW5t3Da1SOl7xlYhoFecddtnmxKr+i/Jhq8rWBg5blfytvs35j5T1/+y44YMqIkE8PUkDJP2eZJ3gfdLH2wO/aGDTURExogVLM7MKUUGdiLI4PBoQEUct8fg5oCafasysElXSIahyODzMzDJQ5dnh8DAzy4J7HmZmVrIqzw6Hh5lZFtzzMDOzklV5djg8zMyy4J6HmZmVrMqzw+FhZpYF9zzMzKxkVZ4dDg8zsyxk1fOQVAPcDryaNr1MMn3SzSRrGU0FjoiIOZIOB04hmdH7uoi4QVJH4EZgXZJVV4dFxFuStiKZSbwWGB8RxzdVR0vNqmtm1qZkPKvu4xFRk95OIlk36eqIGAC8ARwlqStwLrAzyfRKp0pahWS9oZkR0R+4GLg03eflwMkR0Q/oLmmPpgpweJiZZaCF1/OoAe5O799DEhg7AGMjYlZEfA48DfQDBpMstQ0wGugnqROwfkSMXWIfjfJhKzOzDGQ8YL6ZpLtJVio9H+gaEXPS5z4E1gR6sng57gbbI2KhpNq0bUYD2zbK4WFmloEMs+N1ksC4jWQl1Uf58nd5U0t4F9vebPU+bGVmVkEi4t2IGBURtRHxJvA+sLKkLukmvYD30lvPei9dqj0dPC+QDLL3aGDbRjk8zMwykNWAuaTDJZ2X3l8dWB34A7B/usn+wAPAGKCPpJUkdSMZ73gSeAg4MN12L+DRiJgHTJDUP20fku6jUT5sZWaWgQwPW90N3CrpaZJTc08AxgE3SToWmASMjIh5ks4AHiQ5/fb8iJglaRSwi6SngDnA0HS/pwDXSmoHjImI0U0V4fAwM8tAu4zSIyI+JekxLGmXBrb9C/CXJdoWAMMa2PY1YECxdTg8zMwy4CvMzcysZJ7byszMStauurPD4WFmlgX3PMzMrGRVnh0ODzOzLBSav0i7ojk8zMwy0ObHPNLL19eIiCmStgS2Au6IiM8yr87MrEJV+5hHMdOTjAR2lNQLuBPYgmQhETMza0QLT8ne4ooJj14RcRtwMHBNRPyYZBpgMzNrRLtCoaxbpShmzGM5SQVgP+DotK1bdiWZmVW+CsqBshTT83gMmAVMjYj/SDoFiEyrMjOrcBkvQ5u7ZsMjIs4A1omIg9KmvwHHZFqVmVmFa/NjHuki6Hun928B/kHDMzqamVkbUcyYx7nAXmmItAe2Ae4lOfPKzMwaUEmD3+UoZszjs4j4CNgTuDkiZgMLsi3LzKyyFcq8VYpiwqOzpB8BuwMPS9oY6J5tWWZmla3ND5gD3yNZDH1YRHwB7Ab8JNOqzMwqXLtCebdK0eyYR0S8SrK2bZ3rgFuAh7Mqysys0lVSL6IcxcxtdQRwGYuvKl+Ig8PMrElVnh1FnW31A5L5rP5MMmj+bWB2lkWZmVW6au95FDPmMSsi3gfaR8R/I+JaYFjGdZmZVbQ2P+YBLJS0NzBZ0nnAq8BXMq3KzKzCueeRHKaaRDJovlb6+KQsizIzq3TVfp1Hoz0PSXXB8lF6Azgu84rMzKpAtV9h3tRhq/lALYvDsDb9WUjvt8+wLjOzipZldkjqArwCXEhy9uvNJN/JU4EjImKOpMNJjhgtBK6LiBvSlWFvBNYlmSlkWES8JWkrYATJd/v4iDi+uRoaDY+IKOaQlpmZNSDjMY+zgY/T+xcAV0fE7ZIuAY6SdBPJvITbA3OBsZL+SjKp7cyIOFzSrsClJAv9XQ6cHBFjJd0qaY+I+HtTBRQzq+4ASSPrPf6HpJ1K/6xmZm1HVlOyS/oqsClwX9pUA9yd3r8H2BnYARgbEbMi4nPgaaAfMBj4a7rtaKCfpE7A+hExdol9NKmY3sWlJF2jOsembWZm1ogMl6H9FXBavcddI2JOev9DYE2gJzCt3jZLtUfEQpLDVD2BGQ1s2/TnK6LQQkS8UfcgIt7Cs+qamTUpi56HpO8AT0TExMbedhm0F5VgxVzn8Y6kn5MsR9uOZHbdycXs3MzMlqk9gQ0kDSG53m4OMFtSl/TwVC/gvfTWs97regHP1mv/Vzp4XiAZZO+xxLbvNVdIMeExDDgdOIGki/MMnlW3xU0f89u8S8hcW/iM1nZkMWAeEQfX3U8v2p4I7AjsD/wx/fkAMAa4XtJKJGfO9iM582pF4EDgQZLB80cjYp6kCZL6R8RTwBCg2f8Zi5lV9wvgohI+n5lZm9eCp6sOB26SdCzJBd0j00A4gyQkaoHzI2KWpFHALpKeIum1DE33cQpwbXp935iIGN3cmxZqa2ub28Zagc/mVvdf1PKdCnw2t3o/YrtKmrSoDJ07wBfz864iO507lH7x9w/+NqGsX+gr9/1qRfyyFHPYyszMSlTl/14ormclqYek3ul9XzxoZtaMap9Vt5iLBA8lGaW/MW36raSjsyzKzKzSeQ3z5GKUrVh8wcnpJOuam5lZI9p8z4NkMajP6h6k5xLPza4kM7PKl9X0JK1FMQPmH0k6EugiaVuSSbSmNfMaM7M2rdqnZC+m53Ec0AdYAbge6AIck2VRZmaVrl2Zt0pRzEWCM4ETW6AWM7OqUeUdj+bDQ9JkFi8EtUhErJNJRWZmVaDaD1sVM+bRv979TiTzwXfJphwzs+pQ5dlR1GGrSUs0vS7pQeA32ZRkZlb5Kum023IUc9hq0BJNawMbZlOOmVl18GErOKfe/VrgE5IzsMzMrBFVnh1FhcePIuL5zCsxM6si1X7YqpjTin+ZeRVmZlZRiul5TJL0GMnkiIumJYmIc7Mqysys0hVKXwKkohQTHm+nNzMzK1K1H7ZqNDwkHR4Rt0TE+S1ZkJlZNaj28GhqzMNrdpiZlana1/PwMrRmZhmo9p5HU+Gxo6R3GmgvALWe28rMrHEV1IkoS1PhMQ44pKUKMTOrJm35CvMvGpjXyszMitCWD1s912JVmJlVmSrveDQeHhHxk5YsxMysmrTzRYJmZlaqNtvzMDOz8mUx5iFpeeBGYA2gM3Ah8C/gZqA9MBU4IiLmSDocOAVYCFwXETdI6pi+fl1gATAsIt6StBUwgmTm9PERcXxztVTSeutmZhWjXaFQ1q0ZewHPR8Q3gIOAy4ALgKsjYgDwBnCUpK7AucDOQA1wqqRVgMOAmRHRH7gYuDTd7+XAyRHRD+guaY/mCnHPw8wsA1kctoqIUfUerg1MIQmHujWW7gFOBwIYGxGzACQ9DfQjWUb8pnTb0cDvJXUC1o+IsfX2sTPw96Zqcc/DzCwDGfU8AJD0DHAryWGprhExJ33qQ2BNoCcwrd5LlmqPiIUkh6l6AjMa2Lbpz1dUpWZmVpJCobxbMSJiR2Bv4I/wpdO6GttDKe1FVeHwMDOrEJJ6S1oHICJeIhl6+FRSl3STXsB76a1nvZcu1Z4OnhdIBtl7NLBtkxweZmYZaFfmrRkDgNMAJK0BdCMZu9g/fX5/4AFgDNBH0kqSupGMdzwJPAQcmG67F/BoRMwDJkjqn7YPSffR7OczM7NlLKMp2X8HrC7pSeA+4PvAcODItG0VYGREfA6cATxIEi7np4Pno4D2kp5KX3tmut9TgEvTgfU3I2J0s5+vtra21D8Ty8Fnc6v7L2r5TgU+m1u9H7FdlU901LkDfDE/7yqy07lD6ZeL3/T85LJ+ob/Te+2K+GXxqbpmZhloy7PqmplZmao7OhweZmaZqPKOh8PDzCwLlbQeeTkcHmZmGaj2U1kdHmZmGXDPw8zMSlbd0eHwMDPLhHseZmZWMo95mJlZyaq951Ht4WityJ//dAs79N6afl/vzQP338e3Dz2I3XcZyO67DGTLLbfkxOO/B8ClF1/AwJ12pGZAX35+6UU5V23NeXn8eDbccENGXH1V3qW0KoUyb5XCPQ9rEdOnT+fSiy7gqWefZ/bs2Vx8wXD++KfbFj1/4nFHM/SoY5g0cSKvvvIKjz7xDAsWLGCbLTblO0cexZprrZVj9daY//73v/zkR6exyy675F1Kq1PlHQ/3PKxlPPrIaAYOGswKK6zAmmuuyVUjrlv03H8imDlzJr37bM+66623KFRmzJhBu3btWGHFFfMq25qx3HLLcedd99KzZ8/mN25j2lEo61YpHB7WIiZNmshnn3/OgUP2YZdBO/HoIw8veu6aq67gpJNO+tL2p592Mr232ZyfnHU23bp1a+lyrUgdOnSgc+fOeZdhOaio8JC0u6Tjl2h7XtJ6Je5nRUm7pvfPkNQ3vb9/069scF+PSdq8yG23lLRJqe9RDWpra/l4+nT+dNsdXPt/f+C47x1FbW0tc+fO5ZlnnmbgwIFf2v5Xl13BuPH/5vLLfsXEt9/OqWqz8mW5DG1rUFFjHhHR7OpWRdoW2BV4KCJ+BpAG0KHAHcvoPRoyBHge+E+G79Eqrb76GuzQty8dOnRggw03ZIVuKzBt2jReHv8vevfps2i7KZMn88EH77Nd7z6svPLK9O27Iy+8MJb11l8/x+rNSleooENQ5aio8JA0FNgc6AT0BSK9j6S1gBvSxwuAYyLiHUlvAH8jWYZxJrAncDWwoqT/ADsCfwGOB7aXNBz4DrB1RHyaLs14WkQMaaK0IyT1IVnFa2+S9X9HAmsDXYDzgUnAccA0SR8CywGXAPOAycB3I2Lu//yH1ErtvPOufO+YYfzw9J8wY8YMZv93NquuuiovvjCWLbbYatF20z6axik/+D6PPvEMhUKBceNeZNgx38uxcrPyVFIvohwVddgqtQHJF/4OJEsoKm2/EPh1RAwGLgfOqbf9TRHRF1gZ2BL4JTAqIq6rt99fAo9HxPnAX4H90vb9gFubqWlyRAwC/k7Su1gF+EdE7AQcRLIE5Msk6wKfGRHPAVcC+6Sv+4DF6wpXpbV69WK/IftTM6Av++39TX592ZW0a9eO96dOZbXVV1+03TbbbMve++7H4Jr+1Azoy26778FWW22dY+XWlDHPPst2W2/ONddcwy9+fgnbbb0506dPz7usVqHaB8wrqueR2hJ4MCIWApMlvZW27whI0tlAe2Ba2v5JRIxP708BuhfxHjcBP09/DgJ+2sz2T6U/3wV6ADOA3pK+CyxM2xZJF67fGLhTEkDmBFdYAAARq0lEQVRX4KOm3qBzh8pfyvSk7x/HSd8/7kttI65ZfG3A8p2Szzf87LMYfvZZLVqblecb/b/Oq6+8kncZrVK19zwqMTwKJF/Idep6T3OBAyNi6hLbL7mycrN/pRExXtJqkgYDL0XEF828pP57FIDDSHofA9Kfzy+x/Vzg3Yioaa6WOsn60NW7xrfXMK9sbWAN85JVe3hU4mGrfwHbSSpIWpfksBTAGGBfAEmDJB3WxD4WsnRwLtl2KzACuKWMGlcF3k57R/uTjsvUvUdEzEjr3Cz9eZKkLct4HzNrpQpl/lcpKjE83gJeBv4JXAS8lLafB+wr6QlgePp8Y14EDpZ0er22fwPbSvpN+vg2oBvwSBk13gHsJekx4HNgiqRzgSeBK9MezdHAHyQ9CfQnGfw3syrRrlDerVIUamur91DB/yIdr/hKRAzPuxaAz+ZW91+UD1tVtjZw2Krkv8BHJkwv6xd60Fd7VMQvSyWOeWRO0u+B9YB90sfbA79oYNNRETGiBUszswpR7WMe7nlUCPc8Kpt7HpWtnJ7HY/FxWb/QNVqlIn5Z3PMwM8tAlv9ekPQLkrM5OwCXAmOBm0kuU5gKHBERcyQdDpxCcrLOdRFxg6SOwI3AuiQXVA+LiLckbUVyklAtMD4ijqcJlThgbmbW6mV1tpWkgcAW6YXPu5NcFH0BcHVEDADeAI6S1BU4F9gZqAFOlbQKyaUEMyOiP3AxSfiQ7ufkiOgHdJe0R1N1ODzMzDKQ4cSIT7J4RoqZJBcZ1wB3p233kATGDsDYiJgVEZ8DT5NM0zSYZBYNgNFAP0mdgPUjYuwS+2iUw8PMLANZrSQYEfMjYnb68GjgfqBrRMxJ2z4E1gR6snimjQbb02vRatO2GQ1s2yiPeZiZZaBdxqdbSdqHJDx2BV6v91Rjb1xKe7PFu+dhZlZhJO1GMufeHhExC5gtqUv6dC+Smb3fI+lR0Fh7OnheIBlk79HAto1yeJiZZSCrw1aSupPMAv6tiPg4bR5NMhUS6c8HSKZs6iNpJUndSMY7ngQeYvGYyV7AoxExD5iQLkEByezgTa6f5MNWZmZZyO6o1cEk8+fdls7KDXAkcL2kY0nWDhoZEfMknQE8SDKucX5EzJI0CthF0lPAHGBouo9TgGsltQPGRMToporwRYIVwhcJVjZfJFjZyrlIcMybs8r6hd5hw+4V8cvinoeZWQaqfXoSh4eZWQaqPDscHmZmmajy9HB4mJlloJIWdiqHw8PMLAMe8zAzs5JVeXY4PMzMMlHl6eHwMDPLgMc8zMysZB7zMDOzklV5djg8zMwyUeXp4fAwM8uAxzzMzKxk1T7m4fU8zMysZO55mJlloMo7Hg4PM7NMVHl6ODzMzDLgAXMzMytZtQ+YOzzMzDJQ5dnh8DAzy0SVp4fDw8wsAx7zMDOzknnMw8zMSlbl2eHwMDPLRJWnh8PDzCwDHvMwM7OSeczDzMxKlmV2SNoS+Cvwm4i4StLawM1Ae2AqcEREzJF0OHAKsBC4LiJukNQRuBFYF1gADIuItyRtBYwAaoHxEXF8UzV4Vl0zsywUyrw1Q1JX4DLgH/WaLwCujogBwBvAUel25wI7AzXAqZJWAQ4DZkZEf+Bi4NJ0H5cDJ0dEP6C7pD2aqsPhYWaWgUKZ/xVhDvAt4P16bTXA3en9e0gCYwdgbETMiojPgaeBfsBgkl4LwGign6ROwPoRMXaJfTTK4WFmloFCobxbcyJifkR8sURz14iYk97/EFgT6AlMq7fNUu0RsZDkMFVPYEYD2zbKYx5mZhnIcby8sbcupb3Z8t3zMDOrfLMldUnv9wLeS289622zVHs6eF4gGWTv0cC2jXJ4mJllIaMB80aMBvZP7+8PPACMAfpIWklSN5LxjieBh4AD0233Ah6NiHnABEn90/Yh6T4a/3i1tbVlV2st57O51f0XtXynAp/Nrd6P2K5ddZ/037kDfDE/7yqy07lD6V/rk6bPKesXet0eyzX5XpK+DlwPrA7MBz4GdiM5/bYzMInk9Nt5kg4AfkQyrvHbiLhFUvv09RuTDL4PjYjJkjYDriXpVIyJiNOaqsPhUSEcHpXN4VHZygmPdz4uLzzWWaXp8GgtPGBuZpaBikiA/4HDw8wsA56exMzMylDd6eHwMDPLgHseZmZWsirPDodHpVi+U7X/OyY548oqV2d/m3xJtf8f679uM7MMeDEoMzMrXXVnh8PDzCwLVZ4dDg8zsyx4zMPMzErmMQ8zMytddWeHw8PMLAtVnh0OD8uHpA4kawr0iohfSdociHRdATNr5bwYlOXl/4CtWbwoTQ1wU27VWEkkdZB0qKTT08ebp6vSWSqrNcxbC4eH5WXtiPgJ8BlARFwFrJVvSVYCh38zCmX+VykcHpaXTpJWIlnhDEmbAsvlW5KVwOHfjGrveXjMw/JyFvAIsLGkf6dtR+dYj5XG4d/GuedhefkiIrYFNgT6RoS/fCpLXfj3ScP/TqDJNa/bGvc8zJYhSRsBAi6RdAbpGY3p2VdXAuvlV52V4IuI2FbS6sDciJgpaWDeRbUmlTR+UQ6Hh7W0LkBvYHXgoHrtC4Hz8ijIiufwL14l9SLK4fCwFhURLwMvS7ojIl6p/5yks3Mqy4rn8C9SlWeHw8Nys46kG4FV0sedgCnARblVZM1y+JegytPD4WF5GQ4cQHJtwH7p/f/mWpGVwuHfjGof8/DZVpaXzyNiItAuIqZHxLXAUTnXZMWrC/8pQB/gQuCqXCtqZXy2lVk2pkj6DjBO0h+Bt4EeOddkxfs8IiZKahcR04FrJT0C/DHvwlqLCsqBsjg8LC9HAisDtwKHkQTHXrlWZKVw+DenytPD4WF5WRMYAnQn+d+sAAwFLsixJiuew78Z1T7m4fCwvNwNPAC8m3chVhaHfzMqafyiHA4Py8uMiDgr7yKsbA7/ZnTuUN1dj0JtbW3eNVgbImmz9O53gHeAp4D5dc9HxGt51GWlkfRwRAzOuw7Lj8PDWpSkR5t4ujYiBrVYMVYyh7/VcXhYqyPp2PS6D2tlHP5Wx+FhrY6kR/wlVLkc/m2DrzC31qiqBxrbgIPzLsCy5/Cw1sjd4crm8G8DHB5mtqw5/NsAh4e1Rv6Xq1kr54sELReS7gBuAe6NiLlLPP3jHEqyZcfh3wa452F5uQzYEXhW0g2SFp1dFRFj8yvLiiHpDklDJHVq4GmHfxvgU3Utd2lwjAC6Av8H/CoivDBUKyapH8kiXoOAccAtEfFIvlVZS3J4WC4kdSX58jkUWA34E3AbsAdwSETsnGN5VgKHf9vkMQ/Ly7+AO4Ez0nWx61wvqW9ONVmRGgj/37E4/O8CHP5VzuFhebkpIr40fbekX0fEDyPi6LyKsqI5/Ns4H7ayFiVpCMm/VncCHq/3VEdgm4hYL4+6rDSSzm0s/POqyVqWw8NanKT1gKuAX9ZrXgj8OyI+yqUoK4rD3+o4PKxFSdohIsZI2pMGrkSOiPtzKMtK4PA38JiHtbwaYAxwQAPP1QIOj1asXvjXnV1V3/b476/NcHhYi4qIn6d3n46I6+s/J+m0HEqy0tTg8DccHtbCJO0C7AocJGmTek91BA4iufLcWimHv9VxeFhLexaYR3I9wKv12hcC1zf4Cms1HP5Wx+FhLSoiPgUek7QFsAXQncUT6fXIrTArlsPfAIeH5edeYGXg3XpttcAT+ZRjxXD4Wx2Hh+Vl5YjYMe8irGwO/zbO4WF5eUrS1yLi1eY3tVbI4d/G+SJBy4Wk14ENgE9IjqEXgNqIWD3Xwqwokn4BjHT4t10ODzMrmcPfHB6WC0lfAc4lOfxxoKRDgH9GxKScSzOzIngZWsvL9cBfgbp/qX4I3JhbNVYSSV+RdJ2k29PHh0haN++6rOU4PCwv7SPi7yTXB5AuYerfx8rh8G/j/D+r5WVeunxpe0lrSDoO+DzvoqxoDv82zqfqWl6OBi4EVgUeIJlsb1iuFVkpvhT+JEvSOvzbEIeHtShJ69R7WLcSXYHkArOOLV+Rlcnh38Y5PKyl3UESFJ0AAW8B7YH1gZeAHfIrzZrj8Lc6Dg9rURHRB0DSzcC3ImJK+nhdFn8ZWevl8DfAA1yWn03qggMgvb5j4xzrsSJERJ+I2B54Gdg4Ir4WEV8FNgEm5FudtST3PCwvYyQ9R3KsfCHQGxifb0lWgqXCX5LDvw3xFeaWG0mbApuRHDOPiHg555KsSJKuAPqyOPy3A16OiONzLcxajA9bWYuSdGz685ckZ+fsAGwPHJFOtmetWN3fHzAXmAzsDuwJfAB8mldd1vJ82Mpa2sT05yt5FmFlm5j+fCW93Z1fKZYnH7YyM7OS+bCVmZmVzOFhZmYl85iH5ULS1sB3gO4kZ1sBEBFH5VaUmRXN4WF5uQW4EpjS3IZm1vo4PCwvkyPi2ryLMLPy+Gwry4Wki0nmR3oSmF/XHhH351aUmRXNPQ/Ly1rpz/3qtdUCDg+zCuCeh+VG0vrA1sACYFxETM65JDMrkk/VtVxI+hFwO1ADfBO4S5LnRTKrED5sZXnZF9ghIhYASOoAPA6MyLUqMyuKex6WlwLJbKx1FpKMeZhZBXDPw/IyCnhB0j9J/hHzdeC6fEsys2J5wNxyI2k9YBuSHse4dDVBM6sADg9rUZK+09TzEXFTS9ViZuXzYStraYUG2joAxwFfARweZhXAPQ/LlaSDgTOAvwG/ioj/5lySmRXBPQ/LhaSBwMXAC8BuEfFhziWZWQkcHtaiJG0O/AyYDRwREW/mXJKZlcGHraxFSZoPvEbS46j/y1cAar2eh1llcM/DWtqGeRdgZv879zzMzKxknp7EzMxK5vAwM7OSeczDqko65UkA/0ybOgKTgBMiYmaZ+zwG6B8RQyX9GfhhRLzbyLY7Au9HxFtF7rsDMC8iGrp4sqHta4GOETG/2Y3NMuSeh1WjaRFRk976Ae8CZ9ffQFJRX9ZLiohDGguO1DBgg3L2bVZJ3POwtuAJ4FgASRNJZvTdGBgi6SDgJJJThacBx0TEdEknACcAk4H36naUvn5n4C3gSqB3+tSvSdZiPxDYXtKpwBvANcDyQDfgrIgYLUnAH4HPgEcbKlhSF+APwDpp05kR8Xi959dI99Ee6A5cERE3pdfRXAfMSd/3goi4T9LPgEFp+7vAkRExp5Q/RLP63POwqiapPTAEeLJe8+sRMUTS2sBPgZ0joj/wGHCWpO7AhcA3ImIPYNUGdn04sEZEfB3YHRgK3A28RHJY6xGSha1+HRGDgL2B69PDVMOB30fEN4DxjZR+OjA5InYEjgSOWeL5NYGr031/C7gsbf8ucFdEDAT2AlaTtDLwfaBvRAwgmQpmjab+3Mya456HVaPVJD2W3m9HEhy/qff8M+nPviRfwg8mnQGWA94GNgImRsT0dLtHSdZar28HkrAhHUvZEyDdT52BwAqShqeP5wGrA1sAl6ZtjzTyGXYgXVUxIl4Hjlji+anATySdTrIGfI+0/Q7gRknrAvcCIyOiVtKDwOOS/gqMiogpjbyvWVEcHlaNpkVETRPPz01/zgGei4hv1X9SUm++vMph+wb2UUvzPfc5wJCI+GiJ/ddfRbGhfRez/4tIelCHSuoGfAoQEU+kh64Gk/SGvg0cFhEHSPoqScg9Lmn/iHipmfrNGuXDVtaWjSUZn+gJIOlASfsAbwIbSFop/aIf3MBrnyE5XIWk7pLGSOpEEgod022eAg5Kt1lV0uVp+2skvR5Ixk8aUn//G0h6eInn1wBeTe8fDiyUtJykk4CvRMQ9wNHADunrT42ICRHxa+BOYKvm/3jMGufwsDYrIt4DTgbulfQEyZftsxExg2TG3yeBu4CJDbz8NuBtSc8A/wAui4i56f1rJQ0BfgDsJ+lJ4H4WH6K6ADghPZQkkoH2JV0JrJy+9haSnkZ9VwEXSHqEZOD9YeBWYALwJ0mPAveRTHc/BdhG0nNpCK1PcnjLrGyensTMzErmnoeZmZXM4WFmZiVzeJiZWckcHmZmVjKHh5mZlczhYWZmJXN4mJlZyRweZmZWsv8HE8n8izSpG6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc97e1ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "Non identity_hate       0.99      1.00      1.00     79112\n",
      "    identity_hate       0.02      0.00      0.00       674\n",
      "\n",
      "      avg / total       0.98      0.99      0.99     79786\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_confusion_matrix_and_plot(y_test, predictions_int, output_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Area Under Curve ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc98003f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_auc_roc(output_classes, y_test, predictions, auc_roc_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "## Summary of Models\n",
    "We show the AUROC scores obtained from models we tested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear SVM Regularized</td>\n",
       "      <td>0.981723782</td>\n",
       "      <td>16.545900583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>0.980804666</td>\n",
       "      <td>10.725399971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multinomial Naive Bayes</td>\n",
       "      <td>0.957018842</td>\n",
       "      <td>9.688201666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extreme Gradient Boosting Tree</td>\n",
       "      <td>0.782530075</td>\n",
       "      <td>56.283751488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.712185744</td>\n",
       "      <td>857.847550154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model       Score          Time\n",
       "0          Linear SVM Regularized 0.981723782  16.545900583\n",
       "1     Stochastic Gradient Descent 0.980804666  10.725399971\n",
       "2         Multinomial Naive Bayes 0.957018842   9.688201666\n",
       "3  Extreme Gradient Boosting Tree 0.782530075  56.283751488\n",
       "4                   Random Forest 0.712185744 857.847550154"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = {\n",
    "    'Model': ['Linear SVM Regularized', 'Stochastic Gradient Descent', 'Multinomial Naive Bayes', 'Extreme Gradient Boosting Tree', 'Random Forest'],\n",
    "    'Score': [np.mean(auc_roc_svm), np.mean(auc_roc_sgd), np.mean(auc_roc_nb), np.mean(auc_roc_xgb), np.mean(auc_roc_rf)],\n",
    "    'Time': [total_time_svm, total_time_sgd, total_time_nb, total_time_xgb, total_time_rf]\n",
    "}\n",
    "summary = pandas.DataFrame(models)\n",
    "display(summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scores of models, we select the Linear SVM Regularized classifier model to predict the real test set.\n",
    "\n",
    "## Perspective Model vs Best Model\n",
    "The goal is to test our model on the Perspective datasets and compare our AUC ROC with the Perspective model ones to verify if our best model fulfill the objective of this project. This means that our model has to give a better AUC ROC score than the Perspective model.\n",
    "\n",
    "In the notebook https://github.com/conversationai/unintended-ml-bias-analysis/blob/master/unintended_ml_bias/Evaluate%20Models%20Across%20Datasets.ipynb, we see that their best score is done on the debias_random_model which obtained a ROCAUC of 0.972542."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_rnd_perspective = pandas.read_csv(\"dataset/wiki_debias_random_train.csv\")\n",
    "train_rnd_perspective = clean(train_rnd_perspective)\n",
    "\n",
    "test_rnd_perspective = pandas.read_csv(\"dataset/wiki_debias_random_test.csv\")\n",
    "test_rnd_perspective = clean(test_rnd_perspective)\n",
    "\n",
    "dev_rnd_perspective = pandas.read_csv(\"dataset/wiki_debias_random_dev.csv\")\n",
    "dev_rnd_perspective = clean(dev_rnd_perspective)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_perspective = pandas.read_csv(\"dataset/wiki_train.csv\")\n",
    "train_perspective = clean(train_perspective)\n",
    "\n",
    "test_perspective = pandas.read_csv(\"dataset/wiki_test.csv\")\n",
    "test_perspective = clean(test_perspective)\n",
    "\n",
    "dev_perspective = pandas.read_csv(\"dataset/wiki_dev.csv\")\n",
    "dev_perspective = clean(dev_perspective)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki Debias Random Test AUC ROC:  0.9751467703260143\n",
      "Wiki Debias Random Dev AUC ROC:  0.9739739332653962\n"
     ]
    }
   ],
   "source": [
    "apply_best_model('Wiki Debias Random Test', train_rnd_perspective, test_rnd_perspective)\n",
    "apply_best_model('Wiki Debias Random Dev', train_rnd_perspective, dev_rnd_perspective)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiki Test AUC ROC:  0.9744905761181268\n",
      "Wiki Dev AUC ROC:  0.9732874559506474\n"
     ]
    }
   ],
   "source": [
    "apply_best_model('Wiki Test', train_perspective, test_perspective)\n",
    "apply_best_model('Wiki Dev', train_perspective, dev_perspective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is enough to show that our model is better than the Perspective's current models. The objectif is then fulfilled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Model Predictions\n",
    "From the AUROC score we got, the support vector machine is the best one. Therefore, we use this model to get the prediction probabilities of each class using the comments from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             toxic  severe_toxic     obscene      threat      insult  \\\n",
      "0      0.999971472   0.128547014 0.999780194 0.019062572 0.982174675   \n",
      "1      0.003172159   0.001740361 0.002534878 0.000357736 0.004639392   \n",
      "2      0.023961611   0.004693653 0.012005726 0.000968965 0.015170119   \n",
      "3      0.002363576   0.003589101 0.003472929 0.001059903 0.006016317   \n",
      "4      0.016020127   0.002378412 0.005855621 0.001339387 0.007725040   \n",
      "5      0.004634133   0.001894390 0.004197629 0.000812872 0.009307607   \n",
      "6      0.003122302   0.000840731 0.004618959 0.000307620 0.006960938   \n",
      "7      0.515293792   0.001855882 0.018313243 0.001348584 0.054983556   \n",
      "8      0.011664737   0.002016407 0.006510815 0.000659524 0.008057193   \n",
      "9      0.002261111   0.000877943 0.003909395 0.000623779 0.005349244   \n",
      "10     0.440676014   0.000484673 0.082255585 0.000318530 0.005513739   \n",
      "11     0.166727678   0.001916208 0.020124161 0.002000084 0.014869287   \n",
      "12     0.002838098   0.001645479 0.003147785 0.000468317 0.003157851   \n",
      "13     0.004855805   0.001584014 0.007849002 0.000426712 0.003622350   \n",
      "14     0.001119950   0.000909651 0.002297400 0.000235487 0.002007029   \n",
      "15     0.015492266   0.001631413 0.007936870 0.000590156 0.013995140   \n",
      "16     0.008936537   0.003019582 0.006243494 0.000373006 0.006293368   \n",
      "17     0.005592610   0.000929506 0.004375225 0.000269440 0.006583809   \n",
      "18     0.000236950   0.000481888 0.001052995 0.000067160 0.000407362   \n",
      "19     0.008180236   0.003828367 0.006217492 0.000152330 0.006750674   \n",
      "20     0.010901031   0.003415102 0.009375399 0.000328234 0.007671994   \n",
      "21     0.529519008   0.012368638 0.042442207 0.012351218 0.039322558   \n",
      "22     0.038941513   0.001957816 0.008487305 0.002020300 0.016295458   \n",
      "23     0.004526303   0.001792657 0.005399937 0.000583240 0.005043322   \n",
      "24     0.074512803   0.001706310 0.024006899 0.002331058 0.015701606   \n",
      "25     0.007530500   0.001787617 0.006119161 0.001427324 0.009340958   \n",
      "26     0.013537144   0.001681325 0.006708380 0.001072279 0.006631585   \n",
      "27     0.040225738   0.003182128 0.010905403 0.000569404 0.014644393   \n",
      "28     0.244522074   0.005008804 0.018278979 0.001596704 0.057503528   \n",
      "29     0.005084831   0.002091976 0.004594534 0.000486675 0.006554695   \n",
      "...            ...           ...         ...         ...         ...   \n",
      "153134 0.004810660   0.002387120 0.005467257 0.000665627 0.006648491   \n",
      "153135 0.004952495   0.002604940 0.004256387 0.000593975 0.003657233   \n",
      "153136 0.001687406   0.000733862 0.002483131 0.000343813 0.003015424   \n",
      "153137 0.027890476   0.003479354 0.013786785 0.002004821 0.018050591   \n",
      "153138 0.007919434   0.001471397 0.006980952 0.000728880 0.007626192   \n",
      "153139 0.005180320   0.001137304 0.006785281 0.000156630 0.005456816   \n",
      "153140 0.010881164   0.001249311 0.006983964 0.000643056 0.006776996   \n",
      "153141 0.009252980   0.001244321 0.004876542 0.000866248 0.005111799   \n",
      "153142 0.028749674   0.003564902 0.007778704 0.000781663 0.027605226   \n",
      "153143 0.999921915   0.099375428 0.999272189 0.002097129 0.907906175   \n",
      "153144 0.017427603   0.002743303 0.005983580 0.001812002 0.010010872   \n",
      "153145 0.003789645   0.003033176 0.002588342 0.000632135 0.005404839   \n",
      "153146 0.000301826   0.000676729 0.002039441 0.000160071 0.001928760   \n",
      "153147 0.009499912   0.003885928 0.004278693 0.000423821 0.006706821   \n",
      "153148 0.007929795   0.001387163 0.005553281 0.000267687 0.008060480   \n",
      "153149 0.909942496   0.027636049 0.046366944 0.004867106 0.086571541   \n",
      "153150 0.004771426   0.001901870 0.004084436 0.000910117 0.004338705   \n",
      "153151 0.603489353   0.012679212 0.410463730 0.001536715 0.340828396   \n",
      "153152 0.330562381   0.001932098 0.184210990 0.000716769 0.012617290   \n",
      "153153 0.977819510   0.012512771 0.797418167 0.035082661 0.255509825   \n",
      "153154 0.600779082   0.008042407 0.025903811 0.002761837 0.067408497   \n",
      "153155 0.999698867   0.071607804 0.999008980 0.001011022 0.979227014   \n",
      "153156 0.027437799   0.003737781 0.008796321 0.001059581 0.011642509   \n",
      "153157 0.011910276   0.002362020 0.005854007 0.000462231 0.008310293   \n",
      "153158 0.972217187   0.002620902 0.412904213 0.000415659 0.903005018   \n",
      "153159 0.929186038   0.002346781 0.181858225 0.000563724 0.035066963   \n",
      "153160 0.040465477   0.003612304 0.014367931 0.002898069 0.016122148   \n",
      "153161 0.002871804   0.001658172 0.004243825 0.000541686 0.004920706   \n",
      "153162 0.009599346   0.002284290 0.007750776 0.001016336 0.009719751   \n",
      "153163 0.995739116   0.001610577 0.830206060 0.003092951 0.352862702   \n",
      "\n",
      "        identity_hate  \n",
      "0         0.101299804  \n",
      "1         0.003272791  \n",
      "2         0.004326546  \n",
      "3         0.001392106  \n",
      "4         0.003073823  \n",
      "5         0.001672349  \n",
      "6         0.001221443  \n",
      "7         0.003134873  \n",
      "8         0.003343728  \n",
      "9         0.001951765  \n",
      "10        0.002375218  \n",
      "11        0.005160784  \n",
      "12        0.000696538  \n",
      "13        0.001454174  \n",
      "14        0.000457329  \n",
      "15        0.003009611  \n",
      "16        0.001905106  \n",
      "17        0.002745273  \n",
      "18        0.000253791  \n",
      "19        0.002228110  \n",
      "20        0.002841292  \n",
      "21        0.023262319  \n",
      "22        0.008415247  \n",
      "23        0.002341554  \n",
      "24        0.005747208  \n",
      "25        0.002658006  \n",
      "26        0.004839085  \n",
      "27        0.003817496  \n",
      "28        0.050308856  \n",
      "29        0.002524555  \n",
      "...               ...  \n",
      "153134    0.002025789  \n",
      "153135    0.003236706  \n",
      "153136    0.001092799  \n",
      "153137    0.006017538  \n",
      "153138    0.003918893  \n",
      "153139    0.001666453  \n",
      "153140    0.002621982  \n",
      "153141    0.001285842  \n",
      "153142    0.007196126  \n",
      "153143    0.014529203  \n",
      "153144    0.003358729  \n",
      "153145    0.003014950  \n",
      "153146    0.000907694  \n",
      "153147    0.001745492  \n",
      "153148    0.001685624  \n",
      "153149    0.013917320  \n",
      "153150    0.001044649  \n",
      "153151    0.007840895  \n",
      "153152    0.002379474  \n",
      "153153    0.013059006  \n",
      "153154    0.029150101  \n",
      "153155    0.052970670  \n",
      "153156    0.003928136  \n",
      "153157    0.002333393  \n",
      "153158    0.003069307  \n",
      "153159    0.003422246  \n",
      "153160    0.010423612  \n",
      "153161    0.000932290  \n",
      "153162    0.006195491  \n",
      "153163    0.007566179  \n",
      "\n",
      "[153164 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(sublinear_tf=True)\n",
    "X_train_vect = vectorizer.fit_transform(train.comment_text)\n",
    "X_test_vect = vectorizer.transform(test.comment_text)\n",
    "\n",
    "svm = LinearSVC(C=0.12)\n",
    "classifier = CalibratedClassifierCV(svm)\n",
    "\n",
    "submission = pandas.read_csv('dataset/sample_submission.csv')\n",
    "\n",
    "for i, output_class in enumerate(output_classes): \n",
    "    ## Training from the train set data.\n",
    "    classifier.fit(X_train_vect, train[output_class])\n",
    "\n",
    "    ## From what's learned when training, predict the class of each comment of the test set. \n",
    "    submission[output_class] = classifier.predict_proba(X_test_vect)[:, 1]\n",
    "\n",
    "print(submission[output_classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission of Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission_SVM.csv', index=False)"
   ]
  }
 ],
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